bluuebunny/crossref_metadata_2025 huggingface dataset:
Structure of Polyhedral Nanoparticles with Cubic Lattice: Theoretical Analysis
Authors: Hermann Klaus E.
Published: April 2022

We examine the structure of compact metal nanoparticles (NPs) forming polyhedral sections of face-centered cubic (fcc) and body-centered cubic (bcc) lattices, which are confined by facets characterized by highly dense [Formula: see text], [Formula: see text] and [Formula: see text] monolayers. Together with the constraint that the NPs exhibit the same point symmetry as the ideal cubic lattice, i.e., [Formula: see text], different types of generic NPs serve for the definition of general compact polyhedral cubic NPs. Their structural properties, such as shape, size and surface facets, can be described by only three integer-valued polyhedral NP parameters [Formula: see text], [Formula: see text] and [Formula: see text]. Corresponding analytical details are discussed with visualization of characteristic examples. While the overall NP shapes are quite similar between the different cubic lattice types, structural fine details differ. In particular, monolayer planes of adjacent NP facets can join at corners and edges which are not occupied by atoms of the ideal lattice. This gives rise to microfacets and narrow facet strips depending on the lattice type. The discussion illustrates the complexity of seemingly simple NPs in a quantitative account. The geometric relationships of the model particles can also be used to classify shapes and estimate sizes of real compact metal NPs observed by experiment.
Torsional anomaly in Kerr black hole and quantization in Einstein–Cartan gravity
Authors: Garcia de Andrade L. C.
Published: April 2022

Recently, presence of gravitational and axial anomalies in Riemann–Cartan (RC) spacetime [Garcia de Andrade, Class. Quantum Gravity 38 (2021)] indicates that topological densities expressed in terms of torsion may be very useful in understanding the physics involved. Pontryagin and Euler density may be presented in terms of torsion in teleparallelism. In this paper, computation of these topological torsional densities in Kerr black holes is given. The geometric quantisation of torsion is also discussed in terms of the teleparallel metric. Actually, the recent work of Del Grosso and Poplawski [arXiv: 2107.06112] showed that the torsion quantization appears when torsion is the generator of momentum quantum mechanical commutator. Moreover, Poplawski et al. showed that use of quantum torsion in quantum electrodynamic (QED) may avoid the divergences in Feynmann integrals, process called, a torsion regularization. Here, we compute the momenta components in terms of teleparallel Kerr black hole torsion generator, like a Casimir tensor. The quantum generator, while not yet related by torsion, is seen to be naturally associated to axial torsion skew-symmetric in the teleparallel geometry. An important fact is that from torsion quantization, torsion chirality appears naturally in the Einstein–Cartan spin-torsion analogy, where now spin-angular momentum of the black hole is connected to torsion. Torsional chiral anomalies are shown to diverge at black hole singularity, and then they cannot be canceled at singularity.
Anisotropic viable solutions of stellar structures in modified teleparallel gravity
Authors: Ditta Allah , Xia Tiecheng
Published: April 2022

The commitment behind this work is to find the solutions of compact stellar models by using the spherically symmetric static space-time and anisotropic matter distribution in the framework of [Formula: see text] gravity. In this study, we used the new type of metric functions [Formula: see text] and evaluated other functions [Formula: see text] by the well-known Karmarkar condition. In this work, we use the rotated and spinning tetrad named as off-diagonal and most generic function of [Formula: see text] gravity. We find the strange stellar solutions by incorporating the MIT bag equation of state [Formula: see text] to evaluate cosmological constant. Aiming at the simplicity of solutions we utilize the given data for masses and radii of compact objects PSR J1416-2230, 4 U 1608-52, Cen X-3, EXO 1785-248, SMC X-1. By graphical presentation of the core properties of compact models, we show by concluding that our solutions are graphically fit and physically admissible and interesting by having the compatibility with the compact stars study.
PDMS-Based SERS Substrate of High Sensitivity and Uniformity and Its Application in In Situ Detection of Tricyclazole
Authors: Wei Yongkun , Shen Xilong , Zhang Lei , Tang Jie , Xu Tian , Xu Meifeng , Fang Jinghuai , Jin Yonglong , He Chuan , Zhou Ziqiang , Wang Chaonan
Published: April 2022

In this paper, flexible and transparent surface-enhanced Raman scattering (SERS) substrate was prepared. First, silver nanoparticles (Ag NPs) were adsorbed onto 2D stretched polydimethylsiloxane (PDMS) and then the stretch was released and nanowrinkles were formed, bringing the adsorbed Ag NPs close to each other. SERS measurement demonstrated really high sensitivity of the Ag/PDMS substrate fabricated under 30% deformation and 120 min assembling time, the enhancement factor (EF) of which was estimated to be [Formula: see text]. SERS mapping analysis revealed the proposed Ag/PDMS substrate possessed excellent uniformity with relative standard deviation (RSD) of as low as 5.62%. For real-life applications, the versatile Ag/PDMS substrate could be used for both solution detection and in situ detection of solid objects of any shape. Tricyclazole, a commonly used pesticide, might pollute the leaves of crop and even remain in milk, endangering human health. Therefore, there is practical demand for developing direct on-site detection methods. In this paper, tricyclazole of 10[Formula: see text] M was easily probed by dropping the contaminated milk solution on the proposed substrate and 10[Formula: see text][Formula: see text]M tricyclazole sprayed on a leaf exhibited clearly identifiable Raman signals after attaching the proposed substrate to the leaf surface, demonstrating the prepared high-sensitive Ag/PDMS substrate is a competitive tool for rapid, trace and in situ detection.
A novel calculation and simulation method on missile killing effect
Authors: Hou Libing , Zhu Jihong , Shi Heng , Kuang Minchi
Published: April 2022

