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Evidence-Based Complementary and Alternative Medicine
Volume 2017, Article ID 8279109, 10 pages
Research Article

Prescription Function Prediction Using Topic Model and Multilabel Classifiers

1Qianjiang College, Hangzhou Normal University, Hangzhou 310018, China
2College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang 310027, China
3Zhejiang University of Media and Communications, Hangzhou, Zhejiang 310018, China
4Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310053, China

Correspondence should be addressed to Lidong Wang; moc.361@dlw_teloiv

Received 14 September 2016; Accepted 13 June 2017; Published 11 October 2017

Academic Editor: Kenji Watanabe

Copyright © 2017 Lidong Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Determining a prescription’s function is one of the challenging problems in Traditional Chinese Medicine (TCM). In past decades, TCM has been widely researched through various methods in computer science, but none concentrates on the prediction method for a new prescription’s function. In this study, two methods are presented concerning this issue. The first method is based on a novel supervised topic model named Label-Prescription-Herb (LPH), which incorporates herb-herb compatibility rules into learning process. The second method is based on multilabel classifiers built by TFIDF features and herbal attribute features. Experiments undertaken reveal that both methods perform well, but the multilabel classifiers slightly outperform LPH-based method. The prediction results can provide valuable information for new prescription discovery before clinical test.

1. Introduction

Traditional Chinese Medicine (TCM) is a unique medical knowledge system in China and has become a popular complementary treatment in Western countries. Currently there are 100,000 formulae based on the continuous clinical records. A formula is a prescription that is validated by pharmacology and clinics. Researchers have made great efforts to study and utilize those formulae to discover new prescriptions hidden in the formulae data [1]. To discover a new prescription for disease treatment, researchers have to analyze the efficiency of related herbs and collect several herbs with proper proportion according to TCM theory. Then, the function of a new prescription has to be proved through repeated clinical tests, which would require a large amount of manpower and material resources. Actually, if a new prescription’s function can be prepredicted by computer science technology, the results would provide valuable reference for the following clinical practices.

It has been found that data mining approaches play critical roles in TCM related topics, such as new drug discovery [1], syndrome differentiation [24], herbal combinational rule mining [5, 6], symptom name normalization [7], intelligent diagnosis [8], and treatment pattern mining [9]. Most of the previous research was related to relationship mining, such as herb-symptom relationships [8, 10, 11] and herb-herb relationships [6]. Wang et al. [6] created a herbal network to present the herb-herb correlation. Chen et al. [8] detected the patterns between herbs and symptoms by using tripartite information network. Recently, more and more researchers have adopted topic models to mine the correlation between TCM objects. Lin et al. [10] proposed a symptom-herb-therapies-diagnosis topic model to diagnose the disease and administer appropriate drugs and treatments given a patient’s symptoms. Zhang et al. [4] proposed a Symptom-Herb-Diagnosis Topic (SHDT) model to extract multiple relationships among symptoms, herb combinations, and diagnoses from large-scale CM clinical data. The proposed model was useful in discovering the common TCM diagnosis and treatment patterns. Jiang et al. [11] applied Linked LDA to extract the herb-symptom patterns. Yao et al. [9] employed Labeled LDA (Labeled Latent Dirichlet Allocation) to mine treatment patterns in TCM clinical cases, but the mining result was not satisfactory. Unlike these studies, we concentrate on the prescription function prediction through topic detection and incorporate compatibility rule mining into the topic model.

In TCM theory, a prescription’s function can be affected mainly by the following factors: the attributes of herbs, the compatibility rules of paired herbs, and the dosages. Based on this, we present two methods to predict a prescription’s function. The first method is based on topic modeling. A novel topic model named LPH (Label-Prescription-Herb) is proposed to incorporate the results of compatibility rule mining into learning process. It can automatically learn the posterior distribution of each herb in a prescription conditioned on the prescription’s label set (function set). The second method is based on feature extraction and multilabel classifiers. We extract -dimensional feature vector space for each prescription concerning their herbal attributes and TFIDF (Term Frequency-Inverse Document Frequency) Features and then employ several popular and competitive classifiers to validate our method.

The rest of paper is organized as follows. Section 2 presents the detailed steps of our methods for prescription function prediction. Section 3 provides analyses and discussion of our experimental results. Finally, some conclusions and future works are provided in Section 4.

2. Methods

The framework of our methods is shown in Figure 1, with details presented in following subsections.

Figure 1: The framework of our methods.

The herb dataset and formula dataset are extracted from our project CKCEST ( (Chinese Knowledge Center for Engineering Science and Technology). In the first method, we conduct compatibility rule mining from the formula dataset and then incorporate the results into the learning process of topic modeling. The objective of topic modeling is to learn the “topic-word” (function-herb) structure with supervision. The prescription’s most likely labels can then be inferred by thresholding its posterior probability over function labels. In the second method, we treat our prediction task as a multiclass, multilabel classification problem. We extract feature space based on TFIDF weighting and herbal attributes and then train the multilabel classification model by using the features.

2.1. Prediction Based on Topic Model

In this section, we propose a supervised topic model named Label-Prescription-Herb (LPH) to mine treatment patterns in the herbs of the formula dataset. Although a prescription consists of two or more individual herbs, some of them act as pairs in the treatment. In this subsection we introduce the method to mine the compatibility rules.

2.1.1. Compatibility Rule Mining

In TCM theory, compatibility refers to the combination of two or more herbs based on the clinical settings and the properties of herbs [12]. The efficiency of a single herb is usually limited, but when two herbs are used together, their interaction should display their superiority over a single herb in the treatment of diseases; we say that these two herbs have compatibility rule. In China, many herbs have intensive compatibility rule that have been learned from ancient times to the modern period. However, the existing 917 herb pairs in Chinese Paired Herb Database are inadequate for our prediction task. Thus, computer intelligence can be employed to discover more pairs for further research. When two herbs are frequently used in combination with each other, they are more likely to be paired drugs. We propose a method based on support degree [13] and dependency relationship for compatibility rule mining between herb and herb , which is consists of the following steps:

Step 1.

Step 2.

Step 3.

Step 4. Rank all possible herb pairs according to their associated value of .

Step 5. Return top-N pairs.

Here denotes the joint probability of occurrence of two herbs and . In Step 3, we combine the support attribute () and the dependency attribute (the ratio of to ). Note that we remove Glycyrrhizae Radix from the mining results, since it is useless to analyze compatibility rule between Glycyrrhizae Radix and other herbs. The use of this herb is merely in decreasing or moderating medicinal side-effects of all herbs in a prescription.

2.1.2. Topic Model Description on TCM

LDA (Latent Dirichlet Allocation) is a completely unsupervised method that models each document as a mixture of topics [14]. The model outputs a discrete probability distribution over words for each topic and a discrete distribution over topics for each document. However, LDA is not appropriate for multilabeled corpora because it generates automatic summaries of topics that have no direct correspondence with the label set. A simple solution to this problem is to assign a document’s words to its labels rather than to a latent and possibly less interpretable semantic space. At present there exists some related research, such as Labeled LDA [15] and partially Labeled LDA [16].

Analogous to the relationship among documents, topics, and words, we can treat herbs as “words.” A prescription (formula) is a bag of herbs, and we can treat it as a structured “document.” Correspondingly, a prescription’s function can be considered as a “topic.” Thus, we employ topic models to mine the latent relationship between function labels and herbs. The topic model for our prediction task should incorporate supervision by constraining the model to use only those “topics” that correspond to a prescription’s label set. Since the combination of herbs contributes a factor to the function prediction, we consider the role of herb pairs in the topic learning process.

