Detecting and analysing intraspecific genetic variation with eDNA: From population genetics to species abundance.

Advancements in environmental DNA (eDNA) approaches have allowed for rapid and efficient species detections in diverse environments. Although most eDNA research is focused on leveraging genetic diversity to identify taxa, some recent studies have explored the potential for these approaches to detect within-species genetic variation, allowing for population genetic assessments and abundance estimates from environmental samples. However, we currently lack a framework outlining the key considerations specific to generating, analysing and applying eDNA data for these two purposes. Here, we discuss how various genetic markers differ with regard to genetic information and detectability in environmental samples and how analysis of eDNA samples differs from common tissue-based analyses. We then outline how it may be possible to obtain species absolute abundance estimates from eDNA by detecting intraspecific genetic variation in mixtures of DNA under multiple scenarios. We also identify the major causes contributing to allele detection and frequency errors in eDNA data, discuss their consequences for population-level analyses and outline bioinformatic approaches to detect and remove erroneous sequences. This review summarizes the key advances required to harness the full potential of eDNA-based intraspecific genetic variation to inform population-level questions in ecology, evolutionary biology and conservation management.

Biodiversity underpins fisheries resilience to exploitation in the Amazon river basin.

Inland fisheries feed greater than 150 million people globally, yet their status is rarely assessed due to their socio-ecological complexity and pervasive lack of data. Here, we leverage an unprecedented landings time series from the Amazon, Earth's largest river basin, together with theoretical food web models to examine (i) taxonomic and trait-based signatures of exploitation in inland fish landings and (ii) implications of changing biodiversity for fisheries resilience. In both landings time series and theory, we find that multi-species exploitation of diverse inland fisheries results in a hump-shaped landings evenness curve. Along this trajectory, abundant and large species are sequentially replaced with faster growing and smaller species. Further theoretical analysis indicates that harvests can be maintained for a period of time but that continued biodiversity depletion reduces the pool of compensating species and consequently diminishes fisheries resilience. Critically, higher fisheries biodiversity can delay fishery collapse. Although existing landings data provide an incomplete snapshot of long-term dynamics, our results suggest that multi-species exploitation is affecting freshwater biodiversity and eroding fisheries resilience in the Amazon. More broadly, we conclude that trends in landings evenness could characterize multi-species fisheries development and aid in assessing their sustainability.

A machine learning approach to identify barriers in stream networks demonstrates high prevalence of unmapped riverine dams.

Restoring stream ecosystem integrity by removing unused or derelict dams has become a priority for watershed conservation globally. However, efforts to restore connectivity are constrained by the availability of accurate dam inventories which often overlook smaller unmapped riverine dams. Here we develop and test a machine learning approach to identify unmapped dams using a combination of publicly available topographic and geospatial habitat data. Specifically, we trained a random forest classification algorithm to identify unmapped dams using digitally engineered predictor variables and known dam sites for validation. We applied our algorithm to two subbasins in the Hudson River watershed, USA, and quantified connectivity impacts, as well as evaluated a range of predictor sets to examine tradeoffs between classification accuracy and model parameterization effort. The random forest classifier achieved high accuracy in predicting dam sites (true positive rate = 89%, false positive rate = 1.2%) using a subset of variables related to stream slope and presence of upstream lentic habitats. Unmapped dams were prevalent throughout the two test watersheds. In fact, existing dam inventories underestimated the true number of dams by ∼80-94%. Accounting for previously unmapped dams resulted in a 62-90% decrease in dendritic connectivity indices for migratory fishes. Unmapped dams may be pervasive and can dramatically bias stream connectivity information. However, we find that machine learning approaches can provide an accurate and scalable means of identifying unmapped dams that can guide efforts to develop accurate dam inventories, thereby informing and empowering efforts to better manage them.

Twitter sentiment analysis using ensemble based deep learning model towards COVID-19 in India and European countries.

