Miracle-dz/UniFAR-FTUs huggingface dataset:
N,N,N-trimethyl chitosan as an efficient antibacterial agent for polypropylene and polylactide nonwovens

Abstract The paper presents a method of depositing N,N,N-trimethyl chitosan (TMC) layers onto polypropylene and polylactide nonwovens. A two-step modification procedure is applied: first, grafting the nonwovens with acrylic acid and next layer-by-layer deposition. Turbidimetric measurements confirm the creation of polycomplexes between grafted poly(acrylic acid) and deposited TMC. The created material structure is evaluated using gravimetric analysis, reflectance Fourier transform infrared spectroscopy, scanning electron microscopy, energy dispersive spectroscopy measurements and pH-metric titration. The modified material exhibits good antibacterial properties against Gram-positive bacteria Staphylococcus aureus.
Making online content viral through text analysis

Each and every day there are lots and lots of contents being published on the web. May it be blogs, news articles, movie reviews, product reviews, etc. Although this many number of contents are published, how far a content reaches and engages a large number of expected audience is questionable and unpredictable. Our project aims to solve this issue. The goal of our project is to develop a system that, when a post or textual content is given as an input, makes it to go viral on the web. Our project is the first to propose such a solution for this problem. To make a post go viral, we follow two approaches. First one is to improve the content of the post to incorporate emotions and sentiments. The other one is to take a post directly to its potential audience. The initial step of the first approach is deriving some rules. The next step is where we analyze the actual post, measure its popularity and give suggestions as to how to improve the post in order to make it viral, based on the derived rules. The suggestions will be to replace certain words. The final part is the second approach towards solving the problem. Here, we analyze the social media posts of potential viewers of a post and understand what kind of decisions they may take in the future, so that we can recommend them directly with a certain post. Since we do the system for the domain of movies, we mine the text regarding a user's expectations to watch a certain kind of movie in the near future and recommend him with the reviews about the movie he may likely to watch.
His Bundle Pacing: Hemodynamics and Clinical Outcomes

From 1993 to 2009, nearly 2.9 million pacemakers were implanted in the United States; the majority of which were dual-chamber pacemakers. One of the major physiologic advantages of dual-chamber pacing over single-chamber ventricular pacing is atrioventricular synchrony, which prevents the pacemaker syndrome. However, patients who are pacemaker dependent or use right ventricle (RV) apical pacing more than 40% of the time are at a risk of developing heart failure from electromechanical dyssynchrony. Studies have also shown that RV pacing results in nonphysiological activation of the left ventricle, leading to adverse clinical outcomes. Hence, alternative pacing sites, including the RV outflow tract, the high-RV septal region, bi-ventricular pacing, or His bundle pacing, have been explored for a better physiological electromechanical coupling of the ventricles. Although His bundle pacing has gained attention due to favorable data and clinical outcomes, it has not gained widespread acceptance into clinical practice. Hence, we aim to review the current experience with His bundle pacing and its clinical implications in this article.
Sparsity adaptive subspace pursuit based channel estimation algorithm for OFDM based massive MIMO systems

Massive multiple-input multiple-output (massive MIMO) systems, employing very large number of antennas, are attended to yield higher spectrum and energy efficiencies. However, as in basic MIMO, these gains cannot be achieved without accurate channel estimation. With a large number of antennas, the channel estimation is much more requested since its complexity is significantly increased. In this paper, by exploiting the common sparsity properties of massive MIMO channels, we propose a sparsity adaptive subspace pursuit (SASP) algorithm for the massive MIMO channels recovery with unknown sparsity.
Salvianolic Acid A Protects H9c2 Cells from Arsenic Trioxide-Induced Injury via Inhibition of the MAPK Signaling Pathway

