Reduced genetic diversity and the success of the invasive peacock bass (Cichliformes: Cichlidae) / Diversidade genética reduzida e o sucesso da espécie invasora tucunaré (Cichliformes: Cichlidae)

Abstract Several species of Cichla successfully colonized lakes and reservoirs of Brazil, since the 1960's, causing serious damage to local wildlife. In this study, 135 peacock bass were collected in a reservoir complex in order to identify if they represented a single dominant species or multiple ones, as several Cichla species have been reported in the basin. Specimens were identified by color pattern, morphometric and meristic data, and using mitochondrial markers COI, 16S rDNA and Control Region (CR). Overlapping morphological data and similar coloration patterns prevented their identification using the taxonomic keys to species identification available in the literature. However, Bayesian and maximum likelihood from sequencing data demonstrated the occurrence of a single species, Cichla kelberi. A single haplotype was observed for the 16S and CR, while three were detected for COI, with a dominant haplotype present in 98.5% of the samples. The extreme low diversity of the transplanted C. kelberi evidenced a limited number of founding maternal lineages. The success of this colonization seems to rely mainly on abiotic factors, such as increased water transparency of lentic environments that favor visual predators that along with the absence of predators, have made C. kelberi a successful invader of these reservoirs.

Resumo Muitas espécies de Cichla colonizaram com sucesso lagos e reservatórios do Brasil desde os anos 1960, causando graves prejuízos à vida selvagem nesses locais. Neste estudo, 135 tucunarés foram coletados em um complexo de reservatórios a fim de identificar se representavam uma espécie dominante ou múltiplas espécies, uma vez que diversas espécies de Cichla foram registradas na bacia. Os espécimes foram identificados com base na coloração, dados morfométricos e merísticos, e por marcadores mitocondriais COI, 16S rDNA e Região Controle (RC). A sobreposição dos dados morfométricos e o padrão similar de coloração impediram a identificação utilizando as chaves de identificação disponíveis na literatura. Entretanto, as análises bayesiana e de máxima verossimilhança de dados moleculares demonstraram a ocorrência de uma única espécie, Cichla kelberi. Um único haplótipo foi observado para o 16S e RC, enquanto três foram detectados para o COI, com um haplótipo dominante presente em 98,5% das amostras. A baixa diversidade nos exemplares introduzidos de C. kelberi evidenciou um número limitado de linhagens maternas fundadoras. O sucesso da invasão parece depender de fatores abióticos, como a maior transparência da água de ambientes lênticos que favorece predadores visuais que, atrelado à ausência de predadores, fez do C. kelberi um invasor bem-sucedido nesses reservatórios.

Bayesian Optimization in Drug Discovery.

Drug discovery deals with the search for initial hits and their optimization toward a targeted clinical profile. Throughout the discovery pipeline, the candidate profile will evolve, but the optimization will mainly stay a trial-and-error approach. Tons of in silico methods have been developed to improve and fasten this pipeline. Bayesian optimization (BO) is a well-known method for the determination of the global optimum of a function. In the last decade, BO has gained popularity in the early drug design phase. This chapter starts with the concept of black box optimization applied to drug design and presents some approaches to tackle it. Then it focuses on BO and explains its principle and all the algorithmic building blocks needed to implement it. This explanation aims to be accessible to people involved in drug discovery projects. A strong emphasis is made on the solutions to deal with the specific constraints of drug discovery. Finally, a large set of practical applications of BO is highlighted.

Explainable Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Machine Learning Techniques in a Population of 1780 Patients.

