bluuebunny/crossref_metadata_2025 huggingface dataset:
Efficient lipid production using food waste hydrolysate by Schizochytrium sp. HBW10 isolated from Northern China
Authors: Li Xiaofang , Zhang Yong , Yin Zhe , Zhang Dingding , Li Xinyang , Wang Qiuzhen
Published: July 2022

Marine oleaginous thraustochytrids had attracted increasing attention for their great potential in producing physiological active substances using a wide range of industrial and agricultural waste. Food waste containing abundant nutrients was considered as an excellent feedstock for microbial fermentation. In this study, a thraustochytrid strain Schizochytrium sp. HBW10, was isolated from seawater of Bohai Bay in Northern China for the first time. Further lipid production characteristics of S. sp. HBW10 was investigated utilizing sulfuric acid hydrolysate of food waste (FWH) from two different restaurants (FWH1 and FWH2) with the initial pH adjusted by NaOH or NaHCO 3 . Results showed the highest total fatty acids (TFAs) concentration was observed in FWH2 medium with 50% content level on the fifth day, reaching up to 0.34 g/L. Higher initial pH promoted the growth and saturated fatty acids (SFAs) accumulation of S. sp. HBW10, achieving nearly 100% of sum of saturated and monounsaturated fatty acids (SMUFAs) in TFAs with initial pH7 and pH8 in FWH1 medium. Moreover, continuous CO 2 release in the culture medium provided by NaHCO 3 solution was beneficial for TFAs production, especially linolic acid (C18:2), a functional substance used in the chemical and pharmaceutical industries, which was doubled compared with the FWH1NaOH group on the fifth day. This work demonstrated a possible way for lipid production by thraustochytrids using food waste hydrolysate with higher initial pH (pH7~ pH8) adjusted by NaHCO 3 .
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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).
Natural floating Fe films: microbial interactions and function within Fe cycling bacteria in wetlands
Authors: Dong Leheng , Chen Manjia , Liu Chengshuai , Lv Yahui , Wang Xugang , Lei Qinkai , Fang Yujuan , Tong Hui
Published: August 2022

Floating iron (Fe) films are widely found in numerous environments that can form oxic-anoxic boundaries under circumneutral conditions, including wetlands, estuaries, and small lakes. These films play a crucial role in the redox transformations and bioavailability of nutrients, trace metals, and heavy metals. Current studies mainly focus on chemical oxidation during Fe film formation and the characterization of these films. The functional microorganisms and associated microbial processes involved in Fe film formation have yet to be investigated in detail. Here, we investigated the microbial communities and involved microbial processes of floating Fe films in lake wetlands. Ferrihydrite was the dominant Fe(III) phase in films, accompanied by moderate levels of carbon and silicon. The Fe species and microbial analysis indicates that Fe films contain mixed-valent Fe and can form biotically. The analysis of microbial community of these films showed that the dominant genera were Fe-oxidizing and reducing bacteria and methanotrophs, including Leptothrix, Ferriphasclus, Gallionella, Geobacter, and Methylococcales. Leptothrix, Ferriphasclus, and Gallionella, as classical Fe(II)-oxidizing bacteria (FeOB), can oxidize Fe(II) with limited oxygen and form special structures that are closely associated with Fe film formation and are consistent with Fe film morphology. Geobacter can provide a source of Fe(II) for FeOB growth, and Methylococcales can perform methane oxidation to provide energy for Fe cycling. The diversity of floating film community suggest Fe(II) oxidation can trigger carbon fixation, while Fe(III) reduction accelerates Fe and carbon cycling through anaerobic respiration and lithoautotrophic chemosynthesis. These results highlight the contribution of these multiple microbial processes to floating film formation in wetlands. However, further studies are required to fully elucidate the interaction of functional microorganisms involved in floating film formation and their biogeochemical role in wetlands.
Abnormal regional homogeneity (ReHo) in Congenital Amusia: a resting-state functional MRI study
Authors: Yang Ru , Hou Feng , Sun Jun-Jie , Liao Xuan , Jin Zhi-Shuai , Wu Daxing , Liu Jun
Published: July 2022

