Miracle-dz/UniFAR-FTUs huggingface dataset:
Smoking Cessation With Varenicline, a Selective α4β2 Nicotinic Receptor Partial Agonist: Results From a 7-Week, Randomized, Placebo- and Bupropion-Controlled Trial With 1-Year Follow-up

Background: Currently available smoking cessation therapies have limited success rates. Varenicline tartrate is a novel, selective nicotinic receptor partial agonist developed specifically for smoking cessation. This study evaluated the efficacy, tolerability, and safety of 3 varenicline doses for smoking cessation. Bupropion hydrochloride was included as an active control. Methods: A phase 2, multicenter, randomized, doubleblind, placebo-controlled study of healthy smokers (18-65 years old). Subjects were randomized to varenicline tartrate, 0.3 mg once daily (n =1 28), 1.0 mg once daily (n=128), or 1.0 mg twice daily (n=127), for 6 weeks plus placebo for 1 week; to 150-mg sustained-release bupropion hydrochloride twice daily (n=128) for 7 weeks; or to placebo (n=127) for 7 weeks. Results: During the treatment phase, the continuous quit rates for any 4 weeks were significantly higher for varenicline tartrate, 1.0 mg twice daily (48.0%;P.001) and 1.0 mg once daily (37.3%;P.001), than for placebo (17.1%). Thebupropionratewas33.3%(P=.002vsplacebo). Thecarbon monoxide–confirmed continuous quit rates from week 4 to week 52 were significantly higher in the varenicline tartrate, 1.0 mg twice daily, group compared with the placebo group(14.4%vs4.9%;P=.002). Thebupropionratewas6.3%
Smoking cessation with varenicline, a selective alpha4beta2 nicotinic receptor partial agonist: results from a 7-week, randomized, placebo- and bupropion-controlled trial with 1-year follow-up.

BACKGROUND Currently available smoking cessation therapies have limited success rates. Varenicline tartrate is a novel, selective nicotinic receptor partial agonist developed specifically for smoking cessation. This study evaluated the efficacy, tolerability, and safety of 3 varenicline doses for smoking cessation. Bupropion hydrochloride was included as an active control. METHODS A phase 2, multicenter, randomized, double-blind, placebo-controlled study of healthy smokers (18-65 years old). Subjects were randomized to varenicline tartrate, 0.3 mg once daily (n = 128), 1.0 mg once daily (n = 128), or 1.0 mg twice daily (n = 127), for 6 weeks plus placebo for 1 week; to 150-mg sustained-release bupropion hydrochloride twice daily (n = 128) for 7 weeks; or to placebo (n = 127) for 7 weeks. RESULTS During the treatment phase, the continuous quit rates for any 4 weeks were significantly higher for varenicline tartrate, 1.0 mg twice daily (48.0%; P<.001) and 1.0 mg once daily (37.3%; P<.001), than for placebo (17.1%). The bupropion rate was 33.3% (P = .002 vs placebo). The carbon monoxide-confirmed continuous quit rates from week 4 to week 52 were significantly higher in the varenicline tartrate, 1.0 mg twice daily, group compared with the placebo group (14.4% vs 4.9%; P = .002). The bupropion rate was 6.3% (P = .60 vs placebo). Discontinuation owing to treatment-emergent adverse events was 15.9% for bupropion, 11.2% to 14.3% for varenicline, and 9.8% for placebo. No dose-related increases occurred in adverse events for varenicline. CONCLUSIONS Varenicline tartrate demonstrated both short-term (1 mg twice daily and 1 mg once daily) and long-term efficacy (1 mg twice daily) vs placebo. Varenicline was well tolerated and may provide a novel therapy to aid smoking cessation.
Clinical correlates of “pure” essential tremor: the TITAN study

