Bayesian statistics and modelling

Effectiveness of emotion-focused skills training (EFST) for parents: A randomized controlled trial investigating remission of mental health diagnosis and symptom reduction in children

OBJECTIVE

Emotion-focused skills training for parents (EFST) is a parental guidance intervention aimed at strengthening emotional bonds between parent and child and improve children's mental health. In this randomized controlled trial (RCT), EFST was compared to treatment as usual (TAU) in a Norwegian public outpatient clinic for child and adolescent mental health. The hypothesis was that EFST would be equal or superior to TAU in effectiveness on remission of diagnoses and symptomatic change for children.

METHOD

Seventy-two clients were randomly assigned to an EFST intervention or an integrative family-based intervention (TAU). The main outcome measure was the semi-structured diagnostic interview Schedule for Affective Disorders and Schizophrenia (K-SADS-PL), administered at pretreatment and after three months. The secondary outcome was the DSM-IV version of the Strengths and Difficulties Questionnaire (SDQ). Bayesian statistical methods, including clinically informed priors, were used to compare the effectiveness of the two interventions.

RESULTS

Forty-four percent (15 of 34 clients) attained diagnostic remission in the EFST condition compared to 26 percent (10 of 38 clients) in TAU. The proportion of symptomatic decline was equivalent in both conditions.

CONCLUSION

The results support the hypothesis that EFST was equal or superior to TAU in effectiveness in a public outpatient setting.

Causal associations between scapular morphology and shoulder condition estimated with Bayesian statistics

BACKGROUND AND OBJECTIVE

While there is a reported correlation between shoulder condition and scapular morphology, the precise impact of typical anatomical variables remains a subject of ongoing debate. This study aimed to evaluate this causal association, by emphasizing the importance of scientific modeling before statistical analysis.

METHODS

We examined the effect of scapular anatomy on shoulder condition, and conditioning on sex, age, height, and weight. We considered the two most common pathologies: primary osteoarthritis (OA) and cuff tear arthropathy (CTA). We combined the other pathologies into a single category (OTH) and included a control category (CTRL) of adult subjects without pathology. We represented acromion and glenoid morphology by acromion angle (AA), acromion posterior angle (APA), acromion tilt angle (ATA), glenoid inclination angle (GIA), and glenoid version angle (GVA). GVA was negative for posterior orientation. These variables were automatically calculated from CT scans of 396 subjects in the 4 shoulder condition groups by a deep learning model. We applied do-calculus to assess the identifiability of the causal associations and used a multinomial logistic regression Bayesian model to estimate them. To isolate the effect of each anatomical variable on each shoulder condition, we increased it from -2 to 2 z-score while constraining all other variables to their average value, and reported the effect on shoulder condition probability as percentage points (pp) for females and males.

RESULTS

Increasing AA reduced the probability of OA by 44 pp for females and 17 pp for males while increasing the probability of CTA by 36 pp for females and 33 pp for males. Increasing APA raised the probability of OA by 15 pp for females and 4 pp for males and increased the probability of CTA by 12 pp for females and 4 pp for males. Increasing ATA increased the probability of OA by 15 pp for females but decreased it by 25 pp for males, while also raising the probability of CTA by 11 pp for females and 21 pp for males. Increasing GIA decreased the probability of OA by 55 pp for females and 23 pp for males while increasing the probability of CTA by 45 pp for females and 31 pp for males. GVA (more anterior), decreased the probability of OA by 33 pp for females and 63 pp for males. The effects of APA and ATA were less important compared to the other variables. Overall, morphological effects were more pronounced for females than for males, except for GVA's impact on OA.

CONCLUSIONS

We developed a Bayesian causal model to answer interventional questions about the scapular anatomy's effect on shoulder condition. Our results, consistent with clinical knowledge, hold promise for aiding in early pathology detection and optimizing surgical planning within clinical settings.

The Virtual Parkinsonian patient

This study investigates the influence of the pharmacological nigrostriatal dopaminergic stimulation on the entire brain by analyzing EEG and deep electrodes, placed near the subthalamic nuclei, from 10 Parkinsonian patients before (OFF) and after (ON) L-Dopa administration. We characterize large-scale brain dynamics as the spatio-temporal spreading of aperiodic bursts. We then simulate the effects of L-Dopa utilizing a novel neural-mass model that includes the local dopamine concentration. Whole-brain dynamics are simulated for different dopaminergic tones, generating predictions for the expected dynamics, to be compared with empirical EEG and deep electrode data. To this end, we invert the model and infer the most likely dopaminergic tone from empirical data, correctly identifying a higher Dopaminergic tone in the ON-state, and a lower dopaminergic tone in the OFF-state, for each patient. In conclusion, we successfully infer the dopaminergic tone by integrating anatomical and functional knowledge into physiological predictions, using solid ground truth to validate our findings.

Developing and validating a Bayesian clinical risk prediction model for three sexually transmitted infections in key populations from two Canadian provinces

OBJECTIVES

Across Canada, in the last decade, incidence rates of sexually transmitted and blood-borne infections (STBBI) have peaked (syphilis) or plateaued (hepatitis C virus (HCV) and HIV). Key populations (gay, bisexual and other men who have sex with men, trans and gender-diverse people, and people who use injection drugs) are at greater risk for these STBBIs, so correctly predicting risk before screening potentially infected individuals is crucial. We developed and validated a diagnostic clinical risk prediction model (CRPM) estimating HIV, HCV and syphilis risk for two key populations in two Canadian provinces.

METHODS

We used 20 variables and STBBI test results from a cross-sectional study evaluating multiplexed testing (detection of coinfections) in New Brunswick and Quebec (n=400) to develop our CRPM. We randomly split the data into development (n=300) and validation (n=100) datasets using clinic-stratified sampling. We used Bayesian predictive projection with development data to select ranked STBBI predictors. We obtained the ORs of the highest performing submodel measured as area under the receiver operating curve (AUC), sensitivity and specificity with 89% credible intervals (89% CrI) using validation data. Analyses were performed in R (≥V.4.2.3).

RESULTS

Out of 400 participants, 73 were infected with HIV (n=16), HCV (n=60), and/or syphilis (n=5). An internally validated submodel with two predictors (past drug injection, type of past sexually transmitted infection) displayed the highest AUC (0.79; 89% CrI 0.66 to 0.79), sensitivity (0.85; 89% CrI 0.79 to 0.91) and specificity (0.30; 89% CrI 0.15 to 0.50). The predictor contributing most to STBBI risk was past drug injection (OR=7.62; 89% CrI 4.41 to 13.07).

CONCLUSIONS

This Bayesian-based CRPM is the first to identify high-risk individuals for HIV, HCV and syphilis with an overall good performance that minimises case missing. After additional validation, it could serve as a promising novel tool for prescreening key populations and improve Canadian STBBI multiplexed screening strategies.

Mapping global brain reconfigurations following local targeted manipulations

Understanding how localized brain interventions influence whole-brain dynamics is essential for deciphering neural function and designing therapeutic strategies. Using longitudinal functional MRI datasets collected from mice, we investigated the effects of focal interventions, such as thalamic lesions and chemogenetic silencing of cortical hubs. We found that these local manipulations disrupted the brain's ability to sustain network-wide activity, leading to global functional connectivity (FC) reconfigurations. Personalized mouse brain simulations based on experimental data revealed that alterations in local excitability modulate firing rates and frequency content across distributed brain regions, driving these FC changes. Notably, the topography of the affected brain regions depended on the intervention site, serving as distinctive signatures of localized perturbations. These findings suggest that focal interventions produce consistent yet region-specific patterns of global FC reorganization, providing an explanation for the seemingly paradoxical observations of hypo- and hyperconnectivity reported in the literature. This framework offers mechanistic insights into the systemic effects of localized neural modulation and holds potential for refining clinical diagnostics in focal brain disorders and advancing personalized neuromodulation strategies.

Target Agnostic Photoaffinity Labelling by Sulfonylhydrazones

Photoaffinity labeling is a widely used methodology for interrogating small molecule-protein interactions. However, these applications are limited by the few photo-crosslinkers that typically modify the affinity and the binding mode of the original ligand. Here, we report the development of new target agnostic photoaffinity warheads, sulfohydrazones that form a reactive carbene upon UV irradiation. Careful optimization of the reaction conditions allowed us to effectively label five different amino acid residues in proteins. Our approach turned biologically relevant hydrazones and sulfohydrazones to intrinsically irreversible covalent binders without structural modifications by photoactivation as demonstrated on monoamine oxidase A (MAO-A) enzyme and STAT5b (Signal transducer and activator of transcription 5b) transcription factor. Sulfohydrazones are readily accessible by transforming the corresponding carbonyl group of a ligand or a suitable tag that extends the application domain of the method for any ligands exemplified by conditional labelling of the acetylcholine esterase enzyme and the oncogenic mutant of GTP-ase KRasG12D.

