bluuebunny/crossref_metadata_2025 huggingface dataset:
Supplemental Material: Tectonic influence on axial-transverse sediment routing in the Denver Basin
Authors: Sharman Glenn R. , al. et
Published: October 2021

Measured sections from Bijou Creek, U-Pb ages of detrital zircons, U-Pb secondary standard results, (U-Th[Sm])/He analytical results, non-negative matrix factorization results, sample groups for end-member comparison, and sample lithologic information.
Markers of Neuroinflammation and Apoptosis in the Temporal Lobe of Patients with Drug-Resistant Epilepsy
Authors: Litovchenko A. V. , Zabrodskaya Yu. M. , Sitovskaya D. A. , Khuzhakhmetova L. K. , Nezdorovina V. G. , Bazhanova E. D.
Published: October 2021

Current antiepileptic strategies aim to normalize the interaction
of the excitatory and inhibitory systems, which is ineffective in
treating patients with drug-resistant epilepsy. Neuroinflammatory processes
in the epileptic focus and its perifocal area can trigger apoptosis
and also contribute to the development of drug resistance. The level
of pro- and anti-apoptotic proteins (p-NF-kB, TNF-α, p53, FAS, caspase-3,
caspase-9) was analyzed in intraoperative biopsies of the temporal
lobe gray and white matter in the brain of patients with drug-resistant
epilepsy. An increased level of pro-apoptotic proteins was revealed
in the cortex and perifocal area’s white matter against the background
of an imbalance of protective anti-apoptotic proteins. It appears
that the activation of the extrinsic pathway of apoptosis occurs
in the perifocal area, while in the epileptic focus, there are proteins
responsible for the activation of the anti-apoptotic survival pathways.
Active neuroinflammation in the epileptic focus and perifocal area
of the temporal lobe may contribute to the development of the resistance
to antiepileptic drugs and the progression of neurodegeneration in
such patients.
Implementation processes in a cognitive rehabilitation intervention for people with dementia: a complexity-informed qualitative analysis
Authors: Morgan-Trimmer Sarah , Kudlicka Aleksandra , Warmoth Krystal , Leroi Iracema , Oyebode Jan R , Pool Jackie , Woods Robert , Clare Linda
Published: October 2021

Objectives

Healthcare is often delivered through complex interventions. Understanding how to implement these successfully is important for optimising services. This article demonstrates how the complexity theory concept of ‘self-organisation’ can inform implementation, drawing on a process evaluation within a randomised controlled trial of the GREAT (Goal-oriented cognitiveRehabilitation inEarly-stageAlzheimer’s and related dementias: a multi-centre single-blind randomised controlledTrial) intervention which compared a cognitive rehabilitation intervention for people with dementia with usual treatment.

Design

A process evaluation examined experiences of GREAT therapists and participants receiving the intervention, through thematic analysis of a focus group with therapists and interviews with participants and their carers. Therapy records of participants receiving the intervention were also analysed using adapted framework analysis. Analysis adopted a critical realist perspective and a deductive-inductive approach to identify patterns in how the intervention operated.

Setting

The GREAT intervention was delivered through home visits by therapists, in eight regions in the UK.

Participants

Six therapists took part in a focus group, interviews were conducted with 25 participants and 26 carers, and therapy logs for 50 participants were analysed.

Intervention

A 16-week cognitive rehabilitation programme for people with mild-to-moderate dementia.

Results

‘Self-organisation’ of the intervention occurred through adaptations made by therapists. Adaptations included simplifying the intervention for people with greater cognitive impairment, and extending it to meet additional needs. Relational work by therapists produced an emergent outcome of ‘social support’. Self-organised aspects of the intervention were less visible than formal components, but were important aspects of how it operated during the trial. This understanding can help to inform future implementation.

Conclusions

Researchers are increasingly adopting complexity theory to understand interventions. This study extends the application of complexity theory by demonstrating how ‘self-organisation’ was a useful concept for understanding aspects of the intervention that would have been missed by focusing on formal intervention components. Analysis of self-organisation could enhance future process evaluations and implementation studies.

Trial registration number

ISRCTN21027481.
Single-cell sequencing unveils distinct immune microenvironments with CCR6-CCL20 crosstalk in human chronic pancreatitis
Authors: Lee Bomi , Namkoong Hong , Yang Yan , Huang Huang , Heller David , Szot Gregory L , Davis Mark M , Husain Sohail Z , Pandol Stephen J , Bellin Melena D , Habtezion Aida
Published: October 2021

Objective

Chronic pancreatitis (CP) is a potentially fatal disease of the exocrine pancreas, with no specific or effective approved therapies. Due to difficulty in accessing pancreas tissues, little is known about local immune responses or pathogenesis in human CP. We sought to characterise pancreatic immune responses using tissues derived from patients with different aetiologies of CP and non-CP organ donors in order to identify key signalling molecules associated with human CP.

Design

We performed single-cell level cellular indexing of transcriptomes and epitopes by sequencing and T-cell receptor (TCR) sequencing of pancreatic immune cells isolated from organ donors, hereditary and idiopathic patients with CP who underwent total pancreatectomy. We validated gene expression data by performing flow cytometry and functional assays in a second patient with CP cohort.

