bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predicting the Soil Freezing Characteristic From the Particle Size Distribution Based on Micro‐Pore Space Geometry
Authors: Wang Chong , Li Shuangyang , Lai Yuanming , Chen Qian , He Xiaojia , Zhang Hao , Liu Xiaoyu 
Published: December 2021

AbstractKnowledge of freezing characteristic of a porous media is necessary to investigate heat and water transfers in cold regions. The unfrozen water content of soil‐water system is affected by its complicated internal characteristics and external factors such as temperature and pressure. Although various experimental and analytical methods have been proposed to explore the complex relation between unfrozen water and temperature, most of them are empirical or limited. In this study, a sphere‐cylinder dual structure is proposed according to surface adsorption and double electrical layer effect, and then a theoretical upscaling framework is presented based on particle size distribution and the Clapeyron equation. The proposed model predicts the freezing characteristic in soil‐water systems from a new pore structure considering the micro‐pore space geometry. In order to evaluate the new model, 21 data sets from literature and two data sets from nuclear magnetic resonance (NMR) tests in this study are used to compare with predicted results, and excellent match is obtained. The improved method has an advantage in predicting unfrozen water content at lower temperatures. In addition, from the soil freezing characteristic curve, it can be found that the residual unfrozen water contents are positively associated with clay content. Moreover, in low temperature range, T2 cut‐off value decreases with the decrease of temperature. As a fundamental study, the soil freezing characteristic researched in this study is helpful for the research on the hydraulic conductivity of frozen soil, which is significance for the stability analysis in cold region engineering, and the hydro‐ecological process in seasonal frozen soil area.
Impact of molecular tumour board discussion on targeted therapy allocation in advanced prostate cancer
Authors: Slootbeek Peter H. J. , Kloots Iris S. H. , Smits Minke , van Oort Inge M. , Gerritsen Winald R. , Schalken Jack A. , Ligtenberg Marjolijn J. L. , Grünberg Katrien , Kroeze Leonie I. , Bloemendal Haiko J. , Mehra Niven 
Published: December 2021

AbstractBackgroundMolecular tumour boards (MTB) optimally match oncological therapies to patients with genetic aberrations. Prostate cancer (PCa) is underrepresented in these MTB discussions. This study describes the impact of routine genetic profiling and MTB referral on the outcome of PCa patients in a tertiary referral centre.MethodsAll PCa patients that received next-generation sequencing results and/or were discussed at an MTB between Jan 1, 2017 and Jan 1, 2020 were included. Genetically matched therapies (GMT) in clinical trials or compassionate use were linked to actionable alterations. Response to these agents was retrospectively evaluated.ResultsOut of the 277 genetically profiled PCa patients, 215 (78%) were discussed in at least one MTB meeting. A GMT was recommended to 102 patients (47%), of which 63 patients (62%) initiated the GMT. The most recommended therapies were PARP inhibitors (n = 74), programmed death-(ligand) 1 inhibitors (n = 21) and tyrosine kinase inhibitors (n = 19). Once started, 41.3% had a PFS of ≥6 months, 43.5% a PSA decline ≥50% and 38.5% an objective radiographic response.ConclusionRecommendation for a GMT is achieved in almost half of the patients with advanced prostate cancer, with GMT initiation leading to durable responses in over 40% of patients. These data justify routine referral of selected PCa patients to MTB’s.
Temporal and genomic analysis of additive genetic variance in breeding programmes
Authors: Lara Letícia A. de C. , Pocrnic Ivan , Oliveira Thiago de P. , Gaynor R. Chris , Gorjanc Gregor 
Published: December 2021

