Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of the Percutaneous Absorption of Drug Molecules in Zebrafish

In recent decades, zebrafish (Danio rerio) has become a widely used vertebrate animal model for studying development and human diseases. However, studies on skin medication using zebrafish are rare. Here, we developed a novel protocol for percutaneous absorption of molecules via the zebrafish tail skin, by applying a liquid solution directly, or using a filter paper imbibed with a chemical solution (coating). Human skin is capable of absorbing felbinac and loxoprofen sodium hydrate (LSH), but not glycyrrhetinic acid (GA) and terbinafine hydrochloride (TH). To evaluate the possibility and the quality of transdermal absorption in zebrafish, we transdermally administered these four drugs to zebrafish. Pharmacokinetics showed that felbinac was present in the blood of zebrafish subjected to all administration methods. Felbinac blood concentrations peaked at 2 h and disappeared 7 h after administration. GA was not detected following transdermal administrations, but was following exposure. LSH was not found in the circulatory system after transdermal administration, but TH was. A dose-response correlation was observed for felbinac blood concentration. These findings suggest that zebrafish are capable of absorbing drug molecules through their skin. However, the present data cannot demonstrate that zebrafish is a practical model to predict human skin absorption. Further systemic studies are needed to observe the correlations in percutaneous absorption between humans and zebrafish.
Breastfeeding and Postpartum Glucose Regulation Among Women With Prior Gestational Diabetes: A Systematic Review

Background Gestational diabetes mellitus is associated with adverse maternal and fetal outcomes and increases subsequent risk of Type 2 diabetes. Researchers have shown that breastfeeding may reduce diabetes risk in women with recent gestational diabetes. Research aim To assess association between infant feeding and postpartum glucose tolerance in mothers with recent gestational diabetes within 1 year postpartum. Methods A literature search was performed up to December 31, 2019, retrieving articles related to infant feeding, gestational diabetes, and postpartum glucose regulation in four major databases (PubMed, Cochrane, CINAHL, and Embase). Methodological quality was assessed using tools from the United States National Institutes of Health and the National Heart, Lung, and Blood Institute. Results The search yielded 15 cohort studies meeting the selection criteria. Of the 15 studies, 13 (86.7%) examined the influence of breastfeeding on postpartum glycemic status, and eight (53.4%) compared the mean blood glucose values between breastfeeding and non-breastfeeding participants. Of the 13 studies that compared postpartum glycemic status, nine (60%) of the research teams found that breastfeeding lowered rates of impaired glucose tolerance, and four (26.7%) showed no significant change. In eight of the studies reporting mean blood glucose values, six (75%) reported significantly lower fasting plasma glucose in breastfeeding participants, with reductions ranging from 3.7 to 7.4 mg/dL (0.2–0.4 mmol/L). Conclusion Breastfeeding has been associated with improved postpartum glucose regulation in mothers with gestational diabetes. In pregnant women with gestational diabetes, breastfeeding may reduce the risk of Type 2 diabetes, and women with gestational diabetes should be strongly encouraged and supported to breastfeed.
Endoplasmic reticulum stress causes insulin resistance by inhibiting delivery of newly synthesized insulin receptors to the cell surface

Accumulation of unfolded proteins in the endoplasmic reticulum (ER) causes ER stress and activates a signaling network known as the unfolded protein response (UPR). Here we characterize how ER stress and the UPR inhibit insulin signaling. We find that ER stress inhibits insulin signaling by depleting the cell surface population of the insulin receptor. ER stress inhibits proteolytic maturation of insulin proreceptors by interfering with transport of newly synthesized insulin proreceptors from the ER to the plasma membrane. Activation of AKT, a major target of the insulin signaling pathway, by a cytosolic, membrane-bound chimera between the AP20187-inducible FV2E dimerization domain and the cytosolic protein tyrosine kinase domain of the insulin receptor was not affected by ER stress. Hence, signaling events in the UPR, such as activation of the JNK mitogen-activated protein (MAP) kinases or the pseudokinase TRB3 by the ER stress sensors IRE1α and PERK, do not contribute to inhibition of signal transduction in the insulin signaling pathway. Indeed, pharmacologic inhibition and genetic ablation of JNKs, as well as silencing of expression of TRB3, did not restore insulin sensitivity or rescue processing of newly synthesized insulin receptors in ER-stressed cells.
Clinical perspective on pain in multiple sclerosis

