bluuebunny/crossref_metadata_2025_split huggingface dataset:
Interruptible Nodes: Reducing Queueing Costs in Irregular Streaming Dataflow Applications on Wide-SIMD Architectures
Authors: Timcheck Stephen , Buhler Jeremy 
Published: September 2022

Abstract
Streaming dataflow applications are an attractive target to parallelize on wide-SIMD processors such as GPUs. These applications can be expressed as a pipeline of compute nodes connected by edges, which feed outputs from one node to the next. Streaming applications often exhibit irregular dataflow, where the amount of output produced for one input is unknown a priori. Inserting finite queues between pipeline nodes can ameliorate the impact of irregularity and improve SIMD lane occupancy. The sizing of these queues is driven by both performance and safety considerations -- relative queue sizes should be chosen to reduce runtime overhead and maximize throughput, but each node's output queue must be large enough to accommodate the maximum number of outputs produced by one SIMD vector of inputs to the node. When safety and performance considerations conflict, the application may incur excessive memory usage and runtime overhead. In this work, we identify properties of applications that lead to such undesirable behaviors, with examples from applications implemented in our MERCATOR framework for irregular streaming on GPUs. To address these issues, we propose extensions to support interruptible nodes that can be suspended mid-execution if their output queues fill. We illustrate the impacts of adding interruptible nodes to the MERCATOR framework on representative irregular streaming applications from the domains of branching search and bioinformatics.
Interruptible Nodes: Reducing Queueing Costs
In vivo anti-tumor effect of Atractylodes macrocephala Koidz and Zingiber prescriptions combined with paclitaxel in mice bearing ID8 ovarian carcinoma
Authors: Zhang Guo-Nan , Li Xin Ru , Huang Jian-Mei , Pei Li-Xia , He Si-Yi , Wang Xiao-Xin , Huang Jian-Ming 
Published: September 2022

Abstract
Background
Paclitaxel is the first-line chemotherapy drug for ovarian carcinoma, and chemotherapy resistance to paclitaxel is closely related to the recurrence and poor prognosis of ovarian carcinoma. Our previous in vitro experiments demonstrate that Atractylenolide-I and 6-shogaol can be used as natural antagonists to target and block the TLR4 /MyD88-mediated paclitaxel resistance pathway.
Purpose
The majority of traditional herbal preparations are water-based prescriptionss, which results in a low yield of pharmacologically active lipid-soluble components. This preparation follows the decoction method of classic prescriptions (half decoction in wine and water), and maked the Atractylodes macrocephala Koidz and Ginger alcohol /aqueous extracts.
Methods
The effective contents of Atractylenolide-I and 6-shogaol in This preparation were determined by HPLC with main active components.
Results
This preparation were effective in inhibiting the growth of paclitaxel-treated tumors, accompanied by increased apoptosis and improved survival. In combination with paclitaxel, Atractylodes macrocephala Koidz and Zingiber alcohol /aqueous extracts had better anticancer efficacy than Atractylodes-I and 6-shogaol. Furthermore, no hepatic or kidney toxicity could be observed.
Conclusion
Atractylodes macrocephala Koidz and Zingiber alcohol /aqueous extracts may provide a clinically useful natural remedy for the treatment of cancer with active components that promote paclitaxel sensitization.
Combined Pruning for Nested Cross-Validation to Accelerate Automated Hyperparameter Optimization for Embedded Feature Selection in High-Dimensional Data with Very Small Sample Sizes
Authors: May Sigrun , Hartmann Sven , Klawonn Frank 
Published: September 2022

