Miracle-dz/UniFAR-FTUs huggingface dataset:
Typical and disrupted small-world architecture and regional communication in full-term and preterm infants

One fundamental property of conscious experiences is that they are both differentiated and integrated. Adult functional brain networks exhibit an elegant “small-world” architecture. This optimal architecture enables efficient and cost-effective localized information processing and information integration between long-distance regions across the brain. It remains unclear whether the functional small-world architecture is developed in neonates at birth and how this development may be altered by premature birth. To address this gap, we investigated the development of small-world architecture in neonates. To understand the effect of early neonate age on small-world architecture, we also assessed neonates born prematurely or before term-equivalent age (TEA). We used the Developing Human Connectome Project (dHCP), a large neonatal functional magnetic resonance imaging (MRI) dataset with high temporal and spatial resolution. Resting-state functional MRI data for full- term neonates (N = 278, age 41.2 weeks ± 12.2 days) and preterm neonates scanned at TEA (N = 72, 40.9 weeks ± 14.6 days), or before TEA (N = 70, 34.7 weeks ± 12.7 days), were obtained from the dHCP, and for a reference adult group (N = 176, 22–36 years), from the Human Connectome Project. Whole-brain functional network properties were evaluated with comprehensive spatial resolution using graph theoretical analyses. Although different from the adults’, small-world architecture was developed in full-term born neonates at birth. Premature neonates before TEA showed dramatic underdevelopment of small-world architecture and regional communication in 9/11 brain networks, with disruption in 32% of nodes primarily distributed within the somatomotor, dorsal attention, cingulo-opercular, and frontoparietal control network. By TEA, premature neonates showed large-scale recuperation of regional communication, with 1.4% of nodes, distributed in the frontoparietal, salience, and visual networks remaining significantly underdeveloped. Our results suggest that, at full- term birth or by term-equivalent age, infants possess well-developed small-world architecture, which facilitates differentiated and integrated neural processes that give rise to conscious experiences. Conversely, they suggest that this brain infrastructure is significantly underdeveloped before infants reach term-equivalent age. These findings improve understanding of the ontogeny of functional small-world architecture and efficiency of neural communication across distinct brain networks in infants at birth.
Adaptive Incremental Nonlinear Dynamic Inversion for Attitude Control of Micro Air Vehicles

Incremental nonlinear dynamic inversion is a sensor-based control approach that promises to provide high-performance nonlinear control without requiring a detailed model of the controlled vehicle. In the context of attitude control of micro air vehicles, incremental nonlinear dynamic inversion only uses a control effectiveness model and uses estimates of the angular accelerations to replace the rest of the model. This paper provides solutions for two major challenges of incremental nonlinear dynamic inversion control: how to deal with measurement and actuator delays, and how to deal with a changing control effectiveness. The main contributions of this article are 1) a proposed method to correctly take into account the delays occurring when deriving angular accelerations from angular rate measurements; 2) the introduction of adaptive incremental nonlinear dynamic inversion, which can estimate the control effectiveness online, eliminating the need for manual parameter estimation or tuning; and 3) the incorp.
Prediction of Customers' Subscription to Time Deposits Based on SMOTEENN-XGBoost Model

: In the fierce competitive banking industry, accurate prediction of customers’ subscription to time deposits is vital for banks. This can reduce unnecessary time and energy spent on targeted customer service to improve bank efficiency. Traditional prediction methods do not handle the imbalanced data problem very well. In this paper, in order to minimize the impacts from imbalanced data, we combine Synthetic Minority Oversampling Technique (SMOTE)and Edited Nearest Neighbor Technique (ENN) to make the data as balanced as possible. Then, Extreme Gradient Boosting algorithm (XGBoost) is adopted as classification algorithm to improve the accuracy of data classification results. For clarity, this model is called SMOTEENN-XGBoost model. A bank customers dataset published on the Kaggle platform is used to demonstrate its effect by numerical experiments. We compare the performance of the SMOTEENN-XGBoost in this paper with Decision Tree (DT), Adaptive Boosting (AdaBoost), XGBoost, SMOTE-XGBoost in terms of Accuracy (ACC), Area Under ROC Curve (AUC), and Geometric-mean (G-mean). The results show that the mean ACC, AUC and G-mean of SMOTEENN-XGBoost model are 0.92, 0.97, and 0.92, which are better than the other models. It indicates that this model has good classification performance and can effectively dig out potential customers.
Rapid molecular species identification of mammalian scat samples using nanopore adaptive sampling

