Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia

Objective. Schizophrenia (SCZ) is a severe mental disorder associated with persistent or recurrent psychosis, hallucinations, delusions, and thought disorders that affect approximately 26 million people worldwide, according to the World Health Organization. Several studies encompass machine learning (ML) and deep learning algorithms to automate the diagnosis of this mental disorder. Others study SCZ brain networks to get new insights into the dynamics of information processing in individuals suffering from the condition. In this paper, we offer a rigorous approach with ML and deep learning techniques for evaluating connectivity matrices and measures of complex networks to establish an automated diagnosis and comprehend the topology and dynamics of brain networks in SCZ individuals. Approach. For this purpose, we employed an functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) dataset. In addition, we combined EEG measures, i.e. Hjorth mobility and complexity, with complex network measurements to be analyzed in our model for the first time in the literature. Main results. When comparing the SCZ group to the control group, we found a high positive correlation between the left superior parietal lobe and the left motor cortex and a positive correlation between the left dorsal posterior cingulate cortex and the left primary motor. Regarding complex network measures, the diameter, which corresponds to the longest shortest path length in a network, may be regarded as a biomarker because it is the most crucial measure in different data modalities. Furthermore, the SCZ brain networks exhibit less segregation and a lower distribution of information. As a result, EEG measures outperformed complex networks in capturing the brain alterations associated with SCZ. Significance. Our model achieved an area under receiver operating characteristic curve (AUC) of 100% and an accuracy of 98.5% for the fMRI, an AUC of 95%, and an accuracy of 95.4% for the EEG data set. These are excellent classification results. Furthermore, we investigated the impact of specific brain connections and network measures on these results, which helped us better describe changes in the diseased brain.
Effects of berberine and barberry on selected inflammatory biomarkers in adults: A systematic review and dose–response meta‐analysis of randomized clinical trials

The previous meta‐analysis showed an advantageous effect of berberine supplementation on interleukin 6 (IL‐6), tumor necrosis factor‐α (TNF‐α), and serum C‐reactive protein (CRP) concentrations; however, it is unknown the dosage that this component influences inflammatory biomarkers. A comprehensive search was done in Scopus, PubMed, and Web of Science until September 2022 to find randomized controlled trials (RCT) that assessed the effects of berberine/barberry on IL‐6, TNF‐α, and CRP in adults but not trials without a control group. Studies bias was assessed using RoB 2. A random‐effects model was performed to calculate the weighted mean difference (WMD). A dose‐dependent effect was calculated. Eighteen clinical trials with 1600 participants were included in the current meta‐analysis. These interventions significantly mitigate IL‐6 levels (−1.18 pg/mL), TNF‐α levels (−3.72 pg/mL), and CRP levels (−1.33 mg/L). In addition, the non‐linear analysis showed a significant lowering effect of berberine/barberry on IL‐6 and TNF‐α levels in doses <1000 mg/day and less than 5 weeks of intervention. There are limitations to our findings, including low‐quality studies and significant heterogeneity. These interventions might be considered adjunct therapy to managing inflammation status. However, more investigation and high‐quality evidence must be conducted to obtain more comprehensive and generalizable results.
Functional fitness, lifestyle and demographic factors as predictors of perceived physical and mental health in older adults: A structural equation model

