bluuebunny/crossref_metadata_2025_split huggingface dataset:
Unexpected thermal stability of two enveloped megaviruses, Emiliania huxleyi virus and African swine fever virus, as measured by viability PCR
Authors: Balestreri Cecilia , Schroeder Declan C. , Sampedro Fernando , Marqués Guillermo , Palowski Amanda , Urriola Pedro E. , de Ligt Jennifer L. G. van , Yancy Haile F. , Shurson Gerald C. 
Published: January 2023

Abstract
Background: The particle structure of Emiliania huxleyi virus (EhV), an algal infecting member of nucleocytoplasmic large DNA viruses (NCLDVs), contains an outer lipid membrane envelope similar to that found in animal viruses such as African swine fever virus (ASFV). Despite both being enveloped NCLDVs, EhV and ASFV are known for their stability in the environment.
Method: Here we report for the first time, the application of a viability PCR method to describe the unprecedented and similar virion thermal stability of both EhV and ASFV. This result contradicts the bioassay method that suggests that virus “infectivity” is lost in a matter of seconds (EhV) and minutes (ASFV) at temperature greater than 50 °C. Confocal microscopy and analytical flow cytometry methods was used to validate the viability PCR data for EhV.
Results: We observed that both EhV and ASFV particles has unprecedented thermal tolerances. These two NCLDVs are exceptions to the rule that having an enveloped virion anatomy is a predicted weakness, as is often observed in enveloped RNA viruses (i.e., the viruses causing Porcine Reproductive and Respiratory Syndrome (PRRS), COVID-19, Ebola, or seasonal influenza). Using the viability PCR method, we confirm that no PRRSV particles remain after 20 minutes of exposure to temperatures up to 100 °C.
Conclusions: This observation has practical implications for industries involved in animal health and food security. Finally, we propose that EhV could be used as a surrogate for ASFV under certain circumstances.
Development of Low Cost Proximity Sensing System using 24Ghz Radar Sensor for Mining Applications
Authors: Kumari Anjani , Ghorai Swapan Kumar , Tripathy Sanjaya Shankar 
Published: January 2023

Abstract
This paper deals with the design and development of low-cost proximity sensor system for mining application. Proximity sensing system is used in mining vehicle to avoid accidents. Since the size of the mining vehicle is very large, The operator/driver won’t be able to see the back of the vehicle. Since the mining area are dusty environment, the camera will not be able to capture good quality image. Hence the proximity sensing is preferred to identify any human/object behind the vehicle. Conventional ultrasonic sensor range is between 0-2meter but in mining vehicle it is preferred that sensor should able to identify the object from 20meters. The 24-ghz radar sensor meets the necessary requirement of proximity sensing from that distance of 30meters. The K-LD7 is a low-cost digital 24Ghz radar sensor that can be used to measure distance, speed, angle and direction of moving objects from the sensor. It is used to establish the position of both single and multiple targets as well as their motion, direction, speed, and angle of arrival. Low-cost microcontroller ESP32 is used to process the data received from radar sensor. In this paper we have proposed the hardware interfacing and software implementation of the proximity sensing system. The proposed system successfully identifies the targets up to the range of 15 meters, accurately displays the actual distance and produce alarm as per the distance of the objects. Distance information with date and time is stored in micro-SD card for monitoring purpose. The proposed system will able to minimize the accidents in mining area.
Explicit microrelief-controlled decoupling of initial aerobic (in hummocks) and anaerobic (in hollows) decay in superficial layers of Sphagnum-dominated peatlands
Authors: Pérez-Rodríguez Marta , Alten Alexandra , Miler Miloš , Kaal Joeri 
Published: January 2023

