bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improvement of mid-temperature ZT in a Bi-Se-Te via a two two-step sintering process
Authors: Yen Hung-Sheng , Lin Jen-Fin , Shi Shih-Chen , Lin Hao-Tung , Chiu Jung-Chang 
Published: February 2024

Abstract
A wide range of thermoelectric materials are available for selection. The standard classification is based on the temperature range of the application. This study fabricated mid-temperature Bi-Se-Te using spark plasma sintering (SPS). This analysis focused on the impact of sintering conditions on thermoelectric properties. The structural analysis indicated that initial and secondary sintering processes effectively produced the low-temperature thermoelectric material Bi2Se0.45Te2.55. The sintering duration and temperature changes mainly influenced the grain boundary density, with elevated temperatures inducing defects that impacted the performance. Secondary sintering resulted in layered structures with elongated grains, which enhanced the phonon scattering effect. This configuration markedly decreased the thermal conductivity, increasing the ZT value by 60%.
Impact of large kernel size on yield prediction: a case study of corn yield prediction with SEDLA in the U.S. Corn Belt
Authors: Terliksiz Anil Suat , Altilar Deniz Turgay 
Published: February 2024

Abstract
Predicting agricultural yields is imperative for effective planning to sustain the growing global population. Traditionally, regression-based, simulation-based, and hybrid methods were employed for yield prediction. In recent times, there has been a notable shift towards the adoption of Machine Learning (ML) methods, with Deep Learning (DL), particularly Convolutional Neural Networks (CNNs) and Long-Short Term Memory (LSTM) networks, emerging as popular choices for their enhanced predictive accuracy. This research introduces a cost-effective DL architecture tailored for corn yield prediction, considering computational efficiency in processing time, data size, and NN architecture complexity. The proposed architecture, named SEDLA (Simple and Efficient Deep Learning Architecture), leverages the spatial and temporal learning capabilities of CNNs and LSTMs, respectively, with a unique emphasis on exploring the impact of kernel size in CNNs. Simultaneously, the study aims to exclusively employ satellite and yield data, strategically minimizing input variables to enhance the model’s simplicity and efficiency. Notably, the study demonstrates that employing larger kernel sizes in CNNs, especially when processing histogram-based Surface Reflectance (SR) and Land Surface Temperature (LST) data from Moderate Resolution Imaging Spectroradiometer (MODIS), allows for a reduction in the number of hidden layers. The efficacy of the architecture was evaluated through extensive testing on corn yield prediction across 13 states in the United States (U.S.) Corn Belt at county-level. The experimental results showcase the superiority of the proposed architecture, achieving a Mean Absolute Percentage Error (MAPE) of 6.71 and Root Mean Square Error (RMSE) of 14.34, utilizing a single-layer CNN with a 15 × 15 kernel in conjunction with LSTM. These outcomes surpass existing benchmarks in the literature, affirming the efficacy and potential of the suggested DL framework for accurate and efficient crop yield predictions.
Introduction
Authors: Karinkurayil Mohamed Shafeeq 
Published: February 2024

Abstract
This chapter details the historical and structural conditions in Kerala and in the Arab Gulf that have resulted in the Gulf migrant experience of Keralans being presented as a secret in Kerala that calls out for constant revelations through novels, memoirs, and films. The chapter reads both Kerala and the Gulf as borderlands where multiple demarcations crisscross the migrant body. The chapter situates Kerala as the location in which the Keralan Gulf texts are read. The chapter presents translations between the Gulf and Kerala and the affiliation this enables as the main objective of the study. The chapter then discusses methodological issues and concludes with an overview of the book.
The Dead Ends and Alleyways
Authors: Karinkurayil Mohamed Shafeeq 
Published: February 2024