Self-healing crystals

Self-healing is an intrinsically exciting concept as it applies to the process of recovery, a commonplace phenomenon found in living organisms. Self-healing of artificial materials is as beneficial to living creatures as it is to materials science, wherein the effect can considerably prolong lifetimes. Although self-healing sodium chloride crystals were discovered in the 1980s, the field entered a renaissance when healing was observed in the emerging materials class of molecular crystals in 2016. Self-healing properties in polymers, cementitious materials, and coatings have already found commercial applications. The reinvigorated interest in self-healing molecular crystals stems from their prospects as durable, lightweight and flexible emissive or electronic materials. Ideally being defectless and ordered media, organic crystals have unique optical, mechanical and electrical properties, and the possibility of self-healing substantially increases their viability for smart devices.

Evaluating the evolution and inter-individual variability of infant functional module development from 0 to 5 yr old

The segregation and integration of infant brain networks undergo tremendous changes due to the rapid development of brain function and organization. In this paper, we introduce a novel approach utilizing Bayesian modeling to analyze the dynamic development of functional modules in infants over time. This method retains inter-individual variability and, in comparison with conventional group averaging techniques, more effectively detects modules, taking into account the stationarity of module evolution. Furthermore, we explore gender differences in module development under awake and sleep conditions by assessing modular similarities. Our results show that female infants demonstrate more distinct modular structures between these 2 conditions, possibly implying relative quiet and restful sleep compared with male infants.

Bayesian Workflow for Generative Modeling in Computational Psychiatry

Computational (generative) modelling of behaviour has considerable potential for clinical applications. In order to unlock the potential of generative models, reliable statistical inference is crucial. For this, Bayesian workflow has been suggested which, however, has rarely been applied in Translational Neuromodeling and Computational Psychiatry (TN/CP) so far. Here, we present a worked example of Bayesian workflow in the context of a typical application scenario for TN/CP. This application example uses Hierarchical Gaussian Filter (HGF) models, a family of computational models for hierarchical Bayesian belief updating. When equipped with a suitable response model, HGF models can be fit to behavioural data from cognitive tasks; these data frequently consist of binary responses and are typically univariate. This poses challenges for statistical inference due to the limited information contained in such data. We present a novel set of response models that allow for simultaneous inference from multivariate (here: two) behavioural data types. Using both simulations and empirical data from a speed-incentivised associative reward learning (SPIRL) task, we show that models harnessing information from two different data streams (binary responses and continuous response times) ensure robust inference (specifically, identifiability of parameters and models). Moreover, we find a linear relationship between log-transformed response times in the SPIRL task and participants' uncertainty about the outcome. Our analysis illustrates the benefits of Bayesian workflow for a typical use case in TN/CP. We argue that adopting Bayesian workflow for generative modelling helps increase the transparency and robustness of results, which in turn is of fundamental importance for the long-term success of TN/CP.

Turn-taking in grooming interactions of chimpanzees (Pan troglodytes schweinfurthii) in the wild: the role of demographic and social factors

Cooperative turn-taking, a fundamental characteristic of human social interaction, has been postulated as a crucial mechanism for language emergence and is observed across the primate lineage. However, relatively little is known about the influence of demographic and social factors on turn-taking. As according to the sociolinguistic Communication Accommodation Theory, individuals adapt their communication according to their recipient characteristics, which may shape turn-taking. Thus, we aimed to gain insights into the factors (age, relatedness, dominance rank, and social bonds) in relation to the turn-taking infrastructure of one of our closest living relatives, the chimpanzee (Pan troglodytes schweinfurthii) of the Ngogo population in Kibale National Park, Uganda. We specifically focused on a cooperative context, grooming, including gestures and actions, and collected data over nine months (September 2021 to June 2022) involving 42 male chimpanzees. We analysed 311 grooming interactions among 157 dyads concerning the role of demographic and social factors in turn transition infrastructure, turn transition types, and temporal relationships. Our findings demonstrated that turn transitions and types were influenced by age and dominance rank, whereas social bonds and relatedness did not exhibit effects. Specifically, the probability of turn transitions was higher for older initiators and lower-ranking or younger recipients. These effects varied across turn transition types, where initiator's dominance rank and relatedness showed no effects on any type. In addition, no effect was found for the temporal relationships. Although the social dynamics of turn-taking remain largely unexplored across both human and non-human studies, our findings suggest that turn-taking can occur selectively between certain individuals, in line with the Communication Accommodation Theory, underscoring the need for greater focus on investigating how demographic and social factors shape turn-taking.

Climate influence on the early human occupation of South America during the late Pleistocene

The settlement of South America marks one of the final steps in human expansion. This study examines the impact of climate change on this process, focusing on two millennial-scale climatic phases-the Antarctic Cold Reversal and Younger Dryas. Using Bayesian chronological modelling, a cultural timeline was constructed from approximately 150 archaeological sites and 1700 dates, and compared against paleoclimatic records. Findings suggested that human activity likely began in regions most affected by the Antarctic Cold Reversal, specifically in southernmost and high-altitude areas. Together with estimates indicating that the onset of megafaunal exploitation and bifacial point technology occurred before or during the Antarctic Cold Reversal, results suggested that cold conditions did not likely hinder human settlement. Key factors likely included accumulated cultural adaptation and relatively milder climatic changes in the Southern Hemisphere. More widespread occupation likely occurred during or, more likely, after the Younger Dryas as conditions stabilised. Results highlighted the western Andes as a crucial dispersal route and questioned the role of humans and climatic shifts on megafaunal extinctions. An analysis of the compiled archaeo-chronometric dataset revealed significant underrepresentation and reporting gaps, highlighting the need for expanded research and rigorous documentation to improve the reliability of the cultural timeline.

Smart Luminescent Materials for Emerging Sensors: Fundamentals and Advances

Smart luminescent materials have drawn a significant attention owing to their unique optical properties and versatility in sensor applications. These materials, encompassing a broad spectrum of organic, inorganic, and hybrid systems including quantum dots, organic dyes, and metal-organic frameworks (MOFs), offer tunable emission characteristics that can be engineered at the molecular or nanoscale level to respond to specific stimuli, such as temperature, pH, and chemical presence. This adaptability makes them crucial in developing advanced sensor technologies for environmental monitoring, biomedical diagnostics, and industrial applications with the help of the luminescence mechanisms, such as fluorescence, phosphorescence, and upconversion. Recent advancements have been driven by the integration of nanotechnology, which enhances the sensitivity and selectivity of luminescent materials in sensor platforms. The development of photoluminescent and electrochemiluminescent sensors, for instance, has enabled real-time detection and quantification of target analytes with high accuracy. Additionally, the incorporation of these materials into portable, user-friendly devices, such as smartphone-based sensors, broadens their applicability and accessibility. Despite their potential, challenges remain in optimizing the stability, efficiency, and biocompatibility of these materials under different conditions. This review provides a comprehensive overview of the fundamental principles of smart luminescent materials, discusses recent innovations in their use for sensor applications, and explores future directions aimed at overcoming current limitations and expanding their capabilities in meeting the growing demand for rapid and cost-effective sensing solutions.

Light-driven plasmonic microrobot for nanoparticle manipulation

Recently light-driven microdrones have been demonstrated, making use of plasmonic nanomotors based on directional resonant chiral light scattering. These nanomotors can be addressed individually, without requiring the tracking of a focused laser, leading to exceptional 2D maneuverability which renders microdrones a versatile robotic platform in aqueous environments. Here, we incorporate a light-operated manipulator, a plasmonic nano-tweezer, into the microdrone platform, rendering it a microrobot by enabling precise, all-optical transport and delivery of single nanoparticles suspended in solution. The plasmonic nano-tweezer consists of a resonant cross-antenna nanostructure exhibiting a central near-field hot spot, extending the ability of traditional optical tweezers based on focused laser beams to the trapping of nanoparticles. However, most of plasmonic nano-tweezers are fixed to the substrates and lack mobility. Our plasmonic microrobot utilizes circularly polarized light to control both motors and for stable trapping of a 70-nanometer fluorescent nanodiamond in the cross-antenna center. Complex sequences of microrobot operations, including trap-transport-release-trap-transport actions, demonstrate the microrobot's versatility and precision in picking up and releasing nanoparticles. Our microrobot design opens potential avenues in advancing nanotechnology and life sciences, with applications in targeted drug delivery, single-cell manipulation, and by providing an advanced quantum sensing platform, facilitating interdisciplinary research at the nanoscale.