As of November 2021, more than 24.80 crore people are diagnosed with the coronavirus in that around 50.20 lakhs people lost their lives, because of this infectious disease. By understanding the people's sentiment's expressed in their social media (Facebook, Twitter, Instagram etc.) helps their governments in controlling, monitoring, and eradicating the coronavirus. Compared to other social media's, the twitter data are indispensable in the extraction of useful awareness information related to any crisis. In this article, a sentiment analysis model is proposed to analyze the real time tweets, which are related to coronavirus. Initially, around 3100 Indian and European people's tweets are collected between the time period of 23.03.2020 to 01.11.2021. Next, the data pre-processing and exploratory investigation are accomplished for better understanding of the collected data. Further, the feature extraction is performed using Term Frequency-Inverse Document Frequency (TF-IDF), GloVe, pre-trained Word2Vec, and fast text embedding's. The obtained feature vectors are fed to the ensemble classifier (Gated Recurrent Unit (GRU) and Capsule Neural Network (CapsNet)) for classifying the user's sentiment's as anger, sad, joy, and fear. The obtained experimental outcomes showed that the proposed model achieved 97.28% and 95.20% of prediction accuracy in classifying the both Indian and European people's sentiments.

Trajectories of Systolic Blood Pressure in Children: Risk Factors and Cardiometabolic Correlates.

OBJECTIVE: To identify systolic blood pressure (SBP) percentile trajectories in children and to describe the early-life risk factors and cardiometabolic correlates of those trajectories. STUDY DESIGN: Using age-, sex-, and height-specific SBP percentiles based on the American Academy of Pediatrics reference, we examined SBP trajectories using latent class mixed models from ages 3 to 8 years (n = 844) from the Growing Up in Singapore Towards healthy Outcomes-study, a Singaporean mother-offspring cohort study. We analyzed associations between SBP trajectories and early-life risk factors using multinomial logistic regression and differences across trajectories in cardiometabolic outcomes using multiple linear regression. RESULTS: Children were classified into 1 of 4 SBP percentile trajectories: "low increasing" (15%), "high stable" (47%), "high decreasing" (20%), and "low stable" (18%). Maternal hypertension during early pregnancy was a predictor of the "high stable" and "low increasing" SBP trajectories. Rapid child weight gain in the first 2 years of life was only associated with the "high stable" trajectory. Compared with children in the "low stable" trajectory, children in the "high stable" SBP trajectory had greater body mass index z scores, sum of skinfold thicknesses, waist circumference from ages 3 to 8 years, and abdominal adipose tissue (milliliters) at 4.5 years (adjusted mean difference [95% CI]: superficial and deep subcutaneous abdominal adipose tissue: 115.2 [48.1-182.3] and 85.5 [35.2-135.8]). Their fat mass (kilograms) (1.3 [0.6-2.0]), triglyceride levels (mmol/L) (0.10 [0.02-0.18]), and homeostasis model assessment of insulin resistance (0.28 [0.11 0.46]) at age 6 years were also greater but not their arterial thickness and stiffness. CONCLUSIONS: Reducing maternal blood pressure during pregnancy and infant weight gain in the first 2 years of life might help to prevent the development of high SBP.

Nuclear eDNA estimates population allele frequencies and abundance in experimental mesocosms and field samples.

