bluuebunny/crossref_metadata_2025_split huggingface dataset:
A target-driven visual navigation method based on intrinsic motivation exploration and space topological cognition
Authors: Ruan Xiaogang , Li Peng , Zhu Xiaoqing , Liu Pengfei 
Published: March 2022

AbstractTarget-driven visual navigation is essential for many applications in robotics, and it has gained increasing interest in recent years. In this work, inspired by animal cognitive mechanisms, we propose a novel navigation architecture that simultaneously learns exploration policy and encodes environmental structure. First, to learn exploration policy directly from raw visual input, we use deep reinforcement learning as the basic framework and allow agents to create rewards for themselves as learning signals. In our approach, the reward for the current observation is driven by curiosity and calculated by a count-based approach and temporal distance. While agents learn exploration policy, we use temporal distance to find waypoints in observation sequences and incrementally describe the structure of the environment in a way that integrates episodic memory. Finally, space topological cognition is integrated into the model as a path planning module and combined with a locomotion network to obtain a more generalized approach to navigation. We test our approach in the DMlab, a visually rich 3D environment, and validate its exploration efficiency and navigation performance through extensive experiments. The experimental results show that our approach can explore and encode the environment more efficiently and has better capability in dealing with stochastic objects. In navigation tasks, agents can use space topological cognition to effectively reach the target and guide detour behaviour when a path is unavailable, exhibiting good environmental adaptability.
Carbon peak and its mitigation implications for China in the post-pandemic era
Authors: Chen Jiandong , Xu Chong , Gao Ming , Li Ding 
Published: March 2022

AbstractChina’s carbon peak greatly impacts global climate targets. Limited studies have comprehensively analyzed the influence of the COVID-19 pandemic, changing emission network, and recent carbon intensity (CI) reduction on the carbon peak and the corresponding mitigation implications. Using a unique dataset at different levels, we project China’s CO2 emission by 2035 and analyze the time, volume, driver patterns, complex emission network, and policy implications of China’s carbon peak in the post- pandemic era. We develop an ensemble time-series model with machine learning approaches as the projection benchmark, and show that China’s carbon peak will be achieved by 2021–2026 with > 80% probability. Most Chinese cities and counties have not achieved carbon peaks response to the priority-peak policy and the current implementation of CI reduction should thus be strengthened. While there is a "trade off" between the application of carbon emission reduction technology and economic recovery in the post-pandemic era, a close cooperation of interprovincial CO2 emission is also warranted.
Genotyping-in-Thousands by sequencing panel development and application for high-resolution monitoring of introgressive hybridization within sockeye salmon
Authors: Chang Sarah L. , Ward Hillary G. M. , Elliott Lucas D. , Russello Michael A. 
Published: March 2022

AbstractStocking programs have been widely implemented to re-establish extirpated fish species to their historical ranges; when employed in species with complex life histories, such management activities should include careful consideration of resulting hybridization dynamics with resident stocks and corresponding outcomes on recovery initiatives. Genetic monitoring can be instrumental for quantifying the extent of introgression over time, however conventional markers typically have limited power for the identification of advanced hybrid classes, especially at the intra-specific level. Here, we demonstrate a workflow for developing, evaluating and deploying a Genotyping-in-Thousands by Sequencing (GT-seq) SNP panel with the power to detect advanced hybrid classes to assess the extent and trajectory of intra-specific hybridization, using the sockeye salmon (Oncorhynchus nerka) stocking program in Skaha Lake, British Columbia as a case study. Previous analyses detected significant levels of hybridization between the anadromous (sockeye) and freshwater resident (kokanee) forms ofO. nerka, but were restricted to assigning individuals to pure-stock or “hybrid”. Simulation analyses indicated our GT-seq panel had high accuracy, efficiency and power (> 94.5%) of assignment to pure-stock sockeye salmon/kokanee, F1, F2, and B2backcross-sockeye/kokanee. Re-analysis of 2016/2017 spawners previously analyzed using TaqMan® assays and otolith microchemistry revealed shifts in assignment of some hybrids to adjacent pure-stock or B2 backcross classes, while new assignment of 2019 spawners revealed hybrids comprised 31% of the population, ~ 74% of which were B2 backcross or F2. Overall, the GT-seq panel development workflow presented here could be applied to virtually any system where genetic stock identification and intra-specific hybridization are important management parameters.
Gramicidin S and melittin: potential anti-viral therapeutic peptides to treat SARS-CoV-2 infection
Authors: Enayathullah Mohammed Ghalib , Parekh Yash , Banu Sarena , Ram Sushma , Nagaraj Ramakrishnan , Kumar Bokara Kiran , Idris Mohammed M. 
Published: March 2022

