Miracle-dz/UniFAR-FTUs huggingface dataset:
Aerobic exercise improves sleep in U. S. active duty service members following brief treatment for posttraumatic stress disorder symptoms

Introduction Physical exercise is a lifestyle intervention that can positively impact aspects of physical and psychological health. There is a growing body of evidence suggesting that physical exercise, sleep, and PTSD are interrelated. This study investigated possible relationships. Three research questions were posed: (1) Did randomization to an aerobic exercise intervention reduce insomnia more than being randomized to an intervention without exercise, (2) Did change in sleep predict change in PTSD symptoms, and (3) Did change in sleep impact the relationship between exercise and PTSD symptom reductions? Methods Data were collected from 69 treatment-seeking active duty service members with PTSD symptoms randomized into one of four conditions; two conditions included aerobic exercise, and two conditions did not include exercise. Participants in the exercise groups exercised five times per week keeping their heart rate > 60% of their heart rate reserve for 20–25 min. Results At baseline, 58% of participants reported moderate or severe insomnia. PTSD symptom severity decreased following treatment for all groups (p < 0.001). Participants randomized to exercise reported greater reductions in insomnia compared to those in the no exercise group (p = 0.47). However, change in insomnia did not predict change in PTSD symptoms nor did it significantly impact the relationship between exercise and PTSD symptom reductions. Discussion Adding exercise to evidence-based treatments for PTSD could reduce sleep disturbance, a characteristic of PTSD not directly addressed with behavioral therapies. A better understanding of exercise as a lifestyle intervention that can reduce PTSD symptoms and insomnia is warranted.
Hyperinflammatory Immune Response in COVID-19: Host Genetic Factors in Pyrin Inflammasome and Immunity to Virus in a Spanish Population from Majorca Island

The hyperinflammatory response caused by SARS-CoV-2 infection contributes to its severity, and many critically ill patients show features of cytokine storm (CS) syndrome. We investigated, by next-generation sequencing, 24 causative genes of primary immunodeficiencies whose defect predisposes to CS. We studied two cohorts with extreme phenotypes of SARS-CoV-2 infection: critical/severe hyperinflammatory patients (H-P) and asymptomatic patients (AM-risk-P) with a high risk (older age) to severe COVID-19. To explore inborn errors of the immunity, we investigated the presence of pathogenic or rare variants, and to identify COVID-19 severity-associated markers, we compared the allele frequencies of common genetic polymorphisms between our two cohorts. We found: 1 H-P carries the likely pathogenic variant c.887-2 A>C in the IRF7 gene and 5 H-P carries variants in the MEFV gene, whose role in the pathogenicity of the familial Mediterranean fever (FMF) disease is controversial. The common polymorphism analysis showed three potential risk biomarkers for developing the hyperinflammatory response: the homozygous haplotype rs1231123A/A-rs1231122A/A in MEFV gene, the IFNAR2 p.Phe8Ser variant, and the CARMIL2 p.Val181Met variant. The combined analysis showed an increased risk of developing severe COVID-19 in patients that had at least one of our genetic risk markers (odds ratio (OR) = 6.2 (95% CI) (2.430–16.20)).
Black American Women in Olympic Track and Field: A Complete Illustrated Reference

This comprehensive reference work details the accomplishments of over 90 African-American women in Olympic track and field. They overcame racism, sexism and often, abject poverty to compete and frequently to set world records and win Olympic medals. Detailed information is provided on their Olympic participation and accomplishments. Also, through a series of first-person interviews, 20 of the most prominent Olympians (such as Wilma Rudolph and Evelyn Ashford) reveal their struggles both on and off the track.
Assessment of Pre-Trained Models Across Languages and Grammars

We present an approach for assessing how multilingual large language models (LLMs) learn syntax in terms of multi-formalism syntactic structures. We aim to recover constituent and dependency structures by casting parsing as sequence labeling. To do so, we select a few LLMs and study them on 13 diverse UD treebanks for dependency parsing and 10 treebanks for constituent parsing. Our results show that: (i) the framework is consistent across encodings, (ii) pre-trained word vectors do not favor constituency representations of syntax over dependencies, (iii) sub-word tokenization is needed to represent syntax, in contrast to character-based models, and (iv) occurrence of a language in the pretraining data is more important than the amount of task data when recovering syntax from the word vectors.
Radiographic appearance of ossifying fibroma in the left mandible: a case report

