bluuebunny/crossref_metadata_2025 huggingface dataset:
Development and validation of multivariable machine learning algorithms to predict risk of cancer in symptomatic patients referred urgently from primary care: a diagnostic accuracy study
Authors: Savage Richard , Messenger Mike , Neal Richard D , Ferguson Rosie , Johnston Colin , Lloyd Katherine L , Neal Matthew D , Sansom Nigel , Selby Peter , Sharma Nisha , Shinkins Bethany , Skinner Jim R , Tully Giles , Duffy Sean , Hall Geoff
Published: April 2022

Objectives

To develop and validate tests to assess the risk of any cancer for patients referred to the NHS Urgent Suspected Cancer (2-week wait, 2WW) clinical pathways.

Setting

Primary and secondary care, one participating regional centre.

Participants

Retrospective analysis of data from 371 799 consecutive 2WW referrals in the Leeds region from 2011 to 2019. The development cohort was composed of 224 669 consecutive patients with an urgent suspected cancer referral in Leeds between January 2011 and December 2016. The diagnostic algorithms developed were then externally validated on a similar consecutive sample of 147 130 patients (between January 2017 and December 2019). All such patients over the age of 18 with a minimum set of blood counts and biochemistry measurements available were included in the cohort.

Primary and secondary outcome measures

sensitivity, specificity, negative predictive value, positive predictive value, Receiver Operating Characteristic (ROC) curve Area Under Curve (AUC), calibration curves

Results

We present results for two clinical use-cases. In use-case 1, the algorithms identify 20% of patients who do not have cancer and may not need an urgent 2WW referral. In use-case 2, they identify 90% of cancer cases with a high probability of cancer that could be prioritised for review.

Conclusions

Combining a panel of widely available blood markers produces effective blood tests for cancer for NHS 2WW patients. The tests are affordable, and can be deployed rapidly to any NHS pathology laboratory with no additional hardware requirements.
Proactive infliximab optimisation using a pharmacokinetic dashboard versus standard of care in patients with Crohn’s disease: study protocol for a randomised, controlled, multicentre, open-label study (the OPTIMIZE trial)
Authors: Papamichael Konstantinos , Jairath Vipul , Zou Guangyong , Cohen Benjamin , Ritter Timothy , Sands Bruce , Siegel Corey , Valentine John , Smith Michelle , Vande Casteele Niels , Dubinsky Marla , Cheifetz Adam
Published: April 2022

Introduction

Preliminary data indicates that proactive therapeutic drug monitoring (TDM) is associated with better outcomes compared with empiric dose escalation and/or reactive TDM, and that pharmacokinetic (PK) modelling can improve the precision of individual dosing schedules in Crohn’s disease (CD). However, there are no data regarding the utility of a proactive TDM combined PK-dashboard starting early during the induction phase, when disease activity and drug clearance are greatest. The aim of this randomised, controlled, multicentre, open-label trial is to evaluate the efficacy and safety of a proactive TDM combined PK dashboard-driven infliximab dosing compared with standard of care (SOC) dosing in patients with moderately to severely active CD.

Methods and analysis

Eligible adolescent and adult (aged ≥16–80 years) patients with moderately to severely active CD will be randomised 1:1 to receive either infliximab monotherapy with proactive TDM using a PK dashboard (iDose, Projections Research) or SOC infliximab therapy, with or without a concomitant immunomodulator (IMM) (thiopurine or methotrexate) at the discretion of the investigator. The primary outcome of the study is the proportion of subjects with sustained corticosteroid-free clinical remission and no need for rescue therapy from week 14 throughout week 52. Rescue therapy is defined as any IFX dose escalation other than what is forecasted by iDose either done empirically or based on reactive TDM; addition of an IMM after week 2; reintroduction of corticosteroids after initial tapering; switch to another biologic or need for CD-related surgery. The secondary outcomes will include both efficacy and safety end points, such as endoscopic and biological remission, durability of response and CD-related surgery and hospitalisation.

Ethics and dissemination

The protocol has been approved by the Institutional Review Board Committee of the Beth Israel Deaconess Medical Center (IRB#:2021P000391). Results will be disseminated in peer-reviewed journals and presented at scientific meetings.

Trial registration number

NCT04835506.
MUA-Router: Maximizing the Utility-of-Allocation for On-chip Pipelining Routers
Authors: Li Cunlu , Dong Dezun , Liao Xiangke
Published: April 2022

As an important pipeline stage in the router of Network-on-Chips, switch allocation assigns output ports to input ports and allows flits to transit through the switch without conflicts. Previous work designed efficient switch allocation strategies by maximizing the matching efficiency in time series. However, those works neglected the interaction between different router pipeline stages. In this article, we propose the concept of Utility-of-Allocation (UoA) to indicate the quality of allocation to be practically used in on-chip routers. We demonstrate that router pipelines can interact with each other, and the UoA can be maximized if the interaction between router pipelines is taken into consideration. Based on these observations, a novel class of routers, MUA-Router, is proposed to maximize the UoA through the collaborative design (co-design) between router pipelines. MUA-Router achieves this goal in two ways and accordingly implements two novel instance router architectures. In the first, MUA-Router improves the UoA by mitigating the impact of endpoint congestion in the switch allocation, and thus Eca-Router is proposed. Eca-Router achieves an endpoint-congestion-aware switch allocation through the co-design between routing computation and switch allocation. Based on Eca-Router, CoD-Router is proposed to feed back switch allocation information to routing computation stage to provide switch allocator with more conflict-free requests. Through the co-design between pipelines, MUA-Router significantly improves the efficiency of switch allocation and the performance of the entire network. Evaluation results show that our design can achieve significant performance improvement with moderate overheads.
Computational Intelligence in Security of Digital Twins Big Graphic Data in Cyber-physical Systems of Smart Cities
Authors: Lv Zhihan , Chen Dongliang , Feng Hailin , Singh Amit Kumar , Wei Wei , Lv Haibin
Published: April 2022