Congenital amusia (CA) is a disorder of pitch perception, a core function of the human auditory system. A total of seventeen amusics, fitting stringent criteria in Montreal Battery of Evaluation of Amusia (MBEA), and fourteen healthy controls participated in our study. Here, we aimed to characterize a local resting state functional measure, regional homogeneity (ReHo) to evaluate abnormal local consistency of the neurons spontaneous activity in CA group compared with the healthy control group. We focused on potential brain areas with significant differences between the two groups. The partial correlation analyses applied to ReHo values as well as scores of MBEA subtests. The CA group showed a significant decrease ReHo values in the left postcentral gyrus and left middle cingulum cortex when compared to controls. The ReHo values of those two significant regions were both positively correlated with MBEA scores. All in all, this study identified brain regions which were not discussed previously that may partly contribute to or be modulated by CA.
Comparative mitochondrial genome analysis of Spirometra tapeworms from different hosts
Authors: He Jun-Lin , Xie Shi-Chen , Gong Teng-Fang , Chen Shu-Yu , Xie Xin-Rui , Li Wen-Chao , Liu Guo-Hua , Liu Wei
Published: July 2022

Background: Spirometra erinaceieuropaei, the causative agent of food/water-borne sparganosis, has been widely reported worldwide. However, the taxonomy of the genus Spirometra has always been complicated. The main objectives of this study were to assemble 7 complete mitochondrial genomes of Spirometra erinaceieuropaei, collected from different hosts in the Hunan province of China, and to analyze the phylogenetic relationship and genetic diversity of cestode species.Methods: In this study, seven Spirometra erinaceieuropaei (three spargana and 4 adults) were collected from different hosts in Hunan province, China. The long-PCR was performed to amplify the four large fragments of the Spirometra mitochondria (mt) genome by using specific primers reported in a previous study. Then, the mt genome of each S. erinaceieuropaei was assembled and annotated after overlapping four large fragments. Sliding window analysis was carried out to explore the nucleotide variation of the mt genome between 7 isolates obtained in this study and 8 reported Spirometra. The genetic diversity of cestode species was also investigated by Bayesian analysis based on 12 protein-coding genes.Results: Seven mt genomes of S. erinaceieuropaei obtained in this study were successfully assembled and annotated. The genome features of S. erinaceieuropaei are similar to other reported cestode species, containing 12 protein-coding genes (PCGs), 22 transfer RNA genes (tRNAs), and two ribosomal RNA genes (rRNA), large non-coding regions (NC), and small non-coding regions (NR). Sequencing analysis revealed 97.40%-99.90% sequence similarity among seven mt genomes identified in this study. Sliding window analysis indicated that the Korea isolate (KJ599680) might be the differential specie of genus Spirometra, and nad4L, cox3, and nad6 were the top three genes with the lowest variation rates in mt DNA. Phylogenetic analysis based on 12 PCGs further demonstrated that S. mansoni (AB374543) might be the misnamed S. erinaceieuropaei.Conclusion: The results of the current study supports the previously reported conclusion that multiple genotypes exist within S. erinaceieuropaei, and the Korean isolate (KJ599680) be a novel genotype or even a novel species of the genus Spirometra, and we strongly suggests that S. decipines may be a misnamed of S. erinaceieuropaei like S. mansoni is. nad4L, cox3 and nad6 are more suitable molecular genetic markers than cox1 for S. erinaceieuropaei identification. However, the authentic relationships among S. erinaceieuropaei isolates from different hosts and geographical sites are still unknown. More samples must be collected in different host and geographical positions to help us understand the genus Spirometra.
Off-axis Holographic Interferometer with Ensemble Deep Learning for Biological Tissues Identification
Authors: Lam H. H.S. , Zhu Yanmin , Buranasiri Prathan
Published: July 2022

