Predicting the efficiency of arsenic immobilization in soils by biochar using machine learning.

Arsenic (As) pollution in soils is a pervasive environmental issue. Biochar immobilization offers a promising solution for addressing soil As contamination. The efficiency of biochar in immobilizing As in soils primarily hinges on the characteristics of both the soil and the biochar. However, the influence of a specific property on As immobilization varies among different studies, and the development and application of arsenic passivation materials based on biochar often rely on empirical knowledge. To enhance immobilization efficiency and reduce labor and time costs, a machine learning (ML) model was employed to predict As immobilization efficiency before biochar application. In this study, we collected a dataset comprising 182 data points on As immobilization efficiency from 17 publications to construct three ML models. The results demonstrated that the random forest (RF) model outperformed gradient boost regression tree and support vector regression models in predictive performance. Relative importance analysis and partial dependence plots based on the RF model were conducted to identify the most crucial factors influencing As immobilization. These findings highlighted the significant roles of biochar application time and biochar pH in As immobilization efficiency in soils. Furthermore, the study revealed that Fe-modified biochar exhibited a substantial improvement in As immobilization. These insights can facilitate targeted biochar property design and optimization of biochar application conditions to enhance As immobilization efficiency.

Rapid detection of colored and colorless macro- and micro-plastics in complex environment via near-infrared spectroscopy and machine learning.

To better understand the migration behavior of plastic fragments in the environment, development of rapid non-destructive methods for in-situ identification and characterization of plastic fragments is necessary. However, most of the studies had focused only on colored plastic fragments, ignoring colorless plastic fragments and the effects of different environmental media (backgrounds), thus underestimating their abundance. To address this issue, the present study used near-infrared spectroscopy to compare the identification of colored and colorless plastic fragments based on partial least squares-discriminant analysis (PLS-DA), extreme gradient boost, support vector machine and random forest classifier. The effects of polymer color, type, thickness, and background on the plastic fragments classification were evaluated. PLS-DA presented the best and most stable outcome, with higher robustness and lower misclassification rate. All models frequently misinterpreted colorless plastic fragments and its background when the fragment thickness was less than 0.1mm. A two-stage modeling method, which first distinguishes the plastic types and then identifies colorless plastic fragments that had been misclassified as background, was proposed. The method presented an accuracy higher than 99% in different backgrounds. In summary, this study developed a novel method for rapid and synchronous identification of colored and colorless plastic fragments under complex environmental backgrounds.

Callousness, exploitativeness, and tracking of cooperation incentives in the human default network.

Human cognitive capacities that enable flexible cooperation may have evolved in parallel with the expansion of frontoparietal cortical networks, particularly the default network. Conversely, human antisocial behavior and trait antagonism are broadly associated with reduced activity, impaired connectivity, and altered structure of the default network. Yet, behaviors like interpersonal manipulation and exploitation may require intact or even superior social cognition. Using a reinforcement learning model of decision-making on a modified trust game, we examined how individuals adjusted their cooperation rate based on a counterpart's cooperation and social reputation. We observed that learning signals in the default network updated the predicted utility of cooperation or defection and scaled with reciprocal cooperation. These signals were weaker in callous (vs. compassionate) individuals but stronger in those who were more exploitative (vs. honest and humble). Further, they accounted for associations between exploitativeness, callousness, and reciprocal cooperation. Separately, behavioral sensitivity to prior reputation was reduced in callous but not exploitative individuals and selectively scaled with responses of the medial temporal subsystem of the default network. Overall, callousness was characterized by blunted behavioral and default network sensitivity to cooperation incentives. Exploitativeness predicted heightened sensitivity to others' cooperation but not social reputation. We speculate that both compassion and exploitativeness may reflect cognitive adaptations to social living, enabled by expansion of the default network in anthropogenesis.

Mechanism of phosphate release from actin filaments.