Post-stroke depression and anxiety, collectively known as post-stroke adverse mental outcome (PSAMO) are common sequelae of stroke. About 30% of stroke survivors develop depression and about 20% develop anxiety. Stroke survivors with PSAMO have poorer health outcomes with higher mortality and greater functional disability. In this study, we aimed to develop a machine learning (ML) model to predict the risk of PSAMO. We retrospectively studied 1780 patients with stroke who were divided into PSAMO vs. no PSAMO groups based on results of validated depression and anxiety questionnaires. The features collected included demographic and sociological data, quality of life scores, stroke-related information, medical and medication history, and comorbidities. Recursive feature elimination was used to select features to input in parallel to eight ML algorithms to train and test the model. Bayesian optimization was used for hyperparameter tuning. Shapley additive explanations (SHAP), an explainable AI (XAI) method, was applied to interpret the model. The best performing ML algorithm was gradient-boosted tree, which attained 74.7% binary classification accuracy. Feature importance calculated by SHAP produced a list of ranked important features that contributed to the prediction, which were consistent with findings of prior clinical studies. Some of these factors were modifiable, and potentially amenable to intervention at early stages of stroke to reduce the incidence of PSAMO.

Phylogenetic and Phylodynamic Analysis of Delta Strains Circulating in Italy.

The hepatitis delta virus (HDV) exhibits high genetic and evolutionary variability and is classified into eight genotypes (HDV-1 to -8). HDV-1 is the most widespread genotype worldwide and includes several subtypes. It predominates mainly in Europe, the Middle East, North America, and Northern Africa, and is associated with both severe and mild forms of liver disease. In this study, we performed phylogenetic and phylodynamic analyses of HDV strains circulating in Regione Lazio, Italy, to understand when these strains were introduced into the Lazio region and to define their genetic variability in Italy. Fifty HDV RNA positive patient samples were amplified using a nested RT-PCR approach targeting the HDV R0 region and sequenced. A phylogenetic tree of patient-derived sequences and reference sequences representing HDV-1 to -8 was constructed using the GTRGAMMA model in RAxML v8. The results indicated that HDV-1 was the predominant genotype with HDV-1d being the most frequently inferred subtype. HDV-1 sequences clustering with subtypes 1b and 1e were also identified. A phylodynamic analysis of HDV-1 sequences employing a Bayesian birth-death model inferred a clock rate of 3.04 × 10-4 substitutions per site per million years, with a 95% Highest Posterior Density (HPD) interval of 3.45 × 10-5 to 5.72 × 10-4. A Bayesian birth-death analysis with tree calibration based on a sample dating approach indicated multiple original sources of infection (from the late 1950s to late 1980s). Overall, these results suggest that HDV sequences from the native Italian and non-Italian patients analyzed in this study represent multiple lineages introduced across a wide period. A common ancestral origin should be excluded.

On the hazards of relating representations and inductive biases.

The success of models of human behavior based on Bayesian inference over logical formulas or programs is taken as evidence that people employ a "language-of-thought" that has similarly discrete and compositional structure. We argue that this conclusion problematically crosses levels of analysis, identifying representations at the algorithmic level based on inductive biases at the computational level.

Geographic disparities in trends of thyroid cancer incidence and mortality from 1990 to 2019 and a projection to 2030 across income-classified countries and territories.

Background: The rising incidence of thyroid cancer (TC) has generated growing concern globally; yet there are no studies examining whether this incidence was followed by a rise in related mortality. We aimed to comprehensively quantify current trends and future projections of TC incidence and mortality, and to explore the association between the TC burden and socioeconomic inequality in different income strata. Methods: We obtained incidence and mortality data on TC and population from the 2019 Global Burden of Disease (GBD) study and the United Nations' World Population Prospects 2022. We applied an age-period-cohort (APC) model to estimate the overall annual percentage change (net drift) and age, period, and cohort effects from 1990 to 2019, and also constructed a Bayesian APC model to predict the TC burden through 2030. Results: Over a third of global TC cases belonged to the high-income group. From 1990 to 2019, net drifts of TC incidence were >0 in all income groups, while a modest reduction (net drift <0) in mortality was observed in most income groups, except for the lower-middle-income group. Unfavourable age, period, and cohort effects were most notable in Vietnam, China, and Korea. The age-standardised incidence rate (ASIR) is predicted to increase whereas the age-standardized mortality rate (ASMR) is expected to decrease globally between 2020 and 2030, with geographic heterogeneity being detected across income groups. We observed a positive correlation between ASIR and universal health coverage index and health worker density, but a negative one between ASMR and the two indicators, primarily in upper-middle-income and high-income countries. Conclusions: Opposite patterns in incidence and mortality of TC raise concerns about overdiagnosis, particularly in upper-middle-income and high-income countries. Discrepancies in the distribution of health service accessibility, including diagnostic techniques and therapeutic care, should be addressed by narrowing health inequalities in the TC burden across countries.