Background To date, there are no large studies delineating the clinical correlates of “pure” essential tremor (ET) according to its new definition. Methods From the ITAlian tremor Network (TITAN) database, we extracted data from patients with a diagnosis of “pure” ET and excluded those with other tremor classifications, including ET-plus, focal, and task-specific tremor, which were formerly considered parts of the ET spectrum. Results Out of 653 subjects recruited in the TITAN study by January 2022, the data of 208 (31.8%) “pure” ET patients (86M/122F) were analyzed. The distribution of age at onset was found to be bimodal. The proportion of familial cases by the age-at-onset class of 20 years showed significant differences, with sporadic cases representing the large majority of the class with an age at onset above 60 years. Patients with a positive family history of tremor had a younger onset and were more likely to have leg involvement than sporadic patients despite a similar disease duration. Early-onset and late-onset cases were different in terms of tremor distribution at onset and tremor severity, likely as a function of longer disease duration, yet without differences in terms of quality of life, which suggests a relatively benign progression. Treatment patterns and outcomes revealed that up to 40% of the sample was unsatisfied with the current pharmacological options. Discussion The findings reported in the study provide new insights, especially with regard to a possible inversed sex distribution, and to the genetic backgrounds of “pure” ET, given that familial cases were evenly distributed across age-at-onset classes of 20 years. Deep clinical profiling of “pure” ET, for instance, according to age at onset, might increase the clinical value of this syndrome in identifying pathogenetic hypotheses and therapeutic strategies.
Exploring the relationship between lactate metabolism and immunological function in colorectal cancer through genes identification and analysis

Introduction: Metabolic dysregulation is a widely acknowledged contributor for the development and tumorigenesis of colorectal cancer (CRC), highlighting the need for reliable prognostic biomarkers in this malignancy. Methods: Herein, we identified key genes relevant to CRC metabolism through a comprehensive analysis of lactate metabolism-related genes from GSEA MsigDB, employing univariate Cox regression analysis and random forest algorithms. Clinical prognostic analysis was performed following identification of three key genes, and consistent clustering enabled the classification of public datasets into three patterns with significant prognostic differences. The molecular pathways and tumor microenvironment (TME) of these patterns were then investigated through correlation analyses. Quantitative PCR was employed to quantify the mRNA expression levels of the three pivotal genes in CRC tissue. Single-cell RNA sequencing data and fluorescent multiplex immunohistochemistry were utilized to analyze relevant T cells and validate the correlation between key genes and CD4+ T cells. Results: Our analysis revealed that MPC1, COQ2, and ADAMTS13 significantly stratify the cohort into three patterns with distinct prognoses. Additionally, the immune infiltration and molecular pathways were significantly different for each pattern. Among the key genes, MPC1 and COQ2 were positively associated with good prognosis, whereas ADAMTS13 was negatively associated with good prognosis. Single-cell RNA sequencing (scRNA-seq) data illustrated that the relationship between three key genes and T cells, which was further confirmed by the results of fluorescent multiplex immunohistochemistry demonstrating a positive correlation between MPC1 and COQ2 with CD4+ T cells and a negative correlation between ADAMTS13 and CD4+ T cells. Discussion: These findings suggest that the three key lactate metabolism genes, MPC1, COQ2, and ADAMTS13, may serve as effective prognostic biomarkers and support the link between lactate metabolism and the immune microenvironment in CRC.
Comprehensive review and analysis on facial emotion recognition methods

Abstract. Facial emotion recognition (FER) is a significant subject in computer vision and artificial intelligence because of its tremendous academic and commercial potential, such as in cognitive science, health care, virtual reality, and video conferencing in various domains. While FER could be carried out using multiple sensors, this review includes research that exclusively uses face images, since facial expressions are among the key pieces of knowledge in human interactions. We give a brief review of FER research carried out in recent years. We divided the techniques of FER mainly into two parts, i.e., based on the type of approach and based on the type of frame, which is further subdivided into two sub-parts of each classification for more detailed division. First, it explains traditional FER approaches together with a description of the representative classes of FER systems. Deep-learning FER strategies are then addressed using deep networks that allow “end-to-end” learning. This review is also directed toward an up-to-date deep learning strategy, which is a trending topic nowadays. A brief overview of publicly accessible assessment metrics is provided in the later part of this paper, as well as a comparison with the baseline results is presented, which is a norm for quantitative analysis of FER research. The whole analysis also could act as a concise field guide for beginners in the FER sector, providing general details and a common understanding of both the recent state-of-the-art research and established researchers searching for fruitful areas for further research.
Chloroplast magnesium transporters play essential but differential roles in maintaining magnesium homeostasis