Advances in environmental DNA (eDNA) methodologies have led to improvements in the ability to detect species and communities in aquatic environments, yet the majority of studies emphasize biological diversity at the species level by targeting variable sites within the mitochondrial genome. Here, we demonstrate that eDNA approaches also have the capacity to detect intraspecific diversity in the nuclear genome, allowing for assessments of population-level allele frequencies and estimates of the number of genetic contributors in an eDNA sample. Using a panel of microsatellite loci developed for the round goby (Neogobius melanostomus), we tested the similarity between eDNA-based and individual tissue-based estimates of allele frequencies from experimental mesocosms and in a field-based trial. Subsequently, we used a likelihood-based DNA mixture framework to estimate the number of unique genetic contributors in eDNA samples and in simulated mixtures of alleles. In both mesocosm and field samples, allele frequencies from eDNA were highly correlated with allele frequencies from genotyped round goby tissue samples, indicating nuclear markers can be reliably amplified from water samples. DNA mixture analyses were able to estimate the number of genetic contributors from mesocosm eDNA samples and simulated mixtures of DNA from up to 58 individuals, with the degree of positive or negative bias dependent on the filtering scheme of low-frequency alleles. With this study we document the application of eDNA and multiple amplicon-based methods to obtain intraspecific nuclear genetic information and estimate the absolute abundance of a species in eDNA samples. With proper validation, this approach has the potential to advance noninvasive survey methods to characterize populations and detect population-level genetic diversity.

Conformational topography of tris(2-methylbutyl) phosphate and the influence of methyl branching at the non-hyperconjugative carbon on the conformational landscape: insights from matrix isolation infrared spectroscopy and DFT computations.

The branching of a methyl group in a linear chain has a profound influence on the conformational morphology as it wields a strong control in reducing a large number of conformations. To unravel the effect of branching on the second non-hyperconjugative carbon atom on the conformational landscape, the conformations of tris(2-methylbutyl)phosphate (T2MBP) were studied using Density Functional Theory (DFT) computations and matrix isolation infrared spectroscopy. Experimentally, T2MBP along with N2/Ar/Kr/Xe gases was effusively expanded and deposited at a low temperature of 12 K, which was subsequently probed using infrared spectroscopy. The computations of all the conformations were accomplished using the B3LYP level of theory with the 6-311++G(d,p) basis set. A dimethyl(2-methylbutyl) phosphate (DM2MBP) prototype, a molecule containing a single 2-methylbutyl moiety, was examined for its conformations. Computations predicted 18 and 9 conformations each for the 'gauche' and 'trans' families, respectively, in which the third branched carbon completely influences the orientation of the fourth carbon, which simplifies the conformational problem of DM2MBP. Of the 18 and 9 bunches each in the 'gauche' and 'trans' families, only 7 and 3 conformations, respectively, became energetically important, which when extrapolated to T2MBP resulted in 343 and 147 conformational possibilities. The factor of degeneracy further reduced these numbers and a total of 168 conformations effectively contribute to the conformational composition of T2MBP in the gas phase. The role of stereo electronic and steric factors prevalent in the conformational clusters of T2MBP was unravelled respectively using natural bond orbital and non-covalent interaction analyses.

Efficacy of local anaesthetic solutions on the success of inferior alveolar nerve block in patients with irreversible pulpitis: a systematic review and network meta-analysis of randomized clinical trials.

The management of pain during root canal treatment is important. The aim of this systematic review and network meta-analysis was to identify the anaesthetic solution that would provide the best pulpal anaesthesia for inferior alveolar nerve blocks (IANB) treating mandibular teeth with irreversible pulpitis. Two electronic databases (PubMed and Scopus) were searched to identify studies up to October 2018. Randomized clinical trials comparing at least two anaesthetic solutions (lidocaine (lignocaine), articaine, bupivacaine, prilocaine or mepivacaine) used for IANB for treatment of irreversible pulpitis were included. The revised Cochrane risk of bias tool for randomized trials was used to assess the quality of the included studies. Pairwise meta-analysis, network meta-analysis using a random-effects model, and SUCRA ranking were performed. The network meta-analysis estimated the probability of each treatment performing best. The quality of evidence was assessed using the Grading of Recommendations, Assessment, Development and Evaluations approach. In total, 11 studies (n = 750) were included in the meta-analysis. The network meta-analysis revealed that only mepivacaine significantly increased the success rate of IANB compared to lidocaine (RR, 1.42 [95% CI 1.04-1.95]). However, no significant differences in the success rate of IANB were observed between mepivacaine and other anaesthetic agents (articaine and bupivacaine). Of all anaesthetic agents, mepivacaine (SUCRA = 0.81) ranked first in increasing the success rate of IANB, followed by prilocaine (SUCRA = 0.62), articaine (SUCRA = 0.54), bupivacaine (SUCRA = 0.41) and lidocaine (SUCRA = 0.13). The overall quality of evidence was very low to moderate. In conclusion, based on the evidence from the randomized clinical trials included in this review, mepivacaine with epinephrine demonstrated the highest probability of providing effective pulpal anaesthesia using IANB for teeth with irreversible pulpitis compared to prilocaine, articaine, bupivacaine and lidocaine. Further, high-quality clinical trials are needed to support the conclusion of this review.

