bluuebunny/crossref_metadata_2025 huggingface dataset:
Research Challenges for the Design of Human-Artificial Intelligence Systems (HAIS)
Authors: Hevner Alan , Storey Veda
Published: August 2022

Artificial intelligence (AI) capabilities are increasingly common components of all socio-technical information systems that integrate human and machine actions. The impacts of AI components on the design and use of application systems are evolving rapidly as improved deep learning techniques and fresh big data sources afford effective and efficient solutions for broad ranges of applications. New goals and requirements for Human-AI System (HAIS) functions and qualities are emerging, whereas the boundaries between human and machine behaviors continue to blur. This research commentary identifies and addresses the design science research (DSR) challenges facing the field of Information Systems as the demand for human-machine synergies in Human-Artificial Intelligence Systems surges in all application areas. The design challenges of HAIS are characterized by a taxonomy of eight C's - composition, complexity, creativity, confidence, controls, conscience, certification, and contribution. By applying a design science research frame to structure and investigate HAIS design, implementation, use, and evolution, we propose a forward-thinking agenda for relevant and rigorous information systems research contributions.
Generating circuits with generators
Authors: Materzok Marek
Published: August 2022

The most widely used languages and methods used for designing digital hardware fall into two rough categories.
One of them, register transfer level (RTL), requires specifying each and every component in the designed circuit.
This gives the designer full control, but burdens the designer with many trivial details.
The other, the high-level synthesis (HLS) method, allows the designer to abstract the details of hardware away and focus on the problem being solved.
This method however cannot be used for a class of hardware design problems because the circuit's clock is also abstracted away.

We present YieldFSM, a hardware description language that uses the generator abstraction to represent clock-level timing in a digital circuit.
It represents a middle ground between the RTL and HLS approaches: the abstraction level is higher than in RTL, but thanks to explicit information about clock-level timing, it can be used in applications where RTL is traditionally used.
We also present the YieldFSM compiler, which uses methods developed by the functional programming community -- including continuation-passsing style translation and defunctionalization -- to translate YieldFSM programs to Mealy machines.
It is implemented using Template Haskell and the Clash functional hardware description language.
We show that this approach leads to short and conceptually simple hardware descriptions.
Camera Measurement of Physiological Vital Signs
Authors: McDuff Daniel
Published: August 2022

The need for remote tools for healthcare monitoring has never been more apparent. Camera measurement of vital signs leverages imaging devices to compute physiological changes by analyzing images of the human body. Building on advances in optics, machine learning, computer vision, and medicine, these techniques have progressed significantly since the invention of digital cameras. This article presents a comprehensive survey of camera measurement of physiological vital signs, describing the vital signs that can be measured and the computational techniques for doing so. I cover both clinical and non-clinical applications and the challenges that need to be overcome for these applications to advance from proofs of concept. Finally, I describe the current resources (datasets and code) available to the research community and provide a comprehensive webpage (https://cameravitals.github.io/) with links to these resource and a categorized list of all papers referenced in this article.
The book review column
Authors: Green Frederic
Published: August 2022

My predecessor as book review editor, Bill Gasarch, held the post for 17 years. In his final column in January 2015, he said that that's longer than anyone should have the job. I count myself as lucky to have been nominated as his successor. At the time, Bill advised me not to stay on for more than 5 years. Well, here it is 6.5 years out and some 26 columns later, so it is high time for me to retire from the position.
SIGACT news complexity theory column 114
Authors: Hemaspaandra Lane A.
Published: August 2022

This issue's column (which comes immediately after some memories of and comments on Juris Hartmanis, who passed away a few days ago), by Eleni Bakali, Aggeliki Chalki, Andreas Göbel, Aris Pagourtzis, and Stathis Zachos, is a tour---
A Panorama of Counting Problems the Decision Version of which is in P
---of the exciting world of counting functions whose decision version (the set of inputs on which the function evaluates to zero) is in P.
Ergodicity of Time Reversal Process of Stochastic Consensus Formation and Its Application
Authors: Shioda Shigeo , Takehara Kenta
Published: August 2022

The consensus reached in stochastic consensus formation is a random variable whose distribution is generally difficult to determine analytically. We show that the time reversal process for the stochastic consensus formation process is ergodic. This fact allows us to numerically obtain the distribution for the consensus by observing the time reversal process for consensus formation for a fixed sample path.
Queueing-Network Based Applications Under Worst-case Attacks
Authors: Tavori Jhonatan , Levy Hanoch
Published: August 2022

A variety of today's critical applications are based on queueing networks whose performance, mainly delay, depends on routing and resource allocation. These include computer networks (the Internet), load-balancers on cloud systems and vehicular traffic networks.

These applications are vulnerable to malicious attacks which may include the dis-functioning of the network components by malwares, or other computer viruses. For computer networks there are many references (e.g., [1, 5]); for vehicular traffic networks see navigation platforms attacks (e.g., Waze [4]).

Such attacks will increase the delay experienced in the network and degrade its performance. We aim at understanding what are the weak-points of such networks. That is, how can a sophisticated attacker cause the maximal damage to the network using minimal attacking power. Furthermore, we are interested in analyzing how flexible is the network in reacting to such attacks by re-routing its traffic, and whether such flexibility grants significant protection.

