Miracle-dz/UniFAR-FTUs huggingface dataset:
Extracranial Artery Stenosis Is Associated With Total MRI Burden of Cerebral Small Vessel Disease in Ischemic Stroke Patients of Suspected Small or Large Artery Origins

Background and Purpose: Extracranial artery stenosis (ECAS) is related to individual imaging markers of cerebral small vessel disease (cSVD). However, little has been reported on the association between ECAS and the total burden of cSVD as assessed by magnetic resonance imaging (MRI). The purpose of this study was to investigate the relationship between ECAS and cSVD burden in patients with ischemic stroke of suspected small or large artery origin. Methods: We reviewed consecutive patients with ischemic stroke of suspected small or large artery origin who underwent color Doppler ultrasonography and brain MRI. Bilateral extracranial cerebral arteries including common carotid artery, internal carotid artery (ICA), and proximal vertebral artery (VA, ostium, V2–3 segments) were assessed using color Doppler ultrasonography. ECAS severity was classified as no/mild stenosis, moderate stenosis, severe stenosis, or occlusion. The total cSVD score was assessed by awarding one point according to the load of each of these cSVD markers as determined using MRI; lacunar infarction, white matter hyperintensities, cerebral microbleeds, and enlarged perivascular spaces. The relationship between ECAS severity and cSVD burden according to MRI was examined. Results: Two hundred and twenty one patients were included in this study (mean age 61 ± 12 years, 75.6% male). Hypertension, current smoking, hyperlipidaemia, and diabetic mellitus were frequent among the patients (67.4, 45.7, 43.9, and 36.7%, respectively), while the other vascular risk factors including previous stroke or TIA and alcohol excess were less frequent (19.0 and 15.4%, respectively). Patients with higher total cSVD burden was significantly older and had severer ECAS. The frequency of hypertension was significantly higher in patients with higher total cSVD burden. This analysis indicated that that increasing ECAS severity (from no stenosis through to 100%) was independently associated with increasing total cSVD score after adjusting for other vascular risk factors (odds ratio 1.76, 95% CI [1.16–2.69]). Conclusions: In this study, high levels of ECAS from ultrasound evidence were associated with coexisting advanced cerebral cSVD in ischemic stroke patients of suspected small or large artery origin. Further studies are required to determine if and how extracranial arterial imaging helps reduce cSVD burden or improves cognitive function.
TV vs. YouTube: TV Advertisements Capture More Visual Attention, Create More Positive Emotions and Have a Stronger Impact on Implicit Long-Term Memory

In an experiment, effects of commercials that are either shown within a TV program or embedded in YouTube videos were compared. These two media environments have not yet been compared empirically in terms of their advertising impact. A within-subjects design and a multi-method approach were used (N = 36). Eye tracking data show that more attention is allocated to advertisements that appear within a TV program compared to the YouTube-condition and the viewing experience elicited more positive emotions in the TV-condition. Two days after reception, no difference in recognition, likeability, and purchase intention occurred, but in terms of implicit long-term memory: In the TV condition, brands that were previously advertised but no longer remembered elicited stronger skin conductance change than brands for which no advertisements had previously been shown. In terms of advertising impact, TV seems to still be the better choice for advertisers. Presentation mode should be considered in future evaluation of advertisement potential.
Toward Regeneration of the Heart: Bioengineering Strategies for Immunomodulation

