bluuebunny/crossref_metadata_2025_split huggingface dataset:
Solar cycle as a distinct line of evidence constraining Earth’s transient climate response
Authors: Li King-Fai , Tung Ka-Kit 
Published: December 2023

AbstractSeverity of warming predicted by climate models depends on their Transient Climate Response (TCR). Inter-model spread of TCR has persisted at ~ 100% of its mean for decades. Existing observational constraints of TCR are based on observed historical warming response to historical forcing and their uncertainty spread is just as wide, mainly due to forcing uncertainty, and especially that of aerosols. Contrary, no aerosols are involved in solar-cycle forcing, providing an independent, tighter, constraint. Here, we define a climate sensitivity metric: time-dependent response regressed against time-dependent forcing, allowing phenomena with dissimilar time variations, such as the solar cycle with 11-year cyclic forcing, to be used to constrain TCR, which has a linear time-dependent forcing. We find a theoretical linear relationship between the two. The latest coupled atmosphere-ocean climate models obey the same linear relationship statistically. The proposed observational constraint on TCR is about $${{1}}/{{3}}$$
1
/
3
as narrow as existing constraints. The central estimate, 2.2 oC, is at the midpoint of the spread of the latest generation of climate models, which are more sensitive than those of the previous generations.
Direct-to-biology, automated, nano-scale synthesis, and phenotypic screening-enabled E3 ligase modulator discovery
Authors: Wang Zefeng , Shaabani Shabnam , Gao Xiang , Ng Yuen Lam Dora , Sapozhnikova Valeriia , Mertins Philipp , Krönke Jan , Dömling Alexander 
Published: December 2023

AbstractThalidomide and its analogs are molecular glues (MGs) that lead to targeted ubiquitination and degradation of key cancer proteins via the cereblon (CRBN) E3 ligase. Here, we develop a direct-to-biology (D2B) approach for accelerated discovery of MGs. In this platform, automated, high throughput, and nano scale synthesis of hundreds of pomalidomide-based MGs was combined with rapid phenotypic screening, enabling an unprecedented fast identification of potent CRBN-acting MGs. The small molecules were further validated by degradation profiling and anti-cancer activity. This revealed E14 as a potent MG degrader targeting IKZF1/3, GSPT1 and 2 with profound effects on a panel of cancer cells. In a more generalized view, integration of automated, nanoscale synthesis with phenotypic assays has the potential to accelerate MGs discovery.
Ultra-narrow inhomogeneous spectral distribution of telecom-wavelength vanadium centres in isotopically-enriched silicon carbide
Authors: Cilibrizzi Pasquale , Arshad Muhammad Junaid , Tissot Benedikt , Son Nguyen Tien , Ivanov Ivan G. , Astner Thomas , Koller Philipp , Ghezellou Misagh , Ul-Hassan Jawad , White Daniel , Bekker Christiaan , Burkard Guido , Trupke Michael , Bonato Cristian 
Published: December 2023

AbstractSpin-active quantum emitters have emerged as a leading platform for quantum technologies. However, one of their major limitations is the large spread in optical emission frequencies, which typically extends over tens of GHz. Here, we investigate single V4+ vanadium centres in 4H-SiC, which feature telecom-wavelength emission and a coherent S = 1/2 spin state. We perform spectroscopy on single emitters and report the observation of spin-dependent optical transitions, a key requirement for spin-photon interfaces. By engineering the isotopic composition of the SiC matrix, we reduce the inhomogeneous spectral distribution of different emitters down to 100 MHz, significantly smaller than any other single quantum emitter. Additionally, we tailor the dopant concentration to stabilise the telecom-wavelength V4+ charge state, thereby extending its lifetime by at least two orders of magnitude. These results bolster the prospects for single V emitters in SiC as material nodes in scalable telecom quantum networks.
Merizo: a rapid and accurate protein domain segmentation method using invariant point attention
Authors: Lau Andy M. , Kandathil Shaun M. , Jones David T. 
Published: December 2023

