Suggestions and Comparisons of Two Algorithms for the Simplification of Bluetooth Sensor Data in Traffic Cordons.

Bluetooth sensors in intelligent transportation systems possess extensive coverage and access to a large number of identity (ID) data, but they cannot distinguish between vehicles and persons. This study aims to classify and differentiate raw data collected from Bluetooth sensors positioned between various origin-destination (i-j) points into vehicles and persons and to determine their distribution ratios. To reduce data noise, two different filtering algorithms are proposed. The first algorithm employs time series simplification based on Simple Moving Average (SMA) and threshold models, which are tools of statistical analysis. The second algorithm is rule-based, using speed data of Bluetooth devices derived from sensor data to provide a simplification algorithm. The study area was the Historic Peninsula Traffic Cord Region of Istanbul, utilizing data from 39 sensors in the region. As a result of time-based filtering, the ratio of person ID addresses for Bluetooth devices participating in circulation in the region was found to be 65.57% (397,799 person IDs), while the ratio of vehicle ID addresses was 34.43% (208,941 vehicle IDs). In contrast, the rule-based algorithm based on speed data found that the ratio of vehicle ID addresses was 35.82% (389,392 vehicle IDs), while the ratio of person ID addresses was 64.17% (217,348 person IDs). The Jaccard similarity coefficient was utilized to identify similarities in the data obtained from the applied filtering approaches, yielding a coefficient (J) of 0.628. The identity addresses of the vehicles common throughout the two date sets which are obtained represent the sampling size for traffic measurements.

Impact of analytic decisions on test-retest reliability of individual and group estimates in functional magnetic resonance imaging: a multiverse analysis using the monetary incentive delay task.

Empirical studies reporting low test-retest reliability of individual blood oxygen-level dependent (BOLD) signal estimates in functional magnetic resonance imaging (fMRI) data have resurrected interest among cognitive neuroscientists in methods that may improve reliability in fMRI. Over the last decade, several individual studies have reported that modeling decisions, such as smoothing, motion correction and contrast selection, may improve estimates of test-retest reliability of BOLD signal estimates. However, it remains an empirical question whether certain analytic decisions consistently improve individual and group level reliability estimates in an fMRI task across multiple large, independent samples. This study used three independent samples (Ns: 60, 81, 119) that collected the same task (Monetary Incentive Delay task) across two runs and two sessions to evaluate the effects of analytic decisions on the individual (intraclass correlation coefficient [ICC(3,1)]) and group (Jaccard/Spearman rho) reliability estimates of BOLD activity of task fMRI data. The analytic decisions in this study vary across four categories: smoothing kernel (five options), motion correction (four options), task parameterizing (three options) and task contrasts (four options), totaling 240 different pipeline permutations. Across all 240 pipelines, the median ICC estimates are consistently low, with a maximum median ICC estimate of .43 - .55 across the three samples. The analytic decisions with the greatest impact on the median ICC and group similarity estimates are the Implicit Baseline contrast, Cue Model parameterization and a larger smoothing kernel. Using an Implicit Baseline in a contrast condition meaningfully increased group similarity and ICC estimates as compared to using the Neutral cue. This effect was largest for the Cue Model parameterization; however, improvements in reliability came at the cost of interpretability. This study illustrates that estimates of reliability in the MID task are consistently low and variable at small samples, and a higher test-retest reliability may not always improve interpretability of the estimated BOLD signal.

Increased density of conspecifics caused niche contraction in a multispecific passerine assemblage.

