bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of frailty on clinical outcomes in patients with and without COVID-19 pneumonitis admitted to intensive care units in Australia and New Zealand: A retrospective registry data analysis
Authors: Subramaniam Ashwin , Shekar Kiran , Anstey Christopher , Tiruvoipati Ravindranath , Pilcher David 
Published: July 2022

Abstract
Background
It is unclear if the impact of frailty on mortality differs between patients with viral pneumonitis due to COVID-19 or other causes. We aimed to determine if a difference exists between patients with and without COVID-19 pneumonitis.
Methods
This multicenter, retrospective, cohort study using the Australian and New Zealand Intensive Care Society Adult Patient Database included patients aged ≥ 16 years admitted to 153 ICUs between 01/012020 and 12/31/2021 with admission diagnostic codes for viral pneumonia or acute respiratory distress syndrome, and clinical frailty scale (CFS). The primary outcome was hospital mortality.
Results
4,620 patients were studied, 3,077 (66.6%) had COVID-19. The patients with COVID-19 were younger (median [IQR] 57.0 [44.7–68.3] vs. 66.1 [52.0-76.2]; p < 0.001) and less frail (median [IQR] CFS 3 [2–4] vs. 4 [3–5]; p < 0.001), than non-COVID-19 patients. The overall hospital mortality was similar between the patients with and without COVID-19 (14.7% vs. 14.9%; p = 0.82). Frailty alone as a predictor of mortality showed only moderate discrimination in differentiating survivors from those who died but was similar between patients with and without COVID-19 (AUROC 0.68 vs 0.66; p = 0.42). Increasing frailty scores were associated with hospital mortality, after adjusting for Australian and New Zealand Risk-of-Death score and sex. However, the effect of frailty was similar in patients with and without COVID-19 (OR = 1.29; 95%CI 1.19–1.41 vs. OR = 1.24; 95%CI 1.11–1.37).
Conclusion
The presence of frailty was an independent risk factor for mortality. However, the impact of frailty on outcomes was similar in COVID-19 patients compared to other causes of viral pneumonitis.
Trial Registration:
Not applicable
Urban Flood Simulations with Modified Digital Elevation Model
Authors: Dasallas Lea , An Hyunuk , Lee Seungsoo 
Published: August 2022

Abstract
The acquisition of high-resolution digital elevation model (DEM) data is an on-going challenge in urban flood modeling, especially for larger domains. The accuracy of flood simulations is significantly dependent on the precision of DEM data. To address this issue, we propose a method of modifying mid-resolution DEM data to provide a realistic ground elevation for urban flood modeling using geographic information system (GIS) tools. The modified DEM contains the properties of the existing digital terrain and digital surface model (DTM and DSM) data. Flood calculations were performed using 2D shallow water and kinematic equations in the integrated multiscale simulation scheme developed in recent literature and tested for an extreme flood event in an urbanized area in the Philippines. The results show that the underestimated and overestimated flood tendencies for DTM and DSM, respectively, were adequately resolved in MDEM simulations, which were able to accurately capture the flood propagation along the natural and artificial barriers in the urban area, as compared to untreated DTM and DSM data, with root mean square error (RMSE) improvements from RMSEDTM = 30.64 and RMSEDSM = 17.28 to RMSEMDEM = 0.17. The proposed method significantly improved the accuracy of the simulations. It would allow high-resolution urban flood modeling with limited detailed elevation data, which is crucial to propose adequate flood action plans specifically for developing countries experiencing frequent rainfall events.
Machine Learning for the Early Detection of Breast Cancer
Authors: Malakouti Seyed Matin , Ghiasi Amir Rikhtehgar 
Published: July 2022