To accurately describe the killing effect of missiles especially dealing with the small or fast target, a new method for calculating the killing effect of missiles is proposed in this paper. The missile and the target are divided into several basic geometries based on their shapes, and then the equations of these geometries are established in their local frame. The mass center motion equations of the target and the missile are determined in the light of their initial positions and velocities, respectively, and every geometry moves with its center of mass according to its attitude. The equations of the fuze view field and the target can confirm whether the fuze detects the target, and whether the fragment hit the target can be certified by the same approach. This paper also writes a MATLAB simulation program for this calculation method and designs a new algorithm to determine the position relationship of space geometry using existing functions, and the simulation verifies the effectiveness of this method.
Optimization-based Model Predictive Tube Control for Autonomous Ground Vehicles with Minimal Tuning Parameters
Authors: Kovacs Adorjan , Vajk Istvan
Published: April 2022

The trajectory planning and tracking problem are critical points of intelligent vehicles concerning their safety and stability. If these parts are separated, interactions should be made between them, especially when sudden changes and disturbances appear. This paper presents a method that integrates the two parts using a cascade structure. Additionally, the proposed method deals with the interaction between the lateral and the longitudinal trajectory based on dynamical considerations. The whole problem is handled using the model predictive method based on online optimization. This system receives the path borders as input and generates the control requests for the actuators on its output. The configuration space of the system can be maximized to gain stability by handling the lateral-longitudinal parts and the trajectory planning and tracking in one complex system. The main advantage of the proposed approach is that the optimization problem in the predictive method is formulated so that the path and dynamics are considered equality and inequality constraints, and the cost function includes only a physical phenomenon to be minimized without tuning parameters. The evaluation of the proposed algorithm is presented in this paper based on simulation and real-time measurements results.
Rateless codes for near-perfect load balancing in distributed matrix-vector multiplication
Authors: Mallick Ankur , Chaudhari Malhar , Sheth Utsav , Palanikumar Ganesh , Joshi Gauri
Published: April 2022

Large-scale machine learning and data mining applications require computer systems to perform massive matrix-vector and matrix-matrix multiplication operations that need to be parallelized across multiple nodes. The presence of straggling nodes---computing nodes that unpredictably slow down or fail---is a major bottleneck in such distributed computations. Ideal load balancing strategies that dynamically allocate more tasks to faster nodes require knowledge or monitoring of node speeds as well as the ability to quickly move data. Recently proposed fixed-rate erasure coding strategies can handle unpredictable node slowdown, but they ignore partial work done by straggling nodes, thus resulting in a lot of redundant computation. We propose a
rateless fountain coding
strategy that achieves the best of both worlds---we prove that its latency is asymptotically equal to ideal load balancing, and it performs asymptotically zero redundant computations. Our idea is to create linear combinations of the
m
rows of the matrix and assign these encoded rows to different worker nodes. The original matrix-vector product can be decoded as soon as slightly more than
m
row-vector products are collectively finished by the nodes. Evaluation on parallel and distributed computing yields as much as three times speedup over uncoded schemes.
ACM's 2022 general election
Authors: CACM Staff
Published: April 2022

Meet the candidates who introduce their plans---and stands---for the Association.
A Case for Fine-grain Coherence Specialization in Heterogeneous Systems
Authors: Alsop Johnathan , Na Weon Taek , Sinclair Matthew D. , Grayson Samuel , Adve Sarita
Published: April 2022

Hardware specialization is becoming a key enabler of energy-efficient performance. Future systems will be increasingly heterogeneous, integrating multiple specialized and programmable accelerators, each with different memory demands. Traditionally, communication between accelerators has been inefficient, typically orchestrated through explicit DMA transfers between different address spaces. More recently, industry has proposed unified coherent memory which enables implicit data movement and more data reuse, but often these interfaces limit the coherence flexibility available to heterogeneous systems.

This paper demonstrates the benefits of fine-grained coherence specialization for heterogeneous systems. We propose an architecture that enables low-complexity independent specialization of each individual coherence request in heterogeneous workloads by building upon a simple and flexible baseline coherence interface, Spandex. We then describe how to optimize individual memory requests to improve cache reuse and performance-critical memory latency in emerging heterogeneous workloads. Collectively, our techniques enable significant gains, reducing execution time by up to 61% or network traffic by up to 99% while adding minimal complexity to the Spandex protocol.
A News-Based Framework for Uncovering and Tracking City Area Profiles: Assessment in Covid-19 Setting
Authors: Bechini Alessio , Bondielli Alessandro , Bárcena José Luis Corcuera , Ducange Pietro , Marcelloni Francesco , Renda Alessandro
Published: April 2022

