bluuebunny/crossref_metadata_2025_split huggingface dataset:
Losing sleep influences dietary intake in children: a longitudinal compositional analysis of a randomised crossover trial
Authors: Haszard Jillian J. , Jackson Rosie , Morrison Silke , Meredith-Jones Kim A. , Galland Barbara C. , Beebe Dean W. , Elder Dawn E. , Taylor Rachael W. 
Published: June 2024

Abstract
Background
Although inadequate sleep increases the risk of obesity in children, the mechanisms remain unclear. The aims of this study were to assess how sleep loss influenced dietary intake in children while accounting for corresponding changes in sedentary time and physical activity; and to investigate how changes in time use related to dietary intake.

Methods
A randomized crossover trial in 105 healthy children (8–12 years) with normal sleep (~ 8–11 h/night) compared sleep extension (asked to turn lights off one hour earlier than usual for one week) and sleep restriction (turn lights off one hour later) conditions, separated by a washout week. 24-h time-use behaviors (sleep, wake after sleep onset, physical activity, sedentary time) were assessed using waist-worn actigraphy and dietary intake using two multiple-pass diet recalls during each intervention week. Longitudinal compositional analysis was undertaken with mixed effects regression models using isometric log ratios of time use variables as exposures and dietary variables as outcomes, and participant as a random effect.

Results
Eighty three children (10.2 years, 53% female, 62% healthy weight) had 47.9 (SD 30.1) minutes less sleep during the restriction week but were also awake for 8.5 (21.4) minutes less at night. They spent this extra time awake in the day being more sedentary (+ 31 min) and more active (+ 21 min light physical activity, + 4 min MVPA). After adjusting for all changes in 24-h time use, losing 48 min of sleep was associated with consuming significantly more energy (262 kJ, 95% CI:55,470), all of which was from non-core foods (314 kJ; 43, 638). Increases in sedentary time were related to increased energy intake from non-core foods (177 kJ; 25, 329) whereas increases in MVPA were associated with higher intake from core foods (72 kJ; 7,136). Changes in diet were greater in female participants.

Conclusion
Loss of sleep was associated with increased energy intake, especially of non-core foods, independent of changes in sedentary time and physical activity. Interventions focusing on improving sleep may be beneficial for improving dietary intake and weight status in children.

Trial Registration
Australian New Zealand Clinical Trials Registry ANZCTR ACTRN12618001671257, Registered 10th Oct 2018, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=367587&isReview=true
Alpha-6 integrin deletion delays the formation of Brca1/p53-deficient basal-like breast tumors by restricting luminal progenitor cell expansion
Authors: Faraldo Marisa M. , Romagnoli Mathilde , Wallon Loane , Dubus Pierre , Deugnier Marie-Ange , Fre Silvia 
Published: June 2024

Abstract
Background
The aberrant amplification of mammary luminal progenitors is at the origin of basal-like breast cancers associated with BRCA1 mutations. Integrins mediate cell–matrix adhesion and transmit mechanical and chemical signals that drive epithelial stem cell functions and regulate tumor progression, metastatic reactivation, and resistance to targeted therapies. Consistently, we have recently shown that laminin-binding integrins are essential for the expansion and differentiation of mammary luminal progenitors in physiological conditions. As over-expression of the laminin-binding α6 integrin (Itgα6) is associated with poor prognosis and reduced survival in breast cancer, we here investigate the role of Itgα6 in mammary tumorigenesis.

Methods
We used Blg-Cre; Brca1F/F; Trp53F/F mice, a model that phenocopies human basal-like breast cancer with BRCA1 mutations. We generated mutant mice proficient or deficient in Itgα6 expression and followed tumor formation. Mammary tumors and pretumoral tissues were characterized by immunohistochemistry, flow cytometry, RT-qPCR, Western blotting and organoid cultures. Clonogenicity of luminal progenitors from preneoplastic glands was studied in 3D Matrigel cultures.

Results
We show that Itga6 deletion favors activation of p16 cell cycle inhibitor in the preneoplastic tissue. Subsequently, the amplification of luminal progenitors, the cell of origin of Brca1-deficient tumors, is restrained in Itgα6-deficient gland. In addition, the partial EMT program operating in Brca1/p53-deficient epithelium is attenuated in the absence of Itgα6. As a consequence of these events, mammary tumor formation is delayed in Itgα6-deficient mice. After tumor formation, the lack of Itgα6 does not affect tumor growth but rather alters their differentiation, resulting in reduced expression of basal cell markers.