Results

Deep single-cell sequencing revealed distinct immune characteristics and significantly enriched CCR6+ CD4+ T cells in hereditary compared with idiopathic CP. In hereditary CP, a reduction in T-cell clonality was observed due to the increased CD4+ T (Th) cells that replaced tissue-resident CD8+ T cells. Shared TCR clonotype analysis among T-cell lineages also unveiled unique interactions between CCR6+ Th and Th1 subsets, and TCR clustering analysis showed unique common antigen binding motifs in hereditary CP. In addition, we observed a significant upregulation of the CCR6 ligand (CCL20) expression among monocytes in hereditary CP as compared with those in idiopathic CP. The functional significance of CCR6 expression in CD4+ T cells was confirmed by flow cytometry and chemotaxis assay.

Conclusion

Single-cell sequencing with pancreatic immune cells in human CP highlights pancreas-specific immune crosstalk through the CCR6-CCL20 axis, a signalling pathway that might be leveraged as a potential future target in human hereditary CP.
Prognostic performance of the ‘DICA’ endoscopic classification and the ‘CODA’ score in predicting clinical outcomes of diverticular disease: an international, multicentre, prospective cohort study
Authors: Tursi Antonio , Brandimarte Giovanni , Di Mario Francesco , Elisei Walter , Picchio Marcello , Allegretta Leonardo , Annunziata Maria Laura , Bafutto Mauro , Bassotti Gabrio , Bianco Maria Antonietta , Colucci Raffaele , Conigliaro Rita , Dumitrascu Dan , Escalante Ricardo , Ferrini Luciano , Forti Giacomo , Franceschi Marilisa , Graziani Maria Giovanna , Lammert Frank , Latella Giovanni , Maconi Giovanni , Nardone Gerardo , Camara de Castro Oliveira Lucia , Chaves Oliveira Enio , Papa Alfredo , Papagrigoriadis Savvas , Pietrzak Anna , Pontone Stefano , Poskus Tomas , Pranzo Giuseppe , Reichert Matthias Christian , Rodinò Stefano , Regula Jaroslaw , Scaccianoce Giuseppe , Scaldaferri Franco , Vassallo Roberto , Zampaletta Costantino , Zullo Angelo , Piovani Daniele , Bonovas Stefanos , Danese Silvio
Published: October 2021

Objective

To investigate the predictive value of the Diverticular Inflammation and Complication Assessment (DICA) classification and to develop and validate a combined endoscopic-clinical score predicting clinical outcomes of diverticulosis, named Combined Overview on Diverticular Assessment (CODA).

Design

A multicentre, prospective, international cohort study.

Setting

43 gastroenterology and endoscopy centres located in Europe and South America.

Participants

2215 patients (2198 completing the study) at the first diagnosis of diverticulosis/diverticular disease were enrolled. Patients were scored according to DICA classifications.

Interventions

A 3-year follow-up was performed.

Main outcome measures

To predict the acute diverticulitis and the surgery according to DICA classification. Survival methods for censored observation were used to develop and validate a novel combined endoscopic-clinical score for predicting diverticulitis and surgery (CODA score).

Results

The 3-year cumulative probability of diverticulitis and surgery was of 3.3% (95% CI 2.5% to 4.5%) in DICA 1, 11.6% (95% CI 9.2% to 14.5%) in DICA 2 and 22.0% (95% CI 17.2% to 28.0%) in DICA 3 (p<0.001), and 0.15% (95% CI 0.04% to 0.59%) in DICA 1, 3.0% (95% CI 1.9% to 4.7%) in DICA 2 and 11.0% (95% CI 7.5% to 16.0%) in DICA 3 (p<0.001), respectively. The 3-year cumulative probability of diverticulitis and surgery was ≤4%, and ≤0.7% in CODA A; <10% and <2.5% in CODA B; >10% and >2.5% in CODA C, respectively. The CODA score showed optimal discrimination capacity in predicting the risk of surgery in the development (c-statistic: 0.829; 95% CI 0.811 to 0.846) and validation cohort (c-statistic: 0.943; 95% CI 0.905 to 0.981).

Conclusions

DICA classification has a significant role in predicting the risk of diverticulitis and surgery in patients with diverticulosis, which is significantly enhanced by the CODA score.

Trial registration number

NCT02758860.
Aortic distensibility in Marfan syndrome: a potential predictor of aortic events?
Authors: van Andel Mitzi M , de Waard Vivian , Timmermans Janneke , Scholte Arthur J H A , van den Berg Maarten P , Zwinderman Aeilko H , Mulder Barbara J M , Groenink Maarten
Published: October 2021

Objectives

Patients with Marfan syndrome (MFS) are prone to develop aortic aneurysms due to fragmentation of elastic fibres, resulting in reduced distensibility of the aorta. Reduced distensibility was previously shown to predict progressive descending aorta dilatation. Here, we investigated longitudinal changes in distensibility, as a potential predictor of aortic events.

Methods

This retrospective study included all patients with MFS with at least four cardiac magnetic resonance examinations performed between 1996 and 2012. Aortic distensibility was assessed, in the ascending (level 1), proximal descending (level 2) and distal descending (level 3) aorta. Changes in distensibility were studied using linear mixed-effects regression models.