An intriguing question, which we aim at addressing and which may affect network planning, is what will be the nature of an optimal attack: will it be concentrated at few nodes of the network, or would it be scattered over many regions (nodes).

Prior works (e.g., [1, 3, 5]) that dealt with such worstcase attacks on distributed systems did not address queueing delays and their effects.

To allow analytic treatment that will reveal the nature of these networks, we consider here a simplistic queueing model based on k queues which captures the main features of these networks: (i) Multi-commodity arrival flows, (ii) The ability of the network to migrate requests (users) from one route to another. Treatment of general structure networks is the subject of ongoing research which is based on this work.

Our analysis reveals somewhat surprising properties: The nature of an optimal attack varies as a function of the system parameters, and may shift from fully concentrated to fully scattered in the extreme cases. This is in contrast to the (no queueing) model and results of [5] which asserted that optimal attacks are concentrated, even when the system can defend itself using requests migration. This suggests that queueing mechanisms and the consideration of queueing delays may cause scattering of optimal attacks.
CVaR Optimization for MDPs
Authors: Ding Rui , Feinberg Eugene
Published: August 2022

We study the problem of Conditional Value-at-Risk (CVaR) optimization for a finite-state Markov Decision Process (MDP) with total discounted costs and the reduction of this problem to a stochastic game with perfect information. The CVaR optimization problem for finite and infinite-horizon MDPs can be reformulated as a zero-sum stochastic game with a compact state space. This game has the following property: while the second player has perfect information including the knowledge of the decision chosen by the first player at the current time instance, the first player does not directly observe the augmented component of the state and does not know current and past decisions chosen by the second player. Using methods of convex analysis, we show optimal policies exist for this game and an optimal policy of the first player optimizes CVaR of the total discounted costs. In addition to proving existence of optimal policies, we provide algorithms for their computation.
Review—Recent Advances in MoS2 and Its Derivatives-Based Two-Dimensional Gas Sensors
Authors: Kumari Ritu , Kumar Rakesh
Published: August 2022

Sensing of hazardous gases is one of the most significant challenge in front of humans in this technological era, because degradation of eminence air, due to emission of diverse air contaminants by urbanization activities, is increasing day by day. There is an urgent global demand of advanced materials with enhanced sensing features for monitoring the pollutants to prevent the environmental deterioration. Recently, two-dimensional (2D) molybdenum disulfide (MoS2) and its nanostructured materials have gained enormous attention in gas sensing applications because of their novel semiconducting properties with sizeable bandgap, abundant reactive sites and large specific areas. This review summarizes the systematic depiction of gas sensors based on 2D MoS2 and its nanostructures with their structure, morphology and sensing properties. Diverse configurations and gas sensing mechanism, influence of various parameters on the performance of gas sensors based on MoS2 are elucidated. In addition, the gas sensing performance of pristine MoS2 and its derivatives are discussed to further enhance the sensing performances of these devices. Moreover, performance of light assisted of the MoS2 based devices are also summarized. Finally, the existing key challenges which hinder the advance development and practical applications of MoS2 based gas sensors are proposed for future perspectives.

Introduction
Gd-Encapsulated Carbon Nanotubes as Dual-Modal Probes for Magnetic Resonance and Second Near-Infrared Emission
Authors: Nakanishi Yusuke , Nagata Masataka , Yasui Hirotoshi , Sato Kazuhide
Published: August 2022

We report a second near-infrared (NIR-II) fluorescence and magnetic resonance (MR) dual-modal probe composed of fluorescent carbon nanotubes (CNTs) encapsulated with paramagnetic Gd compounds. The Gd-filled CNTs are synthesized via a vapor-phase technique using gadolinium halides as precursors, and the CNTs exhibit the strong photoluminescence even after Gd filling. We have investigated NIR-II fluorescence and MR imaging in vivo as well as in vitro, demonstrating that the Gd-filled CNTs can be used for dual-modal imaging of tissues and vessels in living mice, creating avenues for disease diagnosis and prognosis.
Basic principles of processing of afferent information by spinal interneurons
Authors: Jankowska Elzbieta
Published: August 2022

Integrative functions of spinal interneurons are well recognized but the relative role of different interneuronal populations in this process continues to be investigated. It therefore appeared useful to review the principles of integration of afferent information by the interneurons analyzed so far as these principles should apply also to those remaining to be analyzed. Considering the results of both functional and morphological studies of spinal interneurons and of the morphology and immunochemistry of afferent fibers that provide input to them, the following five basic principles of processing of afferent information by them will be outlined; 1) afferent information of any origin is forwarded to several neuronal populations, 2) information from any sources of input is distributed unevenly, 3) input from several sources is integrated by individual neurons as well as by their populations, 4) specific combinations of input are integrated by different neuronal populations, and 5) afferent input to spinal interneurons is only one of the features distinguishing their functional populations. As the spinal neuronal organization and properties of neurons and afferent fibers in the so far investigated species (cat, rodents, and primates) have been found to resemble, future studies using molecular techniques in the mouse should allow the new data to integrate with those of the preceding studies and the principles outlined earlier as well as any new ones should apply also in humans.
Multiscale Object Detection Method for Track Construction Safety Based on Improved YOLOv5
Authors: Xue Zhen , Zhang Liangliang , Zhai Bo
Published: August 2022