Conclusions
Our data indicate that Itgα6 has a pro-tumorigenic role in Blg-Cre; Brca1F/F; Trp53F/F mice developing basal-like mammary tumors. In particular, we reveal that Itgα6 is required for the luminal progenitor expansion and the aberrant partial EMT program that precedes the formation of BRCA1 deficient tumors.
Optimizing clinico-genomic disease prediction across ancestries: a machine learning strategy with Pareto improvement
Authors: Gao Yan , Cui Yan 
Published: June 2024

Abstract
Background
Accurate prediction of an individual’s predisposition to diseases is vital for preventive medicine and early intervention. Various statistical and machine learning models have been developed for disease prediction using clinico-genomic data. However, the accuracy of clinico-genomic prediction of diseases may vary significantly across ancestry groups due to their unequal representation in clinical genomic datasets.

Methods
We introduced a deep transfer learning approach to improve the performance of clinico-genomic prediction models for data-disadvantaged ancestry groups. We conducted machine learning experiments on multi-ancestral genomic datasets of lung cancer, prostate cancer, and Alzheimer’s disease, as well as on synthetic datasets with built-in data inequality and distribution shifts across ancestry groups.

Results
Deep transfer learning significantly improved disease prediction accuracy for data-disadvantaged populations in our multi-ancestral machine learning experiments. In contrast, transfer learning based on linear frameworks did not achieve comparable improvements for these data-disadvantaged populations.

Conclusions
This study shows that deep transfer learning can enhance fairness in multi-ancestral machine learning by improving prediction accuracy for data-disadvantaged populations without compromising prediction accuracy for other populations, thus providing a Pareto improvement towards equitable clinico-genomic prediction of diseases.
There is a need to better take into account forest soils in the planned soil monitoring law of the European Union
Authors: Wellbrock Nicole , Cools Nathalie , de Vos Bruno , Jandl Robert , Lehtonen Aleksi , Leitgeb Ernst , Mäkipää Raisa , Pavlenda Pavel , Schwärtzel Kai , Šrámek Vít 
Published: June 2024

Abstract
Key message
A Soil Monitoring Law to improve soil health across all land uses has been proposed by the European Commission. As forests soils have different chemical and physical properties as well as biogeochemical dynamics compared to agricultural land, they also face different challenges in maintaining and restoring soil health. Examples are soil acidification, eutrophication by atmospheric deposition, responses to climate change, and loss of biodiversity. Therefore, we propose forest soil specific health descriptors and thresholds based on experience and knowledge from existing long-term monitoring programs.
Efficiently localizing system anomalies for cloud infrastructures: a novel Dynamic Graph Transformer based Parallel Framework
Authors: He Hongxia , Li Xi , Chen Peng , Chen Juan , Liu Ming , Wu Lei 
Published: June 2024

AbstractCloud environment is a virtual, online, and distributed computing environment that provides users with large-scale services. And cloud monitoring plays an integral role in protecting infrastructures in the cloud environment. Cloud monitoring systems need to closely monitor various KPIs of cloud resources, to accurately detect anomalies. However, due to the complexity and highly dynamic nature of the cloud environment, anomaly detection for these KPIs with various patterns and data quality is a huge challenge, especially those massive unlabeled data. Besides, it’s also difficult to improve the accuracy of the existing anomaly detection methods. To solve these problems, we propose a novel Dynamic Graph Transformer based Parallel Framework (DGT-PF) for efficiently detect system anomalies in cloud infrastructures, which utilizes Transformer with anomaly attention mechanism and Graph Neural Network (GNN) to learn the spatio-temporal features of KPIs to improve the accuracy and timeliness of model anomaly detection. Specifically, we propose an effective dynamic relationship embedding strategy to dynamically learn spatio-temporal features and adaptively generate adjacency matrices, and soft cluster each GNN layer through Diffpooling module. In addition, we also use nonlinear neural network model and AR-MLP model in parallel to obtain better detection accuracy and improve detection performance. The experiment shows that the DGT-PF framework have achieved the highest F1-Score on 5 public datasets, with an average improvement of 21.6% compared to 11 anomaly detection models.
Observations of co-existing rising and falling tone emissions of electromagnetic ion cyclotron waves
Authors: Ojha Biswajit , Omura Yoshiharu , Singh Satyavir , Lakhina Gurbax S. 
Published: June 2024