AbstractGenetic variance is a central parameter in quantitative genetics and breeding. Assessing changes in genetic variance over time as well as the genome is therefore of high interest. Here, we extend a previously proposed framework for temporal analysis of genetic variance using the pedigree-based model, to a new framework for temporal and genomic analysis of genetic variance using marker-based models. To this end, we describe the theory of partitioning genetic variance into genic variance and within-chromosome and between-chromosome linkage-disequilibrium, and how to estimate these variance components from a marker-based model fitted to observed phenotype and marker data. The new framework involves three steps: (i) fitting a marker-based model to data, (ii) sampling realisations of marker effects from the fitted model and for each sample calculating realisations of genetic values and (iii) calculating the variance of sampled genetic values by time and genome partitions. Analysing time partitions indicates breeding programme sustainability, while analysing genome partitions indicates contributions from chromosomes and chromosome pairs and linkage-disequilibrium. We demonstrate the framework with a simulated breeding programme involving a complex trait. Results show good concordance between simulated and estimated variances, provided that the fitted model is capturing genetic complexity of a trait. We observe a reduction of genetic variance due to selection and drift changing allele frequencies, and due to selection inducing negative linkage-disequilibrium.
TiMEG: an integrative statistical method for partially missing multi-omics data
Authors: Das Sarmistha , Mukhopadhyay Indranil 
Published: December 2021

AbstractMulti-omics data integration is widely used to understand the genetic architecture of disease. In multi-omics association analysis, data collected on multiple omics for the same set of individuals are immensely important for biomarker identification. But when the sample size of such data is limited, the presence of partially missing individual-level observations poses a major challenge in data integration. More often, genotype data are available for all individuals under study but gene expression and/or methylation information are missing for different subsets of those individuals. Here, we develop a statistical model TiMEG, for the identification of disease-associated biomarkers in a case–control paradigm by integrating the above-mentioned data types, especially, in presence of missing omics data. Based on a likelihood approach, TiMEG exploits the inter-relationship among multiple omics data to capture weaker signals, that remain unidentified in single-omic analysis or common imputation-based methods. Its application on a real tuberous sclerosis dataset identified functionally relevant genes in the disease pathway.
Magnetic structure study of the sawtooth chain antiferromagnet $$\hbox {Fe}_2\hbox {Se}_2\hbox {O}_7$$
Authors: Nawa Kazuhiro , Avdeev Maxim , Berdonosov Peter , Sobolev Alexey , Presniakov Igor , Aslandukova Alena , Kozlyakova Ekaterina , Vasiliev Alexander , Shchetinin Igor , Sato Taku J. 
Published: December 2021

AbstractA magnetic structure of the sawtooth-chain antiferromagnet $$\hbox {Fe}_2\hbox {Se}_2\hbox {O}_7$$

Fe
2

Se
2

O
7

was investigated by magnetization measurements, single crystalline and powder neutron diffraction experiments, and a further analysis on the Mössbauer spectra. These experiments revealed a nearly collinear antiferromagnetic structure with magnetic moments aligned along the b-axis, indicating dominant antiferromagnetic exchanges between Fe(1)–Fe(2) and Fe(2)–Fe(3) sites. The magnon dispersion relation derived from the linear spin wave approximation suggests the possible flat band nature of magnons.
An adaptive shortest-solution guided decimation approach to sparse high-dimensional linear regression
Authors: Yu Xue , Sun Yifan , Zhou Hai-Jun 
Published: December 2021

AbstractHigh-dimensional linear regression model is the most popular statistical model for high-dimensional data, but it is quite a challenging task to achieve a sparse set of regression coefficients. In this paper, we propose a simple heuristic algorithm to construct sparse high-dimensional linear regression models, which is adapted from the shortest-solution guided decimation algorithm and is referred to as ASSD. This algorithm constructs the support of regression coefficients under the guidance of the shortest least-squares solution of the recursively decimated linear models, and it applies an early-stopping criterion and a second-stage thresholding procedure to refine this support. Our extensive numerical results demonstrate that ASSD outperforms LASSO, adaptive LASSO, vector approximate message passing, and two other representative greedy algorithms in solution accuracy and robustness. ASSD is especially suitable for linear regression problems with highly correlated measurement matrices encountered in real-world applications.
Relation of hypertension with episodic primary headaches and chronic primary headaches in population of Rafsanjan cohort study
Authors: Mohammadi Movahedeh , Ayoobi Fatemeh , Khalili Parvin , Soltani Narges , La Vecchia Carlo , Vakilian Alireza 
Published: December 2021

