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.

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.

A model for individualized prediction of liver-related death in outpatients with alcohol-associated cirrhosis.

INTRODUCTION: In alcohol-associated cirrhosis, an accurate estimate of the risk of death is essential for patient care. We developed individualized prediction charts for 5-year liver-related mortality among outpatients with alcohol-associated cirrhosis that take into account the impact of abstinence. METHODS: We collected data on outpatients with alcohol-associated cirrhosis in a prospective registry. The model was derived, internally and externally validated, and compared with the Child-Pugh and the Model For End-Stage Liver Disease (MELD) scores. RESULTS: A total of 527 and 127 patients were included in the derivation and validation data sets, respectively. A model was developed based on the 3 variables independently associated with liver-related mortality in multivariate analyses (age, Child-Pugh score, and abstinence). In the derivation data set, the model combining age, Child-Pugh score, and abstinence outperformed the Child-Pugh and the MELD scores. In the validation data set, the Brier score was lower for the model (0.166) compared with the Child-Pugh score (0.196, p = 0.008) and numerically lower compared with the MELD score (0.190) (p = 0.06). The model had the greatest AUC (0.77; 95% CI 0.68-0.85) compared with the Child-Pugh score (AUC = 0.66; 95% CI 0.56-0.76, p = 0.01) and was numerically higher than that of the MELD score (AUC = 0.66; 95% CI 0.56-0.78, p = 0.06). Also, the Akaike and Bayesian information criterion scores were lower for the model (2163; 2172) compared with the Child-Pugh (2213; 2216) or the MELD score (2205; 2208). CONCLUSION: A model combining age, Child-Pugh score, and abstinence accurately predicts liver-related death at 5 years among outpatients with alcohol-associated cirrhosis. In this study, the model outperformed the Child-Pugh and the MELD scores, although the AUC and the Brier score of the model were not statically different from the MELD score in the validation data set.

Comparative effectiveness of different exercise interventions for elderly patients with hip fracture: A systematic review and Bayesian network meta-analysis protocol of randomized controlled trials.

BACKGROUND: Exercise intervention (EI) is a promising and economical way for elderly patients with hip fracture, but the evidence regarding effective EIs remains fragmented and controversial, and it is unclear which type of exercise is optimal. The purpose of this Bayesian network meta-analysis (NMA) is to compare and rank the efficacy of various EIs in elderly patients with hip fracture. MATERIALS AND METHODS: A comprehensive literature search was performed using a systematic approach across various databases including Medline (via PubMed), CINAHL, CNKI, Web of Science, Wan Fang, Embase, VIP, Cochrane Central Register of Controlled Trials and CBM databases. The search encompasses all available records from the inception of each database until December 2022. The Inclusion literature comprises randomized controlled trials that incorporate at least one EI for elderly patients with hip fracture. We will assess the risk of bias of the studies in accordance with the Cochrane Handbook for Systematic Reviews of Interventions, and assess each evidence of outcome quality in accordance with the Grading of Recommendations Assessment, Development and Evaluation framework. The NMA will be performed by STATA 15.0 software and OpenBUGS version 3.2.3. The identification of publication bias will be accomplished through the utilization of a funnel plot. We will rank the EIs effects according to the cumulative ranking probability curve (surface under the cumulative ranking area, SUCRA). The primary outcomes will be hip function in elderly patients, and the secondary outcomes will be activities of daily living, walking capacity and balance ability of elderly patients. TRIAL REGISTRATION: PROSPERO registration number: CRD4202022340737.

Comparison of survival analysis approaches to modelling age at first sex among youth in Kisesa Tanzania.

