bluuebunny/crossref_metadata_2025_split huggingface dataset:
Forensic characteristics of 4866 violent injury cases in Sichuan Province, China
Authors: Zhang Shilin , Wang Wei , Wei Mengxuan , Luo Yu , Long Wu , Li Lincong , Jiang Chunyue , Zhu Tao , Lin Xia , Jin Bo 
Published: April 2023

AbstractTo evaluate the characteristics of violent injury cases in Sichuan Province, China. Overall, 4866 violent injury cases in Sichuan province, China from 2014 to 2017 were included. The injury evaluation report was used to classify and summarize the injury information, case and injury characteristics, and to describe the characteristics for each risk factor. The majority of cases were males (n = 3851, 79.14%), aged 20–60 (n = 3867, 79.47%), and living in rural areas (n = 3094, 65.55%). Many cases occurred in public areas (n = 3351, 74.19%) and in the evening (n = 1005, 29.49%). Passion was the main motive for the violent injuries (n = 2098, 82.11%) and the main types of injuries were those to the brain, face, and auricula (n = 3075, 63.21%). Blunt instruments (n = 2951, 64.86%) were most commonly used to inflict injury, and the injury evaluation determined that the majority of injuries were simple (n = 2669, 54.85%) and slight (n = 1685, 34.63%). For cases resulting from passion and money, blunt instruments were more commonly used, while sharp instruments were more commonly used for injuries resulting from emotion and revenge (p < 0.05). Compared with grievous injuries, public and entertainment areas and the use of blunt instruments were risk factors for slight injuries. The use of blunt instruments was a risk factor for simple injuries. The cases of violent injury in the Sichuan Province of China have certain characteristics and causes. In order to reduce the frequency of such cases, corresponding intervention measures should be actively conducted at the identified high risk places, times, and populations.
Chemical composition and repellent activity of essential oils of Tithonia diversifolia (Asteraceae) leaves against the bites of Anopheles coluzzii
Authors: Akeumbiwo Tchumkam Cédric , Kojom Foko Loick Pradel , Ndo Cyrille , Essangui Same Estelle , Cheteug Nguetsa Glwadys , Eya’Ane Meva François , Ayong Lawrence , Eboumbou Moukoko Carole Else 
Published: April 2023

AbstractTithonia diversifolia is widely used in African traditional medicine for the treatment of a large number of ailments and disorders, including malaria. In the present study, we evaluated the repellent activity of essential oils (EO) of this plant against Anopheles coluzzii, a major vector of malaria in Africa. Fresh leaves of T. diversifolia were used to extract EO, which were used to perform repellency assays in the laboratory and in the field using commercially available N,N-Diethyl-meta-toluamide (DEET) and Cymbopogon (C.) citratus EO as positive controls and vaseline as negative control. The repellency rates and durations of protection of the human volunteers involved were used as measures of repellent activity. Chemical composition of the T. diversifolia EO was established further by gas chromatography coupled with mass spectrometry. The moisture content and oil yield were 81% and 0.02% respectively. A total of 29 compounds in the T. diversifolia EO was identified, with d-limonene (20.1%), α-Copaene (10.3%) and o-Cymene (10.0%) as the most represented. In field studies, the mean time of protection against mosquito bites was significantly lower in T. diversifolia EO-treated volunteers compared to treatments with C. citratus EO (71 min versus 125 min, p = 0.04), but significantly higher when compared with the non-treated volunteers (71 min vs 0.5 min, p = 0.03). The same pattern was found in laboratory repellency assays against A. coluzzii. In contrast, repulsion rates were statistically similar between T. diversifolia EO and positive controls. In conclusion, the study suggests promising repellent potential of leaves of T. diversifolia EO against A. coluzzii.
Cosmic time calibrator for wireless sensor network
Authors: Tanaka Hiroyuki K. M. 
Published: April 2023