After ATP-actin monomers assemble filaments, the ATP's [Formula: see text]-phosphate is hydrolyzedwithin seconds and dissociates over minutes. We used all-atom molecular dynamics simulations to sample the release of phosphate from filaments and study residues that gate release. Dissociation of phosphate from Mg2+ is rate limiting and associated with an energy barrier of 20 kcal/mol, consistent with experimental rates of phosphate release. Phosphate then diffuses within an internal cavity toward a gate formed by R177, as suggested in prior computational studies and cryo-EM structures. The gate is closed when R177 hydrogen bonds with N111 and is open when R177 forms a salt bridge with D179. Most of the time, interactions of R177 with other residues occlude the phosphate release pathway. Machine learning analysis reveals that the occluding interactions fluctuate rapidly, underscoring the secondary role of backdoor gate opening in Pi release, in contrast with the previous hypothesis that gate opening is the primary event.

Global expansion of marine protected areas and the redistribution of fishing effort.

The expansion of marine protected areas (MPAs) is a core focus of global conservation efforts, with the "30x30" initiative to protect 30% of the ocean by 2030 serving as a prominent example of this trend. We consider a series of proposed MPA network expansions of various sizes, and we forecast the impact this increase in protection would have on global patterns of fishing effort. We do so by building a predictive machine learning model trained on a global dataset of satellite-based fishing vessel monitoring data, current MPA locations, and spatiotemporal environmental, geographic, political, and economic features. We then use this model to predict future fishing effort under various MPA expansion scenarios compared to a business-as-usual counterfactual scenario that includes no new MPAs. The difference between these scenarios represents the predicted change in fishing effort associated with MPA expansion. We find that regardless of the MPA network objectives or size, fishing effort would decrease inside the MPAs, though by much less than 100%. Moreover, we find that the reduction in fishing effort inside MPAs does not simply redistribute outside-rather, fishing effort outside MPAs would also decline. The overall magnitude of the predicted decrease in global fishing effort principally depends on where networks are placed in relation to existing fishing effort. MPA expansion will lead to a global redistribution of fishing effort that should be accounted for in network design, implementation, and impact evaluation.

Potential differences in psychedelic actions based on biological sex.

The resurgence of interest in psychedelics as treatments for psychiatric disorders necessitates a better understanding of potential sex differences in response to these substances. Sex as a biological variable (SABV) has been historically neglected in medical research, posing limits to our understanding of treatment efficacy. Human studies have provided insights into the efficacy of psychedelics across various diagnoses and aspects of cognition, yet sex-specific effects remain unclear, making it difficult to draw strong conclusions about sex-dependent differences in response to psychedelic treatments. Compounding this further, animal studies used to understand biological mechanisms of psychedelics predominantly use one sex and present mixed neurobiological and behavioural outcomes. Studies that do include both sexes often do not investigate sex differences further, which may hinder the translation of findings to the clinic. In reviewing sex differences in responses to psychedelics, we will highlight the direct interaction between estrogen (the most extensively studied steroid hormone) and the serotonin system (central to the mechanism of action of psychedelics), and the potential that estrogen-serotonin interactions may influence the efficacy of psychedelics in female subjects. Estrogen influences serotonin neurotransmission by affecting its synthesis and release, as well as modulating the sensitivity and responsiveness of serotonin receptor subtypes in the brain. This could potentially influence the efficacy of psychedelics in females by modifying their therapeutic efficacy across menstrual cycles and developmental stages. Investigating this interaction in the context of psychedelic research could aid in the advancement of therapeutic outcomes, especially for conditions with sex-specific prevalence.

Deep Learning Used with a Colorimetric Sensor Array to Detect Indole for Nondestructive Monitoring of Shrimp Freshness.

Intelligent colorimetric freshness indicator is a low-cost way to intuitively monitor the freshness of fresh food. A colorimetric strip sensor array was prepared by p-dimethylaminocinnamaldehyde (PDL)-doped poly(vinyl alcohol) (PVA) and chitosan (Chit) for the quantitative analysis of indole, which is an indicator of shrimp freshness. As a result of indole simulation, the array strip turned from faint yellow to pink or mulberry color with the increasing indole concentration, like a progress bar. The indicator film exhibited excellent permeability, mechanical and thermal stability, and color responsiveness to indole, which was attributed to the interactions between PDL and Chit/PVA. Furthermore, the colorimetric strip sensor array provided a good relationship between the indole concentration and the color intensity within a range of 50-350 ppb. The pathogens and spoilage bacteria of shrimp possessed the ability to produce indole, which caused the color changes of the strip sensor array. In the shrimp freshness monitoring experiment, the color-changing progress of the strip sensor array was in agreement with the simulation and could distinguish the shrimp freshness levels. The image classification system based on deep learning were developed, the accuracies of four DCNN algorithms are above 90%, with VGG16 achieving the highest accuracy at 97.89%. Consequently, a "progress bar" strip sensor array has the potential to realize nondestructive, more precise, and commercially available food freshness monitoring using simple visual inspection and intelligent equipment identification.