BACKGROUND: Many studies analyze sexual and reproductive event data using descriptive life tables. Survival analysis has better power to estimate factors associated with age at first sex (AFS), but proportional hazards models may not be right model to use. This study used accelerated failure time (AFT) models, restricted Mean Survival time model (RMST) models, with semi and non-parametric methods to assess age at first sex (AFS), factors associated with AFS, and verify underlying assumptions for each analysis. METHODS: Self-reported sexual debut data was used from respondents 15-24 years in eight cross-sectional surveys between 1994-2016, and from adolescents' survey in an observational community study (2019-2020) in northwest Tanzania. Median AFS was estimated in each survey using non-parametric and parametric models. Cox regression, AFT parametric models (exponential, gamma, generalized gamma, Gompertz, Weibull, log-normal and log-logistic), and RMST were used to estimate and identify factors associated with AFS. The models were compared using Akaike information criterion (AIC) and Bayesian information criterion (BIC), where lower values represent a better model fit. RESULTS: The results showed that in every survey, the Cox regression model had higher AIC and BIC compared to the other models. Overall, AFT had the best fit in every survey round. The estimated median AFS using the parametric and non-parametric methods were close. In the adolescent survey, log-logistic AFT showed that females and those attending secondary and higher education level had a longer time to first sex (Time ratio (TR) = 1.03; 95% CI: 1.01-1.06, TR = 1.05; 95% CI: 1.02-1.08, respectively) compared to males and those who reported not being in school. Cell phone ownership (TR = 0.94, 95% CI: 0.91-0.96), alcohol consumption (TR = 0.88; 95% CI: 0.84-0.93), and employed adolescents (TR = 0.95, 95% CI: 0.92-0.98) shortened time to first sex. CONCLUSION: The AFT model is better than Cox PH model in estimating AFS among the young population.

Big number, big body: Jersey numbers alter body size perception.

Vision has been shown to be an active process that can be shaped by top-down influences. Here, we add to this area of research by showing a surprising example of how visual perception can be affected by cognition (i.e., cognitive penetration). Observers were presented, on each trial, with a picture of a computer-generated football player and asked to rate the slenderness of the player on an analog scale. The results of two experiments showed that observers perceived athletes wearing small jersey numbers as more slender than those with high numbers. This finding suggests that the cognition of numbers quantitatively alters body size perception. We conjecture that this effect is the result of previously learned associations (i.e., prior expectations) affecting perceptual inference. Such associations are likely the result of implicit learning of the statistical regularities of number and size attributes co-occurrences by the nervous system. We discuss how these results are consistent with previous research on statistical learning and how they fit into the Bayesian framework of perception. The current finding supports the notion of top-down influences of cognition on perception.

Clustering microbiome data using mixtures of logistic normal multinomial models.

Discrete data such as counts of microbiome taxa resulting from next-generation sequencing are routinely encountered in bioinformatics. Taxa count data in microbiome studies are typically high-dimensional, over-dispersed, and can only reveal relative abundance therefore being treated as compositional. Analyzing compositional data presents many challenges because they are restricted to a simplex. In a logistic normal multinomial model, the relative abundance is mapped from a simplex to a latent variable that exists on the real Euclidean space using the additive log-ratio transformation. While a logistic normal multinomial approach brings flexibility for modeling the data, it comes with a heavy computational cost as the parameter estimation typically relies on Bayesian techniques. In this paper, we develop a novel mixture of logistic normal multinomial models for clustering microbiome data. Additionally, we utilize an efficient framework for parameter estimation using variational Gaussian approximations (VGA). Adopting a variational Gaussian approximation for the posterior of the latent variable reduces the computational overhead substantially. The proposed method is illustrated on simulated and real datasets.

Learned-SBL-GAMP based hybrid precoders/combiners in millimeter wave massive MIMO systems.