Death of the P Value? Bayesian Statistics for Orthopaedic Surgeons

Statistical interpretation is foundational to evidence-based medicine. Frequentist ( P value testing) and Bayesian statistics are two major approaches for hypothesis testing. Studies analyzed with Bayesian methods are increasingly common with a 4-fold increase in the past 10 years. The Bayesian approach can align with clinical decision making by interpreting smaller differences that are not limited by P values and misleading claims of "trends toward significance." Both methods follow a workflow that includes sampling, hypothesis testing, interpretation, and iteration. Frequentist methodology is familiar and common. However, the limitations are the misunderstanding, misuse, and deceptively simple utility of interpreting dichotomous P values. Bayesian approaches are relatively less common and provide an alternative approach to trial design and data interpretation. Marginal differences elucidated by Bayesian methods may be perceived as less decisive than a P value that may reject a null hypothesis. The purposes of this review are to introduce Bayesian principles and Bayes theorem, define how pretest probability and known information may inform diagnostic testing using an example from prosthetic joint infection, contrast Bayesian and frequentist approaches using an example from the VANCO orthopaedic prospective trial, and describe the criteria for critically reviewing Bayesian studies.

Whole-brain mapping in adult zebrafish and identification of the functional brain network underlying the novel tank test

Zebrafish have gained prominence as a model organism in neuroscience over the past several decades, generating key insight into the development and functioning of the vertebrate brain. However, techniques for whole brain mapping in adult stage zebrafish are lacking. Here, we describe a pipeline built using open-source tools for whole-brain activity mapping in adult zebrafish. Our pipeline combines advances in histology, microscopy, and machine learning to capture cfos activity across the entirety of the brain. Following tissue clearing, whole brain images are captured using light-sheet microscopy and registered to the recently created adult zebrafish brain atlas (AZBA) for automated segmentation. By way of example, we used our pipeline to measure brain activity after zebrafish were subject to the novel tank test, one of the most widely used behaviors in adult zebrafish. Cfos levels peaked 15 minutes following behavior and several regions, including those containing serotoninergic and dopaminergic neurons, were active during exploration. Finally, we generated a novel tank test functional brain network. This revealed that several regions of the subpallium form a cohesive sub-network during exploration. Functional interconnections between the subpallium and other regions appear to be mediated primarily by ventral nucleus of the ventral telencephalon (Vv), the olfactory bulb, and the anterior part of the parvocellular preoptic nucleus (PPa). Taken together, our pipeline enables whole-brain activity mapping in adult zebrafish while providing insight into neural basis for the novel tank test.Significance statement Zebrafish have grown in popularity as a model organism over the past several decades due to their low cost, ease of genetic manipulation, and similarity to other vertebrates like humans and rodents. However, to date, tools for whole-brain mapping in adult stage animals has been lacking. Here, we present an open-source pipeline for whole-brain mapping in adult zebrafish. We demonstrate the use of our pipeline by generating a functional brain network for one of the most widely used behavioral assays in adult zebrafish, the novel tank test. We found that exploration of a novel tank engages the olfactory bulb and a network of subpallial regions that correspond to the mammalian subpallial amygdala and basal ganglia.

The relationship between radiological OA severity or body weight and outcomes following a structured education and exercise therapy program (GLA:D®) for people with knee osteoarthritis

BACKGROUND

Clinicians may presume people with higher bodyweight or greater OA severity do not respond to exercise therapy for knee osteoarthritis (OA), but few studies have examined this.

OBJECTIVE

To examine the relationship between radiographical OA severity or bodyweight and pain and functional outcomes following a structured education and exercise therapy program (Good Life with OsteoArthritis from Denmark: GLA:D®).

METHODS

33 participants with knee OA were assessed at baseline and week 8 following GLA:D®. Outcomes were pain (Visual analogue scale (VAS) 0-100), Knee Injury and Osteoarthritis Outcome Score-12 (KOOS-12 total), 40 m-fast-paced walk and 30-s chair stand. Multilevel models were used to define the severity of OA in medial, lateral and patellofemoral compartments using the Kellgren-Lawrence (KL) system and to examine the relationship between compartment severity, bodyweight and outcomes.

RESULTS

No meaningful relationships between bodyweight and response to GLA:D® were found for any outcome measures. Greater medial OA compartment severity was related to less improvement in pain, KOOS-12 and chair stand repetitions. However, all levels of lateral compartment severity had similar improvements, and greater patellofemoral compartment severity was related to more improvement for KOOS-12 and pain.

CONCLUSION

Bodyweight may have little influence on a person's response to a structured education and exercise therapy program. While people with greater medial compartment severity were less likely to improve following the program, OA severity in the PF and lateral compartments was not a barrier to improvement.

Do Cheaters Prosper? Effect of Externally Supplied Momentum During Resistance Training on Measures of Upper Body Muscle Hypertrophy

Exercise technique, defined as the controlled execution of bodily movements to ensure an exercise effectively targets specific muscle groups while minimizing the risk of injury, is a resistance training (RT) variable frequently highlighted as critical to successful RT program outcomes, with proper technique suggested to play a role in maximizing muscle development. This study examined the effects of externally applied momentum on RT-induced muscular adaptations in the upper extremities. Thirty young adults were recruited to participate in a within-participant design, with one limb randomly allocated to perform biceps curls and triceps pushdowns using strict form (STRICT) and the other using external momentum (CHEAT). Participants completed four sets of each exercise with 8-12 repetitions until momentary muscular failure, twice a week for eight weeks. We obtained pre-post proximal and distal measures of muscle thickness for the elbow flexors and extensors, and assessed circumference changes in the upper arms. Data were analyzed in a Bayesian framework including both univariate and multivariate mixed effect models with random effects. Differences between conditions were estimated as average treatment effects, with inferences based on posterior distributions and Bayes Factors (BFs). Results showed similar between-conditions increases for all muscle thickness sites as well as circumference measures, generating consistent support for the null hypothesis (BF = 0.06 to 0.61). Volume load was markedly greater for CHEAT compared to STRICT across each week of the intervention. In conclusion, the use of external momentum during single-joint RT of the upper extremities neither helped nor hindered hypertrophy of the target muscles.

Comparison of Multiple Techniques for Measuring Geographic Atrophy: A Systematic Review and Network Meta-Analysis

This study compared techniques for measuring geographic atrophy (GA) against the standard autofluorescence blue (AF-Blue). The design was a Bayesian network meta-analysis. PubMed, Scopus, and Cochrane were searched for studies comparing GA imaging techniques. AF-Blue, autofluorescence green (AF-Green), autofluorescence infrared (AF-IR), near-infrared reflectance (NIR), multicolor autofluorescence AF (MC-AF), optical coherence tomography fundus image (OFI), en face sub-retinal pigment epithelium (SubRPE) slab, OCT angiography (OCTA), color fundus photography (CFP), and fluorescein angiography (FA) were included. A ratio of means (ROM) meta-analysis using a threshold based on an established minimally important difference was performed. (Pros-pero ID: CRD42023476459.) Included were 2,001 eyes from 18 studies and no significant differences among measurement techniques was found. The ROM showed that CFP, OCTA, SubRPE slab, OFI, and IR reflectance agreed well with AF-Blue. However, AF-Green, FA, and MC underestimated GA, and AF-IR overestimated it. It appears that there is a fair agreement between AF-Blue and current techniques for measuring GA. [Ophthalmic Surg Lasers Imaging Retina 2025;56:XX-XX.].