Abstract
Background: Embedded feature selection in high-dimensional data with very small sample sizes requires optimized hyperparameters for the model building process. For this hyperparameter optimization, nested cross-validation must be applied to avoid a biased performance estimation. The resulting repeated training with high-dimensional data leads to very long computation times. Moreover, it is likely to observe a high variance in the individual performance evaluation metrics caused by outliers in tiny validation sets. Therefore, early stopping applying standard pruning algorithms to save time risks discarding promising hyperparameter sets.
Result: To speed up feature selection for high-dimensional data with tiny sample size, we adapt the use of a state-of-the-art asynchronous successive halving pruner. In addition, we combine it with two complementary pruning strategies based on domain or prior knowledge. One pruning strategy immediately stops computing trials with semantically meaningless results for the selected hyperparameter combinations. The other is a new extrapolating threshold pruning strategy suitable for nested-cross-validation with a high variance of performance evaluation metrics. In repeated experiments, our combined pruning strategy keeps all promising trials. At the same time, the calculation time is substantially reduced compared to using a state-of-the-art asynchronous successive halving pruner alone. Up to 81.3% fewer models were trained achieving the same optimization result.
Conclusion: The proposed combined pruning strategy accelerates data analysis or enables deeper searches for hyperparameters within the same computation time. This leads to significant savings in time, money and energy consumption, opening the door to advanced, time-consuming analyses.
A Computational approach to uncover the missense mutations in candidate genes of late-onset Alzheimer’s disease
Authors: MUBEEN HIRA , Masood Ammarad , Zafar Asma , Khan Zohaira Qayyum , Khan Muneeeza Qayyum , Nisa Alim un , Naveed Zuniara Afzal 
Published: September 2022

Abstract
Background

Alzheimer’s (induced by ultimate demise of nerve cells) is a neurodegenerative disorder, and experts are still lagging in this disease, owing to the polygenic and multi-factorial nature mainly in Late-Onset-Alzheimer’s-Disease (LOAD). Till now, Amyloid beta (Aβ) aggregation along with neuroinflammation seem to be the main inveterate attribute of Alzheimer’s Disease (AD). Hence, and intriguing area of research that motivates the researchers is to incorporate genes implicated in Aβ clearance and associated regulatory processes.

Aim of study:

The principle purpose of the present study, is to investigate neuro-inflammatory and A cleavage genes (ADAM10, CD33, & TNFSF10).

Methodology:

Assessment of missense mutations of Aβ cleavage genes was done, and their effects were evaluated, followed by analysing the gene-gene and protein-protein interactions. Further, the association of Aβ targeting drugs with genes of investigation was examined. Also, Docking was applied to identify clinical involvement of therapeutic targets.

Result

the results proved that the underlying mechanism and polymorphism in just one nucleotide was related to the disease. It also provides useful information about genes’ network, that contain targeting molecules of NOTCH signalling and Death Induced Signalling Complex (DISC) pathway.

Future Directions

: The research will be valuable to find new directions for therapeutic approaches, with more benefit and success rate in disease treatment.
Paleoclimate constrains future El Niño/Southern Oscillation increase
Authors: Pontes Gabriel , Dias Pedro Silva , Menviel Laurie 
Published: September 2022

Abstract
IPCC projections indicate that El Niño/Southern Oscillation (ENSO) variability will increase throughout the 21st century under global warming. However, the magnitude of the increase greatly varies among models. Here we use paleoclimate model simulations of the 4th phase of the Paleoclimate Model Intercomparison Project (PMIP4) to constrain the projected ENSO increase and propose a common mechanism to explain ENSO changes throughout climate states. We show that the projected ENSO increase is unprecedented in warm climates of the past ~3.3 million years and that ENSO operates in a bi-stable regime dictated by the position of the tropical Pacific Ocean convection centers, namely the Intertropical and South Pacific Convergence Zones. Constraining ENSO variability with the positions of the tropical convection centers indicates a 12% (3 to 19%) ENSO increase in the 21st century. Our results elucidate how tropical Pacific mean state has modulated ENSO activity and alerts for the unprecedented nature of the 21st century global warming.
Intensity of Induction Regimen and Outcomes Among Adults with Ph+ ALL Undergoing Allogeneic Hematopoietic Stem Cell Transplantation
Authors: Luskin Marlise , Raman Hari , Kim Se Eun , DeAngelo Daniel , Stevenson Kristen , Neuberg Donna , Winer Eric , Wadleigh Martha , Garcia Jacqueline , Kim Annette , Stone Richard , Ho Vincent 
Published: September 2022

