Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of methylene blue on experimental postoperative adhesion: A systematic review and meta-analysis

Adhesion is a primary challenge following surgery, and the anti-adhesive effect of methylene blue (MB) has been investigated. This systematic review and meta-analysis aimed to evaluate the effect of MB on postoperative adhesions in experimental studies. We initially searched OVID-MEDLINE, EMBASE, and Google Scholar in February 2021, and then in May 2021. The anti-adhesive efficacy of MB was compared with that of the control (either placebo or nothing) after the surgical procedure. The primary and secondary outcomes were the macroscopic and microscopic adhesion scores, respectively. Traditional meta-analysis, meta-regression, and trial sequential analysis (TSA) were performed to analyze the retrieved outcomes. We included 13 experimental studies of 367 rats (200 rats received MB and 167 rats received placebo or nothing). The macroscopic adhesion scores were significantly lower in the MB-administered group than in the control group (standardized mean difference, 2.313; 95% confidence interval, 1.104 to3.523; I2 = 94.0%, Tau = 2.059). Meta-regression analysis showed that macroscopic adhesion tended to decrease with an increase in MB dose. TSA demonstrated that the cumulative Z curve crossed both the conventional test and trial sequential monitoring boundary for the macroscopic adhesion score. MB had a beneficial effect on intraperitoneal adhesion following laparotomy, and adhesions decreased with increase in dose.
Interplay between SERCA, 4E-BP, and eIF4E in the Drosophila heart

Appropriate cardiac performance depends on a tightly controlled handling of Ca2+ in a broad range of species, from invertebrates to mammals. The role of the Ca2+ ATPase, SERCA, in Ca2+ handling is pivotal, and its activity is regulated, inter alia, by interacting with distinct proteins. Herein, we give evidence that 4E binding protein (4E-BP) is a novel regulator of SERCA activity in Drosophila melanogaster during cardiac function. Flies over-expressing 4E-BP showed improved cardiac performance in young individuals associated with incremented SERCA activity. Moreover, we demonstrate that SERCA interacts with translation initiation factors eIF4E-1, eIF4E-2 and eIF4E-4 in a yeast two-hybrid assay. The specific identification of eIF4E-4 in cardiac tissue leads us to propose that the interaction of elF4E-4 with SERCA may be the basis of the cardiac effects observed in 4E-BP over-expressing flies associated with incremented SERCA activity.
Parallel Calibration for Sensor Array Radio Interferometers

In order to meet the theoretically achievable imaging performance, calibration of modern radio interferometers is a mandatory challenge, especially at low frequencies. In this perspective, we propose a novel parallel iterative multi-wavelength calibration algorithm. The proposed algorithm estimates the apparent directions of the calibration sources, the directional and undirectional complex gains of the array elements and their noise powers, with a reasonable computational complexity. Furthermore, the algorithm takes into account the specific variation of the aforementioned parameter values across wavelength. Realistic numerical simulations reveal that the proposed scheme outperforms the mono-wavelength calibration scheme and approaches the derived constrained Cram\'er-Rao bound even with the presence of non-calibration sources at unknown directions, in a computationally efficient manner.
Explaining the challenges of the Iranian Health System in fighting the COVID-19 pandemic: A Qualitative Study

Summary Introduction The COVID-19 pandemic has become a global threat for the general public and health care workers, and it has created major challenges for all healthcare sectors. The challenges created by this disease can vary in different countries depending on cultural, social, and economic factors. The purpose of this study was to explain the challenges of the Iranian health system in fighting the COVID-19 pandemic from the managers’ and executive authorities’ viewpoints. Methods The present study was a basic-applied research performed using a qualitative approach. It has studied 30 hospitals and medical centers’ managers, deputies of the Ministry of Health, and the universities of medical sciences, which were selected by purposive and snowball sampling with the maximum variety in March-September 2020. Data collection was done through semi-structured interviews and content analysis was used to explain the challenges of the Iranian health system in fighting the COVID-19 pandemic (2020). Results Most of the interviewees (34%) had a Ph.D. degree, and 40% of the participants were graduated in management and health economy and policymaking fields. Analysis and synthesis of the data collected from the interviews led to the creation of 19 sub-themes and 12 main themes classified into four general scopes: organizational factors, resources, management factors, and other factors. Conclusion Identifying the mentioned challenges can provide helpful information for the managers and policy-makers to develop appropriate plans, take the necessary measures to resolve the challenges, and use the available resources to provide the most effective services.
Prevalence of gestational diabetes and its association with stillbirth, preterm birth, macrosomia, abortion and cesarean delivery: a national prevalence study of 11 provinces in Iran