Of causes and symptoms: using monitoring data and expert knowledge to diagnose the causes of stream degradation.

Ecological status assessment under the European Water Framework Directive (WFD) often integrates the impact of multiple stressors into a single index value. This hampers the identification of individual stressors being responsible for status deterioration. As a consequence, management measures are often disentangled from assessment results. To close this gap and to support river basin managers in the diagnosis of stressors, we linked numerous macroinvertebrate assessment metrics and one diatom index with potential causes of ecological deterioration through Bayesian belief networks (BBNs). The BBNs were informed by WFD monitoring data as well as regular consultation with experts and allow to estimate the probabilities of individual degradation causes based upon a selection of biological metrics. Macroinvertebrate metrics were shown to be stronger linked to hydromorphological conditions and land use than to water quality-related parameters (e.g., thermal and nutrient pollution). The modeled probabilities also allow to order the potential causes of degradation hierarchically. The comparison of assessment metrics showed that compositional and trait-based community metrics performed equally well in the diagnosis. The testing of the BBNs by experts resulted in an agreement between model output and expert opinion of 17-92% for individual stressors. Overall, the expert-based validation confirmed a good diagnostic potential of the BBNs; on average 80% of the diagnosed causes were in agreement with expert judgement. We conclude that diagnostic BBNs can assist the identification of causes of stream and river degradation and thereby inform the derivation of appropriate management decisions.

Healthcare-associated infections by multidrug-resistant bacteria in Andalusia, Spain, 2014 to 2021.

BackgroundMultidrug-resistant (MDR) bacteria are among chief causes of healthcare-associated infections (HAIs). In Spain, studies addressing multidrug resistance based on epidemiological surveillance systems are lacking.AimIn this observational study, cases of HAIs by MDR bacteria notified to the epidemiological surveillance system of Andalusia, Spain, between 2014-2021, were investigated. Notified cases and their spatiotemporal distribution were described, with a focus on social determinants of health (SDoH).MethodsNew cases during the study period of HAIs caused by extended-spectrum ß-lactamase (ESBL)-/carbapenemase-producing Enterobacterales, MDR Acinectobacter baumannii, MDR Pseudomonas aeruginosa or meticillin resistant Staphylococcus aureus were considered. Among others, notification variables included sex and age, while socio-economic variables comprised several SDoH. Cases' spatial distribution across municipalities was assessed. The smooth standardised incidence ratio (sSIR) was obtained using a Bayesian spatial model. Association between municipalities' sSIR level and SDoH was evaluated by bivariate analysis.ResultsIn total, 6,389 cases with a median age of 68 years were notified; 61.4% were men (n = 3,921). The most frequent MDR bacteria were ESBL-producing Enterobacterales (2,812/6,389; 44.0%); the main agent was Klebsiella spp. (2,956/6,389; 46.3%). Between 2014 and 2021 case numbers appeared to increase. Overall, up to 15-fold differences in sSIR between municipalities were observed. In bivariate analysis, there appeared to be an association between municipalities' sSIR level and deprivation (p = 0.003).ConclusionThis study indicates that social factors should be considered when investigating HAIs by MDR bacteria. The case incidence heterogeneity between Andalusian municipalities might be explained by SDoH, but also possibly by under-notification. Automatising reporting may address the latter.