We define some notations. Let each prescription be represented by a tuple consisting of a list of herbs, and a list of binary topic presence/absence indicators , where each and each . Here is the prescription length, is the total number of herbs extracted from formula dataset and is the total number of function labels. We set the number of functions in our model to be the number of unique labels .

2.1.3. LPH Model

To incorporate compatibility rules into the topic model, we introduce variable to indicate whether herb has compatibility rule with herb . If , then and are paired herbs; otherwise, they are generated from the distribution associated with their function label. The graphical model of LPH model is shown in Figure 2.

Figure 2: Graphical model of improved Labeled LDA.

In Figure 2, is a vector consisting of the parameters of multinomial distribution corresponding to the function label. is the prior parameter for variable . are the parameters of the Dirichlet topic prior and are the parameters of the herb prior, while is the label prior for function . The generative process for LPH model is given as follows:(1)For each function , generate from a Dirichlet distribution with prior parameter , that is, .(2)For each prescription :(a)For each function , generate function label (topic) presence/absence indicators from a Bernoulli distribution with prior parameter , that is, .(b)Generate the parameters of the Dirichlet function prior from the label projection matrix and the predefined Dirichlet priors , that is, .(c)Generate function mixture from Dirichlet distribution , that is, .(3)For each herb , :(a)Generate from Bernoulli distribution Bernoulli , that is, .(b)Generate function from multinomial distribution , that is, .(c)If , generate a herb from multinomial distribution , that is, ; if , generate herb pair from multinomial distribution , that is, .

During step (2)(b), label projection matrix is used to project the Dirichlet prior vector into a lower dimension . For instance, suppose and that a prescription has labels given by which implies would be The row of has an entry of 1 in column if and only if the label in prescription is equal to the function and 0 otherwise. Then, function mixture is drawn from a Dirichlet distribution with parameters .

During step (3)(a), when the parameter for the herb is observed from the compatibility rule mining results, the prior parameter is separated from the rest of the models. Analogous to Labeled LDA, for prescription , we restrict to be defined over topics corresponding to its prior labels . This restriction ensures that all the topic assignments are limited to the prescription’s labels.

2.1.4. Learning and Inference

The exact inference for LPH is intractable, thus several approximate schemes have been proposed to infer the model. We use collapsed Gibbs sampling [17] to estimate the probability of a function label assigned to the herb in a prescription. We first choose initial states for the Markov chain randomly; then we calculate the conditional distribution and as follows, where denotes all herbs’ function label assignments excluding ; denotes all herbs’ function label assignments excluding and . In (5), is the count of herb in function , is the total number of herbs assigned to function , is the number of times herbs in prescription are assigned to function , and is the number of herbs in . All counts exclude the current assignment. In (6), all counts do not include the current two cases and . Note that once a herb pair is assigned to the function , the two herbs and will be assigned to the topic simultaneously.

After Gibbs sampling iterations, we estimate the function-herb multinomial distribution and the prescription function mixture as follows:

If , then

If , then

2.1.5. Function Prediction

During multilabel prediction, inferring the best set of labels for an unlabeled prescription at test time is more complex: it involves assessing all function label assignments and returning the assignment that has the highest posterior probability. However, the issue is not so simple, since there are possible function label assignments. For the purpose of this paper, we infer the conditional probability of function labels (topics) given a new prescription by using Bayes rules (see (9)). The prescription’s most probable labels can then be inferred by suitably thresholding its posterior probability over function labels. Suppose a new prescription consists of a set of herbs , then is calculated as follows: To simplify calculation, can be treated as a constant and can be calculated as follows:

2.2. Feature Extraction

In this section, we adopt the TFIDF method and herbal attributes to extract a prescription’s features.

2.2.1. TFIDF Features

TFIDF is often used as a weighting factor in information retrieval and text mining. In TCM, some herbs appear frequently to tend to have little influence on a prescription’s function, such as Glycyrrhizae Radix. In this work, we employ TFIDF to reflect the importance of a herb for a prescription in a collection. A prescription is treated as a “document,” and the corresponding herbs are treated as “terms.” So, we denote , which is the frequency of and define , where is the number of prescriptions; is the number of prescriptions containing the herb . Then, the TFIDF feature for the herb can be denoted as follows:Based on this, we use the TFIDF features to represent a prescription:where if the prescription contains herb , otherwise 0. is the total number of unique herbs.

However, a prescription contains no information about the number of occurrences for each herb. Thus, we cannot calculate this way. To solve this problem, we set the herb’s dosage as its initial weight. The dosage information can reflect the importance of a herb in a prescription but should be standardized before our task, since different herbs have different usual dosages. For instance, the usual dosage for Pseudoginseng is 3  g, while that of Dioscoreae Rhizoma is 15  30 g. So, the dosage of herbs in a prescription may not be directly comparable. For a prescription, we first standardize each herb’s dosage before the TFIDF weighting phase by the following rule:where is the actual dosage of herb in a prescription, is its maximum usual dosage, and is the minimum usual dosage. Table 1 shows an example of dosage standardization on prescription “Ma Huang Tang.” The standardized dosage keeps the order of original data; that is, if a herb has higher dose in prescription than in prescription , it remains in the same order after standardization. Then, can be calculated as

Table 1: Dosage standardization for “Ma Huang Tang” (g).
2.2.2. Attribute Features

The attributes of each herb, named “channel tropism,” “nature & flavor,” and “efficiency,” are described with certain terms. For instance, “nature” refers to the temperature characteristics of the herb, such as “cold,” “hot,” and “warm.” “Flavor” refers to the taste property of the herb, such as “sour,” “bitter,” and “sweet.”

For each prescription, we sort the herbs according to its and select top two herbs to represent the prescription. For the herb , we collect 9 attributes in “nature & flavor,” 12 attributes in “channel tropism,” and 46 attributes in “efficiency.” Then, the attribute feature vector for a prescription can be denoted as , where , . If a herb contains feature , the corresponding is 1, otherwise 0. Some specific attributes, such as “slightly bitter” and “slightly hot,” are quantified as 0.5.

We consider our prediction task as a multilabel classification problem: given a training set consisting of prescriptions with multiple function labels, predict the set of labels appropriate for each prescription in the test set. Based on the above features, several multiple one-vs-rest classifiers are trained to test our method. These classifiers are SVM (Support Vector Machine), Adaboost, and Bayes Network, which are popular and extremely competitive baselines used by most previous papers [18].

3. Results

We collected 3055 formulae ( and 972 herbs for our experiments, the former were derived from our project CKCEST (, and the latter were derived from a famous book Great Dictionary of Chinese Medicine ( Examples of formula data and herb data are listed in Tables 2 and 3.

Table 2: An example of a formula.
Table 3: The detailed information about “Ephedrae Herba.”
3.1. Setup

In compatibility rule mining step, our method returned top-N herb pairs according to their associated Cor value, which was used to decide the parameter during the process of topic modeling. The parameters and in (3) were both set to 0.5 through repeated experiments.

In topic modeling-based method, we set the number of topics to be the number of function labels, which were set to 20. The number of unique herbs extracted from 3055 formulae was 972. Moreover, we set the hyperparameters and and the iteration number .

In multilabel classifier-based method, we combined the TFIDF feature space and attribute features to represent a formula. The dimension for TFIDF feature space was set to 972, the number of unique herbs. The dimension for attribute features was 134. Then, the resulting feature vector of each formula was 1106. We adopted several classifiers (SVM, Adaboost, and Bayes Network) using 4-fold cross validation on 3055 formulae.

We designed five experiments to conduct our prediction task:(a)Topic modeling based on Labeled LDA(b)Topic modeling based on LPH(c)TFIDF feature space(d)Attribute feature space(e)TFIDF + attribute feature space.

