Miracle-dz/UniFAR-FTUs huggingface dataset:
Patients’, physicians’, nurses’, and pharmacists’ preferences on the characteristics of biologic agents used in the treatment of rheumatic diseases

Objective To estimate preferences in relevant treatment characteristics evaluated by different groups involved in the management of patients with rheumatic diseases. Subjects and methods We surveyed patients with rheumatic diseases, and rheumatologists, nurses, and pharmacists with experience in treatment with/provision of biologic drugs for these patients. Through a discrete choice experiment, participants evaluated 16 possible scenarios in which pairs of similarly efficacious treatments were described with six characteristics: 1) frequency of administration; 2) mode and place of administration; 3) manner, helpfulness, efficiency, and courtesy of health personnel; 4) frequency of reactions at the site of drug administration; 5) severity of generalized undesired/allergic reactions; and 6) additional cost. The direction and strength of preferences toward each characteristic level and the relative importance of each characteristic were estimated through a random-effects conditional logistic regression model. Results In total, 513 patients, 110 rheumatologists, 51 nurses, and 46 pharmacists from 30 centers in Italy participated. Characteristics 3, 4, and 6 were the most important for every subgroup; 1 was least important for patients and rheumatologists, 2 was least important for pharmacists, and 2 and 5 were least important for nurses. For characteristic 2, pharmacists preferred subcutaneous self-injection with a syringe; nurses preferred assisted infusion at an infusion center close to the patient’s home; patients and rheumatologists preferred subcutaneous self-injection with a pen. Conclusion The different preferences for some characteristics shown by the different groups can play an important role, together with purely clinical aspects, in the choice and consequent benefit of treatments, contributing also to a more satisfactory use of resources.
An Efficient Algorithm for High-Dimensional Log-Concave Maximum Likelihood

The log-concave maximum likelihood estimator (MLE) problem answers: for a set of points $X_1,. X_n \in \mathbb R^d$, which log-concave density maximizes their likelihood? We present a characterization of the log-concave MLE that leads to an algorithm with runtime $poly(n,d, \frac 1 \epsilon,r)$ to compute a log-concave distribution whose log-likelihood is at most $\epsilon$ less than that of the MLE, and $r$ is parameter of the problem that is bounded by the $\ell_2$ norm of the vector of log-likelihoods the MLE evaluated at $X_1,. ,X_n$.
Spironolactone Protects against Diabetic Cardiomyopathy in Streptozotocin-Induced Diabetic Rats

Spironolactone (SPR) has been shown to protect diabetic cardiomyopathy (DCM), but the specific mechanisms are not fully understood. Here, we determined the cardioprotective role of SPR in diabetic mice and further explored the potential mechanisms in both in vivo and in vitro models. Streptozotocin- (STZ-) induced diabetic rats were used as the in vivo model. After the onset of diabetes, rats were treated with either SPR (STZ + SPR) or saline (STZ + NS) for 12 weeks; nondiabetic rats were used as controls (NDCs). In vitro, H9C2 cells were exposed to aldosterone, with or without SPR. Cardiac structure was investigated with transmission electron microscopy and pathological examination; immunohistochemistry was performed to detect nitrotyrosine, collagen-1, TGF-β1, TNF-α, and F4/80 expression; and gene expression of markers for oxidative stress, inflammation, fibrosis, and energy metabolism was detected. Our results suggested that SPR attenuated mitochondrial morphological abnormalities and sarcoplasmic reticulum enlargement in diabetic rats. Compared to the STZ + NS group, cardiac oxidative stress, fibrosis, inflammation, and mitochondrial dysfunction were improved by SPR treatment. Our study showed that SPR had cardioprotective effects in diabetic rats by ameliorating mitochondrial dysfunction and reducing fibrosis, oxidative stress, and inflammation. This study, for the first time, indicates that SPR might be a potential treatment for DCM.
Mechanisms of resistance to BRAF and MEK inhibitors and clinical update of US Food and Drug Administration-approved targeted therapy in advanced melanoma

Approximately 50% of melanomas harbor an activating BRAF mutation. Combined BRAF and MEK inhibitors such as dabrafenib and trametinib, vemurafenib and cobimetinib, and encorafenib and binimetinib are US Food and Drug Administration (FDA)-approved to treat patients with BRAFV600-mutated advanced melanoma. Both genetic and epigenetic alterations play a major role in resistance to BRAF inhibitors by reactivation of the MAPK and/or the PI3K–Akt pathways. The role of BRAF inhibitors in modulating the immunomicroenvironment and perhaps enhancing the efficacy of checkpoint inhibitors is gaining interest. This article provides a comprehensive review of mechanisms of resistance to BRAF and MEK inhibitors in melanoma and summarizes landmark trials that led to the FDA approval of BRAF and MEK inhibitors in metastatic melanoma.
Effects of a preconception lifestyle intervention in obese infertile women on diet and physical activity; A secondary analysis of a randomized controlled trial