Abstract
How does one form a collectivity when the public speech cannot convey one’s experience—this is the question that centrally occupies this chapter. If novels are one of the discursive means through which a collectivity can be imagined in its simultaneity, it is in the reader that this simultaneity is realized. This chapter reads two works—Goat Days by Benyamin and Temporary People by Deepak Unnikrishnan—to demonstrate the strategies through which these texts imagine a collectivity in the selective communicability of speech. The chapter illustrates how these texts operate as borders, passing some readers through while blocking others. It is in this recirculation of readers that the text finds pleasurable enclaves of intimate belonging within the seemingly transparent public speech of deliberation.
SST-editing: in silico spatial transcriptomic editing at single-cell resolution
Authors: Wu Jiqing , Koelzer Viktor H 
Published: February 2024

Abstract

Motivation
Generative Adversarial Nets (GAN) achieve impressive performance for text-guided editing of natural images. However, a comparable utility of GAN remains understudied for spatial transcriptomics (ST) technologies with matched gene expression and biomedical image data.

Results
We propose In Silico Spatial Transcriptomic editing that enables gene expression-guided editing of immunofluorescence images. Using cell-level spatial transcriptomics data extracted from normal and tumor tissue slides, we train the approach under the framework of GAN (Inversion). To simulate cellular state transitions, we then feed edited gene expression levels to trained models. Compared to normal cellular images (ground truth), we successfully model the transition from tumor to normal tissue samples, as measured with quantifiable and interpretable cellular features.

Availability and implementation
https://github.com/CTPLab/SST-editing.
Estimation of optimal treatment regimes with electronic medical record data using the residual life value estimator
Authors: Rhodes Grace , Davidian Marie , Lu Wenbin 
Published: February 2024

Summary
Clinicians and patients must make treatment decisions at a series of key decision points throughout disease progression. A dynamic treatment regime is a set of sequential decision rules that return treatment decisions based on accumulating patient information, like that commonly found in electronic medical record (EMR) data. When applied to a patient population, an optimal treatment regime leads to the most favorable outcome on average. Identifying optimal treatment regimes that maximize residual life is especially desirable for patients with life-threatening diseases such as sepsis, a complex medical condition that involves severe infections with organ dysfunction. We introduce the residual life value estimator (ReLiVE), an estimator for the expected value of cumulative restricted residual life under a fixed treatment regime. Building on ReLiVE, we present a method for estimating an optimal treatment regime that maximizes expected cumulative restricted residual life. Our proposed method, ReLiVE-Q, conducts estimation via the backward induction algorithm Q-learning. We illustrate the utility of ReLiVE-Q in simulation studies, and we apply ReLiVE-Q to estimate an optimal treatment regime for septic patients in the intensive care unit using EMR data from the Multiparameter Intelligent Monitoring Intensive Care database. Ultimately, we demonstrate that ReLiVE-Q leverages accumulating patient information to estimate personalized treatment regimes that optimize a clinically meaningful function of residual life.
Global, regional and national epidemiology of alopecia areata: a systematic review and modelling study
Authors: Jeon Jae Joon , Jung Seung-Won , Kim You Hyun , Parisi Rosa , Lee Ju Yeong , Kim Myung Ha , Lee Won-Soo , Lee Solam 
Published: February 2024

Abstract

Background
Alopecia areata (AA) is a chronic autoimmune disease that leads to a high psychiatric, economic and systemic disease burden. A comprehensive understanding of AA epidemiology is essential for evaluating healthcare source utilization; however, a systematic approach to summarizing epidemiological data on AA is lacking.

Objectives
To investigate systematically the global, regional and national incidence and prevalence of AA.

Methods
A structured search was conducted using the databases MEDLINE, Embase, Cochrane Library, Web of Science, SciELO and Korean Journal Database from their date of inception to 4 October 2023. Studies that reported the prevalence or incidence of AA were included. We used a Bayesian hierarchical linear mixed model to analyse prevalence estimates. The primary outcomes of our study were the global, regional and national prevalence of physician-diagnosed AA for the overall population, for adults and for children. The incidence data were summarized descriptively.

