Miracle-dz/UniFAR-FTUs huggingface dataset:
BKV Related Hemorrhagic Cystitis—An Insight into Risk Factors and Later Complications—An Analysis on Behalf of Polish Adult Leukemia Group

Simple Summary Despite noticeable progress in allogeneic hematopoietic stem cell transplantation, potential viral reactivations are still one of the most challenging complications. The aim of our study was to identify predictive and risk factors associated with the occurrence of the BK virus related hemorrhagic cystitis following hematopoietic stem cell transplantation. Furthermore, we investigated the impact of various factors on the clinical course of patients after hematopoietic stem cell transplantation. We confirmed that >0.75 × 103 BK virus copies/mL in serum at day +21 after allogeneic hematopoietic stem cell transplantation has a strong predictive ability for hemorrhagic cystitis. Thus, we believe that our findings could be helpful in establishing the predictive validity of the BK viral load measurement in polyomavirus BK-associated hemorrhagic cystitis and survival. Abstract BK virus reactivation increases the likelihood of hemorrhagic cystitis (HC) after allogeneic hematopoietic stem cell transplant (HCT). In this study, we aimed to identify predictive and risk factors associated with the increased occurrence of this condition following HCT. On a group of 124 patients aged ≤71 years old (median 40 years) who underwent HCT, we analyzed sex, age, time from diagnosis to transplantation, type of conditioning, donor’s relationship, age, and sex, the impact of immunosuppression with different drugs, and acute and chronic GVHD, BK viremia and viruria as potential factors increasing the risk of BK-related HC after HCT. HC occurred among 24 patients (24/124; 29.2%). A significant correlation was observed between HC incidences after HCT, BK viremia and viruria, and acute GVHD occurrence. Furthermore, the level of BKV DNA in serum at day +21 (>0.75 × 103) significantly impacted the patients’ survival time. According to our results, the likelihood ratio of BKV-DNA on day +21 in serum is 6.25, indicating that this diagnostic test has the potential to be utilized in a clinical setting. These findings may be used as a voice in the discussion on implementing an optimal preemptive treatment in BKV reactivation after allogeneic HCT.
A Video-Based Reflective Design to Prepare First Year Pharmacy Students for Their First Objective Structured Clinical Examination (OSCE)

We explored the use of a video-based reflective design in preparing first-year pharmacy students for their Objective Structured Clinical Examination (OSCE) in Victoria, Australia. This involved pre-workshop activities (a recording of themselves simulating the pharmacist responding to a simple primary care problem, written reflection, review of the OSCE video examples and pre-workshop survey); workshop activities (peer feedback on videos) and post-workshop activities (summative MCQ quiz and post-workshop survey). These activities took place three weeks before their OSCE. A mixed-method study design was employed with quantitative and qualitative analyses of the surveys and a focus group. A total of 137 students (77.4%) completed the pre- and post-workshop surveys, and ten students participated in the focus group. More student participants (54%) reported feeling prepared for the OSCE post-workshop than pre-workshop (13%). The majority (92%) agreed that filming, watching and reflecting on their video allowed them to learn and improve on their skills for the OSCE. The regression analysis found that video recording submissions and written reflections correlated positively with student OSCE performances, and the video-based reflective design learning experience was perceived to be beneficial in multiple ways. Thematic analysis of the focus group data revealed that students acquired metacognitive skills through the self-assessment of their video recordings, developed an awareness of their learning and were able to identify learning strategies to prepare for their first OSCE. Fostering students’ feedback literacy could be considered in future educational designs.
Natural Language Processing Of Helpline Chat Data Before And During The Pandemic Revealed Significant Decrease In Self-image Appreciation And Changes In Other Traits