Summary Background Gestational diabetes mellitus (GDM) is one of the most common metabolic disorders during pregnancy that significantly affects perinatal outcomes. Objective The aim of this study was to determine the prevalence of GDM and its relation with the incidence of stillbirth, preterm birth, macrosomia, abortion and cesarean section (C-section) delivery in pregnant women. Methods This cross-sectional study was conducted on 3675 pregnant women in 11 provinces across Iran. Cluster sampling was used to select samples from mothers covered by health plans in 11 provinces of Iran. Prevalence of adverse pregnancy outcomes, including preterm delivery, type of delivery, macrosomic preterm birth, miscarriage, stillbirth, infant death, and birth weight were measured, using family record and face-to-face interviews. Data were analyzed by logistic regression, using STATA14.2 software. Results About four percent of Iranian pregnant women had GDM during pregnancy. Prevalence of C-section was significantly higher in diabetic women than in the non-diabetic ones (53.19 vs 46.81, respectively, P < 0.001). Abortion in diabetic mothers was more than twice that of the non-diabetic mothers (P < 0.001). In the adjusted logistic regression model, the odds of stillbirth in mothers with GDM were 1.8 (95% CI: 1.11, 2.91, P = 0.018) times higher than that of the non-diabetics. The odds of macrosomia in diabetic women was about 7 times higher than the non-diabetic women (95% CI: 2.81, 17.14, P < 0.001). The odds of GDM had an increasing trend according to the BMI (p < 0.001). The risk of GDM were significantly lower, according to the daily physical activity (PA) (p < 0.001). Conclusion The GDM prevalence has a decreasing trend in Iran. It increases the adverse pregnancy outcomes such as stillbirth, neonatal deaths, macrosomia, preterm birth, abortion and C-section delivery. As, some of these consequences like macrosomia are not treatable, thus early prevention is very crucial.
Optimization and management of cyber-physical systems: smart grid and plug-in hybrid electric vehicles.

of the Dissertation Optimization and Management of Cyber-Physical Systems Smart Grid and Plug-in Hybrid Electric Vehicles by Bingnan Jiang Doctor of Philosophy in Computer Engineering Northeastern University, August 2015 Dr. Yunsi Fei, Adviser In cyber-physical systems (CPS), the bi-directional link between computational and physical elements can significantly increase the efficiency, reliability, and cost-effectiveness of CPS. A precursor generation of CPS can be found in diverse applications, where smart gird and plug-in hybrid electric vehicles (PHEVs) are two exemplary vibrant applications. Compared with traditional power distribution systems and gasoline fueled vehicles, smart grid and PHEVs have much lower cost, higher service provision, and make the environment greener. However, it is challenging to manage the operations of CPS optimally, in view of system complexity, interaction between cyber and physical components and the environment, limited computation resources, and high real-time performance requirement. My dissertation has been focused on the optimization and prediction model design for cost-effective and energy-efficient CPS – smart grid and PHEVs. First, a novel cost-effective energy ecosystem is proposed for a residential microgrid with renewable energy resources. It effectively coordinates demand response (DR), distributed generations (DGs), and energy storage management through a three-level hierarchical optimization, in which particle swarm optimization (PSO) algorithm and environment-adaptive Q-learning algorithm are applied. Second, I explore the application of modern vehicle-to-grid (V2G) technologies on smart grid reactive power compensation. On-board chargers of plug-in electric vehicles (PEVs) are proposed to be utilized as mobile volt-ampere reactive (VAR) resources. Third, an on-road PHEV power management system is proposed which utilizes the information of stochastic vehicle driving states and real-time traffic conditions. With these stochastic elements incorporated, a two-level hierarchical optimization model is developed based on multi-stage stochastic quadratic programming (MSQP) and Markov decision process (MDP). The proposed system makes optimal on-road power management decisions and simulation results
Generation And Verification Of Software Robustness Properties Through Static Analysis

