Mixture Model Research Articles

Mixtures of Dirichlet processes for joint spatial modelling of transcranial magnetic stimulation mapping data

Abstract A patient’s responses to Transcranial Magnetic Stimulation (TMS) pulses on the motor cortex have a complex spatial pattern, making it challenging to understand the response patterns across multiple patients. We developed a mixture of Dirichlet process models to distinguish between patient-specific and shared spatial patterns across multiple patients to provide insight into consistent response patterns essential for developing personalized treatment procedures. The Metropolis–Hastings within Gibbs sampler of the Markov Chain Monte Carlo algorithm was developed for estimation. The model was used to analyse the TMS data of 3 healthy subjects. The study revealed that the primary motor cortex of the hand consistently emerges as a promising region for eliciting optimal responses. This area serves as a key target for brain mapping using TMS to identify cortical hotspots. However, the excitability patterns in this region can vary significantly among patients.

Trajectories of cognitive function development and predictive factors in disabled middle-aged and older adults.

To explore the trajectories of cognitive function development and predictive factors in disabled middle-aged and older adults. Utilizing data from 983 disabled middle-aged and older adults in the China Health and Retirement Longitudinal Study (CHARLS) from 2013 to 2020, latent growth mixture models were constructed to analyze the categories of cognitive function development trajectories and their predictive factors. The cognitive function trajectories of the disabled middle-aged and older adults were classified into three categories: rapid decline (32.6%), Slow decline (36.1%), and Stable (31.2%). Multinomial logistic regression analysis identified age, gender, residence, education, marital status, household income, sleep duration, depression, hearing ability, and social participation as predictors of these trajectories. There is heterogeneity in the cognitive function development trajectories among disabled middle-aged and older adults. Healthcare professionals can implement targeted health management based on the characteristics of different groups to prevent the deterioration of cognitive function in this population.

The Effect of Probiotics on Obesity with Comorbid Depression: A Systematic Review and Meta-Analysis.

Aims/Background Obesity and depression frequently co-occur, and the relationship between them is bidirectional. Being obese or overweight increases the risk of depression, and conversely, depression increases the risk of obesity or overweight. Emerging clinical research has shown that probiotics may be effective in treating obesity and associated depression. Modulating gut microbiota with probiotics may improve obesity-related depression, but current evidence is inconsistent. Methods We systematically searched PubMed, EMBASE, Web of Science, and Cochrane Library for randomized controlled trials examining probiotics for depression in obese adults. Mean difference (MD) in depression scores between probiotic and placebo groups was pooled across studies using random-effects models. Heterogeneity was assessed using I2 to explain heterogeneity in meta-regression analysis. To detect the publication bias of the included studies, a funnel plot, and Begg and Egger tests, were used. Possible heterogeneity moderators were detected by subgroup and sensitivity analyses, Galbraith plot, and graphic display of study heterogeneity (GOSH) analysis. Leave-one-out and Influence analyses were performed to assess sensitivity. Results Five trials (n = 488) were included. Overall, probiotics did not significantly improve depressive symptoms versus placebo (MD = 0.08, 95% confidence interval [CI] = -0.63 to 0.79, p = 0.82). Considerable heterogeneity among studies was observed (I2 = 63%). In subgroup analyses, probiotics significantly reduced depression as measured by the Edinburgh Postnatal Depression Scale (MD = -0.60, 95% CI = -1.17 to -0.03, p = 0.04) with no heterogeneity (I2 = 0%). Probiotics also decreased depressive symptoms after 12 weeks of supplementation (MD = -0.78, 95% CI = -1.58 to -0.01, p = 0.05) versus placebo. No publication bias was found using Begg and Egger tests. The GOSH diagnostics revealed three outliers, among the clusters identified by K-means, DBSCAN (Density-based spatial clustering of applications with noise algorithm), and GMM (Gaussian Mixture Model) analyses. Conclusion Overall, probiotics did not improve depressive symptoms in obesity. However, beneficial effects were observed with the Edinburgh Postnatal Depression Scale and short-term use of probiotic. Additional rigorous randomized controlled trials are warranted to elucidate the therapeutic potential of probiotics for obesity-related depression.