Myocardial Infarction (MI) is the most common cardiovascular disease. An average-sized MI causes the loss of up to 1 billion cardiomyocytes and the adult heart lacks the capacity to replace them. Although post-MI treatment has dramatically improved survival rates over the last few decades, more than 20% of patients affected by MI will subsequently develop heart failure (HF), an incurable condition where the contracting myocardium is transformed into an akinetic, fibrotic scar, unable to meet the body's need for blood supply. Excessive inflammation and persistent immune auto-reactivity have been suggested to contribute to post-MI tissue damage and exacerbate HF development. Two newly emerging fields of biomedical research, immunomodulatory therapies and cardiac bioengineering, provide potential options to target the causative mechanisms underlying HF development. Combining these two fields to develop biomaterials for delivery of immunomodulatory bioactive molecules holds great promise for HF therapy. Specifically, minimally invasive delivery of injectable hydrogels, loaded with bioactive factors with angiogenic, proliferative, anti-apoptotic and immunomodulatory functions, is a promising route for influencing the cascade of immune events post-MI, preventing adverse left ventricular remodeling, and offering protection from early inflammation to fibrosis. Here we provide an updated overview on the main injectable hydrogel systems and bioactive factors that have been tested in animal models with promising results and discuss the challenges to be addressed for accelerating the development of these novel therapeutic strategies.
OBLIVE: Seamless Code Obfuscation for Java Programs and Android Apps

Malicious reverse engineering is a problem when a program is delivered to the end users. In fact, an end user might try to understand the internals of the program, in order to elaborate an attack, tamper with the software and alter its behaviour. Code obfuscation represents a mitigation to these kind of malicious reverse engineering and tampering attacks, making programs harder to analyze (by a tool) and understand (by a human). In this paper, we present Oblive, a tool meant to support developers in applying code obfuscation to their programs. A developer is required to specify security requirements as single-line code annotations only. Oblive, then, reads annotations and applies state-of-the-art data and code obfuscation, namely xormask with opaque mask and java-to-native code, while the program is being compiled. Oblive is successfully applied both to plain Java programs and Android apps. Showcase videos are available for the code obfuscation part https://youtu.be/Bml-BkKP3CU and for the data obfuscation part https://youtu.be/zbizYVK42ps.
Should You Consider Adware as Malware in Your Study?

Empirical validations of research approaches eventually require a curated ground truth. In studies related to Android malware, such a ground truth is built by leveraging Anti-Virus (AV) scanning reports which are often provided free through online services such as VirusTotal. Unfortunately, these reports do not offer precise information for appropriately and uniquely assigning classes to samples in app datasets: AV engines indeed do not have a consensus on specifying information in labels. Furthermore, labels often mix information related to families, types, etc. In particular, the notion of “adware” is currently blurry when it comes to maliciousness. There is thus a need to thoroughly investigate cases where adware samples can actually be associated with malware (e.g., because they are tagged as adware but could be considered as malware as well). In this work, we present a large-scale analytical study of Android adware samples to quantify to what extent “adware should be considered as malware”. Our analysis is based on the Androzoo repository of 5 million apps with associated AV labels and leverages a state-of-the-art label harmonization tool to infer the malicious type of apps before confronting it against the ad families that each adware app is associated with. We found that all adware families include samples that are actually known to implement specific malicious behavior types. Up to 50% of samples in an ad family could be flagged as malicious. Overall the study demonstrates that adware is not necessarily benign.
A Study on the Interplay between Pull Request Review and Continuous Integration Builds

Modern code review (MCR) is nowadays well-adopted in industrial and open source projects. Recent studies have investigated how developers perceive its ability to foster code quality, developers’ code ownership, and team building. MCR is often being used with automated quality checks through static analysis tools, testing or, ultimately, through automated builds on a Continuous Integration (CI) infrastructure. With the aim of understanding how developers use the outcome of CI builds during code review and, more specifically, during the discussion of pull requests, this paper empirically investigates the interplay between pull request discussion and the use of CI by means of 64,865 pull request discussions belonging to 69 open source projects. After having analyzed to what extent a build outcome in uences the pull request merger, we qualitatively analyze the content of 857 pull request discussions. Also, we complement such an analysis with a survey involving 13 developers. While pull requests with passed build have a higher chance of being merged than failed ones, and while survey participants confirmed this quantitative finding, other process-related factors play a more important role in the pull request merge decision. Also, the survey participants point out cases where a pull request can be merged in presence of a CI failure, e.g., when a new pull request is opened to cope with the failure, when the failure is due to minor static analysis warnings. The study also indicates that CI introduces extra complexity, as in many pull requests developers have to solve non-trivial CI configuration issues.
Geographic and Temporal Patterns of Antimicrobial Resistance in Pseudomonas aeruginosa Over 20 Years From the SENTRY Antimicrobial Surveillance Program, 1997–2016