Background Lifestyle changes are notoriously difficult. Since women who intend to become pregnant are more susceptible to lifestyle advice, interventions during this time window might be more effective than interventions during any other period in life. We here report the effects of the first large preconception lifestyle intervention RCT on diet and physical activity in obese infertile women. Methods In total, 577 women were randomized between a six-month lifestyle intervention program (intervention group; N = 290) or prompt infertility treatment (control group; N = 287). Self-reported dietary behaviors and physical activity were assessed at baseline, three, six and twelve months after randomization. Mixed models were used to analyze differences between groups. Results Compared to the control group, the intervention group reduced their intake of sugary drinks at three months (-0.5 glasses/day [95% C.I. = -0.9;-0.2]), of savory snacks at three (-2.4 handful/week [-3.4;-1.4]) and at six months (-1.4 handful/week [-2.6;-0.2]), and of sweet snacks at three (-2.2 portion/week [-3.3;-1.0]) and twelve months after randomization (-1.9 portion/week [-3.5;-0.4]). Also, the intervention group was more moderate to vigorous physically active at three months after randomization compared to the control group (169.0 minutes/week [6.0; 332.1]). Conclusion Our study showed that obese infertile women who followed a six-month preconception lifestyle intervention program decreased their intake of high caloric snacks and beverages, and increased their physical activity. These changes in lifestyle may not only improve women’s health but their offspring’s health too.
Intranasal niosomes of nefopam with improved bioavailability: preparation, optimization, and in-vivo evaluation

Objective One of the greatest challenges drug formulation is facing is poor bioavailability via oral route. In this regard, nasal drug delivery has been commonly used as an alternative route to improve drug bioavailability. Nefopam hydrochloride (NF) is an analgesic drug that suffers from poor bioavailability due to extensive metabolism in liver. Accordingly, the goal of the present study was to improve NF bioavailability via niosomal-based formulation designed for intranasal delivery. Materials and methods Vesicles were developed by mixing surfactants (Span 20, Span 40, Span 80, and Span 85) at four molar ratios of 1:1, 1:2, 1:3, and 1:4 of cholesterol to surfactant. Entrapment efficiency, particle size, zeta potential, release percentage, ex-vivo permeation parameters, and niosomes’ stability were determined. Also, the pharmacokinetic parameters of the optimized formula in in-situ gel base were measured in rats. Results Niosomes showed entrapment efficiency . 80%, particle size ,550 nm, and zeta potential ranging from -16.8±0.13 to -29.7±0.15. The produced vesicles showed significantly higher amounts of drug permeated across nasal mucosa (2.5 folds) and prolonged NF release compared with NF solution. Stability studies of optimum formula showed nonsignificant changes in niosomes parameters over a storage period of 6 months. The in-vivo studies showed a 4.77-fold increase in bioavailability of optimized nasal niosomes compared with oral solution of drug. Conclusion The obtained results revealed the great ability of the produced NF-loaded nio-somes to enhance drug penetration through nasal mucosa and improve its relative bioavailability compared with NF oral solution.
SDN Architecture for 6LoWPAN Wireless Sensor Networks

Wireless sensor networks (WSN) are being increasingly used for data acquisition and control of remote devices. However, they present some constraints in critical and large-scale scenarios. The main limitations come from the nature of their components, such as lossy links, and devices with power supply limitations, poor processing power and limited memory. The main feature of software-defined networks (SDN) is the separation between the control plane and the data plane, making available a logically unified view of the topology in the controllers. In this way, it is possible to build network applications that take into account this unified view, which makes the SDN an alternative approach to solve the mentioned limitations. This paper presents the SD6WSN (software-defined 6LoWPAN wireless sensor network) architecture, developed to control the behavior of the data traffic in 6LoWPAN according to the SDN approach. It takes into account the specific characteristics of WSN devices, such as low data transfer rate, high latency, packet loss and low processing power, and takes advantage of the flexibility provided by flow-based forwarding, allowing the development of specific networking applications based on a unified view. We provide a detailed description of how we have implemented SD6WSN in the Contiki operating system. The new architecture is assessed in two experiments. The first considers a typical advanced metering infrastructure (AMI) network and measures the overhead of SD6WSN control messages in configurations involving different path lengths. The results indicate that the overhead introduced is not excessive, given the advantages that the SDN approach can bring. The second considers a grid-topology to evaluate the average latency of the peer-to-peer communication. It was observed that the average latency in the SD6WSN is considerably lower than that obtained with standard 6LoWPAN, showing the potential of the proposed approach.
Factors influencing variation in participation in the National Diabetes Audit and the impact on the Quality and Outcomes Framework indicators of diabetes care management

