Miracle-dz/UniFAR-FTUs huggingface dataset:
DaRE: A Cross-Domain Recommender System with Domain-aware Feature Extraction and Review Encoder

Recent advents in recommender systems, especially in text-aided methodsandCross-DomainRecommendation(CDR),leadto promising results in solving data-sparsity and cold-start problems. Despite such progress, existing CDR approaches have some critical defects such as requiring overlapping users for the knowledge transfer or ignoring domain-aware features. In addition, text-aided methods, in general, emphasize aggregated item reviews and fail to capture the latent of individual reviews. To overcome such limitations, we propose a novel method, named Domain-aware Feature Extraction and Review Encoder (DaRE), which consists of the key components; domain-aware text analysis module, and review encoders. DaRE debilitates noises by separating domain-invariant features from domain-specific features through selective adversarial training. Then, with the features extracted from aggregated reviews, the review encoder fine-tunes the representations by aligning them with the features derived from individual reviews. The experiments on four real-world datasets show the superiority of DaRE over state-of-the-art single-domain and cross-domain methodologies, achieving 9.2 % and 3.6 % improvements, respectively.
A Reinforcement Learning Approach to Parameter Selection for Distributed Optimization in Power Systems

—With the increasing penetration of distributed en- ergy resources, distributed optimization algorithms have at-tracted signiﬁcant attention for power systems applications due to their potential for superior scalability, privacy, and robustness to a single point-of-failure. The Alternating Direction Method of Multipliers (ADMM) is a popular distributed optimization algorithm; however, its convergence performance is highly de- pendent on the selection of penalty parameters, which are usually chosen heuristically. In this work, we use reinforcement learning (RL) to develop an adaptive penalty parameter selection policy for the AC optimal power ﬂow (ACOPF) problem solved via ADMM with the goal of minimizing the number of iterations until convergence. We train our RL policy using deep Q-learning, and show that this policy can result in signiﬁcantly accelerated convergence (up to a 59% reduction in the number of iterations compared to existing, curvature-informed penalty parameter selection methods). Furthermore, we show that our RL policy demonstrates promise for generalizability, performing well under unseen loading schemes as well as under unseen losses of lines and generators (up to a 50% reduction in iterations). This work thus provides a proof-of-concept for using RL for parameter selection in ADMM for power systems applications.
MFCPji&MFIDji: New ImageJ Macros To Analyze Structure Formed In Magnetic Nanofluid

The aqueous magnetic nanofluid consists of superparamagnetic nanoparticles, with a typical size 10-12 nm. On the application of the magnetic field, these nanoparticles align heterogeneously and form a chain or chain-like structure. This structure is observed using a microscope. Although such chain or microstructure formation is well reported in many articles, the method to identify and determine chain parameters, e.g., chain length, width, and associated counts, are scare. Similarly, inter-chain or successive distance is one of the critical parameters for the development of magnetic nanofluid based devices. This paper describes Magnetic Field induced Chain Parameters (MFCP) and Magnetic Field induced Interchain Distance (MFID) a set of new ImageJ methods to identify and determine (i) chain length, width, and associated counts, along with (ii) successive distance of the chains respectively in the magnetic nanofluid. This utilises a macro files such as MFCPji.txt and MFIDji.txt for ImageJ, which can be used on microscopic image of magnetic nanofluid without and with application of magnetic field. The method requires no specialised scientific equipment and can be run entirely using free to download software. The examples of microstructure formations in two different magnetic fluids (A & B) are discussed. The results of the associated weighted average chain width and counts, as well as the successive distance between the chains, are reported. The chain parameters are useful to determine diffraction grating angle. The MFCPji and MFIDji macros has been integrated into a macro tool set that can be configured to be run on ImageJ start up. The MFCPji and MFIDji are available from the following Uniform Resource Locator (URLs): https://github.com/urveshsoni/ImageJ---Macros https://ruchadesailab.wordpress. com/publication/
Drug Similarity and Link Prediction Using Graph Embeddings on Medical Knowledge Graphs