Results

In total, 35 patients with MFS (age at inclusion 28 (IQR 23–32) years, 54% men) were included. Mean aortic distensibility was already low (between 2.9×10–3/mm Hg/year and 6.4×10–3/mm Hg/year) at all levels at baseline, and significantly decreased over time at levels 2 and 3 (respectively, p=0.012 and p=0.002). The rate of distensibility loss per year (×10-3/mm Hg/year) was 0.01, 0.03 and 0.06×10–3/mm Hg at levels 1, 2 and 3, respectively. At inclusion, men exhibited very low distensibility, whereas women showed moderately reduced distensibility, gradually decreasing with age.

Aortic dilatation rate at level 2 was associated with reduced aortic distensibility. However, we could not demonstrate a direct correlation between distensibility and clinical events during a follow-up of 22 years.

Conclusion

Patients with MFS display reduced aortic distensibility already at an early age, inversely relating to aortic dilatation rate. However, in this selected patient group, distensibility seems less suitable as an individual predictor of aortic events.
Statin treatment and LDL-cholesterol treatment goal attainment among individuals with familial hypercholesterolaemia in primary care
Authors: Iyen Barbara , Akyea Ralph K , Weng Stephen , Kai Joe , Qureshi Nadeem
Published: October 2021

Objectives

Guidance recommends statin treatment in familial hypercholesterolaemia (FH) to achieve at least a 50% reduction in low-density lipoprotein cholesterol (LDL-C). We assessed statin prescribing rates and LDL-C treatment goal attainment among individuals with FH in primary care.

Methods

Using primary care electronic health records from the UK Clinical Practice Research Datalink, we identified adults with recorded diagnosis of FH, statin treatment and measures of LDL-C prior to (baseline) and 12 months after initiating statin treatment. The percentage change in LDL-C was determined, and then baseline and treatment characteristics were assessed by LDL-C treatment goal attainment.

Results

Of 3064 adults (mean age 50.8 years) with recorded diagnosis of FH and repeat LDL-C measures, 50% reduction in LDL-C from baseline was attained in 895 individuals (29.2%) in 12 months. Compared with those who did not attain this goal, these people were predominantly women; they were older at time of FH diagnosis (53.4 years vs 49.7 years) and first statin treatment (53.2 years vs 49.2 years) and had higher pretreatment total cholesterol (8.20 (SD 1.38) mmol/L vs 7.57 (SD 1.39) mmol/L) and pretreatment LDL-C (5.83 (SD 1.36) mmol/L vs 5.25 (SD 1.40) mmol/L). A higher proportion of individuals who attained the treatment goal was prescribed high-potency and medium-potency statins (24.3% and 71.7% vs 20.2% and 69.3%, respectively).

Conclusions

Less than a third of individuals on statin treatment for FH in the community achieve recommended reductions in LDL-C. Greater awareness and optimisation of treatment for FH using higher-potency statins are needed.
Yang–Mills theories on geometric spaces
Authors: Cao Yalong
Published: October 2021

The mathematical development of Yang–Mills theory is an extremely fruitful subject. The purpose of this paper is to give non-experts and researchers in interdisciplinary areas a quick overview of some history, key ideas and recent developments in this subject.
Implications of precision measurements and unitarity on CKM paradigm
Authors: Awasthi Nikhila , Vashisht Aseem , Bagai Aakriti , Ahuja Gulsheen , Gupta Manmohan
Published: October 2021

In the context of precise measurements in some of the CKM elements and angles of the unitarity triangle coupled with latest theoretical improvements of lattice QCD calculations of “hadronic factors,” we have explored the implication of these and unitarity on some of the CKM parameters which are ridden with ambiguities. In particular, we have undertaken a thorough scrutiny of the implications of the precise measurements of the angles of the unitarity triangle along with the element [Formula: see text], on the [Formula: see text], [Formula: see text], [Formula: see text], as well as the CP violating parameter [Formula: see text]. Interestingly, we find a unitarity-based analysis while unambiguously favoring [Formula: see text] exclusive-over-inclusive value which is also in agreement with the present data pertaining to [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], etc. The present analysis also shows that the presence of NP in [Formula: see text] and [Formula: see text] systems, if any, would be less than a few percent only.
Dynamical behaviors of various optical soliton solutions for the Fokas–Lenells equation
Authors: Hendi Awatif A. , Ouahid Loubna , Kumar Sachin , Owyed S. , Abdou M. A.
Published: October 2021

In this work, the new optical soliton solutions and interaction solutions for the space-time fractional Fokas–Lenells equation with fractional [Formula: see text]-derivatives are constructed via three mathematical analytical techniques, namely the extended SE method, unified solver method, and three-wave methods. The results have proved the efficiency of the suggested techniques for obtaining abundant optical soliton solutions to nonlinear evolution equations (NLEEs) and closed-form solutions in the forms of rational function solutions; hyperbolic and trigonometric function solutions and multi-wave interaction solutions are obtained. These techniques are more efficient, robust, and powerful mathematical tools for acquiring several optical soliton solutions for many other fractional space-time NLEEs that arise in optical physics and plasma physics. The graphical representations of the combined optical solitons are demonstrated using three- and two-dimensional graphics.
Khovanov–Lipshitz–Sarkar homotopy type for links in thickened higher genus surfaces
Authors: Kauffman Louis H. , Nikonov Igor Mikhailovich , Ogasa Eiji
Published: October 2021

