bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of ulotaront on brain circuits of reward, working memory, and emotion processing in healthy volunteers with high or low schizotypy
Authors: Perini Francesca , Nazimek Jadwiga Maria , Mckie Shane , Capitão Liliana P. , Scaife Jessica , Pal Deepa , Browning Michael , Dawson Gerard R. , Nishikawa Hiroyuki , Campbell Una , Hopkins Seth C. , Loebel Antony , Elliott Rebecca , Harmer Catherine J. , Deakin Bill , Koblan Kenneth S. 
Published: August 2023

AbstractUlotaront, a trace amine-associated receptor 1 (TAAR1) and serotonin 5-HT1A receptor agonist without antagonist activity at dopamine D2 or the serotonin 5-HT2A receptors, has demonstrated efficacy in the treatment of schizophrenia. Here we report the phase 1 translational studies that profiled the effect of ulotaront on brain responses to reward, working memory, and resting state connectivity (RSC) in individuals with low or high schizotypy (LS or HS). Participants were randomized to placebo (n = 32), ulotaront (50 mg; n = 30), or the D2 receptor antagonist amisulpride (400 mg; n = 34) 2 h prior to functional magnetic resonance imaging (fMRI) of blood oxygen level-dependent (BOLD) responses to task performance. Ulotaront increased subjective drowsiness, but reaction times were impaired by less than 10% and did not correlate with BOLD responses. In the Monetary Incentive Delay task (reward processing), ulotaront significantly modulated striatal responses to incentive cues, induced medial orbitofrontal responses, and prevented insula activation seen in HS subjects. In the N-Back working memory task, ulotaront modulated BOLD signals in brain regions associated with cognitive impairment in schizophrenia. Ulotaront did not show antidepressant-like biases in an emotion processing task. HS had significantly reduced connectivity in default, salience, and executive networks compared to LS participants and both drugs reduced this difference. Although performance impairment may have weakened or contributed to the fMRI findings, the profile of ulotaront on BOLD activations elicited by reward, memory, and resting state is compatible with an indirect modulation of dopaminergic function as indicated by preclinical studies. This phase 1 study supported the subsequent clinical proof of concept trial in people with schizophrenia.Clinical trial registration: Registry# and URL: ClinicalTrials.gov NCT01972711, https://clinicaltrials.gov/ct2/show/NCT01972711
Sensing the DNA-mismatch tolerance of catalytically inactive Cas9 via barcoded DNA nanostructures in solid-state nanopores
Authors: Sandler Sarah E. , Weckman Nicole E. , Yorke Sarah , Das Akashaditya , Chen Kaikai , Gutierrez Richard , Keyser Ulrich F. 
Published: August 2023

AbstractSingle-molecule quantification of the strength and sequence specificity of interactions between proteins and nucleic acids would facilitate the probing of protein–DNA binding. Here we show that binding events between the catalytically inactive Cas9 ribonucleoprotein and any pre-defined short sequence of double-stranded DNA can be identified by sensing changes in ionic current as suitably designed barcoded linear DNA nanostructures with Cas9-binding double-stranded DNA overhangs translocate through solid-state nanopores. We designed barcoded DNA nanostructures to study the relationships between DNA sequence and the DNA-binding specificity, DNA-binding efficiency and DNA-mismatch tolerance of Cas9 at the single-nucleotide level. Nanopore-based sensing of DNA-barcoded nanostructures may help to improve the design of efficient and specific ribonucleoproteins for biomedical applications, and could be developed into sensitive protein-sensing assays.
Spatially resolved protein map of intact human cytomegalovirus virions
Authors: Bogdanow Boris , Gruska Iris , Mühlberg Lars , Protze Jonas , Hohensee Svea , Vetter Barbara , Bosse Jens B. , Lehmann Martin , Sadeghi Mohsen , Wiebusch Lüder , Liu Fan 
Published: August 2023