Pain is an important and frequent symptom in multiple sclerosis (MS), which leads to a low quality of life, increased disability level, and lack of employment and mental health. Recently, studies have shown increased interest in pain in MS and there is a growing evidence of its prevalence. However, the literature suffers from lack of experimental studies focusing on pain reduction. This topical review summarizes the current knowledge about pain in MS with its definitions, assessments, treatments and rehabilitation within a holistic perspective.
The Health Resources and Services Administration (HRSA) Ryan White HIV/AIDS Program's Response to the Opioid Epidemic.

BACKGROUND The United States is in the midst of an unprecedented opioid crisis with increasing injection drug use (IDU)-related human immunodeficiency virus (HIV) outbreaks, particularly in rural areas. The Health Resources and Services Administration (HRSA)'s Ryan White HIV/AIDS Program (RWHAP) is well positioned to integrate treatment for IDU-associated HIV infections with treatment for drug use disorders. These activities will be crucial for the "Ending the HIV Epidemic: A Plan for America" (EHE) initiative, in which 7 southern states were identified with rural HIV epidemics. METHODS The RWHAP Services Report data were used to assess the IDU population and substance use services utilization among RWHAP clients in 2017, nationally and in the 7 EHE-identified states. THe HRSA held a 1-day Technical Expert Panel (TEP) to explore how RWHAP can best respond to the growing opioid crisis. RESULTS During the TEP, 8 key themes emerged and 11 best practices were identified to address opioid use disorder (OUD) among people with HIV. In 2017, among RWHAP clients with reported age and transmission category, 6.7% (31 683) had HIV attributed to IDU; among IDU clients, 6.3% (1988) accessed substance use services. CONCLUSIONS The TEP results and RWHAP data were used to develop implementation science projects that focus on addressing OUD and integrating behavioral health in primary care. These activities are critical to ending the HIV epidemic.
Simultaneous Determination of Metal Ions in Zinc Sulfate Solution Using UV–Vis Spectrometry and SPSE-XGBoost Method

Excessive discharge of heavy metal ions will aggravate environment pollution and threaten human health. Thus, it is of significance to real-time detect metal ions and control discharge in the metallurgical wastewater. We developed an accurate and rapid approach based on the singular perturbation spectrum estimator and extreme gradient boosting (SPSE-XGBoost) algorithms to simultaneously determine multi-metal ion concentrations by UV–vis spectrometry. In the approach, the spectral data is expanded by multi-order derivative preprocessing, and then, the sensitive feature bands in each spectrum are extracted by feature importance (VI score) ranking. Subsequently, the SPSE-XGBoost model are trained to combine multi-derivative features and to predict ion concentrations. The experimental results indicate that the developed “Expand-Extract-Combine” strategy can not only overcome problems of background noise and spectral overlapping but also mine the deeper spectrum information by integrating important features. Moreover, the SPSE-XGBoost strategy utilizes the selected feature subset instead of the full-spectrum for calculation, which effectively improves the computing speed. The comparisons of different data processing methods are conducted. It outcomes that the proposed strategy outperforms other routine methods and can profoundly determine the concentrations of zinc, copper, cobalt, and nickel with the lowest RMSEP. Therefore, our developed approach can be implemented as a promising mean for real-time and on-line determination of multi-metal ion concentrations in zinc hydrometallurgy.
Smart Adaptive Refresh for Optimum Refresh Interval Tracking using in-DRAM ECC