Abstract
Breast cancer is the most common cancer amongst women in the world. It accounts for 25% of all cancer cases and affected over 2.1 Million people in 2015 alone. It starts when cells in the breast begin to grow out of control. These cells usually form tumors that can be seen via X-ray or felt as lumps in the breast area. The critical challenge against its detection is classifying tumors into malignant (cancerous) or benign(non-cancerous). In this article, with the help of machine learning algorithms, healthy people and people with tumors were separated from each other with acceptable accuracy.
Improving the search of optimal prices in envy-free perfect matchings
Authors: Salvatierra Marcos , Colonna Juan G. , Salvatierra Mario , Neto Alcides de C. Amorim 
Published: August 2022

Abstract
We present a method for finding envy-free prices in a combinatorial auction where the consumers’ number n coincides with that of distinct items for sale, each consumer can buy one single item and each item has only one unit available. This is a particular case of the unitdemand envy-free pricing problem , and was recently revisited by Arbib et al. (2019). These authors proved that using a Fibonacci heap for solving the maximum weight perfect matching and the Bellman-Ford algorithm for getting the envy-free prices, the overall time complexity for solving the problem is O ( n
3 ) . We propose a method based on dynamic programming design strategy that seeks the optimal envyfree prices by increasing the consumers’ utilities, which has the same cubic complexity time as the aforementioned approach, but whose theoretical and empirical results indicate that our method performs faster than the shortest paths strategy, obtaining an average time reduction in determining optimal envy-free prices of approximately 48%.
Synthesis and pharmacological evaluation of usnic acid derivatives as hypoglycemic agents
Authors: Borisov Sergey , Luzina Olga , Khvostov Mikhail , Tolstikova Tatyana , Salakhutdinov Nariman 
Published: August 2022

Abstract
Considering the constantly evolving type 2 diabetes mellitus epidemic and the lack of a safe and effective drug therapy, it is of high importance to search for new hypoglycemic drugs. One of this search’s directions is the chemical modification of some known natural metabolites, which potentially possess the necessary pharmacological properties. These criteria are met by usnic acid, which is produced by lichens and exhibit different biological properties, including hypoglycemic. However, this effect becomes noticeable only at relatively high doses, something that may lead to some adverse effects. The chemical modification of its molecule is able to enhance the safety profile and its hypoglycemic properties. With this in view in the present work several usnic acid derivatives were synthesized. As a result of the experiment using the alloxan-induced diabetes mellitus in mice, it was shown that a derivative of usnic acid, containing a 4-trifluoromethylphenyl fragment, exhibits a pronounced hypoglycemic effect, which was observed at a lower dose as compared to usnic acid itself in a similar experimental model.
Telomerase RNA component TERC modulates biological malignant function of colorectal cancer by regulating autophagy via MIF/AKT/mTOR axis and hippo-YAP axis
Authors: He Juan , Ding Jian , Lai Qiuhua , Wang Xinke , Gu Chuncai , Cai Jianqun , Fang Yuxin , Li Qingyuan , Lin Xin , Li Aimin , Liu Side 
Published: July 2022

Abstract
Background
Colorectal cancer (CRC) is the third most frequently diagnosed cancer with rising incidence and mortality rates. Telomere RNA component (TERC) plays a crucial role in various biological processes and tumor development, although its role in CRC development and metastasis remains unknown.
Methods
The proliferation and invasion of TERC-overexpressing and TERC-knockdown CRC cells were analyzed by standard cellular assays and animal models. The underlying mechanisms were investigated by RT-PCR, western blotting and fluorescence in situ hybridization. Nuclear and cytoplasmic RNA fractions were analyzed to confirm the subcellular location of TERC. CHIRP and RNA-binding protein immunoprecipitation were performed to identified TERC-interacting proteins.
Results
TERC was upregulated in CRC tissues and cell lines, and localized to both the nucleus and cytoplasm. Ectopic TERC expression promoted the proliferation and migration of CRC cells in vitro, and tumor growth in vivo. TERC was correlated with cell cycle and the Hippo pathway, and regulated YAP translocation and degradation. The GABPA/TIGD4 pathway and GABPA were predicted as interactive partners of TERC. Eighteen TERC-binding proteins were identified by CHIRP, and functionally annotated to the glycolytic and NADH pathways. Finally, TERC decreased the autophagy flux in CRC cells via the MIF/AKT/mTOR axis.
Conclusion
TERC functions as an oncogene in CRC by activating the Hippo pathway, and regulates metabolic and autophagic pathways.
Comparative Efficacy and Safety of Immunotherapy for Patients with Advanced or Metastatic Esophageal Squamous Cell Carcinoma: A Systematic Review and Network Meta-analysis
Authors: Gao Tian-Tian , Shan Jia-Hui , Yang Yu-Xian , Zhang Ze-Wei , Liu Shi-Liang , Xi Mian , Liu Meng-Zhong , Zhao Lei 
Published: August 2022