Separating states in astronomical sources using hidden Markov models: with a case study of flaring and quiescence on EV Lac

ABSTRACT We present a new method to distinguish between different states (e.g. high and low, quiescent and flaring) in astronomical sources with count data. The method models the underlying physical process as latent variables following a continuous-space Markov chain that determines the expected Poisson counts in observed light curves in multiple passbands. For the underlying state process, we consider several autoregressive processes, yielding continuous-space hidden Markov models of varying complexity. Under these models, we can infer the state that the object is in at any given time. The continuous state predictions from these models are then dichotomized with the help of a finite mixture model to produce state classifications. We apply these techniques to X-ray data from the active dMe flare star EV Lac, splitting the data into quiescent and flaring states. We find that a first-order vector autoregressive process efficiently separates flaring from quiescence: flaring occurs over 30 per cent–40 per cent of the observation durations, a well-defined persistent quiescent state can be identified, and the flaring state is characterized by higher plasma temperatures and emission measures.

Trajectories of negative and positive marital quality during later life

This study aims to establish trajectories of later-life marital quality and assess predictors of trajectory membership. Although numerous studies examine marital quality across the early years of marriage, much less is known about older adults’ trajectories. Existing studies assess average patterns of stability or decline over the life course but have not explored the possibility of multiple trajectories during later life. This study uses nationally-representative data from the 2008–2018 waves of the Health and Retirement Study ( https://www.hrs.isr.umich.edu ). Latent growth mixture models are estimated to identify unique trajectories of positive and negative dimensions of marital quality over an eight-year time period among a sample of adults aged 51 to 97 ( n = 4,467). Results show two distinct patterns of marital quality during later life: (1) high, slightly increasing positive marital quality with low, slightly declining negative marital quality; and (2) moderate, sharply declining positive marital quality with moderate, stable negative marital quality. Significant predictors of trajectory membership include gender, household income, marital duration, remarriage, self-rated health, and depressive symptoms. Rather than patterns of stability or continual decline in marital quality over the later life course, we find the most common trajectory during later life is slight improvement. Only about 12% of older adults experience decline, and that decline is limited to the dimension of positive marital quality, with no uptick in negative marital quality.

An independent base composition of each rate class for improved likelihood-based phylogeny estimation; the 5rf model.

The combination of a time reversible Markov process with a "hidden" mixture of gamma distributed relative site rates plus invariant sites have become the most favoured options for likelihood and other probabilistic models of nucleotide evolution (e.g., tr4gi which approximates a gamma with four rate classes). However, these models assume a homogeneous and stationary distribution of nucleotide (character or base) frequencies. Here, we explore the potential benefits and pitfalls of allowing each rate category (rate class) of a 4gi mixture model to have its own base frequencies. This is achieved by starting each of the five rate classes, at the tree's root, with its own free choice of nucleotide frequencies to create a 4gi5rf model or a 5rf model in shorthand. We assess the practical identifiability of this approach with a BEAST 2 implementation, aiming to determine if it can accurately estimate credibility intervals and expected values for a wide range of plausible parameter values. Practical identifiability, as distinguished from mathematical identifiability, gauges the model's ability to identify parameters in real-world scenarios, as opposed to theoretically with infinite data. One of the most common types of phylogenetic data is mitochondrial DNA (mtDNA) protein coding sequence. It is often assumed current models analyse robustly such data and that higher likelihood/posterior probability models do better. However, this abstract shows that vertebrate mtDNA remains a very difficult type of data to fully model, and that dramatically higher likelihoods do not mean a model is measurably more accurate with respect to recovering key parameters of biological interest (e.g., monophyletic groups, their support and their ages). The 4gi5rf model considerably improves marginal likelihoods and seems to reverse some apparent errors exacerbated by the 4gi model, while introducing others. Problems appear to be linked to non-stationary DNA repair processes that alter the mutation/substitution spectra across lineages and time. We also show such problems are not unique to mtDNA and are encountered in analysing nuclear sequences. Non-stationarity of DNA repair processes mutation/substitution spectra thus pose an active challenge to obtaining reliable inferences of relationships and divergence times near the root of placental mammals, for example. An open source implementation is available under the LGPL 3.0 license in the beastbooster package for BEAST 2, available from https://github.com/rbouckaert/beastbooster.