This paper improved refresh issues by using the in-DRAM ECC. DRAM tends to have in-DRAM ECC to care about single bit DRAM cell fails, so it is easier to use in-DRAM ECC to make DRAM stronger from several leakage issues. We used in-DRAM ECC to detect some particular cells that have a weak characteristic of retention time and we operated refresh operations automatically based on this particular characteristic for saving a lot of power consumptions overcoming VRT issue. Our experimental results improved power consumption by 33% first by managing the retention time of DRAM to reduce refresh overhead. Furthermore, depending on the characteristics of the cell, it will automatically find the most reasonable refresh operating method, which will result in more power consumption gains.
Vitamin B1 Intake in Multiple Sclerosis Patients and its Impact on Depression Presence: A Pilot Study

Vitamin B1, or thiamine, is one of the most relevant vitamins in obtaining energy for the nervous system. Thiamine deficiency or lack of activity causes neurological manifestations, especially symptoms of depression, intrinsic to multiple sclerosis (MS) and related to its pathogenesis. On this basis, the aim of this study was to determine the possible relationship between the nutritional habits of patients with MS and the presence of depression. Therefore, a cross-sectional and observational descriptive study was conducted. An analysis of dietary habits and vitamin B1 consumption in a Spanish population of 51 MS patients was performed by recording the frequency of food consumption. Results showed a vitamin B1 intake within the established range, mainly provided by the consumption of ultra-processed products such as cold meats or pastries, and a total carbohydrate consumption lower than recommended, which stands out for its high content of simple carbohydrates deriving from processed foods such as dairy desserts, juice, snacks, pastries, chocolate bars, soft drinks and fermented alcohol. In addition, a significant negative correlation between depression and the intake of thiamine and total carbohydrates was observed. These findings could explain the influence of MS patients’ eating habits, and consequently vitamin B1 activity, on depression levels.
Bond Graph based Multiphysic Modelling of Anion Exchange Membrane Water Electrolysis Cell

This work is an attempt to develop and validate a graphical dynamical model of an AEM electrolysis cell based on Bond Graphs, an energy based tool that allows to represent multiphysics systems. The model of the cell lays a foundation for developing a complete representation for AEM electrolysers which can be used for simulation as well as for developing control algorithms and fault diagnosis. Parameter identification and model validation is achieved using experimental data.
Biological basis of bone strength: anatomy, physiology and measurement

Understanding how bones are innately designed, robustly developed and delicately maintained through intricate anatomical features and physiological processes across the lifespan is vital to inform our assessment of normal bone health, and essential to aid our interpretation of adverse clinical outcomes affecting bone through primary or secondary causes. Accordingly this review serves to introduce new researchers and clinicians engaging with bone and mineral metabolism, and provide a contemporary update for established researchers or clinicians. Specifically, we describe the mechanical and non-mechanical functions of the skeleton; its multidimensional and hierarchical anatomy (macroscopic, microscopic, organic, inorganic, woven and lamellar features); its cellular and hormonal physiology (deterministic and homeostatic processes that govern and regulate bone); and processes of mechanotransduction, modelling, remodelling and degradation that underpin bone adaptation or maladaptation. In addition, we also explore commonly used methods for measuring bone metabolic activity or material features (imaging or biochemical markers) together with their limitations.
Performance Analysis of Parallel Image Processing Operations

Image processing promotes many of the technological advancements these days. The main aspect while performing image processing operations is the time taken to deal with the application of different routines on these images. Thus, time is an important criterion for the efficiency of the systems. With the given situation, the idea of giving images to the processors and then depending upon code all the cores will be either dealing with one image and performing operations on the image or distributing the images to each core to perform the operations. This uses the idea of Parallel Programming i.e. the use of all computer resources that are cores here. The paper focuses on implementing different image-enhancing techniques integrated into a system that will execute it on single as well as multiple cores. The image processing operations implemented sequentially as well as parallelly in this paper are Image Blurring, Edge Detection, Contrast Stretching, and Image Negation the average speed for all the operations obtained when executed on multiple cores are 9.94, 9.54, 11.12, and 11.21 respectively.
Risk Reduction Strategies for Sudden Cardiac Death in Acute Myocardial Infarction

