bluuebunny/crossref_metadata_2025_split huggingface dataset:
POC viral load testing in an antenatal clinic setting for Ugandan pregnant women living with HIV: a qualitative implementation process analysis
Authors: Ware Norma C. , Wyatt Monique A. , Nakyanzi Agnes , Naddunga Faith , Pisarski Emily E. , Kyomugisha Juliet , Birungi Juliet E. , Bulterys Michelle A. , Kamusiime Brenda , Nalumansi Alisaati , Kasiita Vicent , Mujugira Andrew , Celum Connie L. 
Published: June 2024

Abstract
Introduction
Point of care viral load (POC VL) testing improves viral suppression and retention in HIV care, and is increasingly being integrated into routine health services for African pregnant women living with HIV. We examined processes of implementing POC VL testing in antenatal care and at delivery for Ugandan mothers living with HIV as part of a pilot randomized trial (Clinical Trial Number: NCT05092997).

Methods
We conducted individual qualitative interviews with 12 clinical and research staff who implemented POC VL testing and 22 mothers who received POC VL testing using the Xpert® HIV-1 Viral Load Assay (Cepheid Inc., Sunnyvale, CA, USA). An inductive, content analytic approach was used to examine the interview transcripts. The analysis addressed the question: How did a group of Ugandan health care providers approach the process of implementing POC VL testing in antenatal care and at delivery for mothers living with HIV?

Results
The analysis yielded three themes. (1) Staff created an efficient system of communication and then relied on that system to coordinate testing procedures. (2) They also found ways of increasing the speed and efficiency of the testing process. (3) They adopted a “mother-centered” approach to implementation, prioritizing the needs, preferences, and well-being of women in planning and carrying out testing procedures.

Conclusion
As POC VL testing becomes more widely used across high HIV burden settings, understanding how implementers approach the implementation process and what they do to make an intervention successful will be an important part of evaluating feasibility.
Clinical Trial Number: NCT05092997.
Factors associated with chronic depressive symptoms across adolescence and young adulthood: a UK birth cohort study
Authors: Durdurak B. B. , Williams B. , Zhigalov A. , Moore A. , Mallikarjun P. , Wong D. , Marwaha S. , Morales-Muñoz I. 
Published: June 2024

Abstract

Aims
Identifying children and/or adolescents who are at highest risk for developing chronic depression is of utmost importance, so that we can develop more effective and targeted interventions to attenuate the risk trajectory of depression. To address this, the objective of this study was to identify young people with persistent depressive symptoms across adolescence and young adulthood and examine the prospective associations between factors and persistent depressive symptoms in young people.

Methods
We used data from 6711 participants in the Avon Longitudinal Study of Parents and Children. Depressive symptoms were assessed at 12.5, 13.5, 16, 17.5, 21 and 22 years with the Short Mood and Feelings Questionnaire, and we further examined the influence of multiple biological, psychological and social factors in explaining chronic depressive symptoms.

Results
Using latent class growth analysis, we identified four trajectories of depressive symptoms: persistent high, persistent low, persistent moderate and increasing high. After applying several logistic regression models, we found that loneliness and feeling less connected at school were the most relevant factors for chronic course of depressive symptoms.

Conclusions
Our findings contribute with the identification of those children who are at highest risk for developing chronic depressive symptoms.
Refining Planetary Boundary Layer Height Retrievals From Micropulse‐Lidar at Multiple ARM Sites Around the World
Authors: Roldán‐Henao Natalia , Su Tianning , Li Zhanqing 
Published: June 2024