Learners' Spontaneous Gesture Before a Math Lesson Predicts the Efficacy of Seeing Versus Doing Gesture During the Lesson.

Gestures-hand movements that accompany speech and express ideas-can help children learn how to solve problems, flexibly generalize learning to novel problem-solving contexts, and retain what they have learned. But does it matter who is doing the gesturing? We know that producing gesture leads to better comprehension of a message than watching someone else produce gesture. But we do not know how producing versus observing gesture impacts deeper learning outcomes such as generalization and retention across time. Moreover, not all children benefit equally from gesture instruction, suggesting that there are individual differences that may play a role in who learns from gesture. Here, we consider two factors that might impact whether gesture leads to learning, generalization, and retention after mathematical instruction: (1) whether children see gesture or do gesture and (2) whether a child spontaneously gestures before instruction when explaining their problem-solving reasoning. For children who spontaneously gestured before instruction, both doing and seeing gesture led to better generalization and retention of the knowledge gained than a comparison manipulative action. For children who did not spontaneously gesture before instruction, doing gesture was less effective than the comparison action for learning, generalization, and retention. Importantly, this learning deficit was specific to gesture, as these children did benefit from doing the comparison manipulative action. Our findings are the first evidence that a child's use of a particular representational format for communication (gesture) directly predicts that child's propensity to learn from using the same representational format.

Epileptic seizure forecasting with wearable-based nocturnal sleep features.

OBJECTIVE: Non-invasive biomarkers have recently shown promise for seizure forecasting in people with epilepsy. In this work, we developed a seizure-day forecasting algorithm based on nocturnal sleep features acquired using a smart shirt. METHODS: Seventy-eight individuals with epilepsy admitted to the Centre hospitalier de l'Université de Montréal epilepsy monitoring unit wore the Hexoskin biometric smart shirt during their stay. The shirt continuously measures electrocardiography, respiratory, and accelerometry activity. Ten sleep features, including sleep efficiency, sleep latency, sleep duration, time spent in non-rapid eye movement sleep (NREM) and rapid eye movement sleep (REM), wakefulness after sleep onset, average heart and breathing rates, high-frequency heart rate variability, and the number of position changes, were automatically computed using the Hexoskin sleep algorithm. Each night's features were then normalized using a reference night for each patient. A support vector machine classifier was trained for pseudo-prospective seizure-day forecasting, with forecasting horizons of 16- and 24-h to include both diurnal and nocturnal seizures (24-h) or diurnal seizures only (16-h). The algorithm's performance was assessed using a nested leave-one-patient-out cross-validation approach. RESULTS: Improvement over chance (IoC) performances were achieved for 48.7% and 40% of patients with the 16- and 24-h forecasting horizons, respectively. For patients with IoC performances, the proposed algorithm reached mean IoC, sensitivity and time in warning of 34.3%, 86.0%, and 51.7%, respectively for the 16-h horizon, and 34.2%, 64.4% and 30.2%, respectively, for the 24-h horizon. SIGNIFICANCE: Smart shirt-based nocturnal sleep analysis holds promise as a non-invasive approach for seizure-day forecasting in a subset of people with epilepsy. Further investigations, particularly in a residential setting with long-term recordings, could pave the way for the development of innovative and practical seizure forecasting devices. PLAIN LANGUAGE SUMMARY: Seizure forecasting with wearable devices may improve the quality of life of people living with epilepsy who experience unpredictable, recurrent seizures. In this study, we have developed a seizure forecasting algorithm using sleep characteristics obtained from a smart shirt worn at night by a large number of hospitalized patients with epilepsy (78). A daily seizure forecast was generated following each night using machine learning methods. Our results show that around half of people with epilepsy may benefit from such an approach.