Contribution of the diatom Navicula sp. to the growth of Penaeus vannamei post-larvae in biofloc system: a quantitative stable isotope assessment

ABSTRACT The addition of Navicula sp. to shrimp nurseries can improve the growth of Penaeus vannamei reared in biofloc systems. However, the contribution of microalgae to the biofloc formation and the effective contribution to shrimp nutrition remain unknown. In this study, Navicula sp. was added to biofloc nursery systems of P. vannamei at distinct time frequencies for evaluating its nutritional contribution to shrimp growth. Nursery rearing was carried out in bioflocs for 35 days at a stocking density of 3000 post-larvae m−3. Shrimp were fed using a commercial feed plus fresh culture of Navicula sp. at different frequencies: no addition of Navicula sp. (WN – control), the addition of 10 × 104 cells mL−1 of the diatom every 5, 10 and 15 days (N5, N10 and N15, respectively). Food sources relative contribution to P. vannamei development was estimated using a Bayesian mixture model. The isotopic discrimination factor (Δ15N and Δ13C) for each food source was determined experimentally. After 35 days of culture, survival (∼93 %) was similar across all treatments but there was a significant difference in weight gain and feed conversion ratio. The N10 treatment (0.50 ± 0.05 g, 0.99 ± 0.01) exhibited better growth parameters when compared to the WN treatment (0.33 ± 0.07 g, 11.46 ± 0.30). Biofloc was the food source most assimilated by shrimp followed by Navicula sp. and commercial feed. Contribution of Navicula sp. was higher in the N5 treatment. In the treatments with diatom addition, an inverse correlation was observed between the relative contributions of biofloc and Navicula sp., indicating that Navicula sp. is not in the biofloc composition, but it is directly consumed by P. vannamei post-larvae. Biofloc and Navicula sp. exhibited larger contributions to the growth of shrimp, reinforcing the importance of natural food sources to the aquaculture of P. vannamei post-larvae.

Impact of adiposity indices changes across the lifespan on risk of diabetes in women: trajectory modeling approach

AimsThe impact of life-course different adiposity indices on diabetes mellitus (DM) is poorly understood. We aimed to do trajectory analysis with repeated measurements of adiposity indices in the development of DM among women across the lifespan.MethodsThis study prospectively investigated the 1,681 population of Tehran Lipid and Glucose Study. At baseline, all individuals were free of diabetes. Trajectory analysis was used to identify homogeneous distinct clusters of adiposity indices trajectories and assign individuals to unique clusters.ResultsOf the 1681 healthy women, 320 progressed to the DM. Three distinct body mass index (BMI) trajectories and 2 distinct trajectories of other adiposity indices (waist circumstance (WC), conicity index (C-index), and body roundness index (BRI)) were chosen as the best fitting of the latent class growth mixture model. In the adjusted model, total participants [HR (CI 95%): 2.83 (2.05, 3.91); p < 0.001], and menopause [1.35 (1.10, 2.11); p = 0.001] and reproductive-age women [2.93 (1.80, 4.78); p = 0.003] of the high BMI trajectory compared to the ones in the low trajectory of BMI were more likely to develop DM. The high BRI in menopause had a significantly higher risk of DM compared to the low trajectory. In menopause (1.80 (1.26, 2.58)) and reproductive-age women (4.32 (2.49, 7.47)) with high trajectory of C-index, the DM risk decreased after adjustment.ConclusionsThe risk of DM was greater for high BMI, WC, C-index, and BRI trajectories than for lower trajectories. Hence, the development of general, abdominal, and visceral obesity trajectories in the prevention of DM should be considered by clinicians.

PI gain tuning for pressure-based MFCs with Gaussian mixture model

A vast number of mass flow controllers (MFCs) are used in semiconductor industry. For the stable supply, an efficient production method of MFC is required. The gain tuning of the proportional-integral (PI) control to realize a setting flow rate is essential for efficient mass production. The gains are tuned to meet the specifications required for evaluation indices of response time and overshoot amount in a step response waveform. The tuning is complicated especially for the case of pressure-based MFCs. In this paper, we propose a simple method for the PI gain tuning using the Gaussian mixture model (GMM) and the direct inverse analysis applicable to the pressure-based MFCs’ production. The relationship between the gains and evaluation indices for a standard unit of the MFC is modeled as the GMM. The direct inverse analysis calculates the difference between the standard and a test unit. Under the assumption that the difference can be compensated by a simple shift, gains likely to meet the specifications for the test unit are searched. We applied the method to seven test units. The result showed that the gains of all the test units were tuned within only a few iterations whose numbers were much less than the conventional manual tuning method, and there was no untunable unit.

