bluuebunny/crossref_metadata_2025_split huggingface dataset:
Amino acid substitutions in grapevine (Vitis vinifera) acetolactate synthase conferring herbicide resistance
Authors: Wuddineh Wegi A. , Xu Xia , Zhong Gan-Yuan 
Published: May 2023

AbstractAcetolactate synthase genes (ALS) have successfully been modified for providing resistance to ALS-inhibiting herbicides in many plant species. Based on sequence and expression analyses, we confirmed VvALS1 as the best functional ALS candidate in grapevine. To develop an ALS-based herbicide selection system for facilitating grape transformation, we firstly evaluated the responses of Vitis vinifera cv Chardonnay callus and young in vitro shoots of Vitis vinifera cv Thompson Seedless to several representative ALS-inhibiting herbicides and found a typical linear response curves to some of the herbicides, including chlorsulfuron and imazapyr belonging to the sulfonylurea or imidazolinone families, respectively. Secondly, we created constructs containing amino acid substitutions in the domains which are known to be critical to herbicide resistance and generated transgenic plants for 3 amino acid substitutions using Agrobacterium-mediated transformation of meristematic bulk tissues of Thompson Seedless. Finally, we showed that ectopic expression of two amino acid substitutions (P191S and P191T) at the N-terminal region and another (W568L) at C-terminal region in VvALS resulted in high resistance to chlorsulfuron or imazapyr herbicides in transgenic in vitro shoots. Our work highlighted the potential use of VvALS mutations imparting herbicide resistance as a selectable marker in grapevine transformation research and as a means in fostering grapevine improvement via cisgenesis, paving the way for developing a selectable co-editing system to facilitate transgene-free gene-editing.
Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation
Authors: Hoyer Lukas , Dai Dengxin , Wang Qin , Chen Yuhua , Van Gool Luc 
Published: May 2023

AbstractTraining deep networks for semantic segmentation requires large amounts of labeled training data, which presents a major challenge in practice, as labeling segmentation masks is a highly labor-intensive process. To address this issue, we present a framework for semi-supervised and domain-adaptive semantic segmentation, which is enhanced by self-supervised monocular depth estimation (SDE) trained only on unlabeled image sequences. In particular, we utilize SDE as an auxiliary task comprehensively across the entire learning framework: First, we automatically select the most useful samples to be annotated for semantic segmentation based on the correlation of sample diversity and difficulty between SDE and semantic segmentation. Second, we implement a strong data augmentation by mixing images and labels using the geometry of the scene. Third, we transfer knowledge from features learned during SDE to semantic segmentation by means of transfer and multi-task learning. And fourth, we exploit additional labeled synthetic data with Cross-Domain DepthMix and Matching Geometry Sampling to align synthetic and real data. We validate the proposed model on the Cityscapes dataset, where all four contributions demonstrate significant performance gains, and achieve state-of-the-art results for semi-supervised semantic segmentation as well as for semi-supervised domain adaptation. In particular, with only 1/30 of the Cityscapes labels, our method achieves 92% of the fully-supervised baseline performance and even 97% when exploiting additional data from GTA. The source code is available at https://github.com/lhoyer/improving_segmentation_with_selfsupervised_depth.
Implementing a Social Needs Screening and Referral Program Among Veterans: Assessing Circumstances & Offering Resources for Needs (ACORN)
Authors: Russell Lauren E. , Cohen Alicia J. , Chrzas Steven , Halladay Christopher W. , Kennedy Meaghan A. , Mitchell Kathleen , Moy Ernest , Lehmann Lisa Soleymani 
Published: May 2023

Abstract
Background
The Department of Veterans Affairs (VA) healthcare system routinely screens Veterans for food insecurity, housing instability, and intimate partner violence, but does not systematically screen for other health-related social needs (HRSNs).

Objectives
To (1) develop a process for systematically identifying and addressing Veterans’ HRSNs, (2) determine reported prevalence of HRSNs, and (3) assess the acceptability of HRSN screening among Veterans.