A Novel Internet of Things Framework Integrated with Real Time Monitoring for Intelligent Healthcare Environment.

During mammogram screening, there is a higher probability that detection of cancers is missed, and more than 16 percentage of breast cancer is not detected by radiologists. This problem can be solved by employing image processing algorithms which enhances the accuracy of the diagnostic through image segmentation which reduces the misclassified malignant cancers. By employing segmentation, the unnecessary regions in the breast close to the boundary between the breast tissue and segmented pectoral muscle can be removed, therefore enhancing the accuracy the calculation as well as feature estimation. In-order to enhance the accuracy of classification, the proposed classifier integrates the decision trees and neural network into a system to report the progress of the breast cancer patients in an appropriate manner with the help of technology used in healthcare system. The proposed classifier successfully demonstrated that it achieved more accurate prediction when compared with other widely used algorithms, namely, K-Nearest Neighbors, Support Vector Machine and Naive Bayes algorithm.

Influence of Branching on the Conformational Space: Case Study of Tri- sec-butyl Phosphate Using Matrix Isolation Infrared Spectroscopy and DFT Computations.

The conformational analysis of long chain phosphates poses a serious challenge due to the presence of rotationally flexible multiple alkyl groups. Tri- sec-butyl phosphate (TsBP) is an interesting example, in which branching can be expected to influence the conformational landscape. To solve the conformational problem of TsBP systematically, the conformations of model dimethyl- sec-butyl phosphate (DMsBP), a molecule possessing a single secondary butyl strand, were analyzed. On the basis of the analysis of the energy profile of DMsBP, a few conformational bunches were eliminated. The presence of branched methyl group appears to completely influence the conformational space of TsBP and as a result, the number of conformations is drastically reduced in comparison to its structural isomer, tri- n-butyl phosphate (TBP). B3LYP level of theory in association with 6-311++G(d,p) basis set was used for computing all the conformer geometries. Experimentally, the conformations of TsBP were studied using infrared spectroscopy by trapping the molecule in N2 and Ar matrixes at low temperatures, which were correlated well with the computational results.

Artificial Intelligence Based Skin Classification Using GMM.

This study describes the usage of neural community based on the texture evaluation of pores and skin a variety of similarities in their signs, inclusive of Measles (rubella), German measles (rubella), and Chickenpox etc. In fashionable, these illnesses have similarities in sample of infection and symptoms along with redness and rash. Various skin problems have similar symptoms. For example, in German measles (rubella), Chicken pox and Measles (rubella) a similarity can be observed in skin rashes and redness. The prognosis of skin problems take a long time as the patient's previous medical records, physical examination report and the respective laboratory diagnostic reports have to be studied. The recognition and diagnosis get tough due to the complexity involved. Subsequently, a computer aided analysis and recognition gadget would be handy in such cases. Computer algorithm steps include image processing, picture characteristic extraction and categorize facts with the help of a classifier with Artificial Neural Network (ANN). The ANN can analyze the patterns of symptoms of a particular disease and present faster prognosis and reputation than a human doctor. For this reason, the patients can undergo the treatment for the pores and skin problems based totally on the symptoms detected.