We discuss links in thickened surfaces. We define the Khovanov–Lipshitz–Sarkar stable homotopy type and the Steenrod square for the homotopical Khovanov homology of links in thickened surfaces with genus [Formula: see text]. A surface means a closed oriented surface unless otherwise stated. Of course, a surface may or may not be the sphere. A thickened surface means a product manifold of a surface and the interval. A link in a thickened surface (respectively, a 3-manifold) means a submanifold of a thickened surface (respectively, a 3-manifold) which is diffeomorphic to a disjoint collection of circles. Our Khovanov–Lipshitz–Sarkar stable homotopy type and our Steenrod square of links in thickened surfaces with genus [Formula: see text] are stronger than the homotopical Khovanov homology of links in thickened surfaces with genus [Formula: see text]. It is the first meaningful Khovanov–Lipshitz–Sarkar stable homotopy type of links in 3-manifolds other than the 3-sphere. We point out that our theory has a different feature in the torus case.
Boundary separated and clustered layer positive solutions for an elliptic Neumann problem with large exponent
Authors: Zhang Yibin
Published: October 2021

Given a smooth bounded domain [Formula: see text] in [Formula: see text] with [Formula: see text], we study the existence and the profile of positive solutions for the following elliptic Neumann problem: [Formula: see text] where [Formula: see text] is a large exponent and [Formula: see text] denotes the outer unit normal vector to the boundary [Formula: see text]. For suitable domains [Formula: see text], by a constructive way we prove that, for any non-negative integers k, l with [Formula: see text], if p is large enough, such a problem has a family of positive solutions with k boundary layers and l interior layers which concentrate along [Formula: see text] distinct [Formula: see text]-dimensional minimal submanifolds of [Formula: see text], or collapse to the same [Formula: see text]-dimensional minimal submanifold of [Formula: see text] as [Formula: see text].
The N = 1 super Heisenberg–Virasoro vertex algebra at level zero
Authors: Adamović Dražen , Jandrić Berislav , Radobolja Gordan
Published: October 2021

We study the representation theory of the [Formula: see text] super Heisenberg–Virasoro vertex algebra at level zero, which extends the previous work on the Heisenberg–Virasoro vertex algebra [D. Adamović and G. Radobolja, Free field realization of the twisted Heisenberg–Virasoro algebra at level zero and its applications, J. Pure Appl. Algebra 219(10) (2015) 4322–4342; D. Adamović and G. Radobolja, Self-dual and logarithmic representations of the twisted Heisenberg–Virasoro algebra at level zero, Commun. Contemp. Math. 21(2) (2019) 1850008; Y. Billig, Representations of the twisted Heisenberg–Virasoro algebra at level zero, Can. Math. Bull. 46(4) (2003) 529–537] to the super case. We calculated all characters of irreducible highest weight representations by investigating certain Fock space representations. Quite surprisingly, we found that the maximal submodules of certain Verma modules are generated by subsingular vectors. The formulas for singular and subsingular vectors are obtained using screening operators appearing in a study of certain logarithmic vertex algebras [D. Adamović and A. Milas, On W-algebras associated to (2, [Formula: see text]) minimal models and their representations, Int. Math. Res. Notices 2010(20) (2010) 3896–3934].
Rings with xn + x or xn − x nilpotent
Authors: Abyzov Adel N. , Danchev Peter V. , Tapkin Daniel T.
Published: October 2021

Let [Formula: see text] be a ring and let [Formula: see text] be an arbitrary but fixed positive integer. We characterize those rings [Formula: see text] whose elements [Formula: see text] satisfy at least one of the relations that [Formula: see text] or [Formula: see text] is a nilpotent whenever [Formula: see text]. This extends results from the same branch obtained by Danchev [A characterization of weakly J(n)-rings, J. Math. Appl. 41 (2018) 53–61], Koşan et al. [Rings with [Formula: see text] nilpotent, J. Algebra Appl. 19 (2020)] and Abyzov and Tapkin [On rings with [Formula: see text] nilpotent, J. Algebra Appl. 21 (2022)], respectively.
AUTOMATED RECOGNITION OF DEPRESSION FROM FEWER-SHOT LEANING IN RESTING-STATE fMRI WITH ReHo USING DEEP CONVOLUTIONAL NEURAL NETWORK
Authors: LI MI , ZHANG JINYU , ZHAI QIAN , KANG JIAMING , LU SHENGFU , YANG JIAN
Published: October 2021

Up to now, there is still the absence of research about depression recognition using resting-state functional magnetic resonance imaging (rest_fMRI) and deep learning. Previous studies have shown that regional homogeneity (ReHo) of rest_fMRI (rest_ReHo_fMRI) is a characterization of the functional synchronization of adjacent voxels in brain regions, and the mental and behavioral abnormalities in depression are due to an imbalance of ReHo synchronization in some brain functional areas. Accordingly, this paper presents a method for depression recognition using rest_ReHo_fMRI. First, the rest_ReHo_fMRI is extracted from the preprocessed rest-fMRI by calculation. Then, deep convolutional networks (such as VGG16) pretrained on ImageNet are used to automatically complete extracting the classification features from rest_ReHo_fMRI. Finally, the Kernel Extreme Learning Machine (KELM) was used to classify the depression. The results of the test set show that the proposed method achieves 89.07% in sensitivity and 89.74% in specificity. This study suggests that features of rest_ReHo_fMRI can be used as biomarkers to distinguish depression from normal people.
An overview of entropy principle
Authors: Liu I-Shih
Published: October 2021