Magnesium (Mg2+) is essential for photosynthesis in the chloroplasts of land plants and algae. Being the central ion of chlorophyll, cofactor and activator of many photosynthetic enzymes including RuBisCO, magnesium-deficient plants may suffer from leaf chlorosis symptoms and retarded growth. Therefore, the chloroplast Mg2+ concentration is tightly controlled by magnesium transport proteins. Recently, three different transporters from two distinct families have been identified in the chloroplast inner envelope of the model plant Arabidopsis thaliana: MGT10, MGR8, and MGR9. Here, we assess the individual roles of these three proteins in maintaining chloroplast Mg2+ homeostasis and regulating photosynthesis, and if their role is conserved in the model green alga Chlamydomonas reinhardtii. Phylogenetic analysis and heterologous expression revealed that the CorC-like MGR8 and MGR9 transport Mg2+ by a different mechanism than the CorA-like MGT10. MGR8 and MGT10 genes are highest expressed in leaves, indicating a function in chloroplast Mg2+ transport. MGR9 is important for chloroplast function and plant adaptation in conditions of deficiency or excess of Mg2+. Transmission electron microscopy indicated that MGT10 plays a differential role in thylakoid stacking than MGR8 and MGR9. Furthermore, we report that MGR8, MGR9, and MGT10 are involved in building up the pH gradient across the thylakoid membrane and activating photoprotection in conditions of excess light, however the mechanism has not been resolved yet. While there are no chloroplast MGR-like transporters in Chlamydomonas, we show that MRS4 is a homolog of MGT10, that is required for photosynthesis and cell growth. Taken together, our findings reveal that the studied Mg2+ transporters play essential but differential roles in maintaining chloroplast Mg2+ homeostasis.
A Synchronous Bi-Directional Framework With Temporally Dependent Interaction Modeling for Pedestrian Trajectory Prediction

As an essential part of motion behavior modeling, pedestrian trajectory forecasting with social interactions has become an increasingly important problem in many applications, such as visual navigation and intelligent video surveillance. Most existing methods adopt autoregressive frameworks to forecast the future trajectory, where the trajectory is iteratively generated based on the preceding outputs. Such a process suffers from large accumulated errors over long-term forecasting. To address this issue, in this work, we propose a synchronous bi-directional framework for pedestrian trajectory prediction, where the predicting procedures for two opposite directions are forced to be synchronous through a shared motion characteristic. Unlike previous works, the mutual constraints inherent to our framework from the synchronous opposite predictions can significantly prevent error accumulation. In addition, we devise a temporally dependent interaction model to learn the complex social interactions among pedestrians from correlated historical trajectories. By resorting to a temporally dependent attention scheme and a progressive temporal fusion method, our interaction model can effectively reveal the interacting influence among pedestrians across temporal domains, and also capture the long-term dependencies of the historical trajectory. Experiments conducted on the ETH-UCY benchmark and the Stanford Drone dataset show that our method achieves much better results than existing algorithms. Particularly, our scheme exhibits superior performance in long-term pedestrian trajectory prediction.
Editorial: Methods in genitourinary oncology

COPYRIGHT © 2023 Jang, Sella, Nassar and Koshkin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. TYPE Editorial PUBLISHED 24 August 2023 DOI 10.3389/fonc.2023. 1274264
Could We Predict POAF With a Simple Ambulatory Oscillometry Evaluating Aortic Stiffness?

