bluuebunny/crossref_metadata_2025_split huggingface dataset:
Combined maternal central adiposity measures in relation to infant birth size
Authors: Lindberger Emelie , Ahlsson Fredrik , Junus Katja , Wikström Anna-Karin , Sundström Poromaa Inger 
Published: January 2024

AbstractImprovement of prenatal identification of large-for-gestational-age (LGA) infants could lower the risk for adverse outcomes. Therefore, we sought to evaluate the association of a combination of maternal waist circumference (WC) and abdominal fat depths with infant birth size. A cohort study including 1240 women was performed between 2015 and 2018 at Uppsala University Hospital, Sweden. Maternal WC was measured at the first antenatal visit, and visceral (VF) and subcutaneous (SCF) fat depths by ultrasound at the second-trimester anomaly scan. Waist circumference, VF, and SCF were categorized as low or high (cut-offs WC ≥ 88 cm, VF ≥ 54 mm, SCF ≥ 21 mm). Outcomes were birth weight standard deviation score (BWSDS) and LGA (BWSDS > 90th and > 97th percentile). Secondary outcome was small-for-gestational-age (SGA, BWSDS < 10th and < 3rd percentile). Univariate analysis of variance and logistic regression analyses were performed adjusted for maternal weight, height, parity, smoking, country of birth, pregestational diabetes, and chronic hypertension. For both high and low WC, high VF was positively associated with BWSDS and LGA. There was no association with SGA. The results did not demonstrate any value of the combination of WC and fat depth measures in predicting infant birth size but suggested VF as a marker for large infants.
A twin convolutional neural network with hybrid binary optimizer for multimodal breast cancer digital image classification
Authors: Oyelade Olaide N. , Irunokhai Eric Aghiomesi , Wang Hui 
Published: January 2024

AbstractThere is a wide application of deep learning technique to unimodal medical image analysis with significant classification accuracy performance observed. However, real-world diagnosis of some chronic diseases such as breast cancer often require multimodal data streams with different modalities of visual and textual content. Mammography, magnetic resonance imaging (MRI) and image-guided breast biopsy represent a few of multimodal visual streams considered by physicians in isolating cases of breast cancer. Unfortunately, most studies applying deep learning techniques to solving classification problems in digital breast images have often narrowed their study to unimodal samples. This is understood considering the challenging nature of multimodal image abnormality classification where the fusion of high dimension heterogeneous features learned needs to be projected into a common representation space. This paper presents a novel deep learning approach combining a dual/twin convolutional neural network (TwinCNN) framework to address the challenge of breast cancer image classification from multi-modalities. First, modality-based feature learning was achieved by extracting both low and high levels features using the networks embedded with TwinCNN. Secondly, to address the notorious problem of high dimensionality associated with the extracted features, binary optimization method is adapted to effectively eliminate non-discriminant features in the search space. Furthermore, a novel method for feature fusion is applied to computationally leverage the ground-truth and predicted labels for each sample to enable multimodality classification. To evaluate the proposed method, digital mammography images and digital histopathology breast biopsy samples from benchmark datasets namely MIAS and BreakHis respectively. Experimental results obtained showed that the classification accuracy and area under the curve (AUC) for the single modalities yielded 0.755 and 0.861871 for histology, and 0.791 and 0.638 for mammography. Furthermore, the study investigated classification accuracy resulting from the fused feature method, and the result obtained showed that 0.977, 0.913, and 0.667 for histology, mammography, and multimodality respectively. The findings from the study confirmed that multimodal image classification based on combination of image features and predicted label improves performance. In addition, the contribution of the study shows that feature dimensionality reduction based on binary optimizer supports the elimination of non-discriminant features capable of bottle-necking the classifier.
Circulating microRNAs from plasma as preclinical biomarkers of epileptogenesis and epilepsy
Authors: Szydlowska Kinga , Bot Anna , Nizinska Karolina , Olszewski Maciej , Lukasiuk Katarzyna 
Published: January 2024

