bluuebunny/crossref_metadata_2025 huggingface dataset:
The rise and demise of deep accretionary wedges: A long-term field and numerical modeling perspective
Authors: Angiboust Samuel , Menant Armel , Gerya Taras , Oncken Onno
Published: November 2021

Several decades of field, geophysical, analogue, and numerical modeling investigations have enabled documentation of the wide range of tectonic transport processes in accretionary wedges, which constitute some of the most dynamic plate boundary environments on Earth. Active convergent margins can exhibit basal accretion (via underplating) leading to the formation of variably thick duplex structures or tectonic erosion, the latter known to lead to the consumption of the previously accreted material and eventually the forearc continental crust. We herein review natural examples of actively underplating systems (with a focus on circum-Pacific settings) as well as field examples highlighting internal wedge dynamics recorded by fossil accretionary systems. Duplex formation in deep paleo–accretionary systems is known to leave in the rock record (1) diagnostic macro- and microscopic deformation patterns as well as (2) large-scale geochronological characteristics such as the downstepping of deformation and metamorphic ages. Zircon detrital ages have also proved to be a powerful approach to deciphering tectonic transport in ancient active margins. Yet, fundamental questions remain in order to understand the interplay of forces at the origin of mass transfer and crustal recycling in deep accretionary systems. We address these questions by presenting a suite of two-dimensional thermo-mechanical experiments that enable unravelling the mass-flow pathways and the long-term distribution of stresses along and above the subduction interface as well as investigating the importance of parameters such as fluids and slab roughness. These results suggest the dynamical instability of fluid-bearing accretionary systems causes either an episodic or a periodic character of subduction erosion and accretion processes as well as their topographic expression. The instability can be partly deciphered through metamorphic and strain records, thus explaining the relative scarcity of paleo–accretionary systems worldwide despite the tremendous amounts of material buried by the subduction process over time scales of tens or hundreds of millions of years. We finally stress that the understanding of the physical processes at the origin of underplating processes as well as the forearc topographic response paves the way for refining our vision of long-term plate-interface coupling as well as the rheological behavior of the seismogenic zone in active subduction settings.
A Retrospective Analysis of COVID-19-infected Patients with Acute Hepatitis who Develop Acute Liver Failure in a Safety Net Hospital
Authors: Pellegrini Jr. James Robert , Sabbula Bhanu , Russe-Russe Jose R , Munshi Rezwan F , Meshoyrer Daniel , Sajid Nourma , Gutierrez Alex , Munnangi Swapna , Szydziak Elisa , Akella Jagadish
Published: November 2021

Objective

In early 2019, a new coronavirus called SARS-CoV-2 emerged and changed the course of civilization. Our study aims to analyze the association between acute liver failure (ALF) and mortality in patients infected with COVID-19. A retrospective analysis of 864 COVID-19-infected patients admitted to Nassau University Medical Center in New York was performed.

Design

ALF is identified by acute liver injury (elevations in liver enzymes), hepatic encephalopathy and an international normalised ratio greater than or equal to 1.5. These parameters were analysed via daily blood work and clinical assessment. Multivariate logistic regression model predicting mortality and controlling for confounders such as age, coronary artery disease, intubation, hypertension, diabetes mellitus and acute kidney injury were used to determine the association of ALF with mortality.

Results

A total of 624 patients, out of the initial 864, met the inclusion criteria—having acute hepatitis and COVID-19 infection. Of those 624, 43 (6.9%) patients developed ALF during the course of their hospitalisation and their mortality rate was 74.4%. The majority of patients with ALF were male (60.6%). The logistic model predicting death and controlling for confounders shows COVID-19 patients with ALF had a nearly four-fold higher odds of death in comparison to those without ALF (p=0.0063).

Conclusions

Findings from this study suggest that there is a significant association between mortality and the presence of ALF in patients infected with COVID-19. Further investigation into patients with COVID-19 and ALF can lead to enhanced treatment regimens and risk stratification tools, which can ultimately improve mortality rates during these arduous times.
Emilia-Romagna Regional Blood System accreditation as an example of improvement through application of specific requirements: a retrospective analysis
Authors: Cortellazzi Pilade , Carini Davide , Bolzoni Luana , Cattadori Evelina , Randi Vanda
Published: November 2021

Background

Institutional accreditation in Italy represents the license given by a region to a public or private facility to provide services in the name and on behalf of the National Health Service. This study aims to evaluate the improvement of the Emilia-Romagna Regional Blood System and to highlight its unresolved issues, analysing non-conformities observed during accreditation and maintenance inspections between 2013 and 2018.

Methods

All the Emilia-Romagna Regional Blood facilities were invited to participate in this study voluntarily and anonymously. Participants had to access a web application that we developed specifically. For each of the three inspections evaluated in this study, they had to enter data about the state of their organisation branches and non-conformities observed by regional inspectors. All data entered were finally exported from the web application database and analysed with spreadsheets. Statistical analysis was performed using Wilcoxon signed-rank test with continuity correction.

Results

17 structures took part in the study, with a total of 174 organisation branches. The number of branches changed over the years because of new openings and closures due to reorganisations or non-conformities that were too difficult to correct. Inspectors observed 2381 non-conformities (291 structural, 611 technological and 1479 organisational). As a result of accreditation inspections and consequent improvement actions, non-conformities were reduced by 88%. The most frequent non-conformities concerned the management software and the transportation of blood and blood components.

