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Aim: Functional diversity is a key facet of biodiversity that is increasingly being measured to quantify its changes following disturbance and to understand its effects on ecosystem functioning. Assessing the functional diversity of assemblages based on species traits requires the building of a functional space (dendrogram or multidimensional space) where indices will be computed. However, there is still no consensus on the best method for measuring the quality of functional spaces. Innovation: Here we propose a framework for evaluating the quality of a functional space (i.e. the extent to which it is a faithful representation of the initial functional trait values). Using simulated dataseis, we analysed the influence of the number and type of functional traits used and of the number of species studied on the identity and quality of the best functional space. We also tested whether the quality of the functional space affects functional diversity patterns in local assemblages, using simulated datasets and a real study case. Main conclusions: The quality of functional space strongly varied between situations. Spaces having at least four dimensions had the highest quality, while functional dendrograms and two-dimensional functional spaces always had a low quality. Importantly, we showed that using a poor-quality functional space could led to a biased assessment of functional diversity and false ecological conclusions. Therefore, we advise a pragmatic approach consisting of computing all the possible functional spaces and selecting the most parsimonious one.

Shalini, R.; Arisekar, U.; Jeyasekaran, G; Shakila, R.J.; Sundhar, S.; Sivaraman, B., and Tamizhselvan, S., 2025. Mineral composition of the commercially valuable fish and shellfish caught along the Thoothukudi Coast, Southeast India. Journal of Coastal Research, 41(1), 94–104. Charlotte (North Carolina), ISSN 0749-0208. The SE coast of India boasts a diverse array of fish species, influenced by its geographical features, coastal habitats, and marine ecosystems. To study the nutritional value of fish and shellfish, the concentration of seven minerals (calcium [Ca], magnesium [Mg], iron [Fe], zinc [Zn], copper [Cu], manganese [Mn], and selenium [Se]) in 28 commercially valuable finfish, crustaceans, cephalopods, and bivalves along Thoothukudi region of the SE coast of India was analyzed using an inductively coupled plasma–mass spectrometer. Based on the concentration, the minerals were in the order of Mg > Ca > Fe > Zn > Cu > Mn > Se. The mineral concentration varied among different finfish and shellfish. The highest total mineral concentration was recorded in oysters (Magallana bilineata; 112.8 mg/100 g), followed by ribbonfish (Trichiurus lepturus; 74.7 mg/100 g), and flower shrimp (Penaeus semisulcatus; 71.6 mg/100 g). The Indian mackerel (Rastrelliger kanagurta) and ribbonfish recorded the highest Ca (36.4 mg/100 g) and Fe (7.42 ± 2.25 mg/100 g) concentrations. Among shellfish, M. bilineata recorded the highest concentration of Ca (38.3 mg/100 g), Cu (3.73 mg/ 100 g), Fe (5.02 mg/100 g), and Zn (28.7 mg/100 g). The results indicate that ribbonfish, goldenstripe sardine, Indian mackerel, and oysters are good mineral sources. This study provides the baseline data regarding the mineral concentration of valuable finfish and shellfish along the SE coast of India.

ABSTRACT Tamarind is a tropical fruit highly cultivated in some countries. In Brazil, still there is a little exploitation of tamarind fruits, but its characterization can subsidize the commercial exploitation. Fruit characterization enables the study of genetic diversity between matrices or populations, allowing the identification of possible genitors, or even genotypes with superior characteristics. The aim of this study was to characterize and evaluate the genetic diversity of the physical characteristics of tamarind fruit, in order to provide subsidies for genetic improvement programs and conservation of the species. The fruits were evaluated by weight, diameter and length, color of epidermis and pulp. A completely randomized design was adopted, with 4 treatments (plants) and 7 replicates of 10 fruits, totaling 280 fruits. The data were subjected to analysis of variance, and the means were compared using the Duncan test. The matrix 4 presented higher values of weight, length and diameter, when compared to the others. There is genetic diversity among the tamarind matrices. Two groups were formed using the dendrogram generated by the UPGMA method. A strong correlation was observed between fruit length and weight, peel weight and fruit weight, and peel weight and length, which is crucial information for genetic improvement.

A general scheme for divisive hierarchical clustering algorithms is proposed. It is made of three main steps: first a splitting procedure for the subdivision of clusters into two subclusters, second a local evaluation of the bipartitions resulting from the tentative splits and, third, a formula for determining the node levels of the resulting dendrogram. A set of 12 such algorithms is presented and compared to their agglomerative counterpart (when available). These algorithms are evaluated using the Goodman-Kruskal correlation coefficient. As a global criterion it is an internal goodness-of-fit measure based on the set order induced by the hierarchy compared to the order associated with the given dissimilarities. Applied to a hundred random data tables and to three real life examples, these comparisons are in favor of methods which are based on unusual ratio-type formulas to evaluate the intermediate bipartitions, namely the Silhouette formula, the Dunn's formula and the Mollineda et al. formula. These formulas take into account both the within cluster and the between cluster mean dissimilarities. Their use in divisive algorithms performs very well and slightly better than in their agglomerative counterpart.

