Miracle-dz/UniFAR-FTUs huggingface dataset:
In‐depth evaluation of machine learning methods for semi‐automating article screening in a systematic review of mechanistic literature

We aimed to evaluate the performance of supervised machine learning algorithms in predicting articles relevant for full‐text review in a systematic review. Overall, 16,430 manually screened titles/abstracts, including 861 references identified relevant for full‐text review were used for the analysis. Of these, 40% (n = 6573) were sub‐divided for training (70%) and testing (30%) the algorithms. The remaining 60% (n = 9857) were used as a validation set. We evaluated down‐ and up‐sampling methods and compared unigram, bigram, and singular value decomposition (SVD) approaches. For each approach, Naïve Bayes, Support Vector Machines (SVM), regularized logistic regressions, neural networks, random forest, Logit boost, and XGBoost were implemented using simple term frequency or Tf‐Idf feature representations. Performance was evaluated using sensitivity, specificity, precision and area under the Curve. We combined predictions of the best‐performing algorithms (Youden Index ≥0.3 with sensitivity/specificity≥70/60%). In a down‐sample unigram approach, Naïve Bayes, SVM/quanteda text models with Tf‐Idf, and linear SVM e1071 package with Tf‐Idf achieved >90% sensitivity at specificity >65%. Combining the predictions of the 10 best‐performing algorithms improved the performance to reach 95% sensitivity and 64% specificity in the validation set. Crude screening burden was reduced by 61% (5979) (adjusted: 80.3%) with 5% (27) false negativity rate. All the other approaches yielded relatively poorer performances. The down‐sampling unigram approach achieved good performance in our data. Combining the predictions of algorithms improved sensitivity while screening burden was reduced by almost two‐third. Implementing machine learning approaches in title/abstract screening should be investigated further toward refining these tools and automating their implementation.
Tests with slowed down beams at GSI.

P. Boutachkov1, M. Gorska1, N.A. Kondratjev2, S. Pietri1, F. Naqvi1, J. Gerl1, H. Weick1, W. Prokopowicz1, H. Schaffner1, C. Nociforo1, H. Geissel1, I. Kojouharov1, M.A.G. Alvarez3, I. Mukha3, K. Hadynska4, P. Napiorkowski4, and D. Pietak4 1GSI, Darmstadt, Germany; 2FLNR, JINR, Dubna, Russia; 3Seville University, Seville, Spain; 4Warsaw University, Warsaw, Poland. The NuSTAR HISPEC slowed down beams project at GSI/FAIR aims to produce rare isotopes with energies of 10 MeV/u and less, to be used for spectroscopy and reactions studies. The radioactive beams produced at the NuSTAR FAIR facility of sufficient intensity to perform Coulomb excitation, resonance or transfer reactions studies are included in the nuclear chart shown on Figure 1. There is limited or no information on the excited sates of many of these nuclei. The high production rates of slow down beams at FAIR will allow detail studies of these systems.
A Simple Approach to Enhance the Effectiveness of Passive Currents Balancing in an Interleaved Multiphase Bidirectional DC–DC Converter

This paper studies the high power density bidirectional dc–dc converter with interleaving, soft-switching, and near critical conduction mode (near-CRM) operation. Systematic design and control methods are proposed to enhance the effectiveness of passive currents balancing between multiple interleaved phases without using individual phase current feedbacks. This paper first analyzes the zero voltage turn-on operation, including the turn-off energy diversion by the added snubber capacitor and the turn-off resonance models. Second, the intrinsic inverse co-relation between the phase current deviation and the resulting effective duty cycles variation is quantitatively formulated and modeled as a closed-loop mechanism that balances the currents passively. Then, the design constraints of the system parameters including the snubber capacitance, dead time, and valley currents of the near-CRM mode are formulated to guarantee the effectiveness of counteracting the current unbalance. Finally, a variable switching frequency control is proposed to actively control the valley current and maintain the current balancing effectiveness throughout the operating range. An IGBT-based 20-kW three-phase interleaved prototype with the maximum efficiency of 99.1% is constructed to verify the proposed design and control methods.
The Impact of Workload, Productivity, and Social Support on Burnout Among Marketing Faculty During the COVID-19 Pandemic

