bluuebunny/crossref_metadata_2025_split huggingface dataset:
Experimental Snowball Earth Viscosity Drives the Evolution of Motile Multicellularity
Authors: Halling Andrea , Goodson Brysyn , Hirschmann Anna , Wing Boswell A. , Simpson Carl 
Published: February 2024

AbstractDuring the 70-million-year span of the Cryogenian Snowball Earth glaciations, low ocean temperatures beneath global sea ice increased water viscosity up to fourfold. In the absence of adaptation, unicellular organisms living in this viscous environment were limited in their ability to move and acquire nutrients. We experimentally test the hypothesis that multicellularity evolved in order to overcome this viscosity-induced metabolic deficit. In the presence of Snowball Earth viscosities, we find that populations of unicellular green algae evolve motile multicellular phenotypes in addition to other phenotypes that optimize different combinations of size and speed. As the Snowball Earth subsided and warm seas returned, the novelty of motile multicellularity permitted these organisms to take physical control over their local environment for the first time. This innovation may underpin the evolution of dominant multicellular lineages on Earth today.Significance statementBeginning 720-million years ago, two global glaciations — together known as the Snowball Earth — covered the planet with a thick layer of ice for a total of 70-million years. Several groups of complex multicellular organisms independently radiated at this time, including animals, green algae, and red algae. All of these clades include lineages with large bodies made of thousands of cells, multiple cell types, and spatial organization. At first glance, it seems that life merely survived despite the Snowball Earth glaciations. We find experimental evidence that the Snowball Earth glaciations were instead an evolutionary trigger for the diversification of complex multicellular groups.
vSPACE: Exploring Virtual Spatial Representation of Articular Chondrocytes at the Single-Cell Level
Authors: Zhang Chenyu , Wang Honglin , Hong Seung-Hyun , Olmer Merissa , Swahn Hannah , Lotz Martin K. , Maye Peter , Rowe David , Shin Dong-Guk 
Published: February 2024

AbstractSingle cell RNA sequencing technology has been dramatically changing how gene expression studies are performed. However, its use has been limited to identifying subtypes of cells by comparing cells’ gene expression levels in an unbiased manner to produce a 2D plot (e.g., UMAP/tSNE). We developed a new method of placing cells in 2D space. This system, called vSPACE, shows a virtual spatial representation of scRNAseq data obtained from human articular cartilage by emulating the concept of spatial transcriptomics technology, but virtually. This virtual 2D plot presentation of human articular cartage cells generates several zonal distribution patterns, in one or multiple genes at a time, reveling patterns that scientists can appreciate as imputed spatial distribution patterns along the zonal axis. The discovered patterns are explainable and remarkably consistent across all six healthy doners despite their respectively different clinical variables (age and sex), suggesting the confidence of the discovered patterns.
Prelude to Malignancy: A Gene Expression Signature in Normal Mammary Gland from Breast Cancer Patients Suggests Pre-tumorous Alterations and Is Associated with Adverse Outcomes
Authors: Andreou Maria , Jąkalski Marcin , Duzowska Katarzyna , Filipowicz Natalia , Kostecka Anna , Davies Hanna , Horbacz Monika , Ławrynowicz Urszula , Chojnowska Katarzyna , Bruhn-Olszewska Bożena , Jankau Jerzy , Śrutek Ewa , Las-Jankowska Manuela , Bała Dariusz , Hoffman Jacek , Hartman Johan , Pęksa Rafał , Skokowski Jarosław , Jankowski Michał , Szylberg Łukasz , Zegarski Wojciech , Nowikiewicz Magdalena , Nowikiewicz Tomasz , Dumanski Jan P. , Mieczkowski Jakub , Piotrowski Arkadiusz 
Published: February 2024