AbstractWe report observations of co-existing rising and falling tone emissions of electromagnetic ion cyclotron (EMIC) waves by THEMIS E spacecraft. The investigation of these fine structures of the EMIC waves is essential from the point of view of understanding the connection between the proton holes and the proton hills in velocity phase-space. The wave packets of rising and falling tones are tracked by Poynting vector analysis, where we observe that the rising tones are propagating northward and the falling tones are propagating southward. The nonlinear wave growth theory supports our observations. We propose a model where the proton velocity distribution function evolves through the formation of proton holes on the negative side of the distribution function and mirrored resonant protons forming proton hills on the positive side of the distribution function, allowing us to observe the co-existing rising and falling tone EMIC waves.
Graphical abstract
Connecto-informatics at the mesoscale: current advances in image processing and analysis for mapping the brain connectivity
Authors: Choi Yoon Kyoung , Feng Linqing , Jeong Won-Ki , Kim Jinhyun 
Published: June 2024

AbstractMapping neural connections within the brain has been a fundamental goal in neuroscience to understand better its functions and changes that follow aging and diseases. Developments in imaging technology, such as microscopy and labeling tools, have allowed researchers to visualize this connectivity through high-resolution brain-wide imaging. With this, image processing and analysis have become more crucial. However, despite the wealth of neural images generated, access to an integrated image processing and analysis pipeline to process these data is challenging due to scattered information on available tools and methods. To map the neural connections, registration to atlases and feature extraction through segmentation and signal detection are necessary. In this review, our goal is to provide an updated overview of recent advances in these image-processing methods, with a particular focus on fluorescent images of the mouse brain. Our goal is to outline a pathway toward an integrated image-processing pipeline tailored for connecto-informatics. An integrated workflow of these image processing will facilitate researchers’ approach to mapping brain connectivity to better understand complex brain networks and their underlying brain functions. By highlighting the image-processing tools available for fluroscent imaging of the mouse brain, this review will contribute to a deeper grasp of connecto-informatics, paving the way for better comprehension of brain connectivity and its implications.
Unveiling determinants of household lighting preferences in rural Tanzania: insights for sustainable energy access
Authors: Kamuzora Aurelia Ngirwa 
Published: June 2024

AbstractThis paper investigates the determinants and prospects of household lighting choices in rural Tanzania using a Multinomial Logit Regression Model. The analysis is based on data from 4671 households, focusing on three lighting options: electricity, solar energy, and candle lighting. The results reveal significant factors influencing these choices, including household head characteristics, household size, marital status, education, employment status, number of rooms, and income. Key findings indicate that the age of the household head negatively influences the likelihood of choosing grid-electricity, while having a male head of household significantly reduces the probability of opting for any lighting option. Larger household size is negatively associated with choosing electricity and candle lighting. Marital status shows that married households are more likely to use candle lighting. Employment status positively impacts the likelihood of adopting all three lighting options, with employed household heads being more likely to choose modern lighting solutions. Income levels are crucial, as higher income significantly increases the probability of selecting electricity and candle lighting, but not solar energy. These findings provide valuable insights for policymakers and stakeholders aiming to enhance sustainable energy access in rural Tanzania. It highlights the importance of addressing socio-economic factors to promote the adoption of modern and sustainable lighting technologies.
Synthesis and evaluation of [18F]FBNAF, a STAT3-targeting probe, for PET imaging of tumor microenvironment
Authors: Miyazaki Anna , Kanai Yasukazu , Wakamori Keita , Mizuguchi Serina , Futatsugi Mikiya , Hirano Fuko , Kondo Naoya , Temma Takashi 
Published: June 2024

Abstract
Background
Signal transducer and activator of transcription 3 (STAT3) is a protein that regulates cell proliferation and differentiation, and it is attracting attention as a new index for evaluating cancer pathophysiology, as its activation has been highly correlated with the development and growth of tumors. With the development of STAT3 inhibitors, the demand for imaging probes will intensify. Noninvasive STAT3 imaging can help determine the cancer status and predict the efficacy of STAT3 inhibitors. In this study, we aimed to develop an imaging probe targeting STAT3 and synthesized [18F]FBNAF, which was derived from a STAT3-selective inhibitor as the lead compound, followed by in vitro and in vivo evaluations of [18F]FBNAF in positron emission tomography for STAT3.