During the last two years the COVID-19 pandemic has affected the world population in several ways. An important increase in mental health problems is a consequence of this pandemic that is ubiquitous worldwide. In this work we study the effect of the pandemic on the mental health of a population of teenagers and youth based on the analysis of natural language processing, machine learning algorithms and expert knowledge. The data analysed was obtained from a chat helpline called Safe time from theIt Get’s Better Foundation in Chile. The data consists of 10,986 conversations gathered from 2018 until 2020 between volunteers from the foundation and users of the platform. We compared the conversationsbefore and during the pandemic in terms of their thematic content. Our analysis found: a significantdecrease in self-image appreciation during the pandemic; a significant decrease in the quality of personalrelationships during the pandemic, and a significant increase of performance appreciation.
The Relationship Between Institutional and Organizational Party Power: Evidence from the Minnesota House’s Experimental Setting

How parties manage capitols and constituencies in the American context is the subject of much political inquiry. This research examines whether majority parties’ institutional capability depends on their ability to organize beyond the legislative chamber. An opportunity to examine such a question presents itself in a curious historical occurrence, the Minnesota Legislature’s abrupt de jure ban on political parties. Using data compiled from the Minnesota House of Representatives, I compare partisan characteristics in roll call voting in the context of an experimental setting. The results suggest legislative leadership powers lose efficacy when party’s organizational capacity beyond the chamber diminishes.
Transportation analysis exploring alternative shipping of Marine Expeditionary Brigade forces to seabase in contingency response scenarios

Abstract : As the U.S. national security policy shifts focus toward the Pacific theater and limited availability of amphibious shipping, Marine Forces Pacific must consider the augmentation of alternative shipping to deploy forces to a seabase location to support military operations in the Pacific Command area of operations. Implementing a model-based systems engineering approach, this capstone project examines the effects of augmenting amphibious shipping with commercial, allied nation, and military sealift command ships to achieve force closure at a seabase and reduce fuel consumption. Multiple shipping alternatives supporting a Marine Expeditionary Brigade in anti-access/area denial (A2/AD) and humanitarian assistance/disaster relief (HA/DR) missions formed the basis for measuring the effects of augmenting amphibious shipping. A simulation was developed to model the operational scenarios, and statistical analysis was performed upon the results of each alternative to identify factors affecting force closure time and fuel consumption. Analysis indicated that the effects of augmenting amphibious shipping vary based upon the mission type. Significant statistical evidence suggests that augmentation of amphibious shipping reduces force closure time and fuel consumption for the A2/AD mission. Based on the research, further investigation into the effects of augmented shipping on the Assembly and Employ phases of seabasing operations is recommended.
Immunometabolism in Autoimmune Diseases Special Issue

The activation of immune cells and their differentiation in specialized effector subsets rely on specific cellular metabolic programs. It is therefore not surprising that a number of immunometabolic alterations have been reported in patients with autoimmune diseases, as well as in mouse models of these diseases. The metabolic programs that sustain the activation of healthy lymphocytes are reviewed by Bystrom et al. [1], which are contrasted with the metabolic programs that have been found in CD4+ T cells and B cells in autoimmune diseases, with a focus on rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). These two rheumatic diseases share CD4+ T cells that are fueled by an over-activated mTOR signaling, but that differ by many other metabolic pathways, even when considering the same type of cells, such as Th17 cells. The specific metabolic phenotypes of CD4+ T cells were reviewed in detail comparatively between RA and in SLE by Wu et al. [2]. RA T cells shunt glucose away from glycolysis and energy building toward the pentose phosphate pathway (PPP) and cell-building biosynthesis, which leads to a hyper-reduced state. This defect, rooted in the miss-location of AM PK that limits is activation, increases T cell mobility and their ability to invade the inflamed joints. On the opposite, SLE T cells present an increased glycolysis and mitochondrial metabolism leading to oxidative stress. The molecular pathways that support metabolic defects in SLE T cells is further reviewed by Vukelic et al. [3]. In addition to increased glycolysis and mitochondrial oxidation, there is also evidence from mouse models that SLE T cells present an enhanced glutamine metabolism involved in the expansion of Th17 cells as well as follicular helper T (Tfh) cells, both of which being pathogenic in SLE. Finally, altered lipid metabolism related to lipid rafts and plasma membrane sphingolipids has been linked to the activation of SLE T cells. Mangal et al. provide an extensive review of the metabolic programs that support a broad array of immune cells, some of them with known alterations in RA, and others with potential defects that have not been investigated and may contribute to RA [4]. Building a detailed understanding of metabolic defects in autoimmune diseases, more specifically in RA and SLE T cells has leveraged the therapeutic potentials of metabolic targeting in these diseases. These translational potentials are reviewed by Piranavan et al. [5]. A number of experimental drugs targeting key metabolic enzymes or processes have shown efficacy in mouse models. The inhibition of mTOR with rapamycin Open Access
A NEW APPROACH TO ESTIMATE FREEWAY OD TRAVEL TIME BASED ON DYNAMIC TRAFFIC STATES ESTIMATION USING FEEDBACK ESTIMATOR