A brief overview of the development from classical linear irreversible thermodynamics to the modern rational thermodynamics with Coleman–Noll and Müller–Liu procedures is presented, emphasizing the basic assumptions and formulation details. The major arguments concerned are the improvement of physical assumptions and mathematical formulation differences. Extended thermodynamics is also briefly commented.
Deep Active Context Estimation for Automated COVID-19 Diagnosis
Authors: Chen Bingzhi , Liu Yishu , Zhang Zheng , Li Yingjian , Zhang Zhao , Lu Guangming , Yu Hongbing
Published: October 2021

Many studies on automated COVID-19 diagnosis have advanced rapidly with the increasing availability of large-scale CT annotated datasets. Inevitably, there are still a large number of unlabeled CT slices in the existing data sources since it requires considerable consuming labor efforts. Notably, cinical experience indicates that the neighboring CT slices may present similar symptoms and signs. Inspired by such wisdom, we propose DACE, a novel CNN-based deep active context estimation framework, which leverages the unlabeled neighbors to progressively learn more robust feature representations and generate a well-performed classifier for COVID-19 diagnosis. Specifically, the backbone of the proposed DACE framework is constructed by a well-designed Long-Short Hierarchical Attention Network (LSHAN), which effectively incorporates two complementary attention mechanisms, i.e.,
short-range channel interactions (SCI)
module and
long-range spatial dependencies (LSD)
module, to learn the most discriminative features from CT slices. To make full use of such available data, we design an efficient context estimation criterion to carefully assign the additional labels to these neighbors. Benefiting from two complementary types of informative annotations from

-nearest neighbors, i.e., the majority of high-confidence samples with pseudo labels and the minority of low-confidence samples with hand-annotated labels, the proposed LSHAN can be fine-tuned and optimized in an incremental learning manner. Extensive experiments on the Clean-CC-CCII dataset demonstrate the superior performance of our method compared with the state-of-the-art baselines.
Medical Image Classification based on an Adaptive Size Deep Learning Model
Authors: Liu Xiangbin , He Jiesheng , Song Liping , Liu Shuai , Srivastava Gautam
Published: October 2021

With the rapid development of Artificial Intelligence (AI), deep learning has increasingly become a research hotspot in various fields, such as medical image classification. Traditional deep learning models use Bilinear Interpolation when processing classification tasks of multi-size medical image dataset, which will cause the loss of information of the image, and then affect the classification effect. In response to this problem, this work proposes a solution for an adaptive size deep learning model. First, according to the characteristics of the multi-size medical image dataset, the optimal size set module is proposed in combination with the unpooling process. Next, an adaptive deep learning model module is proposed based on the existing deep learning model. Then, the model is fused with the size fine-tuning module used to process multi-size medical images to obtain a solution of the adaptive size deep learning model. Finally, the proposed solution model is applied to the pneumonia CT medical image dataset. Through experiments, it can be seen that the model has strong robustness, and the classification effect is improved by about 4% compared with traditional algorithms.
A Survey of Flaky Tests
Authors: Parry Owain , Kapfhammer Gregory M. , Hilton Michael , McMinn Phil
Published: October 2021

Tests that fail inconsistently, without changes to the code under test, are described as
flaky
. Flaky tests do not give a clear indication of the presence of software bugs and thus limit the reliability of the test suites that contain them. A recent survey of software developers found that 59% claimed to deal with flaky tests on a monthly, weekly, or daily basis. As well as being detrimental to developers, flaky tests have also been shown to limit the applicability of useful techniques in software testing research. In general, one can think of flaky tests as being a threat to the validity of any methodology that assumes the outcome of a test only depends on the source code it covers. In this article, we systematically survey the body of literature relevant to flaky test research, amounting to 76 papers. We split our analysis into four parts: addressing the causes of flaky tests, their costs and consequences, detection strategies, and approaches for their mitigation and repair. Our findings and their implications have consequences for how the software-testing community deals with test flakiness, pertinent to practitioners and of interest to those wanting to familiarize themselves with the research area.
Utilization of Real Time Behavior and Geographical Attraction for Location Recommendation
Authors: Ren Xinyu , Rahimi Seyyed Mohammadreza , Wang Xin
Published: October 2021

Personalized location recommendation is an increasingly active topic in recent years, which recommends appropriate locations to users based on their temporal and geospatial visiting patterns. Current location recommendation methods usually estimate the users’ visiting preference probabilities from the historical check-ins in batch. However, in practice, when users’ behaviors are updated in real-time, it is often cost-inhibitive to re-estimate and updates users’ visiting preference using the same batch methods due to the number of check-ins. Moreover, an important nature of users’ movement patterns is that users are more attracted to an area where have dense locations with same categories for conducting specific behaviors. In this paper, we propose a location recommendation method called GeoRTGA by utilizing the real time user behaviors and geographical attractions to tackle the problems. GeoRTGA contains two sub-models: real time behavior recommendation model and attraction-based spatial model. The real time behavior recommendation model aims to recommend real-time possible behaviors which users prefer to visit, and the attraction-based spatial model is built to discover the category-based spatial and individualized spatial patterns based on the geographical information of locations and corresponding location categories and check-in numbers. Experiments are conducted on four public real-world check-in datasets, which show that the proposed GeoRTGA outperforms the five existing location recommendation methods.
Salmonellain Swine: Prevalence, Multidrug Resistance, and Vaccination Strategies
Authors: Bearson Shawn M.D.
Published: October 2021