Design
“Assessing Circumstances and Offering Resources for Needs” (ACORN) is a Veteran-tailored HRSN screening and referral quality improvement initiative. Veterans were screened via electronic tablet for nine HRSNs (food, housing, utilities, transportation, legal needs, social isolation, interpersonal violence, employment, and education) and provided geographically tailored resource guides for identified needs. Two-week follow-up interviews with a purposive sample of Veterans explored screening experiences.

Participants
Convenience sample of Veterans presenting for primary care at a VA urban women’s health clinic and suburban community-based outpatient clinic (October 2019–May 2020).

Main Measures
Primary outcomes included prevalence of HRSNs, Veteran-reported acceptability of screening, and use of resources guides. Data were analyzed using descriptive statistics, chi-square tests, and rapid qualitative analysis.

Key Results
Of 268 Veterans screened, 50% reported one or more HRSNs. Social isolation was endorsed most frequently (29%), followed by educational needs (19%), interpersonal violence (12%), housing instability (9%), and utility concerns (7%). One in five Veterans reported at least one form of material hardship. In follow-up interviews (n = 15), Veterans found screening acceptable and felt VA should continue screening. No Veterans interviewed had contacted recommended resources at two-week follow-up, although several planned to use resource guides in the future.

Conclusion
In a VA HRSN screening and referral program, Veterans frequently reported HRSNs, felt screening was important, and thought VA should continue to screen for these needs. Screening for HRSNs is a critical step towards connecting patients with services, identifying gaps in service delivery, and informing future resource allocation.
Lead Recovery From PbO Using Hydrogen as a Reducing Agent
Authors: Rukini A. , Rhamdhani M. A. , Brooks G. A. , Van den Bulck A. 
Published: May 2023

AbstractA systematic isothermal reduction study was carried out on pure PbO pellets using 15 pct H2/85 pct N2 gas. The reduction was carried out at 350 °C to 800 °C at different reaction times (30 minutes to 4 hours) with gas flowrate of 500 mL/min. The kinetics of the reaction were evaluated by measuring the mass change and applying kinetic models to the data. The results from microstructure observation showed that globular and non-wetting lead droplets form on the surface of PbO samples. The droplet's diameter was observed to increase with increasing temperature and reduction time. It was observed that this lead droplets layer, once covers the whole surface, appears to reduce the overall reduction rate. The kinetics analysis showed that PbO pellet reduction is a diffusion-controlled process as supported by the SEM (scanning electron microscope) micrographs, samples’ cross-section observation, and phase analyses using EDS (energy-dispersive spectrometry) and XRD (X-ray diffraction). The energy activation, Ea, was calculated for two temperature ranges, i.e., 61 kJ/mol (for 350 °C to 450 °C) and 224 kJ/mol (for 600 °C to 800 °C), respectively. These results suggest that, in an industrial context, continued reduction process will require constant removal of lead product from the surface of the lead monoxide.
Free-fall drop test with interchangeable surfaces to recreate concussive ice hockey head impacts
Authors: Haid D. , Duncan O. , Hart J. , Foster L. 
Published: May 2023