Characterization and bioinformatic filtering of ambient gRNAs in single-cell CRISPR screens using CLEANSER.

Recent technological developments in single-cell RNA-seq CRISPR screens enable high-throughput investigation of the genome. Through transduction of a gRNA library to a cell population followed by transcriptomic profiling by scRNA-seq, it is possible to characterize the effects of thousands of genomic perturbations on global gene expression. A major source of noise in scRNA-seq CRISPR screens are ambient gRNAs, which are contaminating gRNAs that likely originate from other cells. If not properly filtered, ambient gRNAs can result in an excess of false positive gRNA assignments. Here, we utilize CRISPR barnyard assays to characterize ambient gRNA noise in single-cell CRISPR screens. We use these datasets to develop and train CLEANSER, a mixture model that identifies and filters ambient gRNA noise. This model takes advantage of the bimodal distribution between native and ambient gRNAs and includes both gRNA and cell-specific normalization parameters, correcting for confounding technical factors that affect individual gRNAs and cells. The output of CLEANSER is the probability that a gRNA-cell assignment is in the native distribution over the ambient distribution. We find that ambient gRNA filtering methods impact differential gene expression analysis outcomes and that CLEANSER outperforms alternate approaches by increasing gRNA-cell assignment accuracy.

Changes in Skeletal Muscle Mass in the First 3 Months Following Gastrointestinal Cancer Surgery: A Prospective Study.

Surgical resection is the primary treatment for gastrointestinal (GI) cancers, but postoperative skeletal muscle loss (SML) is common and linked to poor prognosis. This study aims to identify patterns of muscle change, examine its association with quality of life (QoL), and explore predictors of SML in the first 3 months. A prospective cohort study was conducted on patients newly diagnosed with GI cancer and undergoing surgery in China between September 2021 and May 2022. Skeletal muscle mass (SMM) and QoL were assessed at admission, 7 days, 1 month, and 3 months post-surgery. Demographic, clinical data, and biomarkers were collected. Missing data were imputed using multiple imputation. Data were analyzed using growth mixture modelling, bivariate analyses, and logistic regression. A total of 483 patients completed baseline assessment. Of the 242 patients with complete muscle assessments, 92% experienced SML. Three distinct patterns of muscle change were identified: 57% had normal preoperative SMM with mild postoperative SML, 16% had low preoperative SMM with moderate SML, and 27% had normal preoperative mass but severe postoperative SML. Moderate/severe SML was associated with more postoperative complications, poorer health, and higher symptom burden. Independent predictors included advanced age, preoperative sarcopenia, advanced cancer stage, and low prognostic nutrition index (PNI ≤ 45). The results did not change when using imputed values. Although SML is prevalent, patterns of muscle change are heterogeneous among patients. Advanced age, preoperative sarcopenia, advanced cancer stage, and cancer-related inflammation are predictors for moderate/severe SML, highlighting the need for early detection and management.

Human-in-the-Loop-A Deep Learning Strategy in Combination with a Patient-Specific Gaussian Mixture Model Leads to the Fast Characterization of Volumetric Ground-Glass Opacity and Consolidation in the Computed Tomography Scans of COVID-19 Patients.