Faster eating rates are associated with higher energy intakes during an ad libitum meal, higher BMI and greater adiposity among 4·5-year-old children: results from the Growing Up in Singapore Towards Healthy Outcomes (GUSTO) cohort.

Faster eating rates are associated with increased energy intake, but little is known about the relationship between children's eating rate, food intake and adiposity. We examined whether children who eat faster consume more energy and whether this is associated with higher weight status and adiposity. We hypothesised that eating rate mediates the relationship between child weight and ad libitum energy intake. Children (n 386) from the Growing Up in Singapore Towards Healthy Outcomes cohort participated in a video-recorded ad libitum lunch at 4·5 years to measure acute energy intake. Videos were coded for three eating-behaviours (bites, chews and swallows) to derive a measure of eating rate (g/min). BMI and anthropometric indices of adiposity were measured. A subset of children underwent MRI scanning (n 153) to measure abdominal subcutaneous and visceral adiposity. Children above/below the median eating rate were categorised as slower and faster eaters, and compared across body composition measures. There was a strong positive relationship between eating rate and energy intake (r 0·61, P<0·001) and a positive linear relationship between eating rate and children's BMI status. Faster eaters consumed 75 % more energy content than slower eating children (Δ548 kJ (Δ131 kcal); 95 % CI 107·6, 154·4, P<0·001), and had higher whole-body (P<0·05) and subcutaneous abdominal adiposity (Δ118·3 cc; 95 % CI 24·0, 212·7, P=0·014). Mediation analysis showed that eating rate mediates the link between child weight and energy intake during a meal (b 13·59; 95 % CI 7·48, 21·83). Children who ate faster had higher energy intake, and this was associated with increased BMI z-score and adiposity.

Complexation Behavior of the Tri-n-butyl Phosphate Ligand with Pu(IV) and Zr(IV): A Computational Study.

Tri-n-butyl phosphate (TBP), used as the extractant in nuclear fuel reprocessing, shows superior extraction abilities for Pu(IV) over a large number of fission products including Zr(IV). We have applied density functional theory (DFT) calculations to explain this selectivity by investigating differences in electronic structures of Pu(NO3)4·2TBP and Zr(NO3)4·2TBP complexes. On the basis of our quantum chemical calculations, we have established the lowest energy electronic states for both complexes; the quintet is the ground state for the former, whereas the latter exists in the singlet spin state. The calculated structural parameters for the optimized geometry of the plutonium complex are in agreement with the experimental results. Atoms in Molecules analysis revealed a considerable amount of ionic character to M-O{TBP} and M-O{NO3} bonds. Additionally, we have also investigated the extraction behavior of TBP for metal nitrates and have estimated the extraction energies to be -73.1 and -57.6 kcal/mol for Pu(IV) and Zr(IV), respectively. The large extraction energy of Pu(IV) system is in agreement with the observed selectivity in the extraction of Pu.

Structural, optical, and thermoluminescence characterizations of 1 mol% Dy3+ion-activated Fluro Boro-phosphate glass for photonic devices.

A novel Fluro boro-phosphate host matrix doped with the 1 mol% of Dy3+ ions (50B2O3 + 20P2O5 + 10TiO2 + 10SrCO3 + 4BaF2 + 5BaCO3 + 1Dy2O3) was prepared using a conventional melt-quenching mechanism, and its structural characteristics were explored through the Powder-XRD, FT-IR, FT-Raman, EDAX and SEM spectroscopic analysis. The XRD spectrum of the glass confirmed its non-crystalline or amorphous structure. FT-IR and FT-Raman spectrum studies revealed that various borate and phosphate groups present with a variety of stretching and bending vibrations. Scanning Electron Microscope (SEM) and Energy Dispersive X-ray analysis (EDAX) analysis have been used to examine the surface morphology and the presence of elements, respectively in the prepared glass. The optical absorption spectrum was used to explore the electronic band structure through the measurements of optical band-gap energy and Urbach energy. The luminescence spectrum reveals the emission characteristics of Dy3+ ions due to the electric-dipole and magnetic-dipole transitions. It is found that the decay time of the 4F9/2 excited level at a concentration of 1 mol% Dy3+ in the glass matrix is tri-fit non-exponential. The CIE chromaticity coordinates and the concentration influence on Y/B intensity ratios were computed for the creation of white light from the luminescence spectrum. The present work also discusses the findings after figuring out the correlated color temperature associated (CCT) with the color purity (Pe). The Thermoluminescence (TL) characteristics and the kinetic parameters of the glass were studied after the γ-irradiation with a dose of 2 kGy. EPR investigation revealed the paramagnetic characteristics through the hyperfine structure of Dy3+ ions and the electron-hole pair formation upon irradiation in the glass matrix.