AbstractIce hockey has one of the highest concussion rates in sport. During collisions with other players, helmets offer limited protection. Various test protocols exist often requiring various types of laboratory equipment. A simplified test protocol was developed to facilitate testing by more researchers, and modifications to certification standards. Measured kinematics (acceleration vs. time trace shape, peak accelerations, and impact duration) of a Hybrid III headform dropped onto different surfaces were compared to published laboratory representations of concussive impacts. An exemplary comparison of five different helmets, ranging from low (US$50) to high cost (US$300), covering a range of helmet and liner designs, was also undertaken. Different impact conditions were created by changing the impact surface (Modular Elastomer Programmer pad, or 24 to 96 mm of EVAZOTE-50 foam with a Young's modulus of ~ 1 MPa), surface orientation (0 or 45°), impact site, and helmet make/model. With increasing impact surface compliance, peak accelerations decreased and impact duration increased. Impacts onto a 45° anvil covered with 48 mm of foam produced a similar response to reference concussive collisions in ice hockey. Specifically, these impacts gave similar acceleration vs. time trace shapes, while normalized pairwise differences between reference and measured peak acceleration and impact duration, were less than 10% (difference/maximum value), and mean (± SD) of accelerations and duration fell within the interquartile range of the reference data. These results suggest that by modifying the impact surface, a free-fall drop test can produce a kinematic response in a helmeted headform similar to the method currently used to replicate ice hockey collisions. A wider range of impact scenarios, i.e., fall onto different surfaces, can also be replicated. This test protocol for ice hockey helmets could facilitate simplified testing in certification standards and research.
Codifying Wildness: Wild Behaviour for Improving Human-Robot Interaction
Authors: Aguado Esther , Rossi Claudio , Gil Pablo 
Published: May 2023

AbstractAnimals, even when domesticated, maintain a series of innate characteristics that humans perceive as wild. Human responses to animal behavior vary with many factors such as historical time and culture. Even after their domestication, humans perceive animals’ distinctive behavior patterns that are considered as being wild, even in pets. It is common understanding that these wild patterns are linked to the specific animal species but also that there is a common characteristic to wild behavior in general. In this paper, we present our work on coding such a quality, exemplified in an application to robot pets. We consider that an application of wild character in the behavior of the pet robot can improve its realism and make the human/robot social interaction more captivating. To this aim, we have designed and implemented a behavioral architecture to reproduce well-known behavioral patterns of domestic cats that might be considered wild and applied it to a commercial pet robot that then was used to assess human reaction to its behavior. We demonstrate the effectiveness of the proposed approach with experiments carried out with humans that found the robot cat “independent” and “unpredictable” to a significant degree, two distinctive features of the wild side of animals.
Iron Age combustion structures in the north-eastern Iberian Peninsula: an interdisciplinary experimental study
Authors: Belarte Maria Carme , Pastor Quiles María , Mateu Marta , Portillo Marta , Saorin Carme , Pecci Alessandra , Vila Sílvia , Gomar Ada 
Published: May 2023

Abstract
The aim of this research is to investigate the efficiency of combustion structures, the fuels used, the structure maintenance and the range of related domestic activities. An experimental programme was carried out in which replicas of archaeological Iron Age combustion structures were put to work. Based on the available archaeobotanical records, various fuel types (wood, grasses, palm leaves and animal dung) were used. Variables such as increased heating and times were measured in order to determine whether variations occurred depending on the fuels used, the type of structure, the location, or the weather conditions. The possibility of their use for cooking was also tested. A combination of methodologies was applied for integrated analyses: anthracology, phytoliths, calcitic microfossils, Fourier transform infrared spectrometry, micromorphology and chemical analysis of hearth surfaces. Observations and data recorded during the experimental tasks, together with the results of the interdisciplinary analyses, contribute to a better understanding of the Iron Age archaeological combustion structures.
Forecasting of residential unit’s heat demands: a comparison of machine learning techniques in a real-world case study
Authors: Kemper Neele , Heider Michael , Pietruschka Dirk , Hähner Jörg 
Published: May 2023