AbstractThe AlphaFold Protein Structure Database, containing predictions for over 200 million proteins, has been met with enthusiasm over its potential in enriching structural biological research and beyond. Currently, access to the database is precluded by an urgent need for tools that allow the efficient traversal, discovery, and documentation of its contents. Identifying domain regions in the database is a non-trivial endeavour and doing so will aid our understanding of protein structure and function, while facilitating drug discovery and comparative genomics. Here, we describe a deep learning method for domain segmentation called Merizo, which learns to cluster residues into domains in a bottom-up manner. Merizo is trained on CATH domains and fine-tuned on AlphaFold2 models via self-distillation, enabling it to be applied to both experimental and AlphaFold2 models. As proof of concept, we apply Merizo to the human proteome, identifying 40,818 putative domains that can be matched to CATH representative domains.
Endoxifen downregulates AKT phosphorylation through protein kinase C beta 1 inhibition in ERα+ breast cancer
Authors: Jayaraman Swaathi , Wu Xinyan , Kalari Krishna R. , Tang Xiaojia , Kuffel Mary J. , Bruinsma Elizabeth S. , Jalali Shahrzad , Peterson Kevin L. , Correia Cristina , Kudgus Rachel A. , Kaufmann Scott H. , Renuse Santosh , Ingle James N. , Reid Joel M. , Ames Matthew M. , Fields Alan P. , Schellenberg Matthew J. , Hawse John R. , Pandey Akhilesh , Goetz Matthew P. 
Published: December 2023

AbstractEndoxifen, a secondary tamoxifen metabolite, is a potent antiestrogen exhibiting estrogen receptor alpha (ERα) binding at nanomolar concentrations. Phase I/II clinical trials identified clinical activity of Z-endoxifen (ENDX), in endocrine-refractory metastatic breast cancer as well as ERα+ solid tumors, raising the possibility that ENDX may have a second, ERα-independent, mechanism of action. An unbiased mass spectrometry approach revealed that ENDX concentrations achieved clinically with direct ENDX administration (5 µM), but not low concentrations observed during tamoxifen treatment (<0.1 µM), profoundly altered the phosphoproteome of the aromatase expressing MCF7AC1 cells with limited impact on the total proteome. Computational analysis revealed protein kinase C beta (PKCβ) and protein kinase B alpha or AKT1 as potential kinases responsible for mediating ENDX effects on protein phosphorylation. ENDX more potently inhibited PKCβ1 kinase activity compared to other PKC isoforms, and ENDX binding to PKCβ1 was confirmed using Surface Plasma Resonance. Under conditions that activated PKC/AKT signaling, ENDX induced PKCβ1 degradation, attenuated PKCβ1-activated AKTSer473 phosphorylation, diminished AKT substrate phosphorylation, and induced apoptosis. ENDX’s effects on AKT were phenocopied by siRNA-mediated PKCβ1 knockdown or treatment with the pan-AKT inhibitor, MK-2206, while overexpression of constitutively active AKT diminished ENDX-induced apoptosis. These findings, which identify PKCβ1 as an ENDX target, indicate that PKCβ1/ENDX interactions suppress AKT signaling and induce apoptosis in breast cancer.
American local government elections database
Authors: de Benedictis-Kessner Justin , Lee Diana Da In , Velez Yamil R. , Warshaw Christopher 
Published: December 2023

AbstractThe study of urban and local politics in the United States has long been hindered by a lack of centralized sources of election data. We introduce a new database of about 78,000 candidates in 57,000 electoral contests that encompasses races for seven distinct local political offices in most medium and large cities and counties in the U.S. over the last three decades. This is the most comprehensive publicly-available source of information on local elections across the country. We provide partisan and demographic information about candidates in these races as well as electoral outcomes. This new database will facilitate a myriad of new research on representation and elections in local governments.
CFD estimation of gas production in tight carbonates using single and dual-porosity models
Authors: Khadri Syed Oubee , Hussein Ibnelwaleed A. , Sadooni Fadhil , Shirif Ezeddin 
Published: December 2023

