Miracle-dz/UniFAR-FTUs huggingface dataset:
Integration and Segregation of Dynamic Functional Connectivity States for Mild Cognitive Impairment Revealed by Graph Theory Indicators

Mild cognitive impairment (MCI) is an intermediate stage between normal aging and dementia. Researchers tend to discuss its early state (early MCI, eMCI) due to its high conversion rate of dementia and poor treatment effect in the middle and late stages. Currently, the research on the disease evolution of the brain functional networks of patients with MCI has gradually become a research hotspot. In this study, we compare the differences in dynamic functional connectivity among eMCI, late MCI (lMCI), and normal control (NC) groups, and their graph theory indicators reveal the integration and segregation of functional connectivity states. Firstly, dynamic functional network windows were constructed based on the sliding time window method, and then these window samples were clustered by k-means to extract the functional connectivity states. The differences in the three groups were compared by analyzing the graph theory indicators, such as the participation coefficient, module degree distribution, clustering coefficient, global efficiency, and local efficiency, which distinguish the functional connectivity states. The results reveal that the NC group has the strongest integration and segregation, followed by the eMCI group, and the lMCI group has the weakest integration and segregation. We conclude that with the aggravation of MCI, the integration and segregation of dynamic functional connectivity states tend to decline. The results also reflect that the lMCI group has significantly more brain functional connections in some states, such as IPL.L-MTG.R and DCG.R-SMG.L, than the eMCI group, while the lMCI group has significantly less OLF.L-SPG.L than the NC group.
Development of a standardized histopathology scoring system using machine learning algorithms for intervertebral disc degeneration in the mouse model—An ORS spine section initiative

Mice have been increasingly used as preclinical model to elucidate mechanisms and test therapeutics for treating intervertebral disc degeneration (IDD). Several intervertebral disc (IVD) histological scoring systems have been proposed, but none exists that reliably quantitate mouse disc pathologies. Here, we report a new robust quantitative mouse IVD histopathological scoring system developed by building consensus from the spine community analyses of previous scoring systems and features noted on different mouse models of IDD. The new scoring system analyzes 14 key histopathological features from nucleus pulposus (NP), annulus fibrosus (AF), endplate (EP), and AF/NP/EP interface regions. Each feature is categorized and scored; hence, the weight for quantifying the disc histopathology is equally distributed and not driven by only a few features. We tested the new histopathological scoring criteria using images of lumbar and coccygeal discs from different IDD models of both sexes, including genetic, needle‐punctured, static compressive models, and natural aging mice spanning neonatal to old age stages. Moreover, disc sections from common histological preparation techniques and stains including H&E, SafraninO/Fast green, and FAST were analyzed to enable better cross‐study comparisons. Fleiss's multi‐rater agreement test shows significant agreement by both experienced and novice multiple raters for all 14 features on several mouse models and sections prepared using various histological techniques. The sensitivity and specificity of the new scoring system was validated using artificial intelligence and supervised and unsupervised machine learning algorithms, including artificial neural networks, k‐means clustering, and principal component analysis. Finally, we applied the new scoring system on established disc degeneration models and demonstrated high sensitivity and specificity of histopathological scoring changes. Overall, the new histopathological scoring system offers the ability to quantify histological changes in mouse models of disc degeneration and regeneration with high sensitivity and specificity.
Pathogenesis of autoimmune demyelination: from multiple sclerosis to neuromyelitis optica spectrum disorders and myelin oligodendrocyte glycoprotein antibody‐associated disease

Autoimmunity plays a significant role in the pathogenesis of demyelination. Multiple sclerosis (MS), neuromyelitis optica spectrum disorders (NMOSD) and myelin oligodendrocyte glycoprotein antibody‐associated disease (MOGAD) are now recognised as separate disease entities under the amalgam of human central nervous system demyelinating disorders. While these disorders share inherent similarities, investigations into their distinct clinical presentations and lesion pathologies have aided in differential diagnoses and understanding of disease pathogenesis. An interplay of various genetic and environmental factors contributes to each disease, many of which implicate an autoimmune response. The pivotal role of the adaptive immune system has been highlighted by the diagnostic autoantibodies in NMOSD and MOGAD, and the presence of autoreactive lymphocytes in MS lesions. While a number of autoantigens have been proposed in MS, recent emphasis on the contribution of B cells has shed new light on the well‐established understanding of T cell involvement in pathogenesis. This review aims to synthesise the clinical characteristics and pathological findings, discuss existing and emerging hypotheses regarding the aetiology of demyelination and evaluate recent pathogenicity studies involving T cells, B cells, and autoantibodies and their implications in human demyelination.
The role of A268V exon-7 polymorphism of PPARA in development of axial spondyloarthritis