Conclusion

An improvement in the Emilia-Romagna Regional Blood System over time is evident: institutional accreditation certainly pushed it to change and overcome its problems to comply with specific requirements. The remaining non-conformities after the three inspections were mostly organisational and management software was the most critical issue. Despite these non-conformities, all currently active structures are accredited and guarantee high standards of quality and safety of products and services.
PAX-D: study protocol for a randomised placebo-controlled trial evaluating the efficacy and mechanism of pramipexole as add-on treatment for people with treatment resistant depression
Authors: Au-Yeung Sheena Kristine , Griffiths James , Roberts Sophie , Edwards Chloe , Yu Ly-Mee , Bogacz Rafal , Rendell Jennifer , Attenburrow Mary-Jane , Watson Stuart , Chan Fiona , Cipriani Andrea , Cleare Anthony , Harmer Catherine J , Kessler David , Evans Jonathan , Lewis Glyn , Singh Ilina , Simon Judit , Harrison Paul J , Cowen Phil , Shanyinde Milensu , Geddes John , Browning Michael
Published: November 2021

Introduction

Clinical depression is usually treated in primary care with psychological therapies and antidepressant medication. However, when patients do not respond to at least two or more antidepressants within a depressive episode, they are considered to have treatment resistant depression (TRD). Previous small randomised controlled trials suggested that pramipexole, a dopamine D2/3 receptor agonist, may be effective for treating patients with unipolar and bipolar depression as it is known to influence motivational drive and reward processing. PAX-D will compare the effects of pramipexole vs placebo when added to current antidepressant medication for people with TRD. Additionally, PAX-D will investigate the mechanistic effect of pramipexole on reward sensitivity using a probabilistic decision-making task.

Methods and analysis

PAX-D will assess effectiveness in the short- term (during the first 12 weeks) and in the longer-term (48 weeks) in patients with TRD from the UK. The primary outcome will be change in self-reported depressive symptoms from baseline to week 12 post-randomisation measured using the Quick Inventory of Depressive Symptomatology Self-Report (QIDS-SR16). Performance on the decision-making task will be measured at week 0, week 2 and week 12. Secondary outcomes include anhedonia, anxiety and health economic measures including quality of life, capability, well-being and costs. PAX-D will also assess the adverse effects of pramipexole including impulse control difficulties.

Discussion

Pramipexole is a promising augmentation agent for TRD and may be a useful addition to existing treatment regimes. PAX-D will assess its effectiveness and test for a potential mechanism of action in patients with TRD.

Trial registration number

ISRCTN84666271
Antipsychotic use in pregnancy and risk of attention/deficit-hyperactivity disorder and autism spectrum disorder: a Nordic cohort study
Authors: Hálfdánarson Óskar , Cohen Jacqueline M , Karlstad Øystein , Cesta Carolyn E , Bjørk Marte-Helene , Håberg Siri Eldevik , Einarsdóttir Kristjana , Furu Kari , Gissler Mika , Hjellvik Vidar , Kieler Helle , Leinonen Maarit K , Nørgaard Mette , Öztürk Esen Buket , Ulrichsen Sinna Pilgaard , Reutfors Johan , Zoega Helga
Published: November 2021

Background

Antipsychotics are increasingly used among women of childbearing age and during pregnancy.

Objective

To determine whether children exposed to antipsychoticsin uteroare at increased risk of attention-deficit/hyperactivity disorder (ADHD) or autism spectrum disorder (ASD), accounting for maternal diagnoses of bipolar, psychotic and other psychiatric disorders.

Design

Population-based cohort study, including a sibling analysis.

Setting

Nationwide data on all pregnant women and their live-born singletons in Denmark (1997-2017), Finland (1996-2016), Iceland (2004-2017), Norway (2004-2017), and Sweden (2006-2016).

Participants

4 324 086 children were eligible for inclusion to the study cohort.

Intervention

Antipsychotic exposurein utero, assessed by pregnancy trimester, type of antipsychotic, and varying patterns of use.

Main outcome measures

Non-mutually exclusive diagnoses of ADHD and ASD. We used Cox proportional hazard models to calculate hazard ratios (HRs) controlling for maternal psychiatric disorders and other potential confounding factors.

Findings

Among 4 324 086 singleton births, 15 466 (0.4%) were exposed to antipsychoticsin utero. During a median follow-up of 10 years, we identified 72 257 children with ADHD and 38 674 children with ASD. Unadjusted HRs were raised for both outcomes but shifted substantially towards the null after adjustment; 1.10 (95%CI 1.00 to 1.27) for ADHD and 1.12 (0.97 to 1.29) for ASD. Adjusted HRs remained consistent by trimester of exposure and type of antipsychotic. Comparingin uteroexposure with pre-pregnancy use yielded HRs of 0.74 (0.62 to 0.87) for ADHD and 0.88 (0.70 to 1.10) for ASD. Sibling analyses yielded HRs of 1.14 (0.79 to 1.64) for ADHD and 1.34 (0.75 to 2.39) for ASD.

Discussion

Our findings suggest little or no increased risk of child ADHD or ASD afterin uteroexposure to antipsychotics.