AbstractHeadache has a variety of types, such as episodic primary headaches (EPH) and chronic primary headache (CPH) in its primary form. There is a positive correlation between these two types of headaches and hypertension (HTN), but in some works this correlation has been reported negatively. Therefore, we planned to study HTN-CPH as well as HTN-EPH correlation in our population. A sample of Rafsanjan population (10,000 individuals) entered the cohort study, as one of the Prospective Epidemiological Research Studies in Iran (PERSIAN). We compared the frequency of HTN categories in CPH and EPH cases with a normal population. Out of 9933 participants (46.6% males and 53.4% females) about 29% had EPH and 7.5% had CPH. HTN was found in 24.27% of EPH cases and 31.98% of CPH cases. HTN was also found to be associated with EPH and CPH in the crude model. Two Categories of HTN (Long controlled and uncontrolled) were not associated with EPH. On the other hand, CPH showed associations with all of the HTN categories. After included all variables and confounders, EPH and CPH had association with HTN without any considerable changes. There is strong HTN-EPH as well as HTN-CPH correlations in the studied population.
Association between distinct coping styles and heart rate variability changes to an acute psychosocial stress task
Authors: Machado Arthur Viana , Pereira Mirtes Garcia , Souza Gabriela G. L. , Xavier Mariana , Aguiar Carolina , de Oliveira Leticia , Mocaiber Izabela 
Published: December 2021

AbstractModern life comprises a myriad of stressful situations, ranging from life-threatening ones to others not so deadly, all of which activate a physiologic stress response. Engaging in healthy ways to cope can prevent us from wearing out our physiological systems. Heart rate variability (HRV) is often used as an index of emotion regulation response. Hence, our goal is to investigate whether the habitual use of coping strategies is related to a distinct pattern of HRV changes when the individual is exposed to a moderate psychosocial stressor. In this study, 60 female participants performed a psychosocial stress task—oral speech preparation—while ECG signals were collected during the whole experimental procedure. Heart rate (HR), HRV parameters (SDNN, RMSSD, LF, HF) and coping strategies (Brief COPE) were registered. Participants were divided into two groups (low and high groups) as a function of their scores on the maladaptive and adaptive coping strategies of the Brief COPE. As expected, the task alone induced increases in heart rate and reductions in HRV parameters. Additionally, the analyses revealed a different pattern of HRV (SDNN, RMSSD, LF and HF) changes in response to the stressor, with participants using less maladaptive strategies being able to maintain the HRV at baseline levels when confronting the stressor, while those using more maladaptive strategies reducing HRV during the task. These results show a different pattern of HRV changes as a function of the coping style, suggesting a possible autonomic advantage, namely, the maintenance of HRV, in individuals who use maladaptive coping strategies less frequently.
Chemoselectivity-independent Cu-mediated coupling to construct the hydroquinoline skeleton of symbioimine
Authors: Fujita Rie , Hanaya Kengo , Sugai Takeshi , Higashibayashi Shuhei 
Published: December 2021

AbstractConstruction of the hydroquinoline skeleton of symbioimine by Cu-mediated N-alkenylation or O-alkenylation of an allyl aminoalcohol, in which either chemoselectivity could lead to the target compound, was investigated. O-alkenylation followed by Claisen rearrangement was favored with high selectivity under a ligand-free condition. Subsequent intramolecular condensation furnished the hydroquinoline skeleton of symbioimine.
Detection and characterization of small-sized microplastics (≥ 5 µm) in milk products
Authors: Da Costa Filho Paulo A. , Andrey Daniel , Eriksen Bjorn , Peixoto Rafael P. , Carreres Benoit M. , Ambühl Mark E. , Descarrega Josep B. , Dubascoux Stephane , Zbinden Pascal , Panchaud Alexandre , Poitevin Eric 
Published: December 2021

