Bayesian statistics and modelling

Good-enough Care? How Patients' Perceptions of Counselors' Professional Skills Relate to Everyday Life in Forensic Long-stay Units

The overall goal of long-term forensic care is to strive toward acceptable levels of adaptation and quality of life (QoL) of the forensic patient in the institutional context. While the bulk of the literature has focused on the deleterious consequences of personality pathology in this regard, research investigating the contribution of the quality of the therapeutic relationship has remained rather scant. Assuming that the perceived competence of the direct counselor, as perceived by patients, forms an important aspect in this regard, the central aim of this study was to investigate the relationship between patients' perceptions of their therapist's professional skills, their self-reported maladaptive behavior on the ward, and their experienced QoL. To this end, we recruited patients (N = 60) in long-stay forensic units and investigated their perceptions of 10 specific skills displayed by their therapist, along a "too little-too much" rating scale. The results revealed that patients who had the overall impression that their counselor was equipped with an adequate set of professional skills showed less maladaptive behavior and perceived a higher QoL on the ward. Conversely, at a more specific competence level, only a positive relationship between a counselor's predictability and self-reported QoL was found. Taken together, these results highlight that an overall professional skill evaluation matters in the context of forensic patients' adaptation and QoL in their long-stay units, with the counselor's predictability serving as a crucial aspect in obtaining the most favorable outcomes.

Joint modeling of choices and reaction times based on Bayesian contextual behavioral control

In cognitive neuroscience and psychology, reaction times are an important behavioral measure. However, in instrumental learning and goal-directed decision making experiments, findings often rely only on choice probabilities from a value-based model, instead of reaction times. Recent advancements have shown that it is possible to connect value-based decision models with reaction time models. However, typically these models do not provide an integrated account of both value-based choices and reaction times, but simply link two types of models. Here, we propose a novel integrative joint model of both choices and reaction times by combining a computational account of Bayesian sequential decision making with a sampling procedure. This allows us to describe how internal uncertainty in the planning process shapes reaction time distributions. Specifically, we use a recent context-specific Bayesian forward planning model which we extend by a Markov chain Monte Carlo (MCMC) sampler to obtain both choices and reaction times. As we will show this makes the sampler an integral part of the decision making process and enables us to reproduce, using simulations, well-known experimental findings in value based-decision making as well as classical inhibition and switching tasks. Specifically, we use the proposed model to explain both choice behavior and reaction times in instrumental learning and automatized behavior, in the Eriksen flanker task and in task switching. These findings show that the proposed joint behavioral model may describe common underlying processes in these different decision making paradigms.

The Impact of Chlorhexidine as an Endodontic Irrigant/Medicament on Post-Obturation Pain in Patients Following Endodontic Therapy: A Preliminary Bayesian Meta-Analysis

To assess the overall impact of chlorhexidine, when used as an endodontic irrigant/medicament, on post-obturation pain following endodontic treatment by the application of Bayes methods of meta-analysis. This study aimed to combine recently documented evidence using rigorous and specific search criteria. The available data were analyzed using Bayesian methods, which have the advantage of accounting for uncertainties in estimates. This is in contrast to commonly used frequentist meta-analysis methods, which are not able to model uncertainties as effectively, particularly when there is heterogeneity present. A preliminary search was conducted in the Pubmed, Cochrane, and Scielo databases using appropriate keywords. The search aimed to identify human randomized clinical studies that evaluated post-operative endodontic pain and involved the use of chlorhexidine as an endodontic irrigant or medicament. The preliminary Bayesian meta-analysis produced a more accurate estimate compared to a frequentist random effect meta-analysis model, with a narrower credible interval. The frequentist model demonstrated a detrimental impact of chlorhexidine on post-obturation pain, while the Bayesian model indicated a somewhat favorable effect of chlorhexidine on post-obturation pain. Based on the scope of this investigation, it can be inferred that a Bayesian random effects meta-analysis model demonstrated a somewhat favorable impact of chlorhexidine on post-endodontic pain, which carries significant therapeutic significance.

Associations of cholinergic system integrity with cognitive decline in GBA1 and LRRK2 mutation carriers

In Parkinson's disease (PD), GBA1- and LRRK2-mutations are associated with different clinical phenotypes which might be related to differential involvement of the cholinergic system. We investigated cholinergic integrity in 149 asymptomatic GBA1 and 169 asymptomatic LRRK2 mutation carriers, 112 LRRK2 and 60 GBA1 carriers with PD, 492 idiopathic PD, and 180 controls from the PPMI cohort. Basal forebrain volumes were extracted and white matter pathways from nucleus basalis of Meynert (NBM) to cortex and from pedunculopontine nucleus (PPN) to thalamus were assessed with a free water-corrected DTI model. Bayesian ANCOVAs were conducted for group comparisons and Bayesian linear mixed models to assess associations with cognitive decline. Basal forebrain volumes were increased in asymptomatic GBA1 (Bayes Factor against the null hypothesis (BF10) = 75.2) and asymptomatic LRRK2 (BF10 = 57.0) compared to controls. Basal forebrain volumes were increased in LRRK2- compared to GBA1-PD (BF10 = 14.5) and idiopathic PD (BF10 = 3.6*107), with no difference between idiopathic PD and PD-GBA1 (BF10 = 0.25). Mean diffusivity along the medial NBM pathway was decreased in asymptomatic GBA1 compared to controls (BF10 = 30.3). Over 5 years, idiopathic PD and PD-GBA1 declined across all cognitive domains whereas PD-LRRK2 patients only declined in processing speed. We found an interaction between basal forebrain volume and time in predicting multiple cognitive domains in idiopathic PD and PD-GBA1, but not in PD-LRRK2. While LRRK2 and GBA1 mutations are both associated with increased basal forebrain volume at asymptomatic stages, this increase persists at the symptomatic PD stage only in LRRK2 and might be related to slower cognitive decline in these patients.

Unravelling the operation of organic artificial neurons for neuromorphic bioelectronics

Organic artificial neurons operating in liquid environments are crucial components in neuromorphic bioelectronics. However, the current understanding of these neurons is limited, hindering their rational design and development for realistic neuronal emulation in biological settings. Here we combine experiments, numerical non-linear simulations, and analytical tools to unravel the operation of organic artificial neurons. This comprehensive approach elucidates a broad spectrum of biorealistic behaviors, including firing properties, excitability, wetware operation, and biohybrid integration. The non-linear simulations are grounded in a physics-based framework, accounting for ion type and ion concentration in the electrolytic medium, organic mixed ionic-electronic parameters, and biomembrane features. The derived analytical expressions link the neurons spiking features with material and physical parameters, bridging closer the domains of artificial neurons and neuroscience. This work provides streamlined and transferable guidelines for the design, development, engineering, and optimization of organic artificial neurons, advancing next generation neuronal networks, neuromorphic electronics, and bioelectronics.

EfficientNet-based machine learning architecture for sleep apnea identification in clinical single-lead ECG signal data sets

OBJECTIVE

Our objective was to create a machine learning architecture capable of identifying obstructive sleep apnea (OSA) patterns in single-lead electrocardiography (ECG) signals, exhibiting exceptional performance when utilized in clinical data sets.

METHODS

We conducted our research using a data set consisting of 1656 patients, representing a diverse demographic, from the sleep center of China Medical University Hospital. To detect apnea ECG segments and extract apnea features, we utilized the EfficientNet and some of its layers, respectively. Furthermore, we compared various training and data preprocessing techniques to enhance the model's prediction, such as setting class and sample weights or employing overlapping and regular slicing. Finally, we tested our approach against other literature on the Apnea-ECG database.