In track construction, it is an important and necessary guarantee for production safety to check the number of workers and tools before and after the track maintenance. In view of time-consuming, laborious, and low detection efficiency of traditional manual inspection way, we propose an improved YOLOv5 multiscale object detection algorithm for track construction safety in this paper. We improve, from Generalized Intersection over Union (GIoU) to Distance Intersection over Union (DIoU), the loss function for Bounding Box (BBox) regression to speed up the convergence of the model. And we also improve, from the Non-Maximum Suppression (NMS) to DIoU-NMS, the post-processing method to enhance the model’s detection ability for occluded objects and small objects. The experiment results on the Track Maintenance dataset (our self-prepared dataset) and MS COCO dataset show that the mAP value of our improved YOLOv5 algorithm is 94.8% and 38.7%, respectively. Compared with the original YOLOv5 algorithm, the mAP values on above datasets are increased by 5.1% and 5.4%, respectively. The validation experimental results on MS COCO dataset and Track Maintenance dataset indicate that the detection ability of our improved YOLOv5 algorithm for occluded objects and small objects is enhanced. The proposed algorithm can provide technical support for the real-time accurate detection of track construction workers and tools.
[Retracted] Effect of Core Stability Training on Correction and Surface Electronic Signals of Paravertebral in Adolescent Idiopathic Scoliosis
Authors: Weng Huijian , Li Qin
Published: August 2022

Objective. To observe the effect of 12‐week core stability training on Cobb angle and the ratios of AEMG (averaged EMG) and IEMG (integral EMG) of paravertebral electromyography in thoracic and lumbar segments in adolescent idiopathic scoliosis (AIS) patients and offer a practical‐based evidence for the rehabilitation treatment for AIS patients. Methods. 31 AIS female middle school students were randomly divided into an exercise group (n = 16, 12 weeks of core stability training, 3 times/week) and AIS group (n = 15). In addition, 15 female secondary school students without scoliosis were included as the normal group (the AIS group and normal group did not perform any regular physical exercise throughout the trial period and only receive regular evaluation and guidance). Before and after the experiment, the digital diagnostic equipment of Philips was used to examine the Cobb angle and Noraxon was used to measure the surface EMG signals of the back thoracic and lumbar paravertebral muscles. Results. Compared with that before training, after 12 weeks of core stability training, the Cobb angle in the exercise group significantly decreased, and the ratio of AEMG (convex/concave) and IEMG (convex/concave) of paravertebral muscles in the thoracolumbar segment significantly decreased (P < 0.01, respectively); there was no significant difference in the above indicators in the AIS group before and after the experiment (P > 0.05, respectively). After 12 weeks of core stability training, compared with the AIS group, the Cobb angle decreased significantly (P < 0.01), and the ratio of AEMG (convex/concave) and IEMG (convex/concave) of paravertebral muscles in thoracolumbar segment decreased significantly (P < 0.01, P < 0.05). Compared with the normal group, the ratio of AEMG and IEMG of paravertebral muscles in thoracolumbar segment significantly increased (P < 0.01) in the AIS group (P < 0.01, respectively). Conclusions. The core stability training can significantly reduce the Cobb angle of AIS and correct the bad posture of scoliosis patients, which may be related to the balance of the electromyographic activities (convex concave side) of paravertebral muscles in AIS patients.
FPGA-Based Photoelectric Detection Control System with Image Signal Acquisition
Authors: Yu Qing
Published: August 2022

It has as of late been feasible to foster calculations for video picture handling utilizing field programmable gate arrays (FPGAs). A wide scope of video picture handling applications has been made conceivable by the FPGA’s exceptional plan. On two FPGA structures, three such calculations will be executed in FPGAs in this paper. Programming has developed less valuable in video handling as picture sizes and bit profundities have expanded. Video handling requires rapid continuous frameworks like those that are the focal point of this review. Satellite and ground-based identification frameworks create a great deal of information, which may be hard to manage. Specifically, lessening how much information to be handled by DSP frameworks guarantees that main applicable information is given to a human expert. Video handling in what is to come is probably going to depend on DSP frameworks, with minimal human mediation. Human information investigators are costly and may not be totally exact; accordingly, this is obviously invaluable.
Epidemiology of Adult T-Cell Leukaemia/Lymphoma in South Africa over a 10-Year Period
Authors: Nell Erica-Mari , Abdullah Ibtisam , Griesel Carla , Subramony Nadhiya , du Pisani Louis Almero , Chapanduka Zivanai Cuthbert
Published: August 2022