AbstractMicroplastics (MPs) have gained a high degree of public interest since they are associated with the global release of plastics into the environment. Various studies have confirmed the presence of MPs throughout the food chain. However, information on the ingestion of MPs via the consumption of many commonly consumed foods like dairy products are scarce due to the lack of studies investigating the “contamination” of this food group by MPs. This lack of occurrence data is mainly due to the absence of robust analytical methods capable of reliably quantifying MPs with size < 20 µm in foods. In this work, a new methodology was developed to accurately determine and characterize MPs in milk-based products using micro-Raman (μRaman) technology, entailing combined enzymatic and chemical digestion steps. This is the first time that the presence of relatively low amounts of small-sized MP (≥ 5 µm) have been reported in raw milk collected at farm just after the milking machine and in some processed commercial liquid and powdered cow’s milk products.
The illusion of having a large virtual body biases action-specific perception in patients with mild cognitive impairment
Authors: Ryu Hokyoung , Seo Kyoungwon 
Published: December 2021

AbstractThe illusion of having a large body makes us perceive objects as smaller than they really are. This action-specific perception effect occurs because we perceive the property of an object (i.e., size) differently according to our unique action capability (i.e., the affordance of body size). Although the body-ownership illusion contributing to this action-specific perception has been studied, its effects remain unclear in neurological patients. We examined the action-specific perception impairments of MCI patients by means of body-ownership illusion in a non-immersive virtual reality environment. Twenty healthy young adults, 21 healthy older adults, and 15 MCI patients were recruited. We assessed their “original-body action-specific perception” and “enlarged-body action-specific perception” using the original and enlarged sizes of their virtual bodies, respectively. The MCI patients’ original-body action-specific perception was no different than that of the healthy controls (p = 0.679). However, the enlarged-body action-specific perception of the MCI patients was significantly biased (p < 0.001). The inclusion of the enlarged-body action-specific perception provides additional discriminative power for early diagnosis of MCI (89.3% accuracy, 75.0% sensitivity, 100.0% specificity, and 87.5% balanced accuracy).
Prediction across healthcare settings: a case study in predicting emergency department disposition
Authors: Barak-Corren Yuval , Chaudhari Pradip , Perniciaro Jessica , Waltzman Mark , Fine Andrew M. , Reis Ben Y. 
Published: December 2021

AbstractSeveral approaches exist today for developing predictive models across multiple clinical sites, yet there is a lack of comparative data on their performance, especially within the context of EHR-based prediction models. We set out to provide a framework for prediction across healthcare settings. As a case study, we examined an ED disposition prediction model across three geographically and demographically diverse sites. We conducted a 1-year retrospective study, including all visits in which the outcome was either discharge-to-home or hospitalization. Four modeling approaches were compared: a ready-made model trained at one site and validated at other sites, a centralized uniform model incorporating data from all sites, multiple site-specific models, and a hybrid approach of a ready-made model re-calibrated using site-specific data. Predictions were performed using XGBoost. The study included 288,962 visits with an overall admission rate of 16.8% (7.9–26.9%). Some risk factors for admission were prominent across all sites (e.g., high-acuity triage emergency severity index score, high prior admissions rate), while others were prominent at only some sites (multiple lab tests ordered at the pediatric sites, early use of ECG at the adult site). The XGBoost model achieved its best performance using the uniform and site-specific approaches (AUC = 0.9–0.93), followed by the calibrated-model approach (AUC = 0.87–0.92), and the ready-made approach (AUC = 0.62–0.85). Our results show that site-specific customization is a key driver of predictive model performance.
CCNB2 is a novel prognostic factor and a potential therapeutic target in low-grade glioma
Authors: Wang Dengfeng , Sun Hongjiao , Li Xiaohui , Wang Gang , Yan Guizhong , Ren Haijun , Hou Boru 
Published: December 2021