New data on mitogenomes of Thienemanniella Kieffer, 1911 (Diptera: Chironomidae, Orthocladiinae).

The mitochondrial genome (mitogenome) has been widely used as a powerful marker in phylogenetic and evolutionary studies of various Dipteran groups. However, only a few mitogenomes from the Thienemanniella genus have been reported till now. Furthermore, there is still indeterminacy in the phylogenetic relationships of the genus Thienemanniella. In this study, mitogenomes of five Thienemanniella species were sequenced and analyzed newly. Combined with the published mitogenome of Thienemanniella nipponica, the obtained results showed that mitogenomes of Thienemanniella were conserved in structure, and all genes were observed to be arranged in the same gene order as the ancestral mitogenome. Nucleotide composition varied significantly among different genes, and the control region displayed the highest A + T content. All protein coding genes are subjected to purification selection, and the fastest evolving gene is ATP8. Maximum likelihood and Bayesian inference analyses showed the phylogeny of Thienemanniella which was supported in five topologies. Our present study provides valuable insight into the phylogenetic relationships of Thienemanniella species.

Exploring environmental obesogenous effects of organic ultraviolet filters on children from a case-control study.

It has been globally recognized that obesity has become a major public health concern, especially childhood obesity. There is limited information, however, regarding the exposure risk of organic ultraviolet (UV) filters, a kind of emerging contaminant, on childhood obesity. This study would be made on 284 obese and 220 non-obese Chinese children with eight organic UV filters at urinary levels. The eight organic UV filters, including 2-Ethylhexyl 4-aminobenzoate (PABA-E), octisalate (EHS), homosalate (HMS), 2-Ethylhexyl-p-methoxycinnamate (EHMC), benzophenone-3 (BP-3), amiloxate (IAMC), octocrylene (OC) and 4-Methylbenzylidene camphor (4-MBC) were identified in urine samples with detection rates ranged from 35.32% to 100%, among which PABA-E, HMS, IAMC and OC were firstly detected in children' s urine. And the urinary UV filters concentration was associated with genders, living sites, guardian education levels, household income, and dietary factors. Urinary EHMC concentrations and childhood obesity were positively associated for girls [Adjusted OR = 2.642 (95% CI: 1.019, 6.853)], while OC concentrations and childhood obesity were negatively associated for girls [Adjusted OR = 0.022 (95% CI: 0.001, 0.817)]. The results suggest that EHMC exposure may be an environmental obesogen for girls. Moreover, two statistical models were used separately to evaluate the impact of UV filter mixtures on childhood obesity, including the Bayesian kernel machine regression (BKMR) model and the quantile g-computation (qgcomp) model. The negative association between UV filter mixtures and childhood obesity was proposed from both BKMR and qgcomp models. Further experimental and epidemiological studies are called upon to discern the individual and mixture impacts of organic UV filters on childhood obesity.

Reproductive, maternal, newborn, and child health intervention coverage in 70 low-income and middle-income countries, 2000-30: trends, projections, and inequities.

