bluuebunny/crossref_metadata_2025_split huggingface dataset:
Projecting atmospheric N2O rise until the end of the 21st century: an Earth System Model study
Authors: De Sisto M , Somes C , Landolfi A , MacDougall A H 
Published: October 2024

Abstract
Nitrous Oxide (N2O) is a potent greenhouse gas with a centennial-scale lifetime that contributes significantly to global warming. It is emitted from natural and anthropogenic sources. In nature, N2O is released mainly from nitrification and denitrification from the ocean and terrestrial systems. The use of agricultural fertilizers has significantly increased the emission of N2O in the past century. Here we present, to our knowledge, the first coupled ocean and terrestrial N2O modules within an Earth System Model. The coupled modules were used to simulate the six Shared Socioeconomic Pathways (SSPs) scenarios with available nitrogen fertilizer inputs. Our results are compared to projections of atmospheric N2O concentrations used for SSPs scenario experiments. Additionally, an extra set of simulations were prescribed with emulated N2O concentrations available as input in Shared Socioeconomic Pathways scenarios. We report four main drivers for terrestrial N2O uncertainties: atmospheric temperature, agricultural fertilizer input, soil denitrification and agricultural model dynamics. We project an atmospheric N2O concentration range from 401 to 418 ppb in six SSPs simulations with a robust lack of sensitivity to equilibrium climate sensitivity. We found a large difference between our low emission scenarios N2O concentrations by 2100 compared to the concentration provided for SSPs experiments. This divergence is likely explained by strong mitigation assumptions that were not accounted for in this study, which would require a substantial decrease of agricultural N2O emissions. The coupled model and the simulations prescribed with N2O concentrations showed a difference between −0.02 and 0.09 ∘C by 2100. Our model simulation shows a lack of sensitivity to climate mitigation efforts projecting similar N2O concentration in low and high mitigation scenarios, that could indicate the need of further development of agricultural model dynamics. Further improvements in Earth system models should focus on the impact of oxygen decline on N2O dynamics in the ocean and the representation of anaerobic soils and agricultural dynamics on land, including mitigation methods on nitrogen fertilizers.
Do disparities exist in flood risks during monsoon and post-monsoon seasons? Comprehending diametric behaviors over coastal multi-hazard catchments
Authors: Thakur Dev Anand , Mohanty Mohit Prakash 
Published: October 2024

Abstract
Global coastal catchments experience peculiar flood risk attributes due to the confluence of multiple flood drivers. In these regions, the monsoon and post-monsoon seasons impose diverse flood risks that evoke meticulous research for the prudent execution of appropriate flood management options. For the first time in flood management literature, we develop an integrated approach to quantify the distinguishing nature of flood risks during both seasons. We employ a sophisticated 1D–2D coupled flood model to generate high-resolution flood hazards while considering the compound interactions between rainfalls and storm-tides. Flood vulnerability is mapped at the finest administrative scale while considering flood-susceptible physical and socio-economic indicators. With the concept of Bivariate Risk Classifier, we introduce two novel metrics: (a) area index and (b) multi-hazard risk index that advance our understanding of the multi-hazard nature of flood risks during monsoon and post-monsoon seasons. These incisive indices propound case-specific flood management measures for long-term resilience.
Innovative design and process simulation of aviation bracket based on topology optimization and additive manufacturing
Authors: Yu Gao , Ling-qin Xia , Hua Cong , Lu Pan 
Published: October 2024

Abstract
In this paper, topology optimization(TO) and laser powder bed fusion (LPBF) technology were combined to explore the key technology of parts integration design and manufacturing oriented to performance, structure and manufacturing process. With the help of topology optimization(TO), the lightweight design of the aviation bracket achieved the weight loss goal of 87.69% in this work. The thermophysical properties of TC4 were calculated by thermodynamic calculation, and the temperatue interval of Laser Powder Bed Fusion for TC4 is 1800 °C−3000 °C was determined . Additionally, the LPBF macro-scale simulation was carried out. The results showed that the maximum temperature in the printing process is lower than 2500 °C, and the maximum displacement is 4.87 mm, which only appears in the non-design space. Finally, the safety factor and maximum displacement of aviation bracket are 1.2 and 0.8 mm, and the maximum Mises stress is 685 MPa, all of which meet the design requirements.
MEMS capacitive pressure sensor: analysis, theoretical modeling, simulation and performance comparison of the effect of a conical notch
Authors: Gajula Rukshana Bi , Mahata Sanskriti , Pingali Akhila , Jindal Sumit Kumar 
Published: October 2024