Abstract Background The SENTRY Antimicrobial Surveillance Program was established in 1997 and encompasses over 750 000 bacterial isolates from ≥400 medical centers worldwide. Among the pathogens tested, Pseudomonas aeruginosa remains a common cause of multidrug-resistant (MDR) bloodstream infections and pneumonia in hospitalized patients. In the present study, we reviewed geographic and temporal trends in resistant phenotypes of P. aeruginosa over 20 years of the SENTRY Program. Methods From 1997 to 2016, 52 022 clinically significant consecutive isolates were submitted from ≥200 medical centers representing the Asia-Pacific region, Europe, Latin America, and North America. Only 1 isolate per patient per infection episode was submitted. Isolates were identified by standard algorithms and/or matrix-assisted laser desorption ionization-time of flight mass spectrometry. Susceptibility testing was performed by Clinical and Laboratory Standards Institute (CLSI) methods and interpreted using CLSI and European Committee on Antimicrobial Susceptibility Testing 2018 criteria at JMI Laboratories. Results The most common infection from which P. aeruginosa was isolated was pneumonia in hospitalized patients (44.6%) followed by bloodstream infection (27.9%), with pneumonia having a slightly higher rate of MDR (27.7%) than bloodstream infections (23.7%). The region with the highest percentage of MDR phenotypes was Latin America (41.1%), followed by Europe (28.4%). The MDR rates were highest in 2005–2008 and have decreased in the most recent period. Colistin was the most active drug tested (99.4% susceptible), followed by amikacin (90.5% susceptible). Conclusions Over the 20 years of SENTRY Program surveillance, the rate of MDR P. aeruginosa infections has decreased, particularly in Latin America. Whether the trend of decreasing resistance in P. aeruginosa is maintained will be documented in future SENTRY Program and other surveillance reports.
Factors Related to Breast Cancer Screening in Women in the Northern Part of Iran: A Cross-Sectional Study

BACKGROUND: Breast cancer is the most common type of cancer in women and affects many women around the world each year. Breast cancer screening is one of the best strategies that can be used to reduce the death rate from the disease. Different factors influence the breast cancer screening rate. AIM: This study aimed to investigate the factors that affect the screening of women for breast cancer in the northern part of Iran. MATERIAL AND METHODS: This cross-sectional study was conducted in the Mazandaran Province of Iran in 2016 on 1,165 women who participate in breast cancer screening programs, using a cluster-sampling method. A valid and reliable researcher-made questionnaire was used to collect the data. The collected data were analysed using descriptive and inferential statistics via SPSS 21. RESULTS: In this study, 62% of the women had a history of breast self-examinations, 41.1% had breast examinations by healthcare staff, and 21.7% received mammography. The woman’s age, age at first marriage, age at onset of sexual intercourse, occupation, spouse’s occupation, household income, health status, history of infertility, smoking, and decision-maker on issues of sexual and reproductive health (SRH) were the best predictors of participation in screening for breast cancer (P < 0.05). CONCLUSION: To encourage participation in breast cancer screening programs, women should be encouraged to seek preventive care. Also, factors that
Detecting Data Races Caused by Inconsistent Lock Protection in Device Drivers

Data races are often hard to detect in device drivers, due to the non-determinism of concurrent execution. According to our study of Linux driver patches that fix data races, more than 38% of patches involve a pattern that we call inconsistent lock protection. Specifically, if a variable is accessed within two concurrently executed functions, the sets of locks held around each access are disjoint, at least one of the locksets is non-empty, and at least one of the involved accesses is a write, then a data race may occur.In this paper, we present a runtime analysis approach, named DILP, to detect data races caused by inconsistent lock protection in device drivers. By monitoring driver execution, DILP collects the information about runtime variable accesses and executed functions. Then after driver execution, DILP analyzes the collected information to detect and report data races caused by inconsistent lock protection. We evaluate DILP on 12 device drivers in Linux 4.16. 9, and find 25 real data races.
Characterizing and Detecting Inefficient Image Displaying Issues in Android Apps

