bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cigarettes, heated tobacco products and dual use: exhaled carbon monoxide, saliva cotinine and total tobacco consumed by Hong Kong tobacco users
Authors: Zhang Xiaoyu , Sun Yuying , Cheung Yee Tak Derek , Wang Man Ping , Wu Yongda Socrates , Chak Kin Yeung , Chen Jianjiu , Leung Lok Tung , Lam Tai Hing , Ho Sai Yin 
Published: January 2023

Background
Independent studies on exhaled carbon monoxide (CO) and saliva cotinine levels in regular heated tobacco product (HTP) users, and how they compare with conventional cigarette (CC) smokers, are lacking.

Methods
A total of 3294 current users of CCs, HTPs or electronic cigarettes (ECs) from a household survey and a smoking hotspot survey were classified into seven groups: exclusive users of CCs, HTPs, ECs; dual users of CCs and HTPs, CCs and ECs, HTPs and ECs; and triple users. We measured exhaled CO level using the piCo Smokerlyzer (n=780) and saliva cotinine using NicAlert cotinine test strips (n=620). Among the seven groups, the differences in (1) CO and cotinine levels were examined using Kruskal-Wallis test, and (2) the average daily tobacco consumption in the past 30 days was examined using multivariable linear regression.

Results
Both exclusive and dual users of CCs had a higher CO level than exclusive HTP or EC users (p<0.05). Exhaled CO levels were similar between HTP and EC users, as were saliva cotinine levels among the seven groups. Compared with exclusive CC users, those who also used HTPs or ECs smoked fewer CCs (CCs+HTPs: adjusted coefficient −2.79, 95% CI −3.90 to –1.69; CCs+ECs: −1.34, 95% CI −2.34 to –0.34), but consumed more tobacco sticks equivalent in total (2.79 (95% CI 1.61 to 3.96); 1.95 (95% CI 0.79 to 3.12)).

Conclusions
HTP or EC use showed lower exhaled CO but similar saliva cotinine levels compared with CC use. Dual users of CCs and HTPs/ECs smoked fewer CCs than exclusive CC users, but consumed more tobacco in total.
Evaluation of Photos Monte Carlo ambiguities in case of four fermion final states
Authors: Kusina A. , Was Z. 
Published: January 2023

AbstractWith the increasing precision requirements and growing spectrum of applications of Monte Carlo simulations the evaluation of different components of such simulations and their systematic ambiguities become of utmost interest. In the following, we will address the question of systematic errors for Monte Carlo for simulation of bremsstrahlung corrections in final states, which can not, in principle, be identified as a decay of resonances. It is possible, because the program features explicit and exact parametrization of phase space for multi-body plus multi-photon final states. The emission kernel for some processes consist of complete matrix element, in the remaining cases appropriate approximation is used. Comparisons with results of simulations, from generators based on exact phase space and exact fixed order matrix elements, can be used. For the purpose of such validations provides an option to restrict emissions to single photon only. In the current work we concentrate on final state bremsstrahlung in $$q {\bar{q}}(e^+e^-) \rightarrow l^+l^- l^+l^- \gamma $$

q

q

¯

(

e
+

e
-

)

→

l
+

l
-

l
+

l
-

γ

and $$\gamma \gamma \rightarrow l^+l^- \gamma $$

γ
γ
→

l
+

l
-

γ

processes. The reference distributions used as a cross-check are obtained from the fixed-order Monte Carlo simulations. For the purpose of validation we concentrate on those phase space regions where is not expected to work on the basis of its design alone. These phase space regions of hard, non-collinear photons, do not contribute to large logarithmic terms. We find that in these phase space regions the differences between and results do not surpass a few percent and these regions, in turn, contribute about 10% to the observed process rates. This is encouraging in view of the possible ambiguities for precise calculation of realistic observables.
Geometric minimization of softly broken potentials
Authors: de Medeiros Varzielas Ivo , Ivo Diogo 
Published: January 2023

AbstractWe study the minimization of the potentials of multi-Higgs models with symmetries that are softly broken. The powerful method of geometric minimization enables analytic minimization of the potentials of multi-Higgs models invariant under large symmetries. When these symmetries are softly broken, the method needs to be adapted. We propose a useful generalization that considers the effect of the soft-breaking terms to the quadratic part of the potential, by applying the procedure to restricted orbit spaces. We exemplify our novel methodology by finding and classifying the minima for an $$S_4$$