This paper proposes a method with an off-axis interferometer and an ensemble deep learning (I-EDL) hologram-classifier to interpret noisy digital holograms captured from the tissues of flawed biological specimens. The holograms are captured by an interferometer which serves as a digital holographic scanner to scan the tissue with 3D information. A red laser beam penetrates the tissue of the specimen. It scans across the x-y directions to capture thousands of off-axis hologram samples for the training, testing, and recognizing the microstructure of the tissue and, hence, identifying the specimens' identity. The method achieves a high success rate of 99.60% in identifying the specimens through the tissue holograms. It is found that the ensemble deep learning hologram-classifier can effectively adapt to optical aberration coming from dust on mirrors and optical lenses aberration such as the Airy-plaque-like rings out-turn from the lenses in the interferometer. The deep learning network effectively adapts to these irregularities during the training stage and performs well in the later recognition stage without prior optical background compensations. The method does not require an intact sample with a full outline shape of the specimens or the organs to understand the objects' identities. It demonstrates a new paradigm in object identification by ensemble deep learning through a direct wavefront recognition technique.
Determination of concentration of heavy metals and metalloids in grapes grown in Gonabad vineyards and assessment of associated health risks
Authors: Peirovi-Minaee Roya , Alami Ali , Moghaddam Alireza , Zarei Ahmad
Published: July 2022

Metals and metalloids are considered as major public health hazards, they are known to accumulate in fruits, which are heavily consumed by humans because of their unique sweet taste and potential health benefits. Therefore, the aim of this study was to measure the concentration of ten heavy metals and metalloids including arsenic (As), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), Iron (Fe), manganese (Mn), nickel (Ni), lead (Pb) and zinc (Zn) in grapes samples grown in Gonabad vineyards and to estimate the associated health risks of metals in terms of chronic daily intake (CDI), and carcinogenic and non-carcinogenic risks by hazard quotient (HQ), hazard index (HI) and cancer risk (CR). The overall concentration of in red grapes were in range 0.07–0.5 (mean 0.14), 0.08–0.13 (mean 0.10), 0.07–0.13 (mean 0.09), 0.06–1.49 (mean 0.29), 0.52–4.12 (mean 1.65), 6.43–42.17 (mean 19.01), 0.89–4.04 (mean 1.89), 0.07–9.23 (mean 0.71), 0.07–0.37 (mean 0.18), 0.40–4.13 (mean 1.05) mg/kg dry weight for As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn, respectively. Based on the results, cadmium for all samples and Pb in 64.7 % and As in 35.3% of the samples exceeded the FAO/WHO permissible limits. The estimated non-carcinogenic and carcinogenic risk indices showed that the results were lower than the critical value (1) and in acceptable range, respectively, therefore red grape is safe for consumption with no impact on the human health. The obtained data can be used in remediation techniques, as well as in implementing control measures of metals contamination in grapes.
Efficacy of Insect Endogeneous Cellulases for Ligno-cellulosic Biofuels Deciphered Using Molecular Docking Studies
Authors: Nivetha Ramanathan , Bhuvaragavan Sreeramulu , Meenakumari Mani , Janarthanan Sundaram
Published: August 2022

