bluuebunny/crossref_metadata_2025 huggingface dataset:
Codimension-2 bifurcation in a discrete predator–prey system with constant yield predator harvesting
Authors: Singh Anuraj , Sharma Vijay Shankar
Published: August 2022

This work investigates the bifurcation analysis in a discrete-time Leslie–Gower predator–prey model with constant yield predator harvesting. The stability analysis for the fixed points of the discretized model is shown briefly. In this study, the model undergoes codimension-1 bifurcation such as fold bifurcation (limit point), flip bifurcation (period-doubling) and Neimark–Sacker bifurcation at a positive fixed point. Further, the model exhibits codimension-2 bifurcations, including Bogdanov–Takens bifurcation and generalized flip bifurcation at the fixed point. For each bifurcation, by using the critical normal form coefficient method, various critical states are calculated. To validate our analytical findings, the bifurcation curves of fixed points are drawn by using MATCONTM. The system exhibits interesting rich dynamics including limit cycles and chaos. Moreover, it has been shown that the predator harvesting may control the chaos in the system.
From Extreme Weather Events to ‘Cascading Vulnerabilities’: Participatory Flood Research Methodologies in Brazil During COVID-19
Authors: Calvillo Nerea , Garde-Hansen Joanne , Lima-Silva Fernanda , Trajber Rachel , Albuquerque João Porto de
Published: August 2022

Extreme weather events are entangled with each other and with other extreme events, such as the COVID-19 pandemic, anti-racist protests, drought, a housing crisis, strikes, or climate emergencies, as well as with more general inadequacies due to national, economic, and political upheavals and accreted vulnerabilities from long-term policies or inactions. Effects of extreme weather events are intensified by ongoing social injustices like poverty and structural racism, a housing deficit, and the consequent informal and unplanned occupation of hazardous areas, such as riverbanks, and areas of previous social-environmental disasters. In the context of Brazil, the ongoing deforestation in the Amazon (agribusiness, mining and illegal wood) provoking droughts and energy shortages in the region creates further vulnerabilities that are felt globally. In this paper, our primary contribution to these inter-connected scenarios is to describe methodological interventions that were made in response to COVID-19, and to show how those changes provided new insights into vulnerability processes of both subjects and researchers. During a larger project (Waterproofing Data), focused on the case study research areas of São Paulo and Acre (Brazil) wherein our wider team conducted flood-risk community research, we were forced to rethink our approach. We moved away from the singularity of the flood event and its impacts toward acknowledging the cascading conditions of social vulnerability (caused by weather, health, social and political conditions). In this paper, we directly address the ‘cascade of vulnerabilities’ that the flood-prone communities already encounter when researchers seek to engage with them. We open new avenues to reconsider citizenship, space, and innovation in terms of the key challenges that our methods encountered when conducting participatory flood research methodologies, particularly during the first phase of the COVID-19 pandemic from March 2020 to November 2021. Through flood research in Brazil, we articulate methodological contributions from the arts, humanities, and social sciences for more realistic, just, and caring research practices within and about weather in the context of ‘slow violence’ [Nixon, R (2013). Slow Violence and the Environmentalism of the Poor. Cambridge, MA: Harvard UP].
Character-based Joint Word Segmentation and Part-of-Speech Tagging for Tibetan Based on Deep Learning
Authors: Li Yan , Li Xiaomin , Wang Yiru , Lv Hui , Li Fenfang , Duo La
Published: August 2022

Tibetan word segmentation and POS tagging are the primary tasks of Tibetan natural language processing. Most of existing methods of Tibetan word segmentation and POS tagging are based on rules and statistics, which need manual construction of features. In addition, the joint mode has shown stronger capabilities for word segmentation and POS tagging and have received great interests. In this paper, we propose Bi-LSTM+IDCNN+CRF structures, a simple yet effective end-to-end neural network model, for joint Tibetan word segmentation and POS tagging. We conduct step-by-step and joint experiments on the Tibetan datasets. The results demonstrate that the performance of the Bi-LSTM+IDCNN+CRF model is the best regardless of the step-by-step or joint mode. We obtain state-of-the-art performance in the joint tagging mode. The F1 score of the word segmentation task reached 92.31%, and the F1 score of the POS tagging task reached 81.26%.
On the Grounds of Solutionism: Ontologies of Blackness and HCI
Authors: Cunningham Jay , Benabdallah Gabrielle , Rosner Daniela , Taylor Alex
Published: August 2022

Why is the solution the end point to a problem? While many in HCI and design have examined the impulse to solve problems–the solutionist or techno-solutionist mindset–we examine the logic that binds the solution and the problem together as a pair. Focusing on the timely and consequential problem of systemic racial injustice, we think through the paradoxical possibility that the pairing of the problem and solution (so often treated as the default in design and HCI) perpetuates the very conditions we seek to improve. With Calvin Warren’s profound Afro-pessimism, we recognize how the tools used to solve structural inequities around Black life are constructed with inequities themselves. The problem-solution, therefore, is a dead end. We use this paradox as an invitation to rethink ongoing efforts to seek equity and justice more broadly, setting out a fragile but hopeful path for HCI and design.
An Experimental Investigation of Text-based CAPTCHA Attacks and Their Robustness
Authors: Wang Ping , Gao Haichang , Guo Xiaoyan , Xiao Chenxuan , Qi Fuqi , Yan Zheng
Published: August 2022