ABSTRACTDespite advances in early detection and treatment strategies, breast cancer recurrence and mortality remain a significant health issue. Recent insights suggest the prognostic potential of microscopically healthy mammary gland, in the vicinity of the breast lesion. Nonetheless, a comprehensive understanding of the gene expression profiles in these tissues and their relationship to patient outcomes is still missing. Furthermore, the increasing trend towards breast-conserving surgery may inadvertently lead to the retention of existing cancer-predisposing mutations within the normal mammary gland. This study assessed the transcriptomic profiles of 242 samples from 83 breast cancer patients with unfavorable outcomes, including paired uninvolved mammary gland samples collected at varying distances from primary lesions. As a reference, control samples from 53 mammoplasty individuals without cancer history were studied. A custom panel of 634 genes linked to breast cancer progression and metastasis was employed for expression profiling, followed by whole-transcriptome verification experiments and statistical analyses to discern molecular signatures and their clinical relevance. A distinct gene expression signature was identified in uninvolved mammary gland samples, featuring key cellular components encoding keratins, CDH1, CDH3, EPCAM cell adhesion proteins, matrix metallopeptidases, oncogenes, tumor suppressors, along with crucial genes(FOXA1, RAB25, NRG1, SPDEF, TRIM29, andGABRP) having dual roles in cancer. Enrichment analyses revealed disruptions in epithelial integrity, cell adhesion, and estrogen signaling. This signature, named KAOS for Keratin-Adhesion-Oncogenes-Suppressors, was significantly associated with reduced tumor size but increased mortality rates. Integrating molecular assessment of non-malignant mammary tissue into disease management could enhance survival prediction and facilitate personalized patient care.
In vivovalidation of anin situcalibration bead as a reference for backscatter coefficient calculation
Authors: Zhao Yuning , Czarnota Gregory J. , Park Trevor H. , Miller Rita J. , Oelze Michael L. 
Published: February 2024

AbstractObjectivesThe study aims to assess the capability of Quantitative Ul-trasound (QUS) based on the backscatter coefficient (BSC) for classifying disease states, such as breast cancer response to neoadjuvant chemotherapy and quantifying fatty liver disease. We evaluate the effectiveness of anin situtitanium (Ti) bead as a reference target in calibrating the system and mitigating attenuation and transmission loss effects on BSC estimation.MethodsTraditional BSC estimation methods require external references for calibration, which do not account for ultrasound attenuation or transmis-sion losses through tissues. To address this issue, we use anin situtitanium (Ti) bead as a reference target, because it can be used to calibrate the system and mitigate the attenuation and transmission loss effects on estimation of the BSC. The capabilities of thein situcalibration approach were assessed by quantifying consistency of BSC estimates from rabbit mammary tumors (N = 21). Specifically, mammary tumors were grown in rabbits and when a tumor reached 1 cm or greater in size, a 2-mm Ti bead was implanted into the tumor as a radiological marker and a calibration source for ultrasound. Three days later, the tumors were scanned with a L-14/5 38 array transducer connected to a SonixOne scanner with and without a slab of pork belly placed on top of the tumors. The pork belly acted as an additional source of attenu-ation and transmission loss. QUS parameters, specifically effective scatterer diameter (ESD) and effective acoustic concentration (EAC), were calculated using calibration spectra from both an external reference phantom and the Ti bead.ResultsFor ESD estimation, the 95% confidence interval between measure-ments with and without the pork belly layer was (6.0,27.4) using thein situbead and (114, 135.1) with the external reference phantom. For EAC esti-mation, the 95% confidence interval were (−8.1, 0.5) for the bead and (−41.5, −32.2) for the phantom. These results indicate that thein situbead method shows reduced bias in QUS estimates due to intervening tissue losses.ConclusionsThe use of anin situTi bead as a radiological marker not only serves its traditional role but also effectively acts as a calibration target for QUS methods. This approach accounts for attenuation and transmission losses in tissue, resulting in more accurate QUS estimates and offering a promising method for enhanced disease state classification in clinical settings.
USE OF MACHINE LEARNING TO PREDICT INDIVIDUAL POSTPRANDIAL GLYCEMIC RESPONSES TO FOOD AMONG INDIVIDUALS WITH TYPE 2 DIABETES IN INDIA
Authors: Choudhry Niteesh K. , Priyadarshini Shweta , Swamy Jaganath , Mehta Mridul 
Published: February 2024