SHARMA , TANU. Generation And Verification Of Software Robustness Properties Through Static Analysis (Under the direction of Jun Xu). Increasing reliance on computers calls for the need of robust software especially in critical applications such as those used in military, hospital etc. Traditional software testing techniques focus on functionality and ignore stressful conditions and exception handling. Poor programming practices may lead to critical software robustness failures resulting in memory corruption, application crashes and file system failures. Such robustness failures can be detected by many static analysis tools. However the difficulty in using existing tools is that they require users to provide robustness properties which need to be checked. Currently these properties which require source code and interface level information are mostly manually specified. This work proposes an FSA Generator framework that automatically generates concrete properties. Users only need to specify high level generic properties in simple finite state machines. The framework converts these generic properties into concrete language specific properties using source code information from a pattern database and interface level information from an API specification database. The automated cost effective generation of concrete properties makes static analysis scalable and efficient. Experimental evaluation using the generated properties and a static checker has found numerous robustness bugs in more than ten open source packages. GENERATION AND VERIFICATION OF SOFTWARE ROBUSTNESS PROPERTIES THROUGH STATIC ANALYSIS
EXPERIMENTAL STUDIES ON DOA ESTIMATION BASED ON BLIND SIGNAL SEPARATION AND ARRAY SIGNAL PROCESSING 1

In many applications, the direction-of-arrival (DOA) estimation problem is of great interest. After blind separation of the signals incident onto the receiving sensor array, the DOAs can be estimated for each source individually. Non-Gaussian signals with negative kurtosis can be automatically captured by the constant modulus (CM) array, which is the most striking blind beamforming algorithm and widely discussed. However, very little experimental studies have been conducted so far. In this paper, after proposing a new method, we analyze the DOA estimation performance of the multistage CM array via computer simulations and water tank experiments, and compare it with that of other DOA estimation algorithms including ‘non-blind’ and ‘blind’ algorithms. The multistage CM array shows better results in all considered situations.
MicroRNA-135b-5p regulates trophoblast cell function by targeting phosphoinositide-3-kinase regulatory subunit 2 in preeclampsia

ABSTRACT The level of miR‑135b-5p is lower in patients with preeclampsia (PE) superimposed on chronic hypertension than in healthy controls. However, the function of miR‑135b-5p in PE progression remains unknown. In the present study, we investigated the role of miR‑135b-5p in PE development and its possible mechanism for the first time. HTR8/SVneo cells (trophoblast cell line) were exposed to hypoxia/reoxygenation (H/R) to mimic PE in vitro. Hypoxia-inducible factor-1α (HIF-1α), forkhead box O3A (FOXO3a), and miR-135b-5p levels were measured using Real-time PCR. Cell proliferation, apoptosis and migration/invasion were evaluated using the Cell Counting Kit-8 (CCK-8), flow cytometry and transwell assays, respectively. Real-time PCR and Western blotting were performed to determine the levels of several pro- and anti-angiogenic factors. The binding of miR-135b-5p to the PIK3R2-3’ untranslated region (3ʹUTR) was confirmed by bioinformatics analysis and a dual-luciferase reporter assay. H/R exposure greatly upregulated HIF-1α, FOXO3a, and PIK3R2 levels, while downregulating miR-135b-5p levels in HTR8/SVneo cells. H/R exposure resulted in the inhibition of proliferation, migration, invasion, angiogenesis, and the induction of apoptosis. MiR-135b-5p overexpression reversed the effects of H/R on trophoblast cell function, while miR-135b-5p knockdown enhanced the effects. PIK3R2 knockdown had similar effects as miR-135b-5p overexpression on proliferation, apoptosis and angiogenesis. The effect of miR-135b-5p overexpression on H/R-exposed cells was enhanced by PIK3R2 knockdown. MiR-135b-5p downregulated PIK3R2 expression by pairing with its 3ʹUTR. Therefore, miR-135b-5p may regulate trophoblast function by targeting PIK3R2 in PE and could serve as a novel therapeutic target for PE. Graphical abstract
Application of neuroergonomics in the industrial design of mining equipment.