Text-based CAPTCHA has become one of the most popular methods for preventing bot attacks. With the rapid development of deep learning techniques, many new methods to break text-based CAPTCHAs have been developed in recent years. However, a holistic and uniform investigation and comparison of these attacks’ effects is lacking due to inconsistent choices of model structures, training datasets, and evaluation metrics. In this article, we perform an experimental investigation on the effects of existing attacks on text-based CAPTCHA schemes. We first summarize existing text-based CAPTCHAs using a newly proposed taxonomy based on their resistance mechanisms and systematically review corresponding attacks in terms of methods and pros/cons. Then, we introduce a unified attack framework that contains a number of different attack modules and transfer learning strategies. Applying this framework, we extensively evaluate the performance of known attacks on 20 CAPTCHA schemes in terms of accuracy and efficiency; then, we investigate the robustness of these widely used schemes and discover the effects of previously unexplored attacks. Finally, we discuss future CAPTCHA designs based on our experimental results and findings. Our work also contributes to the CAPTCHA community by offering an open-access dataset that contains 22 different CAPTCHA sample sets.
Time Series Compression Survey
Authors: Chiarot Giacomo , Silvestri Claudio
Published: August 2022

Smart objects are increasingly widespread and their ecosystem, also known as the Internet of Things (IoT), is relevant in many application scenarios. The huge amount of temporally annotated data produced by these smart devices demands efficient techniques for the transfer and storage of time series data. Compression techniques play an important role toward this goal and, even though standard compression methods could be used with some benefit, there exist several ones that specifically address the case of time series by exploiting their peculiarities to achieve more effective compression and more accurate decompression in the case of lossy compression techniques. This article provides a state-of-the-art survey of the principal time series compression techniques, proposing a taxonomy to classify them considering their overall approach and their characteristics. Furthermore, we analyze the performances of the selected algorithms by discussing and comparing the experimental results that were provided in the original articles.

The goal of this article is to provide a comprehensive and homogeneous reconstruction of the state-of-the-art, which is currently fragmented across many articles that use different notations and where the proposed methods are not organized according to a classification.
Polynomial-time trace reconstruction in the smoothed complexity model
Authors: Chen Xi , De Anindya , Lee Chin Ho , Servedio Rocco A. , Sinha Sandip
Published: August 2022

In the
trace reconstruction problem
, an unknown source string
x
∈ {0, 1}

n

is sent through a probabilistic
deletion channel
which independently deletes each bit with probability
δ
and concatenates the surviving bits, yielding a
trace
of
x
. The problem is to reconstruct
x
given independent traces. This problem has received much attention in recent years both in the worst-case setting where
x
may be an arbitrary string in {0, 1}

n

[DOS19, NP17, HHP18, HL20, Cha21a, Cha21b] and in the average-case setting where
x
is drawn uniformly at random from {0, 1}

n

[PZ17, HPP18, HL20, Cha21a, Cha21b].

This paper studies trace reconstruction in the
smoothed analysis
setting, in which a “worst-case” string
x
worst
is chosen arbitrarily from {0, 1}

n

, and then a perturbed version x of
x
worst
is formed by independently replacing each coordinate by a uniform random bit with probability
σ
. The problem is to reconstruct x given independent traces from it.

Our main result is an algorithm which, for any constant perturbation rate 0 <
σ
< 1 and any constant deletion rate 0 <
δ
< 1, uses poly(
n
) running time and traces and succeeds with high probability in reconstructing the string x. This stands in contrast with the worst-case version of the problem, for which

\(\text{exp}(\tilde{O}(n^{1/5})) \)

is the best known time and sample complexity [Cha21b].

Our approach is based on reconstructing x from the multiset of its short subwords and is quite different from previous algorithms for either the worst-case or average-case versions of the problem. The heart of our work is a new poly(
n
)-time procedure for reconstructing the multiset of all
O
(log 
n
)-length subwords of any source string
x
∈ {0, 1}

n

given access to traces of
x
.
Towards Effective Telerehabilitation: Assessing Effects of Applying Augmented Reality in Remote Rehabilitation of Patients Suffering from Multiple Sclerosis
Authors: Pruszyńska Magdalena , Milewska-Jędrzejczak Marta , Bednarski Igor , Szpakowski Piotr , Głąbiński Andrzej , Tadeja Sławomir Konrad
Published: August 2022