Cellulose, the substance that makes up most of a plant’s cell wall, is pondered to be one of the most abundant natural organic polymers on earth made up of glucose units linked by β-1, 4 glycosidic bonds. Insects possess cellulolytic system capable of producing variegate enzymes with multifarious specificities to break down complex lignocellulosic products. Astonishingly, endoglucanases, exoglucanase, and β-glycosidases act sequentially in a synergistic system to facilitate the breakdown of cellulose to utilizable energy source glucose. These extremely versatile enzymes are a better source in terms of environmental performance and overall energy efficiency. Pertaining to four main glycosyl hydrolase families (GHF), insect cellulases are distributed in all the insect orders explored up until now. In silico docking studies of endo-β-1,4-glucanase from 19 different insects belonging to six different orders identified that it possesses high affinity for all the six substrates, including CMC, cellulose, cellotriose, cellotetraose, cellopentose, and cellohexaose. Additionally, β-glucosidase from nearly all the reported insect sources also showed considerable affinity towards cellobiose. Van der Waals, conventional hydrogen bonds, and carbon-hydrogen bonds stabilize the interaction between the enzyme and different substrates. Molecular dynamics simulations also held up the stability of various complexes. With lignocelluloses-based biofuels becoming a major focus of industrial and academic communities worldwide, this study can perhaps complement the propensity of insect cellulases for prospected applications.
Effect of discharge teaching quality on readiness for hospital discharge in total knee arthroplasty patients: the role of depression and self-efficacy for rehabilitation
Authors: LI Na , GUO Manjie , YOU Simeng , JI Hong
Published: August 2022

Background: As the number of total knee arthroplasty (TKA) increases and the length of stay decreases, the readiness of patients for discharge needs attention. The purpose of this study is to clarify the relationship between the quality of discharge teaching, depression, self-efficacy for rehabilitation, and readiness for hospital discharge (RHD), thereby providing evidence support for improving readiness for discharge in patients with TKA.Methods: A total of 319 TKA patients participated in this cross-sectional study. Bivariate Pearson’s correlation was used to test the correlation between the four variables. Multivariate regression models and Bootstrap tests were used to examine the mediating role of depression and self-efficacy for rehabilitation. In addition, we used a mediation model 6 to explore the relationship between the quality of discharge teaching, depression, self-efficacy for rehabilitation, and RHD. All regression models were adjusted for age, sex, residential status, education level, number of joint replacements, and number of co-morbidities.Results: Depression was negatively associated with RHD (r=-0.281, P＜0.01) ,and self-efficacy for rehabilitation was positively associated with RHD (r=0.396, P＜0.01), with both playing a partially mediating role between the quality of discharge teaching and RHD, the effect value being 62.7%. The effect of discharge teaching quality on RHD in patients with TKA was weakened when depression and recovery self-efficacy were added.Conclusions: The results of this study emphasized the importance of depression and self-efficacy for rehabilitation on the RHD of patients with TKA. When providing discharge teaching, physicians and nurses could use relevant psychological intervention methods to improve the patient's physical and mental condition, with the final goal of improving their RHD.
Economic Forecasting with Big Data: A Literature Review
Authors: Lin Wencan , Wang Shouyang , Wei Yunjie
Published: July 2022

Big data technology has changed the research paradigm of economic forecasting regardless of the data source, forecasting method or forecasting result. In this paper, by utilizing bibliometric methods, we analyze the existing literature on big data economic forecasting with 821 articles, collected from the ‘‘Web of Science Core Collection’’ database. Using cooccurrence analysis, co-citation analysis, and structural variation analysis, this work not only presents a comprehensive review of big data in economic forecasting research but also captures articles with transformative potential in this field. This study provides meaningful guidance for researchers to enhance the understanding of crucial research, emerging trends, and new developments in big data economic forecasting.
Comparative Analysis of Segmentation Models to Detect Leaf Diseases in Tomato Plant
Authors: Kaur Prabhjot , Harnal Shilpi , Gautam Vinay , Singh Mukund Pratap , Singh Santar Pal
Published: August 2022