Introduction. Adult T-cell leukaemia/lymphoma (ATLL) is a rare and aggressive malignancy of mature T-cells. Limited epidemiological studies have shown that there is substantial variation in age at diagnosis and subtype distribution between different geographical regions. This is the first epidemiological study of ATLL in South Africa. Methods. A national epidemiological study of ATLL in South Africa was performed. All new cases of ATLL from 2009 to 2019 were identified by laboratory database search in public and private health care sectors. Demographic and diagnostic data were obtained, and the cases were subtyped according to the Shimoyama classification. Results. There were 31 patients with ATLL over the 10-year period, with an incidence of 0.06 per 100000 population. The male to female ratio was 1 : 1 and the median age at diagnosis was 37 years. Acute ATLL was the most commonly seen subtype in South Africa. Conclusion. In this, the first epidemiological study of ATLL in South Africa, we demonstrate that ATLL is a rare disease, that acute ATLL is the most commonly diagnosed subtype, and that ATLL is likely under diagnosed. Patients present at a considerably younger age than the reported age in other nations.
Differentially Expressed microRNAs in Peritoneal Dialysis Effluent-Derived Exosomes from the Patients with Ultrafiltration Failure
Authors: Wu Weifei , Wu Xu , Cheng Zhiqun , Yang Zhenzhen , Lu Minhui , Cheng Jing
Published: August 2022

Background. Ultrafiltration failure remains one of the most severe complications of long-term peritoneal dialysis (PD), which results in death. This study aimed to characterize the circulating exosomal microRNA (miRNA) profiles associated with ultrafiltration failure and explore its underlying mechanisms. Methods. Exosomes were isolated from the peritoneal dialysis effluent (PDE) of patients with ultrafiltration failure or success using the ultracentrifugation method, and then transmission electron microscopy (TEM), nanoparticle tracking analysis (NTA), and western blot were used for exosome characterization. After that, the isolated exosomes were sent for small RNA sequencing, and eight differentially expressed miRNAs (DE-miRNAs) were chosen for RT-qPCR validation. Results. TEM, NTA, and western blot revealed that exosomes were successfully isolated. After sequencing, 70 DE-miRNAs involved in ultrafiltration were identified, including 41 upregulated ones and 29 downregulated ones. Functional analyses revealed that these DE-miRNAs were significantly enriched in pathways of cancer, ubiquitin-mediated proteolysis, axon orientation, and the Rap1 and Ras signaling pathways. In addition, the consistency rate of RT-qPCR and sequencing results was 75%, which indicated the relatively high reliability of the sequencing data. Conclusions. Our findings implied that these DE-miRNAs may be potential biomarkers of ultrafiltration failure, which would help us to discover novel therapeutic targets/pathways for ultrafiltration failure in patients with end-stage renal disease.
Semantic Analysis of Public Health Medical Issues Based on Convolution Neural Networks
Authors: Xu Yulong , Zhao Yingying , Rong Hao , Liu Fangfang , Lv Yali , Zhu Honglei
Published: August 2022

Text mining and semantic analysis of medical public health issues are the main points for intelligent medical interaction, but less relevant research has been done on them. This article conceives a convolutional neural network for the semantic classification of public health medical issues. The dual convolution layer is used to further reduce the dimension of the data, extract more in-depth information from the data, and map the features. Each convolution layer includes several convolution nuclei to extract semantic characteristics, and then, the complete connection layer is input to the classifier to obtain the results of the classification. To check the classification effect, the dictionary artificial construction and the double hidden-layers neuronal network are used for semantic classification, and the three methods are compared and tested on the six real datasets. The experimental results show that when the quality of the dataset is high, the convolution neural network method proposed in this paper exceeds the last two methods. The proposed method is higher than the construction of the artificial dictionary and the double hidden-layers neural network in the recall rate: 0.153 and 0.037, and greater than 0.07 and 0.01 for the F1 measure rate, respectively. When the quality of the dataset is general, the models of the three methods do not give good classification results. Finally, it is concluded that the convolutional neural network method conceived has a good semantic recognition performance in public health medical issues.
Evaluation of the Efficacy of Stem Cell Therapy in Animal Models of Intervertebral Disc Degeneration Based on Imaging Indicators: A Systematic Review and Meta-Analysis
Authors: Li Wenhao , Zhao He , Xiong Zhencheng , Li Chuanhong , Guan Jianbin , Liu Tao , Yang Yongdong , Yu Xing
Published: August 2022

Objective. The purpose of this study is to make a systematic review of the therapeutic effect of stem cells in animal models of disc degeneration from an imaging point of view. Methods. Data were extracted by searching electronic databases for RCTs that met the inclusion criteria. Data analysis was performed using RevMan 5.3 and STATA 15.1 software. This meta-analysis was registered with INPLASY, registration number INPLASY202240148. Results. A total of 34 studies were included, covering four species of animals, rabbits, sheep, rats, and mice, with a total of 1163 intervertebral discs. In terms of DHI, the efficacy of stem cell group in rabbits (

P
<
0.001

), mice (

P
<
0.001

), sheep (

P
<
0.001

), and rats (

P
=
0.001

) was better than that in control group. In terms of disc height, the efficacy of stem cell group in rats (

P
<
0.001

) was better than that in control group, while in sheep (

P
=
0.355

), there was no statistical difference between two groups. In terms of MRI index, the efficacy of stem cell group in rats (