Multiple Sclerosis (MS)
is a chronic, incurable disease of the central nervous system that is also one of the most common causes of disability among young adults. Despite available pharmacological treatments, the patients often require ongoing, supervised rehabilitation. Thus, therapists are constantly searching for new, effective ways of improving functional performance and quality of life without frequently visiting medical centers. One of the most promising methods is remote telerehabilitation enhanced with an immersive
augmented reality (AR)
interface. Here, we investigated the effectiveness of using a commercially available AR system in MS patients’ treatment. To evaluate such an approach to rehabilitation, we conducted a medical study with 30 MS patients undergoing immunomodulatory treatment. In this study, we evaluated the influence on the patients’ upper limbs’ hand grip strength and efficiency of the patients’ upper limbs. In addition, we also analyzed the level of neurotrophins to assess the potential impact of the training on the brain plasticity process. Our results show that rehabilitation enhanced with AR significantly improves the strength and efficiency of the patients’ upper limbs. Furthermore, we further infer that AR-enhanced systems are a promising possibility of training without leaving home.
Technical report column
Authors: Kelley Dean
Published: August 2022

Welcome to the Technical Reports Column. If your institution publishes technical reports that you'd like to have included here, please contact me at the email address above.
Optimal Round-Robin Routing to Parallel Servers in Heavy Traffic
Authors: Ye Heng-Qing
Published: August 2022

We study a system with heterogeneous parallel servers. Upon arrival, a job is routed to the queue of one of the servers. We establish the diffusion limit for the round-robin (RR) policy, and show that with properly chosen parameters, it achieves the optimal performance asymptotically over all blind routing policies. Analysis of the diffusion limit yields a number of insights into the performance of the optimal RR policies.
Scheduling under redundancy
Authors: Anton E. , Righter R. , Verloop I.M.
Published: August 2022

In the present extended abstract we investigate the impact that the scheduling policy has on the performance of redundancy systems when the usual exponentially distributed i.i.d. copies assumption is relaxed. In particular, we investigate the performance, in terms of the total number of jobs in the system, not only for redundancy-oblivious policies, such as FCFS (First-Come-First-Serve) and ROS (Random- Order-of-Service), but also for redundancy-aware policies of the form Π1-?2, where Π1 discriminates among job classes and Π2 discriminates among jobs of the same class. Examples of first-level policies are LRF (Least-Redundant-First) and MRF (Most-Redundant-First), where under LRF, respectively MRF, within a server jobs with fewer copies, respectively more copies, have priority over jobs with more copies, respectively fewer copies. Second-level policies could be FCFS or ROS.
Online Selection with Convex Costs
Authors: Tan Xiaoqi , Yu Siyuan , Boutaba Raouf
Published: August 2022

We study a novel online optimization problem, termed online selection with convex costs (OSCC). In OSCC, there is a sequence of items, each with a value that remains unknown before its arrival. At each step when there is a new arrival, we need to make an irrevocable decision in terms of whether to accept this item and take its value, or to reject it. The crux of OSCC is that we must pay for an increasing and convex cost associated with the number of items accepted, namely, it is increasingly more difficult to accommodate additional items. The goal is to develop an online algorithm that accepts/ selects a subset of items, without any prior statistical knowledge of future arrival information, to maximize the social surplus, namely, the sum of the accepted items' values minus the total cost. Our main result is the development of a threshold policy that is logistically-simple and easy to implement, but has provable optimality guarantees among all deterministic online algorithms.
Special Issue on The Workshop on Performance of host-based Network Applications (PerfNA 2022)
Authors: Govindan Kannan Pravein , Naik Priyanka , Tammana Praveen , Vutukuru Mythili
Published: August 2022

With the advancement of highly network-powered paradigms like 5G, Microservices, etc. which are typically deployed as containers/VMs, there is a growing imperative on the host nodes to perform specialized network tasks like monitoring, filtering, tunneling, load-balancing, etc. While traditionally, these tasks were performed using switches and specialized middleboxes in the network, there is a demand to perform these network tasks on commodity hardware comprising of COTS servers. However, a major challenge is to perform these tasks at low-overhead and high reliability while maintaining low latency, high throughput, and flexibility.
Neurophysiological mechanisms of implicit and explicit memory in the process of consciousness
Authors: Tucker Don M. , Luu Phan , Johnson Mark
Published: August 2022

The process of consciousness, generating the qualia that may appear to be irreducible qualities of experience, can be understood to arise from neurophysiological mechanisms of memory. Implicit memory, organized by the lemnothalamic brain stem projections and dorsal limbic consolidation in REM sleep, supports the unconscious field and the quasi-conscious fringe of current awareness. Explicit memory, organized by the collothalamic midbrain projections and ventral limbic consolidation of NREM sleep, supports the focal objects of consciousness.
Solutions of Nonlinear Integro-Partial Differential Equations by the Method of

G

′

/
G
,
1
/
G
Authors: Gusu Daba Meshesha , Bulo Chala
Published: August 2022

In this article, a special expansion method is implemented in solving nonlinear integro-partial differential equations of

2
+
1

-dimensional using a special expansion method of

G

′

/
G
,
1
/
G

. We obtained the solutions for

2
+
1

-dimensional nonlinear integro-differential equations in real physical phenomena. The method is applied on