In agriculture around 22% of crop yield loss is due to living and non-living organisms such as biotic and abiotic stress/disease. The early-stage diagnosis of these stresses is an important issue for farmers through naked eyes. Using computer vision technologies can detect the pattern and clustering of diseases at an early stage. However, inthe last few years, computer vision-based deep learning technology is helpful for the diagnosis of biotic stress (single biotic and multi biotic) in tomato plant leaves.In this work, the PlantVillage dataset is gathered for the segmentation of object detection. The labeled, enhanced and augmented data has been used for training the model. The proposed hybrid Deep Segmentation Convolutional Neural Network (Hybrid-DSCNN)model has been segmenting the diseased objects in the tomato plant. This Hybrid-DSCNN is assembled usingU-Net and Seg-Net pre-trained models with instance segmentation for better detection of objects. The result of the proposed Hybrid DSCNN model is compared with other modified U-Net, M-SegNet, and modified U-SegNet in terms of Accuracy, Precision, Recall, Intersection over Union (IoU), and mean Intersection over Union (mIoU). The proposed model processed 1004 images in 30ns,whichis better than other compared models. The accuracy achieved using the proposed model is 98.24%,which is far better than other modified segmentation models. In this paper, the semantic segmented data has been recognized for the single and multiple leaf diseases for identification and classification.
Neuroepidemiology study of headache in the region of Jammu of north Indian population
Authors: Sudershan Amrit , Younis Mohd , Pushap Aghar Chander , Kumar Hardeep , Sudershan Srishty , Kumar Parvinder
Published: August 2022

Background
Headache disorders represent a major public health problem globally. It is more in developing countries with the rising trends in young adults affecting negatively their quality of life. There has been very little information on the epidemiology of headache disorder in the Jammu division of the north Indian population.
Aim
The present study is aimed to find out the prevalence of headache and its two major types i.e., migraine and TTH (Tension-Type Headache) in population of Jammu division.
Methods
The study was conducted in two phases: (Phase I: Face-face interview and Phase II: E-based sampling) and sufferers of headaches were incorporated into the study based on the ICHD-3 criteria for a representative sample. Frequency distribution and mean ± SD were used in descriptive statistics to describe the data sets, while a t-test, chi-square test, and a logistic regression model using odds ratio were used in inferential statistics.
Results
A total of 3,148 subjects were recruited and found the overall prevalence of headache was 53.84% with a female preponderance (38.18%) in comparison to male 15.66%. Regarding the type, migraine was found to be highly prevalent (33.25%) type than TTH (20.58%). Female suffering from migraine shows the highest prevalence (25.28%). Regarding the environmental factors, bright light OR: 5.81, 95% CIs [4.96–6.81] loud sound OR: 5.17, 95% CIs [4.44–6.03], stress OR: 3.58, 95% CIs [3.07–4.17], empty stomach OR: 3.42, 95% CIs [2.93–3.98] increases the likely hood of the diseases. Also, high comorbidity association was found in between PCOS OR: 8.63, 95% CIs [3.95–18.85], panic disorder OR: 5.04, 95% CIs [1.94–3.05], anxiety OR: 4.53, 95% CIs [3.90–5.27], IBS OR: 3.80, 95% [1.76–8.23] with headache.
Conclusion
The prevalence of headache is high in the Jammu division of J&K India with migraine being the high prevalent type. Light and sound with high intensity, empty stomach, stress, and less water intake are some highly associated environmental risk factor that increases the severity diseases.
A new paradigm in tracking the dynamics of glucose and cortisol: An observational study from human sweat enabled by a skin sensor
Authors: Sardesai Abha Umesh , Greyling Cornelia Felicia , Lin Kai-Chun , Muthukumar Sriram , Prasad Shalini
Published: August 2022