Abstract
Peatlands are important carbon sinks due to their ability to sequester and store large amounts of carbon in the form of organic matter (OM). The microhabitat levels and species distribution within peatlands can significantly influence the mechanism of carbon sequestration, and the quality of OM in moss, specifically its litter, may be a key factor in this process. Sphagnum moss, a common type of moss found in peatlands, has unique physiological, anatomical, and molecular characteristics that can affect the decay of its tissues.
To better understand the early stages of OM decomposition and its conversion into peat at the microhabitat level, we conducted a 400-day decomposition experiment on moss and peat core samples. We used Pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS) and thermally assisted hydrolysis and methylation-GC-MS (THM-GC-MS) techniques to analyse the molecular composition of the samples and determine carbon to nitrogen (C/N) ratios and mass loss over time. We identified two distinct degradation processes: long-term decay in anaerobic conditions, characterized by the gradual loss of polysaccharides and the preservation of lignin and aliphatic OM; and the initial stages of aerobic degradation, characterized by the formation of microbial-origin OM, the degradation of labile phenolic compounds (including 4-isopropenylphenol from sphagnum acid, 4IPP) and its mobilization by leaching along with free carbohydrates. These processes were found to be spatially-separated, with anaerobic decay being the initial process in hollows and aerobic degradation through leaching being the dominant process in hummocks. This suggests that the presence of 4IPP in peat cores might not only depend on general peatland water table fluctuations but also on position in the microrelief during peat accretion. Our data suggests the total mass loss of the samples after incubation was lower when the original sample contained a higher amount of structural carbohydrates. Additionally, we observed that decreases in C/N ratios during the decomposition experiment were not significantly correlated with higher mass loss, as is commonly observed in millennial peat records. Our analysis of subsurface moss core samples suggests that in natural conditions, the initial stages of OM degradation may not be consistently characterized by decreasing C/N values. Although the degradation patterns of OM in incubated samples differed between hollows and hummocks compared to naturally decomposed samples, we were able to identify the transport of trace metals from the peatland, which is associated with phenols and driven by leaching from hummocks or surface aerobic layers, through comparison of our THM products with THM products from the peatland outflow.
Waist to height ratio as a simple tool for predicting mortality: a systematic review and meta-analysis
Authors: Dezfouli Ramin Abdi , Khonsari Nami Mohammadian , Hosseinpour Ali , Asadi Sasan , Ejtahed Hanieh-Sadat , Qorbani Mostafa 
Published: January 2023

Abstract
Background: The association of central obesity with higher rates of mortality is not well studied. This study evaluates the association between waist-to-height ratio (WHtR), as a measure of central obesity, with mortality.
Methods: Documents were retrieved from PubMed, Web of Science, Scopus, and Google Scholar databases until May 2022. Data were extracted from cohort studies reporting effect size (hazard ratio (HR)) regarding the association between WHtR as a continuous (per 1 SD increment) or categorical (highest/lowest) measure and all-cause and cause-specific mortality. Screening of included studies was performed independently by two authors. Moreover, the quality assessment of included studies was performed based on the Newcastle-Ottawa assessment scale. Finally, random effect meta-analysis was performed to pool the data, and the outcomes’ certainty level was assess based on the GRADE criteria
Results: Of the 815 initial studies, 20 were included in the meta-analysis. Random effect meta-analysis showed that in the general population, the all-cause mortality HRs for categorical and continuous measurements of WHtR increased significantly by 23% (HR:1.23; 95%CI: 1.04-1.41) and 16% (HR:1.16; 95%CI: 1.07-1.25), respectively. Moreover, the hazard of cardiovascular (CVD) mortality increased significantly for categorical and continuous measurements of WHtR by 39% (HR:1.39; 95%CI: 1.18-1.59) and 19% (HR:1.19; 95%CI: 1.07-1.31). The quality assessment score of all included studies was high.
Conclusions: Higher levels of WHtR, indicating central obesity, were associated with an increased hazard of CVD and all-cause mortality. This measure can be used in the clinical setting as a simple tool for predicting mortality.
Proteomic analysis of Aquilaria malaccensis seed in relation to seed viability
Authors: Kharnaior Suklang , Thomas Shiny C. 
Published: January 2023