2
+
1

-dimensional space time and solved in three different cases: hyperbolic, trigonometric, and rational functions. The obtained solutions for each result were illustrated by graphical plots using Wolfram Mathematica 9.0 software packages. Furthermore, the obtained results are exactly fit with exact solutions which solves the complicity of finding the solution for nonlinear integro-partial differential equations. Finally, the method is powerful and effective to solve partial differential equations of nonlinear integro form.
Effectiveness of Super Floss and Water Flosser in Plaque Removal for Patients Undergoing Orthodontic Treatment: A Randomized Controlled Trial
Authors: Sawan Nozha , Ben Gassem Afnan , Alkhayyal Faisal , Albakri Aroob , Al-Muhareb Nada , Alsagob Eman
Published: August 2022

Objectives. This study aimed to compare the effectiveness of super floss and water flosser in plaque removal for patients undergoing orthodontic treatment. Methods. A single-blind, randomized, controlled, parallel clinical trial with a split-mouth protocol was conducted on young adult orthodontic patients who were recruited from Riyadh Specialized Dental Center in Riyadh, Saudi Arabia. The type of floss used was randomly assigned to each side of the oral cavity; Super-Floss® (Oral-B) was used on one side, while the Waterpik® water flosser was used on the other. Patients’ plaque level was assessed using Rustogi et al. modified navy plaque index (RMNPI) at baseline and immediately after cleaning. Results. A total of 62 subjects were screened; however, only 34 subjects were enrolled in the study with an equal number of males and females. Overall, the plaque score was significantly reduced from 0.56 ± 0.35 to 0.13 ± 0.26 in the super floss group and from 0.61 ± 0.35 to 0.13 ± 0.28 in the water flosser group. There was no significant difference between the mean difference of super floss and water flosser (

p
=
0.951

). On the other hand, there was no significant difference between both groups in terms of the preintervention plaque score (

p
=
0.379

). The water flosser had a greater effect size on plaque removal compared to super floss on distal interproximal surface of the molar tooth with a mean difference of (−0.21, 95% CI: 00.37 to −0.04,

p
=
0.033

). Conclusions. The use of super floss or water flosser as interproximal aids for plaque removal in patients undergoing orthodontic treatment are both effective. Trial registration. ISRCTN, ISRCTN83875016. Registered 12 September 2021-retrospectively registered, https://www.isrctn.com/ISRCTN83875016.
Research on Emotion Recognition and Mental State Prediction of College Students Based on Deep Neural Network
Authors: Qi Weiyan , Li Yang
Published: August 2022

Since entering the new century, people’s living standards have steadily improved, and their living conditions have also continuously improved. The improvement in living standards has brought greater pressure to people. The recognition of emotion and psychological state has become one of the research hotspots. Because of excessive work pressure, many students now suffer from depression. In this paper, we use a large number of research data and comparative charts to facilitate the analysis of the collected data. At the same time, we use LDA topic classification and NLDA neural network algorithm for refining and concise data processing. The research shows that students’ psychological state will affect their academic achievements. Students should try their best to manage their mental state, learn to know their emotional state, and ensure a good mental state. Emotional leadership is very important for students’ physical and mental health. Emotional leadership is a very important task for students and schools. Schools and students should actively cooperate to improve ’students’ ability to manage emotions, especially their ability to self-regulate emotions, and to jointly help build a harmonious campus. Because of the contradiction between the complexity of interpersonal relationship and society and the simplicity of students, students are often frustrated in interpersonal communication. The biggest reason for students’ psychological state is that they have no courage to express themselves in public, cannot communicate with others, and are interested in and concerned about the failure of various activities.
Construction on Evaluation Index System of Traceability System of Agricultural Internet of Things Based on Network Analysis
Authors: Xie Hui , Zhang Kexin
Published: August 2022

In order to ensure the transparency and openness of each link of agricultural products “from farmland to table” and to solve the problem of consumers’ low trust degree in the safety of agricultural products and food, the application and evaluation of agricultural Internet of Things traceability system are the top priority. However, the traceability system evaluation indicators are rarely discussed in the existing studies. Consequently, this paper attempts to construct the evaluation index system of the Internet of Things traceability system of agricultural products based on an overall perspective of ecological civilization and to determine the weight of each evaluation index by using analytic hierarchy process (AHP). Then, on the basis of the relevant data of the agricultural product traceability system in recent years, the determined weight is modified by using entropy method. Finally, it points out the important practical value for management departments, production subjects, sales subjects, and consumers, providing reference for improving the quality and safety of agricultural products and standardizing the market supervision of agricultural product market.
Structural Evaluation of the Effect of Bone Quality on the Mechanical Interaction between Dental Implants and Bone
Authors: Karami Nazanin , Razeghi Mehdi
Published: August 2022