A sedentary lifestyle is one of the leading risk factors to develop obesity. Abdominal obesity is one of the metabolic disorders that lead to an increased risk of developing cardiovascular disease. In this work, we developed a novel approach of tracking glucose and cortisol. With the aim of observing the dynamic relationship between the target biomarkers as a result of physical activity, nutrition, and the circadian cycle, with the help of the machine learning-assisted wearable sweat-based sensor designed in our previous work. The machine learning approach helps to provide real-time biomarker value and a basis for actionable insight. Through this observational study, we illustrate the real-world performance of our sweat-based sensor by examining the glucose and cortisol levels of 4 healthy participants over multiple days. This novel glucose and cortisol tracking paradigm establishes the foundation for future research on the effect of lifestyle choices on target biomarkers for metabolic disorders.
METTL3 promotes glycolysis and cholangiocarcinoma progression by mediating m6A modification of AKR1B10
Authors: Cai Jingli , Cui Zheng , Zhou Jingyi , Zhang Bosen , Lu Ruiqi , Ding Youcheng , Hu Hai
Published: August 2022

Objective
N6-methyladenosine (m6A) RNA methylation involve in governing mechanism of tumor progression. We aimed to excavate the biological role and mechanism of m6A methyltransferase METTL3 in Cholangiocarcinoma (CCA).
Methods
METTL3 expression was determined by database and tissue microarray. The role of METTL3 in CCA was explored by loss- and gain-of-function experiments. m6A target of METTL3 was detected by RNA sequencing. The role of AKR1B10 in CCA was explored and the association between METTL3 and AKR1B10 was confirmed by rescue experiments.
Result
METTL3 expression was upregulated in CCA tissue and higher METTL3 expression was implicated a poor prognosis in CCA patients. Overexpression of METTL3 facilitated proliferation, migration, invasion, glucose uptake, and lactate production of CCA cells, whereas knockdown of METTL3 had the opposite effect. We further found that METTL3 deficiency inhibited tumor growth of CCA in vivo. RNA sequencing and MeRIP-qPCR confirmed that METTL3 enhanced AKR1B10 expression and m6A modification level. Further, METTL3 directly bind with AKR1B10 at an m6A modification site also confirmed. CCA tissue microarray showed that AKR1B10 expression was upregulated in CCA tissue and silencing of AKR1B10 suppressed the malignant phenotype mentioned-above in CCA. Notably, knockdown of AKR1B10 rescued the tumor-promoting effects induced by METTL3 overexpression.
Conclusion
Elevated METTL3 expression promotes tumor growth and glycolysis in CCA through m6A modification of AKR1B10, indicating that METTL3 is a potential target for blocking glycolysis to apply in CCA therapy.
Changes in soil fungal communities after onset of wheat yellow mosaic virus disease
Authors: Lu Qi-sen , Cai Lin-na , Wu Chuan-fa , Zhang Hao-qing , Wei Liang , Zhang Tian-ye , Hu Hai-chao , Liu Shuang , Lei Jia-jia , Ge Ti-da , Dai Liang-ying , Yang Jian , Chen Jian-ping
Published: August 2022

Rhizosphere-associated microbes have important implications for plant health, but knowledge of the association between the pathological conditions of soil-borne virus-infected wheat and soil microbial communities, especially changes in fungal communities, remains limited. We investigated the succession of fungal communities from bulk soil to wheat rhizosphere soil in both infected and healthy plants using amplicon sequencing methods, and attempted to assess their potential role in plant health. Fungal diversity and richness decreased significantly from the bulk soil to the wheat rhizosphere soil, and had fewer fungi in the bulk soil of infected plant than healthy plants. The structure differences in fungal community at the two wheat health statuses or two compartment niches were obvious, soil physicochemical properties (i.e., NH4+ and soil organic carbon) contribute to differences in fungal community structure and alpha diversity. Comparison analysis showed Mortierellomycetes and Dothideomycetes are found as dominant communities in healthy wheat soils at class level. The genus Cystofilobasidium, Cladosporium, Mortierella and Stephanonectria are significantly enriched in bulk soil of healthy plant. Furthermore, co-occurrence network analysis showed that the fungi in healthy wheat soil has higher mutual benefit and connectivity compared with diseased wheat. In conclusion, we demonstrate that the occurrence of soil-borne viral diseases causes changes in the fungal community that depend on soil abiotic factors.