Clinical implications

Results regarding child neurodevelopment are reassuring for women who need antipsychotics during pregnancy.
Quality of life after transcatheter or surgical aortic valve replacement using the Toronto Aortic Stenosis Quality of Life Questionnaire
Authors: Kennon Simon , Styra Rima , Bonaros Nikolaos , Stastny Lukas , Romano Mauro , Lefèvre Thierry , Di Mario Carlo , Stefàno Pierluigi , Ribichini Flavio Luciano , Himbert Dominique , Urena-Alcazar Marina , Salgado-Fernandez Jorge , Cuenca Castillo Jose Joaquin , Garcia Bruno , Deutsch Cornelia , Sykorova Lenka , Kurucova Jana , Thoenes Martin , Lüske Claudia , Bramlage Peter , Frank Derk
Published: November 2021

Background

The Toronto Aortic Stenosis Quality of Life Questionnaire (TASQ) is a validated instrument for assessing quality of life (QoL) in patients with severe aortic stenosis (AS). In this study, we evaluated health status outcomes, based on the TASQ, in patients with severe AS undergoing transcatheter aortic valve replacement (TAVR) or surgical aortic valve replacement (SAVR).

Methods

The TASQ registry was a prospective observational registry. Patients with severe AS from nine centres in Europe and one in Canada underwent either SAVR or transfemoral TAVR. Patients completed the TASQ, Kansas City Cardiomyopathy Questionnaire and Short Form-12 V.2 prior to the intervention, predischarge, and at 30-day and 3-month follow-ups. Primary end point was the TASQ score.

Results

In both the TAVR (n=137) and SAVR (n=137) cohorts, significant increases were observed in all three scores. The overall TASQ score improved as did all but one of the individual domains at 3 months after the intervention (p<0.001). TASQ health expectations were the only domain which worsened (p<0.001). Across TASQ subscores, significant changes were evident from the time of discharge in the TAVR and 30-day follow-up in the SAVR cohort. In a categorical analysis of the TASQ, 39.7% of the TAVR group and 35.0% of the SAVR group had a substantially improved health status at 3 months compared with baseline.

Conclusions

The TASQ captured changes in QoL among patients with severe AS who were treated with TAVR or SAVR. QoL improved substantially after either intervention, as indicated by changes in the TASQ overall score at 3 months.

Trial registration number

NCT03186339.
There is more Wrangellia — magnetic characterization of southern Alaska crust
Authors: Saltus Richard W. , Hudson Travis L.
Published: November 2021

In southern Alaska, Wrangellia-type magnetic crustal character extends from the Talkeetna Mountains southwest through the Alaska Range to the Bristol Bay region. Magnetic data analyses in the Talkeetna Mountains showed that there are mid-crustal differences in the magnetic properties of Wrangellia and the Peninsular terrane. After converting total field magnetic anomaly data to magnetic potential, we applied Fourier filtering techniques to remove magnetic responses from deep and shallow sources. The resulting mid-crustal magnetic characterization delineates the regional magnetic potential domains that correspond to the Wrangellia and Peninsular terranes throughout southern Alaska. These magnetic potential domains show that Wrangellia-type crust extends southwest to the Iliamna Lake region and that it overlaps the mapped Peninsular terrane. Upon reconsidering geologic ties between Wrangellia, Peninsular, and Alexander terranes, we conclude that Peninsular terrane is part of what we here call Western Wrangellia. Western Wrangellia contains the Lower Jurassic Talkeetna volcanic arc and is similar to Wrangellia of the Vancouver Island area, Canada (Southern Wrangellia), which contains the Lower Jurassic Bonanza volcanic arc. Others have previously made this correlation and proposed that the Talkeetna arc-bearing part of southern Alaska was displaced from the Bonanza arc-bearing part of Canada. We generally agree and propose that about 1000 km of dextral displacement along ancestral Border Ranges fault segments and other faults of south-central Alaska separated Western Wrangellia from Southern Wrangellia. We think this displacement was mostly in the Late Jurassic and earliest Cretaceous, perhaps between about 160 and 130 Ma.
Behavior of entanglement entropy near periodic orbits in a hamiltonian dynamical system
Authors: Bracken Paul
Published: November 2021

Entanglement entropy growth is studied under a form of dynamics that is based on iteration. This approach allows the investigation of the role of decoherence in producing increases of entropy. This has important consequences as far as the study of decoherence is concerned. It is indicated that results are generally independent of Hilbert space partitioning. It is seen that a deep relationship between classical dynamical entropy and the growth of entanglement entropy exists in this kind of model. The former acts to bound the latter and in the asymptotic region, they tend to a common limit.
The 3-Good Neighbor Edge-Fault-Tolerant Strong Menger Edge Connectivity on the Class of BC Networks
Authors: Liu Rong , Li Pingshan
Published: November 2021

A graph [Formula: see text] is called strongly Menger edge connected (SM-[Formula: see text] for short) if the number of disjoint paths between any two of its vertices equals the minimum degree of these two vertices. In this paper, we focus on the maximally edge-fault-tolerant of the class of BC-networks (contain hypercubes, twisted cubes, Möbius cubes, crossed cubes, etc.) concerning the SM-[Formula: see text] property. Under the restricted condition that each vertex is incident with at least three fault-free edges, we show that even if there are [Formula: see text] faulty edges, all BC-networks still have SM-[Formula: see text] property and the bound [Formula: see text] is sharp.
An Effective Model for Consumer Need Prediction Using Big Data Analytics
Authors: Tian Yihao
Published: November 2021