AbstractEpilepsy frequently develops as a result of brain insult; however, there are no tools allowing to predict which patients suffering from trauma will eventually develop epilepsy. microRNAs are interesting candidates for biomarkers, as several of them have been described to change their levels in the brains, and in the plasma of epileptic subjects. This study was conducted to evaluate the usefulness of plasma miRNAs as epileptogenesis/epilepsy biomarkers. In our studies, we used a rat model of temporal lobe epilepsy. An epileptogenic insult was status epilepticus evoked by stimulation of the left lateral nucleus of the amygdala. Next, animals were continuously video and EEG monitored for 3 months. Blood was collected at 14, 30, 60, and 90 days after stimulation. Blood plasma was separated and miRNA levels were analyzed. We compared miRNA levels between sham-operated and stimulated animals, and between animals with high and low numbers of seizures. We propose three miRNAs that could be biomarkers of epilepsy: miR-671, miR-9a-3p and miR-7a-5p. According to us, miR-206-5p is a potential biomarker of epileptogenesis, and miR-221-3p is a potential biomarker of epilepsy severity. We think that these five miRNAs can be considered in the future as potential treatment targets.
Immune and stromal transcriptional patterns that influence the outcome of classic Hodgkin lymphoma
Authors: Menéndez Victoria , Solórzano José L. , García-Cosío Mónica , Alonso-Alonso Ruth , Rodríguez Marta , Cereceda Laura , Fernández Sara , Díaz Eva , Montalbán Carlos , Estévez Mónica , Piris Miguel A. , García Juan F. 
Published: January 2024

AbstractClassic Hodgkin lymphoma (cHL) is characterized by a rich immune microenvironment as the main tumor component. It involves a broad range of cell populations, which are largely unexplored, even though they are known to be essential for growth and survival of Hodgkin and Reed–Sternberg cells. We profiled the gene expression of 25 FFPE cHL samples using NanoString technology and resolved their microenvironment compositions using cell-deconvolution tools, thereby generating patient-specific signatures. The results confirm individual immune fingerprints and recognize multiple clusters enriched in refractory patients, highlighting the relevance of: (1) the composition of immune cells and their functional status, including myeloid cell populations (M1-like, M2-like, plasmacytoid dendritic cells, myeloid-derived suppressor cells, etc.), CD4-positive T cells (exhausted, regulatory, Th17, etc.), cytotoxic CD8 T and natural killer cells; (2) the balance between inflammatory signatures (such as IL6, TNF, IFN-γ/TGF-β) and MHC-I/MHC-II molecules; and (3) several cells, pathways and genes related to the stroma and extracellular matrix remodeling. A validation model combining relevant immune and stromal signatures identifies patients with unfavorable outcomes, producing the same results in an independent cHL series. Our results reveal the heterogeneity of immune responses among patients, confirm previous findings, and identify new functional phenotypes of prognostic and predictive utility.
Indirect impacts of a highway on movement behavioral states of a threatened tortoise and implications for landscape connectivity
Authors: Harju Seth , Cambrin Scott , Berg Jodi 
Published: January 2024

AbstractRoads have often been identified as barriers to the movement of free-ranging animals. However, whether restoration of landscape connectivity across roadways can mitigate barriers to movement is insufficiently understood in light of indirect effects of roads on wildlife movement. We GPS-tagged free-ranging Mojave desert tortoises (Gopherus agassizii) to quantify movement behavioral states using hidden Markov models in relation to a major highway and to document use of existing, permeable culverts. We then used the observed movement behaviors to parameterize simulations of tortoise movement to evaluate alternative culvert designs and placements for enhancing connectivity across the roadway. Tortoises were most active during mid-day, in warm temperatures, and when close to the highway. The highway affected transition probabilities between movement states, as females were more likely than males to switch to an energy-demanding traveling movement state, remain in that state, and move farther than usual within that state. In contrast, males were more likely than females to continue in the low-energy resting state when close to the highway, but if traveling, to travel farther than usual. We observed two highway crossings by a tagged tortoise, which was a higher rate of crossing than in simulated tortoises. Simulated crossing rates increased with culvert size and culvert density, and size and density appeared more important for crossing than if culverts were placed singly or in pairs. Existing culvert densities across the region appeared potentially sufficient for long-term genetic connectivity, but only if retrofitted to allow for tortoise access and passing. We concluded that existing highway traffic may indirectly depress tortoise populations adjacent to the highway, particularly via negative impacts to female movements, and that existing culverts in washes should be retrofitted to allow for periodic tortoise crossings to improve structural connectivity for occasional passage.
$$\mathscr {S}\mathscr {E}\mathscr {I}\mathscr {A}\mathscr {R}\mathscr {S}$$ model for analyzing $$\mathscr {C}\mathscr {O}\mathscr {V}\mathscr {I}\mathscr {D}$$-19 pandemic process via $$\uppsi $$-Caputo fractional derivative and numerical simulation
Authors: Mohammadaliee Behnam , Roomi Vahid , Samei Mohammad Esmael 
Published: January 2024