The paper utilizes the graph embeddings generated for entities of a large biomedical database to perform link prediction to capture various new relationships among different entities. A novel node similarity measure is proposed that utilizes the graph embeddings and link prediction scores to find similarity scores among various drugs which can be used by the medical experts to recommend alternative drugs to avoid side effects from original one. Utilizing machine learning on knowledge graph for drug similarity and recommendation will be less costly and less time consuming with higher scalability as compared to traditional biomedical methods due to the dependency on costly medical equipment and experts of the latter ones.
Training Deep Neural Networks via Branch-and-Bound

In this paper, we propose BPGrad, a novel approximate algorithm for deep nueral network training, based on adaptive estimates of feasible region via branch-and-bound. The method is based on the assumption of Lipschitz continuity in objective function, and as a result, it can adaptively determine the step size for the current gradient given the history of previous updates. We prove that, by repeating such a branch-and-pruning procedure, it can achieve the optimal solution within finite iterations. A computationally efficient solver based on BPGrad has been proposed to train the deep neural networks. Empirical results demonstrate that BPGrad solver works well in practice and compares favorably to other stochastic optimization methods in the tasks of object recognition, detection, and segmentation. The code is available at https://github.com/ RyanCV/BPGrad.
Venomave: Targeted Poisoning Against Speech Recognition

Despite remarkable improvements, automatic speech recognition is susceptible to adversarial perturbations. Compared to standard machine learning architectures, these attacks are significantly more challenging, especially since the inputs to a speech recognition system are time series that contain both acoustic and linguistic properties of speech. Extracting all recognition-relevant information requires more complex pipelines and an ensemble of specialized components. Consequently, an attacker needs to consider the entire pipeline. In this paper, we present Venomave, the first training-time poisoning attack against speech recognition. Similar to the predominantly studied evasion attacks, we pursue the same goal: leading the system to an incorrect and attacker-chosen transcription of a target audio waveform. In contrast to evasion attacks, however, we assume that the attacker can only manipulate a small part of the training data without altering the target audio waveform at runtime. We evaluate our attack on two datasets: TIDIGITS and Speech Commands. When poisoning less than 0.17 % of the dataset, Venomave achieves attack success rates of more than 80.0 %, without access to the victim's network architecture or hyperparameters. In a more realistic scenario, when the target audio waveform is played over the air in different rooms, Venomave maintains a success rate of up to 73.3 %. Finally, Venomave achieves an attack transferability rate of 36.4 % between two different model architectures.
Host Stress Signals Stimulate Pneumococcal Transition from Colonization to Dissemination into the Lungs

Microbes acquire unique lifestyles under different environmental conditions. Although this is a widespread occurrence, our knowledge of the importance of various host signals and their impact on microbial behavior is not clear despite the therapeutic value of this knowledge. ABSTRACT Streptococcus pneumoniae is an asymptomatic colonizer of the nasopharynx, but it is also one of the most important bacterial pathogens of humans, causing a wide range of mild to life-threatening diseases. The basis of the pneumococcal transition from a commensal to a parasitic lifestyle is not fully understood. We hypothesize that exposure to host catecholamine stress hormones is important for this transition. In this study, we demonstrated that pneumococci preexposed to a hormone released during stress, norepinephrine (NE), have an increased capacity to translocate from the nasopharynx into the lungs compared to untreated pneumococci. Examination of NE-treated pneumococci revealed major alterations in metabolic profiles, cell associations, capsule synthesis, and cell size. By systemically mutating all 12 two-component and 1 orphan regulatory systems, we also identified a unique genetic regulatory circuit involved in pneumococcal recognition and responsiveness to human stress hormones. IMPORTANCE Microbes acquire unique lifestyles under different environmental conditions. Although this is a widespread occurrence, our knowledge of the importance of various host signals and their impact on microbial behavior is not clear despite the therapeutic value of this knowledge. We discovered that catecholamine stress hormones are the host signals that trigger the passage of Streptococcus pneumoniae from a commensal to a parasitic state. We identify that stress hormone treatment of this microbe leads to reductions in cell size and capsule synthesis and renders it more able to migrate from the nasopharynx into the lungs in a mouse model of infection. The microbe requires the TCS09 protein for the recognition and processing of stress hormone signals. Our work has particular clinical significance as catecholamines are abundant in upper respiratory fluids as well as being administered therapeutically to reduce inflammation in ventilated patients, which may explain why intubation in the critically ill is a recognized risk factor for the development of pneumococcal pneumonia.
Clinical Outcomes of Hydrogel Spacer Injection Space OAR in Men Submitted to Hypofractionated Radiotherapy for Prostate Cancer

