bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identification of sex-specific compounds in the invasive four-eyed fir bark beetle Polygraphus proximus
Authors: Viklund Lina , Baranchikov Yuri , Schroeder Martin , Efremenko Anton , Demidko Denis , Hedenström Erik 
Published: November 2022

AbstractPolygraphus proximus, a four-eyed fir bark beetle, is an invasive bark beetle species which has caused extensive damage to forests of Abies sibirica in southern and western Siberia and to Abies species in the European part of Russia. There is a high risk that the pest insect will spread to areas where it is currently not considered present, such as the European Union. In these areas, it threatens to attack conifer forests of various species which may result in major environmental and economic impact. The aim of this study was to identify pheromone components of P. proximus that can be used as pheromone baits. Males and females of P. proximus were allowed to bore into the bark of stem sections of Abies sibirica at the laboratory, and volatiles were collected with solid-phase microextraction (SPME). Analyses of these extracts with gas chromatography-mass spectrometry (GC–MS) revealed several sex-specific compounds. In total, twelve male-specific compounds and one female-specific compound were identified. The major male-specific compound determined by GC peak area was (Z)‐2‐(3,3‐dimethylcyclohexylidene)‐ethanol [(Z)-DMCHE] and the minor male-specific compounds were 3-methyl-3-buten-1-ol, 3-methyl-2-buten-1-ol, 3-methyl-2-butenal, benzyl alcohol, fragranol, 7-methyl-3-methylene-6-octen-1-ol, (Z)- and (E)-2-(3,3-dimethylcyclohexylidene)-acetaldehyde, geraniol, geranial and papayanol. The only female-specific compound was identified as 1-hexanol. Two of the male-specific compounds, (Z)‐DMCHE and 3-methyl-2-buten-1-ol were shown to attract males and females of P. proximus in field studies. Thus, we now for the first time can present the structures of two male-specific components that are biologically active parts of P. proximus aggregation pheromone. However, some chemical communication overlap between P. proximus and P. subopacus needs to be further investigated as (Z)‐DMCHE also attracted males and females of P. subopacus.
Multiway clustering with time-varying parameters
Authors: Cerqueti Roy , Mattera Raffaele , Scepi Germana 
Published: November 2022

AbstractThis paper proposes a clustering approach for multivariate time series with time-varying parameters in a multiway framework. Although clustering techniques based on time series distribution characteristics have been extensively studied, methods based on time-varying parameters have only recently been explored and are missing for multivariate time series. This paper fills the gap by proposing a multiway approach for distribution-based clustering of multivariate time series. To show the validity of the proposed clustering procedure, we provide both a simulation study and an application to real air quality time series data.
Bioprospecting and Challenges of Plant Microbiome Research for Sustainable Agriculture, a Review on Soybean Endophytic Bacteria
Authors: Ayilara Modupe Stella , Adeleke Bartholomew Saanu , Babalola Olubukola Oluranti 
Published: November 2022

Abstract
This review evaluates oilseed crop soybean endophytic bacteria, their prospects, and challenges for sustainable agriculture. Soybean is one of the most important oilseed crops with about 20–25% protein content and 20% edible oil production. The ability of soybean root-associated microbes to restore soil nutrients enhances crop yield. Naturally, the soybean root endosphere harbors root nodule bacteria, and endophytic bacteria, which help increase the nitrogen pool and reclamation of another nutrient loss in the soil for plant nutrition. Endophytic bacteria can sustain plant growth and health by exhibiting antibiosis against phytopathogens, production of enzymes, phytohormone biosynthesis, organic acids, and secondary metabolite secretions. Considerable effort in the agricultural industry is focused on multifunctional concepts and bioprospecting on the use of bioinput from endophytic microbes to ensure a stable ecosystem. Bioprospecting in the case of this review is a systemic overview of the biorational approach to harness beneficial plant-associated microbes to ensure food security in the future. Progress in this endeavor is limited by available techniques. The use of molecular techniques in unraveling the functions of soybean endophytic bacteria can explore their use in integrated organic farming. Our review brings to light the endophytic microbial dynamics of soybeans and current status of plant microbiome research for sustainable agriculture.
Comparison of PSA density and lesion volume strategies for selecting men with equivocal PI-RADS 3 lesions on bpMRI for biopsies
Authors: Kortenbach Karen-Cecilie , Løgager Vibeke , Thomsen Henrik S. , Boesen Lars 
Published: November 2022