In the last years, there has been an ever-increasing interest in profiling various aspects of city life, especially in the context of smart cities. This interest has become even more relevant recently when we have realized how dramatic events, such as the Covid-19 pandemic, can deeply affect the city life, producing drastic changes. Identifying and analyzing such changes, both at the city level and within single neighborhoods, may be a fundamental tool to better manage the current situation and provide sound strategies for future planning. Furthermore, such fine-grained and up-to-date characterization can represent a valuable asset for other tools and services, e.g., web mapping applications or real estate agency platforms. In this article, we propose a framework featuring a novel methodology to model and track changes in areas of the city by extracting information from online newspaper articles. The problem of uncovering clusters of news at specific times is tackled by means of the joint use of state-of-the-art language models to represent the articles, and of a density-based streaming clustering algorithm, properly shaped to deal with high-dimensional text embeddings. Furthermore, we propose a method to automatically label the obtained clusters in a semantically meaningful way, and we introduce a set of metrics aimed at tracking the temporal evolution of clusters. A case study focusing on the city of Rome during the Covid-19 pandemic is illustrated and discussed to evaluate the effectiveness of the proposed approach.
A Crop Leaf Disease Image Recognition Method Based on Bilinear Residual Networks
Authors: He Yun , Gao Quan , Ma Zifei
Published: April 2022

Deep learning models are widely used in crop leaf disease image recognition. These models can be divided into two categories: global model and local model. The global model directly takes the whole leaf disease images as input to training and recognition. It can achieve end-to-end training and recognition, which is very convenient to use. But this kind of model cannot very accurately and completely extract the features from the very small diseased spots in the image. Before training and recognizing, the local model needs to extract the diseased spots part from the image by image segmentation technology. Then the local model takes the disease spots part images as input to training and recognition. Features extracted by local model are more accurate and complete. But this kind of model cannot achieve end-to-end training and recognition, and the image segmentation will bring additional overhead. Considering the disadvantage of global model and local model, we proposed a crop leaf disease image recognition method based on bilinear residual networks (named DIR-BiRN). DIR-BiRN extracts features by two residual networks feature extractors and then integrates the features by a bilinear pooling function. By this way, it can extract features more accurately and completely while achieving end-to-end training and recognition. Experiments on the PlantVillage dataset show that, when compared with the standard ResNet-18 model, the DIR-BiRN improves on accuracy performance, recall performance, precision performance, and F1-measure performance by averages of 0.2918, 0.81641, 0.59185, and 0.52151 percentage points, respectively.
An Immunity-Associated lncRNA Signature for Predicting Prognosis in Gastric Adenocarcinoma
Authors: Zhao Xiaowen , Wu Pingfan , Liu Dongling , Li Changtian , Xue Ling , Liu Zhe , Zhu Meng , Yang Jie , Chen Ziyi , Li Yaling , She Yali
Published: April 2022

Background. Gastric adenocarcinoma (GAD) is one of the most common tumors in the world and the prognosis is still very poor. Objective. We sought to identify reliable prognostic biomarkers for the progression of GAD and the sensitivity to drug therapy. Method. The RNA sequencing data of GAD was downloaded from the Cancer Genome Atlas (TCGA) database and used for analysis. Differentially expressed, immune-related lncRNA (DEIRlncRNA) was characterized by differential analysis and correlation analysis. Univariate Cox regression analysis was used to identify DEIRlncRNA associated with prognosis. Least absolute shrinkage and selection operator (LASSO) regression analysis allowed us to determine a signature composed of eight IRlncRNAs. Based on this signature, we further performed gene set enrichment analysis (GSEA) and somatic mutation analysis to evaluate the ability of this signature to predict prognosis. Results. In total, 72 immune-related lncRNAs (DEIRlncRNAs) with prognostic value were identified. These lncRNAs were used to construct a model containing eight immune-related lncRNAs (8-IRlncRNAs). Based on this risk model, we divided GAD patients into high-risk and low-risk groups. The analysis showed that the prognosis of the two groups was different and that the high-risk group had worse overall survival (OS). Immune cell infiltration analysis showed that the proportion of memory B cells increased in the high-risk group while the proportion of macrophages M1, T cells, CD4 memory-activated cells, and T cell follicular helpers decreased. GSEA results showed that 8-IRlncRNA was significantly enriched in tumorigenesis pathways such as myc. The results of somatic mutation analysis showed that the CDH1 gene was significantly mutated in the high-risk group. Conclusion. A prognostic signature of 8-IRlncRNAs in GAD was established and this signature was able to predict the prognosis of GAD patients.
Generalized Lucas Tau Method for the Numerical Treatment of the One and Two-Dimensional Partial Differential Heat Equation
Authors: Youssri Y. H. , Abd-Elhameed W. M. , Sayed S. M.
Published: April 2022

This paper is dedicated to proposing two numerical algorithms for solving the one- and two-dimensional heat partial differential equations (