Big data is an unstructured data set with a considerable volume, coming from various sources such as the internet, business organizations, etc., in various formats. Predicting consumer behavior is a core responsibility for most dealers. Market research can show consumer intentions; it can be a big order for a best-designed research project to penetrate the veil, protecting real customer motivations from closer scrutiny. Customer behavior usually focuses on customer data mining, and each model is structured at one stage to answer one query. Customer behavior prediction is a complex and unpredictable challenge. In this paper, advanced mathematical and big data analytical (BDA) methods to predict customer behavior. Predictive behavior analytics can provide modern marketers with multiple insights to optimize efforts in their strategies. This model goes beyond analyzing historical evidence and making the most knowledgeable assumptions about what will happen in the future using mathematical. Because the method is complex, it is quite straightforward for most customers. As a result, most consumer behavior models, so many variables that produce predictions that are usually quite accurate using big data. This paper attempts to develop a model of association rule mining to predict customers’ behavior, improve accuracy, and derive major consumer data patterns. The finding recommended BDA method improves Big data analytics usability in the organization (98.2%), risk management ratio (96.2%), operational cost (97.1%), customer feedback ratio (98.5%), and demand prediction ratio (95.2%).
A NOVEL DEEP LEARNING NEURAL NETWORK SYSTEM FOR IMBALANCED HEART SOUNDS CLASSIFICATION
Authors: CHEN WEI , SUN QIANG , XIE GANGCAI , XU CHEN
Published: November 2021

This study proposed a novel TFNNS method, which aimed to solve the imbalanced phonocardiogram (PCG) signals’ classification. TFFNS consisted of three submodules: HeartNet, 2D-Maps transformation, and TF-Mask augmentation. HeartNet, deep neural networks (CNNs), was designed to recognize the categories of PCG signals. In particular, on the basis of short-time Fourier transform and Mel filtering, 2D-Maps transformation was used to convert one-dimensional PCG into two-dimensional Savitzky-MFSC feature maps that were fed into HeartNet; TF-Mask augmentation was designed to augment the training datasets by randomly shielded Savitzky-MFSC maps in the domains of time and frequency. We trained our model on the PASCAL heart sounds’ datasets to classify three categories of heart sounds including normal, murmur, and extrasystole. We also evaluated and compared the model with the baselines on the consistent evaluation protocols. The experimental results showed that the proposed TFFNS method significantly promoted the performance of the PCG signals’ classification and exceeded the baselines by giving the mean precision of 94%, heart problem specificity of 99%, and discriminant power of 1.317.
Symmetric Circuits for Rank Logic
Authors: Dawar Anuj , Wilsenach Gregory
Published: November 2021

Fixed-point logic with rank (FPR) is an extension of fixed-point logic with counting (FPC) with operators for computing the rank of a matrix over a finit field. The expressive power of FPR properly extends that of FPC and is contained in P, but it is not known if that containment is proper. We give a circuit characterization for FPR in terms of families of symmetric circuits with rank gates, along the lines of that for FPC given by Anderson and Dawar in 2017. This requires the development of a broad framework of circuits in which the individual gates compute functions that are not symmetric (i.e., invariant under all permutations of their inputs). This framework also necessitates the development of novel techniques to prove the equivalence of circuits and logic. Both the framework and the techniques are of greater generality than the main result.
p-Type Trigate Junctionless Nanosheet MOSFET: Analog/RF, Linearity, and Circuit Analysis
Authors: Vakkalakula Bharath Sreenivasulu , Vadthiya Narendar
Published: November 2021

Silicon (Si) nanosheet (NS) metal-oxide semiconductor field effect transistors (MOSFETs) are realized as an outstanding structure to obtain better area scaling and power performance compared to FinFETs. The Si NS MOSFETs provide high current drivability due to wider effective channel (W
eff) and maintain better short channel performance. Here, the performance of junctionless (JL) SOI NS p-MOSFET is evaluated by invoking HfxTi1−xO2 gate stack to overcome adverse short channel effects (SCEs). The device performance is enhanced using various spacer dielectrics and the electrical characteristics are presented. Moreover, the effect of NS width variation on I
ON/I
OFF, SS, V
th is presented and the analog/RF metrics of the device are evaluated. The power consumption, dynamic power, and static power analyses of NS MOSFET is presented with respect to the ITRS road map. Our investigation reveals that the device exhibits an I
ON/I
OFF ratio of more than ∼106 with NS widths of 10 to 30 nm, respectively. The device exhibits better performance (I
ON) with higher NS widths and ensures potential towards high-performance applications. However, with an increase in NS widths the threshold voltage (V
th) tends to downfall and leads to deterioration in subthreshold performance . With high-k spacer dielectric the device exhibits better static power consumption for the CMOS inverter. By careful control of NS width and effective usage of spacer dielectric ensures better p-MOSFET design for future technology nodes.
Intracochlear Hemorrhage: A Rare Cause of Sudden Sensorineural Hearing Loss
Authors: Jrad Myriam , Zlitni Haifa , Boumediene Miriam , Nasr Atef Ben , Bouzrara Meriem
Published: November 2021

Inner ear hemorrhage is an extremely rare cause of sudden sensorineural hearing loss with few cases reported in the literature. We report the case of a 30-year-old male who presented with a sudden left ear hearing loss, with no tinnitus nor vertigo. The audiogram revealed a profound left sensorineural hearing loss. An MRI of the brain and internal auditory canal was performed 3 weeks after and revealed an increased signal intensity on T1-weighted (T1W) and T2 fluid-attenuated inversion recovery (FLAIR) images in the left cochlea. No other abnormalities were found, in particular no enhancement after intravenous administration of gadolinium. The CISS 3D sequence showed a signal of discreetly lower intensity in the left cochlea compared to the right one. The diagnosis of intracochlear hemorrhage was made. No improvement of the hearing loss has been noted after medical treatment and hyperbaric oxygen therapy.
A Novel Sparse Array Configuration with Low Coarray Redundancy for DOA Estimation in Mobile Wireless Sensor Network
Authors: Zhao Pin-Jiao , Hu Guo-Bing , Wan Liang-Tian
Published: November 2021