An estimated 1.3 million Salmonella infections and 420 deaths occur annually in the United States, with an estimated economic burden of $3.7 billion. More than 50% of US swine operations test positive for Salmonella according to the National Animal Health Monitoring System, and 20% of Salmonella from swine are multidrug resistant (resistant to ≥3 antimicrobial classes) as reported by the National Antimicrobial Resistance Monitoring System. This review on Salmonella in swine addresses the current status of these topics by discussing antimicrobial resistance and metal tolerance in Salmonella and the contribution of horizontal gene transfer. A major challenge in controlling Salmonella is that Salmonella is a foodborne pathogen in humans but is often a commensal in food animals and thereby establishes an asymptomatic reservoir state in such animals, including swine. As food animal production systems continue to expand and antimicrobial usage becomes more limited, the need for Salmonella interventions has intensified. A promising mitigation strategy is vaccination against Salmonella in swine to limit animal, environmental, and food contamination.
New Developments in Social Network Analysis
Authors: Brass Daniel J.
Published: October 2021

This review of social network analysis focuses on identifying recent trends in interpersonal social networks research in organizations, and generating new research directions, with an emphasis on conceptual foundations. It is organized around two broad social network topics: structural holes and brokerage and the nature of ties. New research directions include adding affect, behavior, and cognition to the traditional structural analysis of social networks, adopting an alter-centric perspective including a relational approach to ego and alters, moving beyond the triad in structural hole and brokerage research to consider alters as brokers, expanding the nature of ties to include negative, multiplex/dissonant, and dormant ties, and exploring the value of redundant ties. The challenge is to answer the question “What's next in social network analysis?”
A Balancing Act: p53 Activity from Tumor Suppression to Pathology and Therapeutic Implications
Authors: Wang Mengxiong , Attardi Laura D.
Published: October 2021

TP53, encoding the p53 transcription factor, is the most frequently mutated tumor suppressor gene across all human cancer types. While p53 has long been appreciated to induce antiproliferative cell cycle arrest, apoptosis, and senescence programs in response to diverse stress signals, various studies in recent years have revealed additional important functions for p53 that likely also contribute to tumor suppression, including roles in regulating tumor metabolism, ferroptosis, signaling in the tumor microenvironment, and stem cell self-renewal/differentiation. Not only does p53 loss or mutation cause cancer, but hyperactive p53 also drives various pathologies, including developmental phenotypes, premature aging, neurodegeneration, and side effects of cancer therapies. These findings underscore the importance of balanced p53 activity and influence our thinking of how to best develop cancer therapies based on modulating the p53 pathway.
Avoiding Thermal Issues During Fast Charging Starting with Proper Cell Selection Criteria
Authors: Gonzalez-Aguirre Eneko , Gastelurrutia Jon , Suresh Patil Mahesh , del Portillo-Valdes Luis
Published: October 2021

Proper cell selection is determinant to optimize systems and reduce risks for new and high demanding areas such as electromobility. Thermal performance must be an indispensable selection criterion to avoid thermal issues in these fields, so cells should be correctly characterised and modelled. In this paper, an improved cell selection methodology that focuses on the thermal performance criterion especially for fast charging applications is proposed. After a first selection, two cell candidates were characterised and their heat generation was modelled and compared. With the selected cell, heat generation rate was determined and a 3C fast charge was performed to evidence the predicted thermal performance. The improved methodology identified a cell with an advantageous entropic heat coefficient (EHC) for fast charging, decreasing the heat energy generation by 54% concerning the other candidate cell, which results in optimisation of the thermal management system (TMS). This emphasizes the importance of proper cell selection based on thorough thermal characterization.
Synthesis of Electrospun NASICON Li1.5Al0.5Ge1.5(PO4)3 Solid Electrolyte Nanofibers by Control of Germanium Hydrolysis
Authors: La Monaca Andrea , Girard Gabriel , Savoie Sylvio , Bertoni Giovanni , Krachkovskiy Sergey , Vijh Ashok , Pierini Filippo , Rosei Federico , Paolella Andrea
Published: October 2021

We report the synthesis of ceramic Li1.5Al0.5Ge1.5(PO4)3 (LAGP) nanofibers by combining sol–gel and electrospinning techniques. A homogeneous and stable precursor solution based on chlorides was achieved by controlling Ge hydrolysis. Subsequent electrospinning and heat treatment resulted in highly porous nanostructured NASICON pellets. After a full chemical-physical characterization, various amounts of LAGP nanofibers were used as a filler to develop polyethylene oxide (PEO)-based composite electrolytes. The addition of 10% LAGP nanofibers has allowed doubling the ionic conductivity of the plain polymer electrolyte, by providing longer ion-conductive paths and reducing PEO crystallinity. These findings are promising towards developing solution-based synthesis approaches featuring Ge precursors. In addition, the achieved LAGP nanofibers proved to be a promising nanofiller candidate to develop composite electrolytes for next-generation solid-state batteries.
Physiological roles of mammalian transmembrane adenylyl cyclase isoforms
Authors: Ostrom Katrina F. , LaVigne Justin E. , Brust Tarsis F. , Seifert Roland , Dessauer Carmen W. , Watts Val J. , Ostrom Rennolds S.
Published: October 2021