Neuroergonomics is an interdisciplinary field merging neuroscience and ergonomics to optimize performance. In order to design an optimal user interface, we must understand the cognitive processing involved. Traditional methodology incorporates self-assessment from the user. This dissertation examines the use of neurophysiological techniques in quantifying the cognitive processing involved in allocating cognitive resources. Attentional resources, cognitive processing, memory and visual scanning are examined to test the ecological validity of theoretical laboratory settings and how they translate to real life settings. By incorporating a non-invasive measurement technique, such as the quantitative electroencephalogram (QEEG), we are able to examine connectivity patterns in the brain during operation and discern whether or not a user has obtained expert status. Understanding the activation patterns during each phase of design will allow us to gauge whether our design has balanced the cognitive requirements of the user.
A qualitative analysis of the needs and wishes of people with type 2 diabetes and healthcare professionals for optimal diabetes care

Globally, type 2 diabetes care is often fragmented and still organised in a provider‐centred way, resulting in suboptimal care for many individuals. As healthcare systems seek to implement digital care innovations, it is timely to reassess stakeholders' priorities to guide the redesign of diabetes care. This study aimed to identify the needs and wishes of people with type 2 diabetes, and specialist and primary care teams regarding optimal diabetes care to explore how to better support people with diabetes in a metropolitan healthcare service in Australia.
Marginal Structural Models Using Calibrated Weights With SuperLearner: Application to Type II Diabetes Cohort

As different scientific disciplines begin to converge on machine learning for causal inference, we demonstrate the application of machine learning algorithms in the context of longitudinal causal estimation using electronic health records. Our aim is to formulate a marginal structural model for estimating diabetes care provisions in which we envisioned hypothetical (i.e. counterfactual) dynamic treatment regimes using a combination of drug therapies to manage diabetes: metformin, sulfonylurea and SGLT-2i. The binary outcome of diabetes care provisions was defined using a composite measure of chronic disease prevention and screening elements [27] including (i) primary care visit, (ii) blood pressure, (iii) weight, (iv) hemoglobin A1c, (v) lipid, (vi) ACR, (vii) eGFR and (viii) statin medication. We used several statistical learning algorithms to describe causal relationships between the prescription of three common classes of diabetes medications and quality of diabetes care using the electronic health records contained in National Diabetes Repository. In particular, we generated an ensemble of statistical learning algorithms using the SuperLearner framework based on the following base learners: (i) least absolute shrinkage and selection operator, (ii) ridge regression, (iii) elastic net, (iv) random forest, (v) gradient boosting machines, and (vi) neural network. Each statistical learning algorithm was fitted using the pseudo-population generated from the marginalization of the time-dependent confounding process. Covariate balance was assessed using the longitudinal (i.e. cumulative-time product) stabilized weights with calibrated restrictions. Our results indicated that the treatment drop-in cohorts (with respect to metformin, sulfonylurea and SGLT-2i) may have improved diabetes care provisions in relation to treatment naïve (i.e. no treatment) cohort. As a clinical utility, we hope that this article will facilitate discussions around the prevention of adverse chronic outcomes associated with type II diabetes through the improvement of diabetes care provisions in primary care.
Mapping brain-wide excitatory projectome of primate prefrontal cortex at submicron resolution and comparison with diffusion tractography