AbstractTime synchronization of sensor nodes is critical for optimal operation of wireless sensor networks (WSNs). Since clocks incorporated into each node tend to drift, recurrent corrections are required. Most of these correction schemes involve clients periodically receive RF timing signals from a time server. However, an RF-based scheme is prone to glitches or failure unless operating in a region with almost entirely unobstructed space; hence it only operates well in a limited range of environments. For example, GPS requires open-sky environments. Moreover, the precision of land-based RF schemes is limited to a few micro seconds. In this work, we report on a more versatile and new type of recurrent clock resynchronization scheme called cosmic time calibrator (CTC) and its development and testing. CTC utilizes cosmic-ray muon signals instead of RF signals. Muons are penetrative and continuously precipitating onto the Earth’s surface, and they tend to travel linearly through encountered matter at approximately the speed of light in vacuum. Therefore, muons themselves can periodically transfer the precise timing information from node to node; hence, the performance of the inter-nodal communication device such as Wi-Fi or Bluetooth is minimized/unnecessary for an online/offline WSN analysis. The experimental results have indicated that a resynchronization frequency and precision of 60 Hz and ± 4.3 ns (S.D.) can be achieved. Modelling work of the WSN-based structural health monitoring of aerospace structures has shown that CTC can contribute to the development of new critical and useful applications of WSN in a wider range of environments.
5-methyladenosine regulators play a crucial role in development of chronic hypersensitivity pneumonitis and idiopathic pulmonary fibrosis
Authors: Zhou Yiyi , Hu Zhenli , Sun Qinying , Dong Yuchao 
Published: April 2023

Abstract5-methyladenosine (m5C) modification regulates gene expression and biological functions in oncologic areas. However, the effect of m5C modification in chronic hypersensitivity pneumonitis (CHP) and idiopathic pulmonary fibrosis (IPF) remains unknown. Expression data for 12 significant m5C regulators were obtained from the interstitial lung disease dataset. Five candidate m5C regulators, namely tet methylcytosine dioxygenase 2, NOP2/Sun RNA methyltransferase 5, Y-box binding protein 1, tRNA aspartic acid methyltransferase 1, and NOP2/Sun RNA methyltransferase 3 were screened using random forest and nomogram models to predict risks of pulmonary fibrosis. Next, we applied the consensus clustering method to stratify the samples with different m5C patterns into two groups (cluster A and B). Finally, we calculated immune cell infiltration scores via single-sample gene set enrichment analysis, then compared immune cell infiltration, related functions as well as the expression of programmed cell death 1 (PD-1, PDCD1) and programmed death protein ligand-1 (PD-L1, CD274) between the two clusters. Principal component analysis of m5C-related scores across the 288 samples revealed that cluster A had higher immune-related expression than B. Notably, T helper cell (Th) 2 type cytokines and Th1 signatures were more abundant in clusters A and B, respectively. Our results suggest that m5C is associated with and plays a crucial role in development of pulmonary fibrosis. These m5C patterns could be potential biomarkers for identification of CHP and IPF, and guide future development of immunotherapy or other new drugs strategies for pulmonary fibrosis.
Self-assembling bilayer wiring with highly conductive liquid metal and insulative ion gel layers
Authors: Murakami Koki , Isano Yuji , Asada Juri , Usami Natsuka , Isoda Yutaka , Takano Tamami , Matsuda Ryosuke , Ueno Kazuhide , Fuchiwaki Ohmi , Ota Hiroki 
Published: April 2023

AbstractGa-based liquid metals (LMs) are expected to be suitable for wiring highly deformable devices because of their high electrical conductivity and stable resistance to extreme deformation. Injection and printed wiring, and wiring using LM–polymer composites are the most popular LM wiring approaches. However, additional processing is required to package the wiring after LM patterning, branch and interrupt wiring shape, and ensure adequate conductivity, which results in unnecessary wiring shape changes and increased complexity of the wiring methods. In this study, we propose an LM–polymer composite comprising LM particles and ion gel as a flexible matrix material with low viscosity and specific gravity before curing. Moreover, the casting method is used for wire patterning, and the material is cured at room temperature to ensure that the upper insulative layer of the ion gel self-assembles simultaneously with the formation of LM wiring in the lower layer. High conductivity and low resistance change rate of the formed wiring during deformation are achieved without an activation process. This ion gel–LM bilayer wiring can be used for three-dimensional wiring by stacking. Furthermore, circuits fabricated using ion gel–LM bilayer wiring exhibit stable operation. Therefore, the proposed method can significantly promote the development of flexible electronic devices.
Heat transport in inclined flow towards a rotating disk under MHD
Authors: Mahmud K. , Duraihem Faisal Z. , Mehmood R. , Sarkar S. , Saleem S. 
Published: April 2023