re-entry circuits. 7 Transmural dispersion of repolarization may be exacerbated by limited repolarization reserve in patients with mutations in genes for ion channels, explaining the increased risk of SCD in AMI for subjects with elongated QT intervals. Therefore, risk reduction of SCD should target the instigating plaque, the mechanisms for development of ventricular arrhythmias and prompt defibrillation of lethal arrhythmias. Abstract Sudden cardiac death (SCD) resulting from an acute myocardial infarction (AMI) is the result of a chain of events beginning with the development of the atherosclerotic plaque and ending with the terminal event of an untreated lethal arrhythmia provoked by sudden ischemia. Out-of-hospital arrest has an extremely poor prognosis, thus prevention is critical. Certain risk factors for atherosclerosis are particularly associated with SCD, especially smoking, and smoking cessation is a critical element of prevention. Other cardiac findings such as left ventricular hypertrophy atrial fibrillation have also been associated with SCD. Most patients have a symptomatic prodrome and patients should be educated to heed this warning. Electrocardiogram (ECG) findings at screening are generally non-specific; nevertheless, an elongated QTc interval and signs of left ventricular hypertrophy are markers associated with SCD. There is a genetic component to SCD that is under intense study; patients tend to have a family history of sudden death. Monogenic disorders such as long QT predispose patients to SCD during AMI. Genome-wide association studies have implicated several sites as being associated with SCD in the general population; a single allele linked to SCN5A is found in African-Americans. Treatment with statins is beneficial; other treatments have yet to be proven in primary prevention. Automatic external defibrillators in public sites used promptly by minimally trained personal can be life-saving. Risk stratification is dynamic and should be periodically reassessed. external defibrillator should be assessed. A corrected QT interval (QTc interval) longer than 440ms has a relative risk (RR) of two for SCD. the course of infarction, the sum of ST elevation predicts a poor outcome.
A Flexible SDN-Based Architecture for Identifying and Mitigating Low-Rate DDoS Attacks Using Machine Learning

While there have been extensive studies of denial of service (DoS) attacks and DDoS attack mitigation, such attacks remain challenging to mitigate. For example, Low-Rate DDoS (LR-DDoS) attacks are known to be difficult to detect, particularly in a software-defined network (SDN). Hence, in this paper we present a flexible modular architecture that allows the identification and mitigation of LR-DDoS attacks in SDN settings. Specifically, we train the intrusion detection system (IDS) in our architecture using six machine learning (ML) models (i.e., J48, Random Tree, REP Tree, Random Forest, Multi-Layer Perceptron (MLP), and Support Vector Machines (SVM)) and evaluate their performance using the Canadian Institute of Cybersecurity (CIC) DoS dataset. The findings from the evaluation demonstrate that our approach achieves a detection rate of 95%, despite the difficulty in detecting LR-DoS attacks. We also remark that in our deployment, we use the open network operating system (ONOS) controller running on Mininet virtual machine in order for our simulated environment to be as close to real-world production networks as possible. In our testing topology, the intrusion prevention detection system mitigates all attacks previously detected by the IDS system. This demonstrates the utility of our architecture in identifying and mitigating LR-DDoS attacks.
Enhancing Body-Mounted LiDAR SLAM using an IMU-based Pedestrian Dead Reckoning (PDR) Model

Simultaneous localization and mapping for human body-mounted platforms have been recently the focus of navigation research to support a wide range of applications such as rescue, first-responders, mining, and defense. For vehicular platforms, wheel odometry has been used to enhance the accuracy of SLAM. However, wheel odometry is not available in body-mounted platforms. Using raw inertial measurement unit (IMU) as odometry is not accurate enough to support SLAM due to the large and rapid drifts caused by IMU data integration. To address this challenge, we propose a sensor fusion scheme for body-mounted SLAM that integrates the IMU-based Pedestrian Dead Reckoning (PDR) model with a low-cost lightweight 2D LiDAR sensor. In the proposed fusion, the PDR model is used as a replacement for wheel odometry in vehicular platforms. A system prototype consisting of a helmet-mount IMU from Xsens and RPLIDAR A1 2D LiDAR sensor has been developed and used for field data collection. The developed PDR model was integrated into the Cartographer SLAM engine and compared with Hector SLAM. Our experiments demonstrated that the integration of PDR has enhanced the SLAM accuracy and contributed in bridging featureless portions of the environment leading to an overall average improvement of 71.47%.
A first approach for a passenger-centered behavior on driverless vehicles