For tacking and localizing sources in the mobile wireless sensor network, underdetermined direction of arrival (DOA) estimation with high-accuracy is a crucial issue. In this paper, a novel sparse array configuration is developed for accurate DOA estimation from the perspective of sum-difference coarray (SDCA). As compared with most of the existing sparse array configurations, the proposed array can effectively reduce the overlap between difference coarray (DCA) and sum coarray (SCA) and can achieve more consecutive degrees of freedom (DOF), more sources can be resolved accordingly. Additionally, the proposed array has hole-free DCA and SDCA. Then, the concept of coarray redundancy ratio (CRR) is introduced for evaluating the coarray overlap quantitatively and the closed-form CRR expressions of the proposed array are derived in detail. Based on the good properties of the proposed array, vectorized conjugate augmented MUSIC (VCAM) is adopted for underdetermined DOA estimation. The theoretical propositions and numerical simulations demonstrate the superior performance of the proposed array in terms of CRR, consecutive DOF, and DOA estimation accuracy.
A Network Evolution Model of Credit Risk Contagion between Banks and Enterprises Based on Agent-Based Model
Authors: Mu Pei , Chen Tingqiang , Pan Kun , Liu Meng
Published: November 2021

Credit risk contagion between banks and firms is one of the important triggers of financial crisis, and the credit linkage network is the way of systemic risk contagion triggered by external shocks. Considering the heterogeneity of behavioral rules, learning rules, and interaction rules, this paper constructs a bank-firm credit matching network model based on ABM (agent-based model) model and reinforcement learning algorithm to analyze the interaction behavior and credit risk network contagion mechanism. The results show that (1) macroeconomic cycles are the result of the interaction between banks and enterprises and the interaction of microentities under complex financial conditions; (2) enterprises are heterogeneous and the asset size follows a power-law distribution; (3) the greater the sensitivity of banks and enterprises to market performance, the lower the bank failure rate and enterprise default rate; and (4) shocks to the largest banks and enterprises in terms of assets and entry can all intensify the risk contagion between banks and enterprises. Therefore, the regulation of financial institutions that are “too big to fail” is not sufficient but should be a comprehensive regulation of the banking system.
University Classroom Teaching Model Based on Decision Tree Analysis and Machine Learning
Authors: Guo Yanqi
Published: November 2021

The existing teaching evaluation system partially reflects the teaching effect and other related conditions through statistical reports, but it is difficult to find the useful knowledge hidden in the database, and it cannot effectively assist the decision-making support. In order to improve the evaluation effect of college classroom teaching mode, this paper mainly uses decision tree algorithm and data mining technology of association rules to construct the effectiveness evaluation system of college classroom teaching mode based on decision tree analysis. Moreover, this paper analyzes the teaching information and evaluation data to extract the potentially useful knowledge contained in it, which can help decision-makers find the rules and explore various factors that affect the teaching effect of teachers, thereby improving teaching management and optimizing resource allocation. In addition, this paper uses experimental teaching methods to verify the performance of the system model constructed in this paper. The research results show that the system constructed in this paper has certain reliability and practicability.
On Fault-Tolerant Partition Dimension of Homogeneous Caterpillar Graphs
Authors: Azhar Kamran , Zafar Sohail , Kashif Agha
Published: November 2021

Metric-related parameters in graph theory have several applications in robotics, navigation, and chemical strata. An important such parameter is the partition dimension of graphs that plays an important role in engineering, computer science, and chemistry. In the context of chemical and pharmaceutical engineering, these parameters are used for unique representation of chemical compounds and their structural analysis. The structure of benzenoid hydrocarbon molecules is represented in the form of caterpillar trees and studied for various attributes including UV absorption spectrum, molecular susceptibility, anisotropy, and heat of atomization. Several classes of trees have been studied for partition dimension; however, in this regard, the advanced variant, the fault-tolerant partition dimension, remains to be explored. In this paper, we computed fault-tolerant partition dimension for homogeneous caterpillars

C

p
;
1

,

C

p
;
2

, and

C

p
;
3

for

p
≥
5

,

p
≥
3

, and

p
≥
4

, respectively, and it is found to be constant. Further numerical examples and an application are furnished to elaborate the accuracy and significance of the work.
Edge-Based Detection and Classification of Malicious Contents in Tor Darknet Using Machine Learning
Authors: Li Runchuan , Chen Shuhong , Yang Jiawei , Luo Entao
Published: November 2021