Abstract Objectives Axial spondyloarthritis (axSpA) is a chronic inflammatory disease that mainly affects the axial skeleton. Peroxisome proliferator activated receptor alpha (PPARA) is an intracellular transcription factor, which play a role in inflammation and osteoblasting activity. This study is designed to investigate the relationship of NG_012204. 2:p.Ala268Val polymorphism of PPARA with axSpA risk and its role in disease development. Methods This study was conducted with 168 patients and 181 controls. Genotyping was done with MALDITOF. Gene expression level was analyzed by quantitative real time PCR (RT-qPCR). The protein homology models of PPARA were created with ProMod3. Ligand binding dynamics were tested using the AutoDock4 docking program. Statistical evaluations were made with SPSS (ver24) and GeneGlobe. Results Our results showed that C>T polymorphism causing NG_012204. 2:p.Ala268Val change was associated with disease risk (p=0.024) and T allele increased disease risk 1.7 times (95% CI=1.070–2.594). PPARA expression decreased (p<0.05) in individuals carrying the T allele. We determined that the ligand entry pocket was opened 1.1 Å in the polymorphic PPARA. Polymorphic change caused a decrease in the ligand binding affinity. Conclusions Our results provide an important contribution to elucidating the development of axSpA and demonstrate the potential of PPARA as a marker for the diagnosis of axSpA.
Effects of reward history on decision-making and movement vigor

During foraging, animals explore a site and harvest reward, and then abandon that site and travel to the next opportunity. One aspect of this behavior involves decision-making, while the other involves movement control. We recently proposed that control of decision-making and movements may be linked via a desire to maximize a single normative utility: the sum of all rewards acquired, minus all efforts expended, divided by time. If this is the case, then the history of rewards, and not just its immediate availability, should dictate how long one decides to stay and harvest reward, and how fast one travels to the next opportunity. We tested this theory in a series of experiments in which humans used their hand to harvest tokens at a reward patch, and then used their arm to reach toward a subsequent opportunity. Experiencing a history of poor rewards not only led people to forage longer, but they also reached slower toward the next reward site. Thus, reward history had a consistent effect on both the decision-making process regarding when to abandon a reward site, and the motor control process regarding how fast to move to the next opportunity.
The “neural shift” of sleep quality and cognitive ageing: A resting-state MEG study of transient neural dynamics

Sleep quality changes dramatically from young to old age, but its effects on brain dynamics and cognitive functions are not yet fully specified. We applied Hidden Markov Models (HMMs) to resting-state MEG data from a large cohort (N=564) of population-based adults (aged 18-88), in order to characterize transient neural networks and to relate their temporal dynamics to sleep quality and to cognitive performance. Using multivariate analyses of brain-sleep profiles and of brain-cognition profiles, we found that an age-related “neural shift”, expressed as decreased occurrence of “lower-order” brain networks, coupled with increased occurrence of “higher-order” networks, was associated with both increased sleep dysfunction and decreased fluid intelligence above and beyond age. These results suggest that poor sleep quality, as evident in ageing, may lead to a behavior-related shift in neural dynamics.
Using Deep Learning to Count Monarch Butterflies in Dense Clusters

Monarch butterflies display one of the most fascinating migration patterns of all species, traveling over 3000 miles from their North American breeding grounds to reach overwintering sites in Central Mexico. Recent studies have suggested that monarchs have experienced an alarming decline in population size due to a combination of deforestation, loss of native milkweed and nectaring plants, and climate change. An issue that conservation efforts face is the lack of principled mechanisms to accurately estimate and count the population size of monarchs. This difficulty occurs due to their small size and existence in dense overwintering clusters in forests. We create an open-source tool to aid conservationists estimate the count of monarch butterflies from images automatically. To the best of our knowledge, our approach, based on deep convolutional neural networks, is the first automated application that can count small insects like monarch butterflies in dense clusters. We demonstrate that our approach achieves high accuracy in counting the number of butterflies even in the presence of occlusion. We also release an open-source dataset containing high resolution images of monarch butterflies along with human annotations for each butterfly’s position. Our open-source implementation can be readily used by scientists to estimate monarch numbers in overwintering clusters and could also be adapted for use in other clustering species.
Verification of our empirical understanding of the physiology and ecology of two contrasting plantation species using a trait database