AbstractThe objective of this study is to develop the $$\mathscr {S}\mathscr {E}\mathscr {I}\mathscr {A}\mathscr {R}\mathscr {S}$$

S
E
I
A
R
S

epidemic model for $$\mathscr {C}\mathscr {O}\mathscr {V}\mathscr {I}\mathscr {D}$$

C
O
V
I
D

-$${\textbf {19}}$$
19
utilizing the $$\uppsi $$
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-Caputo fractional derivative. The reproduction number ($$\breve{\mathscr {R}}_0$$

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0

) is calculated utilizing the next generation matrix method. The equilibrium points of the model are computed, and both the local and global stability of the disease-free equilibrium point are demonstrated. Sensitivity analysis is discussed to describe the importance of the parameters and to demonstrate the existence of a unique solution for the model by applying a fixed point theorem. Utilizing the fractional Euler procedure, an approximate solution to the model is obtained. To study the transmission dynamics of infection, numerical simulations are conducted by using MatLab. Both numerical methods and simulations can provide valuable insights into the behavior of the system and help in understanding the existence and properties of solutions. By placing the values $$\texttt{t}$$
t
, $$\ln (\texttt{t})$$

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(
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)

and $$\sqrt{\texttt{t}}$$

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instead of $$\uppsi $$
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, the derivatives of the Caputo and Caputo–Hadamard and Katugampola appear, respectively, to compare the results of each with real data. Besides, these simulations specifically with different fractional orders to examine the transmission dynamics. At the end, we come to the conclusion that the simulation utilizing Caputo derivative with the order of 0.95 shows the prevalence of the disease better. Our results are new which provide a good contribution to the current research on this field of research.
Warming-induced hydrothermal anomaly over the Earth’s three Poles amplifies concurrent extremes in 2022
Authors: Zhang Jie , Chen Haishan , Fang Xinyu , Yin Zhicong , Hu Rui 
Published: January 2024

AbstractClimate warming is causing an increase in the frequency and severity of heatwaves and extreme precipitation events, posing a threat to both socioeconomic stability and human lives. In 2022, five record-breaking heatwaves and floods occurred, it is suggested a new concurrent extreme feature in the study, which is linked to the three Poles warming and ice/snow melting. The independent and synergistic effect of the three Poles warming has led to the poleward movement of westerly jet streams and amplified Rossby waves, as well as the expansion of subtropical highs in both poleward and zonal directions. Consequently, these have intensified heatwaves in the mid-high latitudes and extreme precipitation events in the Asian regions, combined with a southward displacement and weakening of the inter-hemispheric Asian summer monsoon circulations, which are response to poleward of the mid-latitude and subtropical circulations and the hydrothermal effects of the three Poles. This study highlights the significance of three Poles warming in driving amplified concurrent extremes across tropical and extratropical regions, particularly in 2022. These findings provide valuable insights into the role of snow/ice melting and related hydrothermal factors in global climate predictions and disaster prevention efforts.
Cancer drug sensitivity prediction from routine histology images
Authors: Dawood Muhammad , Vu Quoc Dang , Young Lawrence S. , Branson Kim , Jones Louise , Rajpoot Nasir , Minhas Fayyaz ul Amir Afsar 
Published: January 2024