For experiments (a) and (b), we calculated the probability for the new prescription , where . The label was returned when it satisfied the following condition: where was the threshold. For experiments (c)(e), these feature vectors were generated and used as inputs to classifiers. We tuned the SVMs’ shared cost parameter C (=10). The “TFIDF + attributes” features were denoted as . The prediction was considered as a 20-class, multilabel classification problem. Each test was performed 10 times to obtain the average performance. We scored each method based on Precision, Recall, and Micro-F1 as our evaluation measures. These measures were defined as follows:

3.2. Experimental Result
3.2.1. Compatibility Rule Mining

We use Precision@N metric to evaluate the effectiveness of our method and then determine the number of returned herb pairs. Precision@N is the ratio of correct pairs to the N returned pairs. The returned pairs are assumed to be correct when they have compatibility rule according to expert’s instructions. The experimental results are shown in Table 4.

Table 4: Experimental results of compatibility rule mining.

Based on the above results, when the number of returned pairs is more than 1500, the correct sample does not show an obvious increase. Thus, top 1500 herb pairs are returned in our experiment. The mining results are visualized in Figure 3. Each vertex in the graph represents a herb. An edge is drawn between a pair of herbs if they have compatibility rule. As shown in Figure 3, one herb can have compatibility rule with several other herbs. For instance, Ginseng Radix can be combined with Atractylodis Macrocephalae Rhizoma, Zingiberis Rhizoma, Dioscoreae Rhizoma, Angelicae Sinensis Radix, or Cervi Cornu Pantotrichum to promote different treatment effects. It is clear that utilizing powerful computers and efficient algorithms can mine latent compatibility rules, which would be useful for TCM practitioners for further study.

Figure 3: Detected 1500 pairs of herbs.
3.2.2. Topic Discovery

Tables 5 and 6 show the 4 topics detected by LPH model, Table 7 shows the 2 topics detected by Labeled LDA model. Each topic contains top 20 herbs. As shown in Tables 5 and 6, we notice that most of the top 20 herbs have related functions corresponding to the topic, but several detected herbs do not have corresponding function, such as Plantaginis Semen in “cleaning heat” topic and Glycyrrhizae Radix in “relieving uneasiness of mind” topic. Although Plantaginis Semen has low posterior probability and does not have direct correspondence to the topic, the herb is an important component in some prescriptions having the corresponding function. Glycyrrhizae Radix can be detected in most of topics, since it is frequently used in many formulae to regulate actions of all other herbs. It has to be noted that Glycyrrhizae Radix is removed from the combinational rule mining results (see Section 2.1.1), not the topic modeling results; thus it can be assigned to a topic (function) as a single herb in the results of topic discovery.

Table 5: Topics discovered by LPH model.
Table 6: Topics discovered by LPH model.
Table 7: Topics discovered by Labeled LDA model.

In other topics, we can find similar results as well. Most of the herbs (marked by the rectangle) that do not have intensive correlation with the topic have low probability. A pair of herbs tend to indicate more intensive correlation with the corresponding topics than a single herb, such as Ginseng Radix and Atractylodis Macrocephalae Rhizoma from “relieving uneasiness of mind” topic and Atractylodis Macrocephalae Rhizoma and Angelicae Sinensis Radix from “dispelling internal cold” topic. Therapeutic effects can be promoted by the coordination of two herbs. In addition, many individual herbs are inactive in the corresponding topic but become active in combination with other herbs, such as Paeoniae Radix Alba and Szechwan Lovage Rhizome from “dispelling internal cold” topic. However, Labeled LDA cannot discover combinations of effective interacting herbs (see Table 7).

3.2.3. Function Prediction

In employing the LPH model to solve the multilabel classification problem, we should determine the threshold in (15). However, there is no theoretical basis to automatically choose an optimal threshold. In this study, we provide the experimental results using different thresholds (see Table 8).

Table 8: Average performance of topic model-based method.

Table 9 shows the classification performance. Comparing the above two methods, multilabel classifiers perform slightly better than topic model-based methods. As shown in Table 8, the value of threshold has a strong influence on the classification results. We can take as an optimal value to achieve optimal prediction power. LPH substantially outperforms Labeled LDA on Micro-F1 with the optimal . The results demonstrate that incorporating compatibility rule into topic model can promote prediction accuracy. The recall on both two models are not satisfactory, as the posterior probability can highlight the most probable function labels but neglect others.

Table 9: Average performance of multilabel classifiers.

From Table 9, we notice that when using TFIDF features only, the performance is not good. The predictive ability based on herbal attributes is better than TFIDF features. This indicates that “channel tropism,” “nature & flavor,” and “efficiency” are valuable information for function prediction, which is consistent with TCM theory. The combination of the features outperforms individual feature space. SVM produces the highest Micro-F1 on the “TFIDF + attributes” feature space compared with other classifiers.

3.3. Discussion

From the compatibility rule mining results, we can see that our method can effectively discover herb pairs with combinational rules. The method is not meant to perfectly model TCM reality, but to function as a tool for TCM practitioners. Also, it can indicate herbs that are likely to be used together for special therapeutic effects and allow researchers to make attempts at further study.

From the topic discovery results, we can see that it is feasible to employ the supervised topic model to predict the function of a new prescription. The idea of incorporating compatibility rules into the process of topic modeling promotes the accuracy of our task. The results are more satisfactory than Labeled LDA because the efficiency of a pair of herbs is more explicit than a single herb, which contributes to the function prediction on a new prescription.

The two proposed kinds of methods can provide valuable information for new prescription discovery before clinical test procedures [16], but each has its advantages. The method based on multilabel classifiers contains complicated and trivial steps in feature extraction, such as dosage standardization and attributes quantification, while the LPH topic model cannot choose the optimal threshold automatically. Although we may improve the function prediction performance by using SVM classifier and LPH model, the results are not very satisfactory. It is possible to combine these two methods to promote prediction accuracy in our future work.

4. Conclusions

This paper has presented two methods for prescription function prediction. In the first method, we employ a novel supervised topic model named LPH to calculate the prescription’s mostly likely function labels. In the second method, we extract feature space based on TFIDF weighting and herbal attributes and use these features to build multilabel classifiers. Results on real world datasets show the effectiveness of our methods. The results can provide valuable information for new prescription discovery.

When doctors write a prescription for the patient, they should obey the principal named “Jun,” “Chen,” “Zuo,” “Shi”, which plays a significant role in determining a prescription’s function. In the future, we plan to analyze the components of a prescription based on its herbal attributes and dosage information. In other words, the herbs in a prescription may possibly be clustered into four classes by data mining algorithms. The results may further improve the accuracy of our prediction task.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

This study was funded by Zhejiang Provincial Natural Science Foundation of China under Grant no. LQ14F020008, National Natural Science Foundation of China under Grant no. 61602402, and Chinese Knowledge Center for Engineering Science and Technology (CKCEST).

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Table of Contents Author Guidelines Submit a Manuscript
Journal of Ophthalmology
Volume 2017, Article ID 1621640, 7 pages
Research Article

Gradually Then Suddenly? Decline in Vision-Related Quality of Life as Glaucoma Worsens

Division of Optometry and Visual Science, School of Health Sciences, City, University of London, London, UK

Correspondence should be addressed to David P. Crabb;

Received 12 December 2016; Accepted 5 March 2017; Published 2 April 2017

Academic Editor: Antonio M. Fea

Copyright © 2017 Lee Jones et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Purpose. To evaluate the relationship between self-reported vision-related quality of life (VRQL) and visual field (VF) loss in people from glaucoma clinics. Methods. A postal survey using the National Eye Institute Visual Function Questionnaire (NEI VFQ-25) was administered to people with a range of VF loss identified from a UK hospital-based glaucoma service database. Trends were assessed in a composite score from NEI VFQ-25 against better-eye mean deviation (BEMD) using linear regression and a spline-fitting method that can highlight where a monotonic relationship may have different stages. Results. A total of 636 patients (median [interquartile range] BEMD −2.1 [−5.2, −0.4] dB, median age 70 [60, 77] years) were analysed. Analysis of trends in the data revealed an average patient loses approximately 2 units (out of 100) on NEI VFQ-25 for every loss of 1 dB (BEMD) as VF defects first become bilateral, up to BEMD −5 dB. NEI VFQ-25 deterioration then appears to slow before a more rapid phase of change (4–5 units per 1 dB loss) after BEMD worsens beyond −15 dB. Conclusions. Relationship between decline in VRQL and VF worsening in glaucoma is unlikely to be linear; it more likely has different phases, and these should be further explored in longitudinal studies.