AbstractTight Carbonate reservoirs are regarded as one of the most complex reservoir formations due to the heterogeneity and complexity of their mineral composition, pore structure, and storage model. It is uncommon to study the implementation of a transport model appropriate for such formation. Recent studies focused on tight reservoirs and developed models for shale or coal bed methane reservoirs. This study proposes a single and dual-porosity transport model that solely considers the tight matrix and acidized region to shed light on the transport models for tight carbonates. The numerical model included the effect of transport mechanisms such as Knudsen diffusion, desorption, and viscous flow. The proposed transport model includes the apparent permeability model defining these transport mechanisms. Finite element method analysis was conducted on the numerical model using COMSOL Multiphysics. Due to the presence of nanopores in both shale and tight Carbonate, transport models proposed for the former can be utilized to determine the fluid flow behavior in the latter. The adsorption isotherm, rock density, pore structure, porosity, and permeability of the tight carbonate reservoir, which contrasted with the shale results, were the defining features of the reservoir used in the transport model. The dual-porosity model yielded a peak production of 104,000 m3/day, whereas the proposed model represents a shallow production rate from the single-porosity reservoir. The results were validated with an analytical solution proposed in the literature. Based on the literature findings and the production profile, the desorption did not play a significant role in the total production due to calcite’s low affinity towards CH4.
Effects of web-based mindfulness training on psychological outcomes, attention, and neuroplasticity
Authors: Mora Álvarez María Guadalupe , Hölzel Britta Karen , Bremer Benno , Wilhelm Maximilian , Hell Elena , Tavacioglu Ebru Ecem , Koch Kathrin , Torske Alyssa 
Published: December 2023

AbstractMindfulness meditation training (MMT) reliably reduces stress and anxiety while also improving attention. The primary aim of this study was to investigate the relationship between MMT, stress and anxiety reduction, and its impact upon improvements in attention on the behavioral and neuronal levels. As a second aim, we sought to explore any relationship between MMT, attention, and modified states of mind such as flow. 118 healthy, meditation-naïve, participants were either assigned to a 31-day, web-based, MMT or an active control, health training (HT). Participants underwent functional magnetic resonance imaging while performing the attention network test (ANT) to assess functional and behavioural attentional changes, diffusion tensor imaging (DTI) to assess microstructural neuronal changes and completed relevant questionnaires to explore changes in psychological outcomes. Results confirmed a reduction in perceived stress and anxiety levels in the MMT group and significant improvements in the overall reaction time during the ANT, albeit no specific effects on the attentional components were observed. No statistically significant changes were found in the HT group. Interestingly, a significant group-by-time interaction was seen in flow experience. Functional data exhibited an increased activity in the superior frontal gyrus, posterior cingulate cortex, and right hippocampus during the alerting condition of the ANT after the MMT; decreased stress and trait anxiety were significantly correlated with the activation in the right hippocampus, and increased flow was also significantly correlated with all the aforementioned areas. DTI data showed increased fractional anisotropy values in the right uncinate fasciculus indicating white matter microarchitecture improvement between the right hippocampus and frontal areas of the brain. This study, therefore, demonstrates the effectiveness of web-based MMT on overall well-being and attentional performance, while also providing insight into the relationship between psychological outcomes, attention, and neuroplastic changes.
Noninvasive evaluation of pulmonary artery stiffness in heart failure patients via cardiovascular magnetic resonance
Authors: Hou Xuewen , Hashemi Djawid , Erley Jennifer , Neye Marthe , Bucius Paulius , Tanacli Radu , Kühne Titus , Kelm Marcus , Motzkus Laura , Blum Moritz , Edelmann Frank , Kuebler Wolfgang M. , Pieske Burkert , Düngen Hans-Dirk , Schuster Andreas , Stoiber Lukas , Kelle Sebastian 
Published: December 2023

AbstractHeart failure (HF) presents manifestations in both cardiac and vascular abnormalities. Pulmonary hypertension (PH) is prevalent in up 50% of HF patients. While pulmonary arterial hypertension (PAH) is closely associated with pulmonary artery (PA) stiffness, the association of HF caused, post-capillary PH and PA stiffness is unknown. We aimed to assess and compare PA stiffness and blood flow hemodynamics noninvasively across HF entities and control subjects without HF using CMR. We analyzed data of a prospectively conducted study with 74 adults, including 55 patients with HF across the spectrum (20 HF with preserved ejection fraction [HFpEF], 18 HF with mildly-reduced ejection fraction [HFmrEF] and 17 HF with reduced ejection fraction [HFrEF]) as well as 19 control subjects without HF. PA stiffness was defined as reduced vascular compliance, indicated primarily by the relative area change (RAC), altered flow hemodynamics were detected by increased flow velocities, mainly by pulse wave velocity (PWV). Correlations between the variables were explored using correlation and linear regression analysis. PA stiffness was significantly increased in HF patients compared to controls (RAC 30.92 ± 8.47 vs. 50.08 ± 9.08%, p < 0.001). PA blood flow parameters were significantly altered in HF patients (PWV 3.03 ± 0.53 vs. 2.11 ± 0.48, p < 0.001). These results were consistent in all three HF groups (HFrEF, HFmrEF and HFpEF) compared to the control group. Furthermore, PA stiffness was associated with higher NT-proBNP levels and a reduced functional status. PA stiffness can be assessed non-invasively by CMR. PA stiffness is increased in HFrEF, HFmrEF and HFpEF patients when compared to control subjects.Trial registration The study was registered at the German Clinical Trials Register (DRKS, registration number: DRKS00015615).
Energy-absorption analyses of honeycomb-structured Al-alloy and nylon sheets using modified split Hopkinson pressure bar
Authors: Kim Selim , Kim Minu , Kim Ki Jong , Lee Jae Min , Cheong Hae-Won , Kim Hyoung Seop , Lee Sunghak 
Published: December 2023