AbstractA large proportion of the energy consumed by private households is used for space heating and domestic hot water. In the context of the energy transition, the predominant aim is to reduce this consumption. In addition to implementing better energy standards in new buildings and refurbishing old buildings, intelligent energy management concepts can also contribute by operating heat generators according to demand based on an expected heat requirement. This requires forecasting models for heat demand to be as accurate and reliable as possible. In this paper, we present a case study of a newly built medium-sized living quarter in central Europe made up of 66 residential units from which we gathered consumption data for almost two years. Based on this data, we investigate the possibility of forecasting heat demand using a variety of time series models and offline and online machine learning (ML) techniques in a standard data science approach. We chose to analyze different modeling techniques as they can be used in different settings, where time series models require no additional data, offline ML needs a lot of data gathered up front, and online ML could be deployed from day one. A special focus lies on peak demand and outlier forecasting, as well as investigations into seasonal expert models. We also highlight the computational expense and explainability characteristics of the used models. We compare the used methods with naive models as well as each other, finding that time series models, as well as online ML, do not yield promising results. Accordingly, we will deploy one of the offline ML models in our real-world energy management system in the near future.
Mindfulness-Based and Mindfulness-Informed Interventions at the Workplace: A Systematic Review and Meta-Regression Analysis of RCTs
Authors: Michaelsen Maren M. , Graser Johannes , Onescheit Miriam , Tuma Matthias P. , Werdecker Lena , Pieper Dawid , Esch Tobias 
Published: May 2023

Abstract
Objectives
Positive effects of mindfulness-based interventions (MBIs) on occupational health have been demonstrated by several systematic review studies during the last two decades. So far, existing reviews excluded mindfulness-informed interventions (MIIs) that build on informal approaches or mixed techniques aiming at improving mindfulness indirectly. To address this research gap, the present comprehensive meta-analysis synthesizes the results of RCTs of MBIs and MIIs conducted in various workplace settings.

Method
A systematic literature search was conducted in five electronic databases complemented by manual search. Random-effects models were used to synthesize standardized mean differences (SMDs) for 25 outcomes and seven overarching categories of outcomes, and to detect various temporal effects. Meta-regressions were run to elucidate average SMDs between mindfulness intervention types and intervention and population characteristics, with the goal of detecting sources of heterogeneity and help guide the selection of the most appropriate mindfulness intervention type.

Results
Based on 91 eligible studies (from 92 publications), including 4927 participants and 4448 controls, the synthesis shows that MBIs and MIIs significantly improve mindfulness (SMD = 0.43; 95%-CI [0.33;0.52]), well-being (SMD = 0.63; 95%-CI [0.34;0.93]), mental health (SMD = 0.67; 95%-CI [0.48;0.86]), stress (SMD = 0.72; 95%-CI [0.54;0.90]), resilience (SMD = 1.06; 95%-CI [−0.22;2.34]), physical health (SMD = 0.45; 95%-CI [0.32;0.59]), and work-related factors (SMD = 0.62; 95%-CI [0.14;1.10]). Sensitivity analyses demonstrate a tendency towards smaller effect sizes due to extreme outliers. Effect sizes are stable in short-term follow-up assessments (1-12 weeks) for most outcomes, but not for long-term follow-up assessments (13-52 weeks). Meta-regressions suggest that observable intervention characteristics (e.g., online delivery) and population characteristics (e.g., age of participants), as well as study quality, do not explain the prevalence of heterogeneity in effect sizes.

Conclusions
Generally effective, mindfulness interventions are a useful tool to enhance aspects of employee health. However, because of heterogeneity and risk of bias, studies aiming at high-quality data collection and thorough reporting are necessary to draw firm conclusions.

Preregistration
A protocol of this systematic review was registered with PROSPERO (Registration-No. CRD42020159927).
Advances of exosomal miRNAs in the diagnosis and treatment of ovarian cancer
Authors: Xiong Jun , Fu Fen , Yu Feng , He Xiaoju 
Published: May 2023

AbstractOvarian cancer is a tumor with the highest fatalities among female malignant tumors. This disease has no typical symptoms in its early stage, and most of the patients are in an advanced stage when being treated. The treatment effect is poor and it is easy to develop chemotherapy resistance. Therefore, it is particularly urgent to clarify the pathogenesis of ovarian cancer, explore its early diagnosis of biomarkers, and discover new treatment methods. As a carrier of intercellular information and genetic material transfer, exosomes are widely distributed in body fluids (e.g. blood and urine), which are regarded as latent tumor markers and take effects on tumor occurrence and invasion. Several articles have recently signified that exosomal miRNAs are widely implicated in the formation of the ovarian cancer tumor microenvironment, disease initiation and progression, and the generation of chemotherapy resistance. This article reviews the research on exosomal miRNAs in ovarian cancer.
A data-driven model to estimate the pore volume to breakthrough for carbonate acidizing
Authors: Alkathim Murtadha , Aljawad Murtada Saleh , Hassan Amjed , Alarifi Suliman A. , Mahmoud Mohamed 
Published: May 2023