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With no shortage of insurers offering it, medical insurance has now become one of the most commonly purchase types of insurance. To cater to diverse needs, health insurance companies in India are offering a bouquet of insurance plans which cater to a set of needs. The internet has been a great contributor in enabling companies to make healthcare plans available online. Customers can not only compare health insurance plans offered by multiple companies, but also purchase the plan online. Unlike earlier times, customers can now purchase the best medical insurance policy online, without having to go through the tedious process of visiting the insurer’s office or waiting in queues.

When it comes to purchasing medical insurance, many people find it quite challenging, given the complicated mumbo-jumbo of terms, details, etc. To make the task simpler, we have curated a list of some important factors which one must consider before selecting a plan.

How simple is the claim settlement process?

It’s always important to check out the claim settlement process of the insurer if you are purchasing a health insurance or a car insurance policy. In case of health insurance, if the process for settling a claim is simple and quick, the settlement won’t take long either, which is great news for every policyholder. The time taken in settling a medical claim is a very important factor which can make all the difference in the world of insurance. Another thing to look out for is the customer support service offered by the insurer. Good customer service with quick response times can also make a lot of difference.

Waiting period for illnesses

For coverage of pre-existing diseases, a majority of insurance providers will have a waiting period in place. Waiting period is simply the duration after which the policy will begin coverage for pre-existing diseases. Waiting periods can differ from provider to provider, but normally range from 2 to 5 years. Considering this, it is advisable to purchase a health insurance plan at a younger age when you are relatively healthy and are very unlikely to raise a claim during the policy’s waiting period.

Day-care procedures

Most healthcare plans do not provide cover for day-care procedures. Moreover, most plans mandate that the policyholder should have undergone hospitalisation for a minimum of 24 hours to be eligible to raise a claim. However, for a majority of treatments and procedures, hospitalisation is not required. In such a case, it is wise to go for a policy which covers a high number of day-care procedures.

Health insurance for family

If you are planning to buy a family health plan, do so considering their age and medical condition. Take into account any ailment, chronic illness, or pre-existing disease that a family member may be suffering from, along with their medical history.

Deciding the amount of coverage

Medical insurance plans are usually valid for a year, which means the amount of coverage must be decided carefully. Like life insurance, it is advisable to get health insurance while you are still young. The younger you are, the healthier you are likely to be. Also, you will need a lower sum assured when you are younger, which lowers your premiums automatically.

Network hospitals

When choosing a health insurance provider, this is one of the most important things to check for. At the time of a medical emergency, time is of the essence and can mean the difference between life and death. Choose an insurer which has a higher number of network hospitals.

Maternity benefits

If you are looking to start a family and want to get a health insurance plan, look for one which offers maternity benefits. The cost of pregnancy is rising rapidly and can really make a dent in your savings. Take note of the waiting period applicable for maternity benefit claims, sub-limits, coverage of outpatient expenses, and exclusions.

Pre/post-hospitalisation expenses

Pre/post-hospitalisation expenses are incurred either before you are hospitalised or after you have been discharged from the hospital after being hospitalised. When choosing an insurer, look for one which covers these expenses which can come in the form of initial consultation with the doctor, follow-up visits, medicines, follow-up reports, etc.

No claim bonus

Like motor insurance, many health insurance providers offer a no claim bonus to policyholders who haven’t raised a claim on their policies for a year. If a no claim bonus is provided, it can help bump up your sum assured, or bring down your premiums, or do both.

Cumulative bonus

Cumulative bonus is similar to the no claim bonus, except, it is offered when the policy holder has not raised a claim in the previous policy year as well. If you are relatively healthy and haven’t raised a claim on your policy, you can easily accumulate this bonus for a few years, which can help increase your sum assured significantly or lower your premiums.

First Published: Dec 12, 2017 12:40 IST


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How Rekha, Shabana, Javed Akhtar got together to turn Mumbai’s scrapyard into mini forest
Today in New Delhi, India
Jan 15, 2019-Tuesday
New Delhi
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How Rekha, Shabana, Javed Akhtar got together to turn Mumbai’s scrapyard into mini forest

Mumbai city news: Nine citizens change a wasteland to a park with an amphitheatre and jogging track, plan to develop Irla nullah

mumbai Updated: Jul 13, 2017 10:29 IST
mumbai city news,mumbai,eco-friendly
(From left) Lyricist Javed Akhtar, group members Sangeeta Bajpai, Ashok Pandit, Bollywood actress Rekha, architect PK Das, actress Shabana Azmi and Hema Shankar, another group member. (HT)

A two-and-half acre plot, which was once usurped by the land mafia, is now a neighbourhood park with an amphitheatre, thanks to a citizens’ group that waged an 11-year battle to rehabilitate it.

Gulmohar Area Societies Welfare Group (GASWG) in Juhu, comprising nine members, fought tooth and nail with politicians, bureaucrats, and the builders’ lobby and turned the plot into a green zone, complete with an amphitheatre, an under-construction walking path and jogging track.

The one-acre Kishore Kumar Baug, which has the amphitheatre, was merged with the iconic Kaifi Azmi Park along the Irla nullah at Vidyanidhi Marg and was inaugurated by tabla maestro Zakir Hussain in February this year.

“In 2005-06, the space was a forest area that builders had encroached upon. It soon became a den for nefarious activities. They axed over 2,000 trees and even lay a plinth for construction,” said Ashoke Pandit, film-maker and member of Central Board of Film Certification (CBFC), who led the effort.

Between 2005 and 2011, the group made multiple rounds of the municipal corporation and courts to save the space. The land was reserved for a garden according to the 1991 Development Plan (DP), but the builder had barred people from entering the plot.

“Bouncers appointed by the builder attacked me with a knife and stole my car, but it did not stop us,” said Pandit.

The amphitheatre at Kishore Kumar Baug in Juhu. (Pramod Thakur/HT)

After citizens collated land-record documents, the DP and evidence of violations, then assistant municipal commissioner Ramesh Pawar instructed the builder to vacate the premises. A Bombay high court order came next, which further strengthened the residents’ stand. The land was handed over to the Brihanmumbai Municipal Corporation (BMC) after this.

The group then approached then Member of Parliament (MP) and lyricist Javed Akhtar to look into the matter. “Javed sahab helped us after he heard about our efforts and allocated almost all of his MP local area development fund, amounting to Rs30 crore, to getting the land back for us to redevelop,” said Nita Bajpai, a Juhu resident and group member.

Actor and former parliamentarian Shabana Azmi joined their fight.

“I entered the scene after the residents had pushed out the land mafia. Their efforts resulted in the beautiful landmark for the city. They took all the risk, were even threatened, but got the plot vacated. It was decided that the park would be redeveloped,” Akhtar told HT.

