Miracle-dz/UniFAR-FTUs huggingface dataset:
Myeloid Zfhx3 deficiency protects against hypercapnia-induced suppression of host defense against influenza A virus.

Hypercapnia, elevation of the partial pressure of CO2 in blood and tissues, is a risk factor for mortality in patients with severe acute and chronic lung diseases. We previously showed that hypercapnia inhibits multiple macrophage and neutrophil antimicrobial functions, and that elevated CO2 increases the mortality of bacterial and viral pneumonia in mice. Here, we show that normoxic hypercapnia downregulates innate immune and antiviral gene programs in alveolar macrophages (AMØs). We also show that zinc finger homeobox 3 (Zfhx3), a mammalian ortholog of zfh2, which mediates hypercapnic immune suppression in Drosophila, is expressed in mouse and human macrophages. Deletion of Zfhx3 in the myeloid lineage blocked the suppressive effect of hypercapnia on immune gene expression in AMØs and decreased viral replication, inflammatory lung injury and mortality in hypercapnic mice infected with influenza A virus. Our results establish Zfhx3 as the first known mammalian mediator of CO2 effects on immune gene expression and lay the basis for future studies to identify therapeutic targets to interrupt hypercapnic immunosuppression in patients with advanced lung disease.
Systematic review of machine-learning models in orthopaedic trauma

Aims Machine-learning (ML) prediction models in orthopaedic trauma hold great promise in assisting clinicians in various tasks, such as personalized risk stratification. However, an overview of current applications and critical appraisal to peer-reviewed guidelines is lacking. The objectives of this study are to 1) provide an overview of current ML prediction models in orthopaedic trauma; 2) evaluate the completeness of reporting following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement; and 3) assess the risk of bias following the Prediction model Risk Of Bias Assessment Tool (PROBAST) tool. Methods A systematic search screening 3,252 studies identified 45 ML-based prediction models in orthopaedic trauma up to January 2023. The TRIPOD statement assessed transparent reporting and the PROBAST tool the risk of bias. Results A total of 40 studies reported on training and internal validation; four studies performed both development and external validation, and one study performed only external validation. The most commonly reported outcomes were mortality (33%, 15/45) and length of hospital stay (9%, 4/45), and the majority of prediction models were developed in the hip fracture population (60%, 27/45). The overall median completeness for the TRIPOD statement was 62% (interquartile range 30 to 81%). The overall risk of bias in the PROBAST tool was low in 24% (11/45), high in 69% (31/45), and unclear in 7% (3/45) of the studies. High risk of bias was mainly due to analysis domain concerns including small datasets with low number of outcomes, complete-case analysis in case of missing data, and no reporting of performance measures. Conclusion The results of this study showed that despite a myriad of potential clinically useful applications, a substantial part of ML studies in orthopaedic trauma lack transparent reporting, and are at high risk of bias. These problems must be resolved by following established guidelines to instil confidence in ML models among patients and clinicians. Otherwise, there will remain a sizeable gap between the development of ML prediction models and their clinical application in our day-to-day orthopaedic trauma practice. Cite this article: Bone Jt Open 2024;5(1):9–19.
Defense mechanisms are associated with mental health symptoms across six countries

Defense mechanisms are adaptative processes that are related to mental health and psychological functioning and may play an important role in adaptation to distress, as well as in mental health interventions. The present study aimed to compare the use of defense mechanisms and their relationship to mental health symptoms across six countries. In a large-scale descriptive study, we collected data from community- based individuals (N=19,860) in the United States, Australia, Canada, Germany, Italy, and the United Kingdom about the use of defense mechanisms and experienced mental health symptoms during the early phase of the pandemic. We found that the use of defense mechanism categories was similar across countries. Moreover, lower defensive functioning, specifically, neurotic and immature defenses were related to experiencing higher distress across countries, whereas mature defenses were generally inversely related to symptoms. Furthermore, these findings were relatively similar across the six countries. Cross-cultural research on defense mechanisms and mental health has important clinical implications. Our results are consistent with the goal of promoting more adaptive defensive functioning to increase psychological well-being and mitigate the detrimental impact of situational stress.
Development and Validation of a Self-Aligning Knee Exoskeleton With Hip Rotation Capability