This investigation focuses on the application of computational intelligence to the security of Digital Twins (DTs) graphic data of the Cyber-physical System (CPS). The intricate and diverse physical space of CPS in the smart city is mapped in virtual space to construct the DTs CPS in the smart city. Besides, Differential Privacy Frequent Subgraph-Big Multigraph (DPFS-BM) is employed to ensure data privacy security. Moreover, the analysis and prediction model for the DTs big graphic data (BGD) in the CPS is built based on Differential Privacy-AlexNet (DP-AlexNet). Alexnet successfully solves the gradient dispersion problem of the Sigmoid function of deep network structures. Finally, the comparative analysis approach is utilized to verify the performance of the model reported here by comparing it with Long Short-Term Memory, Convolutional Neural Network, Recurrent Neural Network, original AlexNet, and Multi-Layer Perceptron in a simulation experiment. Through the comparison in the root mean square error, the mean absolute error, the mean absolute percentage error, training time, and test time, the model proposed here outperforms other models regarding errors, time delay, and time consumption. In the same environment, the system performs better with multi-hop paths, extra relays, and a high fading index; in that case, the outage probability is minimal. Therefore, the DP-AlexNet model is suitable for processing BGD. Moreover, its speed acceleration is more apparent than that of other models, with a higher SpeedUp indicator. The research effectively combines data mining and data security, which is of significant value for optimizing the privacy protection technology of frequent subgraph mining on a single multi-graph. Besides, the constructed DTs of CPS can provide excellent accuracy and a prominent acceleration effect on the premise of low errors. In addition, the model reported here can provide reference for the intelligent and digital development of smart cities.
A Unified Programmable Edge Matrix Processor for Deep Neural Networks and Matrix Algebra
Authors: George Biji , Omer Om Ji , Choudhury Ziaul , V Anoop , Subramoney Sreenivas
Published: April 2022

Matrix Algebra and Deep Neural Networks represent foundational classes of computational algorithms across multiple emerging applications like Augmented Reality or Virtual Reality, autonomous navigation (cars, drones, robots), data science, and various artificial intelligence-driven solutions. An accelerator-based architecture can provide performance and energy efficiency supporting fixed functions through customized data paths. However, constrained Edge systems requiring multiple applications and diverse matrix operations to be efficiently supported, cannot afford numerous custom accelerators. In this article, we present MxCore, a unified architecture that comprises tightly coupled vector and programmable cores sharing data through highly optimized interconnects along with a configurable hardware scheduler managing the co-execution. We submit MxCore as the generalized approach to facilitate the flexible acceleration of multiple Matrix Algebra and Deep-learning applications across a range of sparsity levels. Unified compute resources improve overall resource utilization and performance per unit area. Aggressive and novel microarchitecture techniques along with block-level sparsity support optimize compute and data-reuse to minimize bandwidth and power requirements enabling ultra-low latency applications for low-power and cost-sensitive Edge deployments. MxCore requires a small silicon footprint of 0.2068 mm
2
, in a modern 7-nm process at 1 GHz and achieves (0.15 FP32 and 0.62 INT8) TMAC/mm
2
, dissipating only 11.66 μW of leakage power. At iso-technology and iso-frequency, MxCore provides an energy efficiency of 651.4×, 159.9×, 104.8×, and 124.2× as compared to the 128-core Nvidia’s Maxwell GPU for dense General Matrix Multiply, sparse Deep Neural Network, Cholesky decomposition, and triangular matrix solve respectively.
iDAM: Iteratively Trained Deep In-loop Filter with Adaptive Model Selection
Authors: Li Yue , Zhang Li , Zhang Kai
Published: April 2022