When one or more teeth are lost for any reason such as accident, caries, and gum disease, they should be replaced with dentures. Researchers discovered that titanium successfully bonds to bone because of the development of osteoblasts on its rough surface. However, the success of dental implants depends on biological, mechanical, and chemical factors. Each of these factors is itself a function of different parameters that, any changes in these factors affect the mechanism of transfering from the prosthesis to the implant and from implant to bone. The role determinant in the success of implant therapy is the function of the geometry and mechanical properties of the implant, jaw and bone-implant joint. Now, due to the increasing use of dental implants, various companies are constantly launching their new products and, as a result, express many benefits of these products, which in most cases are based on their claims. It is not a long-term study, and therefore it is not possible to make an accurate judgment on the designs, macroscopic and microscopic properties, and benefits offered, due to the lack of accurate and practical comparisons between systems. It will not be possible to prove the effectiveness of each of them. Therefore, understanding the design method and its philosophy, along with familiarity with the types of systems available in the market, is a determining factor in the clinical success of dental implant treatment. The presence of threads prevents overload on the surface of the cortical bone and reduces this load by up to 36%. Reducing the thread pitch contributes to the stability of the implant, and increasing the depth of the thread stabilizes patients with poor bone quality. The behavior of the bone and its relation to the implant with regard to applied loads with a maximum inclination angle of 15° have been evaluated by evaluating the tension in the bony areas of the cortex and trabecular bone.
Tridax procumbens Ameliorates Streptozotocin-Induced Diabetic Neuropathy in Rats via Modulating Angiogenic, Inflammatory, and Oxidative Pathways
Authors: Kakkar Munish , Behl Tapan , Cruz Celia Vargas-De-La , Makeen Hafiz A. , Albratty Mohammed , Alhazmi Hassan A. , Meraya Abdulkarim M. , Albadrani Ghadeer M. , Abdel-Daim Mohamed M.
Published: August 2022

Tridax procumbens (TP) is a traditional Indian therapeutic plant and was evaluated for its blood glucose lowering abilities, as well as for its ability to curb diabetic neuropathy (DN). Administrating 45 mg/kg body weight of streptozotocin (STZ) intraperitoneally for four weeks, DN was induced in Wistar rats. After the rats’ tails were clipped, the blood glucose levels were measured. Body weight and urine volume were also assessed. Oxidative stress makers such as superoxide dismutase (SOD), thiobarbituric acid reactive substances (TBARS), catalase (CAT), inflammatory cytokines for instance tumor necrosis factor (TNF)-α, and interleukin (IL)-1β were estimated. Further, protein kinase C (PKC-β) and vascular endothelial growth factor (VEGF) were also estimated as angiogenic markers. Behavioral parameters were also evaluated by using cold allodynia using acetone test, hot allodynia using Eddy’s hot plate, grip strength test using Rota rod, and hyperalgesia test using Tail flick technique. The statistical assessment of findings was done employing one-way (ANOVA) analysis of variance, and subsequently Turkey as post hoc with GraphPad Prism software package. The ingestion of TP for 1 month in DN rats stemmed in a substantial decline in blood glucose concentrations matched to nontreated rats with DN. There had been a considerable improvement in DN as evident from the finding from biochemical markers. The serum level of antioxidant defense enzymes was significantly increased, while the activities of TBARS had been substantially reduced in the TP treated rats with DN. TP averted DN-triggered surge levels of TNF-α and IL-6 in the serum. Further, PKC-β and VEGF concentrations had been also reduced by the treatment TP. The findings of this research demonstrated that the restorative impact of TP on DN rats might be linked to the anti-inflammatory and antioxidative antiangiogenic retorts.
Significance of lncRNA CDKN2B-AS1 in Interventional Therapy of Liver Cancer and the Mechanism under Its Participation in Tumour Cell Growth via miR-199a-5p
Authors: Xu Lu , Wu Haotian , Pan Jianhua , Chen Zhiheng , Du Linan
Published: August 2022

Background and Objective. This research probed the function of lncRNA CDKN2B-AS1 (CDKN2B-AS1) in interventional therapy of liver cancer (LC) and its association with miR-199a-5p, to offer more comprehensive and reliable references for future diagnosis and therapy. Methods. Totally 34 LC patients admitted to our hospital between January 2020 and March 2021 (Obs group) and 32 healthy individuals over the same time span (Con group) were enrolled. CDKN2B-AS1 and miR-199a-5p in the two groups were PCR quantified, and their association and value for the diagnosis and therapy of LC were analyzed. In addition, purchased LC cells were adopted for in vitro assays, and the influences of CDKN2B-AS1 and miR-199a-5p on biological behaviours of LC cells were assessed through CCK-8, Transwell, and flow cytometry experiment, and their regulatory association was verified by the dual luciferase reporter (DLR) assay and rescue assay. And the autophagic protein expression was tested by the western blot to confirm the effect of both on the autophagic capacity of LC cells. Results. CDKN2B-AS1 in LC cases presented high expression and dropped after therapy (

P
<
0.05

), and the opposite situation of miR-199a-5p was found in the LC cases (

P
<
0.05

). In vitro assays, after silencing of CDKN2B-AS1 and upregulation of miR-199a-5p, LC cells presented weaker viability, invasion and migration activities, and stronger apoptotic activity (all

P
<
0.05

). The DLR assay revealed suppressed fluorescence activity of CDKN2B-AS1-WT by miR-199a-5p (

P
<
0.05

). Moreover, according to the rescue assay, the impacts of silencing CDKN2B-AS1 on LC cells could be completely offset by silencing miR-199a-5p (