RESULTS

Our research found that the EfficientNet model achieved the best apnea segment detection using overlapping slicing and sample-weight settings, with an AUC of 0.917 and an accuracy of 0.855. For patient screening with AHI > 30, we combined the trained model with XGBoost, leading to an AUC of 0.975 and an accuracy of 0.928. Additional tests using PhysioNet data showed that our model is comparable in performance to existing models regarding its ability to screen OSA levels.

CONCLUSIONS

Our suggested architecture, coupled with training and preprocessing techniques, showed admirable performance with a diverse demographic dataset, bringing us closer to practical implementation in OSA diagnosis. Trial registration The data for this study were collected retrospectively from the China Medical University Hospital in Taiwan with approval from the institutional review board CMUH109-REC3-018.

Unveiling new genetic insights in rheumatoid arthritis for drug discovery through Taxonomy3 analysis

Genetic support for a drug target has been shown to increase the probability of success in drug development, with the potential to reduce attrition in the pharmaceutical industry alongside discovering novel therapeutic targets. It is therefore important to maximise the detection of genetic associations that affect disease susceptibility. Conventional statistical methods such as genome-wide association studies (GWAS) only identify some of the genetic contribution to disease, so novel analytical approaches are required to extract additional insights. C4X Discovery has developed Taxonomy3, a unique method for analysing genetic datasets based on mathematics that is novel in drug discovery. When applied to a previously published rheumatoid arthritis GWAS dataset, Taxonomy3 identified many additional novel genetic signals associated with this autoimmune disease. Follow-up studies using tool compounds support the utility of the method in identifying novel biology and tractable drug targets with genetic support for further investigation.

Ecological trade-offs drive phenotypic and genetic differentiation of Arabidopsis thaliana in Europe

Plant diversity is shaped by trade-offs between traits related to competitive ability, propagule dispersal, and stress resistance. However, we still lack a clear understanding of how these trade-offs influence species distribution and population dynamics. In Arabidopsis thaliana, recent genetic analyses revealed a group of cosmopolitan genotypes that successfully recolonized Europe from its center after the last glaciation, excluding older (relict) lineages from the distribution except for their north and south margins. Here, we tested the hypothesis that cosmopolitans expanded due to higher colonization ability, while relicts persisted at the margins due to higher tolerance to competition and/or stress. We compared the phenotypic and genetic differentiation between 71 European genotypes originating from the center, and the south and north margins. We showed that a trade-off between plant fecundity and seed mass shapes the differentiation of A. thaliana in Europe, suggesting that the success of the cosmopolitan groups could be explained by their high dispersal ability. However, at both north and south margins, we found evidence of selection for alleles conferring low dispersal but highly competitive and stress-resistance abilities. This study sheds light on the role of ecological trade-offs as evolutionary drivers of the distribution and dynamics of plant populations.

descSPIM: an affordable and easy-to-build light-sheet microscope optimized for tissue clearing techniques

Despite widespread adoption of tissue clearing techniques in recent years, poor access to suitable light-sheet fluorescence microscopes remains a major obstacle for biomedical end-users. Here, we present descSPIM (desktop-equipped SPIM for cleared specimens), a low-cost ($20,000-50,000), low-expertise (one-day installation by a non-expert), yet practical do-it-yourself light-sheet microscope as a solution for this bottleneck. Even the most fundamental configuration of descSPIM enables multi-color imaging of whole mouse brains and a cancer cell line-derived xenograft tumor mass for the visualization of neurocircuitry, assessment of drug distribution, and pathological examination by false-colored hematoxylin and eosin staining in a three-dimensional manner. Academically open-sourced ( https://github.com/dbsb-juntendo/descSPIM ), descSPIM allows routine three-dimensional imaging of cleared samples in minutes. Thus, the dissemination of descSPIM will accelerate biomedical discoveries driven by tissue clearing technologies.

Burden of recreational water illness due to exposure to cyanobacteria and their toxins in freshwater beaches in Canada: protocol of a prospective cohort study

INTRODUCTION

Cyanobacterial blooms are increasingly common in freshwater sources used for swimming and other recreational water contact activities in Canada. Many species of cyanobacteria can produce toxins that affect human and animal health, but there are limited data on the risk of illness associated with water contact at impacted beaches.

METHODS AND ANALYSIS

This study will investigate the incidence of recreational water illness due to exposure to cyanobacterial blooms and their toxins in four targeted and popular freshwater beaches in Ontario, Manitoba and Nova Scotia, Canada. A prospective cohort design and One Health approach will be used. On-site recruitment of recreational water users will be conducted at two beaches per year during the summers of 2024 and 2025. The population of interest includes recreational water users of any age and their pet dogs. After enrolment, an in-person survey will determine beach exposures and confounding factors, and a 3-day follow-up survey will ascertain any acute illness outcomes experienced by participants or their dogs. The target sample size is 2500 recreational water users. Water samples will be taken each recruitment day and analysed for cyanobacterial indicators (pigments), cell counts and toxin levels. Bayesian regression analysis will be conducted to estimate the association with water contact, cyanobacterial levels and risks of different acute illness outcomes.

ETHICS AND DISSEMINATION

This study has been approved by the Toronto Metropolitan University Research Ethics Board (REB 2023-461). Study results will be published in a peer-reviewed journal and as infographics on a project website.

Endorsement of metaphysical idealism mediates a link between past use of psychedelics and wellbeing

It has been proposed that psychedelics promote wellbeing through spiritual-type transformations, involving changes in metaphysical beliefs. Past empirical research shows a link between the use of psychedelics and the endorsement of non-physicalist metaphysical beliefs. However, non-physicalist beliefs encompass a wide range of metaphysical ideas, and their links to wellbeing and psychedelics use remain unclear. We utilized a cross-sectional Internet survey to probe the metaphysical beliefs of participants (N = 701) with past experience of classical psychedelics, using a novel 42-item questionnaire (Core Metaphysical Beliefs, CMB), encompassing a wide range of metaphysical beliefs. Factor analysis of CMB revealed two factors, Idealism and Materialism. In network analyses, Idealism was linked to psychological insight in a past psychedelic experience (E = 0.24) and average use of psychedelics (E = 0.16), and predicted wellbeing (Es = 0.13 and 0.22). Mediation analyses showed an indirect link from past psychedelics use through Idealism to wellbeing (ps ≤ .005). Non-Physicalist Beliefs or Materialism were not significant mediators. The results indicate that Idealism specifically, not non-physicalist beliefs generally, mediate a link between the use of psychedelics and wellbeing. Future research is required to establish whether the link is causal, and to understand what the Idealism factor means.

Machine learning guided rational design of a non-heme iron-based lysine dioxygenase improves its total turnover number

Highly selective C-H functionalization remains an ongoing challenge in organic synthetic methodologies. Biocatalysts are robust tools for achieving these difficult chemical transformations. Biocatalyst engineering has often required directed evolution or structure-based rational design campaigns to improve their activities. In recent years, machine learning has been integrated into these workflows to improve the discovery of beneficial enzyme variants. In this work, we combine a structure-based machine-learning algorithm with classical molecular dynamics simulations to down select mutations for rational design of a non-heme iron-dependent lysine dioxygenase, LDO. This approach consistently resulted in functional LDO mutants and circumvents the need for extensive study of mutational activity before-hand. Our rationally designed single mutants purified with up to 2-fold higher yields than WT and displayed higher total turnover numbers (TTN). Combining five such single mutations into a pentamutant variant, LPNYI LDO, leads to a 40% improvement in the TTN (218±3) as compared to WT LDO (TTN = 160±2). Overall, this work offers a low-barrier approach for those seeking to synergize machine learning algorithms with pre-existing protein engineering strategies.