As a rapid development of neural-network-based machine learning algorithms, deep learning methods are being tentatively used in a much wider range than well-known artificial intelligence applications such as face recognition or auto-driving. Recently, deep learning models are investigated intensively to improve the compression efficiency for video coding, especially at the in-loop filtering stage. Although deep learning-based in-loop filtering methods in prior arts have already shown a remarkable potential capability in video coding, content propagation issue is still not well recognized and addressed yet. Content propagation is the fact that contents of reference frames are propagated to frames referring to them, which typically leads to over-filtering issues. In this article, we develop an iteratively trained deep in-loop filter with adaptive model selection (iDAM) to address the content propagation issue. First, we propose an iterative training scheme, which enables the network to gradually take into account the impacts of content propagation. Second, we propose a filter selection mechanism, i.e., allowing a block to select from a set of candidate filters with different filtering strengths. Besides, we propose a novel approach to design a conditional in-loop filtering method that can deal with multiple quality levels with a single model and serve the functionality of filter selection by modifying the input parameters. Extensive experiments on top of the latest video coding standard (Versatile Video Coding, VVC) have been conducted to evaluate the proposed techniques. Compared with VTM-11.0, our scheme achieves a new state-of-the-art, leading to {7.91%, 20.25%, 20.44%}, {11.64%, 26.40%, 26.50%}, and {10.97%, 26.63%, 26.77%} BD-rate reductions on average for {Y, Cb, Cr} under all-intra, random-access, and low-delay configurations, respectively. As far as we know, our proposed iDAM scheme provides the highest coding performance compared to all existing solutions. In addition, the syntax elements of the proposed scheme were adopted at the 76th meeting of Audio Video coding Standard (AVS) held this year.
Taming DNA clustering in massive datasets with SLYMFAST
Authors: Belcaid Mahdi , Arisdakessian Cedric , Kravchenko Yuliia
Published: April 2022

Data from sequencing instruments are produced at such rates that their analysis is becoming increasingly computationally challenging. Although DNA sequence clustering of very large datasets is an important computational step in various bioinformatics applications, it is a performanceintensive task that often cannot be completed without compromising sensitivity for speed. In order to optimize CPU and RAM usage in DNA clustering, we propose a probabilistic, rigorous, and efficient technique to partition a large DNA sequence dataset into smaller, non-overlapping subsets, which can then be analyzed using more precise clustering algorithms. The approach results in a significant reduction in CPU and RAM requirements, as well as a more intuitive parallelization of the clustering step. We show in our results that our algorithm, implemented in a program called SLYMFAST, can cluster in just a few hours datasets that would otherwise take weeks to cluster without partitioning first.
Cuffless Blood Pressure Measurement
Authors: Mukkamala Ramakrishna , Stergiou George S. , Avolio Alberto P.
Published: April 2022

Cuffless blood pressure (BP) measurement has become a popular field due to clinical need and technological opportunity. However, no method has been broadly accepted hitherto. The objective of this review is to accelerate progress in the development and application of cuffless BP measurement methods. We begin by describing the principles of conventional BP measurement, outstanding hypertension/hypotension problems that could be addressed with cuffless methods, and recent technological advances, including smartphone proliferation and wearable sensing, that are driving the field. We then present all major cuffless methods under investigation, including their current evidence. Our presentation includes calibrated methods (i.e., pulse transit time, pulse wave analysis, and facial video processing) and uncalibrated methods (i.e., cuffless oscillometry, ultrasound, and volume control). The calibrated methods can offer convenience advantages, whereas the uncalibrated methods do not require periodic cuff device usage or demographic inputs. We conclude by summarizing the field and highlighting potentially useful future research directions.
Seismic Performance Assessment of Ordinary Moment Resisting Frame Equipped with Viscous Dampers under Pulse-Like Earthquakes
Authors: Yousefpour Amir , Mazidababdi Farahani Hamid , Ali Shayanfar Mohsen
Published: April 2022

In conventional structures, the earthquake-resistant design is based on flexibility after yielding of structural members to provide a loss of earthquake input energy, while, using dampers, the input energy loss can be concentrated in predetermined points to prevent the nonlinear behavior of the main members that are also in the gravity bearing path. However, near-fault earthquakes might cause unexpected failure and severe structural damage, especially those with the pulse-like effect. A pulsed movement in the near field records in this system will, however, result in unusual behavior. Technology advances and the creation of vibration control systems have helped control this type of behavior since the earthquake forces are applied indirectly to the structure. For further investigation of this issue, in this study, some traditional two-dimensional frames were modeled for 3, 8, and 12 floors. Seven near-fault pulsed and seven far-fault nonpulsed accelerometers are applied. The structural behavior in four modes is examined: (1) without damper and soil-structure interaction effect, (2) without damper and considering soil-structure interaction effect, (3) with damper and considering soil-structure interaction, and (4) with damper without considering soil-structure interaction. Each model is analyzed in OpenSees software under incremental dynamic analysis. Then, the fragility curve is plotted based on the results. The results indicate that frame (4) reaches the failure level at a higher spectral acceleration, which means that the performance of the viscous damper in reducing drift between floors is one of the main criteria for predicting damages. It also shows the effect of soil-structure interaction on increasing the drift between floors and reaching failure at lower spectral acceleration in all models. Also, by comparing the fragility curves of the models under near-field and far-field records, it is found that the probability of failure under far-field documents (without pulse) is less than that under near-field documents (with a pulse).
A Sentiment Classification Model of E-Commerce User Comments Based on Improved Particle Swarm Optimization Algorithm and Support Vector Machines
Authors: Jiang Xuehui
Published: April 2022