AbstractThe COVID19 pandemic has led to multipronged approaches for treatment of the disease. Since de novo discovery of drugs is time consuming, repurposing of molecules is now considered as one of the alternative strategies to treat COVID19. Antibacterial peptides are being recognized as attractive candidates for repurposing to treat viral infections. In this study, we describe the anti-SARS-CoV-2 activity of the well-studied antibacterial peptides gramicidin S and melittin obtained from Bacillus brevis and bee venom respectively. The EC50 values for gramicidin S and melittin were 1.571 µg and 0.656 µg respectively based on in vitro antiviral assay. Significant decrease in the viral load as compared to the untreated group with no/very less cytotoxicity was observed. Both the peptides treated to the SARS-CoV-2 infected Vero cells showed viral clearance from 12 h onwards with a maximal viral clearance after 24 h post infection. Proteomics analysis indicated that more than 250 proteins were differentially regulated in the gramicidin S and melittin treated SARS-CoV-2 infected Vero cells against control SARS-CoV-2 infected Vero cells after 24 and 48 h post infection. The identified proteins were found to be associated in the metabolic and mRNA processing of the Vero cells post-treatment and infection. Both these peptides could be attractive candidates for repurposing to treat SARS-CoV-2 infection.
The human-baited host decoy trap (HDT) is an efficient sampling device for exophagic Anopheles arabiensis within irrigated lands in southern Malawi
Authors: Zembere Kennedy , Chirombo James , Nasoni Peter , McDermott Daniel P. , Tchongwe-Divala Lizzie , Hawkes Frances M. , Jones Christopher M. 
Published: March 2022

AbstractIrrigation schemes provide an ideal habitat for Anopheles mosquitoes particularly during the dry season. Reliable estimates of outdoor host-seeking behaviour are needed to assess the impact of vector control options and this is particularly the case for Anopheles arabiensis which displays a wide range of behaviours that circumvent traditional indoor-insecticide based control. In this study we compared the sampling efficiency of the host decoy trap (HDT) with the human landing catch (HLC) and Suna trap in a repeated Latin square design in two villages (Lengwe and Mwanza) on an irrigated sugar estate in southern Malawi. Over the course of 18 trapping nights, we caught 379 female Anopheles, the majority of which were identified as An. arabiensis. Across both villages, there was no detectable difference in Anopheles catch between the HDT compared with the HLC (RR = 0.85, P = 0.508). The overall sensitivity of the HLC was greater than the Suna trap regardless of mosquito density (Lengwe, α = 2.75, 95% credible interval: 2.03–3.73; Mwanza, α = 3.38, 95% credible interval: 1.50–9.30) whereas the sensitivity of the HDT was only greater than the Suna trap when mosquito numbers were high (Lengwe, α = 2.63, 95% credible interval: 2.00–3.85).We conclude that the HDT is an effective sampling device for outdoor host seeking An. arabiensis in southern Malawi. The presence of An. arabiensis in irrigated lands during the dry season poses a challenge for ongoing indoor vector control efforts.
Synthesis, and docking studies of novel heterocycles incorporating the indazolylthiazole moiety as antimicrobial and anticancer agents
Authors: Dawoud Nadia T. A. , El-Fakharany Esmail M. , Abdallah Abdallah E. , El-Gendi Hamada , Lotfy Doaa R. 
Published: March 2022