P
<
0.05

). According to the clone formation and WB assay, the growth and autophagy of LC cells were under the regulation of CDKN2B-AS1 targeting miR-199a-5p (

P
<
0.05

). Conclusion. With high expression in LC cases, CDKN2B-AS1 is implicated in the development and progression of LC by suppressing cell autophagy through targeting miR-199a-5p.
Integrated Analysis of the lncRNA-Associated ceRNA Network in Wilms Tumor via TARGET and GEO Databases
Authors: An Biao , Hu Yuan , Liang Xiao
Published: August 2022

Wilms tumor (WT) is the most common genitourinary renal tumor that typically occurs in children under 15 and is thought to be linked to somatic and germline mutations. However, the specific functional role of competing endogenous RNAs (ceRNAs) and their potential implications in WT remain unclear. In this study, we developed an lncRNA-mediated (long noncoding RNA-mediated) ceRNA network via the R packages for WT with expression data obtained from the tumor alterations relevant for genomics-driven therapy (TARGET) database. Unsupervised hierarchical clustering analysis revealed that the WT specimens could be clearly distinguished from healthy specimens with respect to the expression of disordered RNAs. A total of 1,607 differentially expressed (DE) lncRNAs, 116 DE microRNAs (DEmiRNAs), and 3,262 DE messenger RNAs (DEmRNAs) were identified as WT-specific RNAs, and a lncRNA-miRNA-mRNA ceRNA network with 159 DElncRNAs, 18 DEmiRNAs, 131 DEmRNAs, and 792 interactions was constructed. According to the clinical survival data, 12 DElncRNAs, 5 DEmRNAs, and 2 DEmiRNAs were selected from the ceRNA network that could significantly impact the overall survival of WT patients (

P
<
0.05

). Functional enrichment analysis showed that the biological processes and pathways of DEmRNAs, such as cell cycle and virus infection, may be associated with WT. The present study constructed a dysregulated lncRNA-mediated ceRNA network in WT and discovered that lncRNA-mediated ceRNAs may serve as important regulators in WT development and progression. Survival-associated RNAs may serve as new potential biomarkers, suggesting that the constructed ceRNA network in WT might be important for determining optimal therapeutic strategies.
Nutrition-Related Mobile Application for Daily Dietary Self-Monitoring
Authors: Ulfa Maria , Setyonugroho Winny , Lestari Tri , Widiasih Esti , Nguyen Quoc Anh
Published: August 2022

Nutrition apps for mobile devices such as smartphones are becoming more widely available. They can help ease the arduous chore of documenting intake for nutritional assessment and self-monitoring. This allows people to control food intake, support their participation in physical activities, and promote a healthy lifestyle. However, there remains a lack of research regarding systematic analysis mapping studies in this area. The objective of this study is to identify dietary self-monitoring implementation strategies on a mobile application. This study analyzed 205 journals from the Scopus database using the descriptive-analytic method. The records used in this exploration study were those released between 2007 and 2021 that were collected based on the keywords “dietary self-monitoring,” or “nutrition application,” or “nutrition apps,” and “calorie application.” Data analysis was conducted using the VOSviewer and NVivo software analytical tools. The results show that research studies on dietary self-monitoring increased in 2017. Results also indicated that the country that contributed the most to this topic was China. The study on mobile applications for dietary self-monitoring revealed seven clusters of dominant themes: attitude to improved dietary behaviors, parameters for disease diagnosis, noncommunicable diseases, methods, nutrition algorithms, mobile health applications, and body mass index. This study also analyzed research trends by year. The current research trends are about dietary self-monitoring using a mobile application that can upgrade people’s lifestyles, enable real-time meal recording and the convenience of automatically calculating the calorie content of foods consumed, and potentially improve the delivery of health behavior modification interventions to large groups of people. The researchers summarized the recent advances in dietary self-monitoring research to shed light on their research frontier, trends, and hot topics through bibliometric analysis and network visualization. These findings may provide valuable guidance for future research and perspectives in this rapidly developing field.
Risk Factors of Benign Stricture of Anastomotic Stoma after Esophagectomy and Therapeutic Effect of Stent Implantation
Authors: Wu Guoliang , Niu Lihua , Yang Yanlin , Tian Shaoyong , Liu Yanru , Wang Chunyan , Zhao Pengfei
Published: August 2022

With the increase in the number of patients and prolongation of their lives after esophagectomy for esophageal cancer, the quality of life after surgery has attracted more and more attention. Although anastomotic stenosis is a common complication, it seriously affects the quality of life and psychological state of patients or even threatens their lives. At present, the exact independent influencing factors of anastomotic stenosis after esophageal cancer surgery have not been determined, and relevant treatment options are still controversial. Here, we analyzed the independent risk factors leading to good postoperative anastomotic stenosis, in order to provide a basis for late prevention. At the same time, we deeply discussed the advantages and safety of stent implantation in the treatment of anastomotic stenosis.
Significance of Immune-Related Genes in the Diagnosis and Classification of Intervertebral Disc Degeneration
Authors: Wu Bo , Huang Xinzhou , Zhang Mu , Chu Wei
Published: August 2022