PDEs

). In these algorithms, generalized Lucas polynomials (

GLPs

) involving two parameters are utilized as basis functions. The two proposed numerical schemes in one and two- dimensions are based on solving the corresponding integral equation to the heat equation, and after that employing, respectively, the tau and collocation methods to convert the heat equations subject to their underlying conditions into systems of linear algebraic equations that can be treated efficiently via suitable numerical procedures. In this article, the convergence analysis is examined for the proposed generalized Lucas expansion. Five illustrative problems are numerically solved via the two proposed numerical schemes to show the applicability and accuracy of the presented algorithms. Our obtained results compare favourably with the exact solutions.
A Successful Esthetic Approach of Gingival Depigmentation Using Microneedling Technique and Ascorbic Acid (Vitamin C)
Authors: Mostafa Diana , Alotaibi Shaden M.
Published: April 2022

A gingival depigmentation is a periodontal plastic procedure that is performed in order to remove melanocytic pigmentation. A variety of different modalities have been proposed for removing hyperpigmentation involving surgical scraping, gingival autograft, cryotherapy, electrosurgery, and lasers. However, the microneedling technique is a nonsurgical procedure that creates microholes to facilitate the penetration of topical medications across the connective tissues. Case Description. A healthy female patient aged 25 years with a pigmented gingiva seeking gingival depigmentation. On examination, a dark brown ribbon of hyperpigmentation was observed within the mandibular attached gingiva. The case was diagnosed as physiological moderate gingival pigmentation (

pigmentation
 
index
 
score
=
3

). The patient was interested in achieving aesthetic results with minimally invasive, nonexpensive procedures. Based on the patient’s concerns, the microneedling technique using vitamin C was suggested and consented. We used a dermapen device to microneedle the gingiva until bleeding pinpoints were observed; then, topical ascorbic acid was applied. After 3 days, our outcomes revealed an excellent aesthetic pink gingival appearance. Conclusions and Practical Implications. Compared to other minimally invasive techniques, our technique is less expensive and more risk-free. Our novel technique of using dermapen and topical ascorbic acid has shown promising results to our case which gives new perspectives for its application in gingival depigmentation.
A Study on Livestreaming E-Commerce Development Scale in China Based on Grey System Theory
Authors: Liu Jiefang , Li ShanShan , Gao Pumei
Published: April 2022

Grey system theory is an effective mathematical method for studying small sample data and solving poor information problems. The grey correlation model and grey prediction model in this theory have been widely used in scientific studies in various industries. Currently, it is quite difficult for China to formulate scientific policies as livestreaming e-commerce is an emerging industry with little available annual data; therefore, the use of grey system theory is crucial to the study of the future scale of livestreaming e-commerce. In this paper, of many factors influencing the development of livestreaming e-commerce, 14 predictors of livestreaming e-commerce development scale were selected to construct a grey correlation model, by which 5 main predictors were determined. Based on the predictors identified above, 4 grey prediction models of GM (1,1), DGM (1,1), NDGM (1,1), and FDGM (1,1) were constructed, and the accuracy of these models was compared. It was concluded that the NDGM (1,1) model had the best simulation effect. The NDGM (1,1) model is then used to forecast and analyse the indicators of livestreaming e-commerce development scale from 2021 to 2023, and some relevant suggestions were made. This paper applies the new modelling approach to livestreaming e-commerce studies, thus broadening the theoretical study field of livestreaming e-commerce. Moreover, the findings can help the Chinese government make more reasonable and effective decisions as a new study on livestreaming e-commerce was conducted from a different perspective in this paper.
Prediction Model of Adverse Effects on Liver Functions of COVID-19 ICU Patients
Authors: Mashraqi Aisha , Halawani Hanan , Alelyani Turki , Mashraqi Mutaib , Makkawi Mohammed , Alasmari Sultan , Shaikh Asadullah , Alshehri Ahmad
Published: April 2022

SARS-CoV-2 is a recently discovered virus that poses an urgent threat to global health. The disease caused by this virus is termed COVID-19. Death tolls in different countries remain to rise, leading to continuous social distancing and lockdowns. Patients of different ages are susceptible to severe disease, in particular those who have been admitted to an ICU. Machine learning (ML) predictive models based on medical data patterns are an emerging topic in areas such as the prediction of liver diseases. Prediction models that combine several variables or features to estimate the risk of people being infected or experiencing a poor outcome from infection could assist medical staff in the treatment of patients, especially those that develop organ failure such as that of the liver. In this paper, we propose a model called the detecting model for liver damage (DMLD) that predicts the risk of liver damage in COVID-19 ICU patients. The DMLD model applies machine learning algorithms in order to assess the risk of liver failure based on patient data. To assess the DMLD model, collected data were preprocessed and used as input for several classifiers. SVM, decision tree (DT), Naïve Bayes (NB), KNN, and ANN classifiers were tested for performance. SVM and DT performed the best in terms of predicting illness severity based on laboratory testing.
Application of Digital Games for Speech Therapy in Children: A Systematic Review of Features and Challenges
Authors: Saeedi Soheila , Bouraghi Hamid , Seifpanahi Mohammad-Sadegh , Ghazisaeedi Marjan
Published: April 2022