Mobile applications (apps for short) often need to display images. However, inefficient image displaying (IID) issues are pervasive in mobile apps, and can severely impact app performance and user experience. This paper presents an empirical study of 162 real-world IID issues collected from 243 popular open-source Android apps, validating the presence and severity of IID issues, and then sheds light on these issues’ characteristics to support future research on effective issue detection. Based on the findings of this study, we developed a static IID issue detection tool TAPIR and evaluated it with real-world Android apps. The experimental evaluations show encouraging results: TAPIR detected 43 previously-unknown IID issues in the latest version of the 243 apps, 16 of which have been confirmed by respective developers and 13 have been fixed.
Deep Learning and Random Forest Approach for Finding the Optimal Traditional Chinese Medicine Formula for Treatment of Alzheimer's Disease

It has demonstrated that glycogen synthase kinase 3β (GSK3β) is related to Alzheimer's disease (AD). On the basis of the world largest traditional Chinese medicine (TCM) database, a network-pharmacology-based approach was utilized to investigate TCM candidates that can dock well with multiple targets. Support vector machine (SVM) and multiple linear regression (MLR) methods were utilized to obtain predicted models. In particular, the deep learning method and the random forest (RF) algorithm were adopted. We achieved R2 values of 0.927 on the training set and 0.862 on the test set with deep learning and 0.869 on the training set and 0.890 on the test set with RF. Besides, comparative molecular similarity indices analysis (CoMSIA) was performed to get a predicted model. All of the training models achieved good results on the test set. The stability of GSK3β protein-ligand complexes was evaluated using 100 ns of MD simulation. Methyl 3- O-feruloylquinate and cynanogenin A induced both more compactness to the GSK3β complex and stable conditions at all simulation times, and the GSK3β complex also had no substantial fluctuations after a simulation time of 5 ns. For TCM molecules, we used the trained models to calculate predicted bioactivity values, and the optimum TCM candidates were obtained by ranking the predicted values. The results showed that methyl 3- O-feruloylquinate contained in Phellodendron amurense and cynanogenin A contained in Cynanchum atratum are capable of forming stable interactions with GSK3β.
Digit ratio predicts the number of lifetime recruits in female collared flycatchers

The early environment in which an organism grows can have long-lasting impacts on both its phenotype and fitness. However, assessing this environment comprehensively is a formidable task. The relative length of the second to the fourth digit (2D : 4D) is a broadly studied skeletal trait that is fixed for life during ontogeny. 2D : 4D has been shown to indicate various early effects including the perinatal steroid milieu in both humans and non-human animals. However, the fitness relevance of the early effects indicated by 2D : 4D remains unknown. Here, we investigated hindlimb 2D : 4D and measures of lifetime performance in wild collared flycatcher (Ficedula albicollis) females. We found that females with higher 2D : 4D had a greater number of recruiting offspring to the breeding population. This was the case despite the fact that such females did not lay more eggs or breed more frequently during their reproductive life. Our results support the suggestion that 2D : 4D, known to be a retrospective marker of perinatal development, positively associates with female quality in the collared flycatcher.
Twenty Years of the SENTRY Antifungal Surveillance Program: Results for Candida Species From 1997–2016