Results
In total, 88 studies from 28 countries were included in the analysis. The reported incidence of AA tended to be higher in adults aged 19–50 years, and this trend was consistent with its estimated prevalence. The reported prevalence in overall populations tended to be higher in men vs. women. The estimated lifetime prevalence rate of AA was 0.10% [95% credible interval (CrI) 0.03–0.39] in the general population worldwide, 0.12% (95% CrI 0.02–0.52) in adults and 0.03% (95% CrI 0.01–0.12) in children. The estimated prevalence of AA was highest in the Asian region and lowest in the African region.

Conclusions
In this study, 48% of the Global Burden of Disease regions had insufficient data on the prevalence or incidence of AA. Further studies are needed to provide epidemiological information on middle- and low-income countries. Our study may serve as a crucial reference in terms of healthcare policy decisions.
Imagine there is no paperwork… it’s easy if you try
Authors: Martín-Noguerol Teodoro , López-Úbeda Pilar , Luna Antonio 
Published: February 2024

Abstract
Artificial Intelligence (AI) applied to radiology is so vast that it provides applications ranging from becoming a complete replacement for radiologists (a potential threat) to an efficient paperwork-saving time assistant (an evident strength). Nowadays, there are AI applications developed to facilitate the diagnostic process of radiologists without directly influencing (or replacing) the proper diagnostic decision step. These tools may help to reduce administrative workload, in different scenarios ranging from assisting in scheduling, study prioritization, or report communication, to helping with patient follow-up, including recommending additional exams. These are just a few of the highly time-consuming tasks that radiologists have to deal with every day in their routine workflow. These tasks hinder the time that radiologists should spend evaluating images and caring for patients, which will have a direct and negative impact on the quality of reports and patient attention, increasing the delay and waiting list of studies pending to be performed and reported. These types of AI applications should help to partially face this worldwide shortage of radiologists.
A neuroanatomical and cognitive model of impaired social behaviour in frontotemporal dementia
Authors: Rouse Matthew A , Binney Richard J , Patterson Karalyn , Rowe James B , Lambon Ralph Matthew A 
Published: February 2024

Abstract
Impaired social cognition is a core deficit in frontotemporal dementia (FTD). It is most commonly associated with the behavioural-variant of FTD, with atrophy of the orbitofrontal and ventromedial prefrontal cortex. Social cognitive changes are also common in semantic dementia, with atrophy centred on the anterior temporal lobes. The impairment of social behaviour in FTD has typically been attributed to damage to the orbitofrontal cortex and/or temporal poles and/or the uncinate fasciculus that connects them. However, the relative contributions of each region are unresolved.
In this review, we present a unified neurocognitive model of controlled social behaviour that not only explains the observed impairment of social behaviours in FTD, but also assimilates both consistent and potentially contradictory findings from other patient groups, comparative neurology and normative cognitive neuroscience. We propose that impaired social behaviour results from damage to two cognitively- and anatomically-distinct components. The first component is social-semantic knowledge, a part of the general semantic-conceptual system supported by the anterior temporal lobes bilaterally. The second component is social control, supported by the orbitofrontal cortex, medial frontal cortex and ventrolateral frontal cortex, which interacts with social-semantic knowledge to guide and shape social behaviour.
Alteration of functional connectivity network in population of objectively-defined subtle cognitive decline
Authors: Zhang Xinyi , Zeng Qingze , Wang Yanbo , Jin Yu , Qiu Tiantian , Li Kaicheng , Luo Xiao , Wang Shuyue , Xu Xiaopei , Liu Xiaocao , Zhao Shuai , Li Zheyu , Hong Luwei , Li Jixuan , Zhong Siyan , Zhang Tianyi , Huang Peiyu , Zhang Baorong , Zhang Minming , Chen Yanxing 
Published: February 2024