Abstract
Purpose
To compare two strategies: Prostate-specific antigen density (PSAd) and lesion volume measurement in ruling out significant prostate cancer (sPCa) in men with equivocal Prostate Imaging Reporting and Data System (PI-RADS) category 3 index lesions on biparametric magnetic resonance imaging.

Methods
In total, 130 men from our database had index lesions with PI-RADS scores of 3. Prostate volume was measured using the ellipsoid method, in accordance with PI-RADS version 2.1 criteria. Index lesion volumes were also measured using the ellipsoidal formula on the diffusion-weighted imaging sequence with the highest b-value and sagittal T2 sequences.

Results
Among 130 men with PI-RADS category 3 index lesions, 23 (18%) had sPCa. In total, 6 of the 89 men with PSAd < 0.15 ng/mL2 (7%) had sPCa, whereas 8 of the 49 men with index lesion volumes < 0.5 mL (16%) had sPCa. The difference was statistically significant (McNemar, p < 0.0001).

Conclusion
The PSAd strategy performed better than the lesion volume strategy in ruling out sPCa in men with equivocal PI-RADS category 3 index lesions.
Assessment of vitamin B6 status in never-pregnant, pregnant and postpartum women and their infants
Authors: Bjørke-Monsen Anne-Lise , Varsi Kristin , Sakkestad Sunniva Todnem , Ulvik Arve , Ueland Per Magne 
Published: November 2022

Abstract
Purpose
Pyridoxal 5´-phosphate (PLP) is the main form of vitamin B6 in humans and functions a coenzyme for more than 160 different enzymatic reactions. The purpose of the study was to find plasma PLP concentrations, which ensure an optimal vitamin B6 status determined by a metabolic marker, in never-pregnant, pregnant and lactating women and their infants.

Methods
In an observational, prospective study, plasma PLP and the metabolic marker, HKr (hydroxykynurenine/(kynurenic acid + anthranilic acid + xanthurenic acid + hydroxyanthranilic acid) were assessed in women (n = 114) from pregnancy week 18 to 6 months postpartum and related to infant status. Never-pregnant women 18–40 years (n = 127) were included as controls.

Results
Compared to controls, plasma PLP decreased during pregnancy and increased postpartum, while HKr increased from week 18 to 6 weeks postpartum, indicating maternal vitamin B6 insufficiency during this period. In never-pregnant women, HKr increased gradually with plasma PLP < 100 nmol/L, and in pregnancy week 28 a sharp increase in HKr was seen at plasma PLP < 30 nmol/L. Despite correcting for maternal vitamin B6 status, infant median plasma PLP decreased with months of exclusive breastfeeding.

Conclusions
Plasma PLP and kynurenine concentrations differ substantially between never-pregnant, pregnant and postpartum women and infants. A plasma PLP concentration in the range of 50–100 nmol/L seems to ensure an optimal vitamin B6 status for never-pregnant women, whereas a plasma PLP > 30 nmol/L in pregnancy week 28 ensures an adequate vitamin B6 status during pregnancy and lactation. Infant vitamin B6 status at age 6 months is inversely correlated to number of months of exclusive breastfeeding.
Circulatory system disease mortality and occupational exposure to radon progeny in the cohort of Newfoundland Fluorspar Miners between 1950 and 2016
Authors: Villeneuve Paul J. , Morrison Howard I. , Volesky Karena , Lane Rachel S. D. 
Published: November 2022

Abstract
Objectives
Exposure to ionizing radiation may increase the risk of circulatory diseases, including heart disease. A limited number of cohort studies of underground miners have investigated these associations. We previously reported a positive but non-statistically significant association between radon progeny and heart disease in a cohort of Newfoundland fluorspar miners. In this study, we report updated findings that incorporate 15 additional years of follow-up.