Deep learning for predicting rehospitalization in acute heart failure: Model foundation and external validation.

AIMS: Assessing the risk for HF rehospitalization is important for managing and treating patients with HF. To address this need, various risk prediction models have been developed. However, none of them used deep learning methods with real-world data. This study aimed to develop a deep learning-based prediction model for HF rehospitalization within 30, 90, and 365 days after acute HF (AHF) discharge. METHODS AND RESULTS: We analysed the data of patients admitted due to AHF between January 2014 and January 2019 in a tertiary hospital. In performing deep learning-based predictive algorithms for HF rehospitalization, we use hyperbolic tangent activation layers followed by recurrent layers with gated recurrent units. To assess the readmission prediction, we used the AUC, precision, recall, specificity, and F1 measure. We applied the Shapley value to identify which features contributed to HF readmission. Twenty-two prognostic features exhibiting statistically significant associations with HF rehospitalization were identified, consisting of 6 time-independent and 16 time-dependent features. The AUC value shows moderate discrimination for predicting readmission within 30, 90, and 365 days of follow-up (FU) (AUC:0.63, 0.74, and 0.76, respectively). The features during the FU have a relatively higher contribution to HF rehospitalization than features from other time points. CONCLUSIONS: Our deep learning-based model using real-world data could provide valid predictions of HF rehospitalization in 1 year follow-up. It can be easily utilized to guide appropriate interventions or care strategies for patients with HF. The closed monitoring and blood test in daily clinics are important for assessing the risk of HF rehospitalization.

Automatic Clinical Assessment of Swallowing Behavior and Diagnosis of Silent Aspiration Using Wireless Multimodal Wearable Electronics.

Dysphagia is more common in conditions such as stroke, Parkinson's disease, and head and neck cancer. This can lead to pneumonia, choking, malnutrition, and dehydration. Currently, the diagnostic gold standard uses radiologic imaging, the videofluoroscopic swallow study (VFSS); however, it is expensive and necessitates specialized facilities and trained personnel. Although several devices attempt to address the limitations, none offer the clinical-grade quality and accuracy of the VFSS. Here, this study reports a wireless multimodal wearable system with machine learning for automatic, accurate clinical assessment of swallowing behavior and diagnosis of silent aspirations from dysphagia patients. The device includes a kirigami-structured electrode that suppresses changes in skin contact impedance caused by movements and a microphone with a gel layer that effectively blocks external noise for measuring high-quality electromyograms and swallowing sounds. The deep learning algorithm offers the classification of swallowing patterns while diagnosing silent aspirations, with an accuracy of 89.47%. The demonstration with post-stroke patients captures the system's significance in measuring multiple physiological signals in real-time for detecting swallowing disorders, validated by comparing them with the VFSS. The multimodal electronics can ensure a promising future for dysphagia healthcare and rehabilitation therapy, providing an accurate, non-invasive alternative for monitoring swallowing and aspiration events.

Modernizing Surgical Quality: A Novel Approach to Improving Detection of Surgical Site Infections in the Veteran Population.

Introduction: Surgical site infections (SSIs) are an important quality measure. Identifying SSIs often relies upon a time-intensive manual review of a sample of common surgical cases. In this study, we sought to develop a predictive model for SSI identification using antibiotic pharmacy data extracted from the electronic medical record (EMR). Methods: A retrospective analysis was performed on all surgeries at a Veteran Affair's Medical Center between January 9, 2020 and January 9, 2022. Patients receiving outpatient antibiotics within 30 days of their surgery were identified, and chart review was performed to detect instances of SSI as defined by VA Surgery Quality Improvement Program criteria. Binomial logistic regression was used to select variables to include in the model, which was trained using k-fold cross validation. Results: Of the 8,253 surgeries performed during the study period, patients in 793 (9.6%) cases were prescribed outpatient antibiotics within 30 days of their procedure; SSI was diagnosed in 128 (1.6%) patients. Logistic regression identified time from surgery to antibiotic prescription, ordering location of the prescription, length of prescription, type of antibiotic, and operating service as important variables to include in the model. On testing, the final model demonstrated good predictive value with c-statistic of 0.81 (confidence interval: 0.71-0.90). Hosmer-Lemeshow testing demonstrated good fit of the model with p value of 0.97. Conclusion: We propose a model that uses readily attainable data from the EMR to identify SSI occurrences. In conjunction with local case-by-case reporting, this tool can improve the accuracy and efficiency of SSI identification.