AbstractThin cylindrical honeycomb-structured aluminum alloy and mono-cast (MC) nylon were studied as superior energy-absorbing materials compared to metallic foams. Their energy-absorbing performance was assessed using a modified split Hopkinson pressure bar (SHPB). Key parameters included maximum impact acceleration (amax) and its reduction ratio (compared to the none-specimen case). The lowest amax reduction ratio was observed in bulk Al sheets without honeycomb cavities. As the cavity fraction increased up to 79% in honeycomb-structured Al specimens, the amax reduction ratio improved due to broadened stress–time curves with a shallow-plateau shape. This made high-cavity-fraction Al specimens preferable for higher-energy absorption and lighter-weight buffering materials. In nylon specimens, the amax reduction ratio increased until the fraction reached 52% due the softer and more deformable nature of the polymeric nylon. Thicker or rotated Al specimens also showed higher amax reduction ratios due to sufficient and continuous energy absorption. The modified SHPB demonstrated effective energy-buffering concepts and provided insightful amax interpretations, overcoming complexities in energy absorption analyses.
Axial compressor blades tip leakage flow control using natural aspiration
Authors: Nejad Peyman Ghashghaie , Zenouz Reza Taghavi 
Published: December 2023

AbstractBlades tip leakage flow structure in axial compressors has dominant effects on flow stability and losses. Accordingly, in the present study, three different ideas are introduced for alleviation of undesirable effects of the blades tip leakage flow. These ideas utilize circumferential tape, S-shape nozzle and natural aspiration slot, all imposed upstream the rotor blades row. The method of investigation is based on numerical simulation of the governing flow field. Results, in terms of the flow field structure and performance curves are compared with those of the untreated case. Final results, showed that the natural aspiration idea works better than the other ones. In comparison to the plane case, it is accompanied by augmentation of 3.5% in the total pressure ratio and 3% in the surge margin. It was also found that the mass flow rate passing through the blades row tip gap has increased by 2.33 g/s in the natural aspiration case.
Transfer learning for versatile and training free high content screening analyses
Authors: Corbe Maxime , Boncompain Gaëlle , Perez Franck , Del Nery Elaine , Genovesio Auguste 
Published: December 2023

AbstractHigh content screening (HCS) is a technology that automates cell biology experiments at large scale. A High Content Screen produces a high amount of microscopy images of cells under many conditions and requires that a dedicated image and data analysis workflow be designed for each assay to select hits. This heavy data analytic step remains challenging and has been recognized as one of the burdens hindering the adoption of HCS. In this work we propose a solution to hit selection by using transfer learning without additional training. A pretrained residual network is employed to encode each image of a screen into a discriminant representation. The deep features obtained are then corrected to account for well plate bias and misalignment. We then propose two training-free pipelines dedicated to the two main categories of HCS for compound selection: with or without positive control. When a positive control is available, it is used alongside the negative control to compute a linear discriminant axis, thus building a classifier without training. Once all samples are projected onto this axis, the conditions that best reproduce the positive control can be selected. When no positive control is available, the Mahalanobis distance is computed from each sample to the negative control distribution. The latter provides a metric to identify the conditions that alter the negative control’s cell phenotype. This metric is subsequently used to categorize hits through a clustering step. Given the lack of available ground truth in HCS, we provide a qualitative comparison of the results obtained using this approach with results obtained with handcrafted image analysis features for compounds and siRNA screens with or without control. Our results suggests that the fully automated and generic pipeline we propose offers a good alternative to handcrafted dedicated image analysis approaches. Furthermore, we demonstrate that this solution select conditions of interest that had not been identified using the primary dedicated analysis. Altogether, this approach provides a fully automated, reproducible, versatile and comprehensive alternative analysis solution for HCS encompassing compound-based or downregulation screens, with or without positive controls, without the need for training or cell detection, or the development of a dedicated image analysis workflow.
ANN deformation prediction model for deep foundation pit with considering the influence of rainfall
Authors: Wei Xing , Cheng Shitao , Chen Rui , Wang Zijian , Li Yanjun 
Published: December 2023