P
<
0.001

), mice (

P
<
0.001

), and rabbits (

P
=
0.016

) was better than that in control group. In terms of MRI signal score, the efficacy of stem cell group in rabbits (

P
<
0.001

) was better than that of control group. In terms of T2 signal intensity, stem cell group was more effective than control group in rabbits (

P
<
0.001

), mice (

P
<
0.001

), and rats (

P
=
0.003

).Conclusion. Stem cell therapy can improve intervertebral disc-related imaging parameters in animal models of disc degeneration, indicating that stem cell therapy has a repairing effect on intervertebral discs. However, given the heterogeneity and limitations of this study, this conclusion still needs to be tested by a large number of studies.
A Study of Contact Detection between Noncircular Particles in Discrete Element Method
Authors: Li Chengbo , Gao Qianqian , Li Xianchang
Published: August 2022

One of the key points of modeling noncircular particles in the discrete element method is the contact detection of particles. In this study, a general contact detection algorithm of two-dimensional particles with analytic shape functions is provided. The contact detection of particles with strictly convex shape function, such as ellipses and superellipses, is solved by Newton–Raphson method, and a grid method is provided to deal with heart-shaped particles. The grid method can be generalized into a particle system, in which the shape function is not convex. The accuracy and stability of the algorithm are verified by a series of tests. For the collision of a pair of ellipses with an aspect ratio of

α

 = 

a
/
b

 = 1000, the efficiency is not worse than the Newton–Raphson method. For random packing under gravity, no residual kinetic energy is observed, and the force that acts on the bottom is equal to the gravity, that is, the system reaches a mechanical equilibrium state. After equilibrium, the process of hopper discharge is also simulated. The present method is suitable for arbitrarily shaped particles with analytic shape functions in two-dimensional cases. In addition to superellipses, the method can also be applied to other particle systems as long as the shape functions are given.
Alterations in cirRNA Expression Profile during the Development of Acute-on-Chronic Liver Failure Induced by Hepatitis B
Authors: Lian Jiangshan , Lin Feiyan , Lu Yinfeng , Hao Shaorui , Cai Huan , Zhang Xiaoli , Gu Yuqing , Yang Yida , Hu Lufeng
Published: August 2022

Background. Hundreds of millions of people worldwide suffer from chronic hepatitis B (CHB), making it one of the most serious public health problems. CHB can lead to serious acute-on-chronic liver failure (ACLF). Since ACLF has a high mortality rate, predicting the risk of ACLF is a critical issue in clinical practice. Methods. To investigate the occurrence of ACLF in CHB, we used high-throughput RNA sequencing to detect the expression of a novel endogenous noncoding RNA (circRNA) in healthy controls (HC) as well as CHB and ACLF patients. Differentially expressed circRNAs were selected and analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) biological pathway analyses, circRNA-miRNA coexpression networks, and statistical analyses. Results. A total of 21,101 circRNAs were identified in HC, CHB, and ACLF subjects. Compared with the HCs, 4 circRNAs were upregulated and 14 circRNAs were downregulated in ACLF subjects, with a consistent trend in all three groups. GO analysis revealed that regulation of lymphocyte activation and macrophage tolerance induction were the most important biological processes in ACLF progression. KEGG pathway analysis revealed that primary immunodeficiency and NOD-like receptor signaling pathways were the most enriched terms. The circRNA-miRNA coexpression network and statistical analyses showed that circRNA_07734, circRNA_08533, and circRNA_16083 were the most important circRNAs in the process of ACLF. Conclusions. Immune dysfunction and deficiency may play a key role in the development of ACLF, especially circRNA_07734, circRNA_08533, and circRNA_16083.
Hsa_circ_0000479/Hsa-miR-149-5p/RIG-I, IL-6 Axis: A Potential Novel Pathway to Regulate Immune Response against COVID-19
Authors: Firoozi Zahra , Mohammadisoleimani Elham , Shahi Abbas , Naghizadeh Mohammad Mehdi , Mirzaei Ebrahim , Asad Ali Ghanbari , Salmanpour Zahra , Javad Nouri Seyed Mohamad , Mansoori Yaser
Published: August 2022