Abstract
Aquilaria malaccensis is one of the woody species and its economic value, high demand in the market, overexploitation, and difficulty in propagation due to the low shelf life of the seed cause the species to face the threat of extinction. The main objective of the study is to understand the role of protein in seed viability. In this experiment, the protein was extracted using tris- HCl/trichloroacetic acid-acetone (TCA-acetone) from the immature seed, premature seed, fresh mature seed, 7 days stored seed, 14 days stored seed, and 30 days stored seed. The extracted protein concentration was estimated using the Bradford method and separated using 1-D SDS PAGE. With the maturity of the seed concentrations of protein increase (immature seed 18.11 µg/ml, premature seed 37.43 µg/ml, and fresh mature seed 68.92 µg/ml ) and it reduces with storage (7 days 23.92 µg/ml, 14 days 20.14 µg/ml, and 30 days 16.08 µg/ml). Proteomic analysis using SDS PAGE, revealed that with the maturity of the seed, development protein was observed, immature seeds show no band, premature seeds showed four bands, and freshly matured seeds show 5 dark bands. The 7 days stored seeds show two bands 14 days stored seeds showed only one band, and no band was observed in 30 days stored seeds. Furthermore, the loss of seed proteins in seed storage might induce a loss of their functional properties in seed viability.
Predicting Ethereum Fraudulency using ChaosNet
Authors: Dutta Anurag ,   Liton Chandra Voumik2, Ray Samrat 
Published: January 2023

Abstract

Cryptocurrencies are in high demand right now, perhaps because of their volatile nature and untraceable difficulties. Bitcoin, Ethereum, Dogecoin, and others are just a few. This research seeks to identify falsehoods and probable fraudulences in Ethereum transactional processes. We have provided this capability to ChaosNet, an Artificial Neural Network constructed using Generalized Luroth Series maps. At many spatiotemporal scales, Chaos has been objectively discovered in the brain. Several synthetic neuronal simulations, including the Hindmarsh-Rose model, possess Chaos, and individual brain neurons are known to display chaotic bursting phenomenon. Although Chaos is included in several Artificial Neural Networks (ANNs), for instance, the Recursively Generating Neural Networks, no ANN exist for classical tasks that is fully made up of Chaoticity. ChaosNet uses the chaotic GLS neurons' topological transitivity property to perform classification problems with cutting-edge performance the pool of data including lower training sample count. This synthetic neural network can perform categorization tasks by gathering from a definite amount of training data. ChaosNet utilizes some of the best traits of network subjected to biological neurons, which derive from the strong Chaotic activity of individual neurons, to solve difficult classification tasks on par with or better than standard Artificial Neural Networks. It has been shown to require much fewer training samples.
Perfromance Analysis of Channel Estimator with Interefrence Detection Architecture
Authors: A Immanuvel , D Arulanantham , S Kumarganesh , S Saranya 
Published: January 2023

Abstract
The evaluation of the wireless channel linking a transmitter and a receiver is essential in many applications. In receiver design, channel estimate is a fundamental and difficult problem. A recent mathematical theory called compressive sensing (CS) makes use of the idea of randomness in the signal to recover sparse or compressible signals. This research work also proposes an efficient channel estimator with interference detection architecture for Multiple Input Multiple Output- Orthogonal Frequency Division Multiplexing (MIMO - OFDM) method. The proposed interference finding architecture efficiently detects and removes the interference between received signals with low power consumption and current consumption with minimum hardware requirements. The power consumption and latency for Virtex Family devices are 19mW and 9 ns respectively. The channel estimator with interference detection architecture is used to achieve the power consumption and latency (CPLD Family devices) are 21.97 and 10.32.
Effectiveness of Manchester Triage Patient Flow Model Regarding Waiting Time in Bahria International Hospital Lahore
Authors: Shaheen Mehwish , Afzal Muhammad , Tahir Syed Naveed , Farid Muhammad Azher , Aziz Zunaira , Shaheen Sumaira 
Published: January 2023

Abstract
Some hospital emergency departments (EDs) suffer from prolonged patient wait times, which decreases hospital productivity. The factors influencing normal ED patient wait times are therefore crucial for hospital administrators to be aware of. An emergency severity index (ESI) and patient flow model for emergency departments were both tested in this causal comparison study, which was founded in the academic framework for business process optimization. Through the use of computer-generated reports, archival data records for one week from the ED of the Bahria International Hospital in Lahore, Pakistan were censused for an ED REU model for patient flow. The results of quantitative descriptive investigation showed that the patient flow model had a considerable temporal effect. The ability to decrease patient wait times while enhancing patient happiness and health outcomes is one of the social change implications. A quantitative study would produce data that would assist medical staff by shortening the wait times for patients.
Predictors of disease course and long-term outcomes of idiopathic intracranial hypertension in children and adolescents
Authors: Senderowich Noam , Bachar Anat , Mitelpunkt Alexis , Tokatly Itay , Klein Ainat , Mezad-Koursh Daphna , Fattal-Valevski Aviva , Hausman-Kedem Moran 
Published: January 2023