ABSTRACTBackgroundType 2 diabetes (DM2) is a leading cause of premature morbidity and mortality globally and affects more than 100 million people in the world’s most populous country, India. Nutrition is a critical and evidence-based component of effective blood sugar control and most dietary advice emphasizes carbohydrate and calorie reduction. Emerging global evidence demonstrates marked inter-individual differences in post-prandial glucose response (PPGR) although no such data exists in India and prior studies have primarily evaluated PPGR variation in individuals without diabetes.MethodsThis prospective cohort study seeks to characterize the PPGR variability in Indians with diabetes and to identify factors associated with these differences. Adults with type 2 diabetes and a hemoglobin A1c ≥7 are being enrolled from 14 sites around India. Subjects wear a continuous glucose monitor, eat a series of standardized meals, and record all free-leaving foods, activities, and medication use for a 14-day period. The study’s primary outcome is PPGR, calculated as the incremental area under the curve 2 hours after each logged meal.DiscussionThis study will provide the first large scale examination variability in blood sugar responses to food in India and will be among the first to estimate PPGR variability for individuals with DM2 in any jurisdiction. Results from our study will generate data to facilitate the creation of machine learning models to predict individual PPGR responses and to facilitate the prescription of personalized diets for individuals with DM2.
Insights into the nature of the microalgal toxins from theChrysochromulina leadbeateriblooms in Northern Norwegian fjords
Authors: Wang Xinhui , Fon Mathias , Andersen Aaron J.C. , Solhaug Anita , Ingebrigtsen Richard A. , Samdal Ingunn A. , Uhlig Silvio , Miles Christopher O. , Edvardsen Bente , Larsen Thomas O. 
Published: February 2024

AbstractIn May–June 2019, the microalgaChrysochromulina leadbeatericaused a massive fish-killing event in several fjords in Northern Norway, resulting in the largest direct impact ever on aquaculture in northern Europe due to toxic algae. Motivated by the fact that no algal toxins have previously been described fromC. leadbeateri, we set out to investigate the chemical nature and toxicity of secondary metabolites in extracts of two strains (UIO 393, UIO 394) isolated from the 2019 bloom, as well as one older strain (UIO 035) isolated during a bloom in Northern Norway in 1991. Initial LC–DAD–MS/MS-based molecular networking analysis of the crude MeOH extracts of the cultivated strains showed that their profiles of small organic molecules, including a large number of known lipids, were very similar, suggesting that the same class of toxin(s) were likely the causative agents of the two harmful algal bloom (HAB) events. Next, bioassay-guided fractionation using the RTgill-W1 cell line and metabolomics analysis pointed to a major compound affording [M+H]+ions atm/z1399.8333 as a possible toxin, corresponding to a compound with the formula C67H127ClO27. Moreover, our study unveiled a series of minor analogues exhibiting distinct patterns of chlorination and sulfation, together defining a new family of compounds, which we propose to name leadbeaterins. Remarkably, these suspected toxins were detectedin situin samples collected during the 2019 bloom close to Tromsø, thereby substantiating their likely role in fish kills. The elemental compositions of the putativeC. leadbeateriichthyotoxins strongly indicate them to be long linear polyhydroxylated polyketides, structurally similar to sterolysins, reported from a number of dinoflagellates.
The Genotype and Phenotypes in Families (GPF) platform manages the large and complex data at SFARI
Authors: Chorbadjiev Liubomir , Cokol Murat , Weinstein Zohar , Shi Kevin , Fleisch Chris , Dimitrov Nikolay , Mladenov Svetlin , Xu Simon , Hall Jake , Ford Steven , Lee Yoon-ha , Yamrom Boris , Marks Steven , Munoz Adriana , Lash Alex , Volfovsky Natalia , Iossifov Ivan 
Published: February 2024

AbstractThe exploration of genotypic variants impacting phenotypes is a cornerstone in genetics research. The emergence of vast collections containing deeply genotyped and phenotyped families has made it possible to pursue the search for variants associated with complex diseases. However, managing these large-scale datasets requires specialized computational tools tailored to organize and analyze the extensive data. GPF (Genotypes and Phenotypes in Families) is an open-source platform (https://github.com/iossifovlab/gpf) that manages genotypes and phenotypes derived from collections of families. The GPF interface allows interactive exploration of genetic variants, enrichment analysis forde novomutations, and phenotype/genotype association tools. In addition, GPF allows researchers to share their data securely with the broader scientific community. GPF is used to disseminate two large-scale family collection datasets (SSC, SPARK) for the study of autism funded by the SFARI foundation. However, GPF is versatile and can manage genotypic data from other small or large family collections. Our GPF-SFARI GPF instance (https://gpf.sfari.org/) provides protected access to comprehensive genotypic and phenotypic data for the SSC and SPARK. In addition, GPF-SFARI provides public access to an extensive collection ofde novomutations identified in individuals with autism and related disorders and to gene-level statistics of the protected datasets characterizing the genes’ roles in autism. Here, we highlight the primary features of GPF within the context of GPF-SFARI.
Model-guided metabolic engineering of curcuminoid Production inPseudomonas putida
Authors: Martin-Pascual Maria , Moreno-Paz Sara , van Rosmalen Rik P. , Dorigo Julia , Demaria Francesca , van Kranenburg Richard , Martins dos Santos Vitor A.P. , Suarez-Diez Maria 
Published: February 2024