Background/Objectives: The accurate quantification of ground-glass opacities (GGOs) and consolidation volumes has prognostic value in COVID-19 patients. Nevertheless, the accurate manual quantification of the corresponding volumes remains a time-consuming task. Deep learning (DL) has demonstrated good performance in the segmentation of normal lung parenchyma and COVID-19 pneumonia. We introduce a Human-in-the-Loop (HITL) strategy for the segmentation of normal lung parenchyma and COVID-19 pneumonia that is both time efficient and quality effective. Furthermore, we propose a Gaussian Mixture Model (GMM) to classify GGO and consolidation based on a probabilistic characterization and case-sensitive thresholds. Methods: A total of 65 Computed Tomography (CT) scans from 64 patients, acquired between March 2020 and June 2021, were randomly selected. We pretrained a 3D-UNet with an international dataset and implemented a HITL strategy to refine the local dataset with delineations by teams of medical interns, radiology residents, and radiologists. Following each HITL cycle, 3D-UNet was re-trained until the Dice Similarity Coefficients (DSCs) reached the quality criteria set by radiologists (DSC = 0.95/0.8 for the normal lung parenchyma/COVID-19 pneumonia). For the probabilistic characterization, a Gaussian Mixture Model (GMM) was fitted to the Hounsfield Units (HUs) of voxels from the CT scans of patients with COVID-19 pneumonia on the assumption that two distinct populations were superimposed: one for GGO and one for consolidation. Results: Manual delineation of the normal lung parenchyma and COVID-19 pneumonia was performed by seven teams on 65 CT scans from 64 patients (56 ± 16 years old (μ ± σ), 46 males, 62 with reported symptoms). Automated lung/COVID-19 pneumonia segmentation with a DSC > 0.96/0.81 was achieved after three HITL cycles. The HITL strategy improved the DSC by 0.2 and 0.5 for the normal lung parenchyma and COVID-19 pneumonia segmentation, respectively. The distribution of the patient-specific thresholds derived from the GMM yielded a mean of -528.4 ± 99.5 HU (μ ± σ), which is below most of the reported fixed HU thresholds. Conclusions: The HITL strategy allowed for fast and effective annotations, thereby enhancing the quality of segmentation for a local CT dataset. Probabilistic characterization of COVID-19 pneumonia by the GMM enabled patient-specific segmentation of GGO and consolidation. The combination of both approaches is essential to gain confidence in DL approaches in our local environment. The patient-specific probabilistic approach, when combined with the automatic quantification of COVID-19 imaging findings, enhances the understanding of GGO and consolidation during the course of the disease, with the potential to improve the accuracy of clinical predictions.

Distinct trajectories of neuropsychiatric symptoms in the 12 months following traumatic brain injury (TBI): a TRACK-TBI study.

Neuropsychiatric symptoms are common after traumatic brain injury (TBI) and often resolve within 3 months post-injury. However, the degree to which individual patients follow this course is unknown. We characterized trajectories of neuropsychiatric symptoms over 12 months post-TBI. We hypothesized that a substantial proportion of individuals would display trajectories distinct from the group-average course, with some exhibiting less favorable courses. Participants were level 1 trauma center patients with TBI (n = 1943), orthopedic trauma controls (n = 257), and non-injured friend controls (n = 300). Trajectories of six symptom dimensions (Depression, Anxiety, Fear, Sleep, Physical, and Pain) were identified using growth mixture modeling from 2 weeks to 12 months post-injury. Depression, Anxiety, Fear, and Physical symptoms displayed three trajectories: Stable-Low (86.2-88.6%), Worsening (5.6-10.9%), and Improving (2.6-6.4%). Among symptomatic trajectories (Worsening, Improving), lower-severity TBI was associated with higher prevalence of elevated symptoms at 2 weeks that steadily resolved over 12 months compared to all other groups, whereas higher-severity TBI was associated with higher prevalence of symptoms that gradually worsened from 3-12 months. Sleep and Pain displayed more variable recovery courses, and the most common trajectory entailed an average level of problems that remained stable over time (Stable-Average; 46.7-82.6%). Symptomatic Sleep and Pain trajectories (Stable-Average, Improving) were more common in traumatically injured groups. Findings illustrate the nature and rates of distinct neuropsychiatric symptom trajectories and their relationship to traumatic injuries. Providers may use these results as a referent for gauging typical v. atypical recovery in the first 12 months post-injury.

Unsupervised discovery of family specific vocal usage in the Mongolian gerbil.