With the increase of data in the network, the load of servers and communication links becomes heavier and heavier. Edge computing can alleviate this problem. Due to a sea of malicious contents in Darknet, it is of high research value to combine edge computing with content detection and analysis. Therefore, this paper illustrates an intelligent classification system based on machine learning and Scrapy that can detect and judge fleetly categories of services with malicious contents. Because of the nondisclosure and short survival time of Tor Darknet domain names, obtaining uniform resource locators (URLs) and resources of the network is challenging. In this paper, we focus on a network based on the Onion Router (tor) anonymous communication system. We designed a crawler program to obtain the contents of the Tor network and label them into six classes. We also construct a dataset which contains URLs, categories, and keywords. Edge computing is used to judge the category of websites. The accuracy of the classifier based on a machine learning algorithm is as high as 89%. The classifier will be used in an operational system which can help researchers quickly obtain malicious contents and categorize hidden services.
Red Blood Cell Transfusion and Postoperative Delirium in Hip Fracture Surgery Patients: A Retrospective Observational Cohort Study
Authors: Raphael Jacob , Hensley Nadia B. , Chow Jonathan , Parr K. Gage , McNeil John S. , Porter Steven B. , Taneja Monica , Tanaka Kenichi , Mazzeffi Michael
Published: November 2021

Background. Patients having hip fracture surgery are at high risk for postoperative delirium. Red blood cell (RBC) transfusion may increase postoperative delirium risk by causing neuroinflammation. We hypothesized that RBC transfusion would be associated with postoperative delirium in patients having hip fracture surgery. Methods. An observational cohort study was performed using the United States National Surgical Quality Improvement Program (NSQIP) participant use files for hip fracture from 2016 to 2018. Propensity score analysis and inverse probability of treatment weighting (IPTW) were used to reduce bias from confounding. An IPTW adjusted odds ratio for developing postoperative delirium was calculated for patients who received RBC transfusion during surgery or in the 72 hours after. Results. There were 20,838 patients who had eligible current procedural terminology (CPT) codes for primary hip fracture surgery and complete study data. After employing strict exclusions to balance covariates and reduce bias, 3,715 patients remained in the IPTW cohort. Of these, 626 patients (16.9%) received RBC transfusion and 665 patients (17.9%) developed postoperative delirium. IPTW adjustment led to good covariate balance between patients who received RBC transfusion and those who did not. Patients who received RBC transfusion had significantly higher odds of postoperative delirium, IPTW adjusted odds ratio = 1.21, 95% CI = 1.03 to 1.43, andP = 0.02. Discharge location also differed significantly between patients who received RBC transfusion and those who did not (P < 0.001) with in-hospital mortality or referral to hospice occurring in 1.6% of patients who received RBC transfusion and 1.3% of patients who were not transfused. Conclusion. RBC transfusion is associated with increased odds of postoperative delirium after hip fracture surgery and may be associated with worse clinical outcome.
Infected Facial Tissue Fillers Caused by Dental Infection
Authors: Berberi Antoine , Hjeij Bouchra , Aad Georges , Aoun Georges
Published: November 2021

Injectable dermal fillers are widely used for facial rejuvenation; they help reshape the facial contours by treating volume loss due to aging changes. Facial fillers may become infected following a dental infection. In this report, we present a case of a 44-year-old female patient who presented with a swelling in her upper right buccal region following dental treatment of her second maxillary right premolar. After a thorough history, clinical, and radiological examinations, the diagnosis of infected dermal filler was made. The lesion was treated by association of two antibiotics (ciprofloxacin IM and clindamycin tablets 300 mg), and a complete healing was observed two months after the end of the dental treatments.
Evaluation and Comparison of Medical Students Stressors and Coping Strategies among Undergraduate Preclinical and Clinical Year Students Enrolled in Medical School of Arsi University, Southeast Ethiopia
Authors: Melaku Leta , Bulcha Guta
Published: November 2021

Background. The transition into higher education is stressful as university students face many stressful events. Medical students must deal with stressors specific to medical education. While many students adjust effectively to the university context, large proportions of students are at risk of developing mental health problems. The objective of the present study was to evaluate the most common medical student’s stressors and coping strategies among undergraduate students enrolled in the Medical School of Arsi University and their association with educational year levels. Methods. An Institutional based cross-sectional study was conducted on 265 medical students by systematic random sampling. Data were collected by pretested self-administrative questionnaire and analyzed by SPSS-21 software. Logistic regression analysis was employed, and statistical significance was accepted at

p
<
0.05

. Result. In the present study, 5 questionnaires were rejected for incompleteness, giving a response rate of 98.1%. The top sources of stress were lack of time to review, conflict with teacher(s), and uncertainty of what was expected. ARS domain was the main cause of high stress, followed by IRS and TLRS. Religious coping, active coping, positive reframing, and planning were the most commonly used coping strategies. When preclinical year students were compared with the clinical years, TLRS and DRS domains were identified as the most common cause of stress in the preclinical years. Furthermore, instrumental support, behavioral disengagement, acceptance, religion, self-blame, and emotional support were the most commonly used coping strategies in the preclinical years compared to the clinical years. Conclusion. We observed that academic-related stressors followed by interpersonal and intrapersonal stressors are the major stressors faced by students. Active coping strategies were the most commonly employed ones rather than avoidant strategies. Stress reduction interventions were recommended.
Deep Learning-Based Detection and Diagnosis of Subarachnoid Hemorrhage
Authors: Gou Xiaohong , He Xuenong
Published: November 2021