Background/Aims: This study aimed to investigate whether Salvianolic acid A (Sal A) conferred cardiac protection against Arsenic trioxide (ATO)-induced cardiotoxicity in H9c2 cells by inhibiting MAPK pathways activation. Methods: H9c2 cardiac cells were exposed to 10 µM ATO for 24 h to induce cytotoxicity. The cells were pretreated with Sal A for 4 h before exposure to ATO. Cell viability was determined utilizing the MTT assay. The percentage of apoptosis was measured by a FITC-Annexin V/PI apoptosis kit for flow cytometry. Mitochondrial membrane potential (∆Ψm) was detected by JC-1. The intracellular ROS levels were measured using an Image-iTTM LIVE Green Reactive Oxygen Species Detection Kit. The apoptosis-related proteins and the MAPK signaling pathways proteins expression were quantified by Western blotting. Results: Sal A pretreatment increased cell viability, suppressed ATO-induced mitochondrial membrane depolarization, and significantly altered the apoptotic rate by enhancing endogenous antioxidative enzyme activity and ROS generation. Signal transduction studies indicated that Sal A suppressed the ATO-induced activation of the MAPK pathway. More importantly, JNK, ERK, and p38 inhibitors mimicked the cytoprotective activity of Sal A against ATO-induced injury in H9c2 cells by increasing cell viability, up-regulating Bcl-2 protein expression, and down-regulating both Bax and caspase-3 protein expression. Conclusion: Sal A decreases the ATO-induced apoptosis and necrosis of H9c2 cells, and the underlying mechanisms of this protective effect of Sal A may be connected with the MAPK pathways.
Processing and properties of poly(ester–urethane)/modified montmorillonite nanocomposite foams derived from novel diol and tolylene-2,4-diisocyanate

In this attempt, we have designed novel poly(ester–urethane) (PEU) using poly(di(ethylene glycol)/trimethylolpropane-alt-adipic acid), tolylene-2,4-diisocyanate, and novel diol. Later, PEU has been employed for the fabrication of nanocomposites and foams with hydroxyl-modified montmorillonite (MMT-OH) via in situ polymerization. Afterward, structure and physical properties of PEU/MMT-OH nanocomposites and foams have been explored using various techniques. Physical characteristics of nanocomposites and foams seemed to be dependent upon polyurethane structure, modification of layered silicate and physical interaction between matrix and organoclay platelets. Field emission scanning electron microscopy revealed distorted honeycomb morphology of PEU/MMT-OH 1–5 foams, while PEU/MMT-OH 1–5 nanocomposites depicted dispersed MMT-OH in the matrix. Increased cell density in nanocomposite foams was also observed relative to pure PEU foam. PEU/MMT-OH 5 (5 wt% MMT-OH) foam showed improved tensile strength of 58.1 MPa relative to PEU/MMT-OH 1 foam (56.8 MPa). The density of the foams was also increased (0.7–1.7 g cm−1) with clay loading. The 10% thermal decomposition temperature of PEU/MMT-OH 1–5 foams measured by thermogravimetric analysis was in the range of 431–465°C. Percentage of water absorption was also measured for the foam materials. Dynamic mechanical thermal analysis of PEU/MMT-OH 5 foam with 5 wt% nanofiller showed higher glass transition temperature (Tg) of 129°C relative to PEU/MMT-OH 1 (Tg 116°C). UL 94 and limiting oxygen index results showed that PEU/MMT-OH 1–5 foam had increased nonflammability (V-0 rating) with the clay loading. Silicate layers of MMT-OH were well exfoliated in PEU matrix due to chemical reaction between the hydroxyl of MMT-OH and functional groups of PEU. The morphology, mechanical, thermal, and flame retardant properties of PEU/MMT-OH 1–5 foams were found to be superior to those of PEU/MMT-OH 1–5 nanocomposites.
BIASES IN FORENSIC JUDGMENTS

This chapter integrates from cognitive neuroscience, cognitive psychology, and social psychology the basic science of bias in human judgment as relevant to judgments and decisions by forensic mental health professionals. Forensic mental health professionals help courts make decisions in cases when some question of psychology is relevant to the legal issue, such as in insanity cases, child custody hearings, and psychological injuries in civil suits. The legal system itself and many people involved, such as jurors, assume mental health experts are “objective” and untainted by bias. However, basic psychological science from several branches of the discipline suggest the law’s assumption about experts’ protection from bias is wrong. Indeed, several empirical studies now show clear evidence of (unintentional) bias in forensic mental experts’ judgments and decisions. In this chapter, we explain the science of how and why human judgments are susceptible to various kinds of bias. We describe dual-process theories from cognitive neuroscience, cognitive psychology, and social psychology that can help explain biased judgments. We review the empirical evidence to date specifically about cognitive and social psychological biases in forensic mental health judgments, weaving in related literature about biases in other types of expert judgment with hypotheses about how forensic experts are likely affected by these biases. We close with a discussion of directions for future research and practice.
Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification

Background Individuals with mild cognitive impairment (MCI) have a substantially increased risk of developing dementia due to Alzheimer's disease (AD). In this study, we developed a multivariate prognostic model for predicting MCI-to-dementia progression at the individual patient level. Methods Using baseline data from 259 MCI patients and a probabilistic, kernel-based pattern classification approach, we trained a classifier to distinguish between patients who progressed to AD-type dementia (n = 139) and those who did not (n = 120) during a three-year follow-up period. More than 750 variables across four data sources were considered as potential predictors of progression. These data sources included risk factors, cognitive and functional assessments, structural magnetic resonance imaging (MRI) data, and plasma proteomic data. Predictive utility was assessed using a rigorous cross-validation framework. Results Cognitive and functional markers were most predictive of progression, while plasma proteomic markers had limited predictive utility. The best performing model incorporated a combination of cognitive/functional markers and morphometric MRI measures and predicted progression with 80% accuracy (83% sensitivity, 76% specificity, AUC = 0.87). Predictors of progression included scores on the Alzheimer's Disease Assessment Scale, Rey Auditory Verbal Learning Test, and Functional Activities Questionnaire, as well as volume/cortical thickness of three brain regions (left hippocampus, middle temporal gyrus, and inferior parietal cortex). Calibration analysis revealed that the model is capable of generating probabilistic predictions that reliably reflect the actual risk of progression. Finally, we found that the predictive accuracy of the model varied with patient demographic, genetic, and clinical characteristics and could be further improved by taking into account the confidence of the predictions. Conclusions We developed an accurate prognostic model for predicting MCI-to-dementia progression over a three-year period. The model utilizes widely available, cost-effective, non-invasive markers and can be used to improve patient selection in clinical trials and identify high-risk MCI patients for early treatment.
Unravelling a new catabolic pathway of C‐19 steroids in Mycobacterium smegmatis

In this work, we have characterized the C-19+ gene cluster (MSMEG_2851 to MSMEG_2901) of Mycobacterium smegmatis. By in silico analysis, we have identified the genes encoding enzymes involved in the modification of the A/B steroid rings during the catabolism of C-19 steroids in certain M. smegmatis mutants mapped in the PadR-like regulator (MSMEG_2868), that constitutively express the C-19+ gene cluster. By using gene complementation assays, resting-cell biotransformations and deletion mutants, we have characterized the most critical genes of the cluster, that is, kstD2, kstD3, kshA2, kshB2, hsaA2, hsaC2 and hsaD2. These results have allowed us to propose a new catabolic route named C-19+ pathway for the mineralization of C-19 steroids in M. smegmatis. Our data suggest that the deletion of the C-19+ gene cluster may be useful to engineer more robust and efficient M. smegmatis strains to produce C-19 steroids from sterols. Moreover, the new KshA2, KshB2, KstD2 and KstD3 isoenzymes may be useful to design new microbial cell factories for the 9α-hydroxylation and/or Δ1-dehydrogenation of 3-ketosteroids.
From molecules to patients: exploring the therapeutic role of soluble guanylate cyclase stimulators

Abstract Nitric oxide (NO) signaling represents one of the major regulatory pathways for cardiovascular function. After the discovery of NO, awarded with the Nobel Prize in 1998, this signaling cascade was stepwise clarified. We now have a good understanding of NO production and NO downstream targets such as the soluble guanylyl cyclases (sGCs) which catalyze cGMP production. Based on the important role of NO-signaling in the cardiovascular system, intense research and development efforts are currently ongoing to fully exploit the therapeutic potential of cGMP increase. Recently, NO-independent stimulators of sGC (sGC stimulators) were discovered and characterized. This new compound class has a unique mode of action, directly binding to sGC and triggering cGMP production. The first sGC stimulator made available to patients is riociguat, which was approved in 2013 for the treatment of different forms of pulmonary hypertension (PH). Besides riociguat, other sGC stimulators are in clinical development, with vericiguat in phase 3 clinical development for the treatment of chronic heart failure (HF). Based on the broad impact of NO/cGMP signaling, sGC stimulators could have an even broader therapeutic potential beyond PH and HF. Within this review, the NO/sGC/cGMP/PKG/PDE-signaling cascade and the major pharmacological intervention sites are described. In addition, the discovery and mode of action of sGC stimulators and the clinical development in PH and HF is covered. Finally, the preclinical and clinical evidence and treatment approaches for sGC stimulators beyond these indications and the cardiovascular disease space, like in fibrotic diseases as in systemic sclerosis (SSc), are reviewed.
Analysis of Data Generated From Multidimensional Type-1 and Type-2 Fuzzy Membership Functions