Abstract
Tyrosine kinase inhibitors are essential in treating Philadelphia chromosome-positive acute lymphoblastic leukemia (Ph + ALL) and have allowed for effective, low intensity induction regimens. Whether the use of low intensity induction regimens impacts outcomes after allogeneic stem cell transplant (alloHSCT) is less understood. We identified consecutive adult patients with Ph + ALL undergoing alloHSCT in first complete remission (CR1) at our center from 2010 to 2021 and examined the impact of induction intensity on outcomes. Among the 87 patients, 44 (51%) received low intensity induction and 43 (49%) received induction with high intensity chemotherapy. Patients receiving low intensity induction were older (median age 60 vs. 47, p < 0.01). Following induction, measurable residual disease (MRD) negativity by BCR::ABL1 RT-PCR was similar in the low and high intensity cohorts (54% and 52% respectively). There was no difference between low and high intensity induction with respect to 2-year disease-free survival (58% vs. 56%), 2-year overall survival (62% vs. 63%), 2-year cumulative incidence of relapse (9% vs. 17%), and 2-year non-relapse mortality (33% vs. 29%). Outcomes were similar when patients were segmented by induction and conditioning regimen intensities. We demonstrate that induction intensity does not impact post-transplant outcomes among Ph + ALL patients Ph + ALL transplanted in CR1.
Bioactive magnesium phosphate/glass composite coatings for magnesium alloy using sol-gel method
Authors: Farag Mohammad M. , Ahmed Hanaa Y. , Al-Rashidy Zainab M. 
Published: September 2022

Abstract
Coating of Mg alloys with Mg-phosphate is usually performed by complex and costly methods. This work was mainly aimed at using Mg-phosphate ceramic for Mg metal implants by simple and cost-effective spin coating combined with a sol-gel approach. Where, Mg-phosphate ceramic particles were dispersed with different percentages (0, 10, and 30 wt. %) in the glass sol (85 SiO2 – 10 CaO – 5 P2O5 system) as a coating solution. The coated substrates were characterized by TGA, XRD, FTIR, contact angle, and SEM/EDX analyses, and the in vitro bioactivity test was performed in revised simulated body fluid (rSBF). The results showed the coating thickness was 8.8 ± 0.8, 5.4 ± 0.6, and 5 ± 0.7 µm for MP0, MP10, and MP30, respectively. Moreover, the coatings increased the hydrophilicity of the metal surface. All coatings enhanced the formation of an apatite-bone like layer on the Mg metal surface, and they were viable with oral epithelial cells at a concentration ≤ 125 µg/ml. Moreover, MP0 and MP10 coatings significantly enhanced the corrosion resistance of the metal, while; MP30 coating did not show a significant effect on it. Thus, the percentage of Mg-phosphate in the coating was valuable for corrosion resistance when it was ≤ 10 wt. %. As a result, the composite coatings showed promising coatings for Mg metal substrate to enhance its corrosion resistance at low percentages of Mg-phosphate ceramic.
Does Endorsement from Political Elites Affect Tweeting Behaviour? Studying the Effects of Indian Prime Minister Narendra Modi’s Tweets in 2019
Authors: Neyazi Taberez Ahmed , Ee Tan Khai , Haque Mohammad Amaan 
Published: September 2022

Abstract
Does interactivity on Twitter initiated by political elites yield dividends in terms of indirect outcomes such as online political mobilization? Twitter has traditionally been utilized by political elites mainly as broadcasting media, characterized by its limited interactions with the wider public. In doing so, political elites may be overlooking Twitter's utility as an interactive media for mobilizing their supporters, which we seek evidence for in this paper. We adopt a naturalistic quasi-experimental design utilizing a unique dataset comprising tweets of users who were mentioned (36.62 million tweets) and not mentioned (29.73 million tweets) by Prime Minister Narendra Modi on 16th March, 2019 during his general election campaign. A controlled intervention analysis using Autoregressive Integrated Moving Average (ARIMA) models was carried out, which suggests that, on average, the total tweets count increased more for users mentioned by Modi (treatment group) as compared to those not mentioned by Modi (control group). However, no substantive lasting effect could be detected, indicating therefore that a mention by political elites helps generate immediate engagement which is not necessarily self-sustaining. To determine if this activation behavior is more prominent for less or more active users, we categorized the users based on their Twitter activity and assessed the significance of any movement between categories; no evidence could be found in support of this claim. Our findings thus provide important insights to understand how Twitter can be used as an effective platform for political campaigns by exploiting some of its interactive features such as replies and mentions to mobilize supporters.
A double-step-length symmetric splitting sequential quadratic optimization algorithm for two-block nonconvex optimization
Authors: Jian Jinbao , Cui Donglei , Ma Guodong , Yin Jianghua 
Published: September 2022