The COVID-19 pandemic was an unprecedented event in modern history, transforming the nature of higher education. As course delivery moved online, marketing educators were faced with higher workloads and more demands on their time. Anecdotal evidence suggested that faculty were working more, and as the 2020–2021 school year progressed, reports of fatigue and burnout were prevalent. An empirical study measures the perceived increases in workload among marketing faculty and the outcomes of that work, as well as levels of burnout. In addition, a model of burnout antecedents is proposed and tested. On average, marketing educators experienced moderate levels of burnout, which was increased by work demands in research and teaching, as well as student interaction, whereas research productivity decreased burnout. Burnout was not influenced by gender, rank, tenure status, or institution type.
A Model of Ridesourcing Demand Generation and Distribution

Ridesourcing has experienced exponential growth in recent years, yet its impact on individual travel are unclear and have not been adequately examined. Recently, an Austin-based ridesourcing company released a large dataset containing disaggregate trip-level information. In this research, we use this new dataset in tandem with several publicly available data sources to estimate two models: a spatially lagged multivariate count model, which is used to describe how many trips are generated in a specific zone on both weekdays and weekend days; and a fractional split model, which helps us identify the characteristics of zones that attract ridesourcing trips. Our results show spatial dependence in ridesourcing trips among proximally located zones, as well as correlation between weekday and weekend day trips originating in a zone. Another interesting finding is the identification of a possible substitution effect between ridesourcing and transit use for weekday trips. Moreover, our results suggest that different income segments in the population may use ridesourcing for different activity purposes. From a travel behavior researcher perspective, the results in this paper identify aggregate area-level variables impacting ridesourcing, which can guide future efforts to better understand the demand for ridesourcing as well as the demand for autonomous and connected vehicles.
Fast non-elitist evolutionary algorithms with power-law ranking selection

Theoretical evidence suggests that non-elitist evolutionary algorithms (EAs) with non-linear selection mechanisms can efficiently overcome broad classes of local optima where elitist EAs fail. However, the analysis assumes a weak selective pressure and mutation rates carefully chosen close to the "error threshold", above which they cease to be efficient. On problems easier for hill-climbing, the populations may slow down these algorithms, leading to worse runtime compared with variants of the elitist (1+1) EA. Here, we show that a non-elitist EA with power-law ranking selection leads to fast runtime on easy benchmark problems, while maintaining the capability of escaping certain local optima where the elitist EAs spend exponential time in the expectation. We derive a variant of the level-based theorem which accounts for power-law distributions. For classical theoretical benchmarks, the expected runtime is stated with small leading constants. For complex, multi-modal fitness landscapes, we provide sufficient conditions for polynomial optimisation, formulated in terms of deceptive regions sparsity and fitness valleys density. We derive the error threshold and show extreme tolerance to high mutation rates. Experiments on NK-Landscape functions, generated according to the Kauffman's model, show that the algorithm outperforms the (1+1) EA and the univariate marginal distribution algorithm (UMDA).
Corporate Co-Agglomeration and Green Economy Efficiency in China

This paper uses panel OLS, IV, and system GMM methods to empirically study the effects of manufacturing and producer service corporate co-agglomeration on green economy efficiency (GEE) in China. Chinese panel data from 2000 to 2019 are collected to assess the GEE and co-agglomeration degrees. The regression results show that there is an “inverted U-shaped” relationship between co-agglomeration and GEE. However, regional heterogeneity is found in the effects of corporate co-agglomeration on GEE. The mediating analysis indicates that corporate co-agglomeration could increase GEE through business entrepreneurship and innovation entrepreneurship. Variables such as transportation infrastructure, human capital, foreign direct investment, and environmental regulations are also found to have an elevating effect on GEE, whereas local fiscal expenditure on environmental protection has little effect. The findings in this paper indicate that entrepreneurship plays an important role in the process of co-agglomeration impacting GEE which differs in different regions and thus provide references for corporate and regional sustainable development.
Functional Drug Screening in the Era of Precision Medicine