Objective Participation in the National Diabetes Audit (NDA) has become a contractual requirement for all general practices in England and is used as part of the assessment framework for sustainability and transformation partnership (STP) footprints. The study aimed to investigate general practice-related factors which may influence participation in the NDA, and the impact that participation in the NDA may have on diabetes management and patient care. Research design A cross-sectional analysis of routine primary care data from 45 725 646 patients aged 17+ years registered across 7779 general practices in England was performed using logistic regression. The main outcome measures included general practice voluntary participation in the NDA, general practice-related factors (practice size, deprivation, diabetes prevalence, geographic area, practice population age) and diabetes management outcomes (cholesterol, blood pressure, hemoglobin A1c (HbA1c)). Results Participation in the NDA differed significantly according to practice size (t(7653)=−9.93, p=0.001), level of deprivation (χ2(9)=36.17, p<0.0001), diabetes prevalence (p<0.0001), practice population age (p<0.0001), and geographic area (χ2(26)=676.9, p<0.0001). In addition, the Quality and Outcomes Framework diabetes indicator HbA1c (OR 1.01, CI 1.0 to 1.01, p=0.0001) but not cholesterol (p=0.055) or blood pressure (p=0.76) was independently associated with NDA participation when controlling for practice-related factors. Conclusion Variation in NDA participation exists. It is suggested that some practices may need additional support when submitting data to the NDA and that NDA participation may have an impact on diabetes outcomes. However, the use of NDA outcomes as a measure of progress with diabetes care by STPs is still unclear and further investigation is needed.
Root-flipped multiband pulses with inherently aligned echoes

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Dereverberation with Differential Microphone Arrays and the Weighted-Prediction-Error Method

Reverberation is one of the most detrimental effects on the quality and performance of hands-free speech communication and human-machine interfaces. Among different approaches investigated in the literature, the weighted-prediction-error (WPE) method has exhibited some promising potential for dealing with reverberation. However, the WPE is highly sensitive to additive noise and the presence of even small levels of noise can cause significant performance degradation to WPE. To improve the robustness of this technique, we study in this paper the combination of WPE and differential microphone arrays (DMAs), resulting a DMA-WPE method. Our approach is as follows. A DMA is used to pick up the sound signal of interest in noisy and reverberant environments. The microphone signals are processed with a differential beamformer to suppress noise, interference, and reverberation. The output of the differential beamformer is then used as the reference signal for WPE to further perform dereverberation. Simulation results demonstrate that the DMA-WPE method outperforms WPE significantly in all the studied conditions.
Fault Detection and Visualization Technique for Hybrid Electric Vehicle Components

The aim of the paper is toefficiently distinguish the abnormalities in huge datasets by employing different mechanisms like visualization techniques and algorithms. These methods classify faulty data from available test dataand represent it visually. In data visualization human interpretation is required to detect abnormalities while the algorithm classifies faulty and healthy data using rule-based algorithms. The parallel coordinates plot is developed in MATLAB. Fault diagnosis in Permanent Magnet Synchronous Motor (PMSM) was doneby means of pattern recognition algorithm based on the Concordia transform. The algorithm is simulated in MATLAB with original vehicle test data. The proposed fault detection algorithm holds good for analysis of variable speed and variable torque drives.
Sources of Uncertainty for Monte Carlo Modelling with Bayesian Analysis for Sub-Saharan Off-Grid Solar PV Systems

This paper outlines a statistical modelling methodology for predicting sustainability of PV systems installed in Sub-Saharan Africa, with a sustainability analysis of an example PV system in Malawi, 3.5 years after installation. Social and economic risks to project sustainability are identified (through expert survey and community consultation) and the methodology for including these qualitative risks is described. The project sustainability results are given in terms of probable system operational time (as a fraction). For scenario modelling, the P10 result (P10 can be considered as a measure for the best case; only 10% of cases are better than this) 3.5 years after installation shows how social and economic impacts are predicted to reduce the fraction of time the system is operational to over 1/4 of the expected P10. The caveat to this conclusion, is that social and economic risks (such as component theft/tampering, lack of management, possibility of the grid extending to the area, community leadership and structure) are usually mitigated against at the project planning stage. However, the level and type of mitigation is sporadic and inconsistent and should be given significantly more consideration at project conception.
CRNN-based Joint Azimuth and Elevation Localization with the Ambisonics Intensity Vector