Visualization of CO2 electrolysis using optical coherence tomography

Electrolysers offer an appealing technology for conversion of CO2 into high-value chemicals. However, there are few tools available to track the reactions that occur within electrolysers. Here we report an electrolysis optical coherence tomography platform to visualize the chemical reactions occurring in a CO2 electrolyser. This platform was designed to capture three-dimensional images and videos at high spatial and temporal resolutions. We recorded 12 h of footage of an electrolyser containing a porous electrode separated by a membrane, converting a continuous feed of liquid KHCO3 to reduce CO2 into CO at applied current densities of 50-800 mA cm-2. This platform visualized reactants, intermediates and products, and captured the strikingly dynamic movement of the cathode and membrane components during electrolysis. It also linked CO production to regions of the electrolyser in which CO2 was in direct contact with both membrane and catalyst layers. These results highlight how this platform can be used to track reactions in continuous flow electrochemical reactors.

Sex based differences in depression, anxiety, and quality of life and predictors of quality of life among South Asian individuals with chronic obstructive pulmonary disease: A Bayesian analysis

BACKGROUND

COPD can affect both men and women leading to deteriorating impact on general well-being, personal, and family life and decreased quality of life. Anxiety, depression, and self-care behaviours can affect the quality of life of individuals with COPD. There is a dearth of sex-based comparative analyses of anxiety, depression, quality of life, and predictors of quality of life among South Asian individuals with COPD.

PURPOSE

To identify the sex-based differences in depression, anxiety, and quality of life and the predictors of quality of life among South Asian individuals with COPD.

METHODS

A cross-sectional survey of 294 men and 114 women with COPD was conducted in Khyber Pakhtunkhwa, Pakistan. Hospital Anxiety and Depression scale, World Health Organization Quality of Life-brief version, Self-Care of Chronic Obstructive Pulmonary Disease Inventory, the Self-Care Self-Efficacy in COPD Scale were used for data collection. Bayesian independent sample t-test was used to compare mean differences in depression, anxiety, and quality of life among men and women. Two regression models were examined to determine if age, years of living with COPD, anxiety, depression, self-care self-efficacy, self-care monitoring, management, and maintenance were predictors of quality of life among men and women.

FINDINGS

Bayesian analysis showed anecdotal evidence that women had higher levels of depression, but lower levels of anxiety compared to men. Anecdotal evidence indicated that the physical quality of life of men was better than women, but strong evidence that their social relationship and environmental quality of life was better than women. Years of living with COPD, anxiety, depression, self-care self-efficacy, self-care management, self-care monitoring, and self-care maintenance were stronger predictors of women's quality of life. Anxiety and depression only predicted men's psychological quality of life, but predicted women's psychological, social relationships, and environmental quality of life.

CONCLUSIONS

The findings contribute to literature highlighting sex-based differences in anxiety, depression, and quality of life among South Asian men and women with COPD. Men generally reported higher levels of quality of life than women across all domains. Women's social relationships and environmental quality of life were greatly impacted by anxiety and depression. Quality of life interventions for women should be targeted at improving their social relationships and environmental satisfaction and addressing anxiety and depression.

Implementation of Bayesian networks and Bayesian inference using a Cu0.1Te0.9/HfO2/Pt threshold switching memristor

Bayesian networks and Bayesian inference, which forecast uncertain causal relationships within a stochastic framework, are used in various artificial intelligence applications. However, implementing hardware circuits for the Bayesian inference has shortcomings regarding device performance and circuit complexity. This work proposed a Bayesian network and inference circuit using a Cu0.1Te0.9/HfO2/Pt volatile memristor, a probabilistic bit neuron that can control the probability of being 'true' or 'false.' Nodal probabilities within the network are feasibly sampled with low errors, even with the device's cycle-to-cycle variations. Furthermore, Bayesian inference of all conditional probabilities within the network is implemented with low power (<186 nW) and energy consumption (441.4 fJ), and a normalized mean squared error of ∼7.5 × 10-4 through division feedback logic with a variational learning rate to suppress the inherent variation of the memristor. The suggested memristor-based Bayesian network shows the potential to replace the conventional complementary metal oxide semiconductor-based Bayesian estimation method with power efficiency using a stochastic computing method.

ePRINT: exonuclease assisted mapping of protein-RNA interactions

RNA-binding proteins (RBPs) regulate key aspects of RNA processing including alternative splicing, mRNA degradation and localization by physically binding RNA molecules. Current methods to map these interactions, such as CLIP, rely on purifying single proteins at a time. Our new method, ePRINT, maps RBP-RNA interaction networks on a global scale without purifying individual RBPs. ePRINT uses exoribonuclease XRN1 to precisely map the 5' end of the RBP binding site and uncovers direct and indirect targets of an RBP of interest. Importantly, ePRINT can also uncover RBPs that are differentially activated between cell fate transitions, including neural progenitor differentiation into neurons.

Extending the quantum tomography of a quasi-photon-number-resolving detector

The ability to discriminate the number of photons in a radiation field has a critical role in the implementation of quantum optical technologies. True photon-number-resolving detectors are rare and complex devices, while a quasi-photon-number-resolving detector (qPNRD) is a practical alternative for real-world applications. Our qPNRD is composed of a fiber demultiplexer and individual non-photon-number-resolving detectors. We perform quantum tomography on our qPNRD based on the positive operator-valued measure and extend the analysis using the Bayesian formalism to uncover how the measurement influences knowledge of the measured photon probability distribution.

A statistical resolution measure of fluorescence microscopy with finite photons

First discovered by Ernest Abbe in 1873, the resolution limit of a far-field microscope is considered determined by the numerical aperture and wavelength of light, approximatelyλ 2 N A . With the advent of modern fluorescence microscopy and nanoscopy methods over the last century, this definition is insufficient to fully describe a microscope's resolving power. To determine the practical resolution limit of a fluorescence microscope, photon noise remains one essential factor yet to be incorporated in a statistics-based theoretical framework. We proposed an information density measure quantifying the theoretical resolving power of a fluorescence microscope in the condition of finite photons. The developed approach not only allows us to quantify the practical resolution limit of various fluorescence and super-resolution microscopy modalities but also offers the potential to predict the achievable resolution of a microscopy design under different photon levels.

Giant organelle vesicles to uncover intracellular membrane mechanics and plasticity

Tools for accessing and studying organelles remain underdeveloped. Here, we present a method by which giant organelle vesicles (GOVs) are generated by submitting cells to a hypotonic medium followed by plasma membrane breakage. By this means, GOVs ranging from 3 to over 10 µm become available for micromanipulation. GOVs are made from organelles such as the endoplasmic reticulum, endosomes, lysosomes and mitochondria, or in contact with one another such as giant mitochondria-associated ER membrane vesicles. We measure the mechanical properties of each organelle-derived GOV and find that they have distinct properties. In GOVs procured from Cos7 cells, for example, bending rigidities tend to increase from the endoplasmic reticulum to the plasma membrane. We also found that the mechanical properties of giant endoplasmic reticulum vesicles (GERVs) vary depending on their interactions with other organelles or the metabolic state of the cell. Lastly, we demonstrate GERVs' biochemical activity through their capacity to synthesize triglycerides and assemble lipid droplets. These findings underscore the potential of GOVs as valuable tools for studying the biophysics and biology of organelles.