With the rapid increase of the number of Internet users and the amount of online comment data, a large number of referable information samples are provided for data mining technology. As a technical application of data mining, text sentiment classification can be widely used in public opinion management, marketing, and other fields. In this study, a combination approach to SVM (support vector machine) and IPSO (improved particle swarm optimization) is proposed to classify sentiment by using text data. First, the text data of 30,000 goods reviews and corresponding ratings are collected through the web crawler. Then, TFIDF (term frequency-inverse document frequency) and Word2vec are used to vectorize the goods review text data. Next, the proposed classification model is trained by the SVM, and the initial parameters of the SVM are optimized by the IPSO. Finally, we applied the trained SVM-IPSO model to the test set and evaluated the performance by several measures. Our experiment results indicate that the proposed model performed the best for text data sentiment classification. Additionally, the traditional machine learning model SVM becomes very effective after parameter optimization, which demonstrates that the parameters’ optimization by IPSO has successfully improved the classification accuracy. Furthermore, our proposed model SVM-IPSO significantly outperforms other benchmark models, indicating that it could be applied to improve the accuracy and efficiency for text data sentiment classification.
Identification of Prognostic Markers of N6-Methylandenosine-Related Noncoding RNAs in Non-Small-Cell Lung Cancer
Authors: Zhang Zexin , Li Jing , Lu Ke , Wu Wenfeng , Huang Ziyi , Zhang Chi , Guo Wei , Li Jiayin , Lin Lizhu
Published: April 2022

Background. Non-small-cell lung cancer (NSCLC) is a major type of lung carcinoma that threatens the health and life of humans worldwide. We aimed to establish an n6-methyladenosine (m6A)-relevant ncRNA model to effectively evaluate the outcome of patients. Methods. m6A-Related ncRNAs (lncRNA/miRNA) were acquired from the UCSC Xena database. Pearson’s correlation analysis among 21 m6A regulatory factors and ncRNAs were implemented to explore m6A-relevant ncRNAs. Weighted gene co-expression network analysis (WGCNA) identified hub modules of gene associated with prognosis of NSCLC patients. Univariate Cox regression analysis identified 80 m6A-related ncRNAs. Least absolute shrinkage and selector operation (LASSO) filtered out redundant factors and established a risk score model (m6A-NSCLC) in the TCGA training data set. Validation of prognostic ability was performed using testing data sets from the TCGA database. We also conducted a correlation analysis among the risk score and different clinical traits. Both univariate and multivariate Cox analyses were combined to verify prognostic factors which have independent value, and a nomogram on the basis of m6A-NSCLC risk scores and clinical traits was constructed to assess the prognosis of patients. In addition, we screened differentially expressed genes (DEGs) based on different risk scores and performed enrichment analysis. Finally, 21 m6A regulators were detected to be differentially expressed between two risk groups. Results. An m6A-NSCLC risk model with 18 ncRNAs was constructed. By comparison with low-risk patients, high-risk score patients had poor prognosis. The distribution of risk score in the tumor size and extent (T), number of near lymph nodes (N), clinical stage, sex, and tumor types was significantly different. The risk score could act as an independent prognostic factor with the nomogram assessing overall survival in NSCLC. DEGs inherent to cell movement and immune regulation were involved in NSCLC development. Furthermore, 18 of 21 m6A regulators were differentially expressed, implying their correlation to survival prognosis. Conclusion. The m6A-NSCLC could be effectively utilized for evaluation of prognosis of patients.
Inertness of Superoxide Dismutase Mimics Mn(II) Complexes Based on an Open-Chain Ligand, Bioactivity, and Detection in Intestinal Epithelial Cells
Authors: Schanne Gabrielle , Zoumpoulaki Martha , Gazzah Géraldine , Vincent Amandine , Preud’homme Hugues , Lobinski Ryszard , Demignot Sylvie , Seksik Philippe , Delsuc Nicolas , Policar Clotilde
Published: April 2022

Oxidative stress is known to play a major role in the pathogenesis of inflammatory bowel diseases (IBDs), and, in particular, superoxide dismutase (SODs) defenses were shown to be weakened in patients suffering from IBDs. SOD mimics, also called SOD mimetics, as low-molecular-weight complexes reproducing the activity of SOD, constitute promising antioxidant catalytic metallodrugs in the context of IBDs. A Mn(II) complex SOD mimic (Mn1) based on an open-chain diaminoethane ligand exerting antioxidant and anti-inflammatory effects on an intestinal epithelial cellular model was shown to experience metal exchanges between the manganese center and metal ions present in the biological environment (such as Zn(II)) to some degrees. As the resulting complexes (mainly Zn(II)) were shown to be inactive, improving the kinetic inertness of Mn(II) complexes based on open-chain ligands is key to improve their bioactivity in a cellular context. We report here the study of three new Mn(II) complexes resulting from Mn1 functionalization with a cyclohexyl and/or a propyl group meant to limit, respectively, (a) metal exchanges and (b) deprotonation of an amine from the 1,2-diaminoethane central scaffold. The new manganese-based SOD mimics display a higher intrinsic SOD activity and also improved kinetic inertness in metal ion exchange processes (with Zn(II), Cu(II), Ni(II), and Co(II)). They were shown to provide anti-inflammatory and antioxidant effects in cells at lower doses than Mn1 (down to 10 μM). This improvement was due to their higher inertness against metal-assisted dissociation and not to different cellular overall accumulations. Based on its higher inertness, the SOD mimic containing both the propyl and the cyclohexyl moieties was suitable for intracellular detection and quantification by mass spectrometry, quantification, that was achieved by using a 13C-labeled Co-based analog of the SOD mimics as an external heavy standard.
Chitosan Oligosaccharides Alleviate H2O2-stimulated Granulosa Cell Damage via HIF-1α Signaling Pathway
Authors: Yang Ziwei , Hong Wenli , Zheng K. , Feng Jingyuan , Hu Chuan , Tan Jun , Zhong Zhisheng , Zheng Yuehui
Published: April 2022