Background. COVID-19, the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to a global pandemic and mortality of people around the world. Some circular RNAs (circRNAs), one of the new types of noncoding RNAs (ncRNAs), act as competing endogenous RNAs (ceRNAs) and compete with mRNAs for shared miRNAs, to regulate gene expression. In the present study, we aimed to evaluate the expression and roles of hsa_circ_0000479/hsa-miR-149-5p/RIG-I, IL-6 in COVID-19 infection. Materials and Methods. After extraction of total RNA from peripheral blood mononuclear cells (PBMC) of 50 patients with symptomatic COVID-19, 50 patients with nonsymptomatic COVID-19, and 50 normal controls, cDNA synthesis was performed. Online in silico tools were applied to evaluate the interaction between the genes in the hsa_circ_0000479/hsa-miR-149-5p/RIG-I, IL-6 axis, and its role in COVID-19-related pathways. Quantification of the expression of these genes and confirmation of their interaction was done using the quantitative real-time PCR (qRT-PCR) technique. Results. The expression levels of hsa_circ_0000479, RIG-I, and IL-6 were increased in COVID-19 patients compared to healthy controls, while hsa-miR-149-5p expression was decreased. Moreover, there was a significant negative correlation between hsa-miR-149-5p and hsa_circ_0000479, RIG-I, IL-6 expressions, and also a positive expression correlation between hsa_circ_0000479 and IL-6, RIG-I. Then, bioinformatics tools revealed the role of hsa_circ_0000479/hsa-miR-149-5p/RIG-I, IL-6 axis in PI3K-AKT and STAT3 signaling pathways. Conclusion. Upregulation of hsa_circ_0000479, RIG-I, and IL-6, and downregulation of hsa-miR-149-5p, along with correlation studies, indicate that hsa_circ_0000479/hsa-miR-149-5p/RIG-I, IL-6 axis could play a role in regulating the immune response against SARS-CoV-2. However, more studies are needed in this area.
Characterization of the Newly Established Homoharringtonine- (HHT-) Resistant Cell Lines and Mechanisms of Resistance
Authors: Li Fenglin , Ling Qing , Hu Chao , Wang Huafeng , Ye Wenle , Li Xia , Zhang Xiang , Lin Xiangjie , Wei Wenwen , Huang Xin , Qian Yu , Zhuang Haihui , Jin Jie , Lu Ying
Published: August 2022

Homoharringtonine- (HHT-) based HHT, aclarubicin, and cytarabine (HAA) induction regimen is the first-line therapy for nonelder acute myeloid leukemia (AML) patients in China. However, drug resistance is a new challenge, and little attention has been devoted to excavating resistant mechanisms. This study used the classic method to construct six HHT-resistant cell lines with a gradually increasing resistance index (RI) to discover HHT drug resistance mechanisms dynamically. After HHT resistance, the cell growth rate decreased, cell cycle delayed, and P-glycoprotein (p-gp, CD243) expression levels increased. Furthermore, we explored the changes in transcriptomics between HHT-sensitive and HHT-resistant cells using RNA-sequence. Through Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and hub gene analyses, we found that immune activity, especially G-protein coupled receptor (GPR) and related molecules, may mediate HHT resistance. Moreover, Calcitonin Receptor-Like (CALCRL) and G Protein Subunit Alpha I1 (GNAI1), which belong to GPRs, were stimulated in HHT-resistant cell strains in vitro and vivo, indicating that they may play a critical role in HHT resistance. In addition, these two genes have prognostic significance for AML patients. Taken together, we successfully constructed HHT-resistant cell lines with dynamic RIs and explored the resistance mechanisms, which will help identify new drugs for HHT-resistant AML patients.
[Retracted] Regulation of Quality of Life and Immune Function in Patients with Thyroid Cancer Treated by Deep Learning Technology
Authors: Fu Xiandong , Yang Xinxin , Wang Yibo , Chi Nannan , Yu Jianan , Feng Yao
Published: August 2022

Background. In order to explore the regulation of quality of life and immune function in patients with thyroid cancer after radiotherapy, a method based on deep learning technology was proposed. A deep learning detection method for thyroid cancer is proposed.Methods. It mainly includes three main modules: data preprocessing, thyroid cancer regional detection module, and thyroid cancer benign and malignant classification module. The data set in the experiment comes from LIDC‐IDRI and is processed by the data preprocessing module to generate a standard data format that can be processed by the framework. The treatment of thyroid cancer can help patients relapse malignant thyroid cancer and prevent recurrence in advance.Results. The results showed that most patients are diagnosed because of obvious swelling of local thyroid mass and conscious compression symptoms in the neck. At this time, they often miss the best treatment time, so as to reduce the surgical effect.Conclusions. The metastasis and invasion of cancer cells are fast, the cancerous lesions are easy to form adhesion with the surrounding tracheal tissue, and the cancer cells invade the surrounding soft tissue, which is also easy to cause the cancerous tissue not to be completely removed.Clinical Trial Registration. Therefore, deep learning technology is used to treat residual cancerous lesions to ensure the surgical effect.
Application of a Realistic Evaluation Approach on Three Public Health Programs Aiming at Prevention of Obesity
Authors: Kolovou Vasiliki
Published: August 2022

Rich literature abounds concerning the clinical effectiveness of programs aiming to produce weight gain/obesity prevention outcomes. However, there is very little evidence on how these outcomes are produced, and what interplay of factors made those programs effective (or not) in the environment that produced those effects. This study aims to describe the application of realistic evaluation in the field of obesity prevention, as an approach to unravel those components that influence the capacity of a program to produce its effects and to examine its significance in an effort to understand those components. The concepts of critical realism have informed the development of an interview topic guide, while three European programs were selected as case studies after a rigorous selection process. In total, 26 in-depth semi-structured interviews were taken, paired with personal observation and secondary data research. Several grounded context-mechanisms-outcomes (CMO) configurations were described within the respective context of each location, with the mechanisms introduced from each project resulting in distinctive outcomes. This study highlights the potential of realistic evaluation as a comprehensive framework to explain in which contextual circumstances of each program’s effects are produced, how certain underlying mechanisms produce those effects, and how to explicitly connect the context and the acting mechanisms into distinct outcome patterns, which will ultimately form unique configuration sets for each of the analyzed projects.
Integration and Optimization of Multimedia Network-Assisted English Teaching Resources Based on Association Rule Algorithm
Authors: Hou Haibing , Zhou Shenghui
Published: August 2022