Background/Aim: To evaluate the clinical outcomes of men with prostate cancer (PCa) submitted to hydrogel spacer injection before hypofractionated radiotherapy (HRT). Patients and Methods: From April 2018 to April 2020, 32 patients with clinically localized PCa underwent hydrogel injection Space OAR before HRT to the prostate and seminal vesicle; the prescription dose was 60 Gy in 20 fractions, 5 days/week over 4 weeks. PSA levels, genitourinary (GU) and gastrointestinal (GI) toxicities, and sexual function were prospectively evaluated. Results: PSA levels at the median follow up of 15 months was 0.52 ng/ml; 28.1% vs. 78.1% patients had GI vs. GU Grade 0 acute toxicity and 93.7% vs. 0% had GI vs. GU Grade 0 late toxicity. Furthermore, 88.1% of patients kept pretreatment sexual potency. Conclusion: The use of the hydrogel Spacer OAR before HRT is useful for reducing acute and late GU and GI toxicities.
Predicting willingness to be vaccinated for Covid-19: Evidence from New Zealand

Governments around the world are seeking to slow the spread of Covid-19 and reduce hospitalisations by encouraging mass vaccinations for Covid-19. The success of this policy depends on most of the population accepting the vaccine and then being vaccinated. Understanding and predicting the motivation of individuals to be vaccinated is, therefore, critical in assessing the likely effectiveness of a mass vaccination programme in slowing the spread of the virus. In this paper we draw on the I3 Response Framework to understand and predict the willingness of New Zealanders to be vaccinated for Covid-19. The Framework differs from most studies predicting willingness to be vaccinated because it is based on the idea that the willingness to adopt a behaviour depends on both involvement (a measure of motivational strength) with the behaviour and attitudes towards the behaviour. We show that predictions of individuals willingness to be vaccinated are improved using involvement and attitudes together, compared to attitudes alone. This result has important implications for the implementation of mass vaccination programmes for Covid-19.
Disease waves of SARS-CoV-2 in Iran closely mirror global pandemic trends

SARS-CoV-2 genome surveillance projects provide a good measure of transmission and monitor the circulating SARS-CoV-2 variants at regional and global scales. Iran is one of the most affected countries still involved with the virus circulating in at least five significant disease waves, as of September 2021. Complete genome sequencing of 50 viral isolates in an early phase of outbreak in Iran, shed light on the origins and circulating lineages at that time. As part of a genomic surveillance program, we provided an additional 319 complete genomes from October 2020 onwards. The current study is the report of complete SARS-CoV-2 genome sequences of Iran in the March 2020-May 2021 time interval. We aimed to characterize the genetic diversity of SARS-CoV-2 in Iran over one year. Overall, 35 different lineages and 8 clades were detected. Temporal dynamics of the prominent SARS-CoV-2 clades/lineages circulating in Iran is comparable to the global perspective and introduces the 19A clade (B.4) dominating the first disease wave, followed by 20A (B.1.36), 20B (B.1.1.413), 20I (B.1.1.7) clades, dominating second, third and fourth disease waves, respectively. We observed a mixture of circulating 20A (B.1.36), 20B (B.1.1.413), 20I (B.1.1.7) clades in winter 2021, paralleled in a diminishing manner for 20A/20B and a growing rise for 20I, eventually prompting the 4th outbreak peak. Furthermore, our study provides evidence on the entry of the Delta variant in April 2021, leading to the 5th disease wave in summer 2021. Three lineages are highlighted as hallmarks of SARS-CoV-2 outbreak in Iran; B4, dominating early periods of the epidemic, B.1. 1.413 (specific B.1.1 lineage carrying a combination of [D138Y-S477N-D614G] spike mutations) in October 2020-February 2021, and the co-occurrence of [I100T-L699I] spike mutations in half of B.1. 1.7 sequences mediating the fourth peak. Continuous monthly monitoring of SARS-CoV-2 genome mutations led to the detection of 1577 distinct nucleotide mutations, in which the top recurrent mutations were D614G, P323L, R203K/G204R, 3037C>T, and 241C>T; the renowned combination of mutations in G and GH clades. The most frequent spike mutation is D614G followed by 13 other frequent mutations based on the prominent circulating lineages; B.1. 1.7 (H69_V70del, Y144del, N501Y, A570D, P681H, T716I, S982A, D1118H, I100T, and L699I), B.1. 1.413 (D138Y, S477N) and B.1. 36 (I210del). In brief, mutation surveillance in this study provided a real-time comprehensive picture of the SARS-CoV-2 mutation profile in Iran, which is beneficial for evaluating the magnitude of the epidemic and assessment of vaccine and therapeutic efficiency in this population.
Smoking and subfertility: multivariable regression and Mendelian randomization analyses in the Norwegian Mother, Father and Child Cohort Study