Multi-Task ADME/PK prediction at industrial scale: leveraging large and diverse experimentaldatasets.

ADME (Absorption, Distribution, Metabolism, Excretion) properties are key parameters to judge whether a drug candidate exhibits a desired pharmacokinetic (PK) profile. In this study, we tested multi-task machine learning (ML) models to predict ADME and animal PK endpoints trained on in-house data generated at Boehringer Ingelheim. Models were evaluated both at the design stage of a compound (i. e., no experimental data of test compounds available) and at testing stage when a particular assay would be conducted (i. e., experimental data of earlier conducted assays may be available). Using realistic time-splits, we found a clear benefit in performance of multi-task graph-based neural network models over single-task model, which was even stronger when experimental data of earlier assays is available. In an attempt to explain the success of multi-task models, we found that especially endpoints with the largest numbers of data points (physicochemical endpoints, clearance in microsomes) are responsible for increased predictivity in more complex ADME and PK endpoints. In summary, our study provides insight into how data for multiple ADME/PK endpoints in a pharmaceutical company can be best leveraged to optimize predictivity of ML models.

Dataset on secondary school teachers' and students' perceptions of teaching-learning activities used in Zambian secondary school biology classrooms.

Teaching and learning activities used in the classroom form an important part of the learning environment. Creating productive learning environments may be influenced by how teachers and students perceive the teaching and learning process. Teachers' and students' perceptions of teaching and learning seem to influence each other. For example, how teachers approach their subject matter impacts how their students learn and view the learning environment or process. Therefore, the degree of teaching and learning quality congruence between teachers' and students' perceptions of teaching-learning process may impact the setting of the learning environment. This article describes a dataset concerning teachers' and students' perceptions of 26 teaching-learning activities used in biology lessons. The data were collected from 57 biology teachers and 469 students from 16 selected secondary schools in four districts of Zambia. Data were collected during the 2022 academic year using separate validated survey questionnaires. The statistical package for the social sciences (SPSS) version 25 was used to analyse the data by calculating descriptive and inferential statistics to describe and compare the participants' perceptions of the teaching-learning activities in biology lessons. The data may provide valuable insight into current teaching practices in biology classrooms based on teachers' and students' perceptions. The data may also provide a basis for comparing teachers' and students' perceptions of teaching-learning activities in biology classrooms.

Formative peer evaluation instrument for a team-based learning course: Content and construct validity.

PURPOSE: Team-based learning (TBL) is an evidence-based approach to promote teamwork. Peer evaluation (PE) is an essential component to shape future team engagement and promote reflection. As PEs vary in use, implementation, and assessment, this study establishes the content and construct validity of a formative PE tool for a TBL course. METHODS: A ten-item instrument was developed based on a comprehensive review of PE literature and was critically edited by a team of experienced educators. Each student in a graduate histology course rated peers at two timepoints on a scale from Never to Always (0-3). The instrument's factor structure was analyzed by dividing the response set (D1 and D2); with D1 utilized for exploratory factor analysis (EFA) and D2 for confirmatory factor analysis (CFA). Cronbach's alpha assessed internal consistency. RESULTS: Data from 158 students across four cohorts were included in the analyses (D1, D2 = 972). A three-factor solution had good overall internal consistency (alpha = 0.82), and within the subscales ranged from 0.67 to 0.81. The factor structures were resonant with existing literature on (1) preparation, (2) providing feedback, and (3) feedback receptivity and attitude. CONCLUSION: This study establishes evidence of content and construct validity of a formative PE instrument for a TBL course.

Hot and cool executive function and theory of mind in children with and without specific learning disorders.