Rapid reaction optimization by robust and economical quantitative benchtop 19F NMR spectroscopy

The instrumental analysis of reaction mixtures is usually the rate-determining step in the optimization of chemical processes. Traditionally, reactions are analyzed by gas chromatography, HPLC or quantitative NMR spectroscopy on high-field spectrometers. However, chromatographic methods require elaborate work-up and calibration protocols, and high-field NMR spectrometers are expensive to purchase and operate. This protocol describes an inexpensive and highly effective analysis method based on low-field benchtop NMR spectroscopy. Its key feature is the use of fluorine-labeled model substrates that, because of the wide chemical shift range and high sensitivity of 19F, enable separate, quantitative detection of product and by-product signals even on low-field, permanent magnet spectrometers. An external lock/shim device obviates the need for deuterated solvents, permitting the direct, noninvasive measurement of crude reaction mixtures with minimal workup. The low field-strength facilitates a homogeneous excitation over a wide chemical shift range, minimizing systematic integration errors. The addition of the optimal amount of the nonshifting relaxation agent tris(acetylacetonato) iron(III) minimizes relaxation delays at full resolution, reducing the analysis time to 32 s per sample. The correct choice of processing parameters is also crucial. A step-by-step guideline is provided, the influence of all parameters, including adjustments needed when using high-field spectrometers, is discussed and potential pitfalls are highlighted. The wide applicability of the analytical protocol for reaction optimization is illustrated by three examples: a Buchwald-Hartwig amination, a Suzuki coupling and a C-H arylation reaction.

Virtual brain twins: from basic neuroscience to clinical use

Virtual brain twins are personalized, generative and adaptive brain models based on data from an individual's brain for scientific and clinical use. After a description of the key elements of virtual brain twins, we present the standard model for personalized whole-brain network models. The personalization is accomplished using a subject's brain imaging data by three means: (1) assemble cortical and subcortical areas in the subject-specific brain space; (2) directly map connectivity into the brain models, which can be generalized to other parameters; and (3) estimate relevant parameters through model inversion, typically using probabilistic machine learning. We present the use of personalized whole-brain network models in healthy ageing and five clinical diseases: epilepsy, Alzheimer's disease, multiple sclerosis, Parkinson's disease and psychiatric disorders. Specifically, we introduce spatial masks for relevant parameters and demonstrate their use based on the physiological and pathophysiological hypotheses. Finally, we pinpoint the key challenges and future directions.

Go/No-Go Ratios Modulate Inhibition-Related Brain Activity: An Event-Related Potential Study

(1) Background: Response inhibition refers to the conscious ability to suppress behavioral responses, which is crucial for effective cognitive control. Currently, research on response inhibition remains controversial, and the neurobiological mechanisms associated with response inhibition are still being explored. The Go/No-Go task is a widely used paradigm that can be used to effectively assess response inhibition capability. While many studies have utilized equal numbers of Go and No-Go trials, how different ratios affect response inhibition remains unknown; (2) Methods: This study investigated the impact of different ratios of Go and No-Go conditions on response inhibition using the Go/No-Go task combined with event-related potential (ERP) techniques; (3) Results: The results showed that as the proportion of Go trials decreased, behavioral performance in Go trials significantly improved in terms of response time, while error rates in No-Go trials gradually decreased. Additionally, the NoGo-P3 component at the central average electrodes (Cz, C1, C2, FCz, FC1, FC2, PCz, PC1, and PC2) exhibited reduced amplitude and latency; (4) Conclusions: These findings indicate that different ratios in Go/No-Go tasks influence response inhibition, with the brain adjusting processing capabilities and rates for response inhibition. This effect may be related to the brain's predictive mechanism model.

A guide to troubleshooting metal sacrificial anodes for organic electrosynthesis

The development of reductive electrosynthetic reactions is often enabled by the oxidation of a sacrificial metal anode, which charge-balances the reductive reaction of interest occurring at the cathode. The metal oxidation is frequently assumed to be straightforward and innocent relative to the chemistry of interest, but several processes can interfere with ideal sacrificial anode behavior, thereby limiting the success of reductive electrosynthetic reactions. These issues are compounded by a lack of reported observations and characterization of the anodes themselves, even when a failure at the anode is observed. Here, we weave lessons from electrochemistry, interfacial characterization, and organic synthesis to share strategies for overcoming issues related to sacrificial anodes in electrosynthesis. We highlight common but underexplored challenges with sacrificial anodes that cause reactions to fail, including detrimental side reactions between the anode or its cations and the components of the organic reaction, passivation of the anode surface by an insulating native surface film, accumulation of insulating byproducts at the anode surface during the reaction, and competitive reduction of sacrificial metal cations at the cathode. For each case, we propose experiments to diagnose and characterize the anode and explore troubleshooting strategies to overcome the challenge. We conclude by highlighting open questions in the field of sacrificial-anode-driven electrosynthesis and by indicating alternatives to traditional sacrificial anodes that could streamline reaction optimization.

Vertical organic electrochemical transistor platforms for efficient electropolymerization of thiophene based oligomers

Organic electrochemical transistors (OECTs) have emerged as promising candidates for various fields, including bioelectronics, neuromorphic computing, biosensors, and wearable electronics. OECTs operate in aqueous solutions, exhibit high amplification properties, and offer ion-to-electron signal transduction. The OECT channel consists of a conducting polymer, with PEDOT:PSS receiving the most attention to date. While PEDOT:PSS is highly conductive, and benefits from optimized protocols using secondary dopants and detergents, new p-type and n-type polymers are emerging with desirable material properties. Among these, low-oxidation potential oligomers are highly enabling for bioelectronics applications, however the polymers resulting from their polymerization lag far behind in conductivity compared with the established PEDOT:PSS. In this work we show that by careful design of the OECT geometrical characteristics, we can overcome this limitation and achieve devices that are on-par with transistors employing PEDOT:PSS. We demonstrate that the vertical architecture allows for facile electropolymerization of a family of trimers that are polymerized in very low oxidation potentials, without the need for harsh chemicals or secondary dopants. Vertical and planar OECTs are compared using various characterization methods. We show that vOECTs are superior platforms in general and propose that the vertical architecture can be expanded for the realization of OECTs for various applications.

Multi-assay approach shows species-associated personality patterns in two socially distinct gerbil species

We aimed to investigate whether two closely related but socially distinct species of gerbils differ in personality patterns. Using a suit of multivariate repeated assays (docility test, dark-light emergence test, startle test, novel object test, elevated platform test, and stranger test), we assessed contextual and temporal consistency of docility, boldness, exploration, anxiety, and sociability in the solitary midday gerbil, Meriones meridianus, and social Mongolian gerbil, M. unguiculatus. We revealed contextually consistent and highly repeatable sex-independent but species-specific personality traits. Species differed in temporal repeatability of different behaviours, and contextual consistency was more pronounced in solitary M. meridianus than in social M. unguiculatus. This finding contradicts the social niche specialization hypothesis, which suggests that personality traits should be more consistent in more social species. Instead, we hypothesize that social complexity should favour more flexible and less consistent behavioural traits. The habituation effect indicative of learning abilities was weak in both species yet stronger in social M. unguiculatus, supporting the relationship between the sociality level and cognitive skills. In both species, only a few different behavioural traits covaried, and the sets of correlated behaviours were species-specific such that the two species did not share any pair of correlated traits. Between-species differences in personality traits, habituation, and behavioural syndromes may be linked to differences in sociality. The lack of prominent behavioural syndromes is consistent with the idea that context-specific individual behavioural traits might be favoured to allow more flexible and adequate responses to changing environments than syndromes of correlated functionally different behaviours.