Locomotion and the early Mesozoic success of Archosauromorpha.

The Triassic was a time of ecological upheaval as life recovered from the Permian-Triassic mass extinction. Archosauromorphs were a key component of the recovery, diversifying substantially during the Triassic and encompassing the origins of dinosaurs, pterosaurs and crocodylomorphs. Here, we explore the evolution of locomotion in Archosauromorpha to test whether dinosaurs show any distinctive locomotory features that might explain their success. We implement geometric morphometrics on limb bone shapes and use limb ratios to calculate bipedality and cursoriality metrics. We find that the Avemetatarsalia (dinosaurs, pterosaurs and relatives) exhibit more variable limb form and limb ratios than any other group, indicating a wider range of locomotory modes. The earliest avemetatarsalians were bipedal and cursorial, and their range of form increased through the Triassic with notable diversification shifts following extinction events. This is especially true of dinosaurs, even though these changes cannot be discriminated from a stochastic process. By contrast, the Pseudosuchia (crocodilians and relatives) were more restricted in limb form and locomotor mode with disparity decreasing through time, suggesting more limited locomotor adaptation and vulnerability to extinction. Perhaps the greater locomotor plasticity of dinosaurs gave them a competitive advantage in the changing climates of the Late Triassic.

Predatory synapsid ecomorphology signals growing dynamism of late Palaeozoic terrestrial ecosystems.

Terrestrial ecosystems evolved substantially through the Palaeozoic, especially the Permian, gaining much new complexity, especially among predators. Key among these predators were non-mammalian synapsids. Predator ecomorphology reflect interactions with prey and competitors, which are key controls on carnivore diversity and ecology. Therefore, carnivorous synapsids may offer insight on wider ecological evolution as the first complex, tetrapod-dominated, terrestrial ecosystems formed through the late Palaeozoic. Using morphometric and phylogenetic comparative methods, we chart carnivorous synapsid trophic morphology from the latest Carboniferous to the earliest Triassic (307-251.2 Ma). We find a major morphofunctional shift in synapsid carnivory between the early and middle Permian, via the addition of new feeding modes increasingly specialised for greater biting power or speed that captures the growing antagonism and dynamism of terrestrial tetrapod predator-prey interactions. The further evolution of new hypo- and hypercarnivorous synapsids highlight the nascent intrinsic pressures and complexification of terrestrial ecosystems across the mid-late Permian.

Multistage time-to-event models improve survival inference by partitioning mortality processes of tracked organisms.

Advances in tagging technologies are expanding opportunities to estimate survival of fish and wildlife populations. Yet, capture and handling effects could impact survival outcomes and bias inference about natural mortality processes. We developed a multistage time-to-event model that can partition the survival process into sequential phases that reflect the tagged animal experience, including handling and release mortality, post-release recovery mortality, and subsequently, natural mortality. We demonstrate performance of multistage survival models through simulation testing and through fish and bird telemetry case studies. Models are implemented in a Bayesian framework and can accommodate left, right, and interval censorship events. Our results indicate that accurate survival estimates can be achieved with reasonable sample sizes ( n ≈ 100 + ) and that multimodel inference can inform hypotheses about the configuration and length of survival stages needed to adequately describe mortality processes for tracked specimens. While we focus on survival estimation for tagged fish and wildlife populations, multistage time-to-event models could be used to understand other phenomena of interest such as migration, reproduction, or disease events across a range of taxa including plants and insects.