Introduction. Treatment of speech disorders during childhood is essential. Many technologies can help speech and language pathologists (SLPs) to practice speech skills, one of which is digital games. This study aimed to systematically investigate the games developed to treat speech disorders and their challenges in children. Methods. A comprehensive search was conducted in four databases, including Medline (through PubMed), Scopus, Web of Science, and IEEE Xplore, to retrieve English articles published by July 14, 2021. The articles in which a digital game was developed to treat speech disorders in children were included in the study. Then, the features of the designed games and their challenges were extracted from the studies. Results. After reviewing the full texts of 69 articles and assessing them in terms of inclusion and exclusion criteria, 27 articles were included in the systematic review. In these articles, 59.25% of the games had been developed in English language and children with hearing impairments had received much attention from researchers compared to other patients. Also, the Mel-Frequency Cepstral Coefficients (MFCC) algorithm and the PocketSphinx speech recognition engine had been used more than any other speech recognition algorithm and tool. In terms of the games, 48.15% had been designed in a way that children could practice with the help of their parents. The evaluation of games showed a positive effect on children’s satisfaction, motivation, and attention during speech therapy exercises. The biggest barriers and challenges mentioned in the studies included sense of frustration, low self-esteem after several failures in playing games, environmental noise, contradiction between games levels and the target group’s needs, and problems related to speech recognition. Conclusion. The results of this study showed that the games positively affect children’s motivation to continue speech therapy, and they can also be used as the SLPs’ aids. Before designing these tools, the obstacles and challenges should be considered, and also, the solutions should be suggested.
Effects of C5a and Receptor CD88 on Glutamate and N-Methyl-D-Aspartic Acid Receptor Expression in the Mouse Model of Optic Neuromyelitis
Authors: Li Ling , Tang Jiangwei , Wang Hongxin , Liu Jiamei , Zhou Lina
Published: April 2022

Objective. To analyze the role of C5a, C5a receptor (CD88), glutamic acid, and N-methyl-D-aspartic acid receptors (NMDAR1 and NMDAR2B) in the onset of neuromyelitis optica (NMO) disease in mice. Method. To select C57BL/6 wild-type (WT) mice and C5a receptor gene knockout (C5aR-/-) mice, use NMO-IgG and hemolytic complement to intervene in spinal cord tissue sections and optic nerves to establish an NMO model in vitro. The experiment was carried out with five groups (control group, WT group, C5aR-/- group, C5a group, and C5a+C5aRA group), with six mice in each group. The differences of American spinal cord injury (ASIA) motor scores were compared among all groups. The expressions of aquaporin (AQP4), glial fibrillary acidic protein (GFAP), NMDAR1, and NMDAR2B in spinal cord and optic nerve tissues were detected. The difference of glutamic acid (Glu) concentrations in culture solutions of the spinal cord and optic nerves was compared. Result. The ASIA motor score of the control group was significantly lower than that of the other four groups. The C5a-/- group was significantly higher than the WT group. The C5a+C5aRA group was significantly higher than the C5a group in terms of ASIA motor score. In the control group, AQP4 and GFAP showed expression loss. The C5aR-/- group’s loss rate was significantly higher than that of the WT group. The loss rate of the C5a+C5aRA group was significantly higher than that of the C5a group. In the control group, the protein expressions of NMDAR1 and NMDAR2B were significantly lower than that of the other four groups. The C5aR-/- group was significantly higher than the WT group. The C5a+C5aRA group was significantly higher than the C5a group in protein expression. In the control group, the concentration of Glu in the C5aR-/- group was significantly higher than that in the WT group, and the C5a group was significantly lower than the C5a+C5aRA group. Conclusion. The deletion of the C5a receptor promotes NMDAR activity, which affects the toxic excitatory effect of NMDAR in NMO and regulates the neurotoxic effect of glutamic acid, thus participating in the pathogenesis of NMO.
Security Assessment of Coal Mine Power Grid Voltage Based on an Improved AHP-FCE
Authors: Yanwen Wang , Peng Chen , Chen Feng
Published: April 2022

The voltage level of a coal mine power grid directly affects the starting and normal operation of the electric motor, but the current grid monitoring system cannot use real-time grid structure parameters and power flow distribution data to conduct early warning research on the running status and voltage level of the power grid, so it cannot eliminate hidden danger in the operation of the power grid. Therefore, it is necessary to accurately and effectively evaluate the voltage safety level of the coal mine power grid. In this paper, an improved AHP-FCE method is proposed to evaluate the voltage safety level of a coal mine power grid. By replacing the comparison judgment matrix with the interval judgment matrix, the objectivity of the weight vector is improved while retaining expert subjectivity. According to the real-time power flow distribution data of the power grid monitoring system, the current voltage safety level of the power grid is evaluated. The results show that the improved AHP-FCE method can accurately evaluate the current grid voltage level, analyze the voltage development trend, give early warnings regarding coal mine grid voltage operation, and improve the monitoring function of the power monitoring system.
Systematic Retesting for Helicobacter pylori: The Potential Overestimation of Suppressive Conditions
Authors: Knoop Richard F. , Gaertner Pauline C. , Petzold Golo , Amanzada Ahmad , Ellenrieder Volker , Neesse Albrecht , Bremer Sebastian C. B. , Kunsch Steffen
Published: April 2022