Methods
The cohort included 2050 miners who worked in the fluorspar mines from 1933 to 1978. Statistics Canada linked the personal identifying data of the miners to Canadian mortality data to identify deaths from 1950 to 2016. We used previously derived individual-level estimates of annual radon progeny exposure in working-level months. Cumulative exposure was categorized into quantiles. We estimated relative risks and their 95% confidence intervals using Poisson regression for deaths from circulatory, ischemic heart disease and acute myocardial infarction. Relative risks were adjusted for attained age, calendar year, and the average number of cigarettes smoked daily.

Results
Relative to the Newfoundland male population, the standardized mortality ratio for circulatory disease in this cohort was 0.82 (95% CI 0.74–0.91). Those in the highest quantile of cumulative radon progeny exposure had a relative risk of circulatory disease mortality of 1.03 (95% CI 0.76–1.40) compared to those in the lowest quantile. The corresponding estimates for ischemic disease and acute myocardial infarction were 0.99 (95% CI 0.66–1.48), and 1.39 (95% CI 0.84–2.30), respectively.

Conclusions
Our findings do not support the hypothesis that occupational exposure to radon progeny increases the risk of circulatory disease.
Predator-induced transgenerational plasticity in animals: a meta-analysis
Authors: MacLeod Kirsty J. , Monestier Chloé , Ferrari Maud C. O. , McGhee Katie E. , Sheriff Michael J. , Bell Alison M. 
Published: November 2022

AbstractThere is growing evidence that the environment experienced by one generation can influence phenotypes in the next generation via transgenerational plasticity (TGP). One of the best-studied examples of TGP in animals is predator-induced transgenerational plasticity, whereby exposing parents to predation risk triggers changes in offspring phenotypes. Yet, there is a lack of general consensus synthesizing the predator–prey literature with existing theory pertaining to ecology and evolution of TGP. Here, we apply a meta-analysis to the sizable literature on predator-induced TGP (441 effect sizes from 29 species and 49 studies) to explore five hypotheses about the magnitude, form and direction of predator-induced TGP. Hypothesis #1: the strength of predator-induced TGP should vary with the number of predator cues. Hypothesis #2: the strength of predator-induced TGP should vary with reproductive mode. Hypothesis #3: the strength and direction of predator-induced TGP should vary among offspring phenotypic traits because some traits are more plastic than others. Hypothesis #4: the strength of predator-induced TGP should wane over ontogeny. Hypothesis #5: predator-induced TGP should generate adaptive phenotypes that should be more evident when offspring are themselves exposed to risk. We found strong evidence for predator-induced TGP overall, but no evidence that parental predator exposure causes offspring traits to change in a particular direction. Additionally, we found little evidence in support of any of the specific hypotheses. We infer that the failure to find consistent evidence reflects the heterogeneous nature of the phenomena, and the highly diverse experimental designs used to study it. Together, these findings set an agenda for future work in this area.
Fuzzy-based hunger games search algorithm for global optimization and feature selection using medical data
Authors: Houssein Essam H. , Hosney Mosa E. , Mohamed Waleed M. , Ali Abdelmgeid A. , Younis Eman M. G. 
Published: November 2022