Due to the numerous applications that utilize different types of fuzzy membership functions (MFs), it may sometimes be difficult to choose an appropriate MF for a particular application. In this paper, we establish preliminary guidelines to direct this selection by proposing a three-stage method. In the “forward” stage, different MFs, such as crisp MFs, type-1 (T1) fuzzy MFs, and type-2 (T2) fuzzy MFs, are generated from multidimensional data sets. Next, in the “reverse” stage, data is generated back from these MFs by considering different bin sizes. In doing so, various data sets may be generated for different applications which require fuzzy data. Finally, for the “similarity analysis” stage, we propose an iterative algorithm that makes use of the results of Wilcoxon signed rank (WSR) and Wilcoxon rank sum (WRS) tests to compare the original data and the generated data. From the results of these tests, recommendations concerning the suitability of MFs for a specific application may be suggested by observing the accuracy of representation and the requirements of the application. With this analysis, the objective is to gain insight on when T2 fuzzy sets may be considered to outperform T1 fuzzy sets, and vice versa. Several examples are provided using synthetic and real data to validate the iterative algorithm for data sets in various dimensions.
Reactive NaN Repair for Applying Approximate Memory to Numerical Applications

Applications in the AI and HPC fields require much memory capacity, and the amount of energy consumed by main memory of server machines is ever increasing. Energy consumption of main memory can be greatly reduced by applying approximate computing in exchange for increased bit error rates. AI and HPC applications are to some extent robust to bit errors because small numerical errors are amortized by their iterative nature. However, a single occurrence of a NaN due to bit-flips corrupts the whole calculation result. The issue is that fixing every bit-flip using ECC incurs too much overhead because the bit error rate is much higher than in normal environments. We propose a low-overhead method to fix NaNs when approximate computing is applied to main memory. The main idea is to reactively repair NaNs while leaving other non-fatal numerical errors as-is to reduce the overhead. We implemented a prototype by leveraging floating-point exceptions of x86 CPUs, and the preliminary evaluations showed that our method incurs negligible overhead.
Design of a compact unidirectional antenna for microwave imaging applications

The article reports the design and analysis of a compact three-dimensional microstrip antenna with an operating bandwidth from 1.14 GHz-2.53 GHz. The proposed design achieves a directional radiation pattern with 75.75% of impedance bandwidth. The lower frequency range enables proper signal penetration into the human body. The antenna shows the front-to-back ratio around 2.28 dB-4.2 dB and efficiency more than 87%. The overall dimension of the designed antenna is 0.62 × 0.08 × 0.012 λ3, where λ represents the effective wavelength of the smallest frequency in the required band of operation. This antenna can be used for microwave imaging systems for biomedical applications.
Live demo: Platform for closed loop neuromodulation based on dual mode biosignals

We demonstrate a novel feedback mechanism to implement closed loop neuromodulation based on a combination of electrical and chemical neural signal recording. The platform consists of our custom designed multi-channel amplifier for recording electrical and chemical signals (such as pH), data processing unit and the stimulator IC unit. A novelty of our approach is that the neural chemical signals are used to initiate stimulation and electrical compound action potentials (CAPs) to determine stimulation dosage. The proposed adaptive stimulation platform has been implemented in the context of Vagus Nerve Stimulation (VNS) for obesity therapy. The demonstration is based on externally generated signals derived from our in vivo experiments on physiological stimulation of the gastric branch of vagus nerve by injection of gut hormone cholecystokinin (CCK).
Electronic sputtering of thin lithium fluoride films induced by swift heavy ions