Abstract
Micro-Electro-Mechanical Systems (MEMS) pressure sensors are widely used in various applications due to their high sensitivity, low power consumption, and compactness. This work involves the design and simulation of a MEMS-based Touch Mode Capacitive Pressure Sensor (TMCPS). The proposed sensor is based on a substrate with an integrated conical notch featuring a circular diaphragm, aiming to enhance the key performance parameters of the sensor. The integration of a conical notch in the substrate increases the touch area between the diaphragm and substrate compared to the literature, ensuring increased capacitance and capacitive sensitivity. In this work the mathematical analysis of deflection of a circular diaphragm employing thin plate theory, capacitance, and capacitive sensitivity, along with step-by-step explanations, is provided. The results are obtained from MATLAB simulations. The deflection of the diaphragm is validated through Finite Element Analysis (FEA) using COMSOL Multiphysics. The proposed work demonstrates a significant improvement in sensor sensitivity compared to the existing literature.
Carbon dots induced homeotropic alignment in a negative dielectric nematic liquid crystal material
Authors: P Priscilla , Gathania Arvind K , Kumar Sandeep , Fisch Michael R , Prakash Jai , Supreet  , Kumar Sanjeev , Singh Gautam 
Published: October 2024

Abstract
Recently, doping guest materials such as quantum dots (QDs) into liquid crystals (LCs) has been of great interest since their addition substantially enhances the properties of LC and opens new avenues for scientific advancement. Here, we report the induction of homeotropic alignment in cells without alignment layers of the negative dielectric nematic liquid crystal, N-(4-Methoxybenzylidene)-4-butylaniline (MBBA) by doping with carbon dots (CDs ∼2.8 ± 0.72 nm). The CDs-MBBA composites (CDs concentration: 0.002, 0.01, 0.03, 0.1 and 0.3 wt%) were investigated using optical polarising microscopy, electro-optical and dielectric techniques. Polarizing optical micrographs and voltage dependent optical transmission revealed the induced homeotropic alignment for all the composites under investigation. Interestingly, the least concentrated sample, 0.002 wt% exhibited partial homeotropic alignment. However, due to light leakage, the optical transmission value below threshold voltage was relatively higher than the rest of the composites. MBBA is a negative dielectric material, hence the application of a voltage across the cell was able to switch the alignment from a dark to a bright state for all composites. However, above a certain voltage (>threshold voltage), the bright state produced some instabilities. The value of dielectric permittivity was observed to decrease with increasing concentration, confirming the effect of CDs in producing homeotropic alignment in MBBA. Measurements as a function of temperature were conducted to examine the thermal stability of the induced alignment. The alignment was found to be stable throughout the nematic phase of MBBA. The induction of such alignment without conventional alignment (i.e., rubbing of polyimides) technique can be helpful in addressing the evolving display demands by making liquid crystal displays (LCDs) and other display devices cost effective.
Beyond ADCs: harnessing bispecific antibodies to directly induce apoptosis for targeted tumor eradication
Authors: Goldmacher Victor S , Gershteyn Iosif M , Kovtun Yelena 
Published: October 2024