The self-aligning capability of an exoskeleton is important to ensure wearing comfort, and the delicate motion ability of the exoskeleton is essential for motion assistance. Designing a self-aligning exoskeleton that offers improved wearing comfort and enhanced motion-assistance functions remains a challenge. This paper proposes a novel spatial self-aligning mechanism for a knee exoskeleton to enable simultaneous assistance in the flexion and extension (FE) of the knee joint and the internal and external rotation (IER) of the hip joint. Additionally, considering the misalignment of the human-robot joint axes, a kinematic model of the knee exoskeleton is established and analyzed to demonstrate the kinematic compatibility of the exoskeleton. Furthermore, a global torque manipulability (GTM) index is proposed to evaluate the effects of dimensional parameters on the exoskeleton’s performance, and then the knee exoskeleton is optimized according to the GTM index. Finally, experiments are conducted to validate the performance of the proposed exoskeleton. The experimental results show that during knee FE and hip IER, the proposed exoskeleton exhibits lower interaction forces and torques than existing exoskeletons.
Late‐Fall, Winter, and Spring Broadcast Applications of Urea to No‐Till Winter Wheat II. Fertilizer N recovery, Yield, and Protein as Affected by NBPT

Surface application of urea to winter wheat (Triticum aestivum L.) during the late-fall, winter, or spring is a common practice in the northern Great Plains (NGP). However, surface applications are susceptible to volatilization, and management strategies are needed to enhance fertilizer-N recovery (FNR). This study was conducted to determine the effect of urea application timing (late-fall, winter, spring) and N-(n-butyl) thiophosphoric triamide (NBPT) on FNR in grain (FNRG) and grain plus straw (FNRT), and grain yield and protein. Replicated small-plot experiments were conducted in no-till winter wheat fields over 3 yr in Montana, and FNR was quantified using ¹⁵N-enriched labeled urea (5% atom). Fertilizer-N recovery was greater (P < 0.05) for spring (FNRG 36.5%, FNRT 46.1%) than late-fall (FNRG 24.9%, FNRT 31.7%) and winter (FNRG 26.8, FNRT 34.1%) applications. Addition of NBPT to urea improved FNR of all urea timings, but the response was greater for late-fall and winter compared with spring applications. Fertilizer-N recovery among the management strategies tested was directly related to cumulative NH₃ emissions (r² = 0.77, P < 0.0001) measured in a companion study, indicating that volatilization represented an important pathway for N loss. Grain yield was not sensitive to application timing or NBPT. However, grain protein increased 6 to 8 g kg–¹ from spring applications or NBPT additions. This study found that management strategies to enhance urea FNR in the NGP should consider delaying applications until the spring or addition of NBPT for late-fall and winter applications that are more susceptible to NH₃ volatilization.
Effects of kitchen waste biofertilizer on growth and development of Wheat (Triticum aestivum L.) crop

The growth and development of Wheat (Triticum aestivum Linn.) plant has been studied using self-prepared kitchen waste biofertilizer. The experimental plants which were grown using the biofertilizer prepared using the kitchen biodegradable wastes excelled on all the parameters tested (rate of seed germination, root – shoot ratio, chlorophyll contents in leaves and Nitrate Reductase activity) in comparison to traditional chemical fertilizers and therefore, the prepared biofertilizer has been found to be more effective nutrient supplier than the traditional chemical fertilizers.
Call graph discovery in binary programs from unknown instruction set architectures

This study addresses the challenge of reverse engineering binaries from unknown instruction set architectures, a complex task with potential implications for software maintenance and cyber-security. We focus on the tasks of detecting candidate call and return opcodes for automatic extraction of call graphs in order to simplify the reverse engineering process. Empirical testing on a small dataset of binary files from different architectures demonstrates that the approach can accurately detect specific opcodes under conditions of noisy data. The method lays the groundwork for a valuable tool for reverse engineering where the reverse engineer has minimal a priori knowledge of the underlying instruction set architecture.
Gap filling and homogenization of climatological datasets in the headwater region of the Upper Blue Nile Basin, Ethiopia

Long‐term time series of climatological data measured at meteorological stations provide one of the most authentic annals of climate in the past. However, there are a host of factors that affect measurements of climate parameters and make the data unsuitable for direct use and analysis. Serial completion and homogenization thus have to be undertaken in order to draw valid conclusions about the climate, or to apply the time series in impact studies. This study reports on gap filling and homogenization results of climatological elements in the headwater region of the Upper Blue Nile Basin from 1980 to 2013. Firstly, approaches for reconstruction of the time series from neighboring stations using different techniques were compared and selected. Neighboring stations were selected based on horizontal distance and coefficient of correlation. Secondly, the reconstructed time series were homogenized using Multiple Analysis of Series for Homogenization (MASH). The results show improved spatial coherence of the final data series. Higher spatial coherence was revealed for maximum temperature than for minimum temperature and rainfall. For rainfall and minimum and maximum temperatures, the coefficient of correlation weighting method outperforms other candidate methods, such as the normal ratio method (NRM), the modified NRM, and the inverse distance weighting method. Rainfall series for half of the stations considered were found to be homogeneous, and thus inhomogeneity corrections were not applied. Inhomogeneity in the remaining stations either underestimates or overestimates annual rainfall series. All stations revealed inhomogeneity in the mean annual maximum and minimum temperature series. These serially complete and homogenized data on rainfall, and minimum and maximum temperatures of the present study can be used for climate change and hydrological studies in the basin.
Adaptoring: Adapter Generation to Provide an Alternative API for a Library