This article reports on ossifying fibroma (OF) which was established based on panoramic radiographic, CBCT and histopathological examination and treatment performed on a 31 years old male patient. The diagnosis is made by comparing with existing theories in the literature. Case Report: A 31 years old man was referred to the Oral Surgery Department of Al Ihsan Hospital. The patient complained of swelling in the lower left jaw. On palpation it feels hard and cannot be moved. Panoramic radiograph examination showed loss of teeth 34-35 and a radiolucent lesion mixed with radiopaque in the left mandible which resulted in a shift in the position of teeth 36 and 37 more superiorly. The CBCT examination performed revealed a mixed radiolucent and radiopaque lesion of teeth 33-38. Histopathological examination also showed the presence of cellular fibrous with a mineralized component. The patient has been treated in the form of excision of the lesion. Conclusion: CBCT can be used as a reliable supporting examination in helping to diagnose cases of benign neoplasms involving hard bone tissue such as ossifying fibroma. OF has distinctive features on radiographs, one of which is the presence of mixed radiolucent and radiopaque lesions with wispy septa which result in resorption and displacement of the teeth involved. The accuracy of the diagnosis of OF can be enforced by a combination of clinical, radiographic and HPA examinations, so that the treatment given to patients is according to the procedure.
Comparative Stability Study of Polysorbate 20 and Polysorbate 80 Related to Oxidative Degradation

The surfactants polysorbate 20 (PS20) and polysorbate 80 (PS80) are utilized to stabilize protein drugs. However, concerns have been raised regarding the degradation of PSs in biologics and the potential impact on product quality. Oxidation has been identified as a prevalent degradation mechanism under pharmaceutically relevant conditions. So far, a systematic stability comparison of both PSs under pharmaceutically relevant conditions has not been conducted and little is known about the dependence of oxidation on PS concentration. Here, we conducted a comparative stability study to investigate (i) the different oxidative degradation propensities between PS20 and PS80 and (ii) the impact of PS concentration on oxidative degradation. PS20 and PS80 in concentrations ranging from 0.1 mg⋅mL−1 to raw material were stored at 5, 25, and 40 °C for 48 weeks in acetate buffer pH 5.5 and water, respectively. We observed a temperature-dependent oxidative degradation of the PSs with strong (40 °C), moderate (25 °C), and weak/no degradation (5 °C). Especially at elevated temperatures such as 40 °C, fast oxidative PS degradation processes were detected. In this case study, a stronger degradation and earlier onset of oxidation was observed for PS80 in comparison to PS20, detected via the fluorescence micelle assay. Additionally, degradation was found to be strongly dependent on PS concentration, with significantly less oxidative processes at higher PS concentrations. Iron impurities, oxygen in the vial headspaces, and the pH values of the formulations were identified as the main contributing factors to accelerate PS oxidation.
Neuroprotective Effects of Human-Induced Pluripotent Stem Cell-Derived Mesenchymal Stem Cell Extracellular Vesicles in Ischemic Stroke Models