S
4

multi-Higgs model that is softly broken with specific terms.
Genomic Complexity Predicts Resistance to Endocrine Therapy and CDK4/6 Inhibition in Hormone Receptor–Positive (HR+)/HER2-Negative Metastatic Breast Cancer
Authors: Davis Andrew A. , Luo Jingqin , Zheng Tiantian , Dai Chao , Dong Xiaoxi , Tan Lu , Suresh Rama , Ademuyiwa Foluso O. , Rigden Caron , Rearden Timothy P. , Clifton Katherine , Weilbaecher Katherine , Frith Ashley , Tandra Pavan K. , Summa Tracy , Haas Brittney , Thomas Shana , Hernandez-Aya Leonel F. , Peterson Lindsay L. , Wang Xiaohong , Luo Shujun J. , Zhou Kemin , Du Pan , Jia Shidong , King Bonnie L. , Krishnamurthy Jairam , Ma Cynthia X. 
Published: January 2023

Abstract

Purpose:
Clinical biomarkers to identify patients unlikely to benefit from CDK4/6 inhibition (CDK4/6i) in combination with endocrine therapy (ET) are lacking. We implemented a comprehensive circulating tumor DNA (ctDNA) analysis to identify genomic features for predicting and monitoring treatment resistance.

Experimental Design:
ctDNA was isolated from 216 plasma samples collected from 51 patients with hormone receptor–positive (HR+)/HER2-negative (HER2−) metastatic breast cancer (MBC) on a phase II trial of palbociclib combined with letrozole or fulvestrant (NCT03007979). Boosted whole-exome sequencing (WES) was performed at baseline and clinical progression to evaluate genomic alterations, mutational signatures, and blood tumor mutational burden (bTMB). Low-pass whole-genome sequencing was performed at baseline and serial timepoints to assess blood copy-number burden (bCNB).

Results:
High bTMB and bCNB were associated with lack of clinical benefit and significantly shorter progression-free survival (PFS) compared with patients with low bTMB or low bCNB (all P &lt; 0.05). Dominant APOBEC signatures were detected at baseline exclusively in cases with high bTMB (5/13, 38.5%) versus low bTMB (0/37, 0%; P = 0.0006). Alterations in ESR1 were enriched in samples with high bTMB (P = 0.0005). There was a high correlation between bTMB determined by WES and bTMB determined using a 600-gene panel (R = 0.98). During serial monitoring, an increase in bCNB score preceded radiographic progression in 12 of 18 (66.7%) patients.

Conclusions:
Genomic complexity detected by noninvasive profiling of bTMB and bCNB predicted poor outcomes in patients treated with ET and CDK4/6i and identified early disease progression before imaging. Novel treatment strategies including immunotherapy-based combinations should be investigated in this population.
Pathogenic Variants in Adult-Onset Cancer Predisposition Genes in Pediatric Cancer: Prevalence and Impact on Tumor Molecular Features and Clinical Management
Authors: McGee Rose B. , Oak Ninad , Harrison Lynn , Xu Ke , Nuccio Regina , Blake Alise K. , Mostafavi Roya , Lewis Sara , Taylor Leslie M. , Kubal Manish , Ouma Annastasia , Hines-Dowell Stacy J. , Cheng Cheng , Furtado Larissa V. , Nichols Kim E. 
Published: January 2023