AbstractHerpesviruses assemble large enveloped particles that are difficult to characterize structurally due to their size, fragility and complex multilayered proteome with partially amorphous nature. Here we used crosslinking mass spectrometry and quantitative proteomics to derive a spatially resolved interactome map of intact human cytomegalovirus virions. This enabled the de novo allocation of 32 viral proteins into four spatially resolved virion layers, each organized by a dominant viral scaffold protein. The viral protein UL32 engages with all layers in an N-to-C-terminal radial orientation, bridging nucleocapsid to viral envelope. We observed the layer-specific incorporation of 82 host proteins, of which 39 are selectively recruited. We uncovered how UL32, by recruitment of PP-1 phosphatase, antagonizes binding to 14-3-3 proteins. This mechanism assures effective viral biogenesis, suggesting a perturbing role of UL32-14-3-3 interaction. Finally, we integrated these data into a coarse-grained model to provide global insights into the native configuration of virus and host protein interactions inside herpesvirions.
Three-dimensional topology dataset of folded radar stratigraphy in northern Greenland
Authors: Franke Steven , Bons Paul D. , Streng Kyra , Mundel Felicitas , Binder Tobias , Weikusat Ilka , Bauer Catherine C. , Paden John D. , Dörr Nils , Helm Veit , Steinhage Daniel , Eisen Olaf , Jansen Daniela 
Published: August 2023

AbstractWe present a dataset of reconstructed three-dimensional (3D) englacial stratigraphic horizons in northern Greenland. The data cover four different regions representing key ice-dynamic settings in Greenland: (i) the onset of Petermann Glacier, (ii) a region upstream of the 79° North Glacier (Nioghalvfjerdsbræ), near the northern Greenland ice divide, (iii) the onset of the Northeast Greenland Ice Stream (NEGIS) and (iv) a 700 km wide region extending across the central ice divide over the entire northern part of central Greenland. In this paper, we promote the advantages of a 3D perspective of deformed englacial stratigraphy and explain how 3D horizons provide an improved basis for interpreting and reconstructing the ice-dynamic history. The 3D horizons are provided in various formats to allow a wide range of applications and reproducibility of results.
SPRC19: A Database of State Policy Responses to COVID-19 in the United States
Authors: Boehmke Frederick J. , Desmarais Bruce A. , Eastman Abbie , Grassel Isabelle , Harden Jeffrey J. , Harper Samuel , Kaboli Liam , Ko Hyein , Oster Elisabeth , Saunders Tracee M. 
Published: August 2023

AbstractSPRC19 is a new database that seeks to capture a wide range of state policy actions in response to COVID-19 in the United States. Since March 2020 we have monitored state governments’ and multi-state associations’ websites for executive orders, agency rules, new legislation, and court decisions. We categorize each policy action into one of 206 distinct policies, then document the branch of government, source document, announcement date, implementation date, and expiration date (if applicable). We also record whether the action represents the introduction of a new policy or the expansion or contraction of an existing policy. The current release of SPRC19, v3.0, captures over 13,000 distinct policy actions through April 2020, which constitutes thousands more actions than similar resources over the same time period.
Machine learning and statistical models for analyzing multilevel patent data
Authors: Qi Sunyun , Zhang Yu , Gu Hua , Zhu Fei , Gao Meiying , Liang Hongxiao , Zhang Qifeng , Gao Yanchao 
Published: August 2023

AbstractA recent surge of patent applications among public hospitals in China has aroused significant research interest. A country’s healthcare innovation capacity can be measured by its number of patents. This paper explores the link between the number of patents and ten independent variables. Multicollinearity was carefully detected and removed by using the variable selection method and LASSO regression, respectively. The Poisson model and the negative binomial model were proposed to analyze the patent data. Three goodness of fit tests, the Pearson test, the deviance test, and the DHARMa non-parametric dispersion test, were conducted to investigate if the model has a good fit. After discovering four clusters by conducting agglomerative hierarchical clustering, these two models were replaced by the negative binomial mixed model. The likelihood ratio test was used to determine which model is more appropriate and the results reveal that the negative binomial mixed model outperforms both the Poisson model and the negative binomial model. Three variables, number of health technicians per 10,000 people, financial expenditure on science and technology as well as number of patent applications per 10,000 health personnel, have a significantly positive relationship with the number of patents in Chinese tertiary public hospitals.
A data-driven reduced-order surrogate model for entire elastoplastic simulations applied to representative volume elements
Authors: Vijayaraghavan S. , Wu L. , Noels L. , Bordas S. P. A. , Natarajan S. , Beex L. A. A. 
Published: August 2023