AbstractThe current study was directed toward developing a new series of fused heterocycles incorporating indazolylthiazole moiety. The newly synthesized compounds were characterized through elemental analysis and spectral data (IR, 1H-NMR, 13C-NMR, and Mass Spectrometry). The cytotoxic effect of the newly synthesized compounds was evaluated against normal human cells (HFB-4) and cancer cell lines (HepG-2 and Caco-2). Among the synthesized compounds, derivatives 4, and 6 revealed a significant selective antitumor activity, in a dose-dependent manner, against both HepG-2 and Caco-2 cell lines, with lower risk toward HFB-4 cells (normal cells). Derivative 8 revealed the maximum antitumor activity toward both tumor cell lines, with an SI value of about 26 and IC50 value of about 5.9 μg/mL. The effect of these derivatives (8, 4, and 6) upon the expression of 5 tumor regulating genes was studied through quantitative real-time PCR, where its interaction with these genes was simulated through the molecular docking study. Furthermore, the antimicrobial activity results revealed that compounds 2, 7, 8, and 9 have a potential antimicrobial activity, with maximum broad-spectrum activity through compound 3 against the three tested pathogens: Streptococcus mutans, Pseudomonas aeruginosa, and Candida albicans. The newly prepared compounds also revealed anti-biofilm formation activity with maximum activity against Streptococcus mutans, Pseudomonas aeruginosa, and Candida albicans, respectively.
In hospital risk factors for acute kidney injury and its burden in patients with Sars-Cov-2 infection: a longitudinal multinational study
Authors: Morieri Mario Luca , Ronco Claudio , Avogaro Angelo , Farnia Filippo , Shestakova Marina , Zaytseva Natalya , Mokrysheva Natalya , Akulkina Larisa , Shepalina Anastasia , Brovko Michail , Moiseev Sergey , Russo Lucia , Mazzocut Sara , Vianello Andrea , Cattellan Anna Maria , Vedovato Monica , Fadini Gian Paolo , Vettor Roberto , Fioretto Paola 
Published: March 2022

AbstractAcute kidney injury (AKI) is associated with increased mortality in most critical settings. However, it is unclear whether its mild form (i.e. AKI stage 1) is associated with increased mortality also in non-critical settings. Here we conducted an international study in patients hospitalized with SARS-CoV-2 infection aiming 1. to estimate the incidence of AKI at each stage and its impact on mortality 2. to identify AKI risk factors at admission (susceptibility) and during hospitalization (exposures) and factors contributing to AKI-associated mortality. We included 939 patients from medical departments in Moscow (Russia) and Padua (Italy). In-hospital AKI onset was identified in 140 (14.9%) patients, mainly with stage 1 (65%). Mortality was remarkably higher in patients with AKI compared to those without AKI (55 [39.3%] vs. 34 [4.3%], respectively). Such association remained significant after adjustment for other clinical conditions at admission (relative risk [RR] 5.6; CI 3.5- 8.8) or restricting to AKI stage 1 (RR 3.2; CI 1.8–5.5) or to subjects with AKI onset preceding deterioration of clinical conditions. After hospital admission, worsening of hypoxic damage, inflammation, hyperglycemia, and coagulopathy were identified as hospital-acquired risk factors predicting AKI onset. Following AKI onset, the AKI-associated worsening of respiratory function was identified as the main contributor to AKI-induced increase in mortality risk. In conclusion, AKI is a common complication of Sars-CoV2 infection in non-intensive care settings where it markedly increases mortality risk also at stage 1. The identification of hospital-acquired risk factors and exposures might help prevention of AKI onset and of its complications.
A hybrid with distributed pooling blockchain protocol for image storage
Authors: Liu Feng , Yang Cheng-yi , Yang Jie , Kong De-li , Zhou Ai-min , Qi Jia-yin , Li Zhi-bin 
Published: March 2022