The park was designed by conservation architect PK Das and is now maintained by the BMC. “Bootlegging, dumping of garbage, car repairs and other such activities had marred the site. The civic body got this land five years back due to the struggle of citizens,” he said. “It was merged with the Kaifi Azmi park and thousands of trees were planted.”

Today, hundreds of citizens visit the park daily. The space has been developed as an educational and interactive garden, with medicinal, aromatic and flowering plants, and mangroves along its periphery.

What the land looks like now. (HT)

The park also opened its doors to redevelop the two-kilometre Irla nullah that runs from north to the south of Juhu. “We are currently developing a cycling-cum-jogging track, which will connect to 10 bridges, cleaning the water of the nullah and will be putting up statues of Bollywood stars that have lived or are living in Juhu,” said Akhtar.

The park boasts of a number of environmental benefits

-Over 2,500 trees are present at the park

-Water purification units and a sewage treatment plant to make sure that water to the plants is not wasted and is reused

-Two composting bins for recycling the trimmed grass and converting it into manure

“Nothing could be done by simply criticizing the authorities but citizens need to fight for their rights. It is an example of citizen movement’s victory. We know the system much better now. We have to ultimately act as a responsible citizen,” said Naveena Shetty, resident and GASWG member

“We will name the cycling-cum-jogging track as Bollywood Walk of Fame. We will install purifiers in the nullah, which will purify the water in six months,” said Sangeeta Bajpai, another member of the group, adding that they will name the nullah, Akash-Ganga.

“This city has given us much more than we can get from any other place. While there has been a huge change from Bombay to Mumbai over the years, it is our responsibility as citizens to ensure we leave behind a deep-rooted heritage for our future generations,” said Javed Akhtar, prominent lyricist and screenwriter.

First Published: Jul 02, 2017 23:58 IST


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Recommend: Good Night Stories for Rebel Girls 1&2

Currently reading: The Cottingley Secret by Hazel Gaynor
Interest in HF: I’ve always loved fiction and history, so historical fiction is my happy reading zone. I think the books that cemented hist fic as my favourite were Random Passage and Waiting for Time by Bernice Morgan. I have a special interest in: WWII, Ireland, England, Italy, Canada (especially Newfoundland and Labrador), women’s history/herstory and folklore.
Favorite HF book: Can’t pick one. Recently enjoyed The Good People by Hannah Kent.
Preferred HF: I have a special interest in WWII period, especially relating to women’s history, codebreaking work, Bletchley Park. I tend to most enjoy literary fiction rather than comercial or popular fiction, but read a range.
Location: Canada

Recommend: Good Night Stories for Rebel Girls 1&2

Postby Baylou » Tue December 19th, 2017, 7:28 pm

I came accross this book last spring and now there’s a second volume and I think it’s so great that I share about it when I can. It’s called Good Night Stories for Rebel Girls and when I’ve given it as gifts it’s really well appreciated. So if you’re looking for a Christmas or birthday gift I recommend it for sure. There are lots of historical figures included and fabulous illustrations. I think it’s a great way to share love of history and books to young and old alike. Info at https://www.facebook.com/TheBletchleyCi ... =3&theater

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History Stories

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6 Historical Figures Who Were Kicked Out of School

From Edgar Allan Poe to Salvador Dalí, learn more about six historical figures who were expelled from school.

1. Edgar Allan Poe

Edgar Allan Poe -

Credit: Culture Club/Getty Images

In 1830, future literary legend Edgar Allan Poe resigned a post in the U.S. Army and enrolled at the United States Military Academy at West Point. The aspiring poet had previously left the University of Virginia after drinking and gambling his way into a mountain of debt, and it appears that his tenure at the Point was equally unsettled. Poe endured the school’s strict military discipline and thrived in his studies, but following a falling out with his foster father, John Allan, he resolved to intentionally get himself kicked out. A popular legend states that he won a court martial by showing up to a drill naked save for a cartridge belt, but in actuality, he simply stopped attending classes, roll call and chapel in favor of passing the time at Benny Havens’, a local watering hole. In total, Poe collected more than 200 offenses and demerits en route to being dismissed from West Point in January 1831. Before leaving, the 22-year-old convinced several of his classmates to donate money to fund the printing costs for his third book of poems. He later dedicated the volume to “the U.S. Corps of Cadets.”

2. William Randolph Hearst

Before he established a news media empire, William Randolph Hearst was one of Harvard University’s most notoriously unruly students. The young mogul-in-waiting struggled to keep up with the school’s rigorous academic program, preferring instead to spend his days working at the “Harvard Lampoon” humor magazine, keeping a pet alligator named “Champagne Charlie” and carousing with friends. Still, it may have been Hearst’s penchant for pranks that finally got him the boot. He famously left a donkey in a teacher’s classroom with a note around its neck that read, “Now there are two of you,” and once assaulted the performers at a Boston theater with custard pies. The final straw came in 1885, when Hearst—already on academic probation—mailed his professors specially made chamber pots with their names and photograph engraved on the inside. Hearst left Harvard in disgrace, but by 1887, he’d convinced his father to put him in charge of the family-owned San Francisco Examiner newspaper, kicking off a media career that would make him one of the world’s richest men.

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3. Benito Mussolini

Benito Mussolini

Credit: Roger Viollet/Getty Images

During his school days in the 1880s and 90s, Italian Fascist dictator Benito Mussolini had a notorious reputation for bullying, stealing and general defiance toward his teachers. “More than once I came back home with my head bleeding from a blow with a stone,” he later wrote of his many fights, “but I knew how to defend myself.” When he was nine, Mussolini’s parents sent him to a strict Catholic boarding school in the hope that the priests could smooth off his rough edges. The boy who would become “Il Duce” didn’t take to church discipline, however, and in 1893 he was expelled after he stabbed a fellow student in the hand with a penknife and threw an inkpot at a priest who tried to discipline him. Mussolini was sent to another boarding school, where he was nearly expelled a second time for yet another stabbing incident. Despite his seeming antipathy toward schooling, Mussolini later got a teaching certification and intermittently worked as an educator. Perhaps not surprisingly, the future dictator was known for his sternness, and was nicknamed “the tyrant” by his students.

4. Marlon Brando

Marlon Brando

Credit: Hulton Archive/Getty Images

One of actor Marlon Brando’s most famous film roles came in 1953’s “The Wild One,” where he played the leader of a rebel motorcycle gang. The biker character may not have been much of a stretch for Brando, an unabashed troublemaker and prankster who supposedly once rode a motorcycle through the halls of his high school in Libertyville, Illinois. “I was a bad student, chronic truant and all-around incorrigible,” Brando later wrote of his high school days. “I was forever being sent to the principal’s office to be disciplined.” Thanks to poor grades and a litany of bad behavior ranging from throwing firecrackers to writing a class essay on a roll of toilet paper, Brando was eventually expelled from Libertyville High in 1941. He then transferred to Shattuck Military Academy in Minnesota, where he continued to exhibit a healthy resentment toward authority. In 1943, Brando was put on probation and confined to campus for talking back to an officer during a drill. When he ignored the order and headed out for a day on the town, he was charged with being AWOL and formally dismissed. Having been expelled from two different schools, Brando moved to New York and dove into acting. He made his Broadway debut only one year later.

5. Percy Bysshe Shelley

Writer Percy Bysshe Shelley is best known as the author of beloved poems such as “Ozymandias” and “Queen Mab,” but he was also a notorious freethinker and rabble-rouser. In 1811, during his first year as a student at Oxford University, an 18-year-old Shelley joined with his friend Thomas Jefferson Hogg in anonymously writing a pamphlet called “The Necessity of Atheism.” The short text laid out the duo’s arguments against the existence of God, and was signed, “Thro’ deficiency of proof, an atheist.” Hoping to spark a theological debate, Shelley advertised the pamphlet extensively and used aliases to send copies to various clergyman and university professors. At the time, atheism was still considered an illicit topic, and before long, Shelley and Hogg were found out and dragged before Oxford’s academic authorities for questioning. When they refused to neither confirm nor deny authorship of the controversial pamphlet, both were expelled. The scandal created a rift between Shelley and his father, who denounced the leaflet as being “criminal” and “improper.” Only a few months later, Shelley cut ties with his family, eloped with a 16-year-old girl and set off to begin his literary career in earnest.