AbstractThis contribution discusses surrogate models that emulate the solution field(s) in the entire simulation domain. The surrogate uses the most characteristic modes of the solution field(s), in combination with neural networks to emulate the coefficients of each mode. This type of surrogate is well known to rapidly emulate flow simulations, but rather new for simulations of elastoplastic solids. The surrogate avoids the iterative process of constructing and solving the linearized governing equations of rate-independent elastoplasticity, as necessary for direct numerical simulations or (hyper-)reduced-order-models. Instead, the new plastic variables are computed only once per increment, resulting in substantial time savings. The surrogate uses a recurrent neural network to treat the path dependency of rate-independent elastoplasticity within the neural network itself. Because only a few of these surrogates have been developed for elastoplastic simulations, their potential and limitations are not yet well studied. The aim of this contribution is to shed more light on their numerical capabilities in the context of elastoplasticity. Although more widely applicable, the investigation focuses on a representative volume element, because these surrogates have the ability to both emulate the macroscale stress-deformation relation (which drives the multiscale simulation), as well as to recover all microstructural quantities within each representative volume element.
Vendor evaluation platform for acquisition of medical equipment based on multi-criteria decision-making approach
Authors: Saleh Neven , Gaber Mohamed N. , Eldosoky Mohamed A. , Soliman Ahmed M. 
Published: August 2023

AbstractThe purchase of medical equipment is a critical issue that should be planned properly. The selection of the most appropriate vendor impacts time, effort, and expenses. Therefore, the challenge is to strike a balance between the available budget and the required equipment. The study aims to select the best vendor for supplying medical equipment based on Emergency Care Research Institute (ECRI) standards. The multi-criteria decision-making approach has been adopted through three methods; Multi-Objective Optimization by Ratio Analysis (MOORA), Simple Additive Weighting (SAW), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The criteria of selection are divided into general, technical, and financial. The criteria are weighted using three methods: CRITIC, entropy, and expert judgment. The Vendor Evaluation Program for Medical Equipment (VEPME) is designed to automatically select the best vendor. Medical imaging equipment is selected to test the program by four modalities: X-ray equipment, CT, MRI, and ultrasound. The best scenario was given by the entropy-TOPSIS. As a result, this methodology was adopted by the program. The results demonstrate the robustness of the proposed methodology by comparing the VEPME output to expert judgment.
Graft-recipient-weight ratio and lowered immunosuppression is important for the success of adult liver retransplantation
Authors: Rhu Jinsoo , Kwon Jieun , Lim Manuel , Oh Namkee , An Sunghyo , Han Seung Wook , Jo Sung Jun , Park Sunghae , Choi Gyu-Seong , Kim Jong Man , Joh Jae-Won 
Published: August 2023

AbstractThis study analyzed the risk of liver retransplantation and factors related to better outcome. Adult liver transplantations performed during 1996–2021 were included. Comparison between first transplantation and retransplantation were performed. Among retransplantation cases, comparison between whole liver and partial liver graft was performed. Multivariable Cox analyses for analyzing risk factors for primary graft and overall patient survival were performed for the entire cohort as well as the subgroup of patients with retransplantation. A total 2237 transplantations from 2135 adults were included and 103 cases were retransplantation. A total of 44 cases (42.7%) were related to acute graft dysfunction while 59 cases (57.3%) were related to subacute or chronic graft dysfunction. Retransplantation was related poor primary graft (HR 3.439, CI 2.230–5.304, P < 0.001) and overall patient survival. (HR 2.905, CI 2.089–4.040, P < 0.001) Among retransplantations, mean serum FK506 trough level ≥ 9 ng/mL was related to poor primary graft (HR 3.692, CI 1.288–10.587, P = 0.015) and overall patient survival. (HR 2.935, CI 1.195–7.211, P = 0.019) Graft-recipient-weight ratio under 1.0% was related to poor overall patient survival in retransplantations. (HR 3.668, CI 1.150–11.698, P = 0.028). Retransplantation can be complicated with poor graft and patient survival compared to first transplantation, especially when the graft size is relatively small. Lowering the FK506 trough level during the first month can be beneficial for outcome.
Research on liver cancer segmentation method based on PCNN image processing and SE-ResUnet
Authors: Zang Lan , Liang Wei , Ke Hanchu , Chen Feng , Shen Chong 
Published: August 2023