A randomised, double-blind, placebo-controlled phase 2 study of trebananib (AMG 386) in combination with FOLFIRI in patients with previously treated metastatic colorectal carcinoma.

BACKGROUND: This phase 2 study evaluated trebananib (AMG 386), an investigational peptide-Fc fusion protein that neutralises the interaction between angiopoietins-1/2 and the Tie2 receptor, plus FOLFIRI as second-line treatment for patients with metastatic colorectal cancer. METHODS: Patients had adenocarcinoma of the colon or rectum with progression within 6 months of receiving only one prior fluoropyrimidine/oxaliplatin-based chemotherapy regimen for metastatic disease. All patients received FOLFIRI and were randomised 2:1 to also receive intravenous trebananib 10 mg kg(-1) once weekly (QW) (Arm A) or placebo QW (Arm B). The primary end point was investigator-assessed progression-free survival (PFS). RESULTS: One hundred and forty-four patients were randomised (Arms A/B, n=95/49). Median PFS in Arms A and B was 3.5 and 5.2 months (hazard ratio (HR) 1.23; 95% CI, 0.81-1.86; P=0.33) and median overall survival (OS) was 11.9 and 8.8 months, respectively (HR 0.90; 95% CI; 0.53-1.54; P=0.70). Objective response rate (ORR) was 14% and 0% in Arms A and B, respectively. Incidence of grade ≥3 adverse events was similar between treatment arms (Arm A, 61%; Arm B, 65%) and included pulmonary embolism (1%/4%), deep vein thrombosis (5%/2%), and hypertension (1%/0%). CONCLUSION: Administration of trebananib plus FOLFIRI did not prolong PFS compared with placebo plus FOLFIRI. Toxicities were manageable and consistent with those known for FOLFIRI and trebananib.

Global fishery development patterns are driven by profit but not trophic level.

Successful ocean management needs to consider not only fishing impacts but drivers of harvest. Consolidating post-1950 global catch and economic data, we assess which attributes of fisheries are good indicators for fishery development. Surprisingly, year of development and economic value are not correlated with fishery trophic levels. Instead, patterns emerge of profit-driven fishing for attributes related to costs and revenues. Post-1950 fisheries initially developed on shallow ranging species with large catch, high price, and big body size, and then expanded to less desirable species. Revenues expected from developed fisheries declined 95% from 1951 to 1999, and few high catch or valuable fishing opportunities remain. These results highlight the importance of economic attributes of species as leading indicators for harvest-related impacts in ocean ecosystems.

A supervised learning approach for the influence of comorbidities in the analysis of COVID-19 mortality in Tamil Nadu.

COVID-19 has created many complications in today's world. It has negatively impacted the lives of many people and emphasized the need for a better health system everywhere. COVID-19 is a life-threatening disease, and a high proportion of people have lost their lives due to this pandemic. This situation enables us to dig deeper into mortality records and find meaningful patterns to save many lives in future. Based on the article from the New Indian Express (published on January 19, 2021), a whopping 82% of people who died of COVID-19 in Tamil Nadu had comorbidities, while 63 percent of people who died of the disease were above the age of 60, as per data from the Health Department. The data, part of a presentation shown to Union Health Minister Harsh Vardhan, show that of the 12,200 deaths till January 7, as many as 10,118 patients had comorbidities, and 7613 were aged above 60. A total of 3924 people (32%) were aged between 41 and 60. Compared to the 1st wave of COVID-19, the 2nd wave had a high mortality rate. Therefore, it is important to find meaningful insights from the mortality records of COVID-19 patients to know the most vulnerable population and to decide on comprehensive treatment strategies.