BACKGROUND: Monitoring the progress in reproductive, maternal, newborn, and child health (RMNCH) using the composite coverage index (CCI) is crucial to evaluate the advancement of low-income and middle-income countries (LMICs) towards the attainment of Sustainable Development Goal target 3. We present current benchmarking for 70 LMICs, forecasting to 2030, and an analysis of inequities within and across countries. METHODS: In this cross-sectional secondary data analysis, we extracted 291 data points from the WHO Equity Monitor, and Demographic and Health Survey Statcompiler for 70 LMICs. We selected countries on the basis of whether they belonged to LMICs, had complete information about the predictors between 2000 and 2030, and had at least one data point related to CCI. CCI was calculated on the basis of eight types of RMNCH interventions in four domains, comprising family planning, antenatal care, immunisations, and management of childhood illnesses. This study examined CCI as the main outcome variable. Bayesian hierarchical models were used to estimate trends and projections of the CCI at regional and national levels, as well as the area of residence, educational level, and wealth quintile. FINDINGS: Despite progress, only 18 countries are projected to reach the 80% CCI target by 2030. Regionally, CCI is projected to increase in all regions of Asia (in southern Asia from 51·8% in 2000 to 89·2% in 2030; in southeastern Asia from 58·8% to 84·4%; in central Asia from 70·3% to 87·0%; in eastern Asia from 76·8% to 82·1%; and in western Asia from 56·5% to 72·1%), Africa (in sub-Saharan Africa from 46·3% in 2000 to 72·2% in 2030 and in northern Africa from 55·0% to 81·7%), and Latin America and the Caribbean (from 67·0% in 2000 to 83·4% in 2030). By contrast, southern Europe is predicted to experience a decline in CCI over the same period (70·1% in 2000 to 55·2% in 2030). Across LMICs, CCIs are higher in urban areas, in populations in which women have higher education levels, and in populations with a high income. INTERPRETATION: Governments of countries where the universal target of 80% CCI has not yet been reached must develop evidence-based policies aimed at enhancing RMNCH coverage. Additionally, they should focus on reducing the extent of existing inequalities within their populations to drive progress in RMNCH. FUNDING: Hitotsubashi University and Japan Society for the Promotion of Science.

A Mathematical Modelling Study of Chemotactic Dynamics in Cell Cultures: The Impact of Spatio-temporal Heterogeneity.

As motivated by studies of cellular motility driven by spatiotemporal chemotactic gradients in microdevices, we develop a framework for constructing approximate analytical solutions for the location, speed and cellular densities for cell chemotaxis waves in heterogeneous fields of chemoattractant from the underlying partial differential equation models. In particular, such chemotactic waves are not in general translationally invariant travelling waves, but possess a spatial variation that evolves in time, and may even oscillate back and forth in time, according to the details of the chemotactic gradients. The analytical framework exploits the observation that unbiased cellular diffusive flux is typically small compared to chemotactic fluxes and is first developed and validated for a range of exemplar scenarios. The framework is subsequently applied to more complex models considering the chemoattractant dynamics under more general settings, potentially including those of relevance for representing pathophysiology scenarios in microdevice studies. In particular, even though solutions cannot be constructed in all cases, a wide variety of scenarios can be considered analytically, firstly providing global insight into the important mechanisms and features of cell motility in complex spatiotemporal fields of chemoattractant. Such analytical solutions also provide a means of rapid evaluation of model predictions, with the prospect of application in computationally demanding investigations relating theoretical models and experimental observation, such as Bayesian parameter estimation.

Associations between urinary heavy metals and anxiety among adults in the National Health and Nutrition Examination Survey (NHANES), 2007-2012.

BACKGROUND: Few studies have investigated the associations between heavy metals and anxiety. The purpose of this study was to examine the associations between single and combined exposure to heavy metals and anxiety. METHODS: This study employed data from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2012. Anxiety was assessed by patients self-reporting the number of anxious days per month. First, we evaluated the associations between 10 heavy metals single exposure and anxiety by multivariable logistic regression. We then selected 5 heavy metals (cadmium, antimony, cobalt, tungsten, and uranium) for further analysis by elastic net regression. Subsequently, principal component analysis (PCA), weighted quantile regression (WQS), and Bayesian kernel machine regression (BKMR) were utilized to evaluate the associations between 5 heavy metals co-exposure and anxiety. RESULTS: This study included 4512 participants, among whom 1206 participants were in an anxiety state. Urinary cadmium and antimony were separately related to an increased risk of anxiety (p for trend <0.01 and < 0.01, respectively). In PCA analysis, PC1 was associated with an increased risk of anxiety (p for trend <0.001). In WQS analysis, the positive WQS index was substantially linked with the risk of anxiety (OR (95%CI): 1.23 (1.04,1.39)). In BKMR analysis, the overall effects of co-exposure to heavy metals were positively connected with anxiety. CONCLUSION: Our study identified a positive correlation between individual exposure to cadmium and antimony and the risk of anxiety. Additionally, the co-exposure to cadmium, antimony, cobalt, tungsten, and uranium was associated with an increased risk of anxiety.