AbstractDrug sensitivity prediction models can aid in personalising cancer therapy, biomarker discovery, and drug design. Such models require survival data from randomised controlled trials which can be time consuming and expensive. In this proof-of-concept study, we demonstrate for the first time that deep learning can link histological patterns in whole slide images (WSIs) of Haematoxylin & Eosin (H&E) stained breast cancer sections with drug sensitivities inferred from cell lines. We employ patient-wise drug sensitivities imputed from gene expression-based mapping of drug effects on cancer cell lines to train a deep learning model that predicts patients’ sensitivity to multiple drugs from WSIs. We show that it is possible to use routine WSIs to predict the drug sensitivity profile of a cancer patient for a number of approved and experimental drugs. We also show that the proposed approach can identify cellular and histological patterns associated with drug sensitivity profiles of cancer patients.
STEM enables mapping of single-cell and spatial transcriptomics data with transfer learning
Authors: Hao Minsheng , Luo Erpai , Chen Yixin , Wu Yanhong , Li Chen , Chen Sijie , Gao Haoxiang , Bian Haiyang , Gu Jin , Wei Lei , Zhang Xuegong 
Published: January 2024

AbstractProfiling spatial variations of cellular composition and transcriptomic characteristics is important for understanding the physiology and pathology of tissues. Spatial transcriptomics (ST) data depict spatial gene expression but the currently dominating high-throughput technology is yet not at single-cell resolution. Single-cell RNA-sequencing (SC) data provide high-throughput transcriptomic information at the single-cell level but lack spatial information. Integrating these two types of data would be ideal for revealing transcriptomic landscapes at single-cell resolution. We develop the method STEM (SpaTially aware EMbedding) for this purpose. It uses deep transfer learning to encode both ST and SC data into a unified spatially aware embedding space, and then uses the embeddings to infer SC-ST mapping and predict pseudo-spatial adjacency between cells in SC data. Semi-simulation and real data experiments verify that the embeddings preserved spatial information and eliminated technical biases between SC and ST data. We apply STEM to human squamous cell carcinoma and hepatic lobule datasets to uncover the localization of rare cell types and reveal cell-type-specific gene expression variation along a spatial axis. STEM is powerful for mapping SC and ST data to build single-cell level spatial transcriptomic landscapes, and can provide mechanistic insights into the spatial heterogeneity and microenvironments of tissues.
Exploring a general multi-pronged activation strategy for natural product discovery in Actinomycetes
Authors: Tay Dillon W. P. , Tan Lee Ling , Heng Elena , Zulkarnain Nadiah , Ching Kuan Chieh , Wibowo Mario , Chin Elaine Jinfeng , Tan Zann Yi Qi , Leong Chung Yan , Ng Veronica Wee Pin , Yang Lay Kien , Seow Deborah C. S. , Lim Yi Wee , Koh Winston , Koduru Lokanand , Kanagasundaram Yoganathan , Ng Siew Bee , Lim Yee Hwee , Wong Fong Tian 
Published: January 2024

AbstractNatural products possess significant therapeutic potential but remain underutilized despite advances in genomics and bioinformatics. While there are approaches to activate and upregulate natural product biosynthesis in both native and heterologous microbial strains, a comprehensive strategy to elicit production of natural products as well as a generalizable and efficient method to interrogate diverse native strains collection, remains lacking. Here, we explore a flexible and robust integrase-mediated multi-pronged activation approach to reliably perturb and globally trigger antibiotics production in actinobacteria. Across 54 actinobacterial strains, our approach yielded 124 distinct activator-strain combinations which consistently outperform wild type. Our approach expands accessible metabolite space by nearly two-fold and increases selected metabolite yields by up to >200-fold, enabling discovery of Gram-negative bioactivity in tetramic acid analogs. We envision these findings as a gateway towards a more streamlined, accelerated, and scalable strategy to unlock the full potential of Nature’s chemical repertoire.
The structure of a tetrameric septin complex reveals a hydrophobic element essential for NC-interface integrity
Authors: Grupp Benjamin , Denkhaus Lukas , Gerhardt Stefan , Vögele Matthis , Johnsson Nils , Gronemeyer Thomas 
Published: January 2024