Oocyte maturation disorder and decreased quality are the main causes of infertility in women, and granulosa cells (GCs) provide the only microenvironment for oocyte maturation through autocrine and paracrine signaling by steroid hormones and growth factors. However, chronic inflammation and oxidative stress caused by ovarian hypoxia are the largest contributors to ovarian aging and GC dysfunction. Therefore, the amelioration of chronic inflammation and oxidative stress is expected to be a pivotal method to improve GC function and oocyte quality. In this study, we detected the protective effect of chitosan oligosaccharides (COS), on hydrogen peroxide- (H2O2-) stimulated oxidative damage in a human ovarian granulosa cell line (KGN). COS significantly increased cell viability, mitochondrial function, and the cellular glutathione (GSH) content and reduced apoptosis, reactive oxygen species (ROS) content, and the levels of 8-hydroxy-2

′

-deoxyguanosine (8-OHdG), 4-hydroxynonenal (4-HNE), hypoxia-inducible factor-1α (HIF-1α), and vascular endothelial-derived growth factor (VEGF) in H2O2-stimulated KGN cells. COS treatment significantly increased levels of the TGF-β1 and IL-10 proteins and decreased levels of the IL-6 protein. Compared with H2O2-stimulated KGN cells, COS significantly increased the levels of E2 and P4 and decreased SA-β-gal protein expression. Furthermore, COS caused significant inactivation of the HIF-1α-VEGF pathway in H2O2-stimulated KGN cells. Moreover, inhibition of this pathway enhanced the inhibitory effects of COS on H2O2-stimulated oxidative injury and apoptosis in GCs. Thus, COS protected GCs from H2O2-stimulated oxidative damage and apoptosis by inactivating the HIF-1α-VEGF signaling pathway. In the future, COS might represent a therapeutic approach for ameliorating disrupted follicle development.
Research on Multiplayer Posture Estimation Technology of Sports Competition Video Based on Graph Neural Network Algorithm
Authors: Guo Xiaoping
Published: April 2022

With the explosive growth of the number of sports videos, the traditional sports video analysis method based on manual annotation has been difficult to meet the growing demand because of its high cost and many limitations. The traditional model is usually based on the target detection algorithm of manual features, and the detection of human posture features is not accurate. Compared with global image features such as line features, texture features and structure features, local image features have the characteristics of rich quantity in the image, low correlation between features, and will not affect the detection and matching of other features due to the disappearance of some features in the case of occlusion. Referring to the practice of Deep-ID network considering both local and global features, this paper adjusts the traditional neural network, and combines the improved neural network with the human joint model to form a human pose detection method based on graph neural network, and then applies the algorithm to multiperson human pose estimation. The results of several groups of comparative experiments show that the algorithm can better estimate the human posture in sports competition video, and has a good performance in solving multiperson pose estimation in sports game video.
A Novel AI-Based Stock Market Prediction Using Machine Learning Algorithm
Authors: M Iyyappan. , Ahmad Sultan , Jha Sudan , Alam Afroj , Yaseen Muhammad , Abdeljaber Hikmat A. M.
Published: April 2022

The time series forecasting system can be used for investments in a safe environment with minimized chances of loss. The Holt–Winters algorithm followed various procedures and observed the multiple factors applied to the neural network. The final module helps filter the system to predict the various factors and provides a rating for the system. This research work uses real-time dataset of fifteen stocks as input into the system and, based on the data, predicts or forecasts future stock prices of different companies belonging to different sectors. The dataset includes approximately fifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not; the forecasting will give an accurate result for the customer investments.
Fluconazole-Like Compounds as Potential Antifungal Agents: QSAR, Molecular Docking, and Molecular Dynamics Simulation
Authors: Salehi Farnaz , Emami Leila , Rezaei Zahra , Khabnadideh Soghra , Tajik Behnaz , Sabet Razieh
Published: April 2022

Today, fungal infection has become more common disease especially in some cases, such as AIDS, cancer, and organ transplant which the immune system is suppressed. On the other hand, due to the increasing resistance to current antifungal drugs, more and more options for design of novel more efficient compounds with higher resistance are needed. In this study, a series of a fluconazole analogues were subjected to quantitative structure-activity relationship analysis to find the structure requirements for modeling adequate candidate. The best multiple linear regression equation was achieved from GA-PLS and MLR modeling. Subsequently, in silico screening study was applied to found new potent lead compounds based on the resulted model. The ability of the best designed compounds for antifungal activity was investigated by using molecular dynamic (MD) and molecular docking simulation. The results showed that compound F13 can efficiently bind to lanestrol 14-α demethylase target similar to other antifungal azoles. The molecular docking studies revealed an interesting binding profile with very high receptor affinity to the CYP51 active site. The triazole moiety of ligand F13 pointed to HEM group in lanestrol 14-α demethylase site and coordinated to Fe of HEM through its N4 atom. Also, there was a convenient relevance between QSAR and docking results. With the compound F13 which demonstrated the most promising minimum inhibitory concentration (MIC) values, it can be concluded that F13 is appropriate candidate for the development as antifungal agent.
Hotspots Identification and Classification of Dockless Bicycle Sharing Service under Electric Fence Circumstances
Authors: Hui Ying , Xie Yingkun , Yu Qing , Liu Xiaolei , Wang Xinyuan
Published: April 2022