Over the past 50 years, socioeconomic development has brought a reduction in birth rates, an increase in life expectancy and consequently in the elderly population. For this reason, there has been an increasing focus on physical and mental health of the elderly, promoting the concept of healthy aging. The aim of this study was to explore the associations between perceived physical and mental health of older adults and a variety of determinants, such as demographic factors, physical functional fitness, physical activity level, adherence to the Mediterranean diet and anthropometric indices, through a structural equation modeling (SEM). A cross-sectional observational study involved 208 elderly (24 men and 184 women) over the age of 60, fully independent and autonomous. Perceived physical and mental health were assessed with the Short Form 12 questionnaire. Basic sociodemographic information was collected; anthropometric features were directly measured, functional fitness was assessed with the Senior Fitness Test, and physical activity level was determined through the International Physical Activity Questionnaire; adherence to Mediterranean Diet (MD) was also collected through the MEDAS questionnaire. The SEM analysis revealed that functional fitness, which was a latent variable of the model described by the six administered fitness tests, was a strong predictor both of perceived physical and perceived mental health in the sample of elderly. Physical activity level was as predictor of the perceived physical component, but not of the mental health, while score of metabolic equivalent task did not result a predictor, as well as the sociodemographic factors and adherence to MD. The present findings suggest that it would be strongly recommended for elderly subjects to engage in physical activity specifically targeted to aged populations, in order to enhance their fitness abilities and enable them to improve the perception of their own health status.
Reading Vision in Adults With Early and Intermediate Age-Related Macular Degeneration Under Mesopic and Photopic Conditions

Purpose Reading is involved in various daily activities that operate under a wide range of luminance levels. Rod- and cone-mediated mesopic visual function is known to be impaired even in early/intermediate age-related macular degeneration (AMD). It remains unclear whether and to what extent mesopic reading is impaired in early/intermediate AMD. Here, we assessed differences in reading vision between photopic and mesopic conditions in early/intermediate AMD and compared their performance to those in older adults with normal macular health. Methods The study included 30 patients with early/intermediate AMD and 30 healthy controls. Reading performance was tested on the MNREAD iPad app under mesopic (2 cd/m2 with a neural-density filter) and photopic (220 cd/m2) conditions. Four reading indices—maximum reading speed (MRS), critical print size (CPS), reading acuity (RA), and reading accessibility index (ACC)—were obtained from the MNREAD test, yielding a function representing reading speed versus print size. Results Compared to photopic conditions, patients with AMD and healthy controls both exhibited noticeable decreases in reading vision under mesopic conditions (P < 0.001) despite normal photopic visual acuity. This decrease was more pronounced in AMD even after adjusting for age (P < 0.001): Under mesopic conditions, MRS and ACC decreased by 8 words per minute and 0.1, respectively; CPS and RA were enlarged by 0.27 and 0.24 logMAR, respectively. Conclusions Reading vision deteriorates under mesopic conditions compared to photopic conditions in early/intermediate AMD and is accentuated compared to this difference in healthy controls. Translational Relevance A mesopic reading test may provide a more sensitive and comprehensive assessment of a patient's reading impairment.
Establishment of paternal methylation imprint at the H19/Igf2 imprinting control region

The insulator model explains the workings of the H19 and Igf2 imprinted domain in the soma, where insulation of the Igf2 promoter from its enhancers occurs by CTCF in the maternally inherited unmethylated chromosome but not the paternally inherited methylated allele. The molecular mechanism that targets paternal methylation imprint establishment to the imprinting control region (ICR) in the male germline is unknown. We tested the function of prospermatogonia-specific broad low-level transcription in this process using mouse genetics. Paternal imprint establishment was abnormal when transcription was stopped at the entry point to the ICR. The germline epimutation persisted into the paternal allele of the soma, resulting in reduced Igf2 in fetal organs and reduced fetal growth, consistent with the insulator model and insulin-like growth factor 2 (IGF2)’s role as fetal growth factor. These results collectively support the role of broad low-level transcription through the H19/Igf2 ICR in the establishment of its paternal methylation imprint in the male germ line, with implications for Silver-Russell syndrome.
Modeling Mentor-Mentee Dialogues in Film