In Millimeter-Wave (mm-Wave) massive Multiple-Input Multiple-Output (MIMO) systems, hybrid precoders/combiners must be designed to improve antenna gain and reduce hardware complexity. Sparse Bayesian learning via Expectation Maximization (SBL-EM) algorithm is not practically feasible for high signal dimensions because estimating sparse signals and designing optimal hybrid precoders/combiners using SBL-EM still provide high computational complexity for higher signal dimensions. To overcome the issues of high computational complexity along with making it suitable for larger data sets, in this paper, we propose Learned-Sparse Bayesian Learning with Generalized Approximate Message Passing algorithm (L-SBL-GAMP) algorithm for designing optimal hybrid precoders/combiners suitable for mmWave Massive MIMO systems. The L-SBL-GAMP algorithm is an extension of the SBL-GAMP algorithm that incorporates a Deep Neural Network (DNN) to improve the system performance. Based on the nature of the training data, the L-SBL-GAMP can design the optimal Hybrid precoders/combiners, which enhances the spectral efficiency of mmWave massive MIMO systems. The proposed L-SBL-GAMP algorithm reduces the iterations, training overhead, and computational complexity compared to the SBL-EM algorithm. The simulation results unveil that the proposed L-SBL-GAMP provides higher achievable rates, better accuracy, and low computational complexity compared to the existing algorithm, such as Orthogonal Matching Pursuit (OMP), Simultaneous Orthogonal Matching Pursuit (SOMP), SBL-EM and SBL-GAMP for mmWave massive MIMO architectures.

A novel framework for classification of two-class motor imagery EEG signals using logistic regression classification algorithm.

Robotics and artificial intelligence have played a significant role in developing assistive technologies for people with motor disabilities. Brain-Computer Interface (BCI) is a communication system that allows humans to communicate with their environment by detecting and quantifying control signals produced from different modalities and translating them into voluntary commands for actuating an external device. For that purpose, classification the brain signals with a very high accuracy and minimization of the errors is of profound importance to the researchers. So in this study, a novel framework has been proposed to classify the binary-class electroencephalogram (EEG) data. The proposed framework is tested on BCI Competition IV dataset 1 and BCI Competition III dataset 4a. Artifact removal from EEG data is done through preprocessing, followed by feature extraction for recognizing discriminative information in the recorded brain signals. Signal preprocessing involves the application of independent component analysis (ICA) on raw EEG data, accompanied by the employment of common spatial pattern (CSP) and log-variance for extracting useful features. Six different classification algorithms, namely support vector machine, linear discriminant analysis, k-nearest neighbor, naïve Bayes, decision trees, and logistic regression, have been compared to classify the EEG data accurately. The proposed framework achieved the best classification accuracies with logistic regression classifier for both datasets. Average classification accuracy of 90.42% has been attained on BCI Competition IV dataset 1 for seven different subjects, while for BCI Competition III dataset 4a, an average accuracy of 95.42% has been attained on five subjects. This indicates that the model can be used in real time BCI systems and provide extra-ordinary results for 2-class Motor Imagery (MI) signals classification applications and with some modifications this framework can also be made compatible for multi-class classification in the future.

Obtaining SF-6D utilities from FACT-H&N in thyroid carcinoma patients: development and results from a mapping study.

Objective: There is limited evidence for mapping clinical tools to preference-based generic tools in the Chinese thyroid cancer patient population. The current study aims to map the FACT-H&N (Functional Assessment of Cancer Therapy-Head and Neck Cancer) to the SF-6D (Short Form Six-Dimension), which will inform future cost-utility analyses related to thyroid cancer treatment. Methods: A total of 1050 participants who completed the FACT-H&N and SF-6D questionnaires were included in the analysis. Four methods of direct and indirect mapping were estimated: OLS regression, Tobit regression, ordered probit regression, and beta mixture regression. We evaluated the predictive performance in terms of root mean square error (RMSE), mean absolute error (MAE), concordance correlation coefficient (CCC), Akaike information criterion (AIC) and Bayesian information criterion (BIC) and the correlation between the observed and predicted SF-6D scores. Results: The mean value of SF-6D was 0.690 (SD = 0.128). The RMSE values for the fivefold cross-validation as well as the 30% random sample validation for multiple models in this study were 0.0833-0.0909, MAE values were 0.0676-0.0782, and CCC values were 0.6940-0.7161. SF-6D utility scores were best predicted by a regression model consisting of the total score of each dimension of the FACT-H&N, the square of the total score of each dimension, and covariates including age and gender. We proposed to use direct mapping (OLS regression) and indirect mapping (ordered probit regression) to establish a mapping model of FACT-H&N to SF-6D. The mean SF-6D and cumulative distribution functions simulated from the recommended mapping algorithm generally matched the observed ones. Conclusions: In the absence of preference-based quality of life tools, obtaining the health status utility of thyroid cancer patients from directly mapped OLS regression and indirectly mapped ordered probit regression is an effective alternative.