Dockless bicycle sharing is one of the low-carbon transportation modes towards sustainable mobilities. Electric fences, as an effective solution for parking management, may have a high potential in guiding the usage of dockless bicycles at a low operation cost. However, new issues arise with the implementation of electric fences. The location of electric fences in hotspots fails to match the parking demand, leading the parking congestion in urban central areas. In this paper, a novel methodology of bicycle hotspots identification and classification is proposed to support parking management. An evaluation framework for bicycle hotspots is also proposed covering three aspects: demand and supply, unbalance, and land use. The methodology is applied to the case of Xiamen Island by using the trip data covering 53,629 bicycles during morning peak hours. Applying the methodology proposed, 47 pick-up hotspots and 53 return hotspots are identified, respectively. The total parking overload of return hotspots during the morning peak is 12,587 bicycles in Xiamen Island. The 53 return hotspots are classified into three clusters, including (1) hotspots where bicycle sharing is in overload status, (2) hotspots where bicycle sharing service quality needs to be improved, and (3) hotspots where bicycle sharing is in stable status. Based on the demand and land use characteristics, parking management schemes and policy implications are proposed. The result of this paper provides guidance for the layout of dockless bicycle sharing electric fences in different areas.
Safety Evaluation of Oleoresin‐Based Turmeric Formulation: Assessment of Genotoxicity and Acute and Subchronic Oral Toxicity
Authors: Nirvanashetty Somashekara , Panda Sanjib Kumar , Jackson-Michel Shavon
Published: April 2022

Turmeric rhizome (Curcuma longa L.) has been used without concern for safety as a culinary spice and traditional medicine under the ancient Ayurvedic medicinal system of India dating back nearly 4000 years. This preclinical safety evaluation was done to determine the safety of an oleoresin‐based turmeric extract (CURCUGEN®). Guidelines from the Organization for Economic Co‐operation and Development (OECD) directed the assessment of safety for the in vitro and in vivo application of CURCUGEN®. Safety of the herbal medicine was evaluated through the toxicological assessment of acute, oral, and 90‐day repeated dosing, genotoxicity, and mutagenicity study. Genotoxicity tests included the in vitro bacterial reverse mutation test, chromosomal aberration test, and in vivo micronucleus test. The single dose of CURCUGEN® administered orally (gavage) to Sprague‐Dawley (SD) rats resulted in a LD50 of >5000 mg/kg body weight. The subchronic assessment of CURCUGEN®, as administered to SD rats over 90 days resulted in a no observed adverse effect level (NOAEL) of 2000 mg/kg body weight/day. CURCUGEN® did not elicit any genotoxic or clastogenic effect in genotoxicity tests. The battery of safety studies carried out demonstrated that CURCUGEN® showed no evidence of general toxicity or genotoxicity.
RPE65 c.393T>A, p.(Asn131Lys): Novel Sequence Variant Detected
Authors: Bjeloš Mirjana , Bušić Mladen , Ćurić Ana , Bosnar Damir , Šarić Borna , Marković Leon , Elabjer Biljana Kuzmanović , Rak Benedict
Published: April 2022

Background. Leber congenital amaurosis (LCA) is a monogenic, but genetically heterogenous disease, and at least 27 genes are implicated. This case report is aimed at providing evidence to link the novel variant RPE65 c.393T>A, p.(Asn131Lys), variant of uncertain significance (VUS), to clinical phenotype and to set the ground for objective assignment of pathogenicity confidence. Case Presentation. A case report of a female patient with LCA who manifested with nystagmus, night blindness, profound visual deficiency, and peripheral involvement of the retina consistent with RPE65 dystrophy. A thorough clinical examination, diagnostic evaluation, and genetic testing were performed. The patient was a compound heterozygote in trans form: RPE65 c.304G>T, p.(Glu102

∗

) pathogenic, and RPE65 c.393T>A, p.(Asn131Lys), VUS. The latter variant is absent in healthy controls and is considered harmful on in silico prediction. Conclusions. We conclude that RPE65 c.393T>A, p.(Asn131Lys) contributed to the pathologic phenotype, demonstrating its significance clearly in the case presented, and should be reclassified according to the criteria of evidence as likely pathogenic. This being the case, patients with this specific variant are likely candidates for genetic treatment.
Involvement of Cancer Stem Cells in Chemoresistant Relapse of Epithelial Ovarian Cancer Identified by Transcriptome Analysis
Authors: Sun Yaoqi , Yao Lin , Wang Chunyan , Xiong Bing , Guo Jing , Wang Lian , Zhu Jihui , Cheng Zhongping , Liu Shupeng
Published: April 2022