Abstract With a view to inform the design of a mentor-like synthetic agent that is to engage in a coherent and consistent in character conversation with human subjects, we conducted a data-driven analysis of verbal communication between fictional mentor and mentee characters in films. While in our earlier work the focus was on the conversation strategies of mentor characters, here we present the extended model, wherein conversation activity of both mentor and mentee characters is accounted for. To examine and to formalize local communication actions and extended goals that the two characters achieve jointly, categories of intents, projects and relationship phases were introduced. The resulting annotated corpus of mentor and mentee characters’ utterances was analyzed qualitatively and quantitatively. In furtherance of the automated in-character dialogue generation task, a range of the state-of-the-art approaches to automated utterances classification was evaluated.
Complete genome sequence of Pectobacterium brasiliense strain 21PCA_AGRO2 with antimicrobial resistance isolated from napa cabbage

ABSTRACT We report a complete genome of Pectobacterium brasiliense strain 21PCA_AGRO2 isolated from napa cabbage, in which the genome consists of a circular chromosome comprising 4,919,671 bp with 4,399 coding DNA sequences, 22 rRNA genes, 77 tRNA genes, and 9 noncoding RNA genes.
Front-Tracking Model for Convective Transport in Flowing Ground Water

Abstract: This report describes a finite-difference numerical model that simulates the convective transport of water or tracer particles through porous media. It can be applied to one- or two-dimensional problems involving either steady-state or transient flow. The model tracks representative water or tracer particles, initially located along specified lines, as they move in response to the ground-water velocity field. Aquifer properties may be both anisotropic and nonhomogeneous. Included in the report is a listing of the program along with input formats and test problem results. The front-tracking model provides a useful first approximation for determining the movement of solutes in an aquifer, particularly in cases where dispersion and dilution is of minor consideration.
DPH1 and DPH2 variants that confer susceptibility to diphthamide deficiency syndrome in human cells and yeast models

ABSTRACT The autosomal-recessive diphthamide deficiency syndrome presents as intellectual disability with developmental abnormalities, seizures, craniofacial and additional morphological phenotypes. It is caused by reduced activity of proteins that synthesize diphthamide on human translation elongation factor 2. Diphthamide synthesis requires seven proteins (DPH1-DPH7), with clinical deficiency described for DPH1, DPH2 and DPH5. A limited set of variant alleles from syndromic patients has been functionally analyzed, but databases (gnomAD) list additional so far uncharacterized variants in human DPH1 and DPH2. Because DPH enzymes are conserved among eukaryotes, their functionality can be assessed in yeast and mammalian cells. Our experimental assessment of known and uncharacterized DPH1 and DPH2 missense alleles showed that six variants are tolerated despite inter-species conservation. Ten additional human DPH1 (G113R, A114T, H132P, H132R, S136R, C137F, L138P, Y152C, S221P, H240R) and two DPH2 (H105P, C341Y) variants showed reduced functionality and hence are deficiency-susceptibility alleles. Some variants locate close to the active enzyme center and may affect catalysis, while others may impact on enzyme activation. In sum, our study has identified functionally compromised alleles of DPH1 and DPH2 genes that likely cause diphthamide deficiency syndrome.
The Rock-Art of Eastern North America: Capturing Images and Insight

In archaeology, rock-art - any long-lasting marking made on a natural surface - is similar to material culture (pottery and tools) because it provides a record of human activity and ideology at that site. Petroglyphs, pictographs, and dendroglyphs (tree carvings) have been discovered and recorded throughout the eastern woodlands of North America - on boulders, bluffs, and trees, in caves and in rock shelters. These cultural remnants scattered on the landscape can tell us much about the belief systems of the inhabitants that left them behind. The Rock-Art of Eastern North America brings together 20 papers from recent research at sites in eastern North America, where humidity and the actions of weather, including acid rain, can be very damaging over time. Contributors to this volume range from professional archaeologists and art historians to avocational archaeologists, including a surgeon, a lawyer, two photographers, and an aerospace engineer. They present information, drawings, and photographs of sites ranging from the Seven Sacred Stones in Iowa to the Bald Friar Petroglyphs of Maryland and from the Lincoln Rise Site in Tennessee to the Nisula Site in Quebec.
Rewiring the pneumococcal capsule pathway for investigating glycosyltransferase specificity and genetic glycoengineering