Bayesian Hierarchical Penalized Spline Models for Immediate and Time-Varying Intervention Effects in Stepped Wedge Cluster Randomized Trials

Stepped wedge cluster randomized trials (SWCRTs) often face challenges related to potential confounding by time. Traditional frequentist methods may not provide adequate coverage of an intervention's true effect using confidence intervals, whereas Bayesian approaches show potential for better coverage of intervention effects. However, Bayesian methods remain underexplored in the context of SWCRTs. To bridge this gap, we propose two innovative Bayesian hierarchical penalized spline models. Our first model accommodates large numbers of clusters and time periods, focusing on immediate intervention effects. To evaluate this approach, we compared this model to traditional frequentist methods. We then extend our approach to account for time-varying intervention effects, conducting a comprehensive comparison with an existing Bayesian monotone effect curve model and alternative frequentist methods. The proposed models were applied in the Primary Palliative Care for Emergency Medicine stepped wedge trial to evaluate the effectiveness of the intervention. Through extensive simulations and real-world application, we demonstrate the robustness of our proposed Bayesian models. Notably, the Bayesian immediate effect model consistently achieves the nominal coverage probability, providing more reliable interval estimations while maintaining high estimation accuracy. Furthermore, our proposed Bayesian time-varying effect model represents a significant advancement over the existing Bayesian monotone effect curve model, offering improved accuracy and reliability in estimation while also achieving higher coverage probability than alternative frequentist methods. To the best of our knowledge, this marks the first development of Bayesian hierarchical spline modeling for SWCRTs. Our proposed models offer promising tools for researchers and practitioners, enabling more precise evaluation of intervention impacts.

Transformer-Based Models for Predicting Molecular Structures from Infrared Spectra Using Patch-Based Self-Attention

Infrared (IR) spectroscopy, a type of vibrational spectroscopy, provides extensive molecular structure details and is a highly effective technique for chemists to determine molecular structures. However, analyzing experimental spectra has always been challenging due to the specialized knowledge required and the variability of spectra under different experimental conditions. Here, we propose a transformer-based model with a patch-based self-attention spectrum embedding layer, designed to prevent the loss of spectral information while maintaining simplicity and effectiveness. To further enhance the model's understanding of IR spectra, we introduce a data augmentation approach, which selectively introduces vertical noise only at absorption peaks. Our approach not only achieves state-of-the-art performance on simulated data sets but also attains a top-1 accuracy of 55% on real experimental spectra, surpassing the previous state-of-the-art by approximately 10%. Additionally, our model demonstrates proficiency in analyzing intricate and variable fingerprint regions, effectively extracting critical structural information.

Impact of prior specifications on performance of Bayesian factor mixture modeling

Factor mixture modeling (FMM) has been increasingly adopted in social, behavioral, and health sciences to identify population heterogeneity by incorporating both continuous latent variables (i.e., latent factors) and categorical latent variables (i.e., latent classes). FMM is known to face a variety of methodological challenges given its model complexity, and this study evaluates the potential of Bayesian estimation, particularly prior specifications, in addressing two challenges of FMM: classification accuracy and parameter recovery. We considered possible scenarios in applied research where subjective beliefs regarding class separation were incorporated into prior specifications such that subjective class separation might be greater or smaller than the true class separation in the population. Results of comprehensive Monte Carlo simulations showed adequate model performance using a moderately informative prior with subjective class separation greater than the true class separation. Practical implications for researchers are provided.

Generative model for the first cell fate bifurcation in mammalian development

The first cell fate bifurcation in mammalian development directs cells toward either the trophectoderm (TE) or inner cell mass (ICM) compartments in preimplantation embryos. This decision is regulated by the subcellular localization of a transcriptional co-activator YAP and takes place over several progressively asynchronous cleavage divisions. As a result of this asynchrony and variable arrangement of blastomeres, reconstructing the dynamics of the TE/ICM cell specification from fixed embryos is extremely challenging. To address this, we developed a live imaging approach and applied it to measure pairwise dynamics of nuclear YAP and its direct target genes, CDX2 and SOX2, key transcription factors of TE and ICM, respectively. Using these datasets, we constructed a generative model of the first cell fate bifurcation, which reveals the time-dependent statistics of the TE and ICM cell allocation. In addition to making testable predictions for the joint dynamics of the full YAP/CDX2/SOX2 motif, the model revealed the stochastic nature of the induction timing of the key cell fate determinants and identified the features of YAP dynamics that are necessary or sufficient for this induction. Notably, temporal heterogeneity was particularly prominent for SOX2 expression among ICM cells. As heterogeneities within the ICM have been linked to the initiation of the second cell fate decision in the embryo, understanding the origins of this variability is of key significance. The presented approach reveals the dynamics of the first cell fate choice and lays the groundwork for dissecting the next cell fate bifurcations in mouse development.

Statistical framework for calling allelic imbalance in high-throughput sequencing data

High-throughput sequencing facilitates large-scale studies of gene regulation and allows tracing the associations of individual genomic variants with changes in gene regulation and expression. Compared to classic association studies, the assessment of an allelic imbalance at heterozygous variants captures functional variant effects with smaller sample sizes, higher sensitivity, and better resolution. Yet, identification of allele-specific variants from allelic read counts remains challenging due to data-dependent biases and overdispersion arising from technical and biological variability. We present MIXALIME, a novel computational framework for calling allele-specific variants in diverse omics data with a repertoire of statistical models accounting for read mapping bias and copy number variation. We benchmark MIXALIME with DNase-Seq, ATAC-Seq, and CAGE-Seq data, and we demonstrate that the allelic imbalance highlights causal variants in GWAS results. Finally, as a showcase of the large-scale practical application of MIXALIME, we present an atlas of variants exhibiting allele-specific chromatin accessibility, built from thousands of available datasets obtained from diverse cell types.

A neuro-immune axis of transcriptomic dysregulation within the subgenual anterior cingulate cortex in schizophrenia

Many genes are linked to psychiatric disorders, but genome-wide association studies (GWAS) and differential gene expression (DGE) analyses in post-mortem brain tissue often implicate distinct gene sets. This disconnect impedes therapeutic development, which relies on integrating genetic and genomic insights. We address this issue using a novel multivariate technique that reduces DGE bias by leveraging gene co-expression networks and controlling for confounds such as drug exposure. Deep RNA sequencing was performed in bulk post-mortem sgACC from individuals with bipolar disorder (BD; N=35), major depression (MDD; N=51), schizophrenia (SCZ; N=44), and controls (N=55). Toxicology data dimensionality was reduced using multiple correspondence analysis; case-control gene expression was then analyzed using 1) traditional DGE and 2) group regularized canonical correlation analysis (GRCCA) - a multivariate regression method that accounts for feature interdependence. Gene set enrichment analyses compared results with established neuropsychiatric risk genes, gene ontology pathways, and cell type enrichments. GRCCA revealed a significant association with SCZ ( P perm =0.001; no significant BD or MDD association), and the resulting gene weight vector correlated with DGE SCZ-control t-statistics ( R =0.53; P <0.05). Both methods indicated down-regulation of immune and microglial genes and upregulation of ion transport and excitatory neuron genes. However, GRCCA - at both the gene and transcript level - showed stronger enrichments (FDR<0.05). Notably, GRCCA results were enriched for SCZ GWAS-implicated genes (FDR<0.05), while DGE results were not. These findings identify a SCZ-specific sgACC gene expression pattern that highlights SCZ risk genes and implicates neuro-immune pathways, thus demonstrating the utility of multivariate approaches to integrate genetic and genomic signals.

Evaluating the efficacy and safety of immune checkpoint inhibitors in first and second-line treatments for recurrent and metastatic head and neck squamous cell carcinoma: a systematic review and network meta-analysis of RCTs with a focus on PD-L1 expression

Introduction

This study systematically reviewed and conducted a network meta-analysis to assess the efficacy and safety of first-line and second-line immunotherapy treatments for recurrent and metastatic head and neck squamous cell carcinoma (R/M HNSCC). The findings aim to provide robust evidence to guide clinical decision-making.

Methods

We conducted an comprehensive literature search in PubMed, Embase, Cochrane Library, and Web of Science. The outcome measures included overall survival (OS), progression-free survival (PFS), overall response rate (ORR), and grade 3 or higher adverse events (AEs ≥3). To compare the efficacy and safety of various first-line and second-line immunotherapy regimens for R/M HNSCC with different PD-L1 expression levels, we conducted a Bayesian network meta-analysis. This study is registered in the Prospective Register of Systematic Reviews (CRD42024551711).