Evaluation of the application of sequence data to the identification of outbreaks of disease using anomaly detection methods.

Anomaly detection methods have a great potential to assist the detection of diseases in animal production systems. We used sequence data of Porcine Reproductive and Respiratory Syndrome (PRRS) to define the emergence of new strains at the farm level. We evaluated the performance of 24 anomaly detection methods based on machine learning, regression, time series techniques and control charts to identify outbreaks in time series of new strains and compared the best methods using different time series: PCR positives, PCR requests and laboratory requests. We introduced synthetic outbreaks of different size and calculated the probability of detection of outbreaks (POD), sensitivity (Se), probability of detection of outbreaks in the first week of appearance (POD1w) and background alarm rate (BAR). The use of time series of new strains from sequence data outperformed the other types of data but POD, Se, POD1w were only high when outbreaks were large. The methods based on Long Short-Term Memory (LSTM) and Bayesian approaches presented the best performance. Using anomaly detection methods with sequence data may help to identify the emergency of cases in multiple farms, but more work is required to improve the detection with time series of high variability. Our results suggest a promising application of sequence data for early detection of diseases at a production system level. This may provide a simple way to extract additional value from routine laboratory analysis. Next steps should include validation of this approach in different settings and with different diseases.

Design and Implementation of an Atrial Fibrillation Detection Algorithm on the ARM Cortex-M4 Microcontroller.

At present, a medium-level microcontroller is capable of performing edge computing and can handle the computation of neural network kernel functions. This makes it possible to implement a complete end-to-end solution incorporating signal acquisition, digital signal processing, and machine learning for the classification of cardiac arrhythmias on a small wearable device. In this work, we describe the design and implementation of several classifiers for atrial fibrillation detection on a general-purpose ARM Cortex-M4 microcontroller. We used the CMSIS-DSP library, which supports Naïve Bayes and Support Vector Machine classifiers, with different kernel functions. We also developed Python scripts to automatically transfer the Python model (trained in Scikit-learn) to the C environment. To train and evaluate the models, we used part of the data from the PhysioNet/Computing in Cardiology Challenge 2020 and performed simple classification of atrial fibrillation based on heart-rate irregularity. The performance of the classifiers was tested on a general-purpose ARM Cortex-M4 microcontroller (STM32WB55RG). Our study reveals that among the tested classifiers, the SVM classifier with RBF kernel function achieves the highest accuracy of 96.9%, sensitivity of 98.4%, and specificity of 95.8%. The execution time of this classifier was 720 µs per recording. We also discuss the advantages of moving computing tasks to edge devices, including increased power efficiency of the system, improved patient data privacy and security, and reduced overall system operation costs. In addition, we highlight a problem with false-positive detection and unclear significance of device-detected atrial fibrillation.

Estimation of forest canopy closure in northwest Yunnan based on multi-source remote sensing data colla-boration.