This paper proposes a paradigm of integration and optimization of English teaching resources based on the association rule algorithm and improves the Apriori algorithm by introducing interest measure and manual labeling through semisupervised learning of the neural network to improve the quality of English instruction assisted by the multimedia network. The efficiency of the method is higher than the original Apriori algorithm and the Apriori algorithm based on hash technology, according to experimental results. The new integration and optimization of the algorithm-based teaching model of English teaching resources also guide multimedia and network-assisted English teaching activities.
[Retracted] Analysis of Causes and Results of Fetal Growth in Utero Caused by Genetic Factors Detected by Ultrasound
Authors: Yu Mei , Liu Ying , Wang Liya
Published: August 2022

In order to investigate the value of the ultrasonic monitoring of maternal and fetal vascular parameters, serum vitamin D, and placental growth factor (PLGF) in predicting fetal growth restriction (FGR), a method of ultrasonic detection of genetic factors causing fetal growth in utero was proposed. 125 pregnant women with FGR diagnosed in our hospital from June 2018 to June 2021 (the FGR group) and 125 pregnant women with a normal prenatal examination (the control group) were collected retrospectively. The systolic/diastolic blood flow ratio (S/D), pulsatile index (PI), and resistance index (RI) of the fetal umbilical artery (UA), middle cerebral artery (MCA), and maternal uterine artery (UtA) were monitored by ultrasound at 20 to 24 weeks of gestation, and the levels of serum vitamin D and PLGF were detected. The receiver operating characteristic curve (ROC curve) was used to evaluate the predictive value of FGR. The results showed that the S/D, PI, and RI of UA in the FGR group were higher than those in the control group, the areas under the curve (AUC) were 0.866, 0.817, and 0.849, and the sensitivity and specificity were (72.8%, 91.2%), (50.4%, 100%), and (72.8%, 91.2%), respectively. The S/D, PI, and RI of MCA were lower than those of the control group. The AUC was 0.882, 0.869, and 0.834, respectively; the sensitivity and specificity were (92.0%, 74.4%), (88.8%, 81.6%), and (90.4%, 72%), respectively. The S/D, PI, and RI of UtA were higher than those of the control group; the AUC was 0.768, 0.729, and 0.732; the sensitivity and specificity were (91.2%, 52%), (48%, 90.4%), and (48.8%, 90.4%), respectively. The serum levels of vitamin D and PLGF were lower than those of the control group (AUC 0.784 and 0.807), and the sensitivity and specificity were (54.4%, 91.2%) and (99.2%, 52%), respectively. It was concluded that the ultrasound monitoring of UA, MCA, and UtA in pregnant women in the middle of pregnancy and detection of serum vitamin D and PLGF levels had a certain predictive value for FGR. Moreover, the comprehensive evaluation could reduce the occurrence of FGR in high‐risk pregnant women.
A Mixed Integer Programming Optimization of Blood Plasma Supply Chain in the Uncertainty Conditions during COVID‐19: A Real Case in Iran
Authors: Baghbani Bijan
Published: August 2022

Blood and its products, like plasma, are among the most sensitive products for the sake of transportation and storage. Special storage conditions, short shelf life, and lack of particular demand for blood products are among the most significant challenges to managing it. In this respect, it is necessary to implement the problem of supply chain network design in uncertain conditions to find a proper solution for the management of blood products. In this study, a multilevel supply chain is designed to supply plasma in the COVID‐19 pandemic. First, the blood is sent to blood donation centers and then to the laboratory. Moreover, after that, it is sent to hospitals. To optimize the transfer rate at each level of the supply chain, a mathematical model is proposed to reduce total costs. Also, the fuzzy programming approach is used to deal with uncertainty in the parameters of the mathematical model. The results of model optimization show that this mathematical model has the required efficiency in finding optimal solutions for the distribution of blood products. According to the obtained results, value objective function in certain and uncertain values is determined. According to the results, the objective certain value is lower than the uncertain value. Uncertain value calculated is of three dimensions. According to this, the categorized objective value increased when the dimension is equal to 0.5. Finally, it shows that when demand increases, more blood and plasma need to be collected to meet the demand, which increases operating and health testing costs and ultimately increases total system costs.
Lightweight Deep Learning Classification Model for Identifying Low-Resolution CT Images of Lung Cancer
Authors: Marappan Shanmugasundaram , Mujib Muhammad Danish , Siddiqui Adnan Ahmed , Aziz Abdul , Khan Samiullah , Singh Mahesh
Published: August 2022