Abstract
Background
The study aimed to compare efficacy and safety of various immune checkpoint inhibitors for patients with advanced or metastatic esophageal squamous cell carcinoma (ESCC).
Methods
We searched Medline, Web of Science, Cochrane Central Register of Controlled Trials, Embase, Clinical Trials.gov and several international conference databases from January 1, 2000 to December 19, 2021. We conducted Bayesian network meta-analysis to assess the relative effects among treatments. Outcomes included overall survival (OS), progression-free survival (PFS), overall response rate and adverse events.
Results
Ten eligible trials with 5250 patients were included. Toripalimab and Camrelizumab plus chemotherapy were preferred to rank first on OS (cumulative probability, 61%) and PFS (cumulative probability, 37%) in the first-line setting, respectively. In refractory patients, Sintilimab and Camrlizumab were most likely to be ranked first on OS (cumulative probability, 37%) and PFS (cumulative probability, 94%). The toxicity related to immunotherapy was manageable in clinical trials. Camrelizumab and Nivolumab had the less adverse events of grade 3 or higher in the first and refractory setting, respectively.
Conclusions
This study found that Toripalimab and Camrelizumab plus chemotherapy were likely to be the best option in terms of OS and PFS in the first-line setting for patients with advanced or metastatic ESCC respectively. Sintilimab and Camrelizumab were the preferred options for OS and PFS in refractory patients respectively. The toxicity of immunotherapy was different from conventional chemotherapy, but manageable in patients with ESCC.
Trial registration:
PROSPERO registration number: (CRD 42021261554).
Identification of sedimentary environment and microfacies of Asmari Formation in one of the Lorestan basin oil fields
Authors: Vakili Nazanin , Paknejadiyan Ali , Yazdjerdi Kouros , mousavi seyed saeed mousapoor 
Published: July 2022

Abstract
Depositional microfacies identification plays a key role in the exploration and development of oil and gas reservoirs. Asmari Formation in Oligo-Miocene is one of the most important reservoir Formations for the production and exploration of hydrocarbons in the Middle East and has a high reputation globally for a long time. In the Lorestan region, the sequences of the Asmari Formation have a carbonate-evaporate mixture of the late Miocene. The main aim of this research is to describe and interpret the different facies observations. Investigation of this sequence in one of the wells in this region has led to the identification of 17 sedimentary microfacies that belong to a homoclinal ramp-type carbonate platform. The nature and distribution of facies and environmental conditions of the Asmari Basin have undergone fundamental changes over time so that in the first part of this Formation (containing the first sequence of this Formation), the facies and their distribution pattern are similar to those of Tertiary and also others Zagros regions like Khuzestan and Fars. Still, in the second part (second sequence), there is a significant change in the Asmari basin, the most important of which is the replacement of red coral algae by a rare group of red algae with Aragonite wall. Our findings allows better characterizing and understanding which sedimentological features control the mechanical and its distribution throughout the formation.
A novel human brainstem map based on true-color sectioned images
Authors: You Yaqian , Park Jin Seo 
Published: July 2022