The effects of climate change on forest ecosystems take on increasing importance more than ever. Information on plant traits is a powerful predictor of ecosystem dynamics and functioning. We reviewed the major ecological traits, such as foliar gas exchange and nutrients, xylem morphology and drought tolerance, of Cryptomeria japonica and Chamaecyparis obtusa, which are major timber species in East Asia, especially in Japan, by using a recently developed functional trait database for both species (SugiHinokiDB). Empirically, C. obtusa has been planted under drier conditions, whereas C. japonica has been planted under wetter conditions. Our analyses revealed followings: The maximum photosynthetic rate, stomatal conductance, foliar nutrient content and soil-to-foliage hydraulic conductance were higher in C. japonica than in C. obtusa and were consistent with the higher growth rate of C. japonica. In contrast, the foliar turgor loss point and xylem pressure corresponding to 50% conductivity, which indicate drought tolerance, were lower in C. obtusa than in C. japonica and are consistent with the drier habitat of C. obtusa. Ontogenetic shifts were also observed; as the age and height of the trees increased, many foliar nutrient concentrations decreased, and the foliar minimum midday water potential and specific leaf area also decreased. This suggests that an ontogenetic reduction in photosynthesis occurred due to an increase in drought stress with tree height and age. However, among the Cupressaceae worldwide, the drought tolerance of C. japonica and C. obtusa is not as high. This may be related to the fact that the Japanese archipelago has historically not been subjected to strong dryness. The maximum photosynthetic rate showed intermediate values within the family, indicating that C. japonica and C. obtusa exhibit relatively high growth rates in the Cupressaceae family, and this is thought to be the reason why they have been selected as economically suitable timber species in Japanese forestry. This study clearly demonstrated that the plant trait database provides us a promising opportunity to verify out empirical knowledge of plantation management and helps us to understand effect of climate change on plantation forests by using trait-based modelling.
DNA mismatch and damage detection using a FRET-based assay for monitoring the loading of multiple MutS sliding clamps

We developed a sensitive, homogeneous fluorescence assay for the detection of DNA mismatches and DNA damage based on the mismatch repair (MMR) protein MutS. The assay is based on Förster resonance energy transfer (FRET) between SYBR Green I (SG), non-covalently bound to DNA, and Alexa Fluor 647 (AF647) conjugated to MutS. In contrast to previous assays using only the mismatch binding activity of MutS, we exploited the ATP-dependent loading of multiple MutS sliding clamps provoked by mismatch/damage to the DNA, which increases the overall sensitivity of the assay. The assay was validated using a well-characterized 3 kb circular DNA containing a single G/T mismatch. We also demonstrate that treatment of long (multiple kb) DNA with various chemical or physical agents including non-denaturing bisulfite conversion of cytosine to uracil, cisplatin modification or ultraviolet light (UVC) results in changes in the DNA that can be detected by the FRET-based MutS biosensor.
Virulence and antimicrobial resistance profile of non-typhoidal Salmonella enterica serovars recovered from poultry processing environments at wet markets in Dhaka, Bangladesh

The rapid emergence of virulent and multidrug-resistant (MDR) non-typhoidal Salmonella (NTS) enterica serovars is a growing public health concern globally. The present study focused on the assessment of the pathogenicity and antimicrobial resistance (AMR) profiling of NTS enterica serovars isolated from the chicken processing environments at wet markets in Dhaka, Bangladesh. A total of 870 samples consisting of carcass dressing water (CDW), chopping board swabs (CBS), and knife swabs (KS) were collected from 29 wet markets. The prevalence of Salmonella was found to be 20% in CDW, 19.31% in CBS, and 17.58% in KS, respectively. Meanwhile, the MDR Salmonella was found to be 72.41%, 73.21%, and 68.62% in CDW, CBS, and KS, respectively. All isolates were screened by polymerase chain reaction (PCR) for eight virulence genes, namely invA, agfA, IpfA, hilA, sivH, sefA, sopE, and spvC. The S. Enteritidis and untyped Salmonella isolates harbored all virulence genes while S. Typhimurium isolates carried six virulence genes, except sefA and spvC. Phenotypic resistance revealed decreased susceptibility to ciprofloxacin, streptomycin, ampicillin, tetracycline, gentamicin, sulfamethoxazole-trimethoprim, amoxicillin-clavulanic acid, and azithromycin. Genotypic resistance showed a higher prevalence of plasmid-mediated blaTEM followed by tetA, sul1, sul2, sul3, and strA/B genes. The phenotypic and genotypic resistance profiles of the isolates showed a harmonic and symmetrical trend. According to the findings, MDR and virulent NTS enterica serovars predominate in wet market conditions and can easily enter the human food chain. The chi-square analysis showed significantly higher associations among the phenotypic resistance, genotypic resistance and virulence genes in CDW, CBS, and KS respectively (p < 0.05).
Differential mitochondrial protein interaction profile between human translocator protein and its A147T polymorphism variant