AbstractThe septins of the yeast Saccharomyces cerevisiae assemble into hetero-octameric rods by alternating interactions between neighboring G-domains or N- and C-termini, respectively. These rods polymerize end to end into apolar filaments, forming a ring beneath the prospective new bud that expands during the cell cycle into an hourglass structure. The hourglass finally splits during cytokinesis into a double ring. Understanding these transitions as well as the plasticity of the higher order assemblies requires a detailed knowledge of the underlying structures. Here we present the first X-ray crystal structure of a tetrameric Shs1-Cdc12-Cdc3-Cdc10 complex at a resolution of 3.2 Å. Close inspection of the NC-interfaces of this and other septin structures reveals a conserved contact motif that is essential for NC-interface integrity of yeast and human septins in vivo and in vitro. Using the tetrameric structure in combination with AlphaFold-Multimer allowed us to propose a model of the octameric septin rod.
Atomic-scale manipulation of buried graphene–silicon carbide interface by local electric field
Authors: Kolmer Marek , Hall Joseph , Chen Shen , Roberts Samuel , Fei Zhe , Han Yong , Tringides Michael C. 
Published: January 2024

AbstractPrecision of scanning tunneling microscopy (STM) enables control of matter at scales of single atoms. However, transition from atomic-scale manipulation strategies to practical devices encounters fundamental problems in protection of the designer structures formed atop the surface. In this context, STM manipulation of subsurface structures on technologically relevant materials is encouraging. Here, we propose a material platform and protocols for precise manipulation of a buried graphene interface. We show that an electric field from the STM tip reversibly controls breaking and restoring of covalent bonds between the graphene buffer layer and the SiC substrate. The process involves charge redistribution at the atomically sharp interface plane under the epitaxial graphene layer(s). This buried manipulation platform is laterally defined by unit cells from the corresponding (6×6)SiC moiré lattice of the epitaxial graphene. Local and reversible electric-field-induced patterning of graphene heterostructures from the bottom interface creates an alternative architecture concept for their applications.
Study on the influence of mechanism dispersion on transient recovery voltage distribution of modular DC vacuum circuit breakers
Authors: Huang Chongyang , Yin Yongshuai , Liu Shuxin , Du Hanwen , Shan Zengyu , Cao Yundong , Yang Shurui 
Published: January 2024

AbstractA simulation analysis and an experiment are carried out to investigate how the gap difference between the breaks of a direct current vacuum circuit breakers with multi‐breaks (MB‐DC VCB) caused by the mechanism dispersion of the breaker influences the distribution of TRV among the breaks. An interruption model of MB‐DC VCB, combining the continuous transition model, is established to analyse the rising rate of transient recovery voltage and the dielectric strength recovery speed of the breaks for MB‐DC VCB under different gap difference conditions. Based on the experimental platform of dual break DC vacuum circuit breaker breaking, the correctness of the simulation model is verified on a DC VCB with the double‐breaks interruption experimental platform. Moreover, a model is applied to the non‐synchronous interruption simulation of a DC VCB with three‐breaks. The relationship between the TRVs of the breaks under different gap difference conditions is analysed using the comparative analysis method, obtaining the maximum gap difference at the moment of breaking failure. The results of this study show that large‐gap breaks have a higher TRV than small‐gap breaks (the fracture of the action delay module), with double fractures reaching 1.4 times and triple fractures reaching a maximum of 1.52 times; the ability of small‐gap breaks to withstand TRV is weak, giving rise to re‐breakdown or even interruption failure; as the number of fractures increases, the maximum gap difference also increases. Improving the synchronous interruption ability of the MB‐DC VCB is conducive to improving the interruption performance and interruption success rate of this type of a circuit breaker.