The focus of precision medicine is providing the right treatment to each unique patient. This scientific movement has incited monumental advances in oncology including the approval of effective, targeted agnostic therapies. Yet, precision oncology has focused largely on genomics in the treatment decision making process, and several recent clinical trials demonstrate that genomics is not the only variable to be considered. Drug screening in three dimensional (3D) models, including patient derived organoids, organs on a chip, xenografts, and 3D-bioprinted models provide a functional medicine perspective and necessary complement to genomic testing. In this review, we discuss the practicality of various 3D drug screening models and each model’s ability to capture the patient’s tumor microenvironment. We highlight the potential for enhancing precision medicine that personalized functional drug testing holds in combination with genomic testing and emerging mathematical models.
New Insights Into Diuretic Use to Treat Congestion in the ICU: Beyond Furosemide

Diuretics are commonly used in critically ill patients with acute kidney injury (AKI) and fluid overload in intensive care units (ICU), furosemide being the diuretic of choice in more than 90% of the cases. Current evidence shows that other diuretics with distinct mechanisms of action could be used with good results in patients with selected profiles. From acetazolamide to tolvaptan, we will discuss recent studies and highlight how specific diuretic mechanisms could help to manage different ICU problems, such as loop diuretic resistance, hypernatremia, hyponatremia, or metabolic alkalosis. The current review tries to shed some light on the potential use of non-loop diuretics based on patient profile and give recommendations for loop diuretic treatment performance focused on what the intensivist and critical care nephrologist need to know based on the current evidence.
Brokering or Sitting Between Two Chairs? A Group Perspective on Workplace Gossip

Brokerage is a central concept in the organization literature. It has been argued that individuals in broker positions—i.e., connecting otherwise disconnected parts within a firm’s social network—can control the flow of information. It would imply their increased relevance in workplace gossip. This allegation, however, has not been addressed empirically yet. To fill this gap, we apply social network analysis techniques to relational data from six organizations in Hungary. First, we identify informal groups and individuals in broker positions. Then, we use this information to predict the likelihood with which positive or negative gossip is reported. We find more gossip when the sender and receiver are part of the same group and more positive gossip about in-group rather than out-group targets. Individuals in broker positions are more likely the senders and targets of negative gossip. Finally, even if both the brokers and the boss(es) are the targets of their colleagues’ negative gossip, the combination of the two categories (bosses in broker positions) does not predict more negative gossip anymore. Results are discussed in relation to the theoretical accounts on brokerage that emphasize its power for information control but fail to recognize the pitfalls of being in such positions.
Peroxisome Proliferator-Activated Receptors Regulate Hepatic Immunity and Assist in the Treatment of Primary Biliary Cholangitis

Fibrates, which are agonists of peroxisome proliferator-activated receptor alpha, have received increasing attention in the treatment of primary biliary cholangitis. Reduced alkaline phosphatase levels and improved clinical outcomes were observed in patients with primary biliary cholangitis with an inadequate response to ursodeoxycholic acid (UDCA) monotherapy4 when treated with bezafibrate or fenofibrate combined with UDCA. In contrast to obeticholic acid, which exacerbates pruritus in patients, fibrates have been shown to relieve pruritus. Clinical trial outcomes show potential for the treatment of primary biliary cholangitis by targeting peroxisome proliferator-activated receptors. It is currently agreed that primary biliary cholangitis is an autoimmune-mediated cholestatic liver disease, and peroxisome proliferator-activated receptor is a nuclear receptor that regulates the functions of multiple immune cells, thus playing an important role in regulating innate and adaptive immunity. Therefore, this review focuses on the immune disorder of primary biliary cholangitis and summarizes the regulation of hepatic immunity when peroxisome proliferator-activated receptors are targeted for treating primary biliary cholangitis.
Generalized Anxiety Disorder and Its Associated Factors Among Pregnant Women During COVID-19 at Public Health Facilities of East Gojjam Zone, 2020: A Multi-Center Cross-Sectional Study