Adenylyl cyclases (ACs) catalyze the conversion of ATP to the ubiquitous second messenger cAMP. Mammals possess nine isoforms of transmembrane ACs, dubbed AC1–9, that serve as major effector enzymes of G protein-coupled receptors (GPCRs). The transmembrane ACs display varying expression patterns across tissues, giving the potential for them to have a wide array of physiological roles. Cells express multiple AC isoforms, implying that ACs have redundant functions. Furthermore, all transmembrane ACs are activated by Gαs, so it was long assumed that all ACs are activated by Gαs-coupled GPCRs. AC isoforms partition to different microdomains of the plasma membrane and form prearranged signaling complexes with specific GPCRs that contribute to cAMP signaling compartments. This compartmentation allows for a diversity of cellular and physiological responses by enabling unique signaling events to be triggered by different pools of cAMP. Isoform-specific pharmacological activators or inhibitors are lacking for most ACs, making knockdown and overexpression the primary tools for examining the physiological roles of a given isoform. Much progress has been made in understanding the physiological effects mediated through individual transmembrane ACs. GPCR-AC-cAMP signaling pathways play significant roles in regulating functions of every cell and tissue, so understanding each AC isoform’s role holds potential for uncovering new approaches for treating a vast array of pathophysiological conditions.
A Novel Crow Search Algorithm Based on Improved Flower Pollination
Authors: Cheng Qian , Huang Huajuan , Chen Minbo
Published: October 2021

Crow search algorithm (CSA) is a new type of swarm intelligence optimization algorithm proposed by simulating the crows’ intelligent behavior of hiding and retrieving food. The algorithm has the characteristics of simple structure, few control parameters, and easy implementation. Like most optimization algorithms, the crow search algorithm also has the disadvantage of slow convergence and easy fall into local optimum. Therefore, a crow search algorithm based on improved flower pollination algorithm (IFCSA) is proposed to solve these problems. First, the search ability of the algorithm is balanced by the reasonable change of awareness probability, and then the convergence speed of the algorithm is improved. Second, when the leader finds himself followed, the cross-pollination strategy with Cauchy mutation is introduced to avoid the blindness of individual location update, thus improving the accuracy of the algorithm. Experiments on twenty benchmark problems and speed reducer design were conducted to compare the performance of IFCSA with that of other algorithms. The results show that IFCSA has better performance in function optimization and speed reducer design problem.
The Effectiveness of Cochlear Implantation for Children of Hereditary Deafness: A Multicenter Retrospective Study
Authors: Liu Yongzhi , Li Yelin , Zhao Yuanyuan , Ao Liying , Wen Yuting , Ding Haitao
Published: October 2021

Aim. To study the effectiveness of cochlear implantation in deaf children with gene mutation. Method. 420 children from three medical centers with cochlear implants were selected. Before wearing the cochlear implant and 6 months after wearing the cochlear implant, the children’s rehabilitation efficacy was evaluated through categories of auditory performance (CAP) and speech intelligibility rating (SIR). The SSF-MCDI and MUSS before and after the treatment were also compared. Results. The CAP and SIR scores of the children after the intervention were higher than those before the intervention (

P

 < 0.05). Univariate analysis found that the age of cochlear implantation, parents’ education level, and monthly family income were all influencing factors of the CAP score of children with prelingual cochlear implantation (

P

 < 0.05). Multivariate logistic regression analysis shows that the age of cochlear implantation <5 years, parents’ education level, and monthly household income are all independent risks of CAP scores in children with prelingual deaf cochlear implantation. Univariate analysis found that the age of cochlear implantation, parents’ education level, and monthly household income are all based on the SIR scores of children with prelingual cochlear implantation. Multivariate logistic regression analysis can be obtained: the age of cochlear implantation less than 5 years, the education level of parents, and the monthly family income are independent risk factors for SIR scores in children with prelingual deaf cochlear implantation (

P

 < 0.05). Conclusion. The rehabilitation effect of cochlear implants is significant, and the age of cochlear implantation, parents’ education level, and monthly family income are all related factors that affect the rehabilitation effect.
Evaluation of the College English Flipped Classroom Teaching Model Based on Data Mining Algorithms
Authors: Xiang Jiangshui
Published: October 2021