Results

This analysis included 9 randomized controlled trials (RCTs) involving 5,946 patients and seven immunotherapy regimens. Among patients with R/M HNSCC, pembrolizumab combined with chemotherapy as a first-line treatment was the only immunotherapy regimen to show a PFS benefit compared to SOC (HR = 0.92, 95% CI: 0.77-1.10); however, the difference was not statistically significant. Meanwhile, nivolumab provided the most pronounced OS benefit (HR=0.71,95%CI:0.52-0.98). Additionally, pembrolizumab exhibited the most favorable safety profile relative to SOC (OR=0.12, 95% CI: 0.05-0.29). In second-line therapy, nivolumab outperformed SOC in multiple aspects, including OS (HR=0.68, 95% CI: 0.54-0.86), ORR (OR=0.40, 95% CI: 0.17-0.95), and grade ≥3 adverse events (OR=0.32, 95% CI: 0.19-0.54). Subgroup analysis by PD-L1 expression revealed that nivolumab, compared to SOC, conferred the greatest OS benefit (HR=0.59, 95% CI: 0.34-1.00) as a first-line therapy in patients with PD-L1 expression ≥1%, while pembrolizumab combined with chemotherapy(pem-chemo) showed the most substantial PFS benefit (HR=0.82, 95% CI: 0.67-1.00). For patients with PD-L1 expression ≥20%, pem-chemo delivered the optimal OS (HR=0.60, 95% CI: 0.44-0.81) and PFS (HR=0.73, 95% CI: 0.55-0.97) outcomes compared to SOC. Furthermore, in patients with PD-L1 expression ≥1%, nivolumab as a second-line treatment demonstrated superior OS (HR=0.55, 95% CI: 0.39-0.78) and PFS (HR=0.59, 95% CI: 0.41-0.84) compared to SOC.

Conclusions

These results suggest that immunotherapy may improve survival outcomes compared to SOC for patients with R/M HNSCC, while maintaining a comparable safety profile. For patients, pembrolizumab combined with chemotherapy and nivolumab as first-line treatments may represent the most optimal options, with nivolumab also showing promise as a second-line therapy. In patients with PD-L1 expression ≥1% or ≥20%, pembrolizumab combined with chemotherapy may be the preferred first-line therapy, while nivolumab remains the most favorable second-line treatment.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42024551711.

Lengthened partial repetitions elicit similar muscular adaptations as full range of motion repetitions during resistance training in trained individuals

Purpose

Resistance training using different ranges of motion may produce varying effects on musclular adaptations. The purpose of this study was to compare the effects of lengthened partial repetitions (LPs) vs. full range of motion (ROM) resistance training (RT) on muscular adaptations.

Methods

In this within-participant study, thirty healthy, resistance-trained participants had their upper extremities randomly assigned to either a lengthened partial or full ROM condition; all other training variables were equivalent between limbs. The RT intervention was an 8-week program targeting upper-body musculature. Training consisted of two training sessions per week, with four exercises per session and four sets per exercise. Muscle hypertrophy of the elbow flexors and elbow extensors was evaluated using B-mode ultrasonography at 45% and 55% of humeral length. Muscle strength-endurance was assessed using a 10-repetition-maximum test on the lat pulldown exercise, both with a partial and full ROM. Data analysis employed a Bayesian framework with inferences made from posterior distributions and the strength of evidence for the existence of a difference through Bayes factors.

Results

Both muscle thickness and unilateral lat pulldown 10-repetition-maximum improvements were similar between the two conditions. Results were consistent across outcomes with point estimates close to zero, and Bayes factors (0.16 to 0.3) generally providing "moderate" support for the null hypothesis of equal improvement across interventions.

Conclusions

Trainees seeking to maximize muscle size should likely emphasize the stretched position, either by using a full ROM or LPs during upper-body resistance training. For muscle strength-endurance, our findings suggest that LPs and full ROM elicit similar adaptations.

Metal-Free Electrocatalytic Alkaline Water Splitting by Porous Macrocyclic Proton Sponges

Macrocycles are unique as they encapsulate and transfer guest molecules or ions and facilitate catalytic processes. Although metalated macrocycles are pivotal in electrocatalytic processes, using metal-free analogs has been rare. Following the strategy of Kanbara et al., we synthesized an azacalixarene macrocycle-N, N', N''-tris(p-aminophenyl)azacalix[3](2,6)pyridine (CalixNH2). The macrocycle encapsulates a proton in its cavity, maintaining the protonation even in highly alkaline media. Notably, it retains almost 50 % protonated form in 1 M KOH (~pH 14)-acting as a proton sponge. As hydrogen evolution is complex in alkaline media owing to sluggish water dissociation, we implemented the proton sponge (CalixNH2) in an alkaline hydrogen evolution reaction. Conjugated Porous polymers, TpCalix and DhaCalix, have been synthesized from the triamine-CalixNH2. The most efficient catalyst, TpCalix, has shown excellent performance in alkaline HER and OER in 1 M KOH (~pH 14), with low overpotentials of only 112(±2) and 290(±2) mV at 10 mA cm-2, respectively, and durable up to 24 hours. A full-cell reaction using TpCalix in both the cathode and anode exhibited a low full-cell voltage of 1.73 V and was stable for 12 hours. DFT calculations verified the tripyridinic core, which acts as the principal site for proton abstraction and binding.

Estimation of Diagnostic Test Accuracy Without Gold Standards

The ideal evaluation of diagnostic test performance requires a reference test that is free of errors. However, for many diseases, obtaining such a "gold standard" reference is either impossible or prohibitively expensive. Estimating test accuracy in the absence of a gold standard is therefore a significant challenge. In this article, we introduce and categorize existing methods for evaluating diagnostic tests without a gold standard, considering factors such as the type and number of tests, as well as the structure of the observed data. For each method, we provide a comprehensive introduction and analysis of its underlying assumptions, model architecture, identifiability, estimation techniques, and inference procedures. We use R to conduct simulations for widely applicable models, validating assumptions, comparing models, and assessing their reliability. Additionally, we present real-world examples along with the corresponding R code for these models, enabling readers to better understand how to apply them effectively. Beyond diagnostic medicine, we underscore that the issue of imperfect gold standards affects other fields, drawing parallels to the noisy label problem in machine learning. By highlighting similarities and differences across these domains, we open pathways for further research. The primary aim of this article is to consolidate existing methods for assessing test accuracy in the absence of a gold standard and to provide practical guidance for researchers seeking to apply these methods effectively.

Clinical Decision Support for Patient Cases with Asymptomatic Carotid Artery Stenosis Using AI Models and Electronic Medical Records

An artificial intelligence (AI) analysis of electronic medical records (EMRs) was performed to analyze the differences between patients with carotid stenosis who developed symptomatic disease and those who remained asymptomatic. The EMRs of 872 patients who underwent a carotid endarterectomy between 2009 and 2022 were analyzed with AI. This included 408 patients who had carotid intervention for symptomatic carotid disease and 464 patients for asymptomatic, >70% stenosis. By analyzing the EMRs, the Support Vector Machine achieved the highest sensitivity at 0.626 for predicting which of these patients would go on to develop a stroke or TIA. Random Forest had the highest specificity at 0.906. The risk for stroke in patients with carotid stenosis was a balance between optimum medical treatment and the underlying disease processes. Risk factors for developing symptomatic carotid disease included elevated glucose, chronic kidney disease, hyperlipidemia, and current or recent smoking, while protective factors included cardiovascular agents, antihypertensives, and beta blockers. An AI review of EMRs can help determine which patients with carotid stenosis are more likely to develop a stroke to assist with decision making as to whether to proceed with intervention or to demonstrate and encourage reduced stroke risk with risk factor modification.

Implementing a Bayesian approach using Stan with Torsten: Population pharmacokinetics analysis of somatrogon

Fully Bayesian approaches are not commonly implemented for population pharmacokinetic (PK) modeling. In this paper, we evaluate the use of Stan with R and Torsten for population PK modeling of somatrogon, a recombinant long-acting growth hormone approved for the treatment of growth hormone deficiency. As a software for Bayesian inference, Stan provides an easy way to conduct MCMC sampling for a wide range of models with efficient sampling algorithms, and there are several diagnostic tools to evaluate the MCMC convergence and other potential issues. Three different sets of priors were evaluated for estimation and prediction: a weakly informative uniform set, a moderately informative set, and a very informative set of priors. All three prior sets showed good performance and all chains mixed well. There were some minor differences in the final parameter posterior distributions while implementing different prior sets, but the posterior predictions covered the observations nicely, not only for the individuals included in posterior sampling but also for new individuals. The impact of a centered versus non-centered parameterization were evaluated, with the non-centered approach improving the estimation time, but it was still computationally intensive. Computational resources had the biggest impact on sampling time. Stan took approximately 2.5 h total for the MCMC sampling on a high-performance computing platform (6 cores) and may be reduced further with additional computational resources. The model and comparisons presented show that with adequate computational resources, the Bayesian approaches using Stan and Torsten are useful for population PK analysis, especially for the analysis of special populations, small sample datasets, and when complex model structures are needed.