AbstractProduction of value-added, plant-derived compounds in microbes increasingly attracts commercially interest in food and pharmaceutical industries. However, plant metabolic pathways are complex, require a robust balance of enzymes, cofactors, ATP and other metabolites, and often result in low production when transplanted to bacteria. This is exemplified by the biosynthesis of curcuminoids from theCurcuma longaplant. Here, we combine dynamic pathway modeling, systematic testing of isoenzymes, and the optimization of gene expression levels and substrate concentrations for the biosynthesis of curcuminoids inPseudomonas putida, leading to unprecedented conversion rates of caffeic acid and tyrosine to curcumin. The development of kinetic ensemble models guided the design of production strains, emphasizing the necessity of high relative expression ofc3h, curs2anddcsand, the low relative expression oftal, comt, ccoaomt, and4cl4. This optimization resulted in a strain that achieved a 10.8 ±1.8% of the maximum theoretical yield of curcumin from tyrosine. This represents a 4.1-fold increase in production efficiency and the highest yield reported to date, demonstrating the potential ofP. putidaas a promising platform for curcuminoid production. Our findings highlight the effectiveness of our strategy not only in the advances in the production of curcuminoids but also in setting a framework for the biosynthesis of other complex compounds.
The utilization of contraceptives by teenagers in Lesotho: A descriptive cross-sectional survey
Authors: Khiba Rets’elisitsoe , Nyangu Isabel 
Published: February 2024

AbstractBackgroundContraceptive service provision to teenagers prevents pregnancy and childbirth complications, which are the leading causes of death among teenage girls globally.AimThe study aimed to assess how teenagers utilized the contraceptive services provided at selected healthcare facilities in Lesotho.MethodologyA quantitative descriptive cross-sectional design was used to collect data from 194 teenagers who were conveniently sampled from two health facilities in Berea district, Lesotho. Permission to conduct the study was sought and granted from the Ministry of Health and written informed consent was sought from the participants before they completed selfadministered questionnaires. Data was captured and analyzed using the SPSS version 24.ResultsMost (64.5%) of the teenagers knew that contraceptives prevented pregnancy, whilst a fifth of them referred to them as the prevention of HIV or sexually transmitted infections. Utilization of contraceptives was reported by just over a third and about two-fifths were able to access them at the public health clinics daily. Injectables and condoms were the most preferred methods used by teenagers. Factors that enhanced contraceptive usage included nurses’ friendliness, as well as an understanding of why teenagers are not fully utilizing the contraceptive services provided. Most teenagers were found not to be utilizing contraceptive services due to the belief that they led to weight gain and unsatisfactory intercourse.ConclusionThe study findings provided a need for and importance of contraceptive service provision to teenagers, including the need for education to improve their understanding and reduce myths.Implications for nursing managementHealthcare service providers need to improve access to contraceptive services to improve their utilization by teenagers.
An increasing Arctic-boreal CO2sink offset by wildfires and source regions
Authors: Virkkala Anna-Maria , Rogers Brendan M. , Watts Jennifer D. , Arndt Kyle A. , Potter Stefano , Wargowsky Isabel , Schuur Edward A. G. , See Craig , Mauritz Marguerite , Boike Julia , Bret-Harte Syndonia M. , Burke Eleanor J. , Burrell Arden , Chae Namyi , Chatterjee Abhishek , Chevallier Frederic , Christensen Torben R. , Commane Roisin , Dolman Han , Elberling Bo , Emmerton Craig A. , Euskirchen Eugenie S. , Feng Liang , Goeckede Mathias , Grelle Achim , Helbig Manuel , Holl David , Järveoja Järvi , Kobayashi Hideki , Kutzbach Lars , Liu Junjie , Liujkx Ingrid , López-Blanco Efrén , Lunneberg Kyle , Mammarella Ivan , Marushchak Maija E. , Mastepanov Mikhail , Matsuura Yojiro , Maximov Trofim , Merbold Lutz , Meyer Gesa , Nilsson Mats B. , Niwa Yosuke , Oechel Walter , Park Sang-Jong , Parmentier Frans-Jan W. , Peichl Matthias , Peters Wouter , Petrov Roman , Quinton William , Rödenbeck Christian , Sachs Torsten , Schulze Christopher , Sonnentag Oliver , St.Louis Vincent , Tuittila Eeva-Stiina , Ueyama Masahito , Varlagin Andrej , Zona Donatella , Natali Susan M. 
Published: February 2024