AbstractDeep foundation pits involving complex soil–water-structure interactions are often at a high risk of failure under heavy rainfall. Predicted deformation is an important index for early risk warning. In the study, an ANN model is proposed based on the Wave Transform (WT), Copula method, Convolutional Neural Network (CNN) and Long Short-Term Memory Neural Network (LSTM). The total deformation was firstly decomposed into low and high frequency components with WT. The CNN and LSTM were then used for prediction of the two components with rolling training and prediction. The input variables of the CNN and LSTM were determined and optimized based on the correlations analysis of Copula method of the two components with different random variables, especially with the rainfall. And finally, the predicted total deformation was obtained by adding the two prediction components. A deep foundation pit in Chengdu, China was taken as a case study, of which the horizontal deformation curves at different measuring points shows three types of developed trend, as unstable, less stable, and stable types. The predictions of the deformations of different development types by the proposed ANN model show high accuracies with a few input variables and can accurately prompt risk warning in advance.
Novel HER2-targeted therapy to overcome trastuzumab resistance in HER2-amplified gastric cancer
Authors: Park Juin , Kang Sun Kyoung , Kwon Woo Sun , Jeong Inhye , Kim Tae Soo , Yu Seo Young , Cho Sang Woo , Chung Hyun Cheol , Rha Sun Young 
Published: December 2023

AbstractTrastuzumab is used to treat HER2-amplified metastatic gastric cancer; however, most patients become trastuzumab-resistant within a year. Knowledge of the mechanisms underlying trastuzumab resistance is required to overcome this limitation. Here, we aimed to elucidate this resistance mechanism using four trastuzumab-resistant (TR) cell lines and investigate the efficacy of HER2-targeted therapies to overcome treatment resistance. Each TR cell line had different phenotypic characteristics. Interestingly, HER2 expression remained as high as the parental cell lines in TR cell lines, suggesting that HER2-targeted agents were still useful. As expected, three tyrosine kinase inhibitors (lapatinib, neratinib, and tucatinib) and one antibody–drug conjugate (trastuzumab deruxtecan: T-DXd) exhibited good antitumor effects against TR cell lines. We further investigated the potential biological mechanism of T-DXd. When treated with trastuzumab or T-DXd, HER2 or its downstream signals were disrupted in parental cell lines, but not in TR cell lines. Moreover, T-DXd induced the expression of pH2A.X and cPARP and caused cell cycle arrest in the S or G2-M phase in TR cell lines. T-DXd showed promising antitumor activity in both parental and TR cell lines, suggesting that it is a potential candidate for overcoming trastuzumab resistance.
Analyzing ChatGPT adoption drivers with the TOEK framework
Authors: Jo Hyeon , Bang Youngsok 
Published: December 2023

AbstractWith the rapid advancements in AI technology and its growing impact on various aspects of daily life, understanding the factors that influence users' adoption intention becomes essential. This study focuses on the determinants affecting the adoption intention of ChatGPT, an AI-driven language model, among university students. The research extends the Technology-Organization-Environment (TOE) framework by integrating the concept of knowledge application. A cross-sectional research design was employed, gathering data through a survey conducted to university students. Structural equation modeling was used to analyze the data, aimed at examining the relationships between key determinants influencing adoption intention. The findings of this research indicate that factors such as network quality, accessibility, and system responsiveness contribute to satisfaction. Furthermore, satisfaction, organizational culture, social influence, and knowledge application significantly affect adoption intention. These findings offer both theoretical and practical implications.