AbstractFlow towards a rotating disk is of highly practical significance in numerous engineering applications such as Turbine disks, rotary type machine systems and many more. In light of this, the current work is an attempt to explore MHD oblique flow towards a rotating disk. Hydromagnetic effects in addition to heat transfer is taken into consideration. The flow governing Partial Differential Equations are altered to a system to coupled non-linear Ordinary Differential Equations through scaling group of transformations which afterwards are tackled using Shooting Algorithm. The impact of obliqueness parameter γ, rotation ratio parameter $$\alpha$$
α
and magnetic field parameter M on 2-dimensional and 3-dimensional stream contours are presented. Location of the shear center varies with magnetic field parameter. Heat flow at the disk surface boosts with magnet field parameter M and rotation ratio parameter $$\alpha$$
α
.
Flubendazole exhibits anti-glioblastoma effect by inhibiting STAT3 and promoting cell cycle arrest
Authors: Vítovcová Barbora , Skarková Veronika , Havelek Radim , Soukup Jiří , Pande Ananya , Caltová Kateřina , Rudolf Emil 
Published: April 2023

AbstractGlioblastoma multiforme (GBM) belongs to most aggressive and invasive primary brain tumor in adults whose prognosis and survival remains poor. Potential new treatment modalities include targeting the cytoskeleton. In our study, we demonstrated that repurposed drug flubendazole (FLU) significantly inhibits proliferation and survival of GBM cells. FLU exerted its effect by affecting microtubule structure and our results also suggest that FLU influences tubulins expression to a certain degree. Moreover, FLU effects decreased activation of STAT3 and also partially inhibited its expression, leading to upregulation of p53 signaling pathway and subsequent cell cycle arrest at G2/M phase as well as caspase-dependent cell death in GBM cells. These results suggest FLU as a promising agent to be used in GBM treatment and prompting further testing of its effects on GBM.
Location-based collective distress using large-scale biosignals in real life for walkable built environments
Authors: Kim Jinwoo , Nirjhar Ehsanul Haque , Lee Hanwool , Chaspari Theodora , Lee Chanam , Ham Youngjib , Winslow Jane Futrell , Ahn Changbum R. 
Published: April 2023

AbstractBiosignals from wearable sensors have shown great potential for capturing environmental distress that pedestrians experience from negative stimuli (e.g., abandoned houses, poorly maintained sidewalks, graffiti, and so forth). This physiological monitoring approach in an ambulatory setting can mitigate the subjectivity and reliability concerns of traditional self-reported surveys and field audits. However, to date, most prior work has been conducted in a controlled setting and there has been little investigation into utilizing biosignals captured in real-life settings. This research examines the usability of biosignals (electrodermal activity, gait patterns, and heart rate) acquired from real-life settings to capture the environmental distress experienced by pedestrians. We collected and analyzed geocoded biosignals and self-reported stimuli information in real-life settings. Data was analyzed using spatial methods with statistical and machine learning models. Results show that the machine learning algorithm predicted location-based collective distress of pedestrians with 80% accuracy, showing statistical associations between biosignals and the self-reported stimuli. This method is expected to advance our ability to sense and react to not only built environmental issues but also urban dynamics and emergent events, which together will open valuable new opportunities to integrate human biological and physiological data streams into future built environments and/or walkability assessment applications.
Discovery and systematic assessment of early biomarkers that predict progression to severe COVID-19 disease
Authors: Hufnagel Katrin , Fathi Anahita , Stroh Nadine , Klein Marco , Skwirblies Florian , Girgis Ramy , Dahlke Christine , Hoheisel Jörg D. , Lowy Camille , Schmidt Ronny , Griesbeck Anne , Merle Uta , Addo Marylyn M. , Schröder Christoph 
Published: April 2023

Abstract
Background
The clinical course of COVID-19 patients ranges from asymptomatic infection, via mild and moderate illness, to severe disease and even fatal outcome. Biomarkers which enable an early prediction of the severity of COVID-19 progression, would be enormously beneficial to guide patient care and early intervention prior to hospitalization.