Abstract
Purpose: To identify predictors for unfavorable disease course and clinical and visual outcomes in pediatric patients with idiopathic intracranial hypertension (IIH).
Methods: A multi-tiered approach analyzing retrospectively collected clinical, ophthalmic, and imaging findings of patients diagnosed with IIH between 2003-2021.
Results: Of the 97 patients, 42 (43%) had an unfavorable disease course, 28 (29%) had persistence of headache at last follow-up, and 16 (18%) had a poor visual outcome. On univariate regression analysis, female sex, overweight/obesity, visual field (VF) defect at presentation, and retinal nerve fiber layer (RNFL) >130μm after treatment initiation served as predictors of an unfavorable disease course as well as poor visual outcome. An unfavorable disease course was significantly associated with higher opening pressure (OP), evidence of polycystic ovary syndrome and higher triglyceride levels whereas optic atrophy at presentation, and disease recurrence were also associated with poor visual outcome. On multivariate regression analysis, female sex and disease recurrence remained significantly associated with poor visual outcomes (OR: 18.5, CI:1.3-270, P = 0.03, and OR: 5.1, CI: 1.2-22.5, P= 0.03, respectively). Patients with a persistent headache had a lower OP, lower papilledema rates and less neuroimaging parameters suggestive of increased intracranial pressure.
Conclusions: We provide multi-layered insights into predictive factors for an unfavorable disease course and poor clinical and visual outcomes in patients with childhood IIH. Patients with persistent headaches may have a variant of a chronic pain syndrome warranting a different therapeutic approach.
Higher preoperative serum lactate dehydrogenase-to-albumin ratio was associated with worse overall survival for stage II gastric cancer
Authors: Liu Fei , Tong Yue , Liu Xu-Rui , Li Zi-Wei , Zhang Wei , Peng Dong 
Published: January 2023

Abstract
Purpose The current study was to evaluate the effect of the preoperative serum lactate dehydrogenase-to-albumin ratio (LAR) on the short-term outcomes and overall survival (OS) of patients with gastric cancer (GC) who received surgery.
Methods GC patients who underwent gastrectomy at a single clinical medical center were enrolled and were divided into the low and the high LAR groups by LAR cutoff (12.8). The baseline information, short-term outcomes, and OS were compared between the two groups. Cox regression analysis was performed to identify independent risk factors for OS. The Kaplan-Meier method was used to estimate the OS between the two groups in different tumor stages.
Results A total of 570 patients were included for analysis. As for baseline information, we found that the high LAR group had older patients (P<0.01), a lower body mass index (BMI) (P=0.042), a higher level of lactate dehydrogenase (P<0.01), and a higher level of albumin (P<0.01). In terms of short-term outcomes, the high LAR group had more total retrieved lymph nodes (P=0.036). After univariate and multivariate analyses, BMI (P=0.015, HR=0.932, 95%CI=0.881-0.986) was an independent protective factor of OS. Meanwhile, neoadjuvant therapy (P<0.01, HR=2.627, 95%CI=1.620-4.259) and tumor stage (P<0.01, HR=2.129, 95%CI=1.643-2.760) were the independent risk factors of OS. However, LAR was not an independent factor for OS (P=0.210, HR=1.334, 95%CI=0.850-1.746). According to the Kaplan-Meier curves, we found that tumor stage II (P<0.01) patients had a worse OS.
Conclusion LAR was not an independent factor of OS. However, LAR might affect the prognosis of stage II GC.
Block-wise Primal-dual Algorithms for Large-scale Doubly Penalized ANOVA Modeling
Authors: Fu Penghui , Tan Zhiqiang 
Published: January 2023