Driverless shuttles are becoming one of the most promising applications of automated vehicle technologies in the short term. For a real system deployment, passenger's acceptance on the shuttle performance will play a key role. This paper presents a passenger-centered control algorithm design for increasing passenger's ride quality. Controllers with different performance but properly following the reference are developed for lane-level autonomous navigation. The whole solution has been implemented and tested in a real vehicle, using a roundabout as use-case scenario, providing encouraging results.
Real-Time Polymerase Chain Reaction For Detection Of Optimal Thermal Profile For DNA Templates From Air-Dried Insect Collection Material

Abstract The thermal profile for polymerase chain reaction (PCR) technique is the main concern in obtaining the PCR product. Determination of optimal thermal profile can be achieved by various techniques, the main objectives of which are simplicity and cost effectiveness of the method. Real-time PCR is a novel molecular biology method widely used in different fields, recognised by its accuracy and precision. In the present work, the thermal profile was estimated for DNA from air-dried collection material of Notiophilus genus and new sequences were obtained for Gene-Bank. Air-dried entomological collections exist around the world and the use of DNA from such type of material is widespread and offers various unique preferences. The purpose of the study was to assess the suitability of different primer annealing temperatures (42 °C – 55 °C) for universal mitochondrial DNA COI gene primer pair HCO/LCO using real-time PCR method. During this study we demonstrate that real-time PCR could be useful to determine the optimal thermal profile for low-yield DNA for increasing the number of qualitative DNA sequences.
A Data-Driven Slip Estimation Approach for Effective Braking Control under Varying Road Conditions

The performances of braking control systems for robotic platforms, e.g., assisted and autonomous vehicles, airplanes and drones, are deeply influenced by the road-tire friction experienced during the maneuver. Therefore, the availability of accurate estimation algorithms is of major importance in the development of advanced control schemes. The focus of this paper is on the estimation problem. In particular, a novel estimation algorithm is proposed, based on a multi-layer neural network. The training is based on a synthetic data set, derived from a widely used friction model. The open loop performances of the proposed algorithm are evaluated in a number of simulated scenarios. Moreover, different control schemes are used to test the closed loop scenario, where the estimated optimal slip is used as the set-point. The experimental results and the comparison with a model based baseline show that the proposed approach can provide an effective best slip estimation.
An Adaptive CSP and Clustering Classification for Online Motor Imagery EEG

A potential limitation of motor imagery (MI) based brain-computer interface (BCI) (MI-BCI) is that it usually requires a relatively long time to record sufficient electroencephalogram (EEG) data for robust feature extraction and classification. Moreover, due to the non-stationarities in EEG signals, the offline training model has poor adaptability and classification ability in cross-session or sample-wise online testing. Methods: To address the problems, we propose a model updating scheme with adaptive and fast operation. Based on the Common Spatial Pattern (CSP), we propose an online and fast generalized eigendecomposition method by Recursive Least Squares updates of the CSP filter coefficients (RLS-CSP), which allows incremental training for CSP spatial filters. Additionally, we present an Incremental Self-training Classification algorithm based on Density Clustering (ISCDC) to select high-confidence samples to update spatial filters and classifier, and classify at the same time. Results: We conducted extensive experiments to validate the efficiency of the proposed adaptive CSP and classifier on the BCI III_IVa and BCI III_V data sets. Experimental results demonstrate that RLS-CSP outperforms significantly in a small sample setting (SSS), and ISCDC has great adaptability in cross-session and non-stationary EEG signals. The results indicate that our proposed methods are feasible to improve the real-time performance of online BCI system.