Objective To investigate the relationship between aortic stiffness and postoperative atrial fibrillation (POAF) in patients undergoing coronary artery bypass grafting (CABG). Methods This study included 110 patients undergoing elective isolated CABG. Aortic stiffness was measured using a noninvasive oscillometric sphygmomanometer before surgery. Characteristics of patients with and without POAF were compared. Results POAF developed in 32 (29.1%) patients. Patients with POAF were older (63.7±8.6 vs. 58.3±8.4; P=0.014). Chronic obstructive pulmonary disease (COPD) was more common in patients with POAF (11.5% vs. 37.5%; P=0.024), whereas the frequency of hypertension, diabetes mellitus, smoking, and previous coronary artery disease did not differ. C-reactive protein and cholesterol levels were similar between patients with and without POAF. Left atrial diameter was greater in patients with POAF (35.9±1.6 vs. 36.7±1.7; P<0.039). Peripheral (p) and central (c) systolic and diastolic blood pressures were also similar between the groups, whereas both p and c pulse pressures (PP) were greater in patients with POAF (pPP: 44.3±11.9 vs. 50.3±11.6; P=0.018, cPP: 31.4±8.1 vs. 36.2±8.9; P=0.008). Pulse wave velocity (PWV) was significantly higher in POAF (8.6+1.3 vs. 9.4+1.3; P=0.006). PWV, pPR and COPD were independent predictors of POAF in multivariate regression analysis. In receiver operating characteristic analysis, PWV and pPP have similar accuracy for predicting POAF (PWV, area under the curve [AUC]: 0.661, 95% confidence interval [CI] [0.547-0.775], P=0.009) (pPP, AUC: 0.656, 95% CI [0.542-0.769], P=0.012). Conclusion COPD, PWV, and PP are predictors of POAF. PP and PWV, easily measured in office conditions, might be useful for detecting patients with a higher risk of POAF.
Transition Mutations in the hTERT Promoter Are Unrelated to Potential i-motif Formation in the C-Rich Strand

Increased expression of the human telomere reverse transcriptase (hTERT) in tumors promotes tumor cell survival and diminishes the survival of patients. Cytosine-to-thymine (C-to-T) transition mutations (C250T or C228T) in the hTERT promoter create binding sites for transcription factors, which enhance transcription. The G-rich strand of the hTERT promoter can form G-quadruplex structures, whereas the C-rich strand can form an i-motif in which multiple cytosine residues are protonated. We considered the possibility that i-motif formation might promote cytosine deamination to uracil and C-to-T mutations. We computationally probed the accessibility of cytosine residues in an i-motif to attack by water. We experimentally examined regions of the C-rich strand to form i-motifs using pH-dependent UV and CD spectra. We then incubated the C-rich strand with and without the G-rich complementary strand DNA under various conditions, followed by deep sequencing. Surprisingly, deamination rates did not vary substantially across the 46 cytosines examined, and the two mutation hotspots were not deamination hotspots. The appearance of mutational hotspots in tumors is more likely the result of the selection of sequences with increased promoter binding affinity and hTERT expression.
RepairNatrix: a Snakemake workflow for processing DNA sequencing data for DNA storage

Abstract Motivation There has been rapid progress in the development of error-correcting and constrained codes for DNA storage systems in recent years. However, improving the steps for processing raw sequencing data for DNA storage has a lot of untapped potential for further progress. In particular, constraints can be used as prior information to improve the processing of DNA sequencing data. Furthermore, a workflow tailored to DNA storage codes enables fair comparisons between different approaches while leading to reproducible results. Results We present RepairNatrix, a read-processing workflow for DNA storage. RepairNatrix supports preprocessing of raw sequencing data for DNA storage applications and can be used to flag and heuristically repair constraint-violating sequences to further increase the recoverability of encoded data in the presence of errors. Compared to a preprocessing strategy without repair functionality, RepairNatrix reduced the number of raw reads required for the successful, error-free decoding of the input files by a factor of 25–35 across different datasets. Availability and implementation RepairNatrix is available on Github: https://github.com/umr-ds/repairnatrix.
A 3D BEM Model for the Hydrodynamic Analysis and Design of Heaving WEC Arrays Attached to a Breakwater