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6. Salvador Dalí

Salvador Dali

Credit: Gianni Ferrari/Cover/Getty Images

In 1922, future Surrealist icon Salvador Dalí entered the Royal Academy of Fine Arts of San Fernando in Madrid. Dalí had only applied to the school after being encouraged by his father, and from the beginning, he was none too impressed with its faculty. “I immediately understood that those old professors covered with honors and decorations could teach me nothing,” he later wrote. While he won acclaim for his bold painting style, Dalí was suspended from the Academy in 1923 for leading a student protest against the faculty selection process. He returned to San Fernando the following year, only to be expelled for good in 1926 after he proclaimed that none of his professors were skilled enough to evaluate his work. Following his dismissal, Dalí entered the Paris art world, adopted his signature upturned moustache and began collaborating with members of the Surrealist movement. By 1931, he had worked on two films and completed “The Persistence of Memory,” his most well known painting.


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Aquinas : A Guide for the Perplexed, Hardback Book

Aquinas : A Guide for the Perplexed Hardback

Part of the Guides for the Perplexed series

Thomas Aquinas is the most widely read and arguably most influential of the medieval philosophers.

He is famous for his impressive and coherent synthesis of Greek Philosophy and Christian Theology and his magisterial Summa Theologiae is a hugely important, and enduring, text in the history of philosophy.

Yet he is also a very difficult thinker and his ideas present a number of challenges to his readers.Aquinas: A Guide for the Perplexed is a clear and thorough account of Aquinas's thought, his major works and ideas, providing an ideal guide to the important and complex writings of this key thinker.

The book introduces all the key concepts and themes in Aquinas's thought and examines the ways in which they have influenced philosophical and theological thought.

Geared towards the specific requirements of students who need to reach a sound understanding of Aquinas's ideas, the book serves as a clear and concise introduction to his philosophy and natural theology.

This is the ideal companion to the study of this most influential and challenging of thinkers.

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Non-Compete Blog

Advice For Registered Representatives: Six Lessons From Cases Implicating The Protocol For Broker Recruiting

The Protocol for Broker Recruiting governs the employment transitions of registered representatives of financial firms, broker-dealers, and wire-houses. Signed with the stated goal of ensuring client privacy while enabling client freedom of choice, the Protocol allows a departing representative to take certain limited client information to his or her new firm. The departing representative can then immediately solicit past clients so long as both the former and hiring firm are signatories to the Protocol, the departing representative does not begin to solicit his or her clients before resigning, and the departing representative leaves behind a list of the information he or she has taken.

Because the Protocol, to which over 900 entities are signatories, trumps non-solicitation covenants, it has enabled smooth transitions for registered representatives while substantially reducing litigation. However, lawsuits between a former firm and its departing employee and/or its hiring firm may implicate the Protocol. Questions might arise about whether the employee or its new firm has breached the Protocol and about whether the Protocol should apply at all. Here are six lessons we’ve learned from those cases.

1. Courts look favorably on those departing employees who act in good faith.

When a Protocol-signatory firm wants to enjoin a departing employee who has left for another signatory firm from retaining information about or soliciting former clients, a court will ask whether it is likely that the departing employee breached the Protocol. Those employees who have acted in good faith are likely to be able to continue to solicit their former clients while those who have clearly exceeded the scope of the Protocol are likely to be enjoined by the court.

In one case, the representatives took a list of mutual fund ticker symbols, the names of three hedge funds and the aggregate amount of assets that their clients had invested in each one. Credit Suisse Securities (USA) LLC v. Lee, No. 11 Civ. 08566(RJH), 2011 WL 6153108 at *5 (S.D.N.Y. Dec. 9, 2011). But this did not violate the Protocol because the defendants only took a vague set of information that did not reveal any single client’s personal information, and because the limited scope of the information would not put the defendants at “any distinct competitive advantage.” Id. at *5. Also, even though there was a file containing client information on a defendant’s personal computer, the defendant acted in good faith by deleting the file upon discovering it. Id.

Courts will not tolerate those who act in bad faith or violate the letter or spirit of the Protocol. One departing employee emailed to his personal email address clients’ financial plans and other confidential information when he resigned. He sent many of those emails “in the course of three minutes, on a Friday night, approximately one week before he attempted to resign.” The court decided his actions suggested he took confidential client information and then tried to use the Protocol to avoid the terms of his other restrictive covenants. His actions went “against the terms and spirit of the Protocol.” American Financial Services, Inc. v. Koenig, Civil Action No. 11-6140-NLH-JS, 2012 WL 379940 (D.N.J. Feb 6, 2012).

2. Do not take the information regarding or attempt to solicit clients that you did not personally acquire, develop, or bring to the firm.

Courts are more likely to allow a departing broker to contact and solicit those clients that that broker earned when working at the former firm as opposed to those clients that the broker acquired via a coworker or the firm’s goodwill. For example, a financial advisor who left a team with whom he had worked and signed a restrictive covenant could not solicit any clients other team members had acquired. UBS Financial Services, Inc. v. Christenson, Civil No. 13-1081 (MJD/JSM), 2013 WL 2145703 at *4 (D. Minn. May 15, 2013). However, the advisor was still allowed to reach out to the disputed clients to inform them of his departure because “there is a difference between soliciting and contacting.” Id at *5.

3. Courts have refused to recognize the Protocol as an “industry standard”

Since the Protocol is a contract, it cannot bind a nonparty. Therefore, non-signatories can sue departing representatives who violate non-solicitation agreements. See Hilliard v. Clark, No. 1:07-cv-911, 2007 WL 2589956 at *8 (W.D. Mich. Aug. 31, 2007).

4. Firms that have signed the Protocol may fail to obtain a preliminary injunction even when the employee has transitioned to a non-Protocol firm.

Signatory firms have struggled to show that a departing employee who complies with the Protocol creates an extreme risk of irreparable harm, even when that employee has gone to a non-signatory firm. The Protocol demonstrates that financial firms implicitly accept that “brokers will leave and take client lists with them.” Merril Lynch, Pierce, Fenner & Smith, Inc. v. Brennan, No.1:07CV475, 2007 WL 632904 at *3 (N.D. Ohio, Feb. 23, 2007). Because the former firm will be in the same position as if the departing advisor had moved to another Protocol firm it will surely survive the transition. Merrill Lynch, Pierce, Fenner & Smith, Inc. v. Baxter, No. 1:09CV45DAK, 2009 WL 960773, at *6 (D. Utah Apr. 8, 2009).

Also, a non-solicitation agreement can have a potentially devastating effect on a departing employee. Individual advisors often spend their whole careers building a client base. An injunction “barring them with any contact with their clients would effectively cripple their careers.” Id at *7.

However, it may be inequitable to allow parties that have not signed to incur the benefits of the Protocol—an incoming financial advisor being allowed to solicit his clients from his former firm—without incurring the risks of a departing financial advisor taking her clients with her. There is, in essence, no reciprocity when one signatory and one non-signatory are involved in the dispute. See Wachovia Securities, L.L.C. v. Stanton, 571 F. Supp.2d 1014, 1040 (N.D. Iowa 2008).