Mechanisms in the Synthesis of S-Alcohols with 1,4-NADH Biomimetic Co-factor N-Benzyl-1,4-dihydronicotinamide using Horse Liver Alcohol Dehydrogenase: A Hybrid Computational Study

The enantioselective reduction of prochiral ketones catalyzed by horse liver alcohol dehydrogenase (HLADH), was investigated via a hybrid computational approach, for molecular reactions involved in chiral synthesis of S-alcohols, when the natural co-factor, 1,4-dihyronicotinamide adenine dinucleotide, 1,4-NADH, was replaced with biomimetic co-factor, N-benzyl-1,4-dihydronicotinamide, 1. We surmised that different hydride and proton transfer mechanisms were involved using co-factor, 1. An alternative mechanism, where the hydride transfer step occurred, via an η1-keto-S-η2-5,6-1,4-dihydronicotinamide-Zn(II) complex, was previously investigated with a model of the HLADH-Zn(II) catalytic site (J. Organometal. Chem. 2021, 943, 121810). Presently, we studied canonical and alternative mechanisms compared to models of the entire enzyme structure. We disproved the η2-Zn(II) complex, and discovered a canonical hydride transfer from biomimetic 1,4-NADH, 1, to the Zn(II) bound prochiral ketone substrate, followed by a new proton relay, consisting of a water chain connecting His51 to Ser48 that accomplished the S-alkoxy anion's protonation to yield the final S-alcohol product. The HLADH catalysis, with biomimetic co-factor, 1, that replaced the ribose group, the 5'-diphosphate groups, and the adenine nucleotide with a N-benzyl group, has provided a new paradigm for the design of other structures of 1,4-NADH biomimetic co-factors, including their economic value in biocatalysis reactions.

The daily reciprocal associations between electroencephalography measured sleep and affect

Self-report studies show that sleep and positive and negative affect are closely and bidirectionally linked. However, studies assessing sleep objectively yield more inconsistent results. This study assessed the reciprocal, daily relationship between sleep as measured with electroencephalography (EEG) and affect (measured in the evening) in a natural setting. We assessed sleep both on the macrolevel (i.e., rapid eye movement [REM] sleep and slow-wave sleep [SWS] duration) and on the microlevel (i.e., REM sleep fragmentation). In this study, 33 participants (i.e., healthy college students, mean [standard deviation] age 21.55 [3.73] years, 67% female) were followed for 2 weeks. Each participant wore an EEG headband for 15 nights and had polysomnography during 3 of the 15 nights providing 72 analysable nights of polysomnography and 271 analysable nights with the EEG headband. Every evening participants reported their momentary negative and positive affect. We examined the relationship between pre-sleep affect and the sleep variables, as well as the reverse relationship, with sleep variables predicting evening affect the next day. We detected that higher negative affect in the evening was related to more fragmented REM sleep. However, this result was only found with polysomnography and not with the EEG headband. No significant associations were found between affect and time spent in REM sleep and SWS. Overall, no support was found for the reciprocal association between negative and positive affect and EEG measured sleep. Only limited support was found for an association in one direction (i.e., evening negative affect was associated with more REM sleep fragmentation at night).

Spatial joint hazard assessment of landslide susceptibility and intensity within a single framework: Environmental insights from the Wenchuan earthquake

To comprehensively assess regional landslide hazards, we propose a geospatial approach that jointly evaluates both the probability of occurrence (susceptibility) and potential destructive power (intensity) within a single framework, overcoming the limitations of previous studies that treated these two disaster scenarios independently. Focusing on the largest landslide event triggered by the Wenchuan earthquake in China, we collected landslide occurrence and count data at the slope unit level, alongside 18 environmental factors, including seismic data. To enable this multi-hazard single-framework evaluation, we employed two Bayesian spatial joint regressions: the spatial shared component model (SSCM) and the spatial shared hyperparameter model (SSHM). This joint assessment focuses on three key components: identifying shared influencing factors, capturing shared spatial autocorrelated random effects, and jointly predicting susceptibility and intensity maps. Additionally, we enhanced the traditional absolute intensity index into the relative intensity by accounting for slope unit size. Both Bayesian SSCM and SSHM, incorporating multiple environmental drivers (seismic, topographical, geological, hydrological, and human activities), successfully evaluated landslide susceptibility and intensity under a single analytical frame. SSHM outperformed SSCM in terms of model fit and predictive accuracy, as revealed by cross-validation. While SSCM overfitted the landslide distribution of spatial autocorrelated random effect, SSHM provided a smoother, more spatially diverse representation. Both models consistently identified slope as the shared key factor influencing susceptibility and intensity, with the top four additional environmental factors varying slightly but all related to seismic activity. The concurrent susceptibility and absolute intensity maps produced by both models exhibited similar patterns, while relative intensity mapping identified new high-hazard areas within smaller slope units that were previously overlooked by susceptibility and absolute intensity. We established a Bayesian-based single modeling framework for joint hazard assessment and prediction of regional susceptibility and intensity, providing a cutting-edge geospatial paradigm for multi-objective hazard assessment in global environmental disaster management.

Statistical flaws of the fitness-fatigue sports performance prediction model

Optimizing athletic training programs with the support of predictive models is an active research topic, fuelled by a consistent data collection. The Fitness-Fatigue Model (FFM) is a pioneer for modelling responses to training on performance based on training load exclusively. It has been subject to several extensions and its methodology has been questioned. In this article, we leveraged a Bayesian framework involving biologically meaningful priors to diagnose the fit and predictive ability of the FFM. We used cross-validation to draw a clear distinction between goodness-of-fit and predictive ability. The FFM showed major statistical flaws. On the one hand, the model was ill-conditioned, and we illustrated the poor identifiability of fitness and fatigue parameters using Markov chains in the Bayesian framework. On the other hand, the model exhibited an overfitting pattern, as adding the fatigue-related parameters did not significantly improve the model's predictive ability (p-value > 0.40). We confirmed these results with 2 independent datasets. Both results question the relevance of the fatigue part of the model formulation, hence the biological relevance of the fatigue component of the FFM. Modelling sport performance through biologically meaningful and interpretable models remains a statistical challenge.

Nanoscale Visualization of Drosophila E-cadherin Ectodomain Fragments and Their Interactions Using DNA Origami Nanoblocks

The adhesive function of cell surface proteins can be visually assessed through direct observation; however, the underlying structures that mediate adhesion typically remain invisible at the nanoscale level. This hinders knowledge on the diversity of molecular architectures responsible for cell-cell adhesion. Drosophila E-cadherin (DE-cadherin), a classical cadherin with a unique domain structure, demonstrates adhesive function; however, it lacks a structural model that explains its adhesion mechanism. Here, we present a novel application of DNA origami technology to create a cell-free, flat environment in which full DE-cadherin ectodomains are anchored using SNAP-tags and biotin-streptavidin interactions. DNA origami was assembled into a 120 nm long block, bearing 5 or 14 biotin:streptavidin sites that were evenly spaced on one lateral face. DE-cadherin ectodomain fragments were attached via biotinylated SNAP-tags. These decorated DNA origami nanoblocks were subjected to transmission electron and high-speed atomic force microscopy, which revealed a hinge-like site that separated the membrane-distal and -proximal portions of the DE-cadherin ectodomain, suggesting a role in mechanical flexibility. We also observed interactions between DE-cadherin ectodomains via their membrane-distal portions on single DNA origami nanoblocks. We reconstituted an adhesion-like process via pairing DNA origami nanoblocks using DE-cadherin ectodomain interactions. Homophilic associations of functional DE-cadherin ectodomains between the paired DNA origami nanoblocks were visualized at the nanoscale, displaying strand-like molecular configurations, likely representing the extracellular cadherin repeats without regular arrays of structural elements. This study introduces a DNA origami-based platform for reconstituting and visualizing cadherin ectodomain interactions, with potential applications for a broader range of adhesion molecules.