AbstractKnowledge of the planetary boundary layer height (PBLH) is crucial for various applications in atmospheric and environmental sciences. Lidar measurements are frequently used to monitor the evolution of the PBLH, providing more frequent observations than traditional radiosonde‐based methods. However, lidar‐derived PBLH estimates have substantial uncertainties, contingent upon the retrieval algorithm used. In addressing this, we applied the Different Thermo‐Dynamic Stabilities (DTDS) algorithm to establish a PBLH data set at five separate Department of Energy's Atmospheric Radiation Measurement sites across the globe. Both the PBLH methodology and the products are subject to rigorous assessments in terms of their uncertainties and constraints, juxtaposing them with other products. The DTDS‐derived product consistently aligns with radiosonde PBLH estimates, with correlation coefficients exceeding 0.77 across all sites. This study delves into a detailed examination of the strengths and limitations of PBLH data sets with respect to both radiosonde‐derived and other lidar‐based estimates of the PBLH by exploring their respective errors and uncertainties. It is found that varying techniques and definitions can lead to diverse PBLH retrievals due to the inherent intricacy and variability of the boundary layer. Our DTDS‐derived PBLH data set outperforms existing products derived from ceilometer data, offering a more precise representation of the PBLH. This extensive data set paves the way for advanced studies and an improved understanding of boundary‐layer dynamics, with valuable applications in weather forecasting, climate modeling, and environmental studies.
Crustal Characterization of the Hengill Geothermal Fields: Insights From Isotropic and Anisotropic Seismic Noise Imaging Using a 500‐Node Array
Authors: Wu Sin‐Mei , Sánchez‐Pastor Pilar , Ágústsdóttir Thorbjörg , Hersir Gylfi Páll , Mordret Aurélien , Hjörleifsdóttir Vala , Obermann Anne 
Published: June 2024

AbstractThe Hengill volcano and its associated geothermal fields represent Iceland's most productive harnessed high‐temperature geothermal fields, where resources are fueled by cooling magmatic intrusions connected to three volcanic systems. The crustal structure in this area is highly heterogeneous and shaped by the intricate interplay between tectonic forces and magmatic/hydrothermal activities. This complexity makes detailed subsurface characterization challenging. In this study, we aim to push the current resolution limits using a 500‐node temporary seismic array and perform an isotropic and, for the first time, radially‐anisotropic velocity model of the area. The high‐resolution isotropic velocity model reveals the characteristic N30ºE fissure swarm that crosses the area within the top 500 m and outlines a deep‐seated low‐velocity body composed of cooling magmatic intrusions at 5 km depth. This deeper body is located near the eastern part of the three volcanic centers and connected to a shallower body at 2–3 km depth that strikes westward toward Hengill volcano. Additionally, our study discovered that non‐induced earthquakes deeper than 2 km align with velocity contrasts that reflect structural variability, indicating the potential to identify deep permeable pathways using dense array imaging. The anisotropic model indicates that the shallow crust of Hengill within the top 2 km is dominated by vertical fractures or cracks, likely attributed to overall divergent deformation from rifting in the study area. This characteristic is diminished at depths greater than 2–3 km, replaced by a layering pattern where the lava flows and/or subhorizontal intrusions become the primary factors influencing the observed anisotropy.
Aspect Differences in Vegetation Type Drive Higher Evapotranspiration on a Pole‐Facing Slope in a California Oak Savanna
Authors: Donaldson Amanda , Dralle David , Barling Nerissa , Callahan Russell P. , Loik Michael E. , Zimmer Margaret 
Published: June 2024

AbstractQuantifying evapotranspiration (ET) is critical to accurately predict vegetation health, groundwater recharge, and streamflow generation. Hillslope aspect, the direction a hillslope faces, results in variable incoming solar radiation and subsequent vegetation water use that drive ET. Previous work in watersheds with a single dominant vegetation type (e.g., trees) have shown that equator‐facing slopes (EFS) have higher ET compared to pole‐facing slopes (PFS) due to higher evaporative demand. However, it remains unclear how differences in vegetation type (i.e., grasses and trees) influence ET and water partitioning between hillslopes with opposing aspects. Here, we quantified ET and root‐zone water storage deficits between a PFS and EFS with contrasting vegetation types within central coastal California. Our results suggest that the cooler PFS with oak trees has higher ET than the warmer EFS with grasses, which is counter to previous work in landscapes with a singule dominant vegetation type. Our root‐zone water storage deficit calculations indicate that the PFS has a higher subsurface storage deficit and a larger seasonal dry down than the EFS. This aspect difference in subsurface water storage deficits may influence the subsequent replenishment of dynamic water storage, groundwater recharge and streamflow generation. In addition, larger subsurface water deficits on PFS may reduce their ability to serve as hydrologic refugia for oaks during multi‐year droughts. This research provides a novel integration of field‐based and remotely‐sensed estimates of ET required to properly quantify hillslope‐scale water balances. These findings emphasize the importance of resolving hillslope‐scale vegetation structure within Earth system models, especially in landscapes with diverse vegetation types.
Thermal noise-driven resonant sensors
Authors: Qiao Yan , Elhady Alaaeldin , Arabi Mohamed , Abdel-Rahman Eihab , Zhang Wenming 
Published: June 2024