Abstract
Existing atlases for the human brainstem were generated from magnetic resonance images or traditional histologically stained slides, but both are insufficient for the identification of detailed brainstem structures at uniform intervals. In this study, a novel brainstem atlas based on sectioned images was generated that shows the true color and shape, as well as the accurate location of the nuclei and tracts; it reveals the striking contrast between gray and white matter, as well as fine structures. In total, 212 structures, including nuclei and tracts, were annotated in axial, coronal, and sagittal sectioned images (48-bit true color images; 0.2 mm intervals, 0.06 mm × 0.06 mm pixel size). To verify the accuracy of the annotations, a volume model of the brainstem was constructed for independent observations of the three planes. In this paper, we describe several interesting structures included in the atlas. These were located in regions of the spinal cord, caudal and rostral medulla oblongata, pons, caudal and rostral midbrain. The junctions of these regions are also shown. By depicting the fine structures of the human brainstem in detail, this atlas allows comprehensive understanding of the complicated topographies of the brainstem. As such, it will be of value for neuroanatomy education and research, in addition to enriching the literature on the human brain.
Human Mobility Networks Manifest Dissimilar Resilience Characteristics at Macroscopic, Substructure, and Microscopic Scales
Authors: Hsu Chia-Wei , Ho Matthew Alexandar , Mostafavi Ali 
Published: July 2022

Abstract
Human mobility networks can reveal insights into resilience phenomena, such as population response to, impacts on, and recovery from crises. The majority of human mobility network resilience characterizations, however, focus mainly on macroscopic network properties; little is known about variation in measured resilience characteristics (i.e., the extent of impact and recovery duration) across macroscopic, substructure (motif), and microscopic mobility scales. To address this gap, in this study, we examine the human mobility network in eight parishes in Louisiana (USA) impacted by the 2021 Hurricane Ida. We constructed human mobility networks using location-based data and examined three sets of measures: (1) macroscopic measures, such as network density, giant component size, and modularity; (2) substructure measures, such motif distribution; and (3) microscopic mobility measures, such as the radius of gyration and average travel distance. To determine the extent of impact and duration of recovery, for each measure, we established the baseline values and examined the fluctuation of measures during the perturbation caused by Hurricane Ida. The results reveal the variation of impact extent and recovery duration obtained from different sets of measures at different scales. Macroscopic measures, such as giant components, tend to recover more quickly than substructure and microscopic measures. In fact, microscopic measures tend to recover more slowly than measures in other scales. These findings suggest that resilience characteristics in human mobility networks are scale-variant, and thus, a single measure at a particular scale may not be representative of the perturbation impacts and recovery duration in the network as a whole. These results spotlight the need to use measures at different scales to properly characterize resilience in human mobility networks.
Taurine, Homotaurine, GABA and hydrophobic amino acids content influences “in vitro” antioxidant and SIRT1 modulation activities of enzymatic protein hydrolysates from algae
Authors: Terriente-Palacios Carlos , Campoy Susana Rubino , Hortós Maria , Peteiro César , Castellari Massimo 
Published: July 2022

Abstract
Prevention and control of diseases and delaying the signs of ageing are nowadays one of the major goals of biomedicine. Sirtuins, a family of NAD+ dependent deacylase enzymes, could be pivotal targets of novel preventive and therapeutic strategies to achieve such aims. SIRT1 activating and inhibiting compounds, such as polyphenols and bioactive peptides, have been proposed to be involved in the development of many human diseases. The objective of this work was to assess and compare the antioxidant and SIRT1 modulation activities of enzymatic protein hydrolysates (EPHs) from a wide number of algae species (24 commercial samples and 12 samples harvested off the Atlantic coast of northern Spain). High antioxidant activities were observed in EPHs from red and green seaweed species. Moreover, 19 samples exhibited SIRT1 activation, while EPHs from the 16 samples were SIRT1 inhibitors. Pearson's correlation test and Principal Component Analysis revealed significant correlations between i) total peptide and hydrophobic amino acid content in EPHs and their antioxidant activities, and ii) concentrations of taurine, homotaurine, and amino acid gamma aminobutyric acid (GABA) in EPHs and their SIRT1 modulation activity.
Handling Compound Hindi OOV words in web queries
Authors: Asthana Amit , Dwivedi Sanjay K. , Dwivedi Anand Prakash 
Published: July 2022