Objective. To investigate the association between smoking-related traits and subfertility. Design. Prospective study. Setting. Nationwide cohort in Norway. Patients. 28,606 women (average age 30) and 27,096 men (average age 33) with questionnaire and genotype information from the Norwegian Mother, Father and Child Cohort Study. Intervention. Self-reported information on smoking (having ever smoked [both sexes], age at smoking initiation [women only], smoking cessation [women only], and cigarettes smoked per week in current smokers [both sexes]) was gathered. Genetically predetermined levels or likelihood of presenting the mentioned traits were estimated for Mendelian randomization (MR) analyses. Main outcome measure. Subfertility, defined as time-to-pregnancy >=12 months. Results. A total of 10% of couples were subfertile. In multivariable regression accounting for age, years of education, body mass index, and number of previous pregnancies, having ever smoked was not linked to subfertility in women or men. A higher intensity of tobacco use in women who were current smokers was related to greater odds of subfertility (+ 1 standard deviation [SD, 48 cigarettes/week]: odds ratio [OR] 1.12, 95% confidence interval [CI] 1.03 to 1.21), also after adjusting for the partner's tobacco use. Later smoking initiation (+ 1 SD [3.2 years]: OR 0.89, 95% CI 0.84 to 0.95) and smoking cessation (relative to not quitting: OR 0.83, 95% CI 0.75 to 0.93) were linked to decreased subfertility in women who had ever smoked. Nevertheless, MR results were not directionally consistent for smoking intensity and cessation and were imprecisely estimated in two-sample MR, with wide confidence intervals that overlapped with the multivariable regression results. In men, greater smoking intensity was marginally linked to greater odds of subfertility in multivariable analyses, but this association was attenuated when adjusting for the partner's smoking intensity (+ 1 SD [54 cigarettes/week]: OR 1.05, 95% CI 0.96 to 1.15). MR estimates were directionally consistent but again imprecisely estimated. Conclusions. We did not find robust evidence of an effect of smoking on subfertility. This may be due to a true lack of effect, weak genetic instruments, or other kinds of confounding. The relevant limitations across all methods highlights the need for larger studies with information on subfertility.
Increased risk for type 2 diabetes in relation to adiposity in middle-aged Black South African men compared to women