Impact of genetic predisposition to late-onset neurodegenerative diseases on early life outcomes and brain structure

Most patients with late-onset neurodegenerative diseases such as Alzheimer's and Parkinson's have a complex aetiology resulting from numerous genetic risk variants of small effects located across the genome, environmental factors, and the interaction between genes and environment. Over the last decade, genome-wide association studies (GWAS) and post-GWAS analyses have shed light on the polygenic architecture of these diseases, enabling polygenic risk scores (PRS) to estimate an individual's relative genetic liability for presenting with the disease. PRS can screen and stratify individuals based on their genetic risk, potentially years or even decades before the onset of clinical symptoms. An emerging body of evidence from various research studies suggests that genetic susceptibility to late-onset neurodegenerative diseases might impact early life outcomes, including cognitive function, brain structure and function, and behaviour. This article summarises recent findings exploring the potential impact of genetic susceptibility to neurodegenerative diseases on early life outcomes. A better understanding of the impact of genetic susceptibility to neurodegenerative diseases early in life could be valuable in disease screening, detection, and prevention and in informing treatment strategies before significant neural damage has occurred. However, ongoing studies have limitations. Overall, our review found several studies focused on APOE haplotypes and Alzheimer's risk, but a limited number of studies leveraging polygenic risk scores or focused on genetic susceptibility to other late-onset conditions.

An end-to-end workflow for nondestructive 3D pathology

Recent advances in 3D pathology offer the ability to image orders of magnitude more tissue than conventional pathology methods while also providing a volumetric context that is not achievable with 2D tissue sections, and all without requiring destructive tissue sectioning. Generating high-quality 3D pathology datasets on a consistent basis, however, is not trivial and requires careful attention to a series of details during tissue preparation, imaging and initial data processing, as well as iterative optimization of the entire process. Here, we provide an end-to-end procedure covering all aspects of a 3D pathology workflow (using light-sheet microscopy as an illustrative imaging platform) with sufficient detail to perform well-controlled preclinical and clinical studies. Although 3D pathology is compatible with diverse staining protocols and computationally generated color palettes for visual analysis, this protocol focuses on the use of a fluorescent analog of hematoxylin and eosin, which remains the most common stain used for gold-standard pathological reports. We present our guidelines for a broad range of end users (e.g., biologists, clinical researchers and engineers) in a simple format. The end-to-end workflow requires 3-6 d to complete, bearing in mind that data analysis may take longer.

Remote Pilates Training Is Effective in Improving Physical Fitness in Healthy Women: A Randomized Controlled Study

Despite its positive impact on physical and mental well-being, adults may refrain from performing regular physical activity, due to inadequate time, accessibility, or funds. Yet remote platforms could overcome such obstacles and increase participation. This study evaluated the effectiveness of remote-synchronous group-Pilates classes compared to in-studio classes in healthy sedentary women. In a randomized controlled design, 40 women, aged 20-45, were assigned to a Zoom or studio group-Pilates training. The intervention included twice-weekly 45 min sessions over an eight-week period. Attendance (adherence) was recorded, and the participants completed physical motor tests (plank, curl-up, stork, push-up, and V-sit and reach), Profile of Mood State Surveys, and Nordic Musculoskeletal Pain Questionnaires. Evaluations were performed at baseline, mid-intervention (4 weeks), and post intervention (8 weeks). Adherence to training was high in the Zoom and studio groups (80% and 74%, respectively). Improvements in physical motor tests were seen in both groups following the Pilates interventions, thereby indicating the effectiveness of group-Pilates Zoom training. In conclusion, remote online physical activity such as Pilates offers a good alternative to in-studio trainings, as a means for improving physical fitness and promoting a healthy lifestyle in adults, by offering a more accessible and less timely alternative to in-studio physical activity programs.

More than double the fun with two-photon excitation microscopy

For generations researchers have been observing the dynamic processes of life through the lens of a microscope. This has offered tremendous insights into biological phenomena that span multiple orders of time- and length-scales ranging from the pure magic of molecular reorganization at the membrane of immune cells, to cell migration and differentiation during development or wound healing. Standard fluorescence microscopy techniques offer glimpses at such processes in vitro, however, when applied in intact systems, they are challenged by reduced signal strengths and signal-to-noise ratios that result from deeper imaging. As a remedy, two-photon excitation (TPE) microscopy takes a special place, because it allows us to investigate processes in vivo, in their natural environment, even in a living animal. Here, we review the fundamental principles underlying TPE aimed at basic and advanced microscopy users interested in adopting TPE for intravital imaging. We focus on applications in neurobiology, present current trends towards faster, wider and deeper imaging, discuss the combination with photon counting technologies for metabolic imaging and spectroscopy, as well as highlight outstanding issues and drawbacks in development and application of these methodologies.

Nanogels: Smart tools to enlarge the therapeutic window of gene therapy

Gene therapy can potentially treat a great number of diseases, from cancer to rare genetic disorders. Very recently, the development and emergency approval of nucleic acid-based COVID-19 vaccines confirmed its strength and versatility. However, gene therapy encounters limitations due to the lack of suitable carriers to vectorize therapeutic genetic material inside target cells. Nanogels are highly hydrated nano-size crosslinked polymeric networks that have been used in many biomedical applications, from drug delivery to tissue engineering and diagnostics. Due to their easy production, tunability, and swelling properties they have called the attention as promising vectors for gene delivery. In this review, nanogels are discussed as vectors for nucleic acid delivery aiming to enlarge gene therapy's therapeutic window. Recent works highlighting the optimization of inherent transfection efficiency and biocompatibility are reviewed here. The importance of the monomer choice, along with the internal structure, surface decoration, and responsive features are outlined for the different transfection modalities. The possible sources of toxicological endpoints in nanogels are analyzed, and the strategies to limit them are compared. Finally, perspectives are discussed to identify the remining challenges for the nanogels before their translation to the market as transfection agents.

Interplay of chloride levels and palladium(ii)-catalyzed O-deallenylation bioorthogonal uncaging reactions

The palladium-mediated uncaging reaction of allene substrates remains a promising yet often overlooked strategy in the realm of bioorthogonal chemistry. This method exhibits high kinetic rates, rivaling those of the widely employed allylic and propargylic protecting groups. In this study, we investigate into the mechanistic aspects of the C-O bond-cleavage deallenylation reaction, examining how chloride levels influence the kinetics when triggered by Pd(ii) complexes. Focusing on the deallenylation of 1,2-allenyl protected 4-methylumbelliferone promoted by Allyl2Pd2Cl2, our findings reveal that reaction rates are higher in environments with lower chloride concentrations, mirroring intracellular conditions, compared to elevated chloride concentrations typical of extracellular conditions. Through kinetic and spectroscopic experiments, combined with DFT calculations, we uncover a detailed mechanism that identifies AllylPd(H2O)2 as the predominant active species. These insights provide the basis for the design of π-allylpalladium catalysts suited for selective uncaging within specific cellular environments, potentially enhancing targeted therapeutic applications.

Exploring Emergent Properties in Enzymatic Reaction Networks: Design and Control of Dynamic Functional Systems

The intricate and complex features of enzymatic reaction networks (ERNs) play a key role in the emergence and sustenance of life. Constructing such networks in vitro enables stepwise build up in complexity and introduces the opportunity to control enzymatic activity using physicochemical stimuli. Rational design and modulation of network motifs enable the engineering of artificial systems with emergent functionalities. Such functional systems are useful for a variety of reasons such as creating new-to-nature dynamic materials, producing value-added chemicals, constructing metabolic modules for synthetic cells, and even enabling molecular computation. In this review, we offer insights into the chemical characteristics of ERNs while also delving into their potential applications and associated challenges.