In nature, animal vocalizations can provide crucial information about identity, including kinship and hierarchy. However, lab-based vocal behavior is typically studied during brief interactions between animals with no prior social relationship, and under environmental conditions with limited ethological relevance. Here, we address this gap by establishing long-term acoustic recordings from Mongolian gerbil families, a core social group that uses an array of sonic and ultrasonic vocalizations. Three separate gerbil families were transferred to an enlarged environment and continuous 20-day audio recordings were obtained. Using a variational autoencoder (VAE) to quantify 583,237 vocalizations, we show that gerbils exhibit a more elaborate vocal repertoire than has been previously reported and that vocal repertoire usage differs significantly by family. By performing gaussian mixture model clustering on the VAE latent space, we show that families preferentially use characteristic sets of vocal clusters and that these usage preferences remain stable over weeks. Furthermore, gerbils displayed family-specific transitions between vocal clusters. Since gerbils live naturally as extended families in complex underground burrows that are adjacent to other families, these results suggest the presence of a vocal dialect which could be exploited by animals to represent kinship. These findings position the Mongolian gerbil as a compelling animal model to study the neural basis of vocal communication and demonstrates the potential for using unsupervised machine learning with uninterrupted acoustic recordings to gain insights into naturalistic animal behavior.

The association between depressive trajectories and disability-free survival among middle-aged and older adults in China: a prospective cohort study.

This study aims to examine the association of depressive trajectories with disability-free-survival (DFS). This prospective cohort study used data from the China Health and Retirement Longitudinal Study, 2011-2015. Depressive symptoms were assessed using the Centre for Epidemiology Studies Depression Scale-10. Disability was assessed using activities of daily living (ADLs) and instrumental ADLs. Trajectories of depressive symptoms were identified and classified by latent mixture modelling. Logistic regression models were used to examine the association between depressive trajectories and DFS. A total of 8373 participants aged 45 years and older were included. We identified four distinct trajectories of depressive symptoms: 'no depressive symptoms', 'decreasing depressive symptoms', 'increasing depressive symptoms', and 'persistent depressive symptoms'. Compared to participants in the no depressive symptom trajectory, those in the decreasing depressive symptoms, increasing depressive symptoms and persistent depressive symptoms trajectories had an increased risk of disability or mortality, with multiple-adjusted hazard ratios (95% confidence intervals) of 1.75 (1.45-2.12), 2.05 (1.77-2.38) and 3.50 (2.77-4.42). Our study shows that among middle-aged and older Chinese adults, individuals with a trajectory of depressive symptoms are at increased risk of disability or mortality. Our findings underscore the importance of early prevention, identification and intervention of depression in clinical care to promote healthy ageing.

Joint trajectory inference for single-cell genomics using deep learning with a mixture prior

Trajectory inference methods are essential for analyzing the developmental paths of cells in single-cell sequencing datasets. It provides insights into cellular differentiation, transitions, and lineage hierarchies, helping unravel the dynamic processes underlying development and disease progression. However, many existing tools lack a coherent statistical model and reliable uncertainty quantification, limiting their utility and robustness. In this paper, we introduce VITAE (Variational Inference for Trajectory by AutoEncoder), a statistical approach that integrates a latent hierarchical mixture model with variational autoencoders to infer trajectories. The statistical hierarchical model enhances the interpretability of our framework, while the posterior approximations generated by our variational autoencoder ensure computational efficiency and provide uncertainty quantification of cell projections along trajectories. Specifically, VITAE enables simultaneous trajectory inference and data integration, improving the accuracy of learning a joint trajectory structure in the presence of biological and technical heterogeneity across datasets. We show that VITAE outperforms other state-of-the-art trajectory inference methods on both real and synthetic data under various trajectory topologies. Furthermore, we apply VITAE to jointly analyze three distinct single-cell RNA sequencing datasets of the mouse neocortex, unveiling comprehensive developmental lineages of projection neurons. VITAE effectively reduces batch effects within and across datasets and uncovers finer structures that might be overlooked in individual datasets. Additionally, we showcase VITAE's efficacy in integrative analyses of multiomic datasets with continuous cell population structures.

Dynamics of Gut Microbiota after Fecal Microbiota Transplantation in Ulcerative Colitis: Success Linked to Control of Prevotellaceae.