Virtually all living cells are covered with glycans. Their structures are primarily controlled by the specificities of glycosyltransferases (GTs). GTs typically adopt one of the three folds, namely, GT-A, GT-B, and GT-C. However, what defines their specificities remain poorly understood. Here, we developed a genetic glycoengineering platform by reprogramming the capsular polysaccharide pathways in Streptococcus pneumoniae to interrogate GT specificity and manipulate glycan structures. Our findings suggest that the central cleft of GT-B enzymes is important for determining acceptor specificity. The constraint of the glycoengineering platform was partially alleviated when the specificity of the precursor transporter was reduced, indicating that the transporter contributes to the overall fidelity of glycan synthesis. We also modified the pneumococcal capsule to produce several medically important mammalian glycans, as well as demonstrated the importance of regiochemistry in a glycosidic linkage on binding lung epithelial cells. Our work provided mechanistic insights into GT specificity and an approach for investigating glycan functions.
It\^o versus H\"anggi-Klimontovich

Interpreting the noise in a stochastic differential equation, in particular the It\^o versus Stratonovich dilemma, is a problem that has generated a lot of debate in the physical literature. In the last decades, a third interpretation of noise, given by the so called H\"anggi-Klimontovich integral, has been proposed as better adapted to describe certain physical systems, particularly in statistical mechanics. Herein, we introduce this integral in a precise mathematical manner and analyze its properties, signaling those that has made it appealing within the realm of physics. Subsequently, we employ this integral to model some statistical mechanical systems, as the random dispersal of Langevin particles and the relativistic Brownian motion. We show that, for these classical examples, the H\"anggi-Klimontovich integral is worse adapted than the It\^o integral and even the Stratonovich one.
Highly Controllable Diffusion-based Any-to-Any Voice Conversion Model with Frame-level Prosody Feature

We propose a highly controllable voice manipulation system that can perform any-to-any voice conversion (VC) and prosody modulation simultaneously. State-of-the-art VC systems can transfer sentence-level characteristics such as speaker, emotion, and speaking style. However, manipulating the frame-level prosody, such as pitch, energy and speaking rate, still remains challenging. Our proposed model utilizes a frame-level prosody feature to effectively transfer such properties. Specifically, pitch and energy trajectories are integrated in a prosody conditioning module and then fed alongside speaker and contents embeddings to a diffusion-based decoder generating a converted speech mel-spectrogram. To adjust the speaking rate, our system includes a self-supervised model based post-processing step which allows improved controllability. The proposed model showed comparable speech quality and improved intelligibility compared to a SOTA approach. It can cover a varying range of fundamental frequency (F0), energy and speed modulation while maintaining converted speech quality.
CenTime: Event-Conditional Modelling of Censoring in Survival Analysis

Survival analysis is a valuable tool for estimating the time until specific events, such as death or cancer recurrence, based on baseline observations. This is particularly useful in healthcare to prognostically predict clinically important events based on patient data. However, existing approaches often have limitations; some focus only on ranking patients by survivability, neglecting to estimate the actual event time, while others treat the problem as a classification task, ignoring the inherent time-ordered structure of the events. Additionally, the effective utilisation of censored samples-data points where the event time is unknown- is essential for enhancing the model's predictive accuracy. In this paper, we introduce CenTime, a novel approach to survival analysis that directly estimates the time to event. Our method features an innovative event-conditional censoring mechanism that performs robustly even when uncensored data is scarce. We demonstrate that our approach forms a consistent estimator for the event model parameters, even in the absence of uncensored data. Furthermore, CenTime is easily integrated with deep learning models with no restrictions on batch size or the number of uncensored samples. We compare our approach to standard survival analysis methods, including the Cox proportional-hazard model and DeepHit. Our results indicate that CenTime offers state-of-the-art performance in predicting time-to-death while maintaining comparable ranking performance. Our implementation is publicly available at https://github.com/ahmedhshahin/CenTime.
The DECam Ecliptic Exploration Project (DEEP). I. Survey Description, Science Questions, and Technical Demonstration