Subarachnoid hemorrhage (SAH) is one of the critical and severe neurological diseases with high morbidity and mortality. Head computed tomography (CT) is among the preferred methods for the diagnosis of SAH, which is confirmed by CT showing high-density shadow in the subarachnoid space. Analysis of these images through a deep learning-based subarachnoid hemorrhage will reduce the approximate rate of misdiagnosis in general and missed diagnosis by clinicians in particular. Deep learning-based detection of subarachnoid hemorrhage mainly includes two tasks, i.e., subarachnoid hemorrhage classification and subarachnoid hemorrhage region segmentation. However, it is difficult to effectively judge reliability of the model and classify bleeding which is based on limited predictive probability of convolutional neural network output. Moreover, deep learning-based bleeding area segmentation requires a large amount of training data to be marked in advance and the large number of network parameters makes the model training unable to reach the optimal. To resolve these problems associated with existing models, Bayesian deep learning and neural network-based hybrid model is presented in this paper to estimate uncertainty and efficiently classify subarachnoid hemorrhage. Uncertainty estimation of the proposed model helps in judging whether the model’s prediction is reliable or not. Additionally, it is used to guide clinicians to find the neglected subarachnoid hemorrhage area. In addition, a teacher-student mechanism deep learning model was designed to introduce observational uncertainty estimation for semisupervised learning of subarachnoid hemorrhage. Observation uncertainty estimation detects the uncertain bleeding areas in CT images and then selects areas with high reliability. Finally, it uses these unlabeled data for model training purposes as well.
Modeling and Vibration Analysis of a 3‐UPU Parallel Vibration Isolation Platform with Linear Motors Based on MS‐DT‐TMM
Authors: Chen Wei , Tong Jiaqi , Yang Hehe , Liu Fulong , Qin Zhen , Ren Zhiyong
Published: November 2021

The dynamics and vibration characteristics of a 3‐UPU parallel mechanism isolator are investigated by theoretical modeling, numerical simulation, and experimental study. The system consists of two platforms, three linear motors, and the Hook hinges. Firstly, the dynamical mathematical model of this vibration isolator is innovatively established and solved by using the discrete‐time transfer matrix method of the multibody system (MS‐DT‐TMM). According to the modeling principle, the transfer matrix of each component is derived, including the upper and lower platforms, Hooke hinges, and linear motors. Then, the dynamical equation of the overall system is obtained by multiplying all transfer matrices. Secondly, the solution of equation is calculated through the setting of boundary conditions. The numerical simulation is carried out according to the known parameters. The dynamical and vibration analysis of the isolation platform is performed, involving the displacement and force characteristics of the branches. Furthermore, in view of the fact that the Hooke hinges and linear motors are simplified as spatial elastic hinges in modeling. The vibration suppression effect caused by adjusting spring stiffness and damping coefficient is discussed. The simulation results verified the correctness of the MS‐DT‐TMM method through the comparison with ADAMS simulation results. Finally, the prototype of the vibration isolator is constructed and assembled, and the vibration experiment is conducted. By testing the responses of the isolation device mounted on the vibration table, the natural frequency of the isolator is obtained. The purpose of this experiment is to avoid resonance when it is applied as a vibration isolator in the future. This paper provides a theoretical basis for the later vibration research and control scheme design of the 3‐UPU parallel vibration isolation platform.
Trastuzumab Plus Endocrine Therapy or Chemotherapy as First-line Treatment for Patients with Hormone Receptor–Positive and HER2-Positive Metastatic Breast Cancer (SYSUCC-002)
Authors: Hua Xin , Bi Xi-Wen , Zhao Jian-Li , Shi Yan-Xia , Lin Ying , Wu Zhi-Yong , Zhang Yuan-Qi , Zhang Le-Hong , Zhang An-Qing , Huang Heng , Liu Xin-Mei , Xu Fei , Guo Ying , Xia Wen , Hong Ruo-Xi , Jiang Kui-Kui , Xue Cong , An Xin , Zhong Yong-Yi , Wang Shu-Sen , Huang Jia-Jia , Yuan Zhong-Yu
Published: November 2021

Purpose:

There is no research evidence demonstrate which is the better partner strategy, endocrine therapy or chemotherapy, to combine with anti-HER2 therapy as the first-line management of hormone receptor (HR)-positive (HR+) and HER2-positive (HER2+) metastatic breast cancer (MBC). We wished to ascertain if trastuzumab plus endocrine therapy is noninferior to trastuzumab plus chemotherapy.

Patients and Methods:

We conducted an open-label, noninferiority, phase III, randomized, controlled trial (NCT01950182) at nine hospitals in China. Participants, stratified by previous adjuvant endocrine therapy and disease status (recurrent disease vs. de novo metastasis), were assigned randomly (1:1) to receive trastuzumab plus endocrine therapy (per investigator's choice of oestrogen-receptor modulators or aromatase inhibitor, with/without concurrent ovarian suppression) or chemotherapy (per investigator's choice of taxanes, capecitabine, or vinorelbine). The primary endpoint was progression-free survival (PFS) with a noninferiority upper margin of 1.35 for the HR. The intention-to-treat population was used in primary and safety analyses.

Results:

A total of 392 patients were enrolled and assigned randomly to receive trastuzumab plus endocrine therapy (ET group, n = 196) or trastuzumab plus chemotherapy (CT group, n = 196). After a median follow-up of 30.2 months [interquartile range (IQR) 15.0–44.7], the median PFS was 19.2 months [95% confidence interval (CI), 16.7–21.7)] in the ET group and 14.8 months (12.8–16.8) in the CT group (hazard ratio, 0.88; 95% CI, 0.71–1.09; Pnoninferiority < 0.0001). A significantly higher prevalence of toxicity was observed in the CT group compared with the ET group.