Competition is a prominent mechanism driving population dynamics and structuring community assemblage, which can be investigated by linking shifts in species' ecological niche and the densities of sympatric species because the ecological release from competitive constraints is a density-dependent process. In this work we determine how a steppe passerine community segregates their ecological niches and evaluate the role of competition in inducing changes in the ecological niche of species. We built multidimensional ecological niches (with Gaussian kernel density estimators) using data on the habitat features used by 10 bird species collected from seven sites in the natural steppes of Central Spain over 2 consecutive years. We computed distance and niche similarity metrics to explore the ecological niche partitioning of the bird community. Next, we ran multivariate linear regression models to evaluate the effects of conspecific and heterospecific density (as proxies of intraspecific and interspecific competition, respectively) on niche breadth and/or position of the three most abundant species. We found low niche overlap in the community assemblage but varying levels of niche similarity among pairs of species, which could increase the likelihood of current competition operating in the community. However, we found no effect of heterospecific density on niche breadth or position, although conspecific density was negatively related to niche breadth. Contrary to predictions of competition theory, increased density of conspecifics caused niche contraction. Our results from a multispecies system contribute to advanced knowledge of the biotic mechanisms structuring wildlife communities within the framework of ecological niche theory.

DeepNet model empowered cuckoo search algorithm for the effective identification of lung cancer nodules.

Introduction: Globally, lung cancer is a highly harmful type of cancer. An efficient diagnosis system can enable pathologists to recognize the type and nature of lung nodules and the mode of therapy to increase the patient's chance of survival. Hence, implementing an automatic and reliable system to segment lung nodules from a computed tomography (CT) image is useful in the medical industry. Methods: This study develops a novel fully convolutional deep neural network (hereafter called DeepNet) model for segmenting lung nodules from CT scans. This model includes an encoder/decoder network that achieves pixel-wise image segmentation. The encoder network exploits a Visual Geometry Group (VGG-19) model as a base architecture, while the decoder network exploits 16 upsampling and deconvolution modules. The encoder used in this model has a very flexible structural design that can be modified and trained for any resolution based on the size of input scans. The decoder network upsamples and maps the low-resolution attributes of the encoder. Thus, there is a considerable drop in the number of variables used for the learning process as the network recycles the pooling indices of the encoder for segmentation. The Thresholding method and the cuckoo search algorithm determines the most useful features when categorizing cancer nodules. Results and discussion: The effectiveness of the intended DeepNet model is cautiously assessed on the real-world database known as The Cancer Imaging Archive (TCIA) dataset and its effectiveness is demonstrated by comparing its representation with some other modern segmentation models in terms of selected performance measures. The empirical analysis reveals that DeepNet significantly outperforms other prevalent segmentation algorithms with 0.962 ± 0.023% of volume error, 0.968 ± 0.011 of dice similarity coefficient, 0.856 ± 0.011 of Jaccard similarity index, and 0.045 ± 0.005s average processing time.

Secure Comparisons of Single Nucleotide Polymorphisms Using Secure Multiparty Computation: Method Development.

BACKGROUND: While genomic variations can provide valuable information for health care and ancestry, the privacy of individual genomic data must be protected. Thus, a secure environment is desirable for a human DNA database such that the total data are queryable but not directly accessible to involved parties (eg, data hosts and hospitals) and that the query results are learned only by the user or authorized party. OBJECTIVE: In this study, we provide efficient and secure computations on panels of single nucleotide polymorphisms (SNPs) from genomic sequences as computed under the following set operations: union, intersection, set difference, and symmetric difference. METHODS: Using these operations, we can compute similarity metrics, such as the Jaccard similarity, which could allow querying a DNA database to find the same person and genetic relatives securely. We analyzed various security paradigms and show metrics for the protocols under several security assumptions, such as semihonest, malicious with honest majority, and malicious with a malicious majority. RESULTS: We show that our methods can be used practically on realistically sized data. Specifically, we can compute the Jaccard similarity of two genomes when considering sets of SNPs, each with 400,000 SNPs, in 2.16 seconds with the assumption of a malicious adversary in an honest majority and 0.36 seconds under a semihonest model. CONCLUSIONS: Our methods may help adopt trusted environments for hosting individual genomic data with end-to-end data security.

Evaluating keyphrase extraction algorithms for finding similar news articles using lexical similarity calculation and semantic relatedness measurement by word embedding.