Abstract
In this paper, a double-step-length symmetric splitting sequential quadratic optimization (DSL-SS-SQO) algorithm for solving two-block nonconvex optimization with nonlinear constraints is proposed. First, at each iteration, the idea of symmetric splitting is embedded into solving the quadratic optimization (QO) subproblem approximating the discussed problem. As a result, the QO subproblem is split into two small-scale QOs, which can generate two improved search directions for the two primal variables. Second, the augmented Lagrangian function is used as the merit function, and two step sizes are yielded by performing the Armijo line search along the two improved directions. Third, under mild conditions, the global convergence, strong convergence, iterative complexity, and Maratos effect of the DSL-SS-SQO algorithm are proven. Finally, some numerical results are reported, comparisons with the results obtained by the IPOPT solver are also provided, which preliminarily show that the proposed algorithm is promising.
Plurilingual Education in Developing Contexts: A Pedagogical Framework
Authors: Giri Ram Ashish 
Published: September 2022

Abstract
This article is a pedagogical response to the theoretical and ideological stance which suggests that not all languages are worthy of inclusion in the school curriculum. It argues that, like any other resource, languages can be developed, conserved, and promoted through education. Very often, languages and cultures in contemporary socio-political discourses are referred to as valuable resources of a nation. However, citing a lack of resources and practical relevance, scholars contest the ways and the extent to which these languages should be developed and preserved. This article challenges such a politico-economic stance and recommends that a plurilingual education policy, based on the ground realities of a nation and a linguistic ecology needs to be considered for the development and promotion of languages.
Presenting Nepal as a case, the article claims that in its complex linguistic context, plurilingual education is more relevant than bi- and tri-lingual education, and therefore, there is a need for a pedagogical shift in language education. Nepal, a small, multilingual Himalayan nation has embarked upon seeking a new political will and a fresh mandate for developing an inclusive language education policy in the federal system of governance. The federal states are seeking to adopt the three-language education policy with Nepali, English, and one locally chosen language. In a context where there are scores of languages, this policy is flawed for more than one reason. The paper analyses the proposed policy and offers a language education framework with a view to re-engineering plurilingualism at the grassroots level. Based on the relevance of fostering individual as well as societal plurilingualism, and an analysis of related literature, it argues that in the developing socio-political scenario and existing linguistic landscape, the much-hyped three-language education formula will damage the existing linguistic fabric and create further divisions in the Nepalese population.
The relationship between COVID-19 protection behaviors and pandemic-related knowledge, perceptions, worry content and public trust in a Turkish sample
Authors: Küçükkarapınar Melike , Karadag Filiz 
Published: September 2022

Abstract
Background: This study aimed to explore the effect of knowledge, COVID-19-related perceptions and public trust on protective behaviors in Turkish people.
Methods: Data were collected from anonline survey (Turkish COVID-19 Snapshot Monitoring) conducted between July 2020 and January 2021.
The recommended protective behaviors (hand cleaning, wearing a face mask, and physical distancing) to prevent COVID-19 were examined. The impacts of the following variables on protective behaviors were investigated using logistic regression analysis: knowledge, cognitive and affective risk perception, pandemic-related worry content, public trust, conspiracy thinking, and COVID-19 vaccine willingness.
Results: Out of a total of 4,210 adult respondents, 13.8% reported nonadherence to protection behavior, and 86.2% reported full adherence. Males and young (18-30 ages) people tend to show less adherence. Perceived self-efficacy and susceptibility, and correct knowledge were positively related to more adherence to protective behavior.
Perceptual and emotional factorsexplaining protective behavior were perceived proximity, stress level, and worrying about the relatives who depended on them. Trust in health professionals and vaccine willingness were positive predictors, while conspiracy thinking and getting less information (<2, daily) were negative predictors. Unexpectedly, trust in the Ministry of Health showed a weak but negative association with protection behavior.
Conclusions: Perceived stress, altruistic worries and public trust seem to shape protection behaviors in addition to individuals’knowledge and cognitive risk perception in respondents. Males and young people may have a greater risk for nonadherence. Reliable, transparent and culture-specific health communication that considers these issues is required.
Targeting TRIM28-mediated SUMOylation of SARS-CoV-2 Nucleocapsid Protein Inhibits SARS-CoV-2 Infection
Authors: Ren Jiang , Wang Shuai , Pan Ting , Ma Weirui , Lu Huasong , Yang Bing , He Xin , Zhang Long , Zhou Fangfang 
Published: September 2022