Results
The results revealed that FBNAF concentration-dependently inhibited STAT3 phosphorylation, similar to the lead compound, thereby supporting radiosynthesis. [18F]FBNAF was easily synthesized from the pinacol boronate ester precursor with suitable radiochemical conversion (46%), radiochemical yield (6.0%), and radiochemical purity (> 97%). [18F]FBNAF exhibited high stability in vitro and in vivo, and radioactivity accumulated in tumor tissues expressing STAT3 with an increasing tumor/blood ratio over time, peaking at 2.6 ± 0.8 at 120 min after injection in tumor-bearing mice. Tumor radioactivity was significantly reduced by the coinjection of a STAT3-selective inhibitor. Furthermore, the localization of radioactivity was almost consistent with STAT3 expression based on ex vivo autoradiography and immunohistochemistry using adjacent tumor sections.

Conclusions
Thus, [18F]FBNAF could be the first promising STAT3-targeting probe for PET imaging. A STAT3 imaging probe provides meaningful information on STAT3-associated cancer conditions and in tumor microenvironment.
Management of spontaneous pyogenic spondylodiscitis: a descriptive cohort study
Authors: Ibrahim Aly , Elshazly Khaled , AbdelFatah Mohamed A. R. , El-Gayar Abdelrahman , Hefny Sameh 
Published: June 2024

Abstract
Background
Spontaneous spondylodiscitis (SD) is an inflammation of the vertebral endplate and the intervertebral disc with no prior surgery or intervention. The treatment of spontaneous pyogenic SD mainly consists of systemic antibiotics and rest. Some cases require surgery due to failure of medical treatment or due to neurological compromise. Due to the disease heterogeneity, there are no standardized, widely adopted treatment protocols. We conducted this study to assess the clinical outcome of the different treatment modalities.

Results
This is a retrospective analysis of prospectively collected data of patients with spontaneous non-tuberculous spondylodiscitis. Thirty-eight patients were identified, including 14 males and 24 females. The mean age was 49 years. Twenty-three patients underwent surgery from the start while 15 patients received conservative medical treatment. Among the latter, 8 patients showed disease progression and required surgical intervention. The ODI/NDI at 1 year (mean = 8) was significantly better than before treatment (mean = 18). Among 11 patients with motor deficit at presentation, 8 improved and 3 remained stable. There were 14 complications, including 11 minor and 3 major, requiring one revision surgery.

Conclusions
Surgical decompression and debridement with or without instrumented fusion for complicated SD cases is a safe and effective treatment modality. Close follow-up is needed in case of conservative treatment for early detection of treatment failure and disease progression. Baseline characteristic variabilities in patients with spontaneous pyogenic SD could predict conservative treatment failure, but this needs to be validated in larger series.
Single-cell RNA sequencing opens a new era for cotton genomic research and gene functional analysis
Authors: Pan Xiaoping , Peng Renhai , Zhang Baohong 
Published: June 2024

AbstractSingle-cell RNA sequencing (scRNA-seq) is one of the most advanced sequencing technologies for studying transcriptome landscape at the single-cell revolution. It provides numerous advantages over traditional RNA-seq. Since it was first used to profile single-cell transcriptome in plants in 2019, it has been extensively employed to perform different research in plants. Recently, scRNA-seq was also quickly adopted by the cotton research community to solve lots of scientific questions which have been never solved. In this comment, we highlighted the significant progress in employing scRNA-seq to cotton genetic and genomic study and its future potential applications.
Identification of key functional pathways: arginine biosynthesis and IL-17 signalling in placental decidua of unexplained recurrent pregnancy loss through RNA sequencing—a case series
Authors: Sultana Shehnaz , Bhanu B. Divya , Ananthapur Venkateshwari 
Published: June 2024

Abstract
Background
Three or more consecutive pregnancy losses before the 20th week of gestation constitute recurrent pregnancy loss (RPL), and about half of these cases are still unsolved despite routine screening tests. The purpose of the current study was to identify the RPL-related placental decidual differential gene expression and to gain new knowledge about the biological mechanisms underlying RPL.