Background: Stroke represents the second leading cause of death and the primary cause of long-term disability in humans. The transplantation of mesenchymal stem cells (MSC) reportedly improves functional outcomes in animal models of cerebral ischemia. Here, we evaluate the neuroprotective potential of extracellular vesicles secreted from human-induced pluripotent stem cell-derived mesenchymal stem cells (hiPS-MSC-EV) using preclinical cell-based and animal-based models of ischemic strokes. Methods: hiPS-MSC-EV were isolated using an ultrafiltration method. HT22 cells were subjected to oxygen-glucose deprivation/reoxygenation (OGD/R) injury for 2 h, followed by treatment with hiPS-MSC-EV (100 μg/mL). Male C57BL/6 mice were subjected to middle cerebral artery occlusion (MCAO) followed by an intravenous injection of hiPS-MSC-EV (100 μg) at three distinct time points. Results: Our experimental approach revealed hiPS-MSC-EV promoted HT22 cell proliferation, reduced apoptosis, and altered cellular morphology following OGD/R. In addition, hiPS-MSC-EV reduced the volume of infarcts, improved spontaneous movement abilities, and enhanced angiogenesis by expressing the VEGF and CXCR4 proteins in the infarcted hemisphere of the MCAO-treated mouse model. Conclusion: Our findings provide evidence of the potential neuroprotective effects of hiPS-MSC-derived extracellular vesicles (hiPS-MSC-EVs) in both in vitro and in vivo mouse models of ischemic stroke. These results suggest that hiPS-MSC-EVs may play a role in neurorestoration and offer insights into potential cell-free strategies for addressing cerebral ischemia.
BOOSTING COST-COMPLEXITY PRUNED TREES ON TWEEDIE RESPONSES: THE ABT MACHINE

This paper proposes a new boosting machine based on forward stagewise additive modeling with cost-complexity pruned trees. In the Tweedie case, it deals directly with observed responses, not gradients of the loss function. Trees included in the score progressively reduce to the root-node one, in an adaptive way. The proposed Adaptive Boosting Tree (ABT) machine thus automatically stops at that time, avoiding to resort to the time-consuming cross validation approach. A case study performed on motor third-party liability insurance claim data demonstrates the performances of the proposed ABT machine for ratemaking, in comparison with regular gradient boosting trees.
One, Two, Three ... An Explosive Outflow in IRAS 12326-6245 Revealed by ALMA

In the last years there has been a substantial increase in the number of the reported massive and luminous star-forming regions with related explosive outflows thanks to the superb sensitivity and angular resolution provided by the new radio, infrared, and optical facilities. Here, we report one more explosive outflow related with the massive and bright star-forming region IRAS 12326−6245 using Band 6 sensitive and high-angular-resolution (∼0.″2) Atacama Large Millimeter/Submillimeter Array observations. We find over 10 molecular and collimated well-defined streamers, with Hubble–Lemaitre–like expansion motions, and pointing right to the center of a dusty and molecular shell (reported for the first time here) localized in the northern part of the UC H ii region known as G301.1A. The estimated kinematic age and energy for the explosion are ∼700 yr and 1048 erg, respectively. Taking into account the recently reported explosive outflows together with IRAS 12326−6245, we estimate an event rate of once every 90 yr in our Galaxy, similar to the formation rate of massive stars.
GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation

Echocardiogram video segmentation plays an important role in cardiac disease diagnosis. This paper studies the unsupervised domain adaption (UDA) for echocardiogram video segmentation, where the goal is to generalize the model trained on the source domain to other unlabelled target domains. Existing UDA segmentation methods are not suitable for this task because they do not model local information and the cyclical consistency of heartbeat. In this paper, we introduce a newly collected CardiacUDA dataset and a novel GraphEcho method for cardiac structure segmentation. Our GraphEcho comprises two innovative modules, the Spatial-wise Cross-domain Graph Matching (SCGM) and the Temporal Cycle Consistency (TCC) module, which utilize prior knowledge of echocardiogram videos, i.e., consistent cardiac structure across patients and centers and the heartbeat cyclical consistency, respectively. These two modules can better align global and local features from source and target domains, leading to improved UDA segmentation results. Experimental results showed that our GraphEcho outperforms existing state-of-the-art UDA segmentation methods. Our collected dataset and code will be publicly released upon acceptance. This work will lay a new and solid cornerstone for cardiac structure segmentation from echocardiogram videos. Code and dataset are available at : https://github.com/xmedlab/GraphEcho
Regularized determinants of the Rumin complex in irreducible unitary representations of the (2,3,5) nilpotent Lie group

We study the Rumin differentials of the 5-dimensional graded nilpotent Lie group that appears as osculating group of generic rank two distributions in dimension five. In irreducible unitary representations of this group, the Rumin differentials provide intriguing generalizations of the classical quantum harmonic oscillator. For the Schrodinger representations, we compute the spectrum and the zeta regularized determinant of each Rumin differential. In the generic representations, we evaluate their alternating product, i.e., the analytic torsion of the Rumin complex.
A Competition-based Pricing Strategy in Cloud Markets using Regret Minimization Techniques