Forest canopy closure (FCC) is an important parameter to evaluate forest resources and biodiversity. Using multi-source remote sensing collaborative means to achieve regional forest canopy closure inversion with low cost and high-precision is a research hotspot. Taking ICESat-2/ATLAS data as the main information source and combined with data of 54 measured plots, we estimated FCC value by the Bayesian optimization (BO) algorithm improved random forest (RF), K-nearest neighbor (KNN), and gradient boosting regression tree (GBRT) model at footprint-scale. Combined with multi-source remote sensing image Sentinel-1/2 and terrain factors, we estimated the regional-scale FCC value of Shangri-La in the northwest Yunnan based on deep neural network (DNN) optimized by BO algorithm. The results showed that six characteristic parameters (percentage of tree canopy, standard deviation of relative height of photons at the top of the canopy, minimum canopy height, difference between 98% canopy height and median canopy height in the segment, number of top canopy photons, apparent surface reflectance) out of the 50 parameters that were extracted from ATLAS lidar footprint had higher contribution rate after RF characteristic variable optimization, which could be used as model variable for footprint-scale remote sensing estimation. Among BO-RF, BO-KNN, and BO-GBRT models, the FCC results estimated by the BO-GBRT model were the best at footprint-scale. The coefficient of determination (R2) was 0.65, the root mean square error (RMSE) was 0.10, the mean absolute residual (RS) was 0.079, and the prediction accuracy (P) was 0.792 for leave-one-out cross validation. It could be used as the FCC estimation model of 74808 ATLAS footprints for forest in the study area. We used the ATLAS footprint-scale FCC value of forest as the large sample data of the regional-scale BO-DNN model and combined with multi-source remote sensing factors to estimate FCC in the study area, the accuracy of the 10-fold cross-validation BO-DNN model was R2=0.47, RMSE=0.22, P=0.558. The mean values of FCC in the study area estimated by BO-DNN model and ordinary Kriging (OK) interpolation were 0.46 and 0.52, respectively, and the values mainly distributed in 0.3-0.6, accounting for 77.8% and 81.4%, respectively. The FCC efficiency obtained directly by the OK interpolation method was higher (R2=0.26), but the prediction accuracy was significantly lower than the BO-DNN model (R2=0.49). The FCC high value was distributed from northwest to southeast in the study area, and the northern and southeastern regions were the main distribution areas of high and low FCC values, respectively. It had certain advantages to estimate mountain area FCC based on ICESat-2/ATLAS high-density footprint, and the estimation results of small sample data at footprint-scale could be used as large sample data of deep learning model at region-scale, which would provide a reference for the low-cost and high-precision to FCC estimation on the footprint-scale up to the extrapolated regional-scale.

Addressing mechanism bias in model-based impact forecasts of new tuberculosis vaccines.

In tuberculosis (TB) vaccine development, multiple factors hinder the design and interpretation of the clinical trials used to estimate vaccine efficacy. The complex transmission chain of TB includes multiple routes to disease, making it hard to link the vaccine efficacy observed in a trial to specific protective mechanisms. Here, we present a Bayesian framework to evaluate the compatibility of different vaccine descriptions with clinical trial outcomes, unlocking impact forecasting from vaccines whose specific mechanisms of action are unknown. Applying our method to the analysis of the M72/AS01E vaccine trial -conducted on IGRA+ individuals- as a case study, we found that most plausible models for this vaccine needed to include protection against, at least, two over the three possible routes to active TB classically considered in the literature: namely, primary TB, latent TB reactivation and TB upon re-infection. Gathering new data regarding the impact of TB vaccines in various epidemiological settings would be instrumental to improve our model estimates of the underlying mechanisms.

Associations between blood essential metal mixture and serum uric acid: a cross-sectional study.

Introduction: Although several studies have explored the associations between single essential metals and serum uric acid (SUA), the study about the essential metal mixture and the interactions of metals for hyperuricemia remains unclear. Methods: We performed a cross-sectional study to explore the association of the SUA levels with the blood essential metal mixture, including magnesium (Mg), calcium (Ca), iron (Fe), copper (Cu), zinc (Zn), manganese (Mn) in Chinese community-dwelling adults (n=1039). The multivariable linear regression, the weighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR) were conducted to estimate the associations of blood essential metals with SUA levels and the BKMR model was also conducted to estimate the interactions of the essential metals on SUA. Results: In the multivariable linear regression, the association of blood Mg, Mn, and Cu with SUA was statistically significant, both in considering multiple metals and a single metal. In WQS regression [ß=13.59 (95%CI: 5.57, 21.60)] and BKMR models, a positive association was found between the mixture of essential metals in blood and SUA. Specifically, blood Mg and Cu showed a positive association with SUA, while blood Mn showed a negative association. Additionally, no interactions between individual metals on SUA were observed. Discussion: In conclusion, further attention should be paid to the relationship between the mixture of essential metals in blood and SUA. However, more studies are needed to confirm these findings.

Investigating the relationship between sleep and migraine in a global sample: a Bayesian cross-sectional approach.