Abstract
The objectively-defined subtle cognitive decline individuals had higher progression rates of cognitive decline and pathological deposition than healthy elderly, indicating a higher risk of progressing to Alzheimer’s disease. However, little is known about the brain functional alterations during this stage. Thus, we aimed to investigate the functional network patterns in objectively-defined subtle cognitive decline cohort. Forty-two cognitive normal, 29 objectively-defined subtle cognitive decline and 55 mild cognitive impairment subjects were included based on neuropsychological measures from the Alzheimer’s disease Neuroimaging Initiative dataset. Thirty cognitive normal, 22 objectively-defined subtle cognitive declines and 48 mild cognitive impairment had longitudinal MRI data. The degree centrality and eigenvector centrality for each participant were calculated by using resting-state functional MRI. For cross-sectional data, analysis of covariance was performed to detect between-group differences in degree centrality and eigenvector centrality after controlling age, sex and education. For longitudinal data, repeated measurement analysis of covariance was used for comparing the alterations during follow-up period among three groups. In order to classify the clinical significance, we correlated degree centrality and eigenvector centrality values to Alzheimer’s disease biomarkers and cognitive function. The results of analysis of covariance showed significant between-group differences in eigenvector centrality and degree centrality in left superior temporal gyrus and left precuneus, respectively. Across groups, the eigenvector centrality value of left superior temporal gyrus was positively related to recognition scores in auditory verbal learning test, whereas the degree centrality value of left precuneus was positively associated with mini-mental state examination total score. For longitudinal data, the results of repeated measurement analysis of covariance indicated objectively-defined subtle cognitive decline group had the highest declined rate of both eigenvector centrality and degree centrality values than other groups. Our study showed an increased brain functional connectivity in objectively-defined subtle cognitive decline individuals at both local and global level, which were associated with Alzheimer’s disease pathology and neuropsychological assessment. Moreover, we also observed a faster declined rate of functional network matrix in objectively-defined subtle cognitive decline individuals during the follow-ups.
Exposure to gestational diabetes mellitus increases subclinical inflammation mediated in part by obesity
Authors: Musumeci Andrea , McElwain Colm John , Manna Samprikta , McCarthy Fergus , McCarthy Cathal 
Published: February 2024

Abstract
Gestational diabetes mellitus (GDM) is a frequent and serious complication of pregnancy, often associated with obesity. Metabolic dysfunction and metainflammation are evident in both obesity and GDM. In this cross-sectional study, we aimed at defining the direct contribution of the immune system in GDM, across the main metabolic tissues, specifically focussing on elucidating the roles of obesity and GDM to the clinical outcome. Using immunoassays and multicolour flow cytometry, cytokine profiles and immune cell frequencies were measured in maternal circulation and central metabolic tissues [placenta and visceral adipose tissue (VAT)] in GDM-diagnosed (n = 28) and normal glucose tolerant (n = 32) women undergoing caesarean section. Participants were sub-grouped as non-obese [body mass index (BMI) &lt; 30 kg/m2] or obese (BMI ≥ 30 kg/m2). Unsupervised data analysis was performed on the flow cytometry data set to identify functional alterations. GDM obese participants had significantly elevated circulating IL-6 and IL-17A levels. GDM non-obese participants had elevated circulating IL-12p70, elevated placental IL-17A, and VAT IFN-γ production. Unsupervised clustering of immune populations across the three biological sites simultaneously, identified different NK- and T-cell phenotypes that were altered in NGT obese and GDM non-obese participants, while a classical tissue monocyte cluster was increased in GDM obese participants. In this study, there was significant evidence of subclinical inflammation, and significant alterations in clusters of NK cells, T cells, and tissue monocyte populations in GDM. While increased adiposity assimilates with increased inflammation in the non-pregnant state, this overt relationship may not be as evident during pregnancy and warrants further examination in future longitudinal studies.
Effective intercalation of [Ru(bpy)3]2+ into titania nanosheet films using bipyridinium analogs as intermediate guests
Authors: Oishi Ko , Sato Tatsuya , Saito Yusei , Koike Shuto , Shibuya Tamami , Umezawa Hibiki , Yui Tatsuto 
Published: February 2024