The sputtering of secondary ions from thin lithium fluoride films of different thickness (2 nm ≤ z ≤ 106 nm) irradiated by swift heavy ions (Ni and Ge: ∼10 MeV/u) was studied under ultrahigh-vacuum conditions by a time-of-flight imaging technique (XY-TOF-SIMS). Most of the positive ions emitted are (LiF)nLi+ clusters. Cluster structure and stability (magic numbers) are analyzed. A pronounced dependence of sputtered particle yields on the film thickness z was observed. The onset of large cluster emission occurs at a thickness of about z = 20 nm. For thinner films, z = 2 and z = 4 nm, only small clusters (n < 6) are ejected. A threshold for the emission of large clusters was also observed as a function of the electronic stopping power Se at around 6 keV nm−1. This leads to the important conclusion that both a sufficient amount of material (z-dependence) and of deposited energy (Se-dependence) are needed for large ionic cluster emission to occur.
Preparation of MoO3 QDs through combining intercalation and thermal exfoliation

Lots of top-down approaches by weakening the van der Waals interaction between adjacent layers and breaking up the covalent chemical bonds in each layer have been reported to prepare QDs of layered materials due to the stacked structures. However, much attention has been focused on graphene and layered transition-metal dichalcogenides (TMDs), seldomly on layered transition-metal oxides (TMOs). Herein, a modified top-down method combining intercalation and thermal exfoliation is reported to prepare high-yield QDs of layered MoO3. Alkylamine was first intercalated into MoO3 layers to weaken the van der Waals forces. Then, the covalent bonds in each MoO3 layer were broken down under a sudden increase in gas pressure generated by the decomposition of alkylamines after rapid heating. These fractured particles were further incised to QDs by sonication. The as-prepared MoO3 QD dispersion showed a plasmon resonance after simulated solar light illumination. Surprisingly, their plasmon peak red shifted with an extended illumination time, which was different from the reported MoO3 nanosheets. This reported method is expected to extend to other QDs of layered materials providing that their bulk materials can also be intercalated.
Enhancing Particle Swarm Algorithm for Multimodal Optimization Problems

Particle swarm optimization (PSO) is an intelligent algorithm inspired by swarm intelligence. It has been shown that PSO is a good optimizer on various optimization problems. Due to the inherent randomness of PSO, it easily falls into local minima when dealing with multimodal optimization problems. In order to enhance the performance of PSO on multimodal problems, this paper proposes a novel PSO algorithm by employing adaptive parameter control and example-based learning. Conducted experiments on nine well-known multimodal problems show that our approach outperforms the standard PSO, unified PSO (UPSO), fully informed PSO (FIPS), fitness-distance-ratio based PSO (FDR-PSO), cooperative PSO (CPSO-H) and comprehensive learning PSO (CLPSO) in terms of the solution accuracy.
ADRB3 Gene Trp64Arg Polymorphism and Essential Hypertension: A Meta-Analysis Including 9,555 Subjects

Background: Presence of the β3-Adrenergic receptor (ADRB3) gene Trp64Arg (T64A) polymorphism may be associated with an increased susceptibility for essential hypertension (EH). A clear consensus, however, has yet to be reached. Objective and methods: To further elucidate the relationship between the ADRB3 gene Trp64Arg polymorphism and EH, a meta-analysis of 9,555 subjects aggregated from 16 individual studies was performed. The combined odds ratios (ORs) and their corresponding 95% confidence intervals (CI) were evaluated using either a random or fixed effect model. Results: We found a marginally significant association between ADRB3 gene Trp64Arg polymorphism and EH in the whole population under the additive genetic model (OR: 1.200, 95% CI: 1.00–1.43, P = 0.049). Association within the Chinese subgroup, however, was significant under allelic (OR: 1.150, 95% CI: 1.002–1.320, P = 0.046), dominant (OR: 1.213, 95% CI: 1.005–1.464, P = 0.044), heterozygous (OR: 1.430, 95% CI:1.040–1.970, P = 0.03), and additive genetic models (OR: 1.280, 95% CI: 1.030–1.580, P = 0.02). A significant association was also found in the Caucasian subgroup under allelic (OR: 1.850, 95% CI: 1. 260–2.720, P = 0.002), dominant (OR: 2.004, 95% CI: 1.316–3.052, P = 0.001), heterozygous (OR: 2.220, 95% CI: 1.450–3.400, P = 0.0002), and additive genetic models (OR: 2.000, 95% CI: 1. 330–3.010, P = 0.0009). Conclusions: The presence of the ADRB3 gene Trp64Arg polymorphism is positively associated with EH, especially in the Chinese and Caucasian population. The Arg allele carriers of ADRB3 gene Trp64Arg polymorphism may be at an increased risk for developing EH.