Executive function (EF) in specific learning disorders (SLD) has been investigated using mainly cool EF tasks, whilst less is known about hot EF and theory of mind (ToM) in this population. The aim of this study was to examine group differences in hot and cool EF and ToM in school-aged children with SLD relative to typically developing peers. It also attempted to investigate whether EF measures are significant predictors of ToM in SLD and typical development. Cross-sectional data were collected from 135 school-aged children with and without SLD (8-10 years old), tested on measures of cool & hot EF and ToM. Significant group differences were observed in EFs inhibition (p= .04), working memory (p= .04) and delay of gratification (p < .001), as well as ToM mental state/emotion recognition (p = .019). Inhibition and planning contributed to 22% of the explained variance of ToM mental state/emotion recognition, but not false belief overall. Results suggest that cool EF may be a crucial predictor of ToM in children with and without SLD. Finally, stepwise logistic regression analysis identified specific hot EF and ToM measures contributing to group differentiation, specifically delay of gratification (odds ratio=.995, 95% CI [.993-.998]) and mental state/emotion recognition (odds ratio= .89, 95% CI [.796-.995]). This study contributes to our understanding of cognitive deficits and socio-cognitive impairment in children with SLD, which hold promise for informing interventions aimed at addressing these cognitive challenges.

Measuring activity-rest rhythms under different acclimation periods in a marine fish using automatic deep learning-based video tracking.

Most organisms synchronize to an approximately 24-hour (circadian) rhythm. This study introduces a novel deep learning-powered video tracking method to assess the stability, fragmentation, robustness and synchronization of activity rhythms in Xyrichtys novacula. Experimental X. novacula were distributed into three groups and monitored for synchronization to a 14/10 hours of light/dark to assess acclimation to laboratory conditions. Group GP7 acclimated for 1 week and was tested from days 7 to 14, GP14 acclimated for 14 days and was tested from days 14 to 21 and GP21 acclimated for 21 days and was tested from days 21 to 28. Telemetry data from individuals in the wild depicted their natural behavior. Wild fish displayed a robust and minimally fragmented rhythm, entrained to the natural photoperiod. Under laboratory conditions, differences in activity levels were observed between light and dark phases. However, no differences were observed in activity rhythm metrics among laboratory groups related to acclimation period. Notably, longer acclimation (GP14 and GP21) led to a larger proportion of individuals displaying rhythm synchronization with the imposed photoperiod. Our work introduces a novel approach for monitoring biological rhythms in laboratory conditions, employing a specifically engineered video tracking system based on deep learning, adaptable for other species.

Accurately deciphering spatial domains for spatially resolved transcriptomics with stCluster.

Spatial transcriptomics provides valuable insights into gene expression within the native tissue context, effectively merging molecular data with spatial information to uncover intricate cellular relationships and tissue organizations. In this context, deciphering cellular spatial domains becomes essential for revealing complex cellular dynamics and tissue structures. However, current methods encounter challenges in seamlessly integrating gene expression data with spatial information, resulting in less informative representations of spots and suboptimal accuracy in spatial domain identification. We introduce stCluster, a novel method that integrates graph contrastive learning with multi-task learning to refine informative representations for spatial transcriptomic data, consequently improving spatial domain identification. stCluster first leverages graph contrastive learning technology to obtain discriminative representations capable of recognizing spatially coherent patterns. Through jointly optimizing multiple tasks, stCluster further fine-tunes the representations to be able to capture complex relationships between gene expression and spatial organization. Benchmarked against six state-of-the-art methods, the experimental results reveal its proficiency in accurately identifying complex spatial domains across various datasets and platforms, spanning tissue, organ, and embryo levels. Moreover, stCluster can effectively denoise the spatial gene expression patterns and enhance the spatial trajectory inference. The source code of stCluster is freely available at https://github.com/hannshu/stCluster.

Researchers' Theater: Enhancing Student Engagement in an Introductory Research Course.

Research coursework can be challenging for occupational therapy students, thus potentially compromising their engagement in learning. A student engagement framework was used to design and implement an innovative assignment called Researchers' Theater with a cohort of 38 first-semester occupational therapy students. At the beginning of each class, a small group of students led a creative activity to review topics from the preceding week. Student feedback survey results and instructors' observations suggest this framework contributed to students' affective, behavioral, and cognitive engagement. Findings also highlight the potential value of student-led, game-based learning for reinforcing course content.