AbstractMEMS/NEMS resonant sensors hold promise for minute mass and force sensing. However, one major challenge is that conventional externally driven sensors inevitably encounter undesired intrinsic noise, which imposes a fundamental limitation upon their signal-to-noise ratio (SNR) and, consequently, the resolution. Particularly, this restriction becomes increasingly pronounced as sensors shrink to the nanoscale. In this work, we propose a counterintuitive paradigm shift that turns intrinsic thermal noise from an impediment to a constituent of the sensor by harvesting it as the driving force, obviating the need for external actuation and realizing ‘noise-driven’ sensors. Those sensors employ the dynamically amplified response to thermal noise at resonances for stimulus detection. We demonstrate that lightly damped and highly compliant nano-structures with high aspect ratios are promising candidates for this class of sensors. To overcome the phase incoherence of the drive force, three noise-enabled quantitative sensing mechanisms are developed. We validated our sensor paradigm by experimental demonstrating noise-driven pressure and temperature sensors. Noise-driven sensors offer a new opportunity for delivering practical NEMS sensors that can function at room temperature and under ambient pressure, and a development that suggests a path to cheaper, simpler, and low-power-consumption sensors.
A meta-analysis of semiconductor materials fabricated in microgravity
Authors: Wilkinson Ashley R. , Brewer Frances , Wright Hannah , Whiteside Ben , Williams Amari , Harper Lynn , Wilson Anne M. 
Published: June 2024

AbstractThis meta-analysis of 160 semiconductor crystals that were grown in microgravity on orbital vehicles between 1973 and 2016 is based on publicly available information documented in the literature. This analysis provides comparisons of crystal metrics including size, structure quality, uniformity, and improved performance between crystals grown in microgravity or terrestrially. Improvement in at least one of these metrics was observed for 86% of those materials that included data in their studies.
The direct effect of SARS-CoV-2 virus vaccination on human ovarian granulosa cells explains menstrual irregularities
Authors: Bar-Joseph Hadas , Raz Yael , Eldar-Boock Anat , Michaan Nadav , Angel Yoel , Saiag Esther , Nemerovsky Luba , Ben-Ami Ido , Shalgi Ruth , Grisaru Dan 
Published: June 2024

AbstractFollowing administration of the SARS-CoV-2 vaccine, many women worldwide reported short-term menstrual irregularities. Although menstrual bleeding, “the fifth vital sign”, is experienced by more than 300 million people on any given day worldwide, these changes were only partially studied. Irregular periods are important well beyond fertility and the discomfort they impose; they are associated with the risk of cardiovascular morbidity, chronic diseases, and premature mortality. Pre-clinical examination of the vaccine polymeric envelope indicates its accumulation in the ovaries. The somatic endocrine cells of the ovarian follicle - the granulosa cells (GCs)—participate in the strict hypothalamic-pituitary-ovarian (HPO) feedback loop that governs the menstrual cycle via endocrine and paracrine regulators, as AMH and Inhibins. We aimed to unravel the direct effect of the COVID-19 vaccine on GCs and link their post-vaccine activity to changes in menstrual patterns. Human primary GCs exposed in-vitro to the Pfizer COVID-19 vaccine BNT162b2, demonstrated no change in their viability but altered mRNA transcripts, specifically of the regulatory key factors: InhibinB was upregulated, whereas AMH was downregulated. We further examined pre- and post-vaccination blood samples from individual women and found a 2–3 folds change in the post-vaccination FSH/InhibinB protein level ratio, compared to their pre-vaccination values. This altered expression of InhibinB could significantly impact the HPO axis in vaccinated women and may ultimately influence the endometrium cyclicity, manifested clinically by the commonly reported changes in menstrual bleeding patterns.
Drosophila immune cells transport oxygen through PPO2 protein phase transition
Authors: Shin Mingyu , Chang Eunji , Lee Daewon , Kim Nayun , Cho Bumsik , Cha Nuri , Koranteng Ferdinand , Song Ji-Joon , Shim Jiwon 
Published: June 2024