AbstractPurpose:Clinical genomic sequencing of pediatric tumors is increasingly uncovering pathogenic variants in adult-onset cancer predisposition genes (aoCPG). Nevertheless, it remains poorly understood how often aoCPG variants are of germline origin and whether they influence tumor molecular profiles and/or clinical care. In this study, we examined the prevalence, spectrum, and impacts of aoCPG variants on tumor genomic features and patient management at our institution.Experimental Design:This is a retrospective study of 1,018 children with cancer who underwent clinical genomic sequencing of their tumors. Tumor genomic data were queried for pathogenic variants affecting 24 preselected aoCPGs. Available tumor whole-genome sequencing (WGS) data were evaluated for second hit mutations, loss of heterozygosity (LOH), DNA mutational signatures, and homologous recombination deficiency (HRD). Patients whose tumors harbored one or more pathogenic aoCPG variants underwent subsequent germline testing based on hereditary cancer evaluation and family or provider preference.Results:Thirty-three patients (3%) had tumors harboring pathogenic variants affecting one or more aoCPGs. Among 21 tumors with sufficient WGS sequencing data, six (29%) harbored a second hit or LOH affecting the remaining aoCPG allele with four of these six tumors (67%) also exhibiting a DNA mutational signature consistent with the altered aoCPG. Two additional tumors demonstrated HRD, of uncertain relation to the identified aoCPG variant. Twenty-one of 26 patients (81%) completing germline testing were positive for the aoCPG variant in the germline. All germline-positive patients were counseled regarding future cancer risks, surveillance, and risk-reducing measures. No patients had immediate cancer therapy changed due to aoCPG data.Conclusions:AoCPG variants are rare in pediatric tumors; however, many originate in the germline. Almost one third of tumor aoCPG variants examined exhibited a second hit and/or conferred an abnormal DNA mutational profile suggesting a role in tumor formation. aoCPG information aids in cancer risk prediction but is not commonly used to alter the treatment of pediatric cancers.
Antibody–Exatecan Conjugates with a Novel Self-immolative Moiety Overcome Resistance in Colon and Lung Cancer
Authors: Weng Weining , Meng Tao , Zhao Qianqian , Shen Yi , Fu Guoxiang , Shi Jing , Zhang Yue , Wang Zhaohui , Wang Mingqiao , Pan Rong , Ma Linjie , Chen Caiwei , Wang Lijun , Zhou Biao , Zhang Hui , Pu Junyi , Zhang Jianjian , Hu Yi Peter , Hua Guoqiang , Qian Yu , Liu Shu-Hui , Hu Wenhao , Meng Xun 
Published: January 2023

AbstractAntibody–drug conjugates (ADC) using DNA topoisomerase I inhibitor DXd/SN-38 have transformed cancer treatment, yet more effective ADCs are needed for overcoming resistance. We have designed an ADC class using a novel self-immolative T moiety for traceless conjugation and release of exatecan, a more potent topoisomerase I inhibitor with less sensitivity to multidrug resistance (MDR). Characterized by enhanced therapeutic indices, higher stability, and improved intratumoral pharmacodynamic response, antibody–T moiety–exatecan conjugates targeting HER2, HER3, and TROP2 overcome the intrinsic or treatment resistance of equivalent DXd/SN-38 ADCs in low-target-expression, large, and MDR+ tumors. T moiety–exatecan ADCs display durable antitumor activity in patient-derived xenograft and organoid models representative of unmet clinical needs, including EGFR ex19del/T790M/C797S triple-mutation lung cancer and BRAF/KRAS–TP53 double-mutant colon cancer, and show synergy with PARP/ATR inhibitor and anti–PD-1 treatment. High tolerability of the T moiety–exatecan ADC class in nonhuman primates supports its potential to expand the responding patient population and tumor types beyond current ADCs.Significance:ADCs combining a novel self-immolative moiety and topoisomerase I inhibitor exatecan as payload show deep and durable response in low-target-expressing and MDR+ tumors resistant to DXd/SN-38 ADCs without increasing toxicity. This new class of ADCs has the potential to benefit an additional patient population beyond current options.See related commentary by Gupta et al., p. 817.This article is highlighted in the In This Issue feature, p. 799
Street-crimes modelled arms recognition technique employing deep learning and quantum deep learning (SMARTED)
Authors: Atif Ali Shah Syed , Abdel-Wahab Ahmed , Ageelani Nasir , Najeeb Najeeb 
Published: January 2023

An <span>increase in population causes loopholes in controlling law and order situations. One of the threatening aspects of peace is the availability of weapons to the general public. As a result, many dangerous situations arise, most notably street crimes. Traditional methods are not sufficient to deal with such situations. Consequently, the police and other security concerns need serious technological reforms to prevent such situations. In modern technology tools, deep learning has made great improvements in various areas of daily life, especially in object detection. This paper presents an efficient technique for detecting weapons from closed-circuit television (CCTV) cameras, videos, or images. Upon the detection of the weapon, the concerned person is automatically informed to take the necessary action; without human intervention. For the first time, RetinaNet has been employed to detect weapons in real-time scenarios. RetinaNet has shown remarkable improvement in this domain, by achieving an average of 90% accuracy in real-time scenarios. With the emergence of quantum computing, many computer environments saw a revolution. Thus, we have also utilized quantum computing technology for real-time weapons detection using quantum deep learning. In this paper, quantum inspired RetinaNet (QIR-Net) is developed for weapons detection and amazing results are observed.</span>
Analysis of facial emotion recognition rate for real-time application using NVIDIA Jetson Nano in deep learning models
Authors: Dudekula Usen , Nalluri Purnachand 
Published: January 2023