Epithelial ovarian cancer (EOC) is the most lethal gynecological malignancy. Despite the initial resection and chemotherapeutic treatment, relapse is common, which leads to poor survival rates in patients. A primary cause of recurrence is the persistence of ovarian cancer stem cells (OCSCs) with high tumorigenicity and chemoresistance. To achieve a better therapeutic response in EOC relapse, the mechanisms underlying acquired chemoresistance associated with relapse-initiating OCSCs need to be studied. Transcriptomes of both chemosensitive primary and chemoresistant relapse EOC samples were obtained from ICGC OV-AU dataset for differential expression analysis. The upregulated genes were further studied using KEGG and GO analysis. Significantly increased expression of eighteen CSC-related genes was found in chemoresistant relapse EOC groups. Upregulation of the expression in four hub genes including WNT3A, SMAD3, KLF4, and PAX6 was verified in chemoresistant relapse samples via immunohistochemistry staining, which confirmed the existence and enrichment of OCSCs in chemoresistant relapse EOC. KEGG and GO enrichment analysis in microarray expression datasets of isolated OCSCs indicated that quiescent state, increased ability of drug efflux, and enhanced response to DNA damage may have caused the chemoresistance in relapse EOC patients. These findings demonstrated a correlation between OCSCs and acquired chemoresistance and illustrated potential underlying mechanisms of OCSC-initiated relapse in EOC patients. Meanwhile, the differentially expressed genes in OCSCs may serve as novel preventive or therapeutic targets against EOC recurrence in the future.
College Information Education Management Relying on Interactive Multimedia Teaching Mode
Authors: Li Ruicheng
Published: April 2022

At present, educational management in colleges and universities has problems such as outdated content and backward methods of management work, fragmentation of structure, poor coordination and communication, surfaceization of work, and lack of joint efforts for all-staff education. In order to improve the effect of information education management in colleges and universities, this paper integrates the interactive multimedia teaching mode English into education management, and improves the multimedia intelligence algorithm to provide reliable technical support for subsequent information education management in colleges and universities. Moreover, with the support of intelligent algorithms, this paper constructs a college teaching information management system based on interactive multimedia teaching mode. The experimental research results show that the college teaching information management system based on the interactive multimedia teaching mode proposed in this paper has good educational data collection effects and educational information management effects.
Cyst Nematode (Heterodera glycines) Problems in Soybean (Glycine max L.) Crops and Its Management
Authors: Wendimu Gebissa Yigezu
Published: April 2022

Soybean is a leguminous crop that originated from Southeast Asia, and it was domesticated in the northeastern parts of China. Recently, it has been highly produced in the United States of America, Brazil, and Argentina for cooking oil, protein, fiber and for the manufacturing of plastics, lubricants, candles, varnishes, soaps, and biodiesel. Nevertheless, in warm, moist, sandy soil conditions, its production is highly challenged by soybean cyst nematodes (Heterodera glycines). It caused more than 30% of soybean yield loss either alone or associated with other soybean pests under suitable environmental conditions. The second-stage juvenile (J2) of this pest inserted its stylet and penetrated into the cells to get its nourishment, shelter, and reproduction site on the soybean roots. Economically, the damage it caused was highly important because it had a wide host range and lacked adequate management methods. Hence, the reason behind the writing of this chapter is to explore the different published scientific papers related to soybean cyst nematode’s economic importance, distribution, symptoms, biology, life cycle, interaction with other pathogens, different management approaches, and its prospects. This chapter shall also embrace the advanced biotechnological innovations that help in achieving effective soybean cyst nematode management that will mitigate its infections in soybean production and will also serve as an asset for the researchers. This review chapter, in addition, plays a vital role in exploring necessary information concerning soybean cyst nematode management.
Effects of Harvesting Times and Germplasm Accessions on the Physical Properties of Roselle (Hibiscus sabdariffa L.) Seeds
Authors: Tandoh Paul Kweku , Banful Ben Kwaku Branoh , Idun Irene Akua
Published: April 2022

Roselle (Hibiscus sabdariffa L.) is an important crop grown successfully in tropical and subtropical climates with huge nutritional, economic, and industrial benefits. The physical properties of seeds, particularly length, width, thickness, mean diameter, angle of repose, degree of sphericity, mass, and surface area, have been found to play significant roles in designing equipment for storage, transportation, and subsequent field operations of the seeds. Therefore, a study was carried out to determine the effects of different harvesting times on the physical properties of seeds of twelve roselle accessions for their subsequent industrial management. In total, 12 × 3 factorial arrangements in Randomized Complete Block Design and Completely Randomized Design were used for the field and laboratory experiments, respectively. The first factor was germplasm accessions at twelve levels (HS08, HS11, HS19, HS25, HS27, HS32, HS41, HS58, HS59, HS69, HS83, and H86), and the second factor was harvesting times at three levels (physiological maturity, one week after physiological maturity, and two weeks after physiological maturity). Seeds of accession HS32 harvested at the physiological maturity stage had the highest moisture content, which was similar to the other 11 accessions harvested at the same stage. The seeds of accession HS08 harvested at physiological maturity had significantly the best values in seed length (4.33 mm), seed width (4.30 mm), seed thickness (2.43 mm), seed geometric mean diameter (3.83 mm), seed angle of repose (20.57°), seed sphericity (0.76), seed surface area (18.57 mm2), and seed mass (0.05 g). Further, there were strongly positive and significant correlations between seed width and seed angle of repose (r = 0.93); seed width and seed length (r = 0.84); seed width and mean seed diameter (r = 0.89); seed width and seed sphericity (r = 0.68); seed width and seed surface area (0.94). There were also strongly positive and significant correlations between seed thickness and seed sphericity (r = 0.79) and also between seed thickness and seed surface area (r = 0.63). In conclusion, the physical properties of accession HS08 harvested at the physiological maturity stage could be very important in designing machinery for roselle seeds storage, transportation, and subsequent field operations.
Multifunctional Robot Grasping System Based on Deep Learning and Image Processing
Authors: Zhang XinYu , Ye Kai
Published: April 2022