Introduction Pregnant women suffer from varying levels of generalized anxiety disorder that result in poor obstetrical outcomes. Therefore, this study aimed to assess the prevalence and factors associated with generalized anxiety disorder among pregnant women attending antenatal care during COVID-19 at the public health facilities in the east Gojjam zone. Methods A health facility-based cross-sectional study was conducted, from 1–30 December 2020. A total of 847 pregnant women were included in the study using a systematic random sampling technique. We used an interviewer-administered questionnaire to collect the data. Bivariate and multivariable logistic regression was used to identify factors associated with the outcome variable. Statistical significance was determined using a p-value < 0.05 and a 95% confidence level. Results The prevalence of generalized anxiety disorder was 43.7%, with a 95% CI (40.28–47.12). Having <3 the number of children (AOR: 1.53; 95% CI: 1.11–2.13, having a negative attitude about COVID (AOR: 1.47; 95% CI: 1.07–2.02 and having a high-risk perception about COVID (AOR: 1.86; 95% CI: 1.34–2.57 were factors significantly associated with generalized anxiety disorder. Conclusions The study found that the prevalence of generalized anxiety disorder was high. Having less than three children, having a negative attitude, and having a high-risk perception of COVID were independent risk factors of generalized anxiety disorder. Appropriate interventions should be considered to address generalized anxiety disorder during the pandemic.
What Is New in the Anti–Pseudomonas aeruginosa Clinical Development Pipeline Since the 2017 WHO Alert?

The spread of antibiotic-resistant bacteria poses a substantial threat to morbidity and mortality worldwide. Carbapenem-resistant Pseudomonas aeruginosa (CRPA) are considered “critical-priority” bacteria by the World Health Organization (WHO) since 2017 taking into account criteria such as patient mortality, global burden disease, and worldwide trend of multi-drug resistance (MDR). Indeed P. aeruginosa can be particularly difficult to eliminate from patients due to its combinatory antibiotic resistance, multifactorial virulence, and ability to over-adapt in a dynamic way. Research is active, but the course to a validated efficacy of a new treatment is still long and uncertain. What is new in the anti–P. aeruginosa clinical development pipeline since the 2017 WHO alert? This review focuses on new solutions for P. aeruginosa infections that are in active clinical development, i.e., currently being tested in humans and may be approved for patients in the coming years. Among 18 drugs of interest in December 2021 anti–P. aeruginosa development pipeline described here, only one new combination of β-lactam/β-lactamase inhibitor is in phase III trial. Derivatives of existing antibiotics considered as “traditional agents” are over-represented. Diverse “non-traditional agents” including bacteriophages, iron mimetic/chelator, and anti-virulence factors are significantly represented but unfortunately still in early clinical stages. Despite decade of efforts, there is no vaccine currently in clinical development to prevent P. aeruginosa infections. Studying pipeline anti–P. aeruginosa since 2017 up to now shows how to provide a new treatment for patients can be a difficult task. Given the process duration, the clinical pipeline remains unsatisfactory leading best case to the approval of new antibacterial drugs that treat CRPA in several years. Beyond investment needed to build a robust pipeline, the Community needs to reinvent medicine with new strategies of development to avoid the disaster. Among “non-traditional agents”, anti-virulence strategy may have the potential through novel and non-killing modes of action to reduce the selective pressure responsible of MDR.
Cardiovascular Toxicity With PD-1/PD-L1 Inhibitors in Cancer Patients: A Systematic Review and Meta-Analysis