Detecting <span>facial emotion expression is a classic research problem in image processing. Face expression detection can be used to help human users monitor their stress levels. Perceiving an individual's failure to communicate specific looks might help analyze early psychological disorders. several issues like lighting changes, rotations, occlusions, and accessories persist. These are not simply traditional image processing issues, yet additionally, action units that make gathering activity of facial acknowledgment troublesome look information, and order of the demeanor. In this study, we use Xception taking into account Xception and convolution neural network (CNN), which is easy to focus on incredible parts like the face, and visual geometric group (VGG-19) used to extract the facial feature using the OpenCV framework classifying the image into any of the basic facial emotions. NVIDIA Jetson Nano has a high video handling outline rate. Accomplishing preferable precision over the recently evolved models on software. The average accuracies for standard data set CK+,” on NVIDIA Jetson Nano, the accuracy rate is 97.1% in the Xception model in the convolutional neural network, 98.4% in VGG-19, and real-time environment accuracy using OpenCV, accuracy rate is 95.6%.</span>
Apolipoprotein Proteomics for Residual Lipid-Related Risk in Coronary Heart Disease
Authors: Clarke Robert , Von Ende Adam , Schmidt Lukas E. , Yin Xiaoke , Hill Michael , Hughes Alun D. , Pechlaner Raimund , Willeit Johann , Kiechl Stefan , Watkins Hugh , Theofilatos Konstantinos , Hopewell Jemma C. , Mayr Manuel ,   on behalf of the PROCARDIS Consortium
Published: January 2023

Background:
Recognition of the importance of conventional lipid measures and the advent of novel lipid-lowering medications have prompted the need for more comprehensive lipid panels to guide use of emerging treatments for the prevention of coronary heart disease (CHD). This report assessed the relevance of 13 apolipoproteins measured using a single mass-spectrometry assay for risk of CHD in the PROCARDIS case-control study of CHD (941 cases/975 controls).

Methods:
The associations of apolipoproteins with CHD were assessed after adjustment for established risk factors and correction for statin use. Apolipoproteins were grouped into 4 lipid-related classes [lipoprotein(a), low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglycerides] and their associations with CHD were adjusted for established CHD risk factors and conventional lipids. Analyses of these apolipoproteins in a subset of the ASCOT trial (Anglo-Scandinavian Cardiac Outcomes Trial) were used to assess their within-person variability and to estimate a correction for statin use. The findings in the PROCARDIS study were compared with those for incident cardiovascular disease in the Bruneck prospective study (n=688), including new measurements of Apo(a).

Results:
Triglyceride-carrying apolipoproteins (ApoC1, ApoC3, and ApoE) were most strongly associated with the risk of CHD (2- to 3-fold higher odds ratios for top versus bottom quintile) independent of conventional lipid measures. Likewise, ApoB was independently associated with a 2-fold higher odds ratios of CHD. Lipoprotein(a) was measured using peptides from the Apo(a)-kringle repeat and Apo(a)-constant regions, but neither of these associations differed from the association with conventionally measured lipoprotein(a). Among HDL-related apolipoproteins, ApoA4 and ApoM were inversely related to CHD, independent of conventional lipid measures. The disease associations with all apolipoproteins were directionally consistent in the PROCARDIS and Bruneck studies, with the exception of ApoM.

Conclusions:
Apolipoproteins were associated with CHD independent of conventional risk factors and lipids, suggesting apolipoproteins could help to identify patients with residual lipid-related risk and guide personalized approaches to CHD risk reduction.
Computational Language Modeling and the Promise of In Silico Experimentation
Authors: Jain Shailee , Vo Vy A. , Wehbe Leila , Huth Alexander G. 
Published: January 2023

Abstract
Language neuroscience currently relies on two major experimental paradigms: controlled experiments using carefully hand-designed stimuli, and natural stimulus experiments. These approaches have complementary advantages which allow them to address distinct aspects of the neurobiology of language, but each approach also comes with drawbacks. Here we discuss a third paradigm—in silico experimentation using deep learning-based encoding models—that has been enabled by recent advances in cognitive computational neuroscience. This paradigm promises to combine the interpretability of controlled experiments with the generalizability and broad scope of natural stimulus experiments. We show four examples of simulating language neuroscience experiments in silico and then discuss both the advantages and caveats of this approach.
Citation metrics covary with researchers’ assessments of the quality of their works
Authors: Aksnes Dag W. , Piro Fredrik Niclas , Fossum Lone Wanderås 
Published: January 2023