We present a source localization system for first-order Ambisonics (FOA) contents based on a stacked convolutional and recurrent neural network (CRNN). We propose to use as input to the CRNN the FOA acoustic intensity vector, which is easy to compute and closely linked to the sound direction of arrival (DoA). The system estimates the DoA of a point source in both azimuth and elevation. We conduct an experimental evaluation in configurations including reverberation, noise, and various speaker w. r. t. microphone orientations. The results show that the proposed architecture and input allow the network to return accurate location estimates in realistic conditions compared to another recent CRNN-based system.
Similarity Learning with Higher-Order Proximity for Brain Network Analysis

In recent years, the similarity learning problem has been widely studied. Most of the existing works focus on images and few of these works could be applied to learn similarity between neuroimages, such as fMRI images and DTI images, which are important data sources for human brain analysis. In this paper, we focus on the similarity learning for fMRI brain network analysis. We propose a general framework called "Multi-hop Siamese GCN" for similarity learning on graphs. This framework provides options for refining the graph representations with high-order structure information, thus can be used for graph similarity learning on various brain network data sets. We apply the proposed Multi-hop Siamese GCN approach on four real fMRI brain network datasets for similarity learning with respect to brain health status and cognitive abilities. Our proposed method achieves an average AUC gain of 82.6% compared to PCA, and an average AUC gain of 42% compared to S-GCN across a variety of datasets, indicating its promising learning ability for clinical investigation and brain disease diagnosis.
SAT-based Explicit LTLf Satisfiability Checking

We present a SAT-based framework for LTLf (Linear Temporal Logic on Finite Traces) satisfiability checking. We use propositional SAT-solving techniques to construct a transition system for the input LTLf formula; satisfiability checking is then reduced to a path-search problem over this transition system. Furthermore, we introduce CDLSC (Conflict-Driven LTLf Satisfiability Checking), a novel algorithm that leverages information produced by propositional SAT solvers from both satisfiability and unsatisfiability results. Experimental evaluations show that CDLSC outperforms all other existing approaches for LTLf satisfiability checking, by demonstrating an approximate four-fold speed-up compared to the second-best solver.
Road Scene Simulation Based on Vehicle Sensors: An Intelligent Framework Using Random Walk Detection and Scene Stage Reconstruction

Road scene model construction is an important aspect of intelligent transportation system research. This paper proposes an intelligent framework that can automatically construct road scene models from image sequences. The road and foreground regions are detected at superpixel level via a new kind of random walk algorithm. The seeds for different regions are initialized by trapezoids that are propagated from adjacent frames using optical flow information. The superpixel level region detection is implemented by the random walk algorithm, which is then refined by a fast two-cycle level set method. After this, scene stages can be specified according to a graph model of traffic elements. These then form the basis of 3D road scene models. Each technical component of the framework was evaluated and the results confirmed the effectiveness of the proposed approach.
As Soon as You Taste It: Evidence for Sequential and Parallel Processing of Gustatory Information

Abstract The quick and reliable detection and identification of a tastant in the mouth regulate nutrient uptake and toxin expulsion. Consistent with the pivotal role of the gustatory system, taste category information (e.g., sweet, salty) is represented during the earliest phase of the taste-evoked cortical response (Crouzet et al., 2015), and different tastes are perceived and responded to within only a few hundred milliseconds, in rodents (Perez et al., 2013) and humans (Bujas, 1935). Currently, it is unknown whether taste detection and discrimination are sequential or parallel processes, i.e., whether you know what it is as soon as you taste it. To investigate the sequence of processing steps involved in taste perceptual decisions, participants tasted sour, salty, bitter, and sweet solutions and performed a taste-detection and a taste-discrimination task. We measured response times (RTs) and 64-channel scalp electrophysiological recordings and tested the link between the timing of behavioral decisions and the timing of neural taste representations determined with multivariate pattern analyses. Irrespective of taste and task, neural decoding onset and behavioral RTs were strongly related, demonstrating that differences between taste judgments are reflected early during chemosensory encoding. Neural and behavioral detection times were faster for the iso-hedonic salty and sour tastes than their discrimination time. No such latency difference was observed for sweet and bitter, which differ hedonically. Together, these results indicate that the human gustatory system detects a taste faster than it discriminates between tastes, yet hedonic computations may run in parallel (Perez et al., 2013) and facilitate taste identification.