BACKGROUND: There is a bidirectional link between sleep and migraine, however causality is difficult to determine. This study aimed to investigate this relationship using data collected from a smartphone application. METHODS: Self-reported data from 11,166 global users (aged 18-81 years, mean: 41.21, standard deviation: 11.49) were collected from the Migraine Buddy application (Healint Pte. Ltd.). Measures included: start and end times of sleep and migraine attacks, and pain intensity. Bayesian regression models were used to predict occurrence of a migraine attack the next day based on users' deviations from average sleep, number of sleep interruptions, and hours slept the night before in those reporting ≥ 8 and < 25 migraine attacks on average per month. Conversely, we modelled whether attack occurrence and pain intensity predicted hours slept that night. RESULTS: There were 724 users (129 males, 412 females, 183 unknown, mean age = 41.88 years, SD = 11.63), with a mean monthly attack frequency of 9.94. More sleep interruptions (95% Highest Density Interval (95%HDI [0.11 - 0.21]) and deviation from a user's mean sleep (95%HDI [0.04 - 0.08]) were significant predictors of a next day attack. Total hours slept was not a significant predictor (95%HDI [-0.04 - 0.04]). Pain intensity, but not attack occurrence was a positive predictor of hours slept. CONCLUSIONS: Sleep fragmentation and deviation from typical sleep are the main drivers of the relationship between sleep and migraine. Having a migraine attack does not predict sleep duration, yet the pain associated with it does. This study highlights sleep as crucial in migraine management.

The temporal dependencies between social, emotional and physical health factors in young people receiving mental healthcare: a dynamic Bayesian network analysis.

AIMS: The needs of young people attending mental healthcare can be complex and often span multiple domains (e.g., social, emotional and physical health factors). These factors often complicate treatment approaches and contribute to poorer outcomes in youth mental health. We aimed to identify how these factors interact over time by modelling the temporal dependencies between these transdiagnostic social, emotional and physical health factors among young people presenting for youth mental healthcare. METHODS: Dynamic Bayesian networks were used to examine the relationship between mental health factors across multiple domains (social and occupational function, self-harm and suicidality, alcohol and substance use, physical health and psychiatric syndromes) in a longitudinal cohort of 2663 young people accessing youth mental health services. Two networks were developed: (1) 'initial network', that shows the conditional dependencies between factors at first presentation, and a (2) 'transition network', how factors are dependent longitudinally. RESULTS: The 'initial network' identified that childhood disorders tend to precede adolescent depression which itself was associated with three distinct pathways or illness trajectories; (1) anxiety disorder; (2) bipolar disorder, manic-like experiences, circadian disturbances and psychosis-like experiences; (3) self-harm and suicidality to alcohol and substance use or functioning. The 'transition network' identified that over time social and occupational function had the largest effect on self-harm and suicidality, with direct effects on ideation (relative risk [RR], 1.79; CI, 1.59-1.99) and self-harm (RR, 1.32; CI, 1.22-1.41), and an indirect effect on attempts (RR, 2.10; CI, 1.69-2.50). Suicide ideation had a direct effect on future suicide attempts (RR, 4.37; CI, 3.28-5.43) and self-harm (RR, 2.78; CI, 2.55-3.01). Alcohol and substance use, physical health and psychiatric syndromes (e.g., depression and anxiety, at-risk mental states) were independent domains whereby all direct effects remained within each domain over time. CONCLUSIONS: This study identified probable temporal dependencies between domains, which has causal interpretations, and therefore can provide insight into their differential role over the course of illness. This work identified social, emotional and physical health factors that may be important early intervention and prevention targets. Improving social and occupational function may be a critical target due to its impacts longitudinally on self-harm and suicidality. The conditional independence of alcohol and substance use supports the need for specific interventions to target these comorbidities.

Challenges, Complexities, and Considerations in the Design and Interpretation of Late-Phase Oncology Trials.