Abstract
For a long time, citation counts have been used to measure scientific impact or quality. Do such measures align with researchers’ assessments of the quality of their work? In this study, we address this issue by decomposing the research quality concept into constituent parts and analyzing their correspondence with citation measures. The focus is on individual publications, their citation counts and how the publications are rated by the authors themselves along quality dimensions. Overall, the study shows a statistically significant relationship for all dimensions analyzed: solidity, novelty/originality, scientific importance and societal impact. The highest correlation is found for scientific importance. However, it is not very strong, but we find distinct gradients when publications are grouped by quality scores. This means that the higher the researchers rate their work, the more they are cited. The results suggest that citation metrics have low reliability as indicators at the level of individual articles, but at aggregated levels, the validity is higher, at least according to how authors perceive quality.
Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering
Authors: Siriwardhana Shamane , Weerasekera Rivindu , Wen Elliott , Kaluarachchi Tharindu , Rana Rajib , Nanayakkara Suranga 
Published: January 2023

Abstract
Retrieval Augment Generation (RAG) is a recent advancement in Open-Domain Question Answering (ODQA). RAG has only been trained and explored with a Wikipedia-based external knowledge base and is not optimized for use in other specialized domains such as healthcare and news. In this paper, we evaluate the impact of joint training of the retriever and generator components of RAG for the task of domain adaptation in ODQA. We propose RAG-end2end, an extension to RAG that can adapt to a domain-specific knowledge base by updating all components of the external knowledge base during training. In addition, we introduce an auxiliary training signal to inject more domain-specific knowledge. This auxiliary signal forces RAG-end2end to reconstruct a given sentence by accessing the relevant information from the external knowledge base. Our novel contribution is that, unlike RAG, RAG-end2end does joint training of the retriever and generator for the end QA task and domain adaptation. We evaluate our approach with datasets from three domains: COVID-19, News, and Conversations, and achieve significant performance improvements compared to the original RAG model. Our work has been open-sourced through the HuggingFace Transformers library, attesting to our work’s credibility and technical consistency.
On the Role of Negative Precedent in Legal Outcome Prediction
Authors: Valvoda Josef , Cotterell Ryan , Teufel Simone 
Published: January 2023

Abstract
Every legal case sets a precedent by developing the law in one of the following two ways. It either expands its scope, in which case it sets positive precedent, or it narrows it, in which case it sets negative precedent. Legal outcome prediction, the prediction of positive outcome, is an increasingly popular task in AI. In contrast, we turn our focus to negative outcomes here, and introduce a new task of negative outcome prediction. We discover an asymmetry in existing models’ ability to predict positive and negative outcomes. Where the state-of-the-art outcome prediction model we used predicts positive outcomes at 75.06 F1, it predicts negative outcomes at only 10.09 F1, worse than a random baseline. To address this performance gap, we develop two new models inspired by the dynamics of a court process. Our first model significantly improves positive outcome prediction score to 77.15 F1 and our second model more than doubles the negative outcome prediction performance to 24.01 F1. Despite this improvement, shifting focus to negative outcomes reveals that there is still much room for improvement for outcome prediction models.
https://github.com/valvoda/Negative-Precedent-in-Legal-Outcome-Prediction
Meta-Learning a Cross-lingual Manifold for Semantic Parsing
Authors: Sherborne Tom , Lapata Mirella 
Published: January 2023

Abstract
Localizing a semantic parser to support new languages requires effective cross-lingual generalization. Recent work has found success with machine-translation or zero-shot methods, although these approaches can struggle to model how native speakers ask questions. We consider how to effectively leverage minimal annotated examples in new languages for few-shot cross-lingual semantic parsing. We introduce a first-order meta-learning algorithm to train a semantic parser with maximal sample efficiency during cross-lingual transfer. Our algorithm uses high-resource languages to train the parser and simultaneously optimizes for cross-lingual generalization to lower-resource languages. Results across six languages on ATIS demonstrate that our combination of generalization steps yields accurate semantic parsers sampling ≤10% of source training data in each new language. Our approach also trains a competitive model on Spider using English with generalization to Chinese similarly sampling ≤10% of training data.1