AbstractAs one of the malignant tumors with high mortality, the initial symptoms of liver cancer are not obvious. In addition, the liver is the largest internal organ of the human body, and its structure and distribution are relatively complex. Therefore, in order to help doctors judge liver cancer more accurately, this paper proposes a variant model based on Unet network. Before segmentation, the image is preprocessed, and Pulse Coupled Neural Network (PCNN) algorithm is used to filter the image adaptively to make the image clearer. For the segmentation model, the SE module is used as the input of the residual network, and then its output is connected to the Unet model through bilinear interpolation to perform the down-sampling and up-sampling operations. The dataset is a combination of Hainan Provincial People's Hospital and some public datasets Lits. The results show that this method has better segmentation performance and accuracy than the original Unet method, and the dice coefficient, mIou and other evaluation indicators have increased by at least 2.1%, which is a method that can be applied to cancer segmentation.
New drugs and stock market: a machine learning framework for predicting pharma market reaction to clinical trial announcements
Authors: Budennyy Semen , Kazakov Alexey , Kovtun Elizaveta , Zhukov Leonid 
Published: August 2023

AbstractPharmaceutical companies operate in a strictly regulated and highly risky environment in which a single slip can lead to serious financial implications. Accordingly, the announcements of clinical trial results tend to determine the future course of events, hence being closely monitored by the public. Most works focus on retrospective analysis of announcement impact on company stock prices, bypassing the consideration of the problem in the predictive paradigm. In this work, we aim to close this gap by proposing a framework that allows predicting the numerical values of announcement-induced changes in stock prices. In fact, it is a problem of the impact prediction of the specific event on the corresponding time series. Our framework includes a BERT model for extracting the sentiment polarity of announcements, a Temporal Fusion Transformer for forecasting the expected return, a graph convolution network for capturing event relationships, and gradient boosting for predicting the price change. We operate with one of the biggest FDA (the Food and Drug Administration) datasets, consisting of 5436 clinical trial announcements from 681 companies for the years 2018–2022. During the study, we get several significant outcomes and domain-specific insights. Firstly, we obtain statistical evidence for the clinical result promulgation influence on the public pharma market value. Secondly, we witness inherently different patterns of responses to positive and negative announcements, reflected in a stronger and more pronounced reaction to negative clinical news. Thirdly, we discover two factors that play a crucial role in a predictive framework: (1) the drug portfolio size of the company, indicating the greater susceptibility to an announcement in the case of low diversification among drug products and (2) the announcement network effect, manifesting through an increase in predictive power when exploiting interdependencies of events belonging to the same company or nosology. Finally, we prove the viability of the forecast setting by getting ROC AUC scores predominantly greater than 0.7 for the classification of price change on historical data. We emphasize the transferability and generalizability of the developed framework on other datasets and domains but on the condition of the presence of two key entities: events and the associated time series.
STENCIL-NET for equation-free forecasting from data
Authors: Maddu Suryanarayana , Sturm Dominik , Cheeseman Bevan L. , Müller Christian L. , Sbalzarini Ivo F. 
Published: August 2023

AbstractWe present an artificial neural network architecture, termed STENCIL-NET, for equation-free forecasting of spatiotemporal dynamics from data. STENCIL-NET works by learning a discrete propagator that is able to reproduce the spatiotemporal dynamics of the training data. This data-driven propagator can then be used to forecast or extrapolate dynamics without needing to know a governing equation. STENCIL-NET does not learn a governing equation, nor an approximation to the data themselves. It instead learns a discrete propagator that reproduces the data. It therefore generalizes well to different dynamics and different grid resolutions. By analogy with classic numerical methods, we show that the discrete forecasting operators learned by STENCIL-NET are numerically stable and accurate for data represented on regular Cartesian grids. A once-trained STENCIL-NET model can be used for equation-free forecasting on larger spatial domains and for longer times than it was trained for, as an autonomous predictor of chaotic dynamics, as a coarse-graining method, and as a data-adaptive de-noising method, as we illustrate in numerical experiments. In all tests, STENCIL-NET generalizes better and is computationally more efficient, both in training and inference, than neural network architectures based on local (CNN) or global (FNO) nonlinear convolutions.
Downregulation of PIK3IP1 in retinal microglia promotes retinal pathological neovascularization via PI3K-AKT pathway activation
Authors: Chen Lushu , Cao Yuan , Shen Yaming , Li Huan , Ye Rong , Yao Jin 
Published: August 2023