Social media is becoming a primary medium to discuss what is happening around the world. Therefore, the data generated by social media platforms contain rich information which describes the ongoing events. Further, the timeliness associated with these data is capable of facilitating immediate insights. However, considering the dynamic nature and high volume of data production in social media data streams, it is impractical to filter the events manually and therefore, automated event detection mechanisms are invaluable to the community. Apart from a few notable exceptions, most previous research on automated event detection have focused only on statistical and syntactical features in data and lacked the involvement of underlying semantics which are important for effective information retrieval from text since they represent the connections between words and their meanings. In this paper, we propose a novel method termed Embed2Detect for event detection in social media by combining the characteristics in word embeddings and hierarchical agglomerative clustering. The adoption of word embeddings gives Embed2Detect the capability to incorporate powerful semantical features into event detection and overcome a major limitation inherent in previous approaches. We experimented our method on two recent real social media data sets which represent the sports and political domain and also compared the results to several state-of-the-art methods. The obtained results show that Embed2Detect is capable of effective and efficient event detection and it outperforms the recent event detection methods. For the sports data set, Embed2Detect achieved 27% higher F-measure than the best-performed baseline and for the political data set, it was an increase of 29%.

Consumption of contaminated meat, milk, and water are among the major routes of human campylobacteriosis. This study aimed to determined the genetic diversity of C. coli and C. jejuni isolated from meat, milk, and water samples collected from different locations. From the 376 samples (meat = 248, cow milk = 72, and water = 56) collected, a total of 1238 presumptive Campylobacter isolates were recovered and the presence of the genus Campylobacter were detected in 402 isolates, and from which, 85 and 67 isolates were identified asC. jejuni and C. coli respectively. Of which, 71 isolates identified as C. coli (n = 35) and C. jejuni (n = 36) were randomly selected from meat, milk, and water samples and were genotyped using enterobacterial repetitive intergenic consensus PCR (ERIC-PCR). The digital images of the ERIC-PCR genotype were analyzed by GelJ v.2.0 software. The diversity and similarity of the isolates were assessed via an unweighted-pair group method using average linkages clustering algorithm. The results showed that the 36 C. jejuni strains separated into 29 ERIC-genotypes and 4 clusters while the 35 C. coli were delineated into 29 ERIC-genotypes and 6 clusters. The study revealed the genetic diversity among C. coli and C. jejuni strains recovered from different matrices characterized by Gelj.

DNA microarray and gene expression problems often require a researcher to perform clustering on their data in a bid to better understand its structure. In cases where the number of clusters is not known, one can resort to hierarchical clustering methods. However, there currently exist very few automated algorithms for determining the true number of clusters in the data. We propose two new methods (mode and maximum difference) for estimating the number of clusters in a hierarchical clustering framework to create a fully automated process with no human intervention. These methods are compared to the established elbow and gap statistic algorithms using simulated datasets and the Biobase Gene ExpressionSet. We also explore a data mixing procedure inspired by cross validation techniques. We find that the overall performance of the maximum difference method is comparable or greater to that of the gap statistic in multi-cluster scenarios, and achieves that performance at a fraction of the computational cost. This method also responds well to our mixing procedure, which opens the door to future research. We conclude that both the mode and maximum difference methods warrant further study related to their mixing and cross-validation potential. We particularly recommend the use of the maximum difference method in multi-cluster scenarios given its accuracy and execution times, and present it as an alternative to existing algorithms.

We analyzed literature data on the composition of 479 glasses used to stabilize radioactive wastes, covering a wide range and including 51 oxides and few fluorides. The most common glass constituents included SiO 2 , B 2 O 3 , Na 2 O, and Fe 2 O 3 in varying amounts, with a predominance of borosilicate glasses. Seven families of waste radioactive glasses were observed, including borosilicates, silicates, boroaluminosilicates, iron phosphates, aluminosilicates, sodium iron phosphates, and boroaluminates. These data were used to estimate an average composition associated with a sodium borosilicate glass. Multivariate exploratory methods were used to analyze and classify the compositions of the waste radioactive glasses using multidimensional scaling and hierarchical clustering. Four main clusters were observed, the largest with 417 glasses, mainly silicates, borosilicates, aluminosilicates, and boroaluminosilicates. The results of this work have shown that it is possible to map radioactive waste glasses according to their composition, promoting a road map for future applications with specific properties.

Cotton (Gossypium hirsutum) is a critical fiber crop and a major source of edible oil, playing a pivotal role in both the textile industry and human nutrition. While various molecular markers have been employed to assess genetic diversity in cotton, there remains an opportunity to further explore its genetic potential. This study aimed to investigate the genetic diversity of cotton using Simple Sequence Repeats (SSR) markers. We applied 10 SSR markers to a collection of 50 cotton genotypes, including 20 interspecific and 30 intraspecific cultivars. Our analysis revealed that the number of bands per marker ranged from 3 to 5 in interspecific genotypes and was consistently 2 in intraspecific genotypes. The Polymorphic Information Content (PIC) values ranged from 0.4886 to 0.8201 in interspecific cotton, with an average PIC value of 0.6602, whereas in intraspecific cotton, PIC values ranged from 0.3047 to 0.3747, with an average of 0.3608. The highest PIC values in interspecific cotton were observed with primers NAU3897 and NAU5172, which had PIC values of 0.8201 and 0.7727, respectively. For intraspecific cotton, the highest PIC values were obtained with primers NAU3897 and NAU3009, which had PIC values of 0.3747. These results indicate a high level of genetic variation among interspecific cotton genotypes, as revealed by the SSR markers. The SSR primers with high PIC values identified in this study are valuable for crop breeders, thereby offering tools for selecting superior germplasm, assessing genetic diversity, and conducting molecular mapping. These insights are crucial for future cotton varietal development programs.