Abstract
Bispecific apoptosis triggers (BATs) are innovative bispecific antibodies designed to simultaneously target both a tumor-associated antigen and a cancer cell’s death receptor, thereby directly activating the extrinsic apoptotic pathway to induce death of cancer cells. This unique mechanism distinguishes BATs from antibody-drug conjugates (ADCs), which rely on cytotoxic drugs, and bispecific immune cell engagers such as bispecific T-cell engagers (BiTEs) and bispecific natural killer cell engagers (NKCEs), which recruit immune cells to eliminate target cancer cells. BATs offer significant potential advantages in clinical efficacy and safety over ADCs and BiTEs. Although the field is still emerging, recent advancements are highly promising, and analysis of preclinical and clinical data of DR5-targeting antibodies have been pivotal in outlining the criteria for the next generation of effective and safe medicines. Antibodies found inactive in preclinical testing were also found to be clinically ineffective, whereas antibodies with minimal preclinical results demonstrated moderate clinical activity. All clinical DR5-targeting antibodies were well tolerated by patients even at high doses (with the exception of TAS266 due to its unique design). These findings underscore the predictive value of robust preclinical models on clinical outcomes. Notably, first-in-class BAT, Cancerlysin™ IMV-M, demonstrated potent efficacy in diverse xenograft cancer models and safety in non-human primates, marking a significant advancement in developing safe and effective anti-cancer drugs.
Characteristics of incident benzodiazepine recipients among US veterans with posttraumatic stress disorder
Authors: Shahid Komel N , Hadlandsmyth Katherine , Brainerd Delaney R , Lund Brian C 
Published: October 2024

Abstract

Purpose
While benzodiazepine prescribing among veterans with posttraumatic stress disorder (PTSD) declined substantially in the Veterans Health Administration over the past decade, little is known about current incident prescribing. Our objective was therefore to describe patient, provider, facility, and prescribing characteristics among veterans with PTSD who were incident benzodiazepine recipients in 2022 and contrast these to the characteristics for incident recipients in 2012.

Methods
This retrospective observational study included all veterans with PTSD who received an incident benzodiazepine prescription during calendar year 2022 and separately for 2012. The distribution of patient, provider, facility, and benzodiazepine prescribing characteristics was contrasted between years. Stratified subanalyses were conducted by potential non-PTSD benzodiazepine indication, including anxiety and sleep disorders.

Results
A total of 28,310 (6.6%) incident benzodiazepine recipients were identified in 2012, which decreased to 16,776 (1.9%) incident recipients in 2022. The proportion of initial prescriptions written for a days’ supply of 30 or more days decreased from 75.6% to 51.7%, and the proportion who received a second prescription within 6 months decreased from 68.7% to 53.5%. The proportion of patients with diagnoses for potential benzodiazepine indications also increased, including for generalized anxiety disorder (15.1% increase), obsessive compulsive disorders (0.6% increase), panic disorder (6.7% increase), and sleep disorders (22.9% increase).

Conclusion
As incident benzodiazepine prescribing among veterans with PTSD decreased over the past decade, so did the volume of drug dispensed and duration of therapy, while the prevalence of documented prescriptions for non-PTSD indications increased.
Tribunal duties and the exclusion of evidence in international arbitration: the tug-of-war of fairness and efficiency
Authors: Godhe Aleksander 
Published: October 2024

Abstract
Arbitral procedure aims to strike the right balance between fairness and efficiency in the specific and unique circumstances of the case. However, in some difficult cases, both considerations can pull in opposite directions. The exclusion of evidence provides a particularly apt example in which the dilemma arises, aggravated by the considerable evidentiary discretion vested in tribunals and the overall lack of guidance in applicable laws and rules on approaching such circumstances. This article argues that the tug-of-war between fairness and efficiency in the context of the exclusion of evidence can be resolved with the help of relevant tribunal duties, which should guide the exercise of tribunal discretion. These include the duty (i) to resolve the dispute; (ii) to observe due process; (iii) to conduct the procedure efficiently and effectively; (iv) to act with impartiality and independence; and (v) to render an award that is enforceable/not subject to set aside.
Multimodal contrastive learning for spatial gene expression prediction using histology images
Authors: Min Wenwen , Shi Zhiceng , Zhang Jun , Wan Jun , Wang Changmiao 
Published: October 2024