Though a court will not always grant a preliminary injunction when an employee leaves a Protocol firm for a non-Protocol firm, the departing advisor and the hiring firm will still likely be liable for damages to the former firm in FINRA arbitration. After all, the Protocol only governs an employee’s transitions between two signatories.

5. Compliance with the Protocol does not imply total immunity.

Courts are quick to stress that even when they do not find that it is likely a departing employee who has breached the Protocol, the Protocol only replaces non-solicitation agreements. A departing representative “could invoke the protections of the Protocol to avoid liability to his prior firm arising out of his solicitation of his former firm’s clients but nonetheless remain liable to his former employer for other wrongful conduct” such as breaching a covenant not to raid coworkers. Lee, 2011 WL 6153108 at *3.

6. Courts respect and enforce the goals of the Protocol.

The Protocol aims to further clients’ privacy interests and maximize their choices in connection with the movement of their representatives between firms. Courts consistently refer to these goals in determining whether or how to apply the Protocol in a given case.

Courts will not look fondly upon those departing brokers who take more information than permitted. The disclosure of confidential information, such as a client’s credit card and social security numbers not only violates client privacy, it also harms a firm who may lose the trust they had built with that client. As the one court explained, “[t]he fact that the Protocol does not permit disclosure of such information suggests that it is to remain highly protected.” Koenig, 2012 WL 379940 at *7.

The best way to maximize client choice is to give both the departing broker and the broker’s original firm an equal chance to solicit and win the client’s business. When both parties are on equal footing, the client can make the most informed choice possible. But if a “financial advisor one day simply disappears without warning” and cannot inform her clients of her departure because of restrictive covenants, a client’s range of choices becomes more restricted. Smith Barney Div. of Citigroup Global Markets Inc. v. Griffin, CIV.A. 08-0022-BLS1, 2008 WL 325269 at *3 (Mass. Super. Jan 23, 2008).

Thus, both employers and brokers should always make sure that their actions benefit the client first and foremost. They should not take actions that violate a client’s privacy or prevent clients from making informed decisions regarding their representation.

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Hoagland Pharmacy

Serving Whatcom, Skagit, Island & Snohomish Counties

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Archive for August 2014

Get your shot, not the Flu!

  • Comments Off on Get your shot, not the Flu!

For further information about how a flu shot can help  you, please talk to your Doctor or a Pharmacist.

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Flu Shot Tidbits

The word “influenza” comes from the Italian influentia because people used to believe that the influence of the planets, stars, and moon caused the flu—for only such universal influence could explain such sudden and widespread sickness.

Annual flu viruses (not including flu pandemics) infect up to 20% of Americans, put 200,000 in the hospital with flu-related complications, and kill about 36,000 people.

The cost of treating annual flu epidemics, including lost wages and productivity of workers, is billions of dollars each year in just the United States alone.

There have been four major global flu pandemics since 1900: the Swine flu (officially named “Novel H1N1 Influenza A”), the Hong Kong flu (1968-1969), the Asian flu pandemic (1957-1958), and the Spanish flu (1918-1919).

The single deadliest flu pandemic in history was the Spanish flu pandemic. It occurred in the three waves of increasing lethality.

  • It killed more people in 24 weeks than AIDS did in 24 years.
  • More people in one year than smallpox or the Black Plague did in 50 years.
  • More Americans in one year than the combined total who died in battle during WWI, WWII, the Korean War, and the Vietnam War.

Viruses mutate more in one day than humans did in several million years.

Flu viruses can live up to 48 hours on hard, nonporous surfaces such as stainless steel and up to 12 hours on cloth and tissues.

It can take 1-2 weeks after being vaccinated before protective immunity develops.

The first time that children younger than 9 years old receive the flu vaccine they need to receive 2 doses at least a month apart from each other.

The flu vaccine can’t give you the flu. The vaccines only contain a dead piece of the flu virus, and a dead virus can’t infect you.

For further information on how a flu shot can help you, please call 360-734-5413 x118.

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Renovated 4 bed holiday gite in the Vendée - LV004472_B

5km from Talmont-Saint-Hilaire, Vendée, Western Loire Reference: LV004472_B Advertiser since: 4 January 2016

This location has multiple holiday properties

Property Board Arrival Bedrooms Sleeps Bathrooms Additional info
La Lumiere Est at Le Village du Bois Self Catering Saturday 4 11 3 Sofabed

About this property

A Superb, Modernised Gite with Great Facilities.. This villa is in a coastal setting.

Renovated to a very high standard, this stylish gîte has 3 double/twin bedrooms plus 1 adjacent twin/double/triple ground floor en suite bedroom, two further bathrooms, a large open plan living/dining area and a separate kitchen plus in-room catering facilities in bedroom four.

The large glazed front doors are set within a larger glazed frontage which sits across the full breadth of the original entrance and retains the original French oak barn doors. These lead to the attractive, modern, main open plan living area. This features warm real wood floors exposed beams and the original stone walls with two comfortable settees.

A feature of this space is the open tread staircases which lead directly from the living and dining areas to the upstairs bedrooms and the outlook onto the attractive terrace.

Kitchen and dining

The gite has an open plan dining area with table and seating for eight immediately adjacent to a separate, brand new, fully fitted kitchen. This has all the equipment needed for modern day living; sink and drainer, four burner hob, microwave, large fridge, dishwasher, work surfaces, and the pots, pans and utensils needed for your self-catering holiday.

The ground floor bedroom also has a small fridge and hob plus its own kettle, crockery, cutlery and basic cooking equipment.

Living rooms

The open plan living area, in addition to the great aspect, features warm real wood floors exposed beams and the original stone walls. There are comfortable settees (one of which is a sofabed).
The open tread staircases lead directly from the living and dining areas to the upstairs bedrooms.

The individually designed open tread staircases take you up to the first floor bedrooms each completely modernised, has its own shape and features French oak beams plus velux windows.

Bedroom one (twin/double) overlooks the gîte patio and, along with bedroom 3 (also twin/double) offers completely private space. Bedroom 2 (twin) located above the dining room/lounge has a more open plan staircase and is perfect for accommodating younger children while adults carry on relaxing below. Each has open and hanger based storage plus bedside cabinets, reading lamps and a fan.

Bedroom four, which has its own immediately adjacent entrance, is a large twin/double/triple perfect for a couple who require just a little independence or perhaps three older children to share. It features French oak beams and original stone walls, and has an integral kitchen/dining area with fridge, kettle, table and chairs.

There are two modern bathrooms at the rear of the Lumiere gîte. Each has ‘power-showers’, hand-basins and a toilet. Bedroom four has a generous en-suite bathroom with toilet, handbasin and a large power shower.

Extra warmth for the cooler months is provided by electric wall heaters in the main living areas. In line with our eco-friendly approach, hot water is supplied by solar powered and electric boilers and is, thus, plentiful at all times.

There is a TV and DVD player; this is not connected to mainstream French/UK channels - but may offer options for this via the wi-fi.

Bedroom four has its own TV/DVD player.

There is wi-fi throughout.

Garden and outside

Outdoors, La Lumière Est has its own extended east facing patio enabling guests to, as they choose, enjoy breakfast in the sun or relax in the shade at any time. Bedroom four has an immediately adjacent small patio

It is close to, and shares, the swimming-pool (which is security fenced) and associated play and park areas with the other gîtes at Le Village.

Parking and garages

There is ample space for guest parking,

Visitor suitability

This gite is suitable for families and/or larger groups of up to 10-11 people.

Information about any additional facilities

A range of bikes of different styles and sizes is kept for free guest use.

A stock of films and books is also available for guest use.