Chloroplast genome, nuclear ITS regions, mitogenome regions, and Skmer analysis resolved the genetic relationship among Cinnamomum species in Sri Lanka.

Cinnamomum species have gained worldwide attention because of their economic benefits. Among them, C. verum (synonymous with C. zeylanicum Blume), commonly known as Ceylon Cinnamon or True Cinnamon is mainly produced in Sri Lanka. In addition, Sri Lanka is home to seven endemic wild cinnamon species, C. capparu-coronde, C. citriodorum, C. dubium, C. litseifolium, C. ovalifolium, C. rivulorum and C. sinharajaense. Proper identification and genetic characterization are fundamental for the conservation and commercialization of these species. While some species can be identified based on distinct morphological or chemical traits, others cannot be identified easily morphologically or chemically. The DNA barcoding using rbcL, matK, and trnH-psbA regions could not also resolve the identification of Cinnamomum species in Sri Lanka. Therefore, we generated Illumina Hiseq data of about 20x coverage for each identified species and a C. verum sample (India) and assembled the chloroplast genome, nuclear ITS regions, and several mitochondrial genes, and conducted Skmer analysis. Chloroplast genomes of all eight species were assembled using a seed-based method.According to the Bayesian phylogenomic tree constructed with the complete chloroplast genomes, the C. verum (Sri Lanka) is sister to previously sequenced C. verum (NC_035236.1, KY635878.1), C. dubium and C. rivulorum. The C. verum sample from India is sister to C. litseifolium and C. ovalifolium. According to the ITS regions studied, C. verum (Sri Lanka) is sister to C. verum (NC_035236.1), C. dubium and C. rivulorum. Cinnamomum verum (India) shares an identical ITS region with C. ovalifolium, C. litseifolium, C. citriodorum, and C. capparu-coronde. According to the Skmer analysis C. verum (Sri Lanka) is sister to C. dubium and C. rivulorum, whereas C. verum (India) is sister to C. ovalifolium, and C. litseifolium. The chloroplast gene ycf1 was identified as a chloroplast barcode for the identification of Cinnamomum species. We identified an 18 bp indel region in the ycf1 gene, that could differentiate C. verum (India) and C. verum (Sri Lanka) samples tested.

Contributed Session III: Identifying Specific Neural Substrates for Bayesian-like Computations in Binocular Vision and Multisensory Processing.

Bayesian thinking is influential in vision but the grounding of Bayesian computation in wetware is poorly understood. Bayesian reliability (inverse variance) weighting of inputs is predicted by Maximum Likelihood Estimation Theory and has some psychophysical support, but evidence for neural reliability weighting is sparse and neural modeling of reliability weighting is tricky. However, reliability averaging is just one possible perceptual weighted average. An alternative - nonlinear magnitude-weighted averaging - was suggested by Schrodinger in 1926 to account for suprathreshold binocular perception and is available to repurpose for other sensory cue combinations. We identified macaque suppressive binocular neurons that implement nonlinear magnitude-weighted averaging and approximate Bayesian averaging, without suffering the computational difficulties that Bayesian averaging implies. We then applied the binocular modeling to suppressive multisensory (visual-tactile, audio-tactile, and audio-visual) neurons. Although magnitude-weighting is a better fit than reliability-weighted averaging for cortical firing rates (in all four cases and in three different species), nonlinear magnitude-weighted averaging is well correlated with reliability averaging. Magnitude-weighted averaging could serve as a surrogate for Bayesian calculations; mildly suppressive binocular and multisensory bimodal neurons could be neural correlates of Bayesian-like computation in the brain.