Abstract
In recent years, the advent of spatial transcriptomics (ST) technology has unlocked unprecedented opportunities for delving into the complexities of gene expression patterns within intricate biological systems. Despite its transformative potential, the prohibitive cost of ST technology remains a significant barrier to its widespread adoption in large-scale studies. An alternative, more cost-effective strategy involves employing artificial intelligence to predict gene expression levels using readily accessible whole-slide images stained with Hematoxylin and Eosin (H&E). However, existing methods have yet to fully capitalize on multimodal information provided by H&E images and ST data with spatial location. In this paper, we propose mclSTExp, a multimodal contrastive learning with Transformer and Densenet-121 encoder for Spatial Transcriptomics Expression prediction. We conceptualize each spot as a “word”, integrating its intrinsic features with spatial context through the self-attention mechanism of a Transformer encoder. This integration is further enriched by incorporating image features via contrastive learning, thereby enhancing the predictive capability of our model. We conducted an extensive evaluation of highly variable genes in two breast cancer datasets and a skin squamous cell carcinoma dataset, and the results demonstrate that mclSTExp exhibits superior performance in predicting spatial gene expression. Moreover, mclSTExp has shown promise in interpreting cancer-specific overexpressed genes, elucidating immune-related genes, and identifying specialized spatial domains annotated by pathologists. Our source code is available at https://github.com/shizhiceng/mclSTExp.
MetaboScope: a statistical toolbox for analyzing 1H nuclear magnetic resonance spectra from human clinical studies
Authors: Loo Ruey Leng , Mosquera Javier Osorio , Zasso Michael , Mathews Jacqueline , Johnston Desmond G , Nicholson Jeremy K , Patiny Luc , Holmes Elaine , Wist Julien 
Published: October 2024

Abstract

Motivation
Metabolic phenotyping, using high-resolution spectroscopic molecular fingerprints of biological samples, has demonstrated diagnostic, prognostic, and mechanistic value in clinical studies. However, clinical translation is hindered by the lack of viable workflows and challenges in converting spectral data into usable information.

Results
MetaboScope is an analytical and statistical workflow for learning, designing and analyzing clinically relevant 1H nuclear magnetic resonance data. It features modular preprocessing pipelines, multivariate modeling tools including Principal Components Analysis (PCA), Orthogonal-Projection to Latent Structure Discriminant Analysis (OPLS-DA), and biomarker discovery tools (multiblock PCA and statistical spectroscopy). A simulation tool is also provided, allowing users to create synthetic spectra for hypothesis testing and power calculations.

Availability and implementation
MetaboScope is built as a pipeline where each module accepts the output generated by the previous one. This provides flexibility and simplicity of use, while being straightforward to maintain. The system and its libraries were developed in JavaScript and run as a web app; therefore, all the operations are performed on the local computer, circumventing the need to upload data. The MetaboScope tool is available at https://www.cheminfo.org/flavor/metabolomics/index.html. The code is open-source and can be deployed locally if necessary. Module notes, video tutorials, and clinical spectral datasets are provided for modeling.
Population-aware permutation-based significance thresholds for genome-wide association studies
Authors: John Maura , Korte Arthur , Todesco Marco , Grimm Dominik G 
Published: October 2024

Abstract

Motivation
Permutation-based significance thresholds have been shown to be a robust alternative to classical Bonferroni significance thresholds in genome-wide association studies (GWAS) for skewed phenotype distributions. The recently published method permGWAS introduced a batch-wise approach to efficiently compute permutation-based GWAS. However, running multiple univariate tests in parallel leads to many repetitive computations and increased computational resources. More importantly, traditional permutation methods that permute only the phenotype break the underlying population structure.

Results
We propose permGWAS2, an improved method that does not break the population structure during permutations and uses an elegant block matrix decomposition to optimize computations, thereby reducing redundancies. We show on synthetic data that this improved approach yields a lower false discovery rate for skewed phenotype distributions compared to the previous version and the commonly used Bonferroni correction. In addition, we re-analyze a dataset covering phenotypic variation in 86 traits in a population of 615 wild sunflowers (Helianthus annuus L.). This led to the identification of dozens of novel associations with putatively adaptive traits, and removed several likely false-positive associations with limited biological support.