AbstractAs a distributed storage scheme, the blockchain network lacks storage space has been a long-term concern in this field. At present, there are relatively few research on algorithms and protocols to reduce the storage requirement of blockchain, and the existing research has limitations such as sacrificing fault tolerance performance and raising time cost, which need to be further improved. Facing the above problems, this paper proposes a protocol based on Distributed Image Storage Protocol (DISP), which can effectively improve blockchain storage space and reduces computational costs in the help of InterPlanetary File System (IPFS). In order to prove the feasibility of the protocol, we make full use of IPFS and distributed database to design a simulation experiment for blockchain. Through distributed pooling (DP) algorithm in this protocol, we can divide image evidence into recognizable several small files and stored in several nodes. And these files can be restored to lossless original documents again by inverse distributed pooling (IDP) algorithm after authorization. These advantages in performance create conditions for large scale industrial and commercial applications.
A deep learning model for molecular label transfer that enables cancer cell identification from histopathology images
Authors: Su Andrew , Lee HoJoon , Tan Xiao , Suarez Carlos J. , Andor Noemi , Nguyen Quan , Ji Hanlee P. 
Published: March 2022

AbstractDeep-learning classification systems have the potential to improve cancer diagnosis. However, development of these computational approaches so far depends on prior pathological annotations and large training datasets. The manual annotation is low-resolution, time-consuming, highly variable and subject to observer variance. To address this issue, we developed a method, H&E Molecular neural network (HEMnet). HEMnet utilizes immunohistochemistry as an initial molecular label for cancer cells on a H&E image and trains a cancer classifier on the overlapping clinical histopathological images. Using this molecular transfer method, HEMnet successfully generated and labeled 21,939 tumor and 8782 normal tiles from ten whole-slide images for model training. After building the model, HEMnet accurately identified colorectal cancer regions, which achieved 0.84 and 0.73 of ROC AUC values compared to p53 staining and pathological annotations, respectively. Our validation study using histopathology images from TCGA samples accurately estimated tumor purity, which showed a significant correlation (regression coefficient of 0.8) with the estimation based on genomic sequencing data. Thus, HEMnet contributes to addressing two main challenges in cancer deep-learning analysis, namely the need to have a large number of images for training and the dependence on manual labeling by a pathologist. HEMnet also predicts cancer cells at a much higher resolution compared to manual histopathologic evaluation. Overall, our method provides a path towards a fully automated delineation of any type of tumor so long as there is a cancer-oriented molecular stain available for subsequent learning. Software, tutorials and interactive tools are available at:https://github.com/BiomedicalMachineLearning/HEMnet
Artificial intelligence to detect malignant eyelid tumors from photographic images
Authors: Li Zhongwen , Qiang Wei , Chen Hongyun , Pei Mengjie , Yu Xiaomei , Wang Layi , Li Zhen , Xie Weiwei , Wu Xuefang , Jiang Jiewei , Wu Guohai 
Published: March 2022

AbstractMalignant eyelid tumors can invade adjacent structures and pose a threat to vision and even life. Early identification of malignant eyelid tumors is crucial to avoiding substantial morbidity and mortality. However, differentiating malignant eyelid tumors from benign ones can be challenging for primary care physicians and even some ophthalmologists. Here, based on 1,417 photographic images from 851 patients across three hospitals, we developed an artificial intelligence system using a faster region-based convolutional neural network and deep learning classification networks to automatically locate eyelid tumors and then distinguish between malignant and benign eyelid tumors. The system performed well in both internal and external test sets (AUCs ranged from 0.899 to 0.955). The performance of the system is comparable to that of a senior ophthalmologist, indicating that this system has the potential to be used at the screening stage for promoting the early detection and treatment of malignant eyelid tumors.
Synthesis of nitrogen and terbium co-doped TiO2nanocrystals with enhanced photocatalytic activity for AO7 degradation under visible-light radiation
Authors: Ma Yunfei , Yue Wenhui , Ye Ziwei , Zhang Jinglong 
Published: March 2022