Accurate species identification is essential to mammalogy. Despite this necessity, rapid and accurate identification of cryptic, understudied, and elusive mammals remains challenging. Traditional barcoding of mitochondrial genes is standard for molecular identification but requires time-consuming wet-lab methodologies. Recent bioinformatic advancements for nanopore sequencing data offer exciting opportunities for non-invasive and field-based identification of mammals. Nanopore adaptive sampling (NAS), a PCR-free method, selectively sequences regions of DNA according to user-specified reference databases. Here, we utilized NAS to enrich mammalian mitochondrial genome sequencing to identify species. Fecal DNA extractions were sequenced from nine mammals, several collected in collaboration with Minnesota Tribal Nations, to demonstrate utility for NAS-barcoding of non-invasive samples. By mapping to the entire National Center for Biotechnology Information (NCBI) mammalian mitochondrial reference genome database and bioinformatically analyzing highly similar matches, we successfully produced species identifications for all of our fecal samples. Eight of nine species identifications matched previous PCR or animal/fecal morphological identifications. For the ninth species, our genetic data indicate a misidentification stemming from the original study. Our approach has a range of applications, particularly field-based wildlife research, conservation, disease surveillance, and monitoring of wildlife trade. Of importance to Minnesota tribes is invasive species monitoring, detections, and confirmation as climate impacts causes changes in biodiversity and shifts in species distributions. The rapid assessment techniques described here will be useful as new introductions and range expansions of native and invasive species may first be detected by the presence of signs such as scat rather than direct observations and will be helpful for chronically understaffed tribal natural resources agencies.
Simultaneous LC-MS determination of glucose regulatory peptides secreted by stem cell-derived islet organoids

For studying stem cell-derived islet organoids (SC-islets) in an organ-on-chip platform, we have developed a reversed phase liquid chromatography tandem mass spectrometry (RPLC-MS/MS) method allowing for simultaneous determination of insulin, somatostatin-14, and glucagon, with improved matrix robustness compared to earlier methodology. Combining phenyl/hexyl-C18 separations using 2.1 mm inner diameter LC columns and triple quadrupole mass spectrometry, identification and quantification were secured with negligible variance in retention time and quantifier/qualifier ratios, negligible levels of carry-over (< 2%), and sufficient precision (± 10% RSD) and accuracy (± 15% relative error) with and without use of internal standard. The here developed RPLC-MS/MS method showed that the SC-islets have an insulin response dependent on glucose concentration, and the SC-islets produce and release somatostatin-14 and glucagon. The RPLC-MS/MS method for these peptide hormones was compatible with an unfiltered off-line sample collection from SC-islets cultivated on a pump-less, recirculating organ-on-chip (rOoC) platform. The SC-islets background secretion of insulin was not significantly different on the rOoC device compared to a standard cell culture well-plate. Taken together, RPLC-MS/MS is well suited for multi-hormone measurements of SC-islets on an organ-on-chip platform.
A New Look at Tests in Multiple Logistic Regression Models

In this paper we consider the response probability in a multiple logistic regression set up when the observations are independent binomial variables and propose the likelihood ratio tests for testing the significance of regression coefficients on the basis of a suitable variance stabilizing transformation. Results of simulation study which evaluate the power of the test procedures and compare the test statistics to earlier ones are presented. An example to illustrate our tests is provided.
CENTER FOR MULTIMODAL SOLUTIONS FOR CONGESTION MITIGATION

DISCLAIMER The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. This document is disseminated under the sponsorship of the Department of Transportation University Transportation Centers Program, in the interest of information exchange. The U.S. Government assumes no liability for the contents or use thereof. In this research, a multimodal transportation model was developed attending the needs of emergency situations , and the solutions provided by the model could be used to moderate congestion during such events. The model incorporated features such as lane reversals as they have a significant impact on the evacuation efficiency. We proposed analytical techniques to solve the model. In this project, we developed a multimodal evacuation model and we hope to apply the model for an event management at the football stadium at University of Florida. The model is used to establish optimal evacuation routes to the bimodal evacuation problem and obtain efficient loading schemes for the staged evacuation. As a future extension, we are working on acceleration of the convergence of the algorithm. In this research, we intend to establish optimal evacuation routes using multimodal transportation. The proposed solution procedure will also provide a loading scheme for the evacuation. The evacuation time obtained from the proposed techniques will be shorter than the contemporary evacuation techniques. We design analytical techniques to develop a staged evacuation procedure. Staged evacuation has empirically shown to handle congestion efficiently. Also, their impact is better realized under an emergency situation. Lane reversals will be implemented as a preprocessing step to further improve the efficiency. An integer program has been formulated to solve the lane reversal problem and it will be used to identify the lanes. We will also provide proof for polynomial time solvability of the lane reversal in maximum dynamic-flow and quickest flow problems. Multimodal flow problems are known to be NP-hard, a class of problems with no known polynomial time algorithm to solve them, making the problem under consideration quite significant. The optimization techniques currently employed could be formulated as a network flow problem, wherein the nodes of the network are the places, which could be a city or a house or a building room that are linked by the arcs, which includes roads, corridors or streets. Static demands and travel times will be considered as a simplistic assumption to make the model …
Eye movement changes as an indicator of mild cognitive impairment