AbstractFeature selection (FS) is one of the basic data preprocessing steps in data mining and machine learning. It is used to reduce feature size and increase model generalization. In addition to minimizing feature dimensionality, it also enhances classification accuracy and reduces model complexity, which are essential in several applications. Traditional methods for feature selection often fail in the optimal global solution due to the large search space. Many hybrid techniques have been proposed depending on merging several search strategies which have been used individually as a solution to the FS problem. This study proposes a modified hunger games search algorithm (mHGS), for solving optimization and FS problems. The main advantages of the proposed mHGS are to resolve the following drawbacks that have been raised in the original HGS; (1) avoiding the local search, (2) solving the problem of premature convergence, and (3) balancing between the exploitation and exploration phases. The mHGS has been evaluated by using the IEEE Congress on Evolutionary Computation 2020 (CEC’20) for optimization test and ten medical and chemical datasets. The data have dimensions up to 20000 features or more. The results of the proposed algorithm have been compared to a variety of well-known optimization methods, including improved multi-operator differential evolution algorithm (IMODE), gravitational search algorithm, grey wolf optimization, Harris Hawks optimization, whale optimization algorithm, slime mould algorithm and hunger search games search. The experimental results suggest that the proposed mHGS can generate effective search results without increasing the computational cost and improving the convergence speed. It has also improved the SVM classification performance.
Light-emitting greenhouses affect daily vocalization behavior in birds
Authors: Skorb Karolina , Jankowiak Łukasz , Zbyryt Adam 
Published: November 2022

AbstractTo date, studies of the effects of artificial light on wildlife have mostly focused on street lighting. However, less attention has been paid to light from greenhouses, which can be sources of substantial light pollution from autumn to early spring. We investigated how daily 4–8 h of nocturnal illumination from a greenhouse influences the time of daily vocalization of forest birds in a non-urban area. Our study revealed that birds exposed to greenhouse lightning start dawn singing and calling earlier and stop dusk vocalization later, especially before the breeding season in the second half of February. The most affected species were the European Robin (entire study period) and Blackbird (early period only), but other species also revealed a significant impact on vocal activity (calls). Our study shows that even short-duration light pollution from greenhouses in a non-urban area in the breeding season can affect bird vocalization behavior.
Towards combining commonsense reasoning and knowledge acquisition to guide deep learning
Authors: Sridharan Mohan , Mota Tiago 
Published: November 2022

AbstractAlgorithms based on deep network models are being used for many pattern recognition and decision-making tasks in robotics and AI. Training these models requires a large labeled dataset and considerable computational resources, which are not readily available in many domains. Also, it is difficult to explore the internal representations and reasoning mechanisms of these models. As a step towards addressing the underlying knowledge representation, reasoning, and learning challenges, the architecture described in this paper draws inspiration from research in cognitive systems. As a motivating example, we consider an assistive robot trying to reduce clutter in any given scene by reasoning about the occlusion of objects and stability of object configurations in an image of the scene. In this context, our architecture incrementally learns and revises a grounding of the spatial relations between objects and uses this grounding to extract spatial information from input images. Non-monotonic logical reasoning with this information and incomplete commonsense domain knowledge is used to make decisions about stability and occlusion. For images that cannot be processed by such reasoning, regions relevant to the tasks at hand are automatically identified and used to train deep network models to make the desired decisions. Image regions used to train the deep networks are also used to incrementally acquire previously unknown state constraints that are merged with the existing knowledge for subsequent reasoning. Experimental evaluation performed using simulated and real-world images indicates that in comparison with baselines based just on deep networks, our architecture improves reliability of decision making and reduces the effort involved in training data-driven deep network models.
Documentation as composing: how medical students and residents use writing to think and learn
Authors: Bowker Dillon , Torti Jacqueline , Goldszmidt Mark 
Published: November 2022