Fecal microbiota transplantation (FMT) is an experimental treatment for ulcerative colitis (UC). We aimed to study microbial families associated with FMT treatment success. We analyzed stools from 24 UC patients treated with four FMTs weekly after randomization for pretreatment during three weeks with budesonide (n = 12) or placebo (n = 12). Stool samples were collected nine times pre-, during, and post FMT. Clinical and endoscopic response was assessed 14 weeks after initiation of the study using the full Mayo score. Early withdrawal due to worsening of UC symptoms was classified as non-response. Nine patients (38%) reached remission at week 14, and 15 patients had a partial response or non-response at or before week 14. With a Dirichlet Multinomial Mixture model we identified five distinct clusters based on the microbiota composition of 180 longitudinally collected patient samples and 27 donor samples. A Prevotellaceae-dominant cluster was associated with poor response to FMT treatment. Conversely, the families Ruminococcaceae and Lachnospiraceae were associated with a successful clinical response. These associations were already visible at the start of the treatment for a subgroup of patients and were retained in repeated measures analyses of family-specific abundance over time. Responders were also characterized by a significantly lower Simpson dominance compared to non-responders. The success of FMT treatment of UC patients appears to be associated with specific gut microbiota families, such as control of Prevotellaceae. Monitoring the dynamics of these microbial families could potentially be used to inform treatment success early during FMT.

Voltage over-limit risk assessment of wind power and photovoltaic access distribution system based on day-night segmentation and Gaussian mixture model

The access of distributed power sources such as wind power and photovoltaic (PV) with randomness and uncertainty makes the operation of distribution system more complicated. It is particularly necessary to comprehensively and efficiently evaluate the risk of voltage over-limit in distribution systems with wind power and photovoltaic. Firstly, aiming at the problem of centralized sampling of Monte Carlo method in risk assessment of distribution system, a day-night segmented Monte Carlo (MC) sampling method is proposed to generate a large number of day-night sampling scenarios. Secondly, the Gaussian mixture model is used to fit the probability density of the voltage obtained from probabilistic power flow calculations, concurrently, the utility preference exponential function characterizes the severity of the over-limit, and a comprehensive evaluation model of voltage over-limit risk is constructed. Finally, the effectiveness and accuracy of the proposed method are verified by taking the improved IEEE 33-bus distribution system as an example. These results offer actionable insights for future risk assessments of new energy grid connections.

Developmental trajectories of loneliness in Chinese children: Environmental and personality predictors

BackgroundThis study aimed to identify the developmental trajectories of loneliness in Chinese children and examine the predictive roles of domain-specific environmental factors (i.e., family dysfunction and satisfaction of relatedness needs at school), personality factors (i.e., neuroticism and extraversion), and their interactions in these developmental trajectories. MethodsA total of 702 Chinese children (Mage = 8.95, SD = 0.76; 54.1 % boys) participated in assessments at six time points over three years at six-month intervals. Growth mixture modeling (GMM) was used to estimate trajectory classes for loneliness, followed by multivariate logistic regression analyses exploring associations between these classes and predictors. ResultsGMM analyses identified three distinct trajectories of loneliness: “low-stable” (81.5 %), “moderate-increasing” (9.4 %), and “high-decreasing” (9.1 %). Multivariate logistic regression analyses revealed that family dysfunction and neuroticism served as risk factors for adverse loneliness trajectories, while satisfaction of relatedness needs at school and extraversion acted as protective factors. Furthermore, the interaction between family dysfunction and extraversion indicated that extraversion did not mitigate the adverse effects of high family dysfunction on children's loneliness, emphasizing the vital need to support positive family functioning among all children. LimitationsThis study did not incorporate biological variables (e.g., genetics), which are crucial in the evolutionary theory of loneliness. ConclusionsThe identification of three distinct trajectory groups of children's loneliness, along with key environmental and personality predictors, suggests that interventions should be tailored to each group's unique characteristics.

Towards Autonomous Operation of UAVs Using Data-Driven Target Tracking and Dynamic, Distributed Path Planning Methods

A hybrid real-time path planning method has been developed that employs data-driven target UAV trajectory tracking methods. It aims to autonomously manage the distributed operation of multiple UAVs in dynamically changing environments. The target tracking methods include a Gaussian mixture model, a long short-term memory network, and extended Kalman filters with pre-specified motion models. Real-time vehicle-to-vehicle communication is assumed through a cloud-based system, enabling virtual, dynamic local networks to facilitate the high demand of vehicles in airspace. The method generates optimal paths by adaptively employing the dynamic A* algorithm and the artificial potential field method, with minimum snap trajectory smoothing to enhance path trackability during real flights. For validation, software-in-the-loop testing is performed in a dynamic environment composed of multiple quadrotors. The results demonstrate the framework’s ability to generate real-time, collision-free flight paths at low computational costs.