Abstract
Background: Cyclin B2 (CCNB2) is an important component of the cyclin pathway and plays a key role in the occurrence and development of cancer. However, the correlation between prognosis of low-grade glioma (LGG), CCNB2, and tumor infiltrating lymphocytes is not clear. Methods: The expression of CCNB2 in LGG was queried in Gene Expression Profiling Interactive Analysis 2 (GEPIA2) and TIMER databases. The relationships between CCNB2 and the clinicopathological features of LGG were analyzed using the Chinese Glioma Genome Atlas (CGGA) database. The relationship between CCNB2 expression and overall survival (OS) was evaluated by GEPIA2. The correlation between CCNB2 and LGG immune infiltration was analyzed by the TIMER database. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was performed to detect CCNB2 expression. Results: The expression of CCNB2 differed across different tumor tissues, but was higher in LGG than in normal tissues. LGG patients with high expression of CCNB2 have poorer prognosis. The expression of CCNB2 was correlated with age, WHO grade, IDH mutational status, 1p/19q codeletion status, and other clinicopathological features. The expression of CCNB2 in LGG was positively correlated with the infiltration level of B cells, dendritic cells, and macrophages. qRT-PCR results revealed that the expression of CCNB2 in LGG tissues was higher than normal tissues and higher expression of CCNB2 was associated with worse prognosis. Conclusion: CCNB2 may be used as a potential biomarker to determine the prognosis of LGG and is also related to immune infiltration.
Spectrum and energy efficient kalman‐based hybrid combiner for mmWave massive MIMO systems
Authors: Abid Wafa , Hajjaj Moufida , Bouallegue Ridha 
Published: December 2021

AbstractThe hybrid beamforming can significantly improve the spectral and energy efficiencies of the millimeter‐wave (mmWave) massive multiple input multiple output (MIMO) systems without obvious performance loss. However, the fundamental limitation of existing hybrid beamforming is that they include high‐resolution analog‐to‐digital converters (ADCs) which consume high power. To overcome this fundamental limitation, in this paper, spectrum and energy efficient mmWave massive MIMO transmission scheme is proposed that jointly optimizes the hybrid combiner and the number of ADC bits. Particularly, the hybrid combiner is presented as a combination of three parts: the analog combiner matrix, the quantization matrix, and the digital baseband matrix which is based on the Kalman filter. Unlike most existing works that rely on sumrate optimization to design the hybrid combiner, in this work, the mean square error (MSE) is chosen as the main performance metric to describe the reliability of the system. Simulations results confirm that the proposed Kalman based hybrid combiner exhibits the best performance in terms of spectral and energy efficiencies compared to minimum mean square error (MMSE) and zero forcing (ZF) receivers.
Design and analysis of hardware Trojans in approximate circuits
Authors: Dou Yuqin , Wang Chenghua , Gu Chongyan , O'Neill Máire , Liu Weiqiang 
Published: December 2021

AbstractApproximate computing can accept errors in computation systems to achieve better utilisation of hardware resources. Approximate computing has been successfully developed in integrated circuits, such as approximate arithemtic circuits and approximate accelerators. Unfortunately, recent works show that approximate circuits also have security vulnerabilities. Hardware Trojan is one of the biggest threats to hardware security. However, very limited research has been conducted on hardware Trojans in approximate circuits. In this letter, two types of hardware Trojans, namely Function‐destructive Trojans and Information‐leaking Trojans, are presented. The Trojans are designed based on the characteristics of approximate circuits. The effectiveness of the hardware Trojans are analysed using the evaluation metrics for approximate circuits.
Fast multi‐physics simulation approach in underwater exploration via deep learning technique
Authors: Zhu Yue , Zhou Yuanguo , Javaid Fawad , Ren Qiang , Liu Wenyuan 
Published: December 2021

AbstractWhen underwater pressure wave is generated, moving water particles cut off the geomagnetic field and produce induced currents, which will simultaneously induce electromagnetic field in the whole space. Due to the large distribution range and slow attenuation of this pseudo‐radiation, it is possible to observe above the sea surface. In this work, we introduce a novel long‐ and short‐term memory neural network and the corresponding training algorithm, to model the multi‐physics process instead of solving magneto‐hydrodynamics equations via numerical methods. Compared with commercial software, the proposed approach is much faster and easier to apply, which puts forward a feasible alternative for predicting the electromagnetic field distribution excited by underwater pressure wave.