Methods
In the current work, we used RNA sequencing (RNA-seq) technology to identify the differentially expressed genes (DEGs) in placental decidua from patients of unexplained recurrent pregnancy loss (RPL). To conduct RNA-seq, two healthy unwanted medically terminated pregnancies (MTPs) and four RPL patients were enlisted.

Results
A total number of 96 significant differentially expressed genes (DEGs) were obtained which includes 73 up- and 23 downregulated genes between the RPL and MTP groups. Histocompatibility genes were significantly upregulated in the RPL. Interleukin 6 (IL-6), matrix metalloproteinase-10 (MMP10), and protein phosphatase 1 regulatory inhibitor subunit 11 (PPP1R11) genes which were significantly upregulated in RPL were further validated in an extended sample size. The validation results were consistent with the sequencing results. To find potential biological pathways connected to RPL, the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were carried out.
The study indicates that arginine biosynthesis is significantly downregulated, while IL-17 signalling pathway is significantly upregulated in RPL.

Conclusion
In conclusion, the findings of the present study indicate involvement of arginine biosynthesis, immune regulatory pathways, and histocompatibility genes in the pathogenesis of recurrent pregnancy loss (RPL). However, to validate these observations, further investigations with a larger sample size are warranted.
Outcomes with trastuzumab deruxtecan (T-DXd) by HER2 status and line of treatment in a large real-world database of patients with metastatic breast cancer.
Authors: Tarantino Paolo , Lee Do , Foldi Julia , Soulos Pamela R. , Gross Cary Philip , Grinda Thomas , Winer Eric P. , Lin Nancy U. , Krop Ian E. , Tolaney Sara M. , Lustberg Maryam B. , Sammons Sarah L 
Published: June 2024

1077 Background: In the clinical trial setting, T-DXd improves survival for patients with HER2+ and HER2-low metastatic breast cancer (MBC), and has also shown preliminary activity in HER2-0 MBC. Little real-world evidence is available on the performance of T-DXd. Methods: We conducted a retrospective observational study using the nationwide Flatiron Health electronic health record (EHR)-derived deidentified database to evaluate the real-world activity of T-DXd. We included patients with MBC who initiated T-DXd between 12/2019 and 1/2023. Tumors were categorized as HER2+ if positive at any timepoint, with HER2- cases further divided into HER2-low (IHC 1+ or 2+ not amplified) and HER2-0 (IHC 0) according to the last biopsy before T-DXd. Real world progression-free survival (rwPFS) and overall survival (OS) were estimated using the Kaplan-Meier method. Results: A total of 930 patients were included: 636 with HER2+, 268 with HER2-low and 26 with HER2-0 MBC. Among HER2- patients, 241 (82.0%) were HR+ and 53 (18.0%) were HR- (i.e., triple-negative). A total of 572 (61.5%) patients were White, 101 (10.9%) Black/African American, 88 (9.4%) Hispanic/Latino, 37 (4.0%) Asian and 132 (14.2%) other or missing. Median age was 59 [range 25, 84] and patients received T-DXd in the following lines of therapy (LOT): 54 (5.8%) as 1st line, 110 (11.8%) as 2nd line, 179 (19.2%) as 3rd line, 182 (19.6%) as 4th line and 405 (43.6%) as ≥5th line. Median number of LOT for advanced disease prior to T-DXd were 3 and 4, respectively, for HER2+ and HER2- patients. In patients with HER2+ MBC, rwPFS and OS were 12.1 and 26 months after initiating T-DXd, respectively. The longest rwPFS, 14.9 months, was observed in the 1st or 2nd LOT, followed by 14.1 months in LOT 3, 12.4 months in LOT 4, and 10.8 months in LOT ≥5. In patients with HER2- MBC, median rwPFS and OS were 6.3 and 15.5 months, respectively. The longest median rwPFS of 8.1 months was observed in the 1st or 2nd LOTs, followed by 6.9 months in LOT 4, 6.2 months in LOTs ≥5, and 6.0 months in LOT 3. Outcomes with T-DXd by HER2 and HR status are provided (Table). Updated data with a larger sample size will be presented. Conclusions: In the largest dataset to date, T-DXd showed favorable real-world activity for treating MBC, although rwPFS appeared shorter than what observed in clinical trials. Encouraging rwPFS was observed in understudied groups, such as patients with triple-negative and HR+/HER2-0 MBC. [Table: see text]