Resolving trajectories of axonal pathways in the primate prefrontal cortex remains crucial to gain insights into higher-order processes of cognition and emotion, which requires a comprehensive map of axonal projections linking demarcated subdivisions of prefrontal cortex and the rest of brain. Here, we report a mesoscale excitatory projectome issued from the ventrolateral prefrontal cortex (vlPFC) to the entire macaque brain by using viral-based genetic axonal tracing in tandem with high-throughput serial two-photon tomography, which demonstrated prominent monosynaptic projections to other prefrontal areas, temporal, limbic, and subcortical areas, relatively weak projections to parietal and insular regions but no projections directly to the occipital lobe. In a common 3D space, we quantitatively validated an atlas of diffusion tractography-derived vlPFC connections with correlative green fluorescent protein-labeled axonal tracing, and observed generally good agreement except a major difference in the posterior projections of inferior fronto-occipital fasciculus. These findings raise an intriguing question as to how neural information passes along long-range association fiber bundles in macaque brains, and call for the caution of using diffusion tractography to map the wiring diagram of brain circuits.
Obstacles to university food pantry use and student-suggested solutions: A qualitative study

Background In the absence of federal programs and policies to alleviate college student food insecurity, the number of food pantries has grown rapidly in the United States. Yet, no studies, to date, have qualitatively examined students’ experiences with this resource. Objective To explore college students’ perspectives on barriers to using an on-campus food pantry and provide insights into student-suggested solutions. Methods In this qualitative study, 41 college students were recruited from a large public university in the southeastern US with a campus food pantry. Students participated in one-on-one, in-person, semi-structured interviews. All interviews were audio-recorded, transcribed verbatim, managed using NVivo 12, and analyzed using inductive, semantic thematic analysis. Results Most students were classified as food insecure (n = 33, 82.5%), and two-thirds identified as pantry users (at least once). The students’ reasons for not using the food pantry indicated resistance and access barriers. Students either ‘chose not to use’ the campus food pantry due to (i) stigma and shame, (ii) perceived insufficient need, (iii) and unsuitable food or they experienced ‘barriers’ due to (i) lack of knowledge and (ii) limited food access. The main reason reported by food insecure non-pantry users was feelings of stigma and shame while that of food insecure pantry users was limited food access. Students suggested three solutions to minimize barriers experienced when utilizing the campus food pantry. These included (i) spreading awareness about the pantry through positive marketing messages that de-stigmatize use, (ii) improving accessibility of fresh produce and protein options, and (iii) improving access through satellite locations and online ordering systems. Conclusion These barriers need to be systematically addressed to normalize food pantry use. Consideration of student recommendations by university program developers and policymakers may be of added value to expand access to food by college students with food insecurity.
The association of the localized pain sensitivity in the residual limb and prosthesis use in male veterans with transtibial amputation

ABSTRACT The association of localized pain sensitivity in the residual limb and prosthesis use has clinical implications, however, rarely been assessed. This study aimed to investigate pain sensitivity and explore its range, variability, and association with prosthesis use alongside other demographic and clinical characteristics of veterans with transtibial amputation. Pain sensitivity was determined as pressure pain threshold (PPT) and pressure tolerance (PT) in 19 male veterans with a mean age of 49.5 years using pressure algometry at 12 anatomical locations on‎ the residual limb. A comparison of pain sensitivity at each location, and among anatomical locations and participants was explored using independent t-test, analysis of variance, and Kruskal–Wallis tests, respectively. Pain sensitivity range (PSR), the difference between PT and PPT, was significantly different (p < 0.05) at mid-patellar tendon, medial tibial flare, and the distal end of the tibia. The lowest PPT and PT (20.5 and 33 Ncm−2, p = 0.13) were recorded at the distal end of the residual limb, and the highest PPT and PT (73.4 and 94.3 Ncm−2, p = 0.03) were recorded at the mid-patellar tendon. Pain sensitivity was significantly different among anatomical locations and participants. The correlation tests (Pearson and partial eta squared) showed non-significant associations of pain sensitivity with participants’ demographic and clinical characteristics except for daily prosthesis use. The mid-patellar tendon, medial tibial flare, and distal end of the tibia revealed the lowest pain hypersensitivity ‎due to higher PSR. Longer daily prosthesis use was associated with increased pain sensitivity.