This paper firstly introduces the general architecture of the multifunctional harvesting robot grasping system; then, deep learning is used to investigate the object target recognition, and a set of target detection algorithm based on convolutional neural network is implemented; then, image processing technology is used to realize the function of target object localization, which can guide the multifunctional harvesting robot to complete the picking of the target multifunctional. The experimental results show that the multifunctional harvesting robot has a small calculation error of multifunctional coordinates and has a strong multifunctional recognition and positioning capability.
Streaming Media Music Classroom Teaching Mode and Effect Analysis Based on Audio Band Analysis Technology
Authors: Wang Huizhong
Published: April 2022

Music teaching mode refers to the basic structure or framework of various types of music teaching activities under the guidance of certain music teaching ideas and music teaching theories. The process of music teaching needs to reflect the procedural coping strategies. With the continuous deepening of quality education reform, higher vocational colleges pay more attention to cultivating students’ professional skills and vocational abilities. Therefore, many schools need to change the current situation of music teaching in higher vocational colleges and explore new music teaching models. School teaching expects to further stimulate students’ interest in music learning and give full play to the aesthetic education function of music teaching. This article focuses on the research and evaluation of the music teaching model around the audio recognition technology and organically combines the music education theory with the music teaching practice. Research through the construction of a new mode of music teaching in higher vocational colleges, enrich the music teaching methods of music courses, and improve the effect of music teaching. The research has realized the aesthetic education function that music teaching should have in quality education. At the same time, the research results also provide a reference for music teaching in similar higher vocational colleges.
Uso del polietileno poroso de alta densidad como alternativa en reconstrucción de fracturas orbitarias. Serie de 22 casos
Authors: Tapia Contreras Pedro , Sarzosa Enríquez Marcelo , Mordoh Cucurella Sebastián , Rosenberg Dueñas Benjamín , Rodríguez Castro Fernando
Published: April 2022

Introducción: las fracturas de pared de órbita pueden producir secuelas funcionales y estéticas. La indicación de reconstrucción quirúrgica, es indispensable para la recuperación de la motilidad y estética ocular. Los materiales reconstructivos más utilizados son los aloplásticos, como el titanio y el polietileno poroso de alta densidad (PPAD), el cual ha resultado exitoso en reconstrucción maxilofacial. El propósito de este estudio retrospectivo, es exponer los resultados tras el uso del PPAD en reconstrucciones orbitarias y sus posibles complicaciones. Materiales y métodos: se realizó una revisión retrospectiva de 22 pacientes donde se utilizó PPAD como material de reconstrucción en fracturas orbitarias. Se consideró para la indicación quirúrgica que el paciente presentara alguno de los siguientes signos: diplopía o síntomas vasovagales por atrapamiento muscular, restricciones en la motilidad ocular, enoftalmo mayor a 4mm. El material utilizado fue PPAD (Medpor Stryker®) puro en 17 casos y PPAD reforzado con una malla de titanio en el interior de su estructura (Medpor Titan Stryker®), en los 5 restantes. Resultados: se registraron 3 casos con complicaciones; ectropión, diplopía y ectropión con diplopía, los cuales todos fueron reversibles. No se registraron casos de infección ni complicaciones oftalmológicas postoperatoria. Discusión: los injertos autólogos fueron los primeros utilizados con resultados de compatibilidad y resistencia óptimos. El uso de PPAD como material de reconstrucción ha sido bien documentado con buenos resultados en términos de una baja incidencia de infección y óptima motilidad ocular a largo plazo.
Peritonitis encapsulante esclerosante (síndrome de cocoon): una causa extremadamente rara de obstrucción intestinal. Reporte de un caso y revisión de la literatura
Authors: Piquimil Meneses Lorena , Vargas gajardo Camilo , Garcés Aburto Sebastián
Published: April 2022

La peritonitis esclerosante encapsulante o síndrome de cocoon es una causa rara de obstrucción intestinal, que puede dividirse en primaria o secundaria, producida por una membrana fibrocolágena que envuelve el intestino delgado. El diagnóstico es particularmente desafiante, al tratarse de una entidad cuya clínica es inespecífica. El conocimiento de los hallazgos imagenológicos, sumados a una correcta anamnesis, permite una adecuada valoración preoperatoria. Presentamos un caso de interés y de difícil diagnóstico, junto con una revisión de la literatura sobre el tema.