Heparin Dose Intensity and Organ Support-Free Days in Patients Hospitalized for COVID-19

Background

Clinical trials suggest that therapeutic-dose heparin may prevent critical illness and vascular complications due to COVID-19, but knowledge gaps exist regarding the efficacy of therapeutic heparin including its comparative effect relative to intermediate-dose anticoagulation.

Objectives

The authors performed 2 complementary secondary analyses of a completed randomized clinical trial: 1) a prespecified per-protocol analysis; and 2) an exploratory dose-based analysis to compare the effect of therapeutic-dose heparin with low- and intermediate-dose heparin.

Methods

Patients who received initial anticoagulation dosed consistently with randomization were included. The primary outcome was organ support-free days (OSFDs), a combination of in-hospital death and days free of organ support through day 21.

Results

Among 2,860 participants, 1,761 (92.8%) noncritically ill and 857 (89.1%) critically ill patients were treated per-protocol. Among noncritically ill per-protocol patients, the posterior probability that therapeutic-dose heparin improved OSFDs as compared with usual care was 99.3% (median adjusted OR: 1.36; 95% credible interval [CrI]: 1.07-1.74). Therapeutic heparin had a high posterior probability of efficacy relative to both low- (94.6%; adjusted OR: 1.26; 95% CrI: 0.95-1.64) and intermediate- (99.8%; adjusted OR: 1.80; 95% CrI: 1.22-2.62) dose thromboprophylaxis. Among critically ill per-protocol patients, the posterior probability that therapeutic heparin improved outcomes was low.

Conclusions

Among noncritically ill patients hospitalized for COVID-19 who were randomized to and initially received therapeutic-dose anticoagulation, heparin, compared with usual care, was associated with improved OSFDs, a combination of in-hospital death and days free of organ support. Therapeutic heparin appeared superior to both low- and intermediate-dose thromboprophylaxis.

Estimating multiple years, tributary-specific, and overall Atlantic salmon smolt abundance in a large Canadian catchment using capture-mark-recapture experiments

Population monitoring of Atlantic salmon (Salmo salar L.) abundance is an essential element to understand annual stock variability and inform fisheries management processes. Smolts are the life stage marking the transition from the freshwater to the marine phase of anadromous Atlantic salmon. Estimating smolt abundance allows for subsequent inferences on freshwater and marine survival rates. Annual abundances of out-migrating Atlantic salmon smolts were estimated using Bayesian models and an 18-year capture-mark-recapture time series from two to five trapping locations within the Restigouche River (Canada) catchment. Some of the trapping locations were at the outlet of large upstream tributaries, and these sampled a portion of the total out-migrating population of smolts for the watershed, whereas others were located just above the head of tide of the Restigouche River and sampled the entire run of salmon smolts. Due to logistic and environmental conditions, not all trapping locations were operational each year. Additionally, recapture rates were relatively low (<5%), and the absolute number of recaptures was relatively few (most often a few dozen), leading to incoherent and highly uncertain estimates of tributary-specific and whole catchment abundance estimates when the data were modeled independently among trapping locations and years. Several models of increasing complexity were tested using simulated data, and the best-performing model in terms of bias and precision incorporated a hierarchical structure among years on the catchability parameters and included an explicit spatial structure to account for the annual variations in the number of sampled locations within the watershed. When the best model was applied to the Restigouche River catchment dataset, the annual smolt abundance estimates varied from 250,000 to 1 million smolts, and the subbasin estimates of abundance were consistent with the spatial structure of the monitoring programme. Ultimately, increasing the probabilities of capture and the absolute number of recaptures at the different traps will be required to improve the precision and reduce the bias of the estimates of smolt abundance for the entire basin and within subbasins of the watershed. The model and approach provide a significant improvement in the models used to date based on independent estimates of abundance by trapping location and year. Total abundance and relative production in discrete spawning, nesting, or rearing areas provide critical information to appropriately understand and manage the threats to species that can occur at subpopulation spatial scales.

A new factor analysis model for factors obeying a Gamma distribution

The traditional factor analysis model assumes that the factors obey a normal distribution, which is not appropriate in fields whose data are nonnegative. For this kind of problem, we construct a more practical factor model, assuming that the factors obey a Gamma distribution. We develop a new factor analysis model and discuss its true loading matrix. Then we study its parameter estimation with the maximum likelihood estimation (MLE) method based on an Expectation-Maximization (EM) algorithm, where step E is realized by the Metropolis-Hastings (M-H) algorithm in the Markov Chain Monte Carlo (MCMC) method. We use the new model to empirically study real data, and evaluate its information extraction ability, using the defined true loading matrix to calculate the true loading of the factor. We compare the new model and traditional factor analysis models on simulated and real data, respectively, whose results show that the new model has better information extraction ability for nonnegative data when the number of factors is the same.

"High-throughput screening of catalytically active inclusion bodies using laboratory automation and Bayesian optimization"

BACKGROUND

In recent years, the production of inclusion bodies that retain substantial catalytic activity was demonstrated. These catalytically active inclusion bodies (CatIBs) are formed by genetic fusion of an aggregation-inducing tag to a gene of interest via short linker polypeptides. The resulting CatIBs are known for their easy and cost-efficient production, recyclability as well as their improved stability. Recent studies have outlined the cooperative effects of linker and aggregation-inducing tag on CatIB activities. However, no a priori prediction is possible so far to indicate the best combination thereof. Consequently, extensive screening is required to find the best performing CatIB variant.

RESULTS

In this work, a semi-automated cloning workflow was implemented and used for fast generation of 63 CatIB variants with glucose dehydrogenase of Bacillus subtilis (BsGDH). Furthermore, the variant BsGDH-PT-CBDCell was used to develop, optimize and validate an automated CatIB screening workflow, enhancing the analysis of many CatIB candidates in parallel. Compared to previous studies with CatIBs, important optimization steps include the exclusion of plate position effects in the BioLector by changing the cultivation temperature. For the overall workflow including strain construction, the manual workload could be reduced from 59 to 7 h for 48 variants (88%). After demonstration of high reproducibility with 1.9% relative standard deviation across 42 biological replicates, the workflow was performed in combination with a Bayesian process model and Thompson sampling. While the process model is crucial to derive key performance indicators of CatIBs, Thompson sampling serves as a strategy to balance exploitation and exploration in screening procedures. Our methodology allowed analysis of 63 BsGDH-CatIB variants within only three batch experiments. Because of the high likelihood of TDoT-PT-BsGDH being the best CatIB performer, it was selected in 50 biological replicates during the three screening rounds, much more than other, low-performing variants.

CONCLUSIONS

At the current state of knowledge, every new enzyme requires screening for different linker/aggregation-inducing tag combinations. For this purpose, the presented CatIB toolbox facilitates fast and simplified construction and screening procedures. The methodology thus assists in finding the best CatIB producer from large libraries in short time, rendering possible automated Design-Build-Test-Learn cycles to generate structure/function learnings.