Background and Aims. In contrast to guideline recommendations, endoscopic testing for Helicobacter pylori is frequently performed under Helicobacter pylori suppressive conditions, e.g., intake of proton‐pump inhibitors (PPI), preceded antibiotic treatment, or recent gastrointestinal bleeding. Our study’s aim was to retest patients with—under suppressive conditions—negative test results. This was carried out in order to examine the rate of false negative tests previously gathered under suppressive conditions. Methods. The trial was conducted in a large patient collective in a university hospital. Every elective esophagogastroduodenoscopy from in‐ and outpatients was included. Prior to endoscopy, suppressive conditions were collected via standardized questionnaire. If Helicobacter pylori testing was indicated, both helicobacter urease test and histology were performed in analogy to the Sydney classification. In case of a negative result under suppressive conditions, the patient was reinvited after, if possible, withdrawal of suppressive condition in order to perform a urea breath test (UBT). Results. 1,216 patients were included (median 59 years, 72.0% inpatients, 28.0% outpatients). Overall, 60.6% (737) were under Helicobacter pylori suppressive conditions. The main suppressive condition was intake of PPIs (54.5%). In 53.7% (653) of all included cases, Helicobacter pylori testing was performed. Of those, 14.1% (92) had a positive test, and 85.9% (561) were negative. Out of the patients with negative result, 50.8% (285) were tested under suppressive conditions and consequently invited for retesting via UBT. In 20.4% (45), suppressive conditions could not be ceased. In 22.8% (65), retesting was conducted. Of those, 98.5% (64) congruently presented a negative result again, and only 1.5% (1) was positive for Helicobacter pylori. Conclusion. Many patients undergoing esophagogastroduodenoscopy in everyday clinical practice are tested for Helicobacter pylori under suppressive conditions leading to a potentially higher risk of false negative results. However, our research shows that this issue might be overestimated.
Prediction of Heart Failure in Children with Congenital Heart Disease Based on Multichannel LSTM
Authors: Bai Jing , Fu Juan , Du Shihua , Chen Xilong , Zhang Chengzu
Published: April 2022

Heart failure (HF) is a complicated clinical illness caused by a variety of primary and secondary causes, as well as increased infection pathways, that are associated with higher risk, illness, and costs. The overall incidence of congenital heart disease is approximately high and is the leading cause of death in infants and children. In this study, we present a novel computational model based on ECNN-LSTM for detecting congenital heart disease in real time and assessing its developing course objectively. The proposed model is a multiresolution singular value decomposition for congenital heart disease prediction in infants and children. Firstly, in the whole-life vibration time-domain signal of newborns and toddlers, the multiresolution singular value decomposition approach is employed to create approximation and detailed signals with different resolutions. Secondly, the health phases are split into the smooth running stage of newborns and toddlers, as well as the standard deviation of the earliest moments. Thirdly, the two-layer one-dimensional convolutional neural network structure divides the quick degradation stage, which can offer degradation information. Finally, the prediction of congenital heart disease is finished on LSTM utilizing the MSE loss function to unify the assessment scale. Validation with lifespan data demonstrates the feasibility and effectiveness of the proposed model. Moreover, the existing models have the insufficient ability for life span feature characterization in congenital heart disease prediction in infants and children.
Condition-Based Monitoring on High-Precision Gearbox for Robotic Applications
Authors: Amin Al Hajj Mohamad , Quaglia Giuseppe , Schulz Ingo
Published: April 2022

This work presents a theoretical and experimental study regarding defect detection in a robotic gearbox using vibration signals in both cyclostationary and noncyclostationary conditions. The existing work focuses on inferring the health of the robot during operation with little regard toward the defective element of the components. This article illustrates the detection of specific element damage of a robotic gearbox during a robotic cycle based on domain knowledge and presents a novel data-driven method for asset health. This starts by studying the robotic gearbox, specifically its kinematics as a planetary 2-stage reduction gearbox to acquire the knowledge of the rotations of each component. The signals acquired from a test bench with four sensors undergo different acquisition methods and signal processing techniques to correlate the elements’ frequencies. The work shows the detection of the artificially created defects from the acquired vibration data, verifying the kinematic methodology and identifying the root cause of failure of such gearboxes. A novel resampling method, Binning, is presented and compared with the traditional signal processing techniques. Binning combined with Principal Component Analysis (PCA) as a data-driven method to infer the state of the gearbox is presented, tested, and validated. This work presents methods as a step toward automatized predictive maintenance on robots in industrial applications.
A Case of Type 1 Triallelic Patterns at D5S818, D18S51, D6S1043, and FGA Demonstrated by Short Tandem Repeat Analysis
Authors: Fan Jing , Zhang Ai-Ping , Zheng Zhong-Zheng , An Lin , Xiao Pei-Li , Li Dai-Yang , Du Ke-Ming , Xiong Hao
Published: April 2022