Dynamically reconfigurable acoustofluidic metasurface for subwavelength particle manipulation and assembly

Particle manipulation plays a pivotal role in scientific and technological domains such as materials science, physics, and the life sciences. Here, we present a dynamically reconfigurable acoustofluidic metasurface that enables precise trapping and positioning of microscale particles in fluidic environments. By harnessing acoustic-structure interaction in a passive membrane resonator array, we generate localized standing acoustic waves that can be reconfigured in real-time. The resulting radiation force allows for subwavelength manipulation and patterning of particles on the metasurface at individual and collective scales, with actuation frequencies below 2 MHz. We further demonstrate the capabilities of the reconfigurable metasurface in trapping and enriching beads and biological cells flowing in microfluidic channels, showcasing its potential in high-throughput bioanalytical applications. Our versatile and biocompatible particle manipulation platform is suitable for applications ranging from the assembly of colloidal particles to enrichment of rare cells.

Optimized molecule detection in localization microscopy with selected false positive probability

Single-molecule localization microscopy (SMLM) allows imaging beyond the diffraction limit. Detection of molecules is a crucial initial step in SMLM. False positive detections, which are not quantitatively controlled in current methods, are a source of artifacts that affect the entire SMLM analysis pipeline. Furthermore, current methods lack standardization, which hinders reproducibility. Here, we present an optimized molecule detection method which combines probabilistic thresholding with theoretically optimal filtering. The probabilistic thresholding enables control over false positive detections while optimal filtering minimizes false negatives. A theoretically optimal Poisson matched filter is used as a performance benchmark to evaluate existing filtering methods. Overall, our approach allows the detection of molecules in a robust, single-parameter and user-unbiased manner. This will minimize artifacts and enable data reproducibility in SMLM.

Development of Bayesian segmented Poisson regression model to forecast COVID-19 dynamics based on wastewater data: a case study in Nanning City, China

INTRODUCTION

COVID-19 has caused tremendous hardships and challenges around the globe. Due to the prevalence of asymptomatic and pre-symptomatic carriers, relying solely on disease testing to screen for infections is not entirely reliable, which may affect the accuracy of predictions about the pandemic trends. This study is dedicated to developing a predictive model aimed at estimating of the dynamics of COVID-19 at an early stage based on wastewater data, to assist in establishing an effective early warning system for disease control.

METHOD

Viral load in wastewater and the number of daily reported COVID-19 cases were collected from Nanning CDC and the Chinese Disease Prevention and Control Information System, respectively. We used the viral load to estimate daily reported cases by a Bayesian linear regression model. Subsequently, a Bayesian (segmented) Poisson regression model was developed, using data from the first wave of the epidemic as prior information, to predict the COVID-19 epidemic trend of the second wave. Finally, in order to explore the optimal training data for predicting outbreak dynamics during the pandemic, we fitted the model using various training sets.

RESULTS

The results revealed the estimated cases, using the viral load with a 3-day lag, were consistent with the actual reported cases, with adjusted R² value of 0.935 (p < 0.001). Our model successfully predicted the epidemic peak time and provided early warnings on the third day after the outbreak began. Furthermore, after using data from the first 6 days of the outbreak, the model's MAPE rapidly decreasing to lower levels (MAPE = 29.34%) and eventually stabilized at approximately 20%. Compared to using non-informative priors, this result allows for an advance warning of approximately two weeks. Importantly, as the inclusion of data from early outbreak increased, the predictive results of the model became more stable and accurate.

CONCLUSION

This study demonstrates the potential of wastewater-based epidemiology combined with Bayesian methods as a monitoring and predictive tool during infectious disease outbreaks.

Spatial control of doping in conducting polymers enables complementary, conformable, implantable internal ion-gated organic electrochemical transistors

Complementary transistors are critical for circuits with compatible input/output signal dynamic range and polarity. Organic electronics offer biocompatibility and conformability; however, generation of complementary organic transistors requires introduction of separate materials with inadequate stability and potential for tissue toxicity, limiting their use in biomedical applications. Here, we discovered that introduction of source/drain contact asymmetry enables spatial control of de/doping and creation of single-material complementary organic transistors from a variety of conducting polymers of both carrier types. When integrated with the vertical channel design and internal ion reservoirs of internal ion-gated organic electrochemical transistors, we produced matched complementary IGTs (cIGTs) that formed high-performance conformable amplifiers with 200 V/V uniform gain and 2 MHz bandwidth. These amplifiers showed long-term in vivo stability, and their miniaturized biocompatible design allowed implantation in developing rodents to monitor network maturation. cIGTs expand the use of organic electronics in standard circuit designs and enhance their biomedical potential.

Prediction of Temperature-Dependent Henry's Law Constants by Matrix Completion

Methods for predicting Henry's law constants Hij describing the solubility of solutes i in solvents j as a function of temperature are essential in chemical engineering. While isothermal properties of binary mixtures can conveniently be predicted with matrix completion methods (MCMs) from machine learning, we advance their application to the temperature-dependent prediction of Hij in the present work by combining them with physical equations describing the temperature dependence. For training the methods, experimental Hij data for 122 solutes and 399 solvents ranging from 173.15 to 573.15 K were taken from the Dortmund Data Bank. Two MCMs are proposed: a data-driven MCM that relies solely on experimental data and a hybrid MCM that incorporates predictions from the established Predictive Soave-Redlich-Kwong (PSRK) equation of state (EoS), effectively combining physical knowledge and machine learning. The performance of these MCMs is assessed via leave-one-out analysis and compared to that of the PSRK-EoS, demonstrating superior prediction accuracy.

Entropy-Based Stochastic Optimization of Multi-Energy Systems in Gas-to-Methanol Processes Subject to Modeling Uncertainties

In gas-to-methanol processes, optimizing multi-energy systems is a critical challenge toward efficient energy allocation. This paper proposes an entropy-based stochastic optimization method for a multi-energy system in a gas-to-methanol process, aiming to achieve optimal allocation of gas, steam, and electricity to ensure executability under modeling uncertainties. First, mechanistic models are developed for major chemical equipments, including the desulfurization, steam boilers, air separation, and syngas compressors. Structural errors in these models under varying operating conditions result in noticeable model uncertainties. Second, Bayesian estimation theory and the Markov Chain Monte Carlo approach are employed to analyze the differences between historical data and model predictions under varying operating conditions, thereby quantifying modeling uncertainties. Finally, subject to constraints in the model uncertainties, equipment capacities, and energy balance, a multi-objective stochastic optimization model is formulated to minimize gas loss, steam loss, and operating costs. The entropy weight approach is then applied to filter the Pareto front solution set, selecting a final optimal solution with minimal subjectivity and preferences. Case studies using Aspen Hysys-based simulations show that optimization solutions considering model uncertainties outperform the counterparts from a standard deterministic optimization in terms of executability.

ChromatinHD connects single-cell DNA accessibility and conformation to gene expression through scale-adaptive machine learning

Gene regulation is inherently multiscale, but scale-adaptive machine learning methods that fully exploit this property in single-nucleus accessibility data are still lacking. Here, we develop ChromatinHD, a pair of scale-adaptive models that uses the raw accessibility data, without peak-calling or windows, to link regions to gene expression and determine differentially accessible chromatin. We show how ChromatinHD consistently outperforms existing peak and window-based approaches and find that this is due to a large number of uniquely captured, functional accessibility changes within and outside of putative cis-regulatory regions. Furthermore, ChromatinHD can delineate collaborating regulatory regions, including their preferential genomic conformations, that drive gene expression. Finally, our models also use changes in ATAC-seq fragment lengths to identify dense binding of transcription factors, a feature not captured by footprinting methods. Altogether, ChromatinHD, available at https://chromatinhd.org , is a suite of computational tools that enables a data-driven understanding of chromatin accessibility at various scales and how it relates to gene expression.

Radiation and DNA Origami Nanotechnology: Probing Structural Integrity at the Nanoscale

DNA nanotechnology has emerged as a groundbreaking field, using DNA as a scaffold to create nanostructures with customizable properties. These DNA nanostructures hold potential across various domains, from biomedicine to studying ionizing radiation-matter interactions at the nanoscale. This review explores how the various types of radiation, covering a spectrum from electrons and photons at sub-excitation energies to ion beams with high-linear energy transfer influence the structural integrity of DNA origami nanostructures. We discuss both direct effects and those mediated by secondary species like low-energy electrons (LEEs) and reactive oxygen species (ROS). Further we discuss the possibilities for applying radiation in modulating and controlling structural changes. Based on experimental insights, we identify current challenges in characterizing the responses of DNA nanostructures to radiation and outline further areas for investigation. This review not only clarifies the complex dynamics between ionizing radiation and DNA origami but also suggests new strategies for designing DNA nanostructures optimized for applications exposed to various qualities of ionizing radiation and their resulting byproducts.

Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy

Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations. We use simulations and experiments to show that applying the trained 'de-aberration' networks outperforms alternative methods, providing restoration on par with adaptive optics techniques; and subsequently apply the networks to diverse datasets captured with confocal, light-sheet, multi-photon, and super-resolution microscopy. In all cases, the improved quality of the restored data facilitates qualitative image inspection and improves downstream image quantitation, including orientational analysis of blood vessels in mouse tissue and improved membrane and nuclear segmentation in C. elegans embryos.

Nanotheranostics: The Fabrication of Theranostics with Nanoparticles and their Application to Treat the Neurological Disorders

BACKGROUND

Theranostics is a method that focuses on providing patient-centred care and is evolving as a targeted, safe, and effective pharmacotherapy. Nanotheranostics combines diagnosis and therapeutic modalities that bridge traditional treatment and personalised medicine. Theranostics provides novel ideas for nanotechnology. This review describes the current state of nanotechnology-based therapies used to treat neurological illnesses. Some patents on theranostics are also discussed in this review.

OBJECTIVE

This study aims to provide a more comprehensive review of the diagnosis and therapeutic properties of nanotheranostics, the present state of nanotechnology-based treatment of neurological disorders, and the future potential of theranostics.

METHODS

The phrase "theranostics" refers to a treatment strategy that integrates therapeutics and diagnostics to monitor treatment response and enhance drug efficacy and safety. Theranostics is a crucial component of personalised medicine and calls for significant advancements in predictive medicine. The term "theranostics" refers to a diagnosis that screens patients for potential adverse drug reactions and targets drug delivery depending on the test results. Theranostics treats neurological disorders (like brain tumours (glioma), Parkinson's disease, Alzheimer's disease, and neurovascular diseases). Many review articles on Google Scholar, PubMed, Google Patents, and Scopus were used to gather information for this review. Data acquired from many sources was compiled in this review to provide more information on theranostics.

RESULTS

The role of various nanocarrier systems as theranostic agents for neurological illnesses and the fabrication of nanomaterials for theranostics are discussed in this article after evaluating a substantial number of review articles.

CONCLUSION

The distinctive intrinsic features of nanoparticles make them useful for functionalization and imaging. Theranostics in nuclear medicine include diagnostic imaging and therapy using the same molecule that is radiolabeled differently or the same medication at various doses. It is possible to determine if a patient will benefit from a given treatment by visualising potential targets. Targeted nuclear therapy has been shown to be beneficial in patients if chosen carefully and has a good safety profile.

The effects of hip flexion angle on quadriceps femoris muscle hypertrophy in the leg extension exercise

This study compared the effects of 90° versus 40° hip flexion in the leg extension exercise on quadriceps femoris muscle hypertrophy. Twenty-two untrained men completed a ten-week intervention comprising two resistance training sessions per week. A within-participant design was used, with the lower limb side randomly allocated to the 40 or 90° condition. Muscle thickness of distal and proximal rectus femoris and vastus lateralis was quantified via ultrasound. Data were analysed within a Bayesian framework including univariate and multivariate mixed effect models with random effects to account for the within participant design. Differences between conditions were estimated as average treatment effects (ATE) and inferences were made based on posterior distributions and Bayes Factors (BF). Results indicated a greater hypertrophic response in the rectus femoris for the 40° condition, with "extreme" evidence supporting a hypertrophic response favouring the 40° hip angle for the rectus femoris (BF > 100; p(Distal/ATE & Proximal/ATE >0) > 0.999), and "strong" evidence supporting no difference in hypertrophic response for the vastus lateralis (BF = 0.07). Therefore, both conditions could be viable options for increasing quadriceps femoris hypertrophy. However, when training for maximizing rectus femoris hypertrophy among untrained men, we suggest training with a reduced hip flexion in the leg extension exercise.

Impact of Vertical Scaling on Normal Probability Density Function Plots

Probability density function (PDF) curves are among the few charts on a Cartesian coordinate system that are commonly presented without y-axes. This design decision may be due to the lack of relevance of vertical scaling in normal PDFs. In fact, as long as two normal PDFs have the same means and standard deviations (SDs), they can be scaled to occupy different amounts of vertical space while still remaining statistically identical. Because unfixed PDF height increases as SD decreases, visualization designers may find themselves tempted to vertically shrink low-SD PDFs to avoid occlusion or save white space in their figures. Although irregular vertical scaling has been explored in bar and line charts, the visualization community has yet to investigate how this visual manipulation may affect reader comparisons of PDFs. In this paper, we present two preregistered experiments (n = 600, n = 401) that systematically demonstrate that vertical scaling can lead to misinterpretations of PDFs. We also test visual interventions to mitigate misinterpretation. In some contexts, we find including a y-axis can help reduce this effect. Overall, we find that keeping vertical scaling consistent, and therefore maintaining equal pixel areas under PDF curves, results in the highest likelihood of accurate comparisons. Our findings provide insights into the impact of vertical scaling on PDFs, and reveal the complicated nature of proportional area comparisons.

INSIHGT: an accessible multi-scale, multi-modal 3D spatial biology platform

Biological systems are complex, encompassing intertwined spatial, molecular and functional features. However, methodological constraints limit the completeness of information that can be extracted. Here, we report the development of INSIHGT, a non-destructive, accessible three-dimensional (3D) spatial biology method utilizing superchaotropes and host-guest chemistry to achieve homogeneous, deep penetration of macromolecular probes up to centimeter scales, providing reliable semi-quantitative signals throughout the tissue volume. Diverse antigens, mRNAs, neurotransmitters, and post-translational modifications are well-preserved and simultaneously visualized. INSIHGT also allows multi-round, highly multiplexed 3D molecular probing and is compatible with downstream traditional histology and nucleic acid sequencing. With INSIHGT, we map undescribed podocyte-to-parietal epithelial cell microfilaments in mouse glomeruli and neurofilament-intensive inclusion bodies in the human cerebellum, and identify NPY-proximal cell types defined by spatial morpho-proteomics in mouse hypothalamus. We anticipate that INSIHGT can form the foundations for 3D spatial multi-omics technology development and holistic systems biology studies.

The Second Law of Infodynamics: A Thermocontextual Reformulation

Vopson and Lepadatu recently proposed the Second Law of Infodynamics. The law states that while the total entropy increases, information entropy declines over time. They state that the law has applications over a wide range of disciplines, but they leave many key questions unanswered. This article analyzes and reformulates the law based on thermocontextual interpretation (TCI). The TCI generalizes Hamiltonian mechanics by defining states and transitions thermocontextually with respect to an ambient-temperature reference state. The TCI partitions energy into exergy, which can do work on the ambient surroundings, and entropic energy with zero work potential. The TCI is further generalized here to account for a reference observer's actual knowledge. This enables partitioning exergy into accessible exergy, which is known and accessible for use, and configurational energy, which is knowable but unknown and inaccessible. The TCI is firmly based on empirically validated postulates. The Second Law of thermodynamics and its information-based analog, MaxEnt, are logically derived corollaries. Another corollary is a reformulated Second Law of Infodynamics. It states that an external agent seeks to increase its access to exergy by narrowing its information gap with a potential exergy source. The principle is key to the origin of self-replicating chemicals and life.

Evaluation of Bayesian Linear Regression derived gene set test methods

BACKGROUND

Gene set tests can pinpoint genes and biological pathways that exert small to moderate effects on complex diseases like Type 2 Diabetes (T2D). By aggregating genetic markers based on biological information, these tests can enhance the statistical power needed to detect genetic associations.

RESULTS

Our goal was to develop a gene set test utilizing Bayesian Linear Regression (BLR) models, which account for both linkage disequilibrium (LD) and the complex genetic architectures intrinsic to diseases, thereby increasing the detection power of genetic associations. Through a series of simulation studies, we demonstrated how the efficacy of BLR derived gene set tests is influenced by several factors, including the proportion of causal markers, the size of gene sets, the percentage of genetic variance explained by the gene set, and the genetic architecture of the traits. By using KEGG pathways, eQTLs, and regulatory elements as different kinds of gene sets with T2D results, we also assessed the performance of gene set tests in explaining more about real phenotypes.

CONCLUSIONS

Comparing our method with other approaches, such as the gold standard MAGMA (Multi-marker Analysis of Genomic Annotation) approach, our BLR gene set test showed superior performance. Combining performance of our method in simulated and real phenotypes, this suggests that our BLR-based approach could more accurately identify genes and biological pathways underlying complex diseases.