Cases of trisomy 21 and trisomy 18 among historic and prehistoric individuals discovered from ancient DNA

Aneuploidies, and in particular, trisomies represent the most common genetic aberrations observed in human genetics today. To explore the presence of trisomies in historic and prehistoric populations we screen nearly 10,000 ancient human individuals for the presence of three copies of any of the target autosomes. We find clear genetic evidence for six cases of trisomy 21 (Down syndrome) and one case of trisomy 18 (Edwards syndrome), and all cases are present in infant or perinatal burials. We perform comparative osteological examinations of the skeletal remains and find overlapping skeletal markers, many of which are consistent with these syndromes. Interestingly, three cases of trisomy 21, and the case of trisomy 18 were detected in two contemporaneous sites in early Iron Age Spain (800-400 BCE), potentially suggesting a higher frequency of burials of trisomy carriers in those societies. Notably, the care with which the burials were conducted, and the items found with these individuals indicate that ancient societies likely acknowledged these individuals with trisomy 18 and 21 as members of their communities, from the perspective of burial practice.

A Software Tool for Estimating Uncertainty of Bayesian Posterior Probability for Disease

The role of medical diagnosis is essential in patient care and healthcare. Established diagnostic practices typically rely on predetermined clinical criteria and numerical thresholds. In contrast, Bayesian inference provides an advanced framework that supports diagnosis via in-depth probabilistic analysis. This study's aim is to introduce a software tool dedicated to the quantification of uncertainty in Bayesian diagnosis, a field that has seen minimal exploration to date. The presented tool, a freely available specialized software program, utilizes uncertainty propagation techniques to estimate the sampling, measurement, and combined uncertainty of the posterior probability for disease. It features two primary modules and fifteen submodules, all designed to facilitate the estimation and graphical representation of the standard uncertainty of the posterior probability estimates for diseased and non-diseased population samples, incorporating parameters such as the mean and standard deviation of the test measurand, the size of the samples, and the standard measurement uncertainty inherent in screening and diagnostic tests. Our study showcases the practical application of the program by examining the fasting plasma glucose data sourced from the National Health and Nutrition Examination Survey. Parametric distribution models are explored to assess the uncertainty of Bayesian posterior probability for diabetes mellitus, using the oral glucose tolerance test as the reference diagnostic method.

Applying water quality standards to pollution from diffuse sources

Water quality standards are instrumental in evaluating the status of water bodies, and in providing protective and restorative endpoints. To date, much of the infrastructure used to implement water quality standards has been directed towards remediating and managing pollution from point source discharges. However, pollution from diffuse sources is the leading cause of water quality impairment, especially by nutrients. Although the effects of nutrient enrichment on streams is well studied, and ecological thresholds identified, those thresholds have not been widely adopted as standards primarily because they are not attainable by point sources. Clearly, a framework for adopting and applying standards to manage pollution from diffuse sources needs to be decoupled from those intended for point sources. This paper argues for a relatively unstructured distributional approach to predict how ecological responses might shift in response to management of diffuse sources. The approach calls for first developing a deterministic model of stressor and response variables, followed by a reformulation as a Bayesian model. In the case here, a structural equation model was developed that linked nutrient enrichment, habitat quality, and chloride and manganese concentrations to an index of macroinvertebrate quality. Results from the Bayesian representation suggest that in landscapes where the drainage network has been highly modified for agricultural production, reduction in total phosphorus alone is expected to have a modest (but non-trivial) effect on macroinvertebrate condition, shifting the distribution of scores up by 1 point. The addition of habitat restoration is likely to shift the distribution upwards by 4 points, an effect size observed in Ohio, USA from other large-scale restoration efforts.

Gaining more from doing less? The effects of a one-week deload period during supervised resistance training on muscular adaptations

Background

Based on emerging evidence that brief periods of cessation from resistance training (RT) may re-sensitize muscle to anabolic stimuli, we aimed to investigate the effects of a 1-week deload interval at the midpoint of a 9-week RT program on muscular adaptations in resistance-trained individuals.

Methods

Thirty-nine young men (n = 29) and women (n = 10) were randomly assigned to 1 of 2 experimental, parallel groups: An experimental group that abstained from RT for 1 week at the midpoint of a 9-week, high-volume RT program (DELOAD) or a traditional training group that performed the same RT program continuously over the study period (TRAD). The lower body routines were directly supervised by the research staff while upper body training was carried out in an unsupervised fashion. Muscle growth outcomes included assessments of muscle thickness along proximal, mid and distal regions of the middle and lateral quadriceps femoris as well as the mid-region of the triceps surae. Adaptions in lower body isometric and dynamic strength, local muscular endurance of the quadriceps, and lower body muscle power were also assessed.

Results

Results indicated no appreciable differences in increases of lower body muscle size, local endurance, and power between groups. Alternatively, TRAD showed greater improvements in both isometric and dynamic lower body strength compared to DELOAD. Additionally, TRAD showed some slight psychological benefits as assessed by the readiness to train questionnaire over DELOAD.

Conclusion

In conclusion, our findings suggest that a 1-week deload period at the midpoint of a 9-week RT program appears to negatively influence measures of lower body muscle strength but has no effect on lower body hypertrophy, power or local muscular endurance.

Genomic data integration tutorial, a plant case study

BACKGROUND

The ongoing evolution of the Next Generation Sequencing (NGS) technologies has led to the production of genomic data on a massive scale. While tools for genomic data integration and analysis are becoming increasingly available, the conceptual and analytical complexities still represent a great challenge in many biological contexts.

RESULTS

To address this issue, we describe a six-steps tutorial for the best practices in genomic data integration, consisting of (1) designing a data matrix; (2) formulating a specific biological question toward data description, selection and prediction; (3) selecting a tool adapted to the targeted questions; (4) preprocessing of the data; (5) conducting preliminary analysis, and finally (6) executing genomic data integration.

CONCLUSION

The tutorial has been tested and demonstrated on publicly available genomic data generated from poplar (Populus L.), a woody plant model. We also developed a new graphical output for the unsupervised multi-block analysis, cimDiablo_v2, available at https://forgemia.inra.fr/umr-gdec/omics-integration-on-poplar , and allowing the selection of master drivers in genomic data variation and interplay.

Plant science in the age of simulation intelligence

Historically, plant and crop sciences have been quantitative fields that intensively use measurements and modeling. Traditionally, researchers choose between two dominant modeling approaches: mechanistic plant growth models or data-driven, statistical methodologies. At the intersection of both paradigms, a novel approach referred to as "simulation intelligence", has emerged as a powerful tool for comprehending and controlling complex systems, including plants and crops. This work explores the transformative potential for the plant science community of the nine simulation intelligence motifs, from understanding molecular plant processes to optimizing greenhouse control. Many of these concepts, such as surrogate models and agent-based modeling, have gained prominence in plant and crop sciences. In contrast, some motifs, such as open-ended optimization or program synthesis, still need to be explored further. The motifs of simulation intelligence can potentially revolutionize breeding and precision farming towards more sustainable food production.

Structural heterogeneity of the ion and lipid channel TMEM16F

Transmembrane protein 16 F (TMEM16F) is a Ca2+-activated homodimer which functions as an ion channel and a phospholipid scramblase. Despite the availability of several TMEM16F cryogenic electron microscopy (cryo-EM) structures, the mechanism of activation and substrate translocation remains controversial, possibly due to restrictions in the accessible protein conformational space. In this study, we use atomic force microscopy under physiological conditions to reveal a range of structurally and mechanically diverse TMEM16F assemblies, characterized by variable inter-subunit dimerization interfaces and protomer orientations, which have escaped prior cryo-EM studies. Furthermore, we find that Ca2+-induced activation is associated to stepwise changes in the pore region that affect the mechanical properties of transmembrane helices TM3, TM4 and TM6. Our direct observation of membrane remodelling in response to Ca2+ binding along with additional electrophysiological analysis, relate this structural multiplicity of TMEM16F to lipid and ion permeation processes. These results thus demonstrate how conformational heterogeneity of TMEM16F directly contributes to its diverse physiological functions.