Methods
Here we describe the identification of plasma protein biomarkers using an antibody microarray-based approach in order to predict a severe cause of a COVID-19 disease already in an early phase of SARS-CoV-2 infection. To this end, plasma samples from two independent cohorts were analyzed by antibody microarrays targeting up to 998 different proteins.

Results
In total, we identified 11 promising protein biomarker candidates to predict disease severity during an early phase of COVID-19 infection coherently in both analyzed cohorts. A set of four (S100A8/A9, TSP1, FINC, IFNL1), and two sets of three proteins (S100A8/A9, TSP1, ERBB2 and S100A8/A9, TSP1, IFNL1) were selected using machine learning as multimarker panels with sufficient accuracy for the implementation in a prognostic test.

Conclusions
Using these biomarkers, patients at high risk of developing a severe or critical disease may be selected for treatment with specialized therapeutic options such as neutralizing antibodies or antivirals. Early therapy through early stratification may not only have a positive impact on the outcome of individual COVID-19 patients but could additionally prevent hospitals from being overwhelmed in potential future pandemic situations.
A multi‐stage model for bird's eye view prediction based on stereo‐matching model and RGB‐D semantic segmentation
Authors: Rao Zhongyu , Cai Yingfeng , Wang Hai , Chen Long , Li Yicheng 
Published: April 2023

AbstractBird's‐Eye‐View (BEV) map is a powerful and detailed scene representation for intelligent vehicles that provides both the location and semantic information about nearby objects from a top‐down perspective. BEV map generation is a complex multi‐stage task, and the existing methods typically perform poorly for distant scenes. Thus, the authors introduce a novel multi‐stage model that infers to obtain more accurate BEV map. First, the authors propose the Adaptive Aggregation with Stereo Mixture Density (AA‐SMD) model, which is an improved stereo matching model that eliminates bleeding artefacts and provides more accurate depth estimation. Next, the authors employ the RGB‐Depth (RGB‐D) semantic segmentation model to improve the semantic segmentation performance and connectivity of their model. The depth map and semantic segmentation maps are then combined to create an incomplete BEV map. Finally, the authors propose a Multi Strip Pooling Unet (MSP‐Unet) model with a hierarchical multi‐scale (HMS) attention and strip pooling (SP) module to improve prediction with BEV generation. The authors evaluate their model with a Car Learn to Act (CARLA)‐generated synthetic dataset. The experiment results demonstrate that the authors’ model generates a highly accurate representation of the surrounding environment achieving a state‐of‐the‐art result of 61.50% Mean Intersection‐over‐Union (MIoU) across eight classes.
A double‐modulation‐wave discontinuous pulse‐width‐modulation for dead‐time‐effect elimination and switching‐loss reduction in two‐level power converters
Authors: Yan Qingzeng , Zhou Zanrong , Wu Mingbo 
Published: April 2023

AbstractHigh current qualities and high efficiencies are always two important characteristics for power converters. However, it is challenging to achieve the two targets simultaneously. In this paper, the dead‐time‐effect elimination pulse‐width‐modulation (PWM) for improving current qualities and the discontinuous PWM (DPWM) for reducing switching losses are combined, proposing a double‐modulation‐wave DPWM. An additional modulation wave is added, whose amplitude is adjusted according to the current polarity. By utilizing the added drive pulses in the current freewheeling stage, the proposed double‐modulation‐wave DPWM successfully reduces the dependence on the current polarities. The overmodulation phenomenon brought by the added modulation wave is analysed, which is proved to be negligible for achieving the maximum modulation index of 1.15. Finally, the effectiveness of the proposed double‐modulation‐wave DPWM is experimentally verified at a switching frequency of 40 kHz.
Clustering the properties of near-Earth objects: physico-dynamical links among NEOs
Authors: Deshapriya Jasinghege Don Prasanna , Perna Davide , Bott Nicolas , Hasselmann Pedro Henrique , Granvik Mikael , Dotto Elisabetta , Fulchignoni Marcello , Giunta Alessio , Perozzi Ettore , Ieva Simone , Petropoulou Vasiliki , Epifani Elena Mazzotta 
Published: April 2023