Award Lecture: 2021 Tillyer Award Lecture: The Early Visual Encoding: Computations and Consequences.

The visual system takes sensory measurements of the light incident at the eyes and uses these to make perceptual inferences about external world. The sensory measurements do not preserve all of the information available in the light signal. One approach to understanding the implications of the initial stages of visual processing is ideal observer analysis, which evaluates the information available to support psychophysical discriminations at various stages of the early visual representation. We are interested in extending this type of analysis to take into account the statistical structure of natural images. To do so, we developed an open-source computational model of the initial visual encoding, ISETBio (isetbio.org). ISETBio incorporates specification of visual displays, retinal image formation through the eye's optics, spatio-spectral sampling by the retinal cone mosaic, Poisson noise in the cone photopigment excitations, transduction of excitations to photocurrent, and fixational eye movements. In this talk, I will introduce ISETBio and illustrate a set of insights it enables about visual processing by reviewing a number of computational examples. These examples will include ways that combining ISETBio with Bayesian image-reconstruction methods helps us understand how the interaction of the visual encoding and the statistical structure of natural images shapes visual performance.

A review of Coelostegus prothales Carroll and Baird, 1972 from the Upper Carboniferous of the Czech Republic and the interrelationships of basal eureptiles.

We redescribe the holotype and only known specimen of the early eureptile Coelostegus prothales from the Upper Carboniferous of the Czech Republic using photogrammetric scanning and a virtual 3D rendition of its skull. New information is available on several skull and lower jaw bones, including the postorbital, supratemporal, tabular, postparietal, angular, and prearticular. The new data also permit the correct identification of previously undetected or mis-identified elements (e.g., supratemporal; quadratojugal; angular). We provide an amended diagnosis of Coelostegus and a new reconstruction of the skull in dorsal and lateral views. To evaluate the affinities of Coelostegus, we code this taxon in two recently published taxon-character matrices. Parsimony and Bayesian analyses do not permit firm conclusions on the phylogenetic position of Coelostegus or, indeed, the status and extrinsic relationships of protorothyridid amniotes. Coelostegus emerges either as the sister taxon to the recently redefined Diapsida (Araeoscelidia; Varanopidae; Parareptilia; Neodiapsida), as one of the most basal protorothyridids, or as a derived stem-group amniote in various parsimony-based analyses, or as the basalmost protorothyridid in one Bayesian analysis, with protorothyridids forming a paraphyletic array relative to Diapsida. We review the cranial similarities and differences between Coelostegus and other protorothyridid genera and discuss the implications that various phylogenetic results have for our understanding of early amniote relationships.

Alterations of brainstem volume in patients with first-episode and recurrent major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) is a prevalent mental health condition characterized by recurrent episodes in a substantial proportion of patients. The number of previous episodes is one of the most crucial predictors of depression recurrence. However, the underlying neural mechanisms remain unclear. To date, there have been limited neuroimaging studies investigating morphological changes of the brainstem in patients with first-episode MDD (FMDD) and recurrent MDD (RMDD). This study aimed to examine volumetric changes of individual brainstem regions in relation to the number of previous episodes and disease duration. METHOD: A total of 111 individuals including 36 FMDD, 25 RMDD, and 50 healthy controls (HCs) underwent T1-weighted structural magnetic resonance imaging scans. A Bayesian segmentation algorithm was used to analyze the volume of each brainstem region, including the medulla oblongata, pons, midbrain, and superior cerebellar peduncle (SCP), as well as the whole brainstem volume. Analyses of variance (ANOVA) were performed to obtain brain regions with significant differences among three groups and then post hoc tests were calculated for inter-group comparisons. Partial correlation analyses were further conducted to identify associations between regional volumes and clinical features. RESULTS: The ANOVA revealed significant brainstem volumetric differences among three groups in the pons, midbrain, SCP, and the whole brainstem (F = 3.996 ~ 5.886, adjusted p = 0.015 ~ 0.028). As compared with HCs, both groups of MDD patients showed decreased volumes in the pons as well as the entire brainstem (p = 0.002 ~ 0.034), however, only the FMDD group demonstrated a significantly reduced volume in the midbrain (p = 0.003). Specifically, the RMDD group exhibited significantly decreased SCP volume when comparing to both FMDD (p = 0.021) group and HCs (p = 0.008). Correlation analyses revealed that the SCP volumes were negatively associated with the number of depressive episodes (r=-0.36, p < 0.01) and illness duration (r=-0.28, p = 0.035) in patients with MDD. CONCLUSION: The present findings provided evidence of decreased brainstem volume involving in the pathophysiology of MDD, particularly, volumetric reduction in the SCP might represent a neurobiological marker for RMDD. Further research is needed to confirm our observations and deepen our understanding of the neural mechanisms underlying depression recurrence.