Variable selection for nonlinear dimensionality reduction of biological datasets through bootstrapping of correlation networks

Identifying the most relevant variables or features in massive datasets for dimensionality reduction can lead to improved and more informative display, faster computation times, and more explainable models of complex systems. Despite significant advances and available algorithms, this task generally remains challenging, especially in unsupervised settings. In this work, we propose a method that constructs correlation networks using all intervening variables and then selects the most informative ones based on network bootstrapping. The method can be applied in both supervised and unsupervised scenarios. We demonstrate its functionality by applying Uniform Manifold Approximation and Projection for dimensionality reduction to several high-dimensional biological datasets, derived from 4D live imaging recordings of hundreds of morpho-kinetic variables, describing the dynamics of thousands of individual leukocytes at sites of prominent inflammation. We compare our method with other standard ones in the field, such as Principal Component Analysis and Elastic Net, showing that it outperforms them. The proposed method can be employed in a wide range of applications, encompassing data analysis and machine learning.

Design and Execution of Clinical Trials in the Cardiac Intensive Care Unit

Clinical practice in the contemporary cardiac intensive care unit (CICU) has evolved significantly over the last several decades. With more frequent multisystem organ failure, increasing use of advanced respiratory support, and the advent of new mechanical circulatory support platforms, clinicians in the CICU are increasingly managing patients with complex comorbid disease in addition to their high-acuity cardiovascular illnesses. Here, the authors discuss challenges associated with traditional trial design in the CICU setting and review novel clinical trial designs that may facilitate better evidence generation in the CICU.

The Bayesian simulation study (BASIS) framework for simulation studies in statistical and methodological research

Statistical simulation studies are becoming increasingly popular to demonstrate the performance or superiority of new computational procedures and algorithms. Despite this status quo, previous surveys of the literature have shown that the reporting of statistical simulation studies often lacks relevant information and structure. The latter applies in particular to Bayesian simulation studies, and in this paper the Bayesian simulation study framework (BASIS) is presented as a step towards improving the situation. The BASIS framework provides a structured skeleton for planning, coding, executing, analyzing, and reporting Bayesian simulation studies in biometrical research and computational statistics. It encompasses various features of previous proposals and recommendations in the methodological literature and aims to promote neutral comparison studies in statistical research. Computational aspects covered in the BASIS include algorithmic choices, Markov-chain-Monte-Carlo convergence diagnostics, sensitivity analyses, and Monte Carlo standard error calculations for Bayesian simulation studies. Although the BASIS framework focuses primarily on methodological research, it also provides useful guidance for researchers who rely on the results of Bayesian simulation studies or analyses, as current state-of-the-art guidelines for Bayesian analyses are incorporated into the BASIS.

A practical guide to adopting Bayesian analyses in clinical research

Background

Bayesian statistical approaches are extensively used in new statistical methods but have not been adopted at the same rate in clinical and translational (C&T) research. The goal of this paper is to accelerate the transition of new methods into practice by improving the C&T researcher's ability to gain confidence in interpreting and implementing Bayesian analyses.

Methods

We developed a Bayesian data analysis plan and implemented that plan for a two-arm clinical trial comparing the effectiveness of a new opioid in reducing time to discharge from the post-operative anesthesia unit and nerve block usage in surgery. Through this application, we offer a brief tutorial on Bayesian methods and exhibit how to apply four Bayesian statistical packages from STATA, SAS, and RStan to conduct linear and logistic regression analyses in clinical research.

Results

The analysis results in our application were robust to statistical package and consistent across a wide range of prior distributions. STATA was the most approachable package for linear regression but was more limited in the models that could be fitted and easily summarized. SAS and R offered more straightforward documentation and data management for the posteriors. They also offered direct programming of the likelihood making them more easily extendable to complex problems.

Conclusion

Bayesian analysis is now accessible to a broad range of data analysts and should be considered in more C&T research analyses. This will allow C&T research teams the ability to adopt and interpret Bayesian methodology in more complex problems where Bayesian approaches are often needed.

Green synthesis aspects of (R)-(-)-mandelic acid; a potent pharmaceutically active agent and its future prospects

(R)-(-)-mandelic acid is an important carboxylic acid known for its numerous potential applications in the pharmaceutical industry as it is an ideal starting material for the synthesis of antibiotics, antiobesity drugs and antitumor agents. In past few decades, the synthesis of (R)-(-)-mandelic acid has been undertaken mainly through the chemical route. However, chemical synthesis of optically pure (R)-(-)-mandelic acid is difficult to achieve at an industrial scale. Therefore, its microbe mediated production has gained considerable attention as it exhibits many merits over the chemical approaches. The present review focuses on various biotechnological strategies for the production of (R)-(-)-mandelic acid through microbial biotransformation and enzymatic catalysis; in particular, an analysis and comparison of the synthetic methods and different enzymes. The wild type as well as recombinant microbial strains for the production of (R)-(-)-mandelic acid have been elucidated. In addition, different microbial strategies used for maximum bioconversion of mandelonitrile into (R)-(-)-mandelic acid are discussed in detail with regard to higher substrate tolerance and maximum bioconversion.HighlightsMandelonitrile, mandelamide and o-chloromandelonitrile can be used as substrates to produce (R)-(-)-mandelic acid by enzymes.Three enzymes (nitrilase, nitrile hydratase and amidase) are systematically introduced for production of (R)-(-)-mandelic acid.Microbial transformation is able to produce optically pure (R)-(-)-mandelic acid with 100% productive yield.

A novel approach to predict acute radiation dermatitis in patients with head and neck cancer using a model based on Bayesian probability

PURPOSE

In this study, we aimed to establish a method for predicting the probability of each acute radiation dermatitis (ARD) grade during the head and neck Volumetric Modulated Arc Therapy (VMAT) radiotherapy planning phase based on Bayesian probability.

METHODS

The skin dose volume >50 Gy (V50), calculated using the treatment planning system, was used as a factor related to skin toxicity. The empirical distribution of each ARD grade relative to V50 was obtained from the ARD grades of 119 patients (55, 50, and 14 patients with G1, G2, and G3, respectively) determined by head and neck cancer specialists. Using Bayes' theorem, the Bayesian probabilities of G1, G2, and G3 for each value of V50 were calculated with an empirical distribution. Conversely, V50 was obtained based on the Bayesian probabilities of G1, G2, and G3.

RESULTS

The empirical distribution for each graded patient group demonstrated a normal distribution. The method predicted ARD grades with 92.4 % accuracy and provided a V50 value for each grade. For example, using the graph, we could predict that V50 should be ≤24.5 cm3 to achieve G1 with 70 % probability.

CONCLUSIONS

The Bayesian probability-based ARD prediction method could predict the ARD grade at the treatment planning stage using limited patient diagnostic data that demonstrated a normal distribution. If the probability of an ARD grade is high, skin care can be initiated in advance. Furthermore, the V50 value during treatment planning can provide radiation oncologists with data for strategies to reduce ARD.

Prior beliefs and the interpretation of scientific results

How do prior beliefs affect the interpretation of scientific results? I discuss a hypothetical scenario where researchers publish results that could either support a theory they believe in, or refute that theory, and ask if the two instances carry the same weight. More colloquially, I ask if we should overweigh scientific results supporting a given theory and reported by a researcher, or a team, that initially did not support that theory. I illustrate the challenge using two examples from psychology: evidence accumulation models, and extra sensory perception.