Third-party libraries are a cornerstone of fast application development. To enable efficient use, libraries must provide a well-designed API. An obscure API instead slows down the learning process and can lead to erroneous use. The usual approach to improve the API of a library is to edit its code directly, either keeping the old API but deprecating it (temporarily increasing the API size) or dropping it (introducing breaking changes). If maintainers are unwilling to make such changes, others need to create a hard fork, which they can refactor. But then it is difficult to incorporate changes to the original library, such as bug fixes or performance improvements. In this paper, we instead explore the use of the adapter pattern to provide a new API as a new library that calls the original library internally. This allows the new library to leverage all implementation changes to the original library, at no additional cost. We call this approach adaptoring. To make the approach practical, we identify API transformations for which adapter code can be generated automatically, and investigate which transformations can be inferred automatically, based on the documentation and usage patterns of the original library. For cases where automated inference is not possible, we present a tool that lets developers manually specify API transformations. Finally, we consider the issue of migrating the generated adapters if the original library introduces breaking changes. We implemented our approach for Python, demonstrating its effectiveness to quickly provide an alternative API even for large libraries.
Pedestrian Detection in Low-Light Conditions: A Comprehensive Survey

Pedestrian detection remains a critical problem in various domains, such as computer vision, surveillance, and autonomous driving. In particular, accurate and instant detection of pedestrians in low-light conditions and reduced visibility is of utmost importance for autonomous vehicles to prevent accidents and save lives. This paper aims to comprehensively survey various pedestrian detection approaches, baselines, and datasets that specifically target low-light conditions. The survey discusses the challenges faced in detecting pedestrians at night and explores state-of-the-art methodologies proposed in recent years to address this issue. These methodologies encompass a diverse range, including deep learning-based, feature-based, and hybrid approaches, which have shown promising results in enhancing pedestrian detection performance under challenging lighting conditions. Furthermore, the paper highlights current research directions in the field and identifies potential solutions that merit further investigation by researchers. By thoroughly examining pedestrian detection techniques in low-light conditions, this survey seeks to contribute to the advancement of safer and more reliable autonomous driving systems and other applications related to pedestrian safety. Accordingly, most of the current approaches in the field use deep learning-based image fusion methodologies (i.e., early, halfway, and late fusion) for accurate and reliable pedestrian detection. Moreover, the majority of the works in the field (approximately 48%) have been evaluated on the KAIST dataset, while the real-world video feeds recorded by authors have been used in less than six percent of the works.
A Bayesian Approach to Modeling Variance of Intensive Longitudinal Biomarker Data as a Predictor of Health Outcomes

Intensive longitudinal biomarker data are increasingly common in scientific studies that seek temporally granular understanding of the role of behavioral and physiological factors in relation to outcomes of interest. Intensive longitudinal biomarker data, such as those obtained from wearable devices, are often obtained at a high frequency typically resulting in several hundred to thousand observations per individual measured over minutes, hours, or days. Often in longitudinal studies, the primary focus is on relating the means of biomarker trajectories to an outcome, and the variances are treated as nuisance parameters, although they may also be informative for the outcomes. In this paper, we propose a Bayesian hierarchical model to jointly model a cross-sectional outcome and the intensive longitudinal biomarkers. To model the variability of biomarkers and deal with the high intensity of data, we develop subject-level cubic B-splines and allow the sharing of information across individuals for both the residual variability and the random effects variability. Then different levels of variability are extracted and incorporated into an outcome submodel for inferential and predictive purposes. We demonstrate the utility of the proposed model via an application involving bio-monitoring of hertz-level heart rate information from a study on social stress.
Detecting coronal mass ejections with machine learning methods

Flares on the Sun are often associated with ejected plasma: these events are known as coronal mass ejections (CMEs). These events, although are studied in detail on the Sun, have only a few dozen known examples on other stars, mainly detected using the Doppler-shifted absorption/emission features in Balmer lines and tedious manual analysis. We present a possibility to find stellar CMEs with the help of high-resolution solar spectra.
Harnessing Deep Learning and Satellite Imagery for Post-Buyout Land Cover Mapping