A textual data processing task that involves the automatic extraction of relevant and salient keyphrases from a document that expresses all the important concepts of the document is called keyphrase extraction. Due to technological advancements, the amount of textual information on the Internet is rapidly increasing as a lot of textual information is processed online in various domains such as offices, news portals, or for research purposes. Given the exponential increase of news articles on the Internet, manually searching for similar news articles by reading the entire news content that matches the user's interests has become a time-consuming and tedious task. Therefore, automatically finding similar news articles can be a significant task in text processing. In this context, keyphrase extraction algorithms can extract information from news articles. However, selecting the most appropriate algorithm is also a problem. Therefore, this study analyzes various supervised and unsupervised keyphrase extraction algorithms, namely KEA, KP-Miner, YAKE, MultipartiteRank, TopicRank, and TeKET, which are used to extract keyphrases from news articles. The extracted keyphrases are used to compute lexical and semantic similarity to find similar news articles. The lexical similarity is calculated using the Cosine and Jaccard similarity techniques. In addition, semantic similarity is calculated using a word embedding technique called Word2Vec in combination with the Cosine similarity measure. The experimental results show that the KP-Miner keyphrase extraction algorithm, together with the Cosine similarity calculation using Word2Vec (Cosine-Word2Vec), outperforms the other combinations of keyphrase extraction algorithms and similarity calculation techniques to find similar news articles. The similar articles identified using KPMiner and the Cosine similarity measure with Word2Vec appear to be relevant to a particular news article and thus show satisfactory performance with a Normalized Discounted Cumulative Gain (NDCG) value of 0.97. This study proposes a method for finding similar news articles that can be used in conjunction with other methods already in use.

The Statistics of k-mers from a Sequence Undergoing a Simple Mutation Process Without Spurious Matches.

k-mer-based methods are widely used in bioinformatics, but there are many gaps in our understanding of their statistical properties. Here, we consider the simple model where a sequence S (e.g., a genome or a read) undergoes a simple mutation process through which each nucleotide is mutated independently with some probability r, under the assumption that there are no spurious k-mer matches. How does this process affect the k-mers of S? We derive the expectation and variance of the number of mutated k-mers and of the number of islands (a maximal interval of mutated k-mers) and oceans (a maximal interval of nonmutated k-mers). We then derive hypothesis tests and confidence intervals (CIs) for r given an observed number of mutated k-mers, or, alternatively, given the Jaccard similarity (with or without MinHash). We demonstrate the usefulness of our results using a few select applications: obtaining a CI to supplement the Mash distance point estimate, filtering out reads during alignment by Minimap2, and rating long-read alignments to a de Bruijn graph by Jabba.

Human drug-pathway association prediction based on network consistency projection.

We present a novel computational method for drug-pathway association prediction based on known drug-pathway associations. The association between a drug and a pathway needs to be examined to not only explain the cause and enable the identification, therapy, and diagnosis of a human disease. Though, biological studies and clinical trials require substantial time and resources to identify drug-pathway associations. Considerable research attention has been devoted to many scientists have developed computer models to predict the future interactions of drug-pathway organizations. We proposed a novel computing approach known as the Network Consistency Projection for Human Drug-Pathway Association (NCPHDPA). This method was based on the drug pathway target wherein biologically related drugs appear to interact with pathway targets in identical diseases and vice versa. We computed the pathway-pathway-interaction similarity of drugs sharing similarities on the basis of pairwise Jaccard similarity and then computed the drug-drug-interaction similarity of drugs sharing similar drug targets based on Jaccard similarity. The system was combined because of the cosine similarity drug network, the pathway cosine resemblance network, and the interaction network for recognized drug-pathway. NCPHDPA was a parameter less solution and did not require negative tests. Notably, NCPHDPA could be used to predict drugs without any known related pathway. Test results showed that our proposed NCPHDPA method with LOOCV achieved a high ROC of AUC = 0.7479, and with10-fold CV obtained ROC of AUC = 0.7566. The Result of ROC (AUC) comparison of NCPHDPA with other methods, such as SIMCCDA LOOCV (AUC = 0.7364), LOMDA LOOCV (AUC = 0.6729) and DMTHNDM LOOCV (AUC = 0.50.00) obtained. The robust predictive capability of the NCPHDPA was demonstrated in three case studies on drugs involved in pathways, cancer pathways, and hepatocellular carcinoma. Few attempts have been made to compared with other methods, our proposed NCPHDPA method had reliable predictive performance. The results yielded some interesting findings as that interaction of these proteins can cause a change in its associated pathway, leading to the onset of cancer.