Assessment of the performance of arrays of wave energy converters (WECs) of the type of simple heaving floaters, operating in nearshore and coastal areas, is essential to estimate their power production capacity. The effective design and layout of such WEC farms can be supported by studying the system’s operational characteristics based on the wave climatology of the deployment region. Installation along the exposed side of harbor breakwaters in sea areas with substantial incoming energy potential is a promising option. In this study, a 3D hydrodynamic model based on the boundary element method (BEM) is presented and discussed, aiming to evaluate the performance of WEC arrays consisting of multiple heaving bodies attached to the exposed side of a harbor breakwater, modelled as a vertical wall, considering its reflective effects, as well as hydrodynamic interactions between the multiple floating devices. Numerical results of the predicted power performance for various configurations, including interactions of multiple WECs with the nearshore topography and the breakwater wall, as well as the effects of power take off (PTO) parameters, are presented and discussed. Finally, a case study is presented for a selected coastal site at the port of Heraklion, located in the north-central part of Crete Island in the South Aegean Sea, characterized by relatively increased wave energy potential, using long-term climatological data, illustrating the method and its applicability as a supporting tool for optimal design of WEC arrangements.
Machine learning and UAV path following identification algorithm based on navigation spoofing

With advances in navigation technology, unmanned aerial vehicles (UAVs) have become widely available to all sections of society. Given the potential hazards of UAVs, as seen from their use in the Russia–Ukraine war and security incidents at London and Baghdad airports, counter-UAV technology is receiving unprecedented attention. This paper describes a method for taking control of a UAV indirectly by navigation spoofing and luring it to a designated area from which it can be neutralized. Our contributions are threefold: first, we analyze the limitations of the traditional state estimation and control model for UAV navigation based on the integration of the Global Navigation Satellite System and Inertial Navigation System. Second, we propose a particle hypothesis and planning model that is insensitive to the particular UAV navigation system, and decompose the UAV navigation spoofing process into two steps: identification and planning. Finally, to overcome the poor heading angle prediction accuracy of unary polynomial regression, we propose a support vector regression algorithm that improves the prediction accuracy from 1.5° to 1.01° under navigation spoofing. Experimental results using the proposed navigation spoofing method prove that machine learning offers significant advantages in modeless identification.
Industry 4.0 Base Technologies and Business Models: A Bibliometric Analysis

PurposeThe objective of this study is to examine current business and management research on “Industry 4.0 base technologies” and “business models” to shed light on this vast literature and to point out future research agenda.Design/methodology/approachThe authors conducted a bibliometric analysis of scientific publications based on 482 documents collected from the Scopus database and a co-citation analysis to provide an overview of business model studies related to Industry 4.0 base technologies. After that a qualitative analysis of the articles was also conducted to identify research trends and trajectories.FindingsThe results reveal the existence of five research themes: smart products (cluster 1); business model innovation (cluster 2); technological platforms (cluster 3); value creation and appropriation (cluster 4); and digital business models (cluster 5). A qualitative analysis of the articles was also conducted to identify research trends and trajectories.Research limitations/implicationsFirst, the dataset was collected through Scopus. The authors are aware that other databases, such as Web of Science, can be used to deepen the focus of quantitative bibliometric analysis. Second, the authors based this analysis on the Industry 4.0 base technologies identified by Frank et al. (2019). The authors recognize that Industry 4.0 comprises other technologies beyond IoT, cloud computing, big data and analytics.Practical implicationsDrawing on these analyses, the authors submit a useful baseline for developing Industry 4.0 base technologies and considering their implications for business models.Originality/valueIn this paper, the authors focus their attention on the relationship between technologies underlying the fourth industrial revolution, identified by Frank et al. (2019), and the business model, with a particular focus on the developments that have occurred over the last decade and the authors performed a bibliometric analysis to consider all the burgeoning literature on the topic.