Aiming at the problem of the inability to classify data due to the excessive amount of teaching resources, which leads to the college English flipped classroom teaching model’s low resource sharing rate and the poor accuracy of score statistical analysis, a university-based data mining algorithm is designed. Research on the evaluation of english flipped classroom teaching model is conducted, the strategy of applying the flipped classroom in college English teaching is analyzed, the characteristics and advantages of this model are explored, the data mining algorithm to practical teaching is applied, and the decision tree C4.5 classification technology is used to achieve accurate classification of massive student test scores. The classification technology selects classification attributes based on the information gain rate. It uses the postpruning method to process data to improve the accuracy of data classification. Finally, the statistical analysis results of the business logic layer are transmitted to the user through the browser application layer using the WEB server. The experimental results show that using this article’s evaluation method, the college English flipped classroom teaching model can achieve a high resource sharing rate, high accuracy of performance statistics analysis, and a good teaching effect.
The Short‐Term Load Forecasting Using an Artificial Neural Network Approach with Periodic and Nonperiodic Factors: A Case Study of Tai′an, Shandong Province, China
Authors: Sun Jiuyun , Dong Huanhe , Gao Ya , Fang Yong , Kong Yuan
Published: October 2021

Accurate electricity load forecasting is an important prerequisite for stable electricity system operation. In this paper, it is found that daily and weekly variations are prominent by the power spectrum analysis of the historical loads collected hourly in Tai’an, Shandong Province, China. In addition, the influence of the extraneous variables is also very obvious. For example, the load dropped significantly for a long period of time during the Chinese Lunar Spring Festival. Therefore, an artificial neural network model is constructed with six periodic and three nonperiodic factors. The load from January 2016 to August 2018 was divided into two parts in the ratio of 9 : 1 as the training set and the test set, respectively. The experimental results indicate that the daily prediction model with selected factors can achieve higher forecasting accuracy.
An Efficient and Privacy-Preserving Biometric Identification Scheme Based on the FITing-Tree
Authors: Yang Xiaopeng , Zhu Hui , Zhang Songnian , Lu Rongxing , Gao Xuesong
Published: October 2021

Biometric identification services have been applied to almost all aspects of life. However, how to securely and efficiently identify an individual in a huge biometric dataset is still very challenging. For one thing, biometric data is very sensitive and should be kept secure during the process of biometric identification. On the other hand, searching a biometric template in a large dataset can be very time-consuming, especially when some privacy-preserving measures are adopted. To address this problem, we propose an efficient and privacy-preserving biometric identification scheme based on the FITing-tree, iDistance, and a symmetric homomorphic encryption (SHE) scheme with two cloud servers. With our proposed scheme, the privacy of the user’s identification request and service provider’s dataset is guaranteed, while the computational costs of the cloud servers in searching the biometric dataset can be kept at an acceptable level. Detailed security analysis shows that the privacy of both the biometric dataset and biometric identification request is well protected during the identification service. In addition, we implement our proposed scheme and compare it to a previously reported M-Tree based privacy-preserving identification scheme in terms of computational and communication costs. Experimental results demonstrate that our proposed scheme is indeed efficient in terms of computational and communication costs while identifying a biometric template in a large dataset.
Experimental Study on Seismic Behavior of Composite Shear Wall with Inclined Bars
Authors: Wang Y. L. , Hu D. D. , Fang B.
Published: October 2021

The composite shear wall is the core component of the thermal insulation integrated structure, which is a load‐bearing shear wall with good thermal insulation, sound insulation, and seismic resistance. To improve the applicable height of the composite shear wall structure, a cohesive sandwich heat‐insulation composite shear wall with door frame inclined tendon and diamond‐shaped inclined tendon is proposed, and the quasistatic force of four 1/2‐scale shear wall test specimens is carried out. Different specimens are analyzed, including failure modes, hysteresis curves, skeleton curves, stiffness degradation, ductility performance, and energy dissipation capacity. The following conclusions are drawn: the failure modes of the specimens are bending and shear failure; the ultimate strength and deformation performance of the composite wall close to the solid wall; the composite wall with the door frame inclined tendon can effectively delay the wall cracking and improve the bearing capacity and energy consumption capacity of the composite wall; the configuration of the diamond‐shaped inclined tendon improves the ductility and energy dissipation capacity of the composite wall.
Usability of the Level of the S100B Protein, the Gosling Pulsatility Index, and the Jugular Venous Oxygen Saturation for the Prediction of Mortality and Morbidity in Patients with Severe Traumatic Brain Injury
Authors: Tomasiuk Ryszard , Dzierzęcki Sebastian , Zaczyński Artur , Ząbek Mirosław
Published: October 2021

The high frequency of traumatic brain injury imposes severe economic stress on health and insurance services. The objective of this study was to analyze the association between the serum S100B protein, the Gosling pulsatility index (PI), and the level of oxygen saturation at the tip of the internal jugular vein (SjVO2%) in patients diagnosed with severe TBI. The severity of TBI was assessed by a GCS score ≤ 8 stratified by Glasgow outcome scale (GOS) measured on the day of discharge from the hospital. Two groups were included: GOS < 4 (unfavorable group (UG)) and GOS ≥ 4 (favorable group (UG)). S100B levels were higher in the UG than in the FG. PI levels in the UG were also substantially higher than in the FG. There were similar levels of SjVO2 in the two groups. This study confirmed that serum S100B levels were higher in patients with unfavorable outcomes than in those with favorable outcomes. Moreover, a clear demarcation in PI between unfavorable and FGs was observed. This report shows that mortality and morbidity rates in patients with traumatic brain injury can be assessed within the first 4 days of hospitalization using the S100B protein, PI values, and SjVO2.