高速道路上の起終点 (OD) 旅行時間を推定する新手法を提案した. 各道路区間の交通状態を感知器からのフィードバック作用により推定し, 区間旅行時間の時空間的な合計によってOD旅行時間を推定した. 筆者らは現在までニューロカルマンフィルタ (NKF) を用い, 交通状態を推定せずにOD旅行時間を直接推定する手法を提案してきた. 今回既存のNKFによる手法と新提案手法を用いてOD旅行時間を推定した結果, 新提案手法が精度の高い推定を行うことを確認した. さらに提案手法を実際の高速道路単路部へ適用した結果, 密に感知器を設置することにより混雑状態を含めた広範な交通状態の推定が可能となる見通しを得た. これらの数値計算結果から, OD旅行時間推定に対する提案手法の実用化へ向けての課題を整理した.
The Motivations and Practices of Impact Assessment in Socially Responsible Investing: The French Case and its Implications for the Accounting and Impact Investing Communities

ABSTRACT This research note elaborates on the impact assessment practices of the French Socially Responsible Investing (SRI) industry. The research was conducted by the Scientific Committee of the French public SRI label based on interviews, participative observation, a survey, and documentary evidence. SRI is usually distinguished from impact investing in terms of investors’ different intentions (contributing to sustainable development in a financially savvy way for SRI vs. demonstrating a societal impact for impact investing). We show that, beyond this distinction, the meanings and motivations behind impact assessment in the SRI community are broadly different from impact assessment practices in impact investing, creating a distance between the two communities. In fact, little is known about impact assessment practices in SRI, despite the market power of this asset class. We address this shortcoming by investigating 1) who is interested in impact assessment in the SRI industry, 2) why SRI investors want impact assessment, and 3) what impact assessment looks like in the SRI industry. We develop this analysis to suggest areas of concern and opportunities for the SRI, impact investing, and accounting communities. SRI investors’ recent appropriation of impact assessment indicates that the three communities’ interests and success will increasingly be linked to one another. The topic therefore warrants investigation.
Predicted Releases of Engineered Nanomaterials: From Global to Regional to Local

A key question for industry, regulators, toxicologists, and risk assessors working with nanomaterials is what relevant environmental engineered nanomaterial (ENM) concentrations should be considered. Answering this question requires ENM material flow estimates at the local level. Using a life-cycle approach, global ENM production and application data were used to estimate releases at global, regional, national, and local levels. Local level emissions were then used to estimate releases to water (direct and from wastewater treatment effluent), soils (direct and from runoff and biosolids), and air (direct and from incineration of ENM-containing products). Waste management data for dozens of countries were used to estimate the flow of 10 major ENMs through eight world regions. A national and local release example was conducted with data from the United States, providing predicted wastewater effluent concentrations for the San Francisco Bay area, ranging from low nanograms per liter to micrograms per liter de.
Chamada Remota de Métodos na Plataforma J2ME/CLDC

This paper presents the RME system, a middleware plataform that aims to provide application developers with a remote method invocation service. The platform has been developed to the J2ME version of the Java language, which is directed to small devices like cell phones and palmtops. More specifically, it targets a configura tion of J2ME called CLDC. Its implementation uses several design patterns which make it easy to reconfigure the system in order to address differen t requirements imposed by the execution environment. Because of this approch, RME can be used in limited resource devices as well as in more powerful computers.
Guidance and Control system design for Lunar Descent and Landing