Background. With the extensive development of intervertebral disc degeneration (IDD) research, IDD has been found to be a complex disease associated with immune-related gene (IRGs) changes. Nonetheless, the roles of IRGs in IDD are unclear. Methods. In our study, 11 IRGs were chosen using differential analysis between nondisc degeneration and degenerative patients from the GEO database. Then, we utilized a random forest (RF) model to screen six candidate IRGs to predict the risk of IDD. A nomogram was developed on the basis of six candidate IRGs, and DCA showed that patients could benefit from the nomogram. Based on the selected significant IRGs, a consensus clustering approach was used to differentiate disc degeneration patients into two immune patterns (immune cluster A and B). The PCA algorithm was constructed to compute immune scores for every sample, to quantify immune patterns. The immune scores of immune cluster B patients were higher than those of immune cluster A. Results. Through differential expression analysis between healthy and IDD samples, 11 significant IRGs (CTSS, S100Z, STAT3, KLRK1, FPR1, C5AR2, RLN1, IFGR2, IL2RB, IL17RA, and IL6R) were recognized through significant IRGs. The “Reverse Cumulative Distribution of Residual” and “Boxplots of Residual” indicate that the RF model has minimal residuals. The majority of samples in the model have relatively small residuals, demonstrating that the model is better. Besides, the nomogram model was constructed based on importance and the IRGs with importance scores greater than 2 (FPR1, RLN1, S100Z, IFNGR2, KLRK1, and CTSS). The nomogram model revealed that decision-making based on an established model might be beneficial for IDD patients, and the predictive power of the nomogram model was significant. In addition, we identified two different immune cluster patterns (immune cluster A and immune cluster B) based on the 11 IRGs. We found that immune cluster A had significantly higher levels of MDSC, neutrophil, plasmacytoid dendritic cell, and type 17 T helper cell expression than immune cluster B. And we calculated the score for each sample to quantify the gene patterns. The patients in immune cluster B or gene cluster B had higher immune scores than those in immune cluster A or gene cluster A. Conclusion. In conclusion, IRGs play an extremely significant role in the occurrence of IDD. Our study of immune patterns may guide the strategies of prevention and treatment for IDD in the future.
Production of Mycoblock from the Mycelium of the Fungus Pleurotus ostreatus for Use as Sustainable Construction Materials
Authors: Alemu Digafe , Tafesse Mesfin , Gudetta Deressa Yohannes
Published: August 2022

As the global population rises, resource depletion and environmental pollution also aggravate. To meet the needs of the population, different products have been manufactured. However, most industrially manufactured products are not eco-friendly, costly, and locally unavailable. To solve these problems, using and enhancing locally available biomaterials are the key option. Three substrates sawdust, bagasse, and coffee husk and the fungus Pleurotus ostreatus were used. Mycelium was fully colonized by 9, 14, and 27 days on potato dextrose agar (PDA), sorghum grain, and substrate, respectively. The mycelium growth on coffee husk showed the fastest growth rate whereas that of the sawdust was slowest. The fully colonized substrates were molded for 7, 14, and 21 days by plastic mold to maintain their regular 3D structure. The result shows that the block made with sawdust at 21 molding period has higher compressive strength and density of 750 kPa and 343.44 Kg/m3, respectively, followed by bagasse and coffee husk. These variations were due to the mycelium density difference between the substrates. Physicochemical and mechanical characteristics such as mycelium morphology, bimolecular and elemental analysis of substrates, density, water absorption, and compressive strength of the block were analyzed. This technology has the potential to replace conventional construction and packaging materials used for indoor applications such as insulation, partition walls, and other design and architectural applications. It also benefits in terms of its low cost, green synthesis approach, nontoxicity, low environmental emission, recyclability, and local availability.
Macroeconomic Forecasting Based on Mixed Frequency Vector Autoregression and Neural Network Models
Authors: Cheng Fangze , Fu Zitian
Published: August 2022

Macroeconomic indicators include gross domestic product (GDP), consumer price index (CPI), and retail price index (RPI). These indicators are important for understanding the macroeconomic situation and controlling the macroeconomic trend, as they provide a macroscopic view of a country or region’s economic performance. If macroeconomic indicators can be predicted accurately in advance, the government and relevant macroeconomic control departments can propose more forward-looking and targeted macroeconomic control policies and deploy the necessary control measures. In addition, individuals can make more reasonable decisions on their investments and savings if they know the macroeconomic indexes in advance. From these two aspects, prediction of macroeconomic indexes is of great research significance. In this paper, we propose a prediction model based on chaotic vector autoregression and neural networks for macroeconomic forecasting, and we model and test the prediction with GDP and inflation as the main concerns, and we find that the improvement of GDP forecasting shows an increase of expected inflation rate, indicating the usefulness of using expected GDP data.
Exploring the Paradox of COVID-19 in Neurological Complications with Emphasis on Parkinson’s and Alzheimer’s Disease
Authors: Rai Sachchida Nand , Tiwari Neeraj , Singh Payal , Singh Anurag Kumar , Mishra Divya , Imran Mohd. , Singh Snigdha , Hooshmandi Etrat , Vamanu Emanuel , Singh Santosh K. , Singh Mohan P.
Published: August 2022