Cloud computing as a fairly new commercial paradigm, widely investigated by different researchers, already has a great range of challenges. Pricing is a major problem in Cloud computing marketplace; as providers are competing to attract more customers without knowing the pricing policies of each other. To overcome this lack of knowledge, we model their competition by an incomplete-information game. Considering the issue, this work proposes a pricing policy related to the regret minimization algorithm and applies it to the considered incomplete-information game. Based on the competition based marketplace of the Cloud, providers update the distribution of their strategies using the experienced regret. The idea of iteratively applying the algorithm for updating probabilities of strategies causes the regret get minimized faster. The experimental results show much more increase in profits of the providers in comparison with other pricing policies. Besides, the efficiency of a variety of regret minimization techniques in a simulated marketplace of Cloud are discussed which have not been observed in the studied literature. Moreover, return on investment of providers in considered organizations is studied and promising results appeared.
OpenChat: Advancing Open-source Language Models with Mixed-Quality Data

Nowadays, open-source large language models like LLaMA have emerged. Recent developments have incorporated supervised fine-tuning (SFT) and reinforcement learning fine-tuning (RLFT) to align these models with human goals. However, SFT methods treat all training data with mixed quality equally, while RLFT methods require high-quality pairwise or ranking-based preference data. In this study, we present a novel framework, named OpenChat, to advance open-source language models with mixed-quality data. Specifically, we consider the general SFT training data, consisting of a small amount of expert data mixed with a large proportion of sub-optimal data, without any preference labels. We propose the C(onditioned)-RLFT, which regards different data sources as coarse-grained reward labels and learns a class-conditioned policy to leverage complementary data quality information. Interestingly, the optimal policy in C-RLFT can be easily solved through single-stage, RL-free supervised learning, which is lightweight and avoids costly human preference labeling. Through extensive experiments on three standard benchmarks, our openchat-13b fine-tuned with C-RLFT achieves the highest average performance among all 13b open-source language models. Moreover, we use AGIEval to validate the model generalization performance, in which only openchat-13b surpasses the base model. Finally, we conduct a series of analyses to shed light on the effectiveness and robustness of OpenChat. Our code, data, and models are publicly available at https://github.com/imoneoi/openchat and https://huggingface.co/openchat.
Deep generative model conditioned by phase picks for synthesizing labeled seismic waveforms with limited data

Shortage of labeled seismic field data poses a significant challenge for deep-learning related applications in seismology. One approach to mitigate this issue is to use synthetic waveforms as a complement to field data. However, traditional physics-driven methods for synthesizing data are computationally expensive and often fail to capture complex features in real seismic waveforms. In this study, we develop a deep-learning-based generative model, PhaseGen, for synthesizing realistic seismic waveforms dictated by provided P- and S-wave arrival labels. Contrary to previous generative models which require a large amount of data for training, the proposed model can be trained with only 100 seismic events recorded by a single seismic station. The fidelity, diversity and alignment for waveforms synthesized by PhaseGen with diverse P- and S-wave arrival labels are quantitatively evaluated. Also, PhaseGen is used to augment a labelled seismic dataset used for training a deep neural network for the phase picking task, and it is found that the picking capability trained with the augmented dataset is unambiguously improved. It is expected that PhaseGen can offer a valuable alternative for synthesizing realistic waveforms and provide a promising solution for the lack of labeled seismic data.
Universal direction in thermoosmosis of a near-critical binary fluid mixture

We consider thermoosmosis of a near-critical binary fluid mixture, lying in the one-phase region, through a capillary tube in the presence of preferential adsorption of one component. The critical composition is assumed in the two reservoirs linked by the tube. With coarse-grained approach, we evaluate the flow field induced by the thermal force density. We predict a universal property; if the mixture is near the upper (lower) consolute point, the flow direction is the same as (opposite to) the direction of the temperature gradient, irrespective of which component is adsorbed onto the wall.
PSDiff: Diffusion Model for Person Search with Iterative and Collaborative Refinement