The triallelic pattern of short tandem repeat (STR) is rare; especially, the case where this pattern exists at 4 loci has not been reported. Here, we report the type 1 triallelic patterns at D5S818, D18S51, D6S1043, and FGA from a Chinese family, which were observed during our routine chimerism assays. Before hematopoietic stem cell transplantation, the blood sample of the certain patient was analyzed by performing chimerism analysis. A preliminary STR analysis was also performed on the samples of the patient’s parents. STR signal data illustrated that the sum of the peak chart areas of the two types inherited from the father was basically the same as that of the mother, belonging to the type 1 triallelic pattern. In addition, the patient’s elder sister’s STR result appeared to be normal. Altogether, we presented a pedigree, in which the triallelic pattern was linked by inheritance in the family. This is the first reported case of the triallelic pattern at D5S818, D18S51, D6S1043, and FGA all around the world. We hope that in the future there will be any tools to achieve accurate verification against this possibility.
The Clinical Efficacy of Platelet-Rich Plasma versus Conventional Drug Injection in the Treatment of Knee Osteoarthritis: A Study Protocol for a Randomized Controlled Trial
Authors: Ma Qi , Hei Xiaoyan , Zhu Shiqiang , Tian Xinbao , Chen Yundong , Zhu Ning , Zhang Jinchen , Feng Ping , Liu Yujuan , Xing Lijuan , Li Xiaojun , Liu Qiang , Li Fengqiao , Li Xiaolong , Lai Zheying , Lin Ruizhu , Xu Jianfeng
Published: April 2022

Knee osteoarthritis is a common chronic degenerative joint disease in middle-aged and elderly people. Intra-articular injection for the treatment of knee osteoarthritis is a regularly utilized nonsurgical treatment in modern medicine. Hyaluronic acid (HA) and platelet-rich plasma (PRP) are two frequently employed intra-articular devices. Hyaluronic acid (HA) is an accepted nonsurgical treatment for symptomatic KOA, and platelet-rich plasma is a popular option in the treatment of KOA in recent years. The purpose of this research is to compare the efficacy and safety of intra-articular injection of platelet-rich plasma (PRP) versus hyaluronic acid (HA) on the pain score scale, knee function, and related inflammatory biomarkers in KOA patients using a clinical randomized controlled trial. Participants are being randomized into either the hyaluronic acid (HA) or into the platelet-rich plasma (PRP) group. All patients receive 4 weeks of treatment (once a week), and well-being support and quadriceps training (3 times a week). The primary outcomes are measured using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and the visual analog scale (VAS). The secondary outcomes include the activities of daily living score, erythrocyte sedimentation rate, C-reactive protein testing, interleukin-6 levels, and X-ray examination. In order to monitor the occurrence of irregularities and abnormalities, patients are assessed at each visit, and restorative treatment is given if necessary. The results of this clinical trial will verify the efficacy of PRP and HA in the treatment of KOA and provide important evidence for the clinical treatment of KOA. The trial was enlisted at the Chinese Clinical Trial Registry on 26 September 2020 (ChiCTR2000038635).
Polyclonal Immunotactoid Glomerulopathy Associated with Monoclonal Gammopathy of IgM Type and Underlying Plasmacellular Disease: Successful Treatment with Rituximab Alone
Authors: Neukirchen Wolfgang , Oesterling Anne , Wennmann Dirk Oliver , Heitplatz Barbara , Ritter Peter , Merz Hartmut , Busch Veit
Published: April 2022

Immunotactoid glomerulopathy (ITG) occurs infrequently and is characterized by organized IgG containing deposits. It most usually manifests as a concomitant disease of a broad spectrum of oncologic entities. We here present an exceptional case of ITG without glomerular light chain restriction secondary to a IgM kappa type monoclonal gammopathy of undetermined significance. Due to nephrotic syndrome and deterioration of kidney function a rituximab monotherapy was initiated without targeting the plasmacellular augmentation, which was confirmed as the underlying process. The treatment led to a long-term improvement of proteinuria and stabilization of glomerular filtration rate. Its therapeutic effect has to be attributed to immunomodulatory capacities and targeting of podocytes rather than to be interpreted as directed against a bone marrow or glomerular clone. We conclude that rituximab therapy may be a valuable part of the therapeutic options in ITG irrespective of the underlying oncologic entity.
Nanomolar Responsiveness of Marine Phaeobacter inhibens DSM 17395 toward Carbohydrates and Amino Acids
Authors: Weiten Arne , Kalvelage Kristin , Neumann-Schaal Meina , Buschen Ramona , Scheve Sabine , Winklhofer Michael , Rabus Ralf
Published: April 2022

Phaeobacter inhibens DSM 17395 is a heterotrophic member of the ubiquitous, marine Roseobacter group and specializes in the aerobic utilization of carbohydrates and amino acids via pathways widespread among roseobacters. The in vivo responsiveness of P. inhibens DSM 17395 was studied with nonadapted cells (succinate-grown), which were exposed to a single pulse (100–0.01 µM) each of N-acetylglucosamine, mannitol, xylose, leucine, phenylalanine, or tryptophan (effectors). Responsiveness was then determined by time-resolved transcript analyses (quantitative reverse transcription-PCR) of “degradation” and “uptake” genes selected based on previously reported substrate-specific proteome profiles. The transcriptional response thresholds were: 50–100 nM for nagK (N-acetylglucosamine kinase), paaA (ring 1,2-phenylacetyl-CoA epoxidase), and kynA (tryptophan 2,3-dioxygenase), 10–50 nM for xylA (xylose isomerase), and around 10 nM for mtlK (mannitol 2-dehydrogenase). A threshold for leucine could not be determined due to the elevated intrinsic presence of leucine in the exometabolome of succinate-grown cells (no effector addition). Notably, the response thresholds for presumptive carbohydrate-binding proteins of ABC-transporters were in the same range or even lower: 0.1–1 µM for c27930 (N-acetylglucosamine) and even below 10 nM for c13210 (mannitol) and xylF (xylose). These results shed new light on the sensory/regulatory sensitivity of a well-studied roseobacter for recognizing potential substrates at low ambient concentrations and on the concentration threshold below which these might escape biodegradation (“emergent recalcitrance” concept of dissolved organic matter persistence).
Sex Differences in Poststroke Inflammation: a Focus on Microglia Across the Lifespan
Authors: Ugidos Irene F. , Pistono Cristiana , Korhonen Paula , Gómez-Budia Mireia , Sitnikova Valeriia , Klecki Pamela , Stanová Iveta , Jolkkonen Jukka , Malm Tarja
Published: April 2022