Hyper-parameter tuned deep learning approach for effective human monkeypox disease detection.

Human monkeypox is a very unusual virus that can devastate society. Early identification and diagnosis are essential to treat and manage an illness effectively. Human monkeypox disease detection using deep learning models has attracted increasing attention recently. The virus that causes monkeypox may be passed to people, making it a zoonotic illness. The latest monkeypox epidemic has hit more than 40 nations. Computer-assisted approaches using Deep Learning techniques for automatically identifying skin lesions have shown to be a viable alternative in light of the fast proliferation and ever-growing problems of supplying PCR (Polymerase Chain Reaction) Testing in places with limited availability. In this research, we introduce a deep learning model for detecting human monkeypoxes that is accurate and resilient by tuning its hyper-parameters. We employed a mixture of convolutional neural networks and transfer learning strategies to extract characteristics from medical photos and properly identify them. We also used hyperparameter optimization strategies to fine-tune the Model and get the best possible results. This paper proposes a Yolov5 model-based method for differentiating between chickenpox and Monkeypox lesions on skin pictures. The Roboflow skin lesion picture dataset was subjected to three different hyperparameter tuning strategies: the SDG optimizer, the Bayesian optimizer, and Learning without Forgetting. The proposed Model had the highest classification accuracy (98.18%) when applied to photos of monkeypox skin lesions. Our findings show that the suggested Model surpasses the current best-in-class models and may be used in clinical settings for actual Human Monkeypox disease detection and diagnosis.

Integrated transcriptomic meta-analysis and comparative artificial intelligence models in maize under biotic stress.

Biotic stress imposed by pathogens, including fungal, bacterial, and viral, can cause heavy damage leading to yield reduction in maize. Therefore, the identification of resistant genes paves the way to the development of disease-resistant cultivars and is essential for reliable production in maize. Identifying different gene expression patterns can deepen our perception of maize resistance to disease. This study includes machine learning and deep learning-based application for classifying genes expressed under normal and biotic stress in maize. Machine learning algorithms used are Naive Bayes (NB), K-Nearest Neighbor (KNN), Ensemble, Support Vector Machine (SVM), and Decision Tree (DT). A Bidirectional Long Short Term Memory (BiLSTM) based network with Recurrent Neural Network (RNN) architecture is proposed for gene classification with deep learning. To increase the performance of these algorithms, feature selection is made from the raw gene features through the Relief feature selection algorithm. The obtained finding indicated the efficacy of BiLSTM over other machine learning algorithms. Some top genes ((S)-beta-macrocarpene synthase, zealexin A1 synthase, polyphenol oxidase I, chloroplastic, pathogenesis-related protein 10, CHY1, chitinase chem 5, barwin, and uncharacterized LOC100273479 were proved to be differentially upregulated under biotic stress condition.