The translocator protein (TSPO) has been implicated in mitochondrial transmembrane cholesterol transport, brain inflammation, and other mitochondrial functions. It is upregulated in glial cells during neuroinflammation in Alzheimer’s disease. High affinity TSPO imaging radioligands are utilized to visualize neuroinflammation. However, this is hampered by the common A147T polymorphism which compromises ligand binding. Furthermore, this polymorphism has been linked to increased risk of neuropsychiatric disorders, and possibly reduces TSPO protein stability. Here, we used immunoprecipitation coupled to mass-spectrometry (IP-MS) to establish a mitochondrial protein binding profile of wild-type (WT) TSPO and the A147T polymorphism variant. Using mitochondria from human glial cells expressing either WT or A147T TSPO, we identified 30 WT TSPO binding partners, yet only 23 for A147T TSPO. Confirming that A147T polymorphism of the TSPO might confer loss of function, we found that one of the identified interactors of WT TSPO, 14-3-3 theta (YWHAQ), a protein involved in regulating mitochondrial membrane proteins, interacts much less with A147T TSPO. Our data presents a network of mitochondrial interactions of TSPO and its A147T polymorphism variant in human glial cells and indicate functional relevance of A147T in mitochondrial protein networks.
DCIS genomic signatures define biology and clinical outcome: Human Tumor Atlas Network (HTAN) analysis of TBCRC 038 and RAHBT cohorts

Ductal carcinoma in situ (DCIS) is the most common precursor of invasive breast cancer (IBC), with variable propensity for progression. We have performed the first multiscale, integrated profiling of DCIS with clinical outcomes by analyzing 677 DCIS samples from 481 patients with 7.1 years median follow-up from the Translational Breast Cancer Research Consortium (TBCRC) 038 study and the Resource of Archival Breast Tissue (RAHBT) cohorts. We identified 812 genes associated with ipsilateral recurrence within 5 years from treatment and developed a classifier that was predictive of DCIS or IBC recurrence in both cohorts. Pathways associated with recurrence include proliferation, immune response, and metabolism. Distinct stromal expression patterns and immune cell compositions were identified. Our multiscale approach employed in situ methods to generate a spatially resolved atlas of breast precancers, where complementary modalities can be directly compared and correlated with conventional pathology findings, disease states, and clinical outcome. HIGHLIGHTS ⍰ Development of a new classifier for DCIS recurrence or progression ⍰ Outcome associated pathways identified across multiple data types and compartments ⍰ Four stroma-specific signatures identified ⍰ CNAs characterize DCIS subgroups associated with high risk invasive cancers
A competitive advantage through fast dead matter elimination in confined cellular aggregates

Competition of different species or cell types for limited space is relevant in a variety of biological processes such as biofilm development, tissue morphogenesis and tumor growth. Predicting the outcome for non-adversarial competition of such growing active matter is non-trivial, as it depends on how processes like growth, proliferation and the degradation of cellular matter are regulated in confinement; regulation that happens even in the absence of competition to achieve the dynamic steady state known as homeostasis. Here, we show that passive by-products of the processes maintaining homeostasis can significantly alter fitness. Even for purely pressure-regulated growth and exclusively mechanical interactions, this enables cell types with lower homeostatic pressure to outcompete those with higher homeostatic pressure. We reveal that interfaces play a critical role for this specific kind of competition: there, growing matter with a higher proportion of active cells can better exploit local growth opportunities that continuously arise as the active processes keep the system out of mechanical equilibrium. We elucidate this effect in a theoretical toy model and test it in an agent-based computational model that includes finite-time mechanical persistence of dead cells and thereby decouples the density of growing cells from the homeostatic pressure. Our results suggest that self-organization of cellular aggregates into active and passive matter can be decisive for competition outcomes and that optimizing the proportion of growing (active) cells can be as important to survival as sensitivity to mechanical cues.
Identifying cell state–associated alternative splicing events and their coregulation

Alternative splicing shapes the transcriptome and contributes to each cell’s unique identity, but single-cell RNA sequencing has struggled to capture the impact of alternative splicing. We previously showed that low recovery of mRNAs from single cells led to erroneous conclusions about the cell-to-cell variability of alternative splicing (1). Here, we present a method, Psix, to confidently identify splicing that changes across a landscape of single cells, using a probabilistic model that is robust against the data limitations of scRNA-seq. Its autocorrelation-inspired approach finds patterns of alternative splicing that correspond to patterns of cell identity, such as cell type or developmental stage, without the need for explicit cell clustering, labeling, or trajectory inference. Applying Psix to data that follow the trajectory of mouse brain development, we identify exons whose alternative splicing patterns cluster into modules of co-regulation. We show that the exons in these modules are enriched for binding by distinct neuronal splicing factors, and that their changes in splicing correspond to changes in expression of these splicing factors. Thus, Psix reveals cell-type-dependent splicing patterns and the wiring of the splicing regulatory networks that control them. Our new method will enable scRNA-seq analysis to go beyond transcription to understand the roles of post-transcriptional regulation in determining cell identity.