With an astounding five million fatal cases every year, lung cancer is among the leading causes of mortality worldwide for both men and women. The diagnosis of lung illnesses can benefit from the information a computed tomography (CT) scan can offer. The major goals of this study are to diagnose lung cancer and its seriousness and to identify malignant lung nodules from the provided input lung picture. This paper applies unique deep learning techniques to identify the exact location of the malignant lung nodules. Using a DenseNet model, mixed ground glass is analyzed in low-dose, low-resolution CT scan images of nodules (mGGNs) with a slice thickness of 5 mm in this study. This was done to categorize and identify many histological subtypes of lung cancer. Low-resolution CT scans are used to pathologically classify invasive adenocarcinoma (IAC) and minimally invasive adenocarcinoma (MIA). 105 low-resolution CT images with 5 mm thick slices from 105 patients at Lishui Central Hospital were selected. To detect and distinguish, IAC and MIA, extend and enhance deep learning two- and three-dimensional DenseNet models are used. The two-dimensional DenseNet model was shown to perform much better than the three-dimensional DenseNet model in terms of classification accuracy (76.67%), sensitivity (63.3%), specificity (100%), and area under the receiver operating characteristic curve (0.88). Finding the histological subtypes of persons with lung cancer should aid doctors in making a more precise diagnosis, even if the image quality is not outstanding.
Travel Patterns Analysis Using Tensor-Based Model from Large-Scale License Plate Recognition Data
Authors: Liu Zhaoxin , Wang Xiaolu , Bi Yufeng , Kong Jun , Xu Run , Chen Yuanpei , Tang Jinjun
Published: August 2022

Travel patterns reflect the regularity of residents’ mobility, and it is a crucial factor to evaluate the reasonability of urban spatial structure and connectivity of road networks. Therefore, exploring travel patterns is of practical significance for urban planning, traffic management, and improvement of the operational efficiency of the transportation system. In this study, we apply the tensor model to explore travel patterns under temporal and spatial dimensions based on the license plate recognition (LPR) data collected from the Changsha city, China. As travel patterns are influenced by many variables, a method framework based on the tensor model is proposed to explore the influence of variables on travel characteristics. Firstly, we apply clustering algorithms and the principal component analysis method to extract main feature variables, which can achieve the purpose of dimensionality reduction and eliminate the complex collinearity among variables. Then, the tensor decomposition and reconstruction algorithms are performed based on extracted feature variables to analyze their influence on travel patterns. The experiments demonstrate the advantages of the proposed method framework.
The Theme Evolution and Strategic Decision-Making of the Public’s Attention to the Theme of Aging in China Based on Big Data Analysis
Authors: Lu Ning
Published: August 2022

Actively responding to population aging has become the new national condition of China’s aging society in the new era. To do a good job of actively coping with aging in the new era, it is necessary to take multiple measures and work together on the basis of clarifying the major realities and problems faced by China’s aging society. From the perspective of big data technology, this paper systematically studies the concerns of China’s aging through the method of artificial intelligence. In addition, with two typical aging indicators, three coupled intelligent algorithms are introduced in this paper. The prediction results show that the multi-dimensional support vector machine optimized by the immune algorithm shows good advantages compared with the other two algorithms. The multi-dimensional support vector machine optimized by the immune algorithm, one of the big data, has a good prediction effect on the performance indicators of China’s aging, which indirectly illustrates the applicability of big data analysis to the concerns of China’s aging.
The Differences in Clinical Characteristic and Outcomes of New Onset Typical versus Atypical Right Branch Bundle Block in Acute Myocardial Infarction
Authors: Li Jingchao , Cui Luqian , Ma Lingkun , Yu Haijia , Song Huihui , Dong Shujuan , Chu Yingjie
Published: August 2022

Objective. The purpose of this study is to explore the clinical characteristics and estimate the new‐onset atypical right branch bundle block (ATRBBB) predictive value in short‐term and long‐term mortality by comparing the typical right branch bundle block (TRBBB) subset in acute myocardial infarction (AMI) patients. Methods. A total of 224 AMI patients combined with new onset RBBB who received primary coronary angiography were included, being admitted to Henan Provincial People’s Hospital in China from July 2010 to June 2021. Patients were divided into typical RBBB group (n = 104) and atypical RBBB group (n = 120). The differences in clinical characteristics between the two groups were analyzed. Logistic and Cox regression analysis were performed to identify independent predictors of in‐hospital Major Adverse Cardiovascular Events (MACE). Result. The ATRBBB group had a higher proportion of smoking and alcohol consumption, higher body mass index, worse cardiac function (killip ≧ II proportion), higher peak value of CK‐MB, lower LVEF%, longer total ischemia time, higher proportion of LAD (left anterior descending coronary artery) occlusion, and multivessel lesions, compared to the TRBBB group. The ATRBBB group had a higher proportion of in‐hospital MACE and 1‐year all‐cause mortality compared to the TRBBB group. ATRBBB was an independent predictor of in‐hospital MACE and 1‐year mortality in patients with AMI combined with new onset RBBB. Conclusions. ATRBBB group had more serious clinical symptoms and clinical prognosis. New ATRBBB is an independent predictor of in‐hospital MACE and 1‐year death in patients with AMI combined with RBBB. If the infarct‐related vessel was opened immediately, the evolution of TRBBB to ATRBBB may be avoided, leading to a better prognosis.