Abstract
Viruses, as opportunistic intracellular parasites, hijack the cellular machinery of host cells to support their survival and propagation. Consequently, numerous viral proteins are subjected to host-mediated post-translational modifications. Here, we demonstrate that the SARS-CoV-2 nucleocapsid protein (SARS2-NP) is modified by a small ubiquitin-like modifier (SUMO) on the lysine 65 residue. SARS2-NP SUMOylation is essential for executing efficiently SARS2-NP’s ability in homo-oligomerization, RNA association, liquid-liquid phase separation (LLPS), thereby the innate antiviral immune response is suppressed robustly both in vitro and in vivo. These roles played by SARS2-NP SUMOylation can be achieved through intermolecular association between SUMO conjugation and a newly identified SUMO-interacting motif (SIM) in SARS2-NP. Importantly, the widespread SARS2-NP R203K mutation in SARS-CoV-2 variants gains a novel site of SUMOylation which further increases SARS2-NP’s LLPS and immunosuppression. Notably, we discover that the SUMO E3 ligase TRIM28 is responsible for catalyzing SARS2-NP SUMOylation. An interfering peptide targeting the TRIM28 and SARS2-NP interaction was screened out to block SARS2-NP SUMOylation and LLPS, and consequently inhibit SARS-CoV-2 replication and rescue innate antiviral immunity. Collectively, these data support SARS2-NP SUMOylation as an essential modification for SARS-CoV-2 virulence, and therefore provide a strategy to antagonize SARS-CoV-2.
Machine Learning Approaches for Predicting Suicidal Behaviors among University Students in Bangladesh during the COVID-19 Pandemic
Authors: Mahmud Sultan , Mohsin Md , Muyeed Abdul , Nazneen Shaila , Sayed Md. Abu , Murshed Nabil , Islam Ariful 
Published: September 2022

Abstract
Background
Psychological and behavioral stresses increased enormously during the global COVID-19 pandemic. This study intends to identify the best machine learning model to forecast suicide risk among university students in Bangladesh.
Methodology:
An anonymous online survey utilizing DASS-21 and Insomnia Severity Index (ISI) to assess depression, anxiety, and stress levels; Suicidal Behaviors Questionnaire-Revised (SBQ-R) to identify suicidal risk was conducted from 1 to 30 June 2022. We compared six popular machine learning models (MLM), including Logistic Regression (LR), Support Vector Machine (SVM), Naïve Bayes (NB), Classification Tree (CT), and Random Forest (RF), to identify the most efficient predictive model for suicidal behavior through several metrics such as accuracy, Kappa, and receiver operating characteristic curve (ROC).
Result
Determinants predicting suicidal behavior include depression, insomnia, anxiety, and stress. Besides, sex, relationship status, family income, loss of jobs, and death within the family from COVID-19 are crucial suicidal risk predictors. The performance evaluation and comparison of MLM show that all models behaved consistently and were comparable in predicting suicidal risk determinants since the ranges were for accuracy (0.76 to 0.79); Kappa (0.52 to 0.59); ROC (0.76 to 0.89); sensitivity (0.76 to 0.81), and specificity (0.72 to 0.82). SVM was the best and most consistent performing model among all MLM in terms of accuracy (79%), Kappa (0.59), ROC (0.89), sensitivity (0.81), and specificity (0.81).
Conclusion
SVM is the best MLM in identifying predictors of suicidal risk among university students to develop a screening tool that can guide policymakers and universities in designing appropriate, timely suicide prevention interventions.