Aims: Despite a higher prevalence of overweight and obesity in black South African women compared to men, the prevalence of type 2 diabetes does not differ. We explored if this could be due to sex differences in insulin sensitivity, clearance and or beta cell function, and also sex-specific associations with total and regional adiposity. Methods: This cross-sectional study included 804 black South African men (n=388) and women (n=416). Dual-energy x ray absorptiometry was used to measure total and regional adiposity. Insulin sensitivity (Matsuda index), secretion (C peptide index) and clearance (C peptide/insulin ratio) were estimated from an oral glucose tolerance test. Results: After adjusting for sex differences in fat mass index, men were less insulin sensitive and had lower beta cell function than women (p<0.001), with the strength of the associations with measures of total and central adiposity being greater in men than women (p<0.001 for interactions). Further, the association between total adiposity and type 2 diabetes risk was also greater in men than women (relative risk ratio (95% confidence interval): 2.05 (1.42 to 2.96), p<0.001 vs. 1.38 (1.03 to 1.85), p=0.031). Conclusion: With increasing adiposity, particularly increased centralisation of body fat linked to decreased insulin sensitivity and beta cell function, Black African men are at greater risk for type 2 diabetes than their female counterparts.
Downregulation of STOX1 is a novel prognostic biomarker for glioma patients

Abstract Storkhead box 1 (STOX1) is a winged helix transcription factor structurally and functionally related to the forkhead family of transcription factors. Recent studies have highlighted its role in the central nervous system and revealed hints in the development of glioma. However, the expression profiles of STOX1, its association with clinicopathological characteristics, and potential functions in glioma remain unknown. In this study, we analyzed three publicly available datasets including CGGA, TCGA, and Rembrandt and revealed a grade-dependent reduction in STOX1 expression in glioma (P < 0.001). Chi-square test demonstrated that low STOX1 expression was significantly associated with older age at initial diagnosis (P < 0.001), less IDH1 mutation (P < 0.001), and advanced WHO grade (P < 0.001). Moreover, multivariate Cox regression analysis showed that STOX1 expression may serve as a novel independent prognostic biomarker in glioma patients. Bioinformatic functional analysis (GSEA) predicted that STOX1 was related to many key cancer pathways including P53 signaling pathway (P < 0.01), DNA replication (P < 0.05), homologous recombination (P < 0.05), and Wnt signaling pathway (P < 0.05). Taken together, these findings suggested that STOX1 may be used as a novel predictive molecular biomarker for glioma grading and overall patient survival. Further investigations on the functional roles and therapeutic value of STOX1 in glioma are warranted.
Comparing Money and Time Donation: What Do Experiments Tell Us?

Abstract Money donation and time donation, as charitable donations from individuals to organizations, are two forms of prosocial behavior that have been increasingly studied in recent years. Despite the vast amount of research about money and/or time donation, however, only limited work has been done on reviewing the research on these two forms of charitable donations as comparable or parallel entities. In this paper, we seek to help fill this gap by reviewing the existing research. We applied the backwards and forwards snowballing technique to arrive at a review sample of 39 experimental papers published in 2000–2020 that have compared money and time donation, or at least analyzed them as two parallel entities. We examine the issues that are predominantly considered in these experimental papers and summarize the general directions of their findings. We also point out certain gaps in the existing literature and posit some potentially fruitful directions for future experimental research regarding money and time donation.
De novo motor learning of a bimanual control task over multiple days of practice

Although much research on motor learning has focused on how we adapt our movements to maintain performance in the face of imposed perturbations, in many cases we must learn new skills from scratch, or de novo. In comparison to adaptation, relatively little is known about de novo learning. In part, this is because learning a new skill can involve many challenges, including learning to recognize new patterns of sensory input and generate new patterns of motor output. However, even with familiar sensory cues and well-practiced movements, the problem of quickly selecting the appropriate actions in response to the current state is challenging. Here, we devised a bimanual hand-to-cursor mapping which isolates this control problem. We find that participants initially struggled to control the cursor under this bimanual mapping, despite explicit knowledge of the mapping. Performance improved steadily over multiple days of practice, however. Participants exhibited no aftereffects when reverting to a veridical cursor, confirming that participants learned the new task de novo, rather than through adaptation. Corrective responses to mid-movement perturbations of the target were initially weak, but with practice, participants gradually became able to respond rapidly and robustly to these perturbations. After four days of practice, participants’ behavior under the bimanual mapping almost matched performance with a veridically mapped cursor. However, there remained a small but persistent difference in performance level. Our findings illustrate the dynamics and limitations of learning a novel controller and introduce a promising paradigm for tractably investigating this aspect of motor skill learning.