Background PD-1/PD-L1 inhibitors have significantly improved the outcomes of those patients with various malignancies. However, the incidence of adverse events also increased. This meta-analysis aims to systematically evaluate the risk of cardiovascular toxicity in patients treated with PD-1/PD-L1 inhibitors. Materials and methods We searched PubMed, Embase, the Cochrane Library databases for all randomized controlled trials (RCTs) comparing all-grade and grade 3-5 cardiovascular toxicity of single-agent PD-1/PD-L1 inhibitors to placebo/chemotherapy, PD-1/PD-L1 inhibitors combined with chemotherapy to chemotherapy, or PD-1/PD-L1 inhibitors combined with CTLA-4 inhibitors to single-agent immune checkpoint inhibitors (ICIs) and pooled our data in a meta-analysis stratified by tumor types and PD-1 or PD-L1 inhibitors. The Mantel-Haenszel method calculated the odds ratio (OR) and its corresponding 95% confidence intervals (CIs). Results A total of 50 trials were included in the analysis. Single-agent PD-1/PD-L1 inhibitors increased the risk of all-grade cardiotoxicity compared with placebo (OR=2.11, 95%CI 1.02-4.36, P=0.04). Compared with chemotherapy, patients receiving PD-1/PD-L1 inhibitors combined with chemotherapy had a significant higher risk of all-grade (OR=1.53, 95%CI 1.18-1.99, P=0.001) and grade 3-5 cardiotoxicity (OR=1.63, 95%CI 1.11-2.39, P=0.01) cardiotoxicity, especially patients with non-small cell lung cancer (NSCLC) [all-grade cardiotoxicity (OR=1.97, 95%CI 1.14-3.41, P=0.02) and grade 3-5 cardiotoxicity (OR=2.15, 95%CI 1.08-4.27, P=0.03)]. Subgroup analysis showed that PD-1 inhibitors combined with chemotherapy were associated with a higher risk of grade 3-5 cardiotoxicity (OR=2.08, 95%CI 1.18-3.66, P=0.01). Compared with placebo or chemotherapy, single-agent PD-1/PD-L1 inhibitors did not increase the risk of all-grade of myocarditis, arrhythmia and hypertension. However, PD-1/PD-L1 inhibitors combined with chemotherapy increased the risk of all-grade arrhythmia (OR=1.63, 95%CI 1.07-2.46, P=0.02) [PD-L1 inhibitor-containing treatment (OR=1.75, 95%CI 1.09-2.80, P=0.02)], and the risk of all-grade hypertension (OR=1.34, 95%CI 1.02-1.77, P=0.04) and grade 3-5 hypertension (OR=1.54, 95%CI 1.10-2.15, P=0.01). Conclusions Our results suggest that single-agent PD-1/PD-L1 inhibitors increase the risk of all-grade cardiotoxicity, PD-1/PD-L1 inhibitors combined with chemotherapy increase the risk of all-grade and grade 3-5 cardiotoxicity, especially in those patients treated with PD-1 inhibitor-containing treatment and those with NSCLC. In addition, PD-1/PD-L1 inhibitors combined with chemotherapy increase the risk of arrhythmia and hypertension. Therefore, this evidence should be considered when assessing the benefits and risks of PD-1/PD-L1 inhibitors in treating malignancies. Systematic Review Registration https://www.crd. york.ac. uk/prospero/, identifier CRD42022303115.
A Classroom Simulation Based on Political Polling To Help Students Understand Sampling Distributions

Abstract Sampling distributions are central to understanding statistical inference, yet they are one of the most difficult concepts for introductory statistics students. Although hands-on teaching methods are preferred, finding the right balance between theory and practical experience has not been easy. Simulation activities have not always captured the research situations that statisticians work with. This paper describes a method developed by the author to teach sampling distributions using a collaborative learning simulation based on political polling. Anecdotally, students found the polling scenario easy to understand, interesting, and enjoyable, and they were able to explain the meaning of sample results and inferences about the population. Sample examination questions are included, with examples of students' responses that suggest that the method helped them to understand sampling error and its role in statistical inference.
A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks

Strong adversarial attacks are important for evaluating the true robustness of deep neural networks. Most existing attacks search in the input space, e.g., using gradient descent, and may miss adversarial examples due to non-convexity. In this work, we systematically search adversarial examples in the activation space of ReLU networks to tackle hard instances where none of the existing adversarial attacks succeed. Unfortunately, searching the activation space typically relies on generic mixed integer programming (MIP) solvers and is limited to small networks and easy problem instances. To improve scalability and practicability, we use branch and bound (BaB) with specialized GPU-based bound propagation methods, and propose a top-down beam-search approach to quickly identify the subspace that may contain adversarial examples. Moreover, we build an adversarial candidates pool using cheap attacks to further assist the search in activation space via diving techniques and a bottom-up large neighborhood search. Our adversarial attack framework, BaB-Attack, opens up a new opportunity for designing novel adversarial attacks not limited to searching the input space, and enables us to bor-row techniques from integer programming theory and neural network veriﬁcation. In experiments, we can successfully generate adversarial examples when existing attacks on input space fail. Compared to off-the-shelf MIP solver based attacks that requires signiﬁcant computations, we outperform in both success rates and efﬁciency.