Availability and implementation
permGWAS2 is open-source and publicly available on GitHub for download: https://github.com/grimmlab/permGWAS.
Differential expression and co-expression reveal cell types relevant to genetic disorder phenotypes
Authors: Alías-Segura Sergio , Pazos Florencio , Chagoyen Monica 
Published: October 2024

Abstract

Motivation
Knowledge of the specific cell types affected by genetic alterations in rare diseases is crucial for advancing diagnostics and treatments. Despite significant progress, the cell types involved in the majority of rare disease manifestations remain largely unknown. In this study, we integrated scRNA-seq data from non-diseased samples with known genetic disorder genes and phenotypic information to predict the specific cell types disrupted by pathogenic mutations for 482 disease phenotypes.

Results
We found significant phenotype-cell type associations focusing on differential expression and co-expression mechanisms. Our analysis revealed that 13% of the associations documented in the literature were captured through differential expression, while 42% were elucidated through co-expression analysis, also uncovering potential new associations. These findings underscore the critical role of cellular context in disease manifestation and highlight the potential of single-cell data for the development of cell-aware diagnostics and targeted therapies for rare diseases.

Availability and implementation
All code generated in this work is available at https://github.com/SergioAlias/sc-coex
Modeling longitudinal skewed functional data
Authors: Alam Mohammad Samsul , Staicu Ana-Maria 
Published: October 2024

Abstract
This paper introduces a model for longitudinal functional data analysis that accounts for pointwise skewness. The proposed procedure decouples the marginal pointwise variation from the complex longitudinal and functional dependence using copula methodology. Pointwise variation is described through parametric distribution functions that capture varying skewness and change smoothly both in time and over the functional argument. Joint dependence is quantified through a Gaussian copula with a low-rank approximation-based covariance. The introduced class of models provides a unifying platform for both pointwise quantile estimation and prediction of complete trajectories at new times. We investigate the methods numerically in simulations and discuss their application to a diffusion tensor imaging study of multiple sclerosis patients. This approach is implemented in the R package sLFDA that is publicly available on GitHub.
The incremental value of histogram analysis in the differentiation between hyperdense cysts and solid renal masses on unenhanced CT images
Authors: de Souza Torres Cecília Vidal , de Freitas Secaf André , Maia Vieira David Freire , de Moraes Morgado Alexandre Souto , de Moraes Palma Matheus , Andrade Ramos Gabriel , Elias Jr. Jorge , Reis Rodolfo B , Muglia Valdair F 
Published: October 2024

Abstract

Objective
To investigate the utility of voxel histogram analysis (HA) for differentiating hyperdense renal cysts from small solid masses on unenhanced CT scans.

Methods
A retrospective analysis of 99 hyperdense cystic lesions and 28 solid malignant lesions was conducted using a radiological database (from 2015 to 2021) and a pathological database (from 2010 to 2020). The study investigated the distribution of voxel attenuation values using percentiles to establish reliable criteria for differentiation after drawing a region of interest (ROI) in the centre of the lesions. The standard of reference was a histopathological diagnosis for malignant masses and contrast-enhanced CT or MRI for cysts.

Results
HA provided higher diagnostic accuracy than the conventional mean attenuation value of 70 Hounsfield Units (HU). For the 75th and 90th percentiles ± 1 standard deviation, accuracies of 77.2% (95% confidence interval 68.9%-84.2%) for the 75th and 68.5% (59.7%-76.4%) for the 90th were found, versus 37.0% (28.6%-46.0%) for the 70 HU threshold criterion. A Gaussian distribution of voxel attenuation values was observed in 88.9% of the lesions, suggesting that it is feasible to calculate these parameters from a single measurement.

Conclusion
The study underscores the potential of HA as a valuable tool for characterizing hyperdense cysts on unenhanced CT by using the same ROI for measuring lesion attenuation. HA could offer additional value beyond the 70 HU criterion and possibly influence clinical decisions. Multi-institutional studies are necessary for external validation to confirm its generalizability and more extensive applicability.

Advances in knowledge
(1) A single measurement on unenhanced CT images, using mean attenuation and standard deviation, accurately reflects the voxel distribution of both cystic and solid masses, allowing for the application of histogram analysis. (2) The 75th percentile threshold of 65 HU or higher could potentially increase sensitivity in diagnosing hyperdense cysts, compared to the 70 HU mean attenuation threshold, without compromising specificity.