Detection of Genes in Arabidopsis thaliana L. Responding to DNA Damage from Radiation and Other Stressors in Spaceflight.

Ionizing radiation present in extraterrestrial environment is an important factor that affects plants grown in spaceflight. Pearson correlation-based gene regulatory network inferencing from transcriptional responses of the plant Arabidopsis thaliana L. grown in real and simulated spaceflight conditions acquired by GeneLab, followed by topological and spectral analysis of the networks is performed. Gene regulatory subnetworks are extracted for DNA damage response processes. Analysis of radiation-induced ATR/ATM protein-protein interactions in Arabidopsis reveals interaction profile similarities under low radiation doses suggesting novel mechanisms of DNA damage response involving non-radiation-induced genes regulating other stress responses in spaceflight. The Jaccard similarity index shows that the genes AT2G31320, AT4G21070, AT2G46610, and AT3G27060 perform similar functions under low doses of radiation. The incremental association Markov blanket method reveals non-radiation-induced genes linking DNA damage response to root growth and plant development. Eighteen radiation-induced genes and sixteen non-radiation-induced gene players have been identified from the ATR/ATM protein interaction complexes involved in heat, salt, water, osmotic stress responses, and plant organogenesis. Network analysis and logistic regression ranking detected AT3G27060, AT1G07500, AT5G66140, and AT3G21280 as key gene players involved in DNA repair processes. High atomic weight, high energy, and gamma photon radiation result in higher intensity of DNA damage response in the plant resulting in elevated values for several network measures such as spectral gap and girth. Nineteen flavonoid and carotenoid pigment activations involved in pigment biosynthesis processes are identified in low radiation dose total light spaceflight environment but are not found to have significant regulations under very high radiation dose environment.

Multi-Person Tracking and Crowd Behavior Detection via Particles Gradient Motion Descriptor and Improved Entropy Classifier.

To prevent disasters and to control and supervise crowds, automated video surveillance has become indispensable. In today's complex and crowded environments, manual surveillance and monitoring systems are inefficient, labor intensive, and unwieldy. Automated video surveillance systems offer promising solutions, but challenges remain. One of the major challenges is the extraction of true foregrounds of pixels representing humans only. Furthermore, to accurately understand and interpret crowd behavior, human crowd behavior (HCB) systems require robust feature extraction methods, along with powerful and reliable decision-making classifiers. In this paper, we describe our approach to these issues by presenting a novel Particles Force Model for multi-person tracking, a vigorous fusion of global and local descriptors, along with a robust improved entropy classifier for detecting and interpreting crowd behavior. In the proposed model, necessary preprocessing steps are followed by the application of a first distance algorithm for the removal of background clutter; true-foreground elements are then extracted via a Particles Force Model. The detected human forms are then counted by labeling and performing cluster estimation, using a K-nearest neighbors search algorithm. After that, the location of all the human silhouettes is fixed and, using the Jaccard similarity index and normalized cross-correlation as a cost function, multi-person tracking is performed. For HCB detection, we introduced human crowd contour extraction as a global feature and a particles gradient motion (PGD) descriptor, along with geometrical and speeded up robust features (SURF) for local features. After features were extracted, we applied bat optimization for optimal features, which also works as a pre-classifier. Finally, we introduced a robust improved entropy classifier for decision making and automated crowd behavior detection in smart surveillance systems. We evaluated the performance of our proposed system on a publicly available benchmark PETS2009 and UMN dataset. Experimental results show that our system performed better compared to existing well-known state-of-the-art methods by achieving higher accuracy rates. The proposed system can be deployed to great benefit in numerous public places, such as airports, shopping malls, city centers, and train stations to control, supervise, and protect crowds.