Optimal management of cancer patients relies heavily on late-phase oncology randomized controlled trials. A comprehensive understanding of the key considerations in designing and interpreting late-phase trials is crucial for improving subsequent trial design, execution, and clinical decision-making. In this review, we explore important aspects of late-phase oncology trial design. We begin by examining the selection of primary endpoints, including the advantages and disadvantages of using surrogate endpoints. We address the challenges involved in assessing tumor progression and discuss strategies to mitigate bias. We define informative censoring bias and its impact on trial results, including illustrative examples of scenarios that may lead to informative censoring. We highlight the traditional roles of the log-rank test and hazard ratio in survival analyses, along with their limitations in the presence of nonproportional hazards as well as an introduction to alternative survival estimands, such as restricted mean survival time or MaxCombo. We emphasize the distinctions between the design and interpretation of superiority and noninferiority trials, and compare Bayesian and frequentist statistical approaches. Finally, we discuss appropriate utilization of phase II and phase III trial results in shaping clinical management recommendations and evaluate the inherent risks and benefits associated with relying on phase II data for treatment decisions.

Integrative Genome-Based Survey of the SARS-CoV-2 Omicron XBB.1.16 Variant.

The XBB.1.16 SARS-CoV-2 variant, also known as Arcturus, is a recent descendant lineage of the recombinant XBB (nicknamed Gryphon). Compared to its direct progenitor, XBB.1, XBB.1.16 carries additional spike mutations in key antigenic sites, potentially conferring an ability to evade the immune response compared to other circulating lineages. In this context, we conducted a comprehensive genome-based survey to gain a detailed understanding of the evolution and potential dangers of the XBB.1.16 variant, which became dominant in late June. Genetic data indicates that the XBB.1.16 variant exhibits an evolutionary background with limited diversification, unlike dangerous lineages known for rapid changes. The evolutionary rate of XBB.1.16, which amounts to 3.95 × 10-4 subs/site/year, is slightly slower than that of its direct progenitors, XBB and XBB.1.5, which have been circulating for several months. A Bayesian Skyline Plot reconstruction suggests that the peak of genetic variability was reached in early May 2023, and currently, it is in a plateau phase with a viral population size similar to the levels observed in early March. Structural analyses indicate that, overall, the XBB.1.16 variant does not possess structural characteristics markedly different from those of the parent lineages, and the theoretical affinity for ACE2 does not seem to change among the compared variants. In conclusion, the genetic and structural analyses of SARS-CoV-2 XBB.1.16 do not provide evidence of its exceptional danger or high expansion capability. Detected differences with previous lineages are probably due to genetic drift, which allows the virus constant adaptability to the host, but they are not necessarily connected to a greater danger. Nevertheless, continuous genome-based monitoring is essential for a better understanding of its descendants and other lineages.

The complete mitochondrial genome of Wellcomia compar (Spirurina: Oxyuridae) and its genome characterization and phylogenetic analysis.

Wellcomia compar (Spirurina: Oxyuridae) is a pinworm that infects wild and captive porcupines. Despite clear records of its morphological structure, its genetics, systematics, and biology are poorly understood. This study aimed to determine the complete mitochondrial (mt) genome of W. compar and reconstruct its phylogenetic relationship with other nematodes. We sequenced the complete mt genome of W. comparand conducted phylogenetic analyses using concatenated coding sequences of 12 protein-coding genes (PCGs) by maximum likelihood and Bayesian inference. The complete mt genome is 14,373 bp in size and comprises 36 genes, including 12 protein-coding, two rRNA and 22 tRNA genes. Apart from 28 intergenic regions, one non-coding region and one overlapping region also occur. A comparison of the gene arrangements of Oxyuridomorpha revealed relatively similar features in W. compar and Wellcomia siamensis. Phylogenetic analysis also showed that W. compar and W. siamensis formed a sister group. In Oxyuridomorpha the genetic distance between W. compar and W. siamensis was 0.0805. This study reports, for the first time, the complete W. compar mt genome sequence obtained from Chinese porcupines. It provides genetic markers for investigating the taxonomy, population genetics, and phylogenetics of pinworms from different hosts and has implications for the diagnosis, prevention, and control of parasitic diseases in porcupines and other animals.