AbstractRetinal pathological neovascularization involves endothelial cells, pericytes, photoreceptor cells, ganglion cells, and glial cells, whose roles remain unclear. Using the Scissor algorithm, we found that microglia are associated with formation of fibrovascular membranes and can promote pathological neovascularization. GO and KEGG results showed that PI3K-AKT pathway activation in retinal microglia was associated with pathological neovascularization, and PIK3IP1 was associated with retinal microglia activation. Then we used PCR, Western blot and Elisa techniques to confirm that the expression of VEGFA, FGF2, HGFα and MMP9 was increased in microglia after Lipopolysaccharide (LPS) induction. We also used cell flow cytometry and OIR models to verify the role of PI3K-AKT pathway and PIK3IP1 in microglia. Targeting of PIK3IP1 regulated the activation of the PI3K-AKT pathway in microglia, microglia function activation, and pro-angiogenic effects. These findings reveal the role of M1-type microglia in pathological neovascularization and suggests that targeting the PI3K-AKT pathway in microglia may be a new strategy for treating retinal pathological neovascularization.
A brighter shade of future climate on Himalayan musk deer Moschus leucogaster
Authors: Mainali Kumar P. , Singh Paras Bikram , Evans Michael , Adhikari Arjun , Hu Yiming , Hu Huijian 
Published: August 2023

AbstractHimalayan musk deer (Moschus leucogaster) is classified as an endangered species by IUCN with a historically misunderstood distribution due to misidentification with other species of musk deer, Moschus spp. Taking advantage of recent genetic analyses confirming the species of various populations in Nepal and China, we produced an accurate estimate of the species’ current and future distribution under multiple climate change scenarios. We collected high-quality occurrence data using systematic surveys of various protected areas of Nepal to train species distribution models. The most influential determinants of the distribution of Himalayan musk deer were precipitation of the driest quarter, temperature seasonality, and annual mean temperature. These variables, and precipitation in particular, determine the vegetation type and structure in the Himalaya, which is strongly correlated with the distribution of Himalayan musk deer. We predicted suitable habitats between the Annapurna and Kanchenjunga region of Nepal Himalaya as well as the adjacent Himalaya in China. Under multiple climate change scenarios, the vast majority (85–89%) of current suitable sites are likely to remain suitable and many new areas of suitable habitat may emerge to the west and north of the current species range in Nepal and China. Two-thirds of current and one-third of future suitable habitats are protected by the extensive network of protected areas in Nepal. The projected large gains in suitable sites may lead to population expansion and conservation gains, only when the threat of overexploitation and population decline is under control.
Changes in peripheral blood immune cell population in thyroid cancer patients treated with lenvatinib
Authors: Jin Meihua , Kim Chae A. , Bae Dong Jun , Kim Sang-Yeob , Kim Tae Yong , Kim Won Bae , Shong Young Kee , Kim Won Gu , Jeon Min Ji 
Published: August 2023

AbstractThis study evaluated changes in the peripheral blood immune cell population in patients with advanced thyroid cancer receiving lenvatinib treatment to confirm the immune-modulatory effect of lenvatinib. After obtaining informed consent from patients, we prospectively collected 20 ml of whole blood at 2–3 months intervals 2–4 times from each patient; peripheral blood mononuclear cells (PBMCs) were separated, and the Maxpar Direct Immune Profiling Assay was performed. A total of 10 patients were enrolled, and 31 blood samples were obtained. The median age of patients was 65 years, and all patients showed durable responses to the lenvatinib treatment. When we compared the PBMC profiles between the pre-treatment, on-treatment, and off-treatment samples, the peripheral natural killer (NK) cell proportion differed significantly. The proportion of NK cells among total live cells significantly increased from 9.3 ± 4.5 (%) in the pre-treatment samples to 20.8 ± 7.9 (%) in the on-treatment samples (P = 0.009) and decreased to 13.3 ± 3.1 (%) in the off-treatment samples (P = 0.07). There was a significant increase in the peripheral NK cell population with lenvatinib treatment in advanced thyroid cancer patients. This finding confirms the immune-modulatory effect of lenvatinib.