Niche theory-based modeling of assembly processes of viral communities in bats.

Understanding the assembly processes of symbiont communities, including viromes and microbiomes, is important for improving predictions on symbionts' biogeography and disease ecology. Here, we use phylogenetic, functional, and geographic filters to predict the similarity between symbiont communities, using as a test case the assembly process in viral communities of Mexican bats. We construct generalized linear models to predict viral community similarity, as measured by the Jaccard index, as a function of differences in host phylogeny, host functionality, and spatial co-occurrence, evaluating the models using the Akaike information criterion. Two model classes are constructed: a "known" model, where virus-host relationships are based only on data reported in Mexico, and a "potential" model, where viral reports of all the Americas are used, but then applied only to bat species that are distributed in Mexico. Although the "known" model shows only weak dependence on any of the filters, the "potential" model highlights the importance of all three filter types-phylogeny, functional traits, and co-occurrence-in the assemblage of viral communities. The differences between the "known" and "potential" models highlight the utility of modeling at different "scales" so as to compare and contrast known information at one scale to another one, where, for example, virus information associated with bats is much scarcer.

Can Knowledge of Genetic Distances, Genome Sizes and Chromosome Numbers Support Breeding Programs in Hardy Geraniums?

Breeding programs in ornamentals can be facilitated by integrating knowledge of phylogenetic relatedness of potential parents along with other genomic information. Using AFLP, genetic distances were determined for 59 Geranium genotypes, comprising 55 commercial cultivars of the three subgenera of a total collection of 61 Geranium genotypes. A subgroup of 45 genotypes, including intragroup and intergroup hybrids, were selected and further characterized for genome sizes and chromosome numbers. The variation in genome size ranged from 1.51 ± 0.01 pg/2C to 12.94 ± 0.07 pg/2C. The chromosome numbers ranged from 26 to 108-110 with some hybrids showing an aberrant number of chromosomes based on their parents' constitution. All chromosome numbers of Geranium are an even number, which presumes that unreduced gametes occur in some cross combinations. Overall, parental difference in genome size and chromosome number were not limiting for cross compatibility. Good crossing compatibility was correlated to a Jaccard similarity coefficient as parameter for parental relatedness of about 0.5. Additionally, parent combinations with high differences in the DNA/chromosome value could not result in a successful cross. We expect that our results will enable breeding programs to overcome crossing barriers and support further breeding initiatives.

A novel essential protein identification method based on PPI networks and gene expression data.

BACKGROUND: Some proposed methods for identifying essential proteins have better results by using biological information. Gene expression data is generally used to identify essential proteins. However, gene expression data is prone to fluctuations, which may affect the accuracy of essential protein identification. Therefore, we propose an essential protein identification method based on gene expression and the PPI network data to calculate the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network. Our experiments show that the method can improve the accuracy in predicting essential proteins. RESULTS: In this paper, we propose a new measure named JDC, which is based on the PPI network data and gene expression data. The JDC method offers a dynamic threshold method to binarize gene expression data. After that, it combines the degree centrality and Jaccard similarity index to calculate the JDC score for each protein in the PPI network. We benchmark the JDC method on four organisms respectively, and evaluate our method by using ROC analysis, modular analysis, jackknife analysis, overlapping analysis, top analysis, and accuracy analysis. The results show that the performance of JDC is better than DC, IC, EC, SC, BC, CC, NC, PeC, and WDC. We compare JDC with both NF-PIN and TS-PIN methods, which predict essential proteins through active PPI networks constructed from dynamic gene expression. CONCLUSIONS: We demonstrate that the new centrality measure, JDC, is more efficient than state-of-the-art prediction methods with same input. The main ideas behind JDC are as follows: (1) Essential proteins are generally densely connected clusters in the PPI network. (2) Binarizing gene expression data can screen out fluctuations in gene expression profiles. (3) The essentiality of the protein depends on the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network.

Patent relatedness and velocity in the Chinese pharmaceutical industry: A dataset of Jaccard similarity indices.