Context. At present, near-Earth objects (NEOs) are being discovered at an ever-increasing rate. However, their physical characterisation is still significantly lagging behind. In particular, the taxonomic classification of newly discovered NEOs is of great importance with regard to improving our understanding of the population of NEOs.
Aims. In this context, our goal is to probe potential links between orbital properties of NEOs and their composition. We investigate whether we can make a reasonable guess about the taxonomic class of an NEO upon its discovery with a decent orbital accuracy.
Methods. We used a G-mode multivariate statistical clustering method to find homogeneous clusters in a dataset composed of orbital elements of NEOs. We adopted two approaches, using two sets of variables as inputs to the G-mode method. In each approach, we analysed the available taxonomic distribution of resulting clusters to find potential correlations with several unique parameters that distinctively characterise NEOs. We then applied a dynamical model on the same clusters to trace their escape regions.
Results. Approach 1 (A1) led us to obtain NEO clusters that can be linked to a primitive composition. This result was further strengthened by the dynamical model, which mapped outer-belt sources as escape regions for these clusters. We remark on the finding of a cluster akin to S-type NEOs in highly eccentric orbits during the same approach (A1). Two clusters, one with small NEOs in terrestriallike orbits and one with relatively high inclinations, were found to be common to both approaches. Approach 2 (A2) revealed three clusters that are only separable by their arguments of perihelion. Taken altogether, they make up the majority of known Atira asteroids.
Conclusions. For an NEO whose orbit is relatively well determined, we propose a model to determine whether the taxonomy of an NEO is siliceous or primitive if the orbital elements of the NEO fall within the presented combinations of inclination, eccentricity, and semi-major axis ranges.
Red-emitting and antibacterial carbon dots for cellular imaging and skin wound healing
Authors: Karami Sara , Shamsipur Mojtaba , Barati Ali , Fartootzadeh Reza , Molaabasi Fatemeh , Alipour Mohsen , Radi Hamid Cheraghian 
Published: April 2023

Abstract
Carbon dots (CDs) as a new type of fluorescent nanomaterials have attracted considerable attention from researchers in chemistry, biology, and materials science. Here, we employed a hydrothermal method for synthesis of red-emitting and antibacterial CDs displaying an excitation-independent emission characteristic with a maximum emission intensity at 610 nm (pH 7.4). The synthesised CDs exhibited a high capability for bioimaging in HeLa cells, because of their negligible cytotoxicity and acceptable red photoluminescence. Moreover, the antibacterial activity of CDs against P. aeruginosa was studied and showed a minimum inhibitory concentration (MIC) of 256 μg ml−1. Further experimental results showed the high ability of the CDs to heal skin wounds in the rat model. According to the observations, wound healing in the presence of the CDs was calculated four days faster with 20.48% better performance than the control group.
Latent disease similarities and therapeutic repurposing possibilities uncovered by multi-modal generative topic modeling of human diseases
Authors: Kozawa Satoshi , Yokoyama Hirona , Urayama Kyoji , Tejima Kengo , Doi Hotaka , Takagi Shunki , Sato Thomas N 
Published: April 2023

Abstract

Motivation
Human diseases are characterized by multiple features such as their pathophysiological, molecular and genetic changes. The rapid expansion of such multi-modal disease-omics space provides an opportunity to re-classify diverse human diseases and to uncover their latent molecular similarities, which could be exploited to repurpose a therapeutic-target for one disease to another.

Results
Herein, we probe this underexplored space by soft-clustering 6955 human diseases by multi-modal generative topic modeling. Focusing on chronic kidney disease and myocardial infarction, two most life-threatening diseases, unveiled are their previously underrecognized molecular similarities to neoplasia and mental/neurological-disorders, and 69 repurposable therapeutic-targets for these diseases. Using an edit-distance-based pathway-classifier, we also find molecular pathways by which these targets could elicit their clinical effects. Importantly, for the 17 targets, the evidence for their therapeutic usefulness is retrospectively found in the pre-clinical and clinical space, illustrating the effectiveness of the method, and suggesting its broader applications across diverse human diseases.

Availability and implementation
The code reported in this article is available at: https://github.com/skozawa170301ktx/MultiModalDiseaseModeling

Supplementary information
Supplementary data are available at Bioinformatics Advances online.