Environmental disasters such as floods, hurricanes, and wildfires have increasingly threatened communities worldwide, prompting various mitigation strategies. Among these, property buyouts have emerged as a prominent approach to reducing vulnerability to future disasters. This strategy involves governments purchasing at-risk properties from willing sellers and converting the land into open space, ostensibly reducing future disaster risk and impact. However, the aftermath of these buyouts, particularly concerning land-use patterns and community impacts, remains under-explored. This research aims to fill this gap by employing innovative techniques like satellite imagery analysis and deep learning to study these patterns. To achieve this goal, we employed FEMA's Hazard Mitigation Grant Program (HMGP) buyout dataset, encompassing over 41,004 addresses of these buyout properties from 1989 to 2017. Leveraging Google's Maps Static API, we gathered 40,053 satellite images corresponding to these buyout lands. Subsequently, we implemented five cutting-edge machine learning models to evaluate their performance in classifying land cover types. Notably, this task involved multi-class classification, and our model achieved an outstanding ROC-AUC score of 98.86%
DynaHull: Density-centric Dynamic Point Filtering in Point Clouds

In the field of indoor robotics, accurately navigating and mapping in dynamic environments using point clouds can be a challenging task due to the presence of dynamic points. These dynamic points are often represented by people in indoor environments, but in industrial settings with moving machinery, there can be various types of dynamic points. This study introduces DynaHull, a novel technique designed to enhance indoor mapping accuracy by effectively removing dynamic points from point clouds. DynaHull works by leveraging the observation that, over multiple scans, stationary points have a higher density compared to dynamic ones. Furthermore, DynaHull addresses mapping challenges related to unevenly distributed points by clustering the map into smaller sections. In each section, the density factor of each point is determined by dividing the number of neighbors by the volume these neighboring points occupy using a convex hull method. The algorithm removes the dynamic points using an adaptive threshold based on the point count of each cluster, thus reducing the false positives. The performance of DynaHull was compared to state-of-the-art techniques, such as ERASOR, Removert, OctoMap, and a baseline statistical outlier removal from Open3D, by comparing each method to the ground truth map created during a low activity period in which only a few dynamic points were present. The results indicated that DynaHull outperformed these techniques in various metrics, noticeably in the Earth Mover's Distance. This research contributes to indoor robotics by providing efficient methods for dynamic point removal, essential for accurate mapping and localization in dynamic environments.
Incremental Extractive Opinion Summarization Using Cover Trees

Extractive opinion summarization involves automatically producing a summary of text about an entity (e.g., a product's reviews) by extracting representative sentences that capture prevalent opinions in the review set. Typically, in online marketplaces user reviews accrue over time, and opinion summaries need to be updated periodically to provide customers with up-to-date information. In this work, we study the task of extractive opinion summarization in an incremental setting, where the underlying review set evolves over time. Many of the state-of-the-art extractive opinion summarization approaches are centrality-based, such as CentroidRank. CentroidRank performs extractive summarization by selecting a subset of review sentences closest to the centroid in the representation space as the summary. However, these methods are not capable of operating efficiently in an incremental setting, where reviews arrive one at a time. In this paper, we present an efficient algorithm for accurately computing the CentroidRank summaries in an incremental setting. Our approach, CoverSumm, relies on indexing review representations in a cover tree and maintaining a reservoir of candidate summary review sentences. CoverSumm's efficacy is supported by a theoretical and empirical analysis of running time. Empirically, on a diverse collection of data (both real and synthetically created to illustrate scaling considerations), we demonstrate that CoverSumm is up to 25x faster than baseline methods, and capable of adapting to nuanced changes in data distribution. We also conduct human evaluations of the generated summaries and find that CoverSumm is capable of producing informative summaries consistent with the underlying review set.
Semi-supervised Semantic Segmentation using Redesigned Self-Training for White Blood Cell

Artificial Intelligence (AI) in healthcare, especially in white blood cell cancer diagnosis, is hindered by two primary challenges: the lack of large-scale labeled datasets for white blood cell (WBC) segmentation and outdated segmentation methods. These challenges inhibit the development of more accurate and modern techniques to diagnose cancer relating to white blood cells. To address the first challenge, a semi-supervised learning framework should be devised to efficiently capitalize on the scarcity of the dataset available. In this work, we address this issue by proposing a novel self-training pipeline with the incorporation of FixMatch. Self-training is a technique that utilizes the model trained on labeled data to generate pseudo-labels for the unlabeled data and then re-train on both of them. FixMatch is a consistency-regularization algorithm to enforce the model's robustness against variations in the input image. We discover that by incorporating FixMatch in the self-training pipeline, the performance improves in the majority of cases. Our performance achieved the best performance with the self-training scheme with consistency on DeepLab-V3 architecture and ResNet-50, reaching 90.69%, 87.37%, and 76.49% on Zheng 1, Zheng 2, and LISC datasets, respectively.