Abstract
For multivariate nonparametric regression, doubly penalized ANOVA modeling(DPAM) has recently been proposed, using hierarchical total variations (HTVs) and empirical norms as penalties on the component functions such as main effects and multi-way interactions in a functional ANOVA decomposition of the underlying regression function. The two penalties play complementary roles: the HTV penalty promotes sparsity in the selection of basis functions within each component function, whereas the empirical-norm penalty promotes sparsity in the selection of component functions. We adopt backfitting or block minimization for training DPAM, and develop two suitable primal-dual algorithms, including both batch and stochastic versions, for updating each component function in single-block optimization. Existing applications of primal-dual algorithms are intractable in our setting with both HTV and empirical-norm penalties. Through extensive numerical experiments, we demonstrate the validity and advantage of our stochastic primal-dual algorithms, compared with their batch versions and a previous active-set algorithm, in large-scale scenarios.
Genome wide association studies and candidate gene mining for understanding genetic basis of straw silica content in a set of Oryza nivara (Sharma et Shastry) accessions
Authors: Gowda Rakshith S R , Sharma Sandeep , Gill Ranvir Singh , Mangat Gurjit Singh , Bhatia Dharminder 
Published: January 2023

Abstract
Background
Rice is a high silicon-accumulator. Silicon (Si) is designated as a beneficial element associated with multiple positive effects on crop plants. However, presence of high silica content is detrimental in rice straw and its management, hampering its use as animal feed and as raw material in multiple industries. Rice straw management is a serious concern in North-Western India and it is eventually burned in situ by the farmers contributing to air pollution. A practical solution to the said issue could lie in reducing the silica content in rice, while also attaining sound plant growth.
Results
A set of 258 Oryza nivara accessions along with 25 cultivated varieties were used to assess the variation for straw silica content using molybdenum blue colorimetry method. Large and continuous variation was observed for straw silica content in O. nivara accessions, ranging from 5.08-16%, while it varied from 7.95–15.81% in the cultivated varieties. The O. nivara accessions containing 43–54% lower straw silica content than the currently prominent cultivated varieties in the region were identified. A set of 22,528 high-quality single nucleotide polymorphism (SNP) among the 258 O. nivara accessions were used for estimating population structure and genome wide association studies (GWAS). A weak population structure with 59% admixtures was identified among O. nivara accessions. Further, multi-locus GWAS revealed the presence of 14 marker trait associations (MTAs) for straw silica content, with six of them co-localizing with previously reported QTLs. Twelve out of the 14 MTAs showed statistically significant allelic differences. Thorough candidate gene analyses among the QTL regions revealed the presence of promising candidate genes, including ABC transporter, Casparian thickening genes, multi-drug and toxin extrusion (MATE) protein, F-box domains, and MYB-transcription factor domains. Besides ortho-QTLs among rice and maize genomes were also identified which could open up ways for further genetic analysis of this trait.
Conclusion
The findings of the study could aid in further understanding and characterizing genes for Si transport and regulation in the plant body. The donors carrying the alleles for lower straw silica content can be used in further marker-assisted breeding programmes in order to develop rice varieties with lower silica content along with higher yield potential.
Predicting antimicrobial resistance using historical bacterial resistance data with machine learning algorithms
Authors: Urena Raquel , Sabine Camiade , Baalla Yasser , Piarroux Martine , HALFON Philippe , Gaudart Jean , Dufour Jean Charles , Rebaudet Stanislas 
Published: January 2023

Abstract
Antibiotic resistance of bacterial pathogens is considered by the World Health Organization as a major threat to global health aggravated by the misuse of antibiotics. In clinical practice results of bacterial cultures and antibiograms can take several days. In the meantime, prescribing an empirical antimicrobial treatment constitutes a challenge in which the practitioner has to strike a balance between antibiotics spectrum and expected susceptibility probability. In this contribution, we report the development and testing of a machine-learning-based system that early predicts the antimicrobial susceptibility probability and provides explanations of the contribution of the different cofactors at 4 different stages prior to the antibiogram (sampling, direct examination, culture, and species identification stages). A comparative analysis of different state of the art machine learning and probabilistic methods was carried out using 7 years of historical bacterial resistance data from the Hôpital Européen Marseille, France. Our results suggest that dense neural network-based models and Bayesian models are suitable to early predict antibiotics susceptibility (average AUC 0.91 at the species identification stage) even for the less frequent situations.