The area is fantastic for things to to. In addition to the blue flag beach which is ideal for swimming, surfing and kite-surfing, there is an excellent golf course, Port Bourgenay, the Seven Ocean Aquarium, a racecourse, and the chateaux at Talmont St Hilaire. 20 minutes down the road is Les Sables d'Olonne and Puy du Fou is just an hour away.

  • Cleaning

  • Internet

  • Linen

  • Shared pool

Fishing nearby

Species of fish: Various
Name of nearest fishing location: On site

Fishing on-site

Name and address of nearest golf course: Port Bourgenay Golf
Distance: 4 km

Horse riding

Name and address of nearest stables: Various
Distance: 5 km

Name/number of nearest marked trail: Walking/cycling on le Vélodyssée

About the Owner/Advertiser, Mr John EADY

John Eady, the owner, combines family and business interests in Manchester with running his gite complex.

Owner's website
Year property purchased 2013
Why this location? This is a superb part of France; it has great weather, beautiful countryside, the Vendee's finest beaches (La Plage du Veillon and Les Sables d'Olonne) and towns and loads of things to do. It is also an excellent location for serious and leisure cyclists, golfers, walkers and water-sports enthusiasts.

It is something of a cliche but, as all our guests have discovered and confirmed, while we are right in the middle of the countryside we are just 5 minutes from wonderful beaches, Port Bourgenay, Richard the Lionheart's chataue at Talmont and superb local restaurants - to cater for every taste and dining expectation. The cosmoplitan Quayside at Les Sables d'Olonne with its marvellous array of bars and restaurants, O'Gliss Park and Indian Forest are all just 20 minutes away and even Puy de Fou is just an hour.
We are biased but this is one of the best locations in the Vendee - its got it all.
Unique benefits of property Le Village du Bois opened in 2016 and is just a lovely place to live and stay. Our gites all have their own style, shape and character and the layout of the site means that each offers a high degree of privacy.

The location is rural and tranquil. Each gite has been fully modernised and is of high quality while retaining the features and character of the historic buildings in which it is contained. They are complemented by our superb heated pool.

It is 5 minutes from one of the Vendee's finest beaches, Port Bourgenay, an excellent golf course, the beautiful town of Talmont St Hilaire, superb places to eat (from the upmarket Cayola to some wonderful local restaurants). Supermarkets/shops and a surprisingly broad range of first class visitor attractions are all close by.
It is also very accessible; an easy 3-5 hour drive from the main channel ports of Caen, Le Havre, Roscoff and St Malo and just over an hour from Nantes and La Rochelle airports.

The Owner Recommends

The beach: La Plage du Veillon

Described by many as one of the finest beaches on the Vendee, and less than 5 minutes drive from us (15 minutes off-road cycling) is La Plage du Veillon. In addition to just enjoying the sunshine and the golden sandy beach you can take part in water-sports including kite-surfing, surfing and paddle-boarding, rock pools or take the gorgeous 30 minute coastal walk across to Port Bourgenay. Its recently refurbished 'arrival' has a lovely coffee/cake shop and restaurant and outlets from which you can hire wet-suits and water-sports equipment. It backs onto beautiful sand-dunes and pine forests and is an outstanding environment in which to walk, cycle, jog or simply enjoy with your family.

Beach based wter-sports are available at Plage du Veillon, just 5 minutes away, which has in-season lifeguarding and is excellent for surfing, kitesurfing and body-boarding.
The beach at Les Sables d'Olonne is also a great location for surfing and body boarding.
Windsurfing and canoeing is available on the lake in the centre of Talmont St Hilaire.

Le Puy du Fou

In visitor number terms Puy du Fou is the second most popular (after Disneyland Paris) theme park in France, Just one hour's drive from Le Village du Bois it is described by the Guardian as France's 'best kept theme park secret' offering 'a range of spectacular history extravaganza shows – where thousands of actors and animals recreate high drama, from Roman gladiators to King Arthur, with mind-blowing special effects'.

Le Château de Talmont Saint Hilaire

The Chateau at Talmont (again just 5 minutes from Le Village) is set up to enable visitors to experience life in the middle ages in the Fortress of Richard the Lion Heart. It includes an entertaining and instructive trail exploring the castle and in the summer a variety of events encompass the ordeals of young knights, crossbow shooting and the spectacle of knights on horseback. These include the Spectacle Nocturne summer evening pageant plus various medieval festivities (including falconry) throughout the week.

Visitor attractions and water parks - young people

There are several excellent attractions geared to young people within 20-30 minutes drive. These include the 'back to back' O'Gliss Waterpark and Indian Forest Park at Le Bernard and Moutiers-les-Mauxfaits respectively plus the Le Cairn - Centre de la Préhistoire at Saint-Hilaire-la-Forêt.
Even closer to home, just a short drive or cycle from Le Village, is the Aquarium Le 7ème Continent (a form of sea-life centre) located in Port Bourgenay.

Excellent on and off road cycling on le Vélodyssée and a range of inland and coastal cycle routes.

Le Village du Bois is ideally situated for golf breaks with Port Bourgenay on the doorstep and five courses located within a one hour drive. The quality and variety is excellent while the approach taken at the various courses is, within reason, pretty relaxed in respect of clothing and footwear. All venues hire clubs and buggies and it is also possible to combine your golf with some very good food.

Eating out

There is a superb choice of restaurants of all types within close proximity. Seafood is a speciality and much of what is on offer is caught locally.

Horse racing

The Hippodrome de la Malbrande which features a range of flat racing and trotting events throughout the Spring, Summer and Autumn is located just 5 minutes drive from Le Village du Bois.

From Le Village du Bois, you can walk for miles through rolling countryside. There are off-road routes to the beach, the port, Talmont St Hilaire or you can stroll down to the local 'commerces' to get your fresh bread and local fish, meat and cheese.

1 Review
  • Cleanliness: 10
  • Comfort: 9
  • Condition: 10
  • Facilities: 9
  • Local Amenities: 8
  • Setting: 10
  • Value for Money: 10
9.7 / 10 Cesouthern 8 July, 2016

We had a great time at le village de Bois, location is stunning, really quiet and peaceful. Great bike rides, the port is lovely- just 5 mins drive and the local beach is ideal...great for families and big groups. Very relaxing and enjoyable.

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David M. Benett/Dave Benett/Getty Images

Craig died at his home in Studio City on Tuesday.

Craig Raymond Turner, the first son of Tina Turner, has died at the age of 59, The Hollywood Reporter has confirmed.

Craig was "pronounced dead at his residence in Studio City at 12:38 p.m. today,” Los Angeles County coroner’s office assistant chief Ed Winter confirmed to THR. Winter said Turner's death was reported as a suicide and that he had a gunshot wound. An autopsy is pending.

Craig is the son of Tina and saxophonist Raymond Hill. Tina's first husband, Ike Turner, adopted Craig. Turner was a real estate agent in the San Fernando Valley, with his Rodeo Realty profile noting that he belonged to the National Organization of Realtors and California Association of Realtors.

THR has reached out to representatives for Tina Turner.


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For other neighbourhoods, please use our search bar to access the selection of holiday accommodations available.

Can I rent Townhouses near Seaside Village?

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      Can I book a holiday accommodation directly online or instantly near Seaside Village?

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What is the best area to stay when travelling to Podgora?

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What are the most popular points of interest and landmarks to stay near when travelling to Podgora?

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But there are many other points of interest around Podgora listed on HomeAway where you can find your place to stay. Please use our search bar to access the selection of holiday rentals available near the point of interest of your choice.

Can I rent Studios in Podgora?

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But you can also enjoy a great stay in one of our other holiday rentals including Bungalows and more.

Can I find a holiday accommodation with pool in Podgora?

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Looking for Appliance Repair & Installation Services in Thornlie
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Appliance Repair & Installation Services - Thornlie (6108)

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