AbstractInsect respiration has long been thought to be solely dependent on an elaborate tracheal system without assistance from the circulatory system or immune cells1,2. Here we describe that Drosophila crystal cells—myeloid-like immune cells called haemocytes—control respiration by oxygenating Prophenoloxidase 2 (PPO2) proteins. Crystal cells direct the movement of haemocytes between the trachea of the larval body wall and the circulation to collect oxygen. Aided by copper and a neutral pH, oxygen is trapped in the crystalline structures of PPO2 in crystal cells. Conversely, PPO2 crystals can be dissolved when carbonic anhydrase lowers the intracellular pH and then reassembled into crystals in cellulo by adhering to the trachea. Physiologically, larvae lacking crystal cells or PPO2, or those expressing a copper-binding mutant of PPO2, display hypoxic responses under normoxic conditions and are susceptible to hypoxia. These hypoxic phenotypes can be rescued by hyperoxia, expression of arthropod haemocyanin or prevention of larval burrowing activity to expose their respiratory organs. Thus, we propose that insect immune cells collaborate with the tracheal system to reserve and transport oxygen through the phase transition of PPO2 crystals, facilitating internal oxygen homeostasis in a process that is comparable to vertebrate respiration.
Cenozoic history of the tropical marine biodiversity hotspot
Authors: Tian Skye Yunshu , Yasuhara Moriaki , Condamine Fabien L. , Huang Huai-Hsuan M. , Fernando Allan Gil S. , Aguilar Yolanda M. , Pandita Hita , Irizuki Toshiaki , Iwatani Hokuto , Shin Caren P. , Renema Willem , Kase Tomoki 
Published: June 2024

AbstractThe region with the highest marine biodiversity on our planet is known as the Coral Triangle or Indo-Australian Archipelago (IAA)1,2. Its enormous biodiversity has long attracted the interest of biologists; however, the detailed evolutionary history of the IAA biodiversity hotspot remains poorly understood3. Here we present a high-resolution reconstruction of the Cenozoic diversity history of the IAA by inferring speciation–extinction dynamics using a comprehensive fossil dataset. We found that the IAA has exhibited a unidirectional diversification trend since about 25 million years ago, following a roughly logistic increase until a diversity plateau beginning about 2.6 million years ago. The growth of diversity was primarily controlled by diversity dependency and habitat size, and also facilitated by the alleviation of thermal stress after 13.9 million years ago. Distinct net diversification peaks were recorded at about 25, 20, 16, 12 and 5 million years ago, which were probably related to major tectonic events in addition to climate transitions. Key biogeographic processes had far-reaching effects on the IAA diversity as shown by the long-term waning of the Tethyan descendants versus the waxing of cosmopolitan and IAA taxa. Finally, it seems that the absence of major extinctions and the Cenozoic cooling have been essential in making the IAA the richest marine biodiversity hotspot on Earth.
ROCOv2: Radiology Objects in COntext Version 2, an Updated Multimodal Image Dataset
Authors: Rückert Johannes , Bloch Louise , Brüngel Raphael , Idrissi-Yaghir Ahmad , Schäfer Henning , Schmidt Cynthia S. , Koitka Sven , Pelka Obioma , Abacha Asma Ben , G. Seco de Herrera Alba , Müller Henning , Horn Peter A. , Nensa Felix , Friedrich Christoph M. 
Published: June 2024

AbstractAutomated medical image analysis systems often require large amounts of training data with high quality labels, which are difficult and time consuming to generate. This paper introduces Radiology Object in COntext version 2 (ROCOv2), a multimodal dataset consisting of radiological images and associated medical concepts and captions extracted from the PMC Open Access subset. It is an updated version of the ROCO dataset published in 2018, and adds 35,705 new images added to PMC since 2018. It further provides manually curated concepts for imaging modalities with additional anatomical and directional concepts for X-rays. The dataset consists of 79,789 images and has been used, with minor modifications, in the concept detection and caption prediction tasks of ImageCLEFmedical Caption 2023. The dataset is suitable for training image annotation models based on image-caption pairs, or for multi-label image classification using Unified Medical Language System (UMLS) concepts provided with each image. In addition, it can serve for pre-training of medical domain models, and evaluation of deep learning models for multi-task learning.
Features and quality metrics datasets for video coding in DASH
Authors: Micó-Enguídanos Francisco , Salcedo-Navarro Andoni , Garcia-Pineda Miguel , Gutiérrez-Aguado Juan 
Published: June 2024