Conclusions:

Trastuzumab plus endocrine therapy was noninferior to trastuzumab plus chemotherapy in patients with HR+HER2+ MBC.
Deregulation of AKT–mTOR Signaling Contributes to Chemoradiation Resistance in Lung Squamous Cell Carcinoma
Authors: Shen Changxian , Shyu Duan-Liang , Xu Min , Yang Linlin , Webb Amy , Duan Wenrui , Williams Terence M.
Published: November 2021

Lung squamous cell carcinoma (LUSC) accounts for one of three of non–small cell lung carcinoma (NSCLC) and 30% of LUSC patients present with locally advanced, unresectable/medically inoperable disease, who are commonly treated with definitive chemoradiation. However, disease relapse in the radiation fields occurs in one of three cases. We aim to explore the underlying molecular mechanisms of chemoradiation resistance of LUSC. Patient-derived xenograft (PDX) models of LUSC were established in immunodeficient mice, followed by treatment with cisplatin in combination with clinically relevant courses of ionizing radiation (20, 30, and 40 Gy). The recurrent tumors were extracted for functional proteomics using reverse phase protein analysis (RPPA). We found that phospho-AKT-S473, phospho-AKT-T308, phospho-S6-S235/6, and phospho-GSK3β-S9 were upregulated in the chemoradiation-resistant 20 Gy + cisplatin and 40 Gy + cisplatin tumors compared with those in the control tumors. Ingenuity pathway analysis of the RPPA data revealed that AKT–mTOR signaling was the most activated signaling pathway in the chemoradiation-resistant tumors. Similarly, elevated AKT–mTOR signaling was observed in stable 40 Gy and 60 Gy resistant HARA cell lines compared with the parental cell line. Accordingly, pharmacologic inhibition of mTOR kinase by Torin2 significantly sensitized LUSC cell lines to ionizing radiation. In conclusion, using chemoradiation-resistant PDX models coupled with RPPA proteomics analysis, we revealed that deregulation of AKT–mTOR signaling may contribute to the chemoradiation resistance of LUSC.

Implications:

Clonal selection of subpopulations with high AKT–mTOR signaling in heterogeneous tumors may contribute to relapse of LUSC after chemoradiation. mTOR kinase inhibitors may be promising radiosensitizing agents in upfront treatment to prevent acquired resistance.
Homoharringtonine Inhibits Alzheimer’s Disease Progression by Reducing Neuroinflammation via STAT3 Signaling in APP/PS1 Mice
Authors: Jiang Xinyuan , Wu Qingdong , Zhang Cuicui , Wang Maobo
Published: November 2021

Background: Accumulating evidence suggests an implication of neuroinflammation in Alzheimer’s disease (AD) pathogenesis. Homoharringtonine (HHT) is an antitumor reagent with anti-inflammatory activity. This study investigates whether and how HHT plays a role in disease progression in a mouse AD model. Methods: HHT was injected into APP/PS1 mice every other day for 6 months. The effects of HHT on cognitive function were assessed by behavioral assays. β-Amyloid accumulation was assessed by ELISA analysis of Aβ40 and Aβ42. Neuronal loss and synaptic function were determined by levels of NeuN, synaptophysin, and PSD95. Neuroinflammation was assessed by glial markers and pro-inflammatory cytokines. Signal transducer and activator of transcription 3 (STAT3) signaling was evaluated by phosphorylated STAT3 and SOCS3 expression. Results: We found that HHT at 2 mg/kg significantly alleviated cognitive deficits in APP/PS1 mice. HHT reduced soluble and insoluble Aβ40 and Aβ42 accumulation and attenuated the impairments of synaptic function in the AD mouse hippocampus. Finally, HHT inhibited neuroinflammation, suppressed STAT3 activation, and increased SOCS3 expression in the APP/PS1 mouse hippocampus. Conclusion: Our results indicate that HHT inhibits disease progression in APP/PS1 mice by suppressing neuroinflammation through modulating the STAT3 signaling. Our findings suggest that HHT may potentially be used for preventing or slowing down AD pathogenesis and warrants further investigation.
Economic Impact of Home-Use versus Office-Use Diphenylcyclopropenone in Extensive Alopecia Areata
Authors: Lekhavat Chinmanat , Rattanaumpawan Pinyo , Juengsamranphong Isree
Published: November 2021

Introduction: Diphenylcyclopropenone (DPCP) is the medication of choice for the treatment of severe alopecia areata (AA) according to AA treatment guidelines. Precise initiation and application are important factors for successful treatment. However, it is difficult for patients who live far away to visit their doctor weekly. Methods: We conducted a retrospective cohort study to assess the cost, effectiveness, and side effects of DPCP treatment between office-use DPCP (O-DPCP) and home-use DPCP (H-DPCP) in severe AA patients. A cost-effectiveness analysis was performed from the perspective of healthcare providers and patients using real-world data and the national cost statistics for hospital services comparing O-DPCP and H-DPCP in patients with severe AA at 24, 36, and 48 weeks. Results: Two groups of 41 patients treated with O-DPCP and H-DPCP were enrolled. There was no significant difference in the proportion of patients who showed a favorable outcome (≥50% improvement) with minimal side effects between both groups at 24 (O-DPCP 43.9% vs. H-DPCP 26.8%, p = 0.11), 36 (O-DPCP 58.5% vs. H-DPCP 43.9%, p = 0.19), or 48 weeks (O-DPCP 63.4% vs. H-DPCP 56.1%, p = 0.49). The cost of H-DPCP was half of the cost of O-DPCP. Discussion/Conclusion: H-DPCP is a cost-effective and time-efficient alternative treatment option for severe AA patients.