The dataset is about innovation dynamics in the pharmaceutical industry in China. Innovation dynamics is interpreted as knowledge transfer across technologies and through time (velocity). The dataset provides access to 143,916 Jaccard similarity indices. A Jaccard similarity indice is a distance measure between two units. Here, they proxy relatedness across technologies (classes) and through time (velocity). The Jaccard similarity indices are computed based on a Natural Language Processing treatment of 69,923 patents in the pharmaceutical industry in China from 1990 to 2017.

Spectral Jaccard Similarity: A New Approach to Estimating Pairwise Sequence Alignments.

Pairwise sequence alignment is often a computational bottleneck in genomic analysis pipelines, particularly in the context of third-generation sequencing technologies. To speed up this process, the pairwise k-mer Jaccard similarity is sometimes used as a proxy for alignment size in order to filter pairs of reads, and min-hashes are employed to efficiently estimate these similarities. However, when the k-mer distribution of a dataset is significantly non-uniform (e.g., due to GC biases and repeats), Jaccard similarity is no longer a good proxy for alignment size. In this work, we introduce a min-hash-based approach for estimating alignment sizes called Spectral Jaccard Similarity, which naturally accounts for uneven k-mer distributions. The Spectral Jaccard Similarity is computed by performing a singular value decomposition on a min-hash collision matrix. We empirically show that this new metric provides significantly better estimates for alignment sizes, and we provide a computationally efficient estimator for these spectral similarity scores.

Novel similarity measures in spherical fuzzy environment and their applications.

Spherical fuzzy sets (SFSs) have gained great attention from researchers in various fields. The spherical fuzzy set is characterized by three membership functions expressing the degrees of membership, non-membership and the indeterminacy to provide a larger preference domain. It was proposed as a generalization of picture fuzzy sets and Pythagorean fuzzy sets in order to deal with uncertainty and vagueness information. The similarity measure is one of the essential and advantageous tools to determine the degree of similarity between items. Several studies on similarity measures have been developed due to the importance of similarity measure and application in decision making, data mining, medical diagnosis, and pattern recognition in the literature. The contribution of this study is to present some novel spherical fuzzy similarity measures. We develop the Jaccard, exponential, and square root cosine similarity measures under spherical fuzzy environment. Each of these similarity measures is analyzed with respect to decision-makers' optimistic or pessimistic point of views. Then, we apply these similarity measures to medical diagnose and green supplier selection problems. These similarity measures can be computed easily and they can express the dependability similarity relation apparently.

Evaluation of Automatic Facial Wrinkle Detection Algorithms.

Facial wrinkles (considered to be natural features) appear as people get older. Wrinkle detection is an important aspect of applications that depend on facial skin changes, such as face age estimation and soft biometrics. While existing wrinkle detection algorithms focus on forehead horizontal lines, it is necessary to develop new methods to detect all wrinkles (vertical and horizontal) on whole face. Therefore, we evaluated the performance of wrinkle detection algorithms on the whole face and proposed an enhancement technique to improve the performance. More specifically, we used 45 images of the Face Recognition Technology dataset (FERET) and 25 images of the Sudanese dataset. For ground truth annotations, the selected images were manually annotated by the researcher. The experiments showed that the method with enhancement performed better at detecting facial wrinkles when compared to the state-of-the-art methods. When evaluated on FERET, the average Jaccard similarity indices were 56.17%, 31.69% and 15.87% for the enhancement method, Hybrid Hessian Filter and Gabor Filter, respectively.

Comparative Analysis of Algorithmic Approaches for Auto-Coding with ICD-10-AM and ACHI.

Clinical coding is done using ICD-10-AM (International Classification of Diseases, version 10, Australian Modification) and ACHI (Australian Classification of Health Interventions) in acute and sub-acute hospitals in Australia for funding, insurance claims processing and research. The task of assigning a code to an episode of care is a manual process. This has posed challenges due to increase set of codes, the complexity of care episodes, and large training and recruitment costs of clinical coders. Use of Natural Language Processing (NLP) and Machine Learning (ML) techniques is considered as a solution to this problem. This paper carries out a comparative analysis on a selected set of NLP and ML techniques to identify the most efficient algorithm for clinical coding based on a set of standard metrics: precision, recall, F-score, accuracy, Hamming loss and Jaccard similarity.