This study aims at the preliminary design of a propulsion system concept able to achieve soft precision landing at the Lunar South Pole with an optimal payload mass. The design is constrained by the use of two types of engine sets: main engines with fixed thrust and pulsed assist engines. The requirements are derived from the NEXT Lunar Lander feasibility study performed by the European Space Agency. A guidance strategy has been derived and implemented in software to simulate a complete Descent and Landing profile from circular orbit to touchdown. The trajectory has been optimized taking into account fuel performance and visibility conditions of the landing site. Retargeting opportunities have been determined so that the lander can divert to a hazard-free area once the sensors are close enough to analyse the boulders, shadows and slopes found at the landing site. The effects on the required thrust profile of these divert manoeuvres, as well as the effects of GNC dispersions at key-points, have been assessed and worst case thrust profile has been derived. Finally, the optimal sets of engines have been designed for the 1500 kg on-orbit mass case, achieving a landing at the selected site with the best performance in terms of landed mass, based on a trade-off between higher Isp, lower engine mass and thrust modulation capabilities.
On the Immunometabolic Role of NF-κB in Adipocytes

Two decades of research have established that Nuclear Factor-κB (NF-κB) signaling plays a critical role in reprogramming the fat cell transcriptome towards inflammation in response to overnutrition and metabolic stress. Several groups have suggested that inhibition of NF-κB signaling could have metabolic benefits for obesity-associated adipose tissue inflammation. However, two significant problems arise with this approach. The first is how to deliver general NF-κB inhibitors into adipocytes without allowing these compounds to disrupt normal functioning in cells of the immune system. The second issue is that general inhibition of canonical NF-κB signaling in adipocytes will likely lead to a massive increase in adipocyte apoptosis under conditions of metabolic stress, leading full circle into a secondary inflammation (However, this problem may not be true for non-canonical NF-κB signaling.). This review will focus on the research that has examined canonical and non-canonical NF-κB signaling in adipocytes, focusing on genetic studies that examine loss-of-function of NF-κB specifically in fat cells. Although the development of general inhibitors of canonical NF-κB signaling seems unlikely to succeed in alleviating adipose tissue inflammation in humans, the door remains open for more targeted therapeutics. In principle, these would include compounds that interrogate NF-κB DNA binding, protein-protein interactions, or post-translational modifications that partition NF-κB activity towards some genes and away from others in adipocytes. I also discuss the possibility for inhibitors of non-canonical NF-κB signaling to realize success in mitigating fat cell dysfunction in obesity. To plant the seeds for such approaches, much biochemical “digging” in adipocytes remains; this includes identifying—in an unbiased manner–NF-κB direct and indirect targets, genomic DNA binding sites for all five NF-κB subunits, NF-κB protein-protein interactions, and post-translational modifications of NF-κB in fat cells.
Efficacy of Acupuncture Treatment for Incidence of Poststroke Comorbidities: A Systematic Review and Meta-Analysis of Nationalized Cohort Studies