Dominant Person Search methods aim to localize and recognize query persons in a unified network, which jointly optimizes two sub-tasks, \ie, pedestrian detection and Re-IDentification (ReID). Despite significant progress, current methods face two primary challenges: 1) the pedestrian candidates learned within detectors are suboptimal for the ReID task. 2) the potential for collaboration between two sub-tasks is overlooked. To address these issues, we present a novel Person Search framework based on the Diffusion model, PSDiff. PSDiff formulates the person search as a dual denoising process from noisy boxes and ReID embeddings to ground truths. Distinct from the conventional Detection-to-ReID approach, our denoising paradigm discards prior pedestrian candidates generated by detectors, thereby avoiding the local optimum problem of the ReID task. Following the new paradigm, we further design a new Collaborative Denoising Layer (CDL) to optimize detection and ReID sub-tasks in an iterative and collaborative way, which makes two sub-tasks mutually beneficial. Extensive experiments on the standard benchmarks show that PSDiff achieves state-of-the-art performance with fewer parameters and elastic computing overhead.
Dynamic Interdependence Between Volatility of Shariah Stock and Bond

The purpose of this research is to investigate the correlation between bond yields and the Shariah equity index from 2007 to 2019. The Multivariate-GARCH Dynamic Conditional Correlation (DCC) model is applied to the daily data indices of five bond markets, namely conventional bond, corporate bond, corporate sukuk, government bond, and government sukuk, as well as the daily index of the Islamic equity market, which is represented by FTSE Bursa Malaysia EMAS Shariah. The empirical evidence reveals a substantial correlation between these sharia stock and sukuk indexes, demonstrating that investors' risk tolerance fluctuates over time. Co-movement power fluctuates throughout time, and the government bond is dominant.
A Tutorial on the Structure of Distributed Optimization Algorithms

We consider the distributed optimization problem for a multi-agent system. Here, multiple agents cooperatively optimize an objective by sharing information through a communication network and performing computations. In this tutorial, we provide an overview of the problem, describe the structure of its algorithms, and use simulations to illustrate some algorithmic properties based on this structure.
Rebellions and Impeachments in a Neural Network Society

Basede on a study of the modern presidencial democracies in South America, we present a statistical mechanics exploration of the collective, coordinated action of political actors in the legislative chamber that may result on the impeachment of the executive. By representing the legislative political actors with neurla networks, we observed that the larger the effective number of presidential-agenda items are treated, the smaller the chances for a cross-party dialogue, which, if combined with a decrement in the president's public approval rating, could trigger an impeachment process.
Early Alzheimer’s Disease Screening Approach Using Plasma Biomarkers

Alzheimer’s disease (AD) is the most prevalent dementia, but it shows similar initial symptoms to other neurocognitive diseases (Lewy body disease (LBD) and frontotemporal dementia (FTD)). Thus, the identification of reliable AD plasma biomarkers is required. The aim of this work is to evaluate the use of a few plasma biomarkers to develop an early and specific AD screening method. Plasma p-Tau181, neurofilament light (NfL), and glial fibrillary acid protein (GFAP) were determined by Single Molecule Assay (SIMOA® Quanterix, Billerica, MA, USA) in patients with mild cognitive impairment due to AD (MCI-AD, n = 50), AD dementia (n = 10), FTD (n = 20), LBD (n = 5), and subjective cognitive impairment (SCI (n = 21)). Plasma p-Tau181 and GFAP showed the highest levels in AD dementia, and significant correlations with clinical AD characteristics; meanwhile, NfL showed the highest levels in FTD, but no significant correlations with AD. The partial least squares (PLS) diagnosis model developed between the AD and SCI groups showed good accuracy with a receiver operating characteristic (ROC) area under curve (AUC) of 0.935 (CI 95% 0.87–0.98), sensitivity of 86%, and specificity of 88%. In a first screen, NfL plasma levels could identify FTD patients among subjects with cognitive impairment. Then, the developed PLS model including p-Tau181 and GFAP levels could identify AD patients, constituting a simple, early, and specific diagnosis approach.