AbstractThe Arctic-Boreal Zone (ABZ) is rapidly warming, impacting its large soil carbon stocks. We use a new compilation of terrestrial ecosystem CO2fluxes, geospatial datasets and random forest models to show that although the ABZ was an increasing terrestrial CO2sink from 2001 to 2020 (mean ± standard deviation in net ecosystem exchange: −548 ± 140 Tg C yr-1; trend: −14 Tg C yr-1, p<0.001), more than 30% of the region was a net CO2source. Tundra regions may have already started to function on average as CO2sources, demonstrating a critical shift in carbon dynamics. After factoring in fire emissions, the increasing ABZ sink was no longer statistically significant (budget: −319 ± 140 Tg C yr-1; trend: −9 Tg C yr-1), with the permafrost region becoming CO2neutral (budget: −24 ± 123 Tg C yr-1; trend: −3 Tg C yr-1), underscoring the importance of fire in this region.
Prediction of protein-carbohydrate binding sites from protein primary sequence
Authors: Nawar Quazi Farah , Nafi Md Muhaiminul Islam , Islam Tasnim Nishat , Rahman M Saifur 
Published: February 2024

AbstractA protein is a large complex macromolecule that has a crucial role in performing most of the work in cells and tissues. It is made up of one or more long chains of amino acid residues. Another important biomolecule, after DNA and protein, is carbohydrate. Carbohydrates interact with proteins to run various biological processes. Several biochemical experiments exist to learn the protein-carbohydrate interactions, but they are expensive, time consuming and challenging. Therefore developing computational techniques for effectively predicting protein-carbohydrate binding interactions from protein primary sequence has given rise to a prominent new field of research. In this study, we proposeStackCBEmbed, an ensemble machine learning model to effectively classify protein-carbohydrate binding interactions at residue level. StackCBEmbed combines traditional sequence-based features along with features derived from a pre-trained transformer-based protein language model. To the best of our knowledge, ours is the first attempt to apply protein language model in predicting protein-carbohydrate binding interactions. StackCBEmbed achieved sensitivity, specificity and balanced accuracy scores of 0.730, 0.821, 0.776 and 0.666, 0.818, 0.742 in two separate independent test sets. This performance is superior compared to the earlier prediction models benchmarked in the same datasets. We thus hope that StackCBEmbed will discover novel protein-carbohydrate interactions and help advance the related fields of research. StackCBEmbed is freely available as python scripts athttps://github.com/nafiislam/StackCBEmbed.
Federated Learning for Predicting Compound Mechanism of Action Based on Image-data from Cell Painting
Authors: Ju Li , Hellander Andreas , Spjuth Ola 
Published: February 2024

ABSTRACTHaving access to sufficient data is essential in order to train accurate machine learning models, but much data is not publicly available. In drug discovery this is particularly evident, as much data is withheld at pharmaceutical companies for various reasons. Federated Learning (FL) aims at training a joint model between multiple parties but without disclosing data between the parties. In this work, we leverage Federated Learning to predict compound Mechanism of Action (MoA) using fluorescence image data from cell painting. Our study evaluates the effectiveness and efficiency of FL, comparing to non-collaborative and data-sharing collaborative learning in diverse scenarios. Specifically, we investigate the impact of data heterogeneity across participants on MoA prediction, an essential concern in real-life applications of FL, and demonstrate the benefits for all involved parties. This work highlights the potential of federated learning in multi-institutional collaborative machine learning for drug discovery and assessment of chemicals, offering a promising avenue to overcome data-sharing constraints.