Acupuncture has been applied as a complementary therapy in stroke survivors worldwide and approved to be beneficial to stroke recovery. However, there is little medical evidence regarding the association between acupuncture and the risk of poststroke comorbidities. We reviewed big data studies from the Taiwan National Health Insurance Research Database to investigate the risk of poststroke comorbidities after acupuncture treatment in a real-world situation. Ten English (PubMed, Embase, Medline, Cochrane, Alt HealthWatch, CINAHL, Health Source, PsycINFO, PsycARTICLES, and Psychology and Behavioral Sciences Collection) and two Chinese (AiritiLibray and Visualizing Health Data) electronic databases were searched from inception until December 2020 for nationalized cohort studies comparing the effects of acupuncture treatment with a nonacupuncture control group among stroke patients. Eight nationalized cohort studies were included. Six of eight studies showed a moderate overall risk of bias, while two studies showed a serious overall risk of bias. Included studies have investigated the effect of acupuncture in reducing the risk of seven medical conditions after stroke, including stroke recurrence, new-onset acute myocardial infarction (AMI), pneumonia, dementia, epilepsy, urinary tract infection (UTI), and depression. The meta-analysis showed clinically significant reductions in the risk of poststroke comorbidities in the acupuncture group compared to the nonacupuncture group (HR, 0.776; 95% CI, 0.719–0.838; p < 0.0001). In this systematic review and meta-analysis of nationalized cohort studies, acupuncture showed clinically relevant benefits in reducing the incidence of poststroke comorbidities, such as stroke recurrence, new-onset acute myocardial infarction (AMI), pneumonia, dementia, epilepsy, and UTI.
Knowledge-Driven Genome-Wide Analysis of Multigenic Interactions Impacting HDL Cholesterol Level

Genome-wide association studies (GWAS) are routinely being used to examine the genetic contribution to complex human traits, such as high-density lipoprotein cholesterol (HDL-C). Although HDL-C levels are highly heritable (h2∼0.7), the genetic determinants identified through GWAS contribute to a small fraction of the variance in this trait. Reasons for this discrepancy may include rare variants, structural variants, gene-environment (GxE) interactions, and gene-gene (GxG) interactions. Clinical practice-based biobanks now allow investigators to address these challenges by conducting GWAS in the context of comprehensive electronic medical records (EMRs). Here we apply an EMR-based phenotyping approach, within the context of routine care, to replicate several known associations between HDL-C and previously characterized genetic variants: CETP (rs3764261, p = 1.22e-25), LIPC (rs11855284, p = 3.92e-14), LPL (rs12678919, p = 1.99e-7), and the APOA1/C3/A4/A5 locus (rs964184, p = 1.06e-5), all adjusted for age, gender, body mass index (BMI), and smoking status. By using a novel approach which censors data based on relevant co-morbidities and lipid modifying medications to construct a more rigorous HDL-C phenotype, we identified an association between HDL-C and TRIB1, a gene which previously resisted identification in studies with larger sample sizes. Through the application of additional analytical strategies incorporating biological knowledge, we further identified 11 significant GxG interaction models in our discovery cohort, 8 of which show evidence of replication in a second biobank cohort. The strongest predictive model included a pairwise interaction between LPL (which modulates the incorporation of triglyceride into HDL) and ABCA1 (which modulates the incorporation of free cholesterol into HDL). These results demonstrate that gene-gene interactions modulate complex human traits, including HDL cholesterol.
Improving Arabic Cognitive Distortion Classification in Twitter using BERTopic

Social media platforms allow users to share thoughts, experiences, and beliefs. These platforms represent a rich resource for natural language processing techniques to make inferences in the context of cognitive psychology. Some inaccurate and biased thinking patterns are defined as cognitive distortions. Detecting these distortions helps users restructure how to perceive thoughts in a healthier way. This paper proposed a machine learning-based approach to improve cognitive distortions’ classification of the Arabic content over Twitter. One of the challenges that face this task is the text shortness, which results in a sparsity of co-occurrence patterns and a lack of context information (semantic features). The proposed approach enriches text representation by defining the latent topics within tweets. Although classification is a supervised learning concept, the enrichment step uses unsupervised learning. The proposed algorithm utilizes a transformer-based topic modeling (BERTopic). It employs two types of document representations and performs averaging and concatenation to produce contextual topic embeddings. A comparative analysis of F1-score, precision, recall, and accuracy is presented. The experimental results demonstrate that our enriched representation outperformed the baseline models by different rates. These encouraging results suggest that using latent topic distribution, obtained from the BERTopic technique, can improve the classifier’s ability to distinguish between different CD categories. Keywords—Arabic tweets; cognitive distortions’ classification; machine learning; social media; supervised learning; unsupervised learning; transformers; BERTopic; topic modeling