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a human coronavirus (HCoV) that has created a pandemic situation worldwide as COVID-19. This virus can invade human cells via angiotensin-converting enzyme 2 (ACE2) receptor-based mechanisms, affecting the human respiratory tract. However, several reports of neurological symptoms suggest a neuroinvasive development of coronavirus. SARS-CoV-2 can damage the brain via several routes, along with direct neural cell infection with the coronavirus. The chronic inflammatory reactions surge the brain with proinflammatory elements, damaging the neural cells, causing brain ischemia associated with other health issues. SARS-CoV-2 exhibited neuropsychiatric and neurological manifestations, including cognitive impairment, depression, dizziness, delirium, and disturbed sleep. These symptoms show nervous tissue damage that enhances the occurrence of neurodegenerative disorders and aids dementia. SARS-CoV-2 has been seen in brain necropsy and isolated from the cerebrospinal fluid of COVID-19 patients. The associated inflammatory reaction in some COVID-19 patients has increased proinflammatory cytokines, which have been investigated as a prognostic factor. Therefore, the immunogenic changes observed in Parkinson’s and Alzheimer’s patients include their pathogenetic role. Inflammatory events have been an important pathophysiological feature of neurodegenerative diseases (NDs) such as Parkinson’s and Alzheimer’s. The neuroinflammation observed in AD has exacerbated the Aβ burden and tau hyperphosphorylation. The resident microglia and other immune cells are responsible for the enhanced burden of Aβ and subsequently mediate tau phosphorylation and ultimately disease progression. Similarly, neuroinflammation also plays a key role in the progression of PD. Several studies have demonstrated an interplay between neuroinflammation and pathogenic mechanisms of PD. The dynamic proinflammation stage guides the accumulation of α-synuclein and neurodegenerative progression. Besides, few viruses may have a role as stimulators and generate a cross-autoimmune response for α-synuclein. Hence, neurological complications in patients suffering from COVID-19 cannot be ruled out. In this review article, our primary focus is on discussing the neuroinvasive effect of the SARS-CoV-2 virus, its impact on the blood-brain barrier, and ultimately its impact on the people affected with neurodegenerative disorders such as Parkinson’s and Alzheimer’s.
Biopsychosocial Approach for Conservative Management of Abdominal Pregnancy in Previously Infertile Mother
Authors: Indarti Junita , Saroyo Yudianto Budi , Anggraheni Beta Andewi Resti , Widyatama Andika
Published: August 2022

Background. Abdominal pregnancy is a rare condition in which early termination is generally recommended. However, there are cases of abdominal pregnancies treated using expectant management with satisfactory outcomes. This management may be considered in special cases, such as previously infertile couples. Case. A case of a 27-year-old woman with infertility history complaining of vaginal bleeding was presented. Physical and ultrasound examination revealed an abdominal pregnancy with 18 weeks of gestation. Although she was ready to abort the pregnancy, she insisted to try expectant management for her pregnancy. Her baby was finally born at 25 weeks via laparotomy. Conclusion. Abdominal pregnancy is a rare and highly morbid form of ectopic pregnancy. It demands a skilled approach in management. A comprehensive and holistic approach is required to deliver the best outcome for the patient and her family.
A Car-Following Model considering the Effect of Following Vehicles under the Framework of Physics-Informed Deep Learning
Authors: Xu Le , Ma Jianxiao , Wang Yuchen
Published: August 2022

Car-following models have been studied for a long time, and many traffic engineers and researchers have devoted attention to them. With the increase in machine learning, this paper proposes a fusion model based on the physics-informed deep learning framework. The purpose of this paper is to inherit the predecessors’ ideas, transform them to fit a new framework, and improve the framework’s accuracy. The IDM-D (intelligent driver model development) involves reenabling the effect of the following vehicle to form a complementary model (not car-following model) with the IDM (intelligent driver model). The pretreated NGSIM data are used for calibration and validation. The IDM and the IDM-D are combined with the LSTM under the framework of physics-informed deep learning, and the results are mixed in a ratio to form the final result. Using test data for simulation, the results reveal that the IDM-informed LSTM shows better performance than the LSTM and that the fusion model further improves the MSE (mean square error) of the IDM-informed LSTM. The fusion increases the accuracy during the deceleration process, which is better than just a single IDM-informed LSTM. The fusion model further explains drivers’ deceleration behaviors.
Hermite–Hadamard and Jensen‐Type Inequalities via Riemann Integral Operator for a Generalized Class of Godunova–Levin Functions
Authors: Zhang Xiaoju , Shabbir Khurram , Afzal Waqar , Xiao He , Lin Dong
Published: August 2022

The generalization of Godunova–Levin interval‐valued functions has been drastically studied in last few decades, as it has a remarkable applications in both pure and applied mathematics. The goal of this study is to introduce the notion of h‐Godunova–Levin interval‐valued functions. We establish Hermite–Hadamard and Jensen‐type inequalities via Riemann integral operator.