Daphniid zooplankton assemblage shifts in response to eutrophication and metal contamination during the Anthropocene.

Human activities during the Anthropocene result in habitat degradation that has been associated with biodiversity loss and taxonomic homogenization of ecological communities. Here we estimated effects of eutrophication and heavy metal contamination, separately and in combination, in explaining zooplankton species composition during the past 125-145 years using analysis of daphniid diapausing egg banks from four lakes in the northeastern USA. We then examined how these community shifts influenced patterns of diversity and homogenization. Analysis of past lake production (via subfossil pigments) and metal contamination (via sedimentary metals) demonstrated that eutrophication alone (19-39%) and in combination with metal pollution (17-54%) explained 36-79% of historical variation in daphniid species relative abundances in heavily fertilized lakes. In contrast, metal pollution alone explained the majority (72%) of historical variation in daphniid assemblages at the oligotrophic site. Several species colonization events in eutrophying lakes resulted in increased species richness and gamma diversity through time. At the same time, daphniid assemblages in three eutrophied lakes became more similar to each other (homogenized), but this pattern was only seen when accounting for species presence/absence. We did not observe consistent patterns of divergence between the assemblages in the eutrophying lakes and the low-nutrient reference site. Given the pervasive nature of fertilization and metal pollution, and the sensitivity of cladocerans to these factors, we suggest that many inhabited lake districts may already exhibit similar patterns of daphniid assemblage shifts.

Ethnopharmacological survey of medicinal plants used in Daraa-Tafilalet region (Province of Errachidia), Morocco.

ETHNOPHARMACOLOGICAL RELEVANCE: We carried out an ethnobotanical survey in Tafilalet region. This region is classified by the UNESCO as a reserve of biosphere and represents an important area with important knowledge of traditional medicine, especially the use of medicinal plants for human healthcare. Furthermore, the geographic location of this region makes this site a diverse and interesting resource of herbal biodiversity. THE AIM OF THE STUDY: The study aimed to collect information about medicinal plants used in Tafilalet region as well as the indigenous knowledge related to the use of this natural resource in healthcare by the local population in order to preserve and protect this invaluable inheritance from loss and overlook. We aimed also to compare taxa used by the indigenous people of Tafilalet for health-care purposes in comparison with other regions of Morocco as well as neighboring countries. MATERIAL AND METHODS: The total of informants interviewed in this study was 1616 (1500 were local inhabitants and 116 were herbalists). This enquiry was carried out through semi-structured and unstructured interviews and the sampling technique used was the stratified sample (9 stratums). Data obtained were analyzed calculating 6 indices: Use Value (UV), Family Use Value (FUV), Fidelity Level (FL), Rank Order Priority (ROP), Informant Consensus Factor (Fic) and Jaccard similarity Index (JI). RESULTS: 194 species belonging to 69 families were inventoried in this survey and 17 species were cited for the first time in an ethnobotanical survey in Morocco. The highest value of UV was obtained for Rosmarinus officinalis L. (UV=0.24) and Liliaceae was the family frequently used by inhabitants of Tafilalet (FUV=0.106). In addition, the highest value of FL was recorded for Cistus salviifolius L. and Daphne gnidium L. with FL value of 100% for both species and Origanium vulgare L. had the highest ROP with a value of 53% while the highest value of FIC was mentioned for digestive system disorders (FIC=0.29). Concerning the level of similarity between our study and other regions of Morocco, the province of Tata seems to be the most similar to Tafilalet (JI=42.97), while M'sila (Algeria) was the most similar to Tafilalet among areas in neighboring countries (JI=13.00). CONCLUSION: Despite the richness which characterizes Tafilalet regarding diversity and effectiveness of medicinal plants as well as the largest culture and knowledge related to the popular phytotherapy among local people in this region, several procedures must be realized to protect and to valorize this interesting inheritance.