We present here the DECam Ecliptic Exploration Project (DEEP), a 3 yr NOAO/NOIRLab Survey that was allocated 46.5 nights to discover and measure the properties of thousands of trans-Neptunian objects (TNOs) to magnitudes as faint as VR ∼ 27 mag, corresponding to sizes as small as 20 km diameter. In this paper we present the science goals of this project, the experimental design of our survey, and a technical demonstration of our approach. The core of our project is “digital tracking,” in which all collected images are combined at a range of motion vectors to detect unknown TNOs that are fainter than the single exposure depth of VR ∼ 23 mag. Through this approach, we reach a depth that is approximately 2.5 mag fainter than the standard LSST “wide fast deep” nominal survey depth of 24.5 mag. DEEP will more than double the number of known TNOs with observational arcs of 24 hr or more, and increase by a factor of 10 or more the number of known small (<50 km) TNOs. We also describe our ancillary science goals, including measuring the mean shape distribution of very small main-belt asteroids, and briefly outline a set of forthcoming papers that present further aspects of and preliminary results from the DEEP program.
Neutrinos and Gamma Rays from Galaxy Clusters Constrained by the Upper Limits of IceCube

Clusters of galaxies possess the capability to accelerate cosmic rays (CRs) to very high energy up to ∼1018 eV due to their large size and magnetic field strength, which favor CR confinement for cosmological times. During their confinement, they can produce neutrinos and γ-rays out of interactions with the background gas and photon fields. In recent work, Hussain et al. have conducted three-dimensional cosmological magnetohydrodynamical simulations of the turbulent intracluster medium combined with multidimensional Monte Carlo simulations of CR propagation for redshifts ranging from z ∼ 5 to z = 0 to study the multimessenger emission from these sources. They found that when CRs with a spectral index in the range 1.5–2.5 and cutoff energy Emax=1016–1017 eV are injected into the system, they make significant contributions to the diffuse background emission of both neutrinos and γ-rays. In this work, we have revisited this model and undertaken further constraints on the parametric space. This was achieved by incorporating the recently established upper limits on neutrino emission from galaxy clusters, as obtained by the IceCube experiment. We find that for CRs injected with spectral indices in the range 2.0–2.5, cutoff energy Emax=1016–1017 eV, and power corresponding to (0.1–1)% of the cluster luminosity, our neutrino flux aligns with the upper limits estimated by IceCube. Additionally, the resulting contribution from clusters to the diffuse γ-ray background remains significant with values of the order of ∼10−5 MeV cm−2 s−1 sr−1 at energies above 500 GeV.
Supervised Learning and Large Language Model Benchmarks on Mental Health Datasets: Cognitive Distortions and Suicidal Risks in Chinese Social Media

In the realm of social media, users frequently convey personal sentiments, with some potentially indicating cognitive distortions or suicidal tendencies. Timely recognition of such signs is pivotal for effective interventions. In response, we introduce two novel annotated datasets from Chinese social media, focused on cognitive distortions and suicidal risk classification. We propose a comprehensive benchmark using both supervised learning and large language models, especially from the GPT series, to evaluate performance on these datasets. To assess the capabilities of the large language models, we employed three strategies: zero-shot, few-shot, and fine-tuning. Furthermore, we deeply explored and analyzed the performance of these large language models from a psychological perspective, shedding light on their strengths and limitations in identifying and understanding complex human emotions. Our evaluations underscore a performance difference between the two approaches, with the models often challenged by subtle category distinctions. While GPT-4 consistently delivered strong results, GPT-3.5 showed marked improvement in suicide risk classification after fine-tuning. This research is groundbreaking in its evaluation of large language models for Chinese social media tasks, accentuating the models' potential in psychological contexts. All datasets and code are made available.