Background Early identification of patients at risk of dementia, alongside timely medical intervention, can prevent disease progression. Despite their potential clinical utility, the application of diagnostic tools, such as neuropsychological assessments and neuroimaging biomarkers, is hindered by their high cost and time-consuming administration, rendering them impractical for widespread implementation in the general population. We aimed to develop non-invasive and cost-effective classification models for predicting mild cognitive impairment (MCI) using eye movement (EM) data. Methods We collected eye-tracking (ET) data from 594 subjects, 428 cognitively normal controls, and 166 patients with MCI while they performed prosaccade/antisaccade and go/no-go tasks. Logistic regression (LR) was used to calculate the EM metrics’ odds ratios (ORs). We then used machine learning models to construct classification models using EM metrics, demographic characteristics, and brief cognitive screening test scores. Model performance was evaluated based on the area under the receiver operating characteristic curve (AUROC). Results LR models revealed that several EM metrics are significantly associated with increased odds of MCI, with odds ratios ranging from 1.213 to 1.621. The AUROC scores for models utilizing demographic information and either EM metrics or MMSE were 0.752 and 0.767, respectively. Combining all features, including demographic, MMSE, and EM, notably resulted in the best-performing model, which achieved an AUROC of 0.840. Conclusion Changes in EM metrics linked with MCI are associated with attentional and executive function deficits. EM metrics combined with demographics and cognitive test scores enhance MCI prediction, making it a non-invasive, cost-effective method to identify early stages of cognitive decline.
Antimicrobial activity of different nanocellulose films embedded with thyme, cinnamon, and oregano essential oils for active packaging application on raspberries

The study focuses on the antimicrobial activity of nanocellulose films (NFC) embedded with thyme, cinnamon, and oregano essential oils for active packaging application. The activity against model pathogenic bacteria was first tested to obtain each oil's minimum inhibitory concentration (MIC). The tests showed that the surface area of the films and the quantity of essential oil strongly influenced the antimicrobial effect. Then, the different active packaging systems were tested directly on packed raspberries to study their actual commercial packaging conditions. Through 12 days of storage at 1°C, it was observed that thyme and oregano essential oils were more effective in maintaining the firmness and reducing the weight loss of the fruits compared to cinnamon essential oil or the control; no significant effect was observed on the other quality parameters that were considered. The essential oils independently proved effective in preventing fungal growth. However, the combined impact of thyme+oregano (NAP_TO) demonstrated the best performance.
Glutathione affinity chromatography for the scalable purification of an oncolytic virus immunotherapy from microcarrier cell culture

Therapeutic viral vectors are an emerging technology with several clinical applications in gene therapy, vaccines, and immunotherapy. Increased demand has required the redevelopment of conventional, low-throughput cell culture and purification manufacturing methods such as static cell stacks and ultracentrifugation. In this work, scalable methods were investigated for the manufacture of an oncolytic virus immunotherapy application consisting of a prototype strain of coxsackievirus A21 (CVA21) produced in adherent MRC-5 cells. Cell culture was established in stirred-tank microcarrier bioreactors, and an efficient affinity chromatography method was developed for the purification of harvested CVA21 through binding of the viral capsids to an immobilized glutathione (GSH) ligand. Bioreactor temperature during infection was investigated to maximize titer, and a decrease in temperature from 37°C to 34°C yielded a two–three-fold increase in infectivity. After purification of the 34°C harvests, the GSH affinity chromatography elution not only maintained a >two-fold increase in infectivity and viral genomes but also increased the proportion of empty capsids compared to 37°C harvests. Using material generated from both infection temperature setpoints, chromatographic parameters and mobile phase compositions were studied at the laboratory scale to maximize infectious particle yields and cell culture impurity clearance. Empty capsids that co-eluted with full capsids from 34°C infection temperature harvests were poorly resolved across the conditions tested, but subsequent polishing anion exchange and cation exchange chromatography steps were developed to clear residual empty capsids and other impurities. Oncolytic CVA21 production was scaled-up 75-fold from the laboratory scale and demonstrated across seven batches in 250 L single-use microcarrier bioreactors and purified with customized, prepacked, single-use 1.5 L GSH affinity chromatography columns. The large-scale bioreactors controlled at 34°C during infection maintained a three-fold increase in productivity in the GSH elution, and excellent clearance of host cell and media impurities was observed across all batches. This study presents a robust method for the manufacture of an oncolytic virus immunotherapy application that may be implemented for the scalable production of other viruses and viral vectors which interact with glutathione.
Algorithm-Based Fault Tolerance for Two-Sided Dense Matrix Factorizations