Abstract
Handling of Out of Vocabulary (OOV) words is still a problem in NLP. If there is a word for which morphological analyser is not able to find a morpheme, that word is known as OOV word. These words if not identified, may restrict to understand the proper meaning of the sentence. It may also have severe impact on the IR system involving the queries. Detection and identification of OOV words in information retrieval is a challenging task. This problem may become more challenging in case of cross lingual information retrieval (CLIR) due to issues in query translation. The objective of this paper is to understand the impact of web queries involving these words on the retrieval effectiveness of web searches. Subsequently, we have also proposed an algorithm to successfully detect and handle the impact of Hindi web queries involving compound OOV. Our results have shown increased precision of 8.53% for one-word web queries involving only OOV word and 15.68% with queries having more than one word having at least one OOV word.
Spatiotemporal changes in droughts in Iraq for shared socioeconomic pathways
Authors: Hamed Mohammed Magdy , Sammen Saad Sh. , Nashwan Mohamed Salem , Shahid Shamsuddin 
Published: August 2022

Abstract
Semiarid climate has made Iraq one of the most vulnerable regions to droughts. With temperatures rising and precipitation falling, projected climate models indicate a possible aggravation of droughts in the country. Iraq's shared socioeconomic pathways (SSPs) are the focus of this study, which uses CMIP6 climate models to examine the spatiotemporal variation of drought. The historical simulations of 21 GCMs were evaluated to choose a selection of GCMs. A support vector machine was used to downscaled the simulations of the selected GCM to 0.5° resolution. Downscaled simulations were used to estimate droughts employing standardized precipitation evapotranspiration index (SPEI) for the near (2020–2059) and far future (2060–2099) periods in comparison to the reference period (1975–2014). EC-Earth3-Veg, BCC-CSM2-MR and ACCESS-CM2 performed the best in simulating Iraq's climate. Historical simulation of droughts revealed northern Iraq as most prone to droughts of all severities and time scales. The projections of droughts revealed a decline in drought frequency in the near but a large rise in the late period. SSP 2-4.5 and SSP5-8.5 saw a greater decline in the near future and a rise in the far future. In both scenarios, drought frequency decreased between 0 and 40% in the near future, while an increase of up to 45% in moderate and severe droughts in the far future. In contrast, most scenarios showed a decrease in extreme droughts up to 30% in the drought-prone northern region, signifying a shift in the extreme drought-prone zone in Iraq.
Analyzing IoT big data in healthcare using Deep learning and Distributed Fog Computing
Authors: Iranpak Somayeh , Shahbahrami Asadollah , Shakeri Hassan 
Published: August 2022

Abstract
High amounts of patient and healthcare data are generated daily on the Internet of Things (IoT). The processing time and analyzing the big data received from IoT devices, as well as providing the necessary accuracy for data classification, are considered as the most important challenges in IoT. Therefore, this study aims to improve the accuracy of data classification by implementing hybrid approaches of feature selection (FS) and deep learning (DL) within the fog computing infrastructure while reducing the response time (RT) and bandwidth usage. Also, the major goal is to analyze and classify the states of patients remotely through IoT, cloud computing and fog computing technologies. The information processing time is reduced by implementing Particle Swarm Optimization (PSO) and Imperialist Competitive Algorithm (ICA) in fog computing to select the prominent features, and the classification accuracy is improved through classifying new data by a deep neural network (DNN) model. The simulation results show that the accuracy of the classification and remote monitoring of patients is 98.54%, which is about 4.5% better than the case without using PSO-ICA algorithms, and it is also improved by about 10% on average compared to other methods such as Linear Regression (LR), K Nearest Neighbors (KNN), Neural Network (NN), and Bayesian Belief Net (BBN).