Stroke is one of the leading causes of death worldwide and currently only few therapeutic options are available. Stroke is a sexually dimorphic disease contributing to the difficulty in finding efficient treatments. Poststroke neuroinflammation is geared largely by brain microglia and infiltrating peripheral immune cells and largely contributes to sex differences in the outcome of stroke. Microglia, since very early in the development, are sexually divergent, imprinting specific sex-related features. The diversity in terms of microglial density, morphology, and transcriptomic and proteomic profiles between sexes remains in the adulthood and is likely to contribute to the observed sex-differences on the postischemic inflammation. The impact of sexual hormones is fundamental: changes in terms of risk and severity have been observed for females before and after menopause underlining the importance of altered circulating sexual hormones. Moreover, aging is a driving force for changes that interact with sex, shifting the inflammatory response in a sex-dependent manner. This review summarizes the present literature on sex differences in stroke-induced inflammatory responses, with the focus on different microglial responses along lifespan.
Multi-Agent Systems: Technical & Ethical Challenges of Functioning in a
Mixed Group
Authors: Gal Kobi , Grosz Barbara J.
Published: April 2022

In today's highly interconnected, open-networked computing world, artificial intelligence computer agents increasingly interact in groups with each other and with people both virtually and in the physical world. AI's current core challenges concern determining ways to build AI systems that function effectively and safely for people and the societies in which they live. To incorporate reasoning about people, research in multi-agent systems has engendered paradigmatic shifts in computer-agent design, models, and methods, as well as the development of new representations of information about agents and their environments. These changes have raised technical as well as ethical and societal challenges. This essay describes technical advances in computer-agent representations, decision-making, reasoning, and learning methods and highlights some paramount ethical challenges.
AI, Great Power Competition & National Security
Authors: Schmidt Eric
Published: April 2022

Breakthroughs in AI are accelerating global commercial competition and transforming the international security environment. The reach and influence of foreign-based network platforms present risks to American society and require us to confront questions about their origin and purpose. Meanwhile, AI technologies are enhancing several existing national security threats, and will change the way states try to gain leverage against adversaries and exercise coercion and influence in other societies. The open nature of free and democratic societies, combined with their increasing reliance on poorly secured digital networks, makes them especially vulnerable. In the military realm, AI holds the prospect of augmenting cyber, conventional, and nuclear capabilities in ways that make security relationships among rivals more challenging to predict and maintain, and conflicts more difficult to limit. Even as they compete, rivals should explore limits on AI capabilities. The AI ecosystems of the principal global competitors, the United States and China, remain intertwined, and a calibration of the bilateral technology relationship requires both selective decoupling and continued collaboration in areas of mutual interest. These changes require a comprehensive national strategy for the next decade that preserves global leadership advantages for America's economy and security.
Artificially Intelligent Regulation
Authors: Cuéllar Mariano-Florentino , Huq Aziz Z.
Published: April 2022

This essay maps the potential, and risks, of artificially intelligent regulation: regulatory arrangements that use a complex computational algorithm or another artificial agent either to define a legal norm or to guide its implementation. The ubiquity of AI systems in modern organizations all but guarantees that regulators or the parties they regulate will make use of learning algorithms or novel techniques to analyze data in the process of defining, implementing, or complying with regulatory requirements. We offer an account of the possible benefits and harms of artificially intelligent regulation. Its mix of costs and rewards, we show, depend primarily on whether AI is deployed in ways aimed merely at shoring up existing hierarchies, or whether AI systems are embedded in and around legal frameworks carefully structured and evaluated to better our lives, environment, and future.
Visual Working Memory Adapts to the Nature of Anticipated Interference
Authors: Wang Benchi , Knapen Tomas , Olivers Christian N. L.
Published: April 2022

Visual working memory has been proven to be relatively robust against interference. However, little is known on whether such robust coding is obligatory, or can be flexibly recruited depending on its expected usefulness. To address this, participants remembered both the color and orientation of a grating. During the maintenance, we inserted a secondary color/orientation memory task, interfering with the primary task. Crucially, we varied the expectations of the type of interference by varying the probability of the two types of intervening task. Behavioral data indicate that to-be-remembered features for which interference is expected are bolstered, whereas to-be-remembered features for which no interference is expected are left vulnerable. This was further supported by fMRI data obtained from visual cortex. In conclusion, the flexibility of visual working memory allows it to strengthen memories for which it anticipates the highest risk of interference.