TiO2 nanocrystals co-doped with nitrogen and terbium exhibited extended light absorption, improved charge separation efficiency, and enhanced AO7 adsorption efficiency, giving rise to enhanced AO7 degradation activity which was 2× better than undoped TiO2 nanocrystals.
A nonlinear double‐integral sliding mode controller design for hybrid energy storage systems and solar photovoltaic units to enhance the power management in DC microgrids
Authors: Ghosh Subarto Kumar , Roy Tushar Kanti , Pramanik Md Abu Hanif , Mahmud Md Apel 
Published: March 2022

AbstractIn this paper, a nonlinear decentralized double‐integral sliding mode controller (DI‐SMC) is designed along with an energy management system (EMS) for the DC microgrid (DCMG). This DCMG includes having a hybrid energy storage system (HESS) that incorporates a battery energy storage system (BESS) and supercapacitor energy storage system (SCESS) while the load demand is met through the power generated from solar photovoltaic (SPV) units. First, dynamical models of each subsystem of DCMGs such as the SPV system, BESS, and SCESS are developed to capture highly nonlinear behaviors of DCMGs under various operating conditions. The proposed nonlinear DI‐SMC is then designed for each power unit in DCMGs to ensure the desired voltage level at the common DC‐bus and appropriate power dispatch of different components to fulfill the load requirement of the DCMG. On the other hand, an energy management system (EMS) is designed to determine the set point for the controller with an aim of ensuring the power balance within DCMGs under various operating conditions where the overall stability is assessed using the Lyapunov theory. Simulation studies along with the processor‐in‐loop validation, including a comparative study with a proportional‐integral (PI) controller, verify the applicability and effectiveness of the EMS‐based DI‐SMC under different operating conditions of the DCMG.
Facile hydrothermal synthesis CuO microflowers for non‐enzymatic glucose sensors
Authors: Wang Aihua , Yang Yan , Zhao Qinghuai , Ji Xiaoxu 
Published: March 2022

AbstractWith diabetes mellitus increasing, developing non‐enzymatic glucose sensors to replace enzyme‐based sensors has become more urgent, owing to its intrinsic disadvantages of enzymes. Herein, CuO microflowers were successfully synthesized through a green hydrothermal method with no template or surfactant. X‐ray diffraction, X‐ray photoelectron spectroscopy, scanning electron microscopy and transmission electron microscopy were used to study the crystalline structure, elemental composition and morphology of the as‐obtained sample. Then, the CuO microflowers acted as non‐enzymatic glucose sensor electrode material. The electrochemical test results indicate that the glucose oxidation controlled by diffusion at 0.5 V had a short response time(5 s), the linear range was wide(∼11 mM), and the detection limit was low(1.3 µm). Moreover, the CuO microflowers exhibit great selectivity to glucose and have high poison resistance to chloride ions. Thus, all these results imply that the as‐fabricated CuO has a great application prospect for non‐enzymatic glucose sensor electrode material.
Deadbeat predictive current control of PMSM drives with an adaptive flux‐weakening controller
Authors: Fan Shengwen , Zhang Yongchang , Jin Jialin , Wang Xing , Tong Chaonan 
Published: March 2022

AbstractIn practical application, the motor speed may need to be greater than the rated speed, so field‐weakening control is required. However, the conventional flux‐weakening control requires much tuning work, because multiple PI controllers are used. In order to make the tuning work easier, this paper combines flux‐weakening control with deadbeat predictive current control (DPCC) to eliminate two PI controllers in current loop. In addition, this paper also proposes a speed adaptive flux‐weakening controller, which can further simplify the tuning work. The effectiveness of this method has been confirmed on a 2.2 kW surface permanent magnet synchronous motor (SPMSM) experimental platform.