AbstractSome educators have described clinical documentation as “scut”. Research in medicine has focused on documentation’s communicative value and not its function in learning. With time being an important commodity and electronic health records changing how we document, understanding the learning value of documentation is essential. The purpose of this study was to explore how trainee composing practices shape learning. Qualitative methods employing Rhetorical Genre Theory were used to explore clinical documentation practices among medical trainees. Data collection and analysis occurred in iterative cycles. Data included field notes and field interviews from 110 h of observing junior trainees and senior internal medicine residents participating in patient admission and follow-up visits. Analysis was focused on Paré and Smart’s framework for studying documentation as composing. From a composing lens, documentation plays a vital role in learning in clinical settings. Junior trainees were observed to be reliant on using writing to support their thinking around patient care. Before patient encounters, writing helped trainees focus on what was already known and develop a preliminary understanding of the patient’s problem(s). After encounters, writing helped trainees synthesize the data and develop an assessment and plan. Before and after the encounter, through writing, trainees also identified knowledge and data collection gaps. Our findings highlight clinical documentation as more than a communication task. Rather, the writing process itself appeared to play a pivotal role in supporting thinking. While some have proposed strategies for reducing trainee involvement, we argue that writing can be time well spent.
Mobility is Associated with Higher-risk Sexual Partnerships Among Both Men and Women in Co-resident Couples in Rural Kenya and Uganda: A Longitudinal Cohort Study
Authors: Gutin Sarah A. , Neilands Torsten B. , Charlebois Edwin D. , Getahun Monica , Okiring Jaffer , Akullian Adam , Maeri Irene , Eyul Patrick , Ssali Sarah , Cohen Craig R. , Kamya Moses R. , Bukusi Elizabeth A. , Camlin Carol S. 
Published: November 2022

AbstractPopulation mobility is associated with higher-risk sexual behaviors in sub-Saharan Africa and is a key driver of the HIV epidemic. We conducted a longitudinal cohort study to estimate associations between recent mobility (overnight travel away from home in past six months) or migration (changes of residence over defined geopolitical boundaries) and higher-risk sexual behavior among co-resident couples (240 couples aged ≥ 16) from 12 rural communities in Kenya and Uganda. Data on concurrent mobility and sexual risk behaviors were collected every 6-months between 2015 and 2020. We used sex-pooled and sex-stratified multilevel models to estimate associations between couple mobility configurations (neither partner mobile, male mobile/female not mobile, female mobile/male not mobile, both mobile) and the odds of higher-risk (casual, commercial sex worker/client, one night stand, inherited partner, stranger) and concurrent sexual partnerships based on who was mobile. On average across all time points and subjects, mobile women were more likely than non-mobile women to have a higher-risk partner; similarly, mobile men were more likely than non-mobile men to report a higher-risk partnership. Men with work-related mobility versus not had higher odds of higher-risk partnerships. Women with work-related mobility versus not had higher odds of higher-risk partnerships. Couples where both members were mobile versus neither had greater odds of higher-risk partnerships. In analyses using 6-month lagged versions of key predictors, migration events of men, but not women, preceded higher-risk partnerships. Findings demonstrate HIV risks for men and women associated with mobility and the need for prevention approaches attentive to the risk-enhancing contexts of mobility.
A methodology for refined evaluation of neural code completion approaches
Authors: Le Kim Tuyen , Rashidi Gabriel , Andrzejak Artur 
Published: November 2022

AbstractCode completion has become an indispensable feature of modern Integrated Development Environments. In recent years, many approaches have been proposed to tackle this task. However, it is hard to compare between the models without explicitly re-evaluating them due to the differences of used benchmarks (e.g. datasets and evaluation metrics). Besides, almost all of these works report the accuracy of the code completion models as aggregated metrics averaged over all types of code tokens. Such evaluations make it difficult to assess the potential improvements for particularly relevant types of tokens (i.e. method or variable names), and blur the differences between the performance of the methods. In this paper, we propose a methodology called Code Token Type Taxonomy (CT3) to address the issue of using aggregated metrics. We identify multiple dimensions relevant for code prediction (e.g. syntax type, context, length), partition the tokens into meaningful types along each dimension, and compute individual accuracies by type. We illustrate the utility of this methodology by comparing the code completion accuracy of a Transformer-based model in two variants: with closed, and with open vocabulary. Our results show that the refined evaluation provides a more detailed view of the differences and indicates where further work is needed. We also survey the state-of-the-art of Machine Learning-based code completion models to illustrate that there is a demand for a set of standardized benchmarks for code completion approaches. Furthermore, we find that the open vocabulary model is significantly more accurate for relevant code token types such as usage of (defined) variables and literals.