Abstract Background The emergence of antifungal resistance threatens effective treatment of invasive fungal infection (IFI). Invasive candidiasis is the most common health care–associated IFI. We evaluated the activity of fluconazole (FLU) against 20 788 invasive isolates of Candida (37 species) collected from 135 medical centers in 39 countries (1997–2016). The activity of anidulafungin, caspofungin, and micafungin (MCF) was evaluated against 15 308 isolates worldwide (2006–2016). Methods Species identification was accomplished using phenotypic (1997–2001), genotypic, and proteomic methods (2006–2016). All isolates were tested using reference methods and clinical breakpoints published in the Clinical and Laboratory Standards Institute documents. Results A decrease in the isolation of Candida albicans and an increase in the isolation of Candida glabrata and Candida parapsilosis were observed over time. Candida glabrata was the most common non–C. albicans species detected in all geographic regions except for Latin America, where C. parapsilosis and Candida tropicalis were more common. Six Candida auris isolates were detected: 1 each in 2009, 2013, 2014, and 2015 and 2 in 2016; all were from nosocomial bloodstream infections and were FLU-resistant (R). The highest rates of FLU-R isolates were seen in C. glabrata from North America (NA; 10.6%) and in C. tropicalis from the Asia-Pacific region (9.2%). A steady increase in isolation of C. glabrata and resistance to FLU was detected over 20 years in the United States. Echinocandin-R (EC-R) ranged from 3.5% for C. glabrata to 0.1% for C. albicans and C. parapsilosis. Resistance to MCF was highest among C. glabrata (2.8%) and C. tropicalis (1.3%) from NA. Mutations on FKS hot spot (HS) regions were detected among 70 EC-R isolates (51/70 were C. glabrata). Most isolates harboring FKS HS mutations were resistant to 2 or more ECs. Conclusions EC-R and FLU-R remain uncommon among contemporary Candida isolates; however, a slow and steady emergence of resistance to both antifungal classes was observed in C. glabrata and C. tropicalis isolates.
Mobile phone use while riding a motorcycle and crashes among university students

Abstract Objective: Motorcycle crashes are a significant road safety challenge, particularly in many low- and middle-income countries where motorcycles represent the vast majority of their vehicle fleet. Though risky riding behaviors, such as speeding and riding under the influence of alcohol, have been identified as important contributors to motorcycle crashes, little is understood about the effect of using a mobile phone while riding on motorcycle crash involvement. This article investigates crash involvement among motorcycle riders with risky riding behaviors, particularly using a mobile phone while riding. Methods: Data were obtained from an online survey of university students’ risky riding behaviors in Vietnam administered between March and May 2016 (n = 665). Results: Results show that 40% of motorcycle riders reported to have experienced a crash/fall and nearly 24% of motorcycle riders indicated that they had been injured in a crash/fall. Effects of mobile phone use while riding on safety of motorcycle riders are highlighted. Specifically, more frequent use of a mobile phone for texting or searching for information while riding is associated with a higher chance of being involved in a crash/fall. The results also show that drink riding is associated with a higher chance of being injured. Conclusions: Overall this article reveals significant safety issues of using a mobile phone while riding a motorcycle, providing valuable insight for designing education and publicity campaigns.
RMut: R package for a Boolean sensitivity analysis against various types of mutations

There have been many in silico studies based on a Boolean network model to investigate network sensitivity against gene or interaction mutations. However, there are no proper tools to examine the network sensitivity against many different types of mutations, including user-defined ones. To address this issue, we developed RMut, which is an R package to analyze the Boolean network-based sensitivity by efficiently employing not only many well-known node-based and edgetic mutations but also novel user-defined mutations. In addition, RMut can specify the mutation area and the duration time for more precise analysis. RMut can be used to analyze large-scale networks because it is implemented in a parallel algorithm using the OpenCL library. In the first case study, we observed that the real biological networks were most sensitive to overexpression/state-flip and edge-addition/-reverse mutations among node-based and edgetic mutations, respectively. In the second case study, we showed that edgetic mutations can predict drug-targets better than node-based mutations. Finally, we examined the network sensitivity to double edge-removal mutations and found an interesting synergistic effect. Taken together, these findings indicate that RMut is a flexible R package to efficiently analyze network sensitivity to various types of mutations. RMut is available at https://github.com/csclab/RMut.