AbstractThis research investigates the impact of different rock, acid, and reaction dynamic properties on the pore volume to breakthough (PVBT) at different acid injection rates using in-house developed two-scale continuum simulation model. We analyzed the parameters relation and developed a reliable machine learning model to accurately predict the PVBT at similar range of investigated parameters. In the simulation, it was found that different acid concentrations result in the same optimum injection velocity but at large contrast in PVBT between low and high acid concentration. However, other parameters such as diffusion coefficient and reaction rate exhibited an inverse PVBT behavior across optimum injection velocity due to change in acid transport and reaction behavior. After that, different reliable machine learning algorithms were employed to predict the optimum PVBT for carbonates matrix acidizing. The utilized machine learning models undergone multiple optimizations and comparison to obtain the most accurate prediction performance. The artificial neural network model with 2 hidden layers outperforms the other optimizations with 11.27% estimation error, 0.96 R2and 0.98 correlation coefficient for the testing data set. Finally, an empirical correlation was developed to accurately estimate PVBT at a low cost and very short time compared to lab experiments and numerical simulation models. The novelty of this research stems from examining PVBT curves behavior by varying five matrix acidizing parameters independently, analyzing the correlation between these parameters and developing machine learning model for handy and reliable optimum PVBT estimation.
Game-theoretical problems for fractional-order nonstationary systems
Authors: Matychyn Ivan , Onyshchenko Viktoriia 
Published: May 2023

AbstractNonstationary fractional-order systems represent a new class of dynamic systems characterized by time-varying parameters as well as memory effect and hereditary properties. Differential game described by system of linear nonstationary differential equations of fractional order is treated in the paper. The game involves two players, one of which tries to bring the system’s trajectory to a terminal set, whereas the other strives to prevent it. Using the technique of set-valued maps and their selections, sufficient conditions for reaching the terminal set in a finite time are derived. Theoretical results are supported by a model example.
Rational Design of Cellulosic Triboelectric Materials for Self-Powered Wearable Electronics
Authors: Meng Xiangjiang , Cai Chenchen , Luo Bin , Liu Tao , Shao Yuzheng , Wang Shuangfei , Nie Shuangxi 
Published: May 2023

AbstractWith the rapid development of the Internet of Things and flexible electronic technologies, there is a growing demand for wireless, sustainable, multifunctional, and independently operating self-powered wearable devices. Nevertheless, structural flexibility, long operating time, and wearing comfort have become key requirements for the widespread adoption of wearable electronics. Triboelectric nanogenerators as a distributed energy harvesting technology have great potential for application development in wearable sensing. Compared with rigid electronics, cellulosic self-powered wearable electronics have significant advantages in terms of flexibility, breathability, and functionality. In this paper, the research progress of advanced cellulosic triboelectric materials for self-powered wearable electronics is reviewed. The interfacial characteristics of cellulose are introduced from the top-down, bottom-up, and interfacial characteristics of the composite material preparation process. Meanwhile, the modulation strategies of triboelectric properties of cellulosic triboelectric materials are presented. Furthermore, the design strategies of triboelectric materials such as surface functionalization, interfacial structure design, and vacuum-assisted self-assembly are systematically discussed. In particular, cellulosic self-powered wearable electronics in the fields of human energy harvesting, tactile sensing, health monitoring, human–machine interaction, and intelligent fire warning are outlined in detail. Finally, the current challenges and future development directions of cellulosic triboelectric materials for self-powered wearable electronics are discussed.