AbstractNowadays, the majority of media Internet traffic consists of H.264-encoded streaming videos due to its high compatibility. One of the most popular streaming technology used to deliver videos over Internet is Dynamic Adaptive Streaming over HTTP (DASH). It transmit the video as a sequence of independent short video segments tailored to the receiver’s limitations (related to several factors such as available bandwidth or resolution on reception), aiming to enhance the Quality of Experience. In this paper, we present two types of datasets created from 4065 video segments of 2 seconds. The first type consists of extracting features related to color, space, and time from the segments across different vertical resolutions (240, 360, 480, 720, 1080, 1440 and 4K). The second one includes several quality metrics obtained from the same segments when they are encoded with different compression levels in the different resolutions.
Nuclear and mitochondrial genomes of the plum fruit moth Grapholita funebrana
Authors: Cao Li-Jun , Yang Fangyuan , Chen Jin-Cui , Wei Shu-Jun 
Published: June 2024

AbstractThe plum fruit moth Grapholita funebrana (Tortricidae, Lepidoptera) is an important pest of many wild and cultivated stone fruits and other plants in the family Rosaceae. Here, we assembled its nuclear and mitochondrial genomes using Illumina, Nanopore, and Hi-C sequencing technologies. The nuclear genome size is 570.9 Mb, with a repeat rate of 51.28%, and a BUCSO completeness of 97.7%. The karyotype for males is 2n = 56. We identified 17,979 protein-coding genes, 5,643 tRNAs, and 94 rRNAs. We also determined the mitochondrial genome of this species and annotated 13 protein-coding genes, 22 tRNAs, and 2 rRNA. These genomes provide resources to understand the genetics, ecology, and genome evolution of the tortricid moths.
A morphological traits dataset of Heteroptera sampled in biodiversity priority areas of Southwest China
Authors: Gao Shutong , Yu Wenbo , Tian Ting , Lu Zhixing , Zhang Xiang , Li Qiao , Chen Youqing 
Published: June 2024

AbstractFunctional traits reveal the adaptive strategies of species to their environment, and are relevant to the formation of communities, the function of ecosystems, and the mechanisms underlying biodiversity. However, trait databases have not been established for most biological taxa, especially for insects, which encompass a vast number of species. This study measured the morphological traits of 307 species of Heteroptera insects collected in 2019 from the “Xishuangbanna Priority Conservation Area” in Southwest China using sweep netting and light trapping methods. This study provides a dataset for 307 Heteroptera species, comprising 34 morphometric measurements and 17 morphological traits. The dataset contains information on species sex, abundance, and the average, maximum, and minimum values of traits. This dataset facilitates an enhanced understanding of the functional traits and ecological associations of Heteroptera insects and offers opportunities for exploring a more diverse range of research topics.
Deep learning model integrating cfDNA methylation and fragment size profiles for lung cancer diagnosis
Authors: Kim Minjung , Park Juntae , Seonghee Oh  , Jeong Byeong-Ho , Byun Yuree , Shin Sun Hye , Im Yunjoo , Cho Jong Ho , Cho Eun-Hae 
Published: June 2024

AbstractDetecting aberrant cell-free DNA (cfDNA) methylation is a promising strategy for lung cancer diagnosis. In this study, our aim is to identify methylation markers to distinguish patients with lung cancer from healthy individuals. Additionally, we sought to develop a deep learning model incorporating cfDNA methylation and fragment size profiles. To achieve this, we utilized methylation data collected from The Cancer Genome Atlas and Gene Expression Omnibus databases. Then we generated methylated DNA immunoprecipitation sequencing and genome-wide Enzymatic Methyl-seq (EM-seq) form lung cancer tissue and plasma. Using these data, we selected 366 methylation markers. A targeted EM-seq panel was designed using the selected markers, and 142 lung cancer and 56 healthy samples were produced with the panel. Additionally, cfDNA samples from healthy individuals and lung cancer patients were diluted to evaluate sensitivity. Its lung cancer detection performance reached an accuracy of 81.5% and an area under the receiver operating characteristic curve of 0.87. In the serial dilution experiment, we achieved tumor fraction detection of 1% at 98% specificity and 0.1% at 80% specificity. In conclusion, we successfully developed and validated a combination of methylation panel and a deep learning model that can distinguish between patients with lung cancer and healthy individuals.