The mean time between failure (MTBF) of large supercomputers is decreasing, and future exascale computers are expected to have a MTBF of around 30 minutes. Therefore, it is urgent to prepare important algorithms for future machines with such a short MTBF. Eigenvalue problems (EVP) and singular value problems (SVP) are common in engineering and scientific research. Solving EVP and SVP numerically involves two-sided matrix factorizations: the Hessenberg reduction, the tridiagonal reduction, and the bidiagonal reduction. These three factorizations are computation intensive, and have long running times. They are prone to suffer from computer failures. We designed algorithm-based fault tolerant (ABFT) algorithms for the parallel Hessenberg reduction and the parallel tridiagonal reduction. The ABFT algorithms target fail-stop errors. These two fault tolerant algorithms use a combination of ABFT and diskless checkpointing. ABFT is used to protect frequently modified data . We carefully design the ABFT algorithm so the checksums are valid at the end of each iterative cycle. Diskless checkpointing is used for rarely modified data. These checkpoints are in the form of checksums, which are small in size, so the time and storage cost to store them in main memory is small. Also, there are intermediate results which need to be protected for a short time window. We store a copy of this data on the neighboring process in the process grid. We also designed algorithm-based fault tolerant algorithms for the CPU-GPU
The effects of exercise training on body composition in postmenopausal women: a systematic review and meta-analysis

Introduction We conducted a systematic review and meta-analysis to investigate the effect of exercise training on body composition outcomes in postmenopausal women. Methods PubMed, Web of Science, CINAHL, and Medline were searched to identify the randomized controlled trials which evaluated effect of exercise training versus control in postmenopausal women. Standardized mean differences (SMD), weighted mean differences (WMD) and 95% confidence intervals (95% CIs) were calculated using random effects model. Results One hundred and one studies involving 5,697 postmenopausal women were included in the meta-analysis. Results indicated that exercise training effectively increased muscle mass/ volume, muscle and fiber cross-sectional area and fat-free mass, and decreased fat mass, body fat percentage, waist circumference and visceral fat. Furthermore, subgroup analyses results revealed that aerobic and combined training had greater beneficial effects on fat mass outcomes, whereas resistance and combined training had greater beneficial effects on muscle mass outcomes. Discussion Overall, our results revealed that exercise training is effective for improving body composition in postmenopausal women. To be specific, aerobic training is effective on fat loss, whereas resistance training is effective on muscle gain. However, combination of aerobic and resistance trainings may be considered a viable strategy to improve body composition in postmenopausal women. Systematic review registration https://www.crd. york.ac. uk/prospero/, identifier CRD42021283425.
Association between war-related traumatic events and blood pressure trajectory: a population-based study among the mid-aged and older Palestinian adults living in Gaza

Background Little is known regarding health status in an environment characterized by instability and ongoing war risks. This study investigated hypertension disease burden and associations of war-related traumatic events with blood pressure (BP) trajectory over time amongst mid-aged and older Palestinian adults in Gaza Strip. Methods From nine primary healthcare centers, medical records between 2013 and 2019 were collected for 1,000 mid-aged and older Palestinian adults living in Gaza. Multinomial logistic regression analysis examined associations between war-related traumatic events and BP trajectories derived using latent class trajectory analysis (LCTA). Results The prevalence of self-reported injury (of participants or their family members), death of a family member, and violence due to house bombing was 51.4%, 54.1%, and 66.5%, respectively. In total, 22.4% and 21.4% of participants had constant-very-high (CVH) systolic BP (SBP) (>160 mmHg) and diastolic BP (DBP) (>95 mmHg), and normal-stable SBP and DBP was found only 54.9% and 52.6%, respectively. Injury (participants or family members), death of a family member, and violence due to house bombing during wars were associated with CVH SBP with odds ratios [95 CI, OR = 1.79 (1.28–2.48), 1.90 (1.36–2.65), and 1.44 (1.01–2.05)], respectively. The corresponding figures were [95 CI, OR = 1.92 (1.36–2.71), 1.90 (1.35–2.68), and 1.62 (1.13–2.38)] for CVH DBP. Living in debt was positively associated with CVH SBP, [95 CI, OR = 2.49 (1.73–3.60)] and CVH DBP, [95 CI, OR = 2.37 (1.63–3.45)]. Conclusion The disease burden related to war-related traumatic events is high and positively related to adverse BP trajectory among the mid-aged and older Palestinians living in Gaza. Intervention programs are needed to manage and prevent chronic diseases in this vulnerable population.