Abstract
The intercalation of a large photofunctional molecule, tris(bipyridine)ruthenium (II) ([Ru(bpy)3]2+), into titania nanosheet (TNS) films via a guest-exchange method was investigated using 6 bipyridinium analogs as intermediate guest (IG) molecules. The interlayer distance of the IG intercalated TNS films affects the subsequent ion-exchange reaction and the adsorption rate of [Ru(bpy)3]2+. The IG 3,3′-diethylbipyridinium (3-Etbpy2+) was as the most effective interlayer expansion molecule among the studied analogs.
Overwintering site selection and associated microclimates for the redbanded stink bug (Hemiptera: Pentatomidae), a non-native pest of soybean
Authors: Paul Thomas G , Catchot Angus L , Musser Fred R , Chakrabarti Priyadarshini , Ward Samuel F 
Published: February 2024

Abstract
Cold winter temperatures govern the distribution and abundance of many insect species, but refugia that provide microclimates can moderate temperature-driven mortality. Winter temperatures have been implicated in limiting the survival and range of Piezodorus guildinii (Westwood) (Hemiptera: Pentatomidae; redbanded stink bug), an economically damaging invasive pest in the southeastern United States, but the role of refugia in overwintering survival of this pest is poorly understood. We conducted 2 studies in successive years to evaluate how leaf litter from hardwoods, pines, and soybeans modulate overwintering site selection and survival of P. guildinii. In the second-year study, we also quantified the buffering effect of the 3 leaf litter types compared to ambient conditions and assessed diapause. In the first-year study, we found that stink bugs preferentially dispersed into leaf litter compared with remaining unsheltered on bare soil; no clear preference among leaf litter types was found. In the second year, however, no clear differences were found among leaf litter types and bare soil. Means of daily minimum temperatures under leaf litter were at least 3.0 ± 0.9 °C (SE) warmer and generally less variable than ambient conditions. While high mortality in both studies illustrates that more work must be done to fully understand overwintering survival, limited survival through potentially lethal conditions in the first-year study nonetheless emphasizes the possibility of populations persisting and rebounding in the following spring. Furthermore, our study highlights the potential for stink bugs to persist in areas with lethal ambient temperatures by dispersing into widely available substrates.
A model to reduce tyrosine crystallization in Chinese fermented soybean curd by adjusting NaCl, ethanol and temperature during fermentation
Authors: Deng Jia , Yin Lijun , Yang Xi , Jia Xin 
Published: February 2024

Abstract

Objectives
White spots, mainly consisting of tyrosine, refer to small white particles that exist on the surface of mature Chinese fermented soybean curd or in the fermentation broth and have affected the appearance of Chinese fermented soybean curd.

Materials and Methods
The study determined the composition of the white spots throughout mass spectrometry and high-performance liquid chromatography and investigated the relationship between tyrosine content, amino acid nitrogen content, depth of the proteolysis index and protease activity to interpret the mechanism of white spot formation during fermentation.

Results
In this study, the results confirm that tyrosine accounts for 78.22% of the white spots. Excessive tyrosine content can promote the formation of white spots during fermentation. Free tyrosine content, amino acid nitrogen content, depth of the proteolysis index and protease activity during the fermentation process increased during fermentation. Correlation analysis results show that free tyrosine content was positively correlated with amino acid nitrogen content, depth of the proteolysis index and protease activity, which showed that the production of tyrosine is related to the degree of proteolysis. Solubility studies show that high concentrations of NaCl and ethanol affect the production of tyrosine and reduce its solubility, while the increase in temperature increases the solubility of tyrosine. Taking NaCl, ethanol and temperature as influencing factors, a mathematical model of tyrosine solubility was established.

Conclusion
This study lays the groundwork for understanding the mechanism of white spot formation during Chinese fermented soybean curd fermentation and provides solutions for controlling white spot formation.