bluuebunny/crossref_metadata_2025_split huggingface dataset:
Nano-additive manufacturing of multilevel strengthened aluminum matrix composites
Authors: Shao Chenwei , Li Haoyang , Zhu Yankun , Li Peng , Yu Haoyang , Zhang Zhefeng , Gleiter Herbert , McDonald André , Hogan James 
Published: October 2022

Abstract
Nanostructured materials are being actively developed, while it remains an open question how to rapidly scale them up to bulk engineering materials for broad industrial applications. This study propose an industrial approach to rapidly fabricate high-strength large-size nanostructured metal matrix composites and attempts to investigate and optimize the deposition process and strengthening mechanism. Here, advanced nanocrystalline aluminum matrix composites (nanoAMCs) were assembled for the first time by a novel nano-additive manufacturing method that was guided by numerical simulations (i.e. the in-flight particle model and the porefree deposition model). The present nanoAMC with a mean grain size <50 nm in matrix exhibited hardness eight times higher than the bulk aluminum and shows the highest hardness among all Al–Al2O3 composites reported to date in the literature, which are the outcome of controlling multiscale strengthening mechanisms from tailoring solution atoms, dislocations, grain boundaries, precipitates, and externally introduced reinforcing particles. The present high-throughput strategy and method can be extended to design and architect advanced coatings or bulk materials in a highly efficient (synthesizing a nanostructured bulk with dimensions of 50 × 20 × 4 mm3 in 9 min) and highly flexible (regulating the gradient microstructures in bulk) way, which is conducive to industrial production and application.
Effect of local inhomogeneous permeability of Ni-Mn-Ga in dependency of constrains
Authors: Titsch Christian , Henkner Rick , Drossel Welf-Guntram 
Published: October 2022

Abstract
Magnetic shape memory alloys are a promising but rather novel smart material class. A test stand was developed and verified to characterize commercially available samples. The test stand generates a homogeneous magnetic field within the air gap when no sample is inserted. Once a Ni-Mn-Ga sample is inserted, the field remains homogeneous in the horizontal but not in the vertical direction. Rotating the sample around the long edge leads to a mirrored behavior. An FEM simulation based on the simplified description of the switching effect was built up. The simulation reproduced the experimental data principle but not in detail. The simplified description of the effect helps to understand the behavior of magnetic memory alloys, but for a deeper understanding micro-simulations with precise boundary conditions are necessary.
Machine-learning accelerated identification of exfoliable two-dimensional materials
Authors: Tohidi Vahdat Mohammad , Varoon Agrawal Kumar , Pizzi Giovanni 
Published: October 2022

Abstract
Two-dimensional (2D) materials have been a central focus of recent research because they host a variety of properties, making them attractive both for fundamental science and for applications. It is thus crucial to be able to identify accurately and efficiently if bulk three-dimensional (3D) materials are formed by layers held together by a weak binding energy that, thus, can be potentially exfoliated into 2D materials. In this work, we develop a machine-learning (ML) approach that, combined with a fast preliminary geometrical screening, is able to efficiently identify potentially exfoliable materials. Starting from a combination of descriptors for crystal structures, we work out a subset of them that are crucial for accurate predictions. Our final ML model, based on a random forest classifier, has a very high recall of 98%. Using a SHapely Additive exPlanations analysis, we also provide an intuitive explanation of the five most important variables of the model. Finally, we compare the performance of our best ML model with a deep neural network architecture using the same descriptors. To make our algorithms and models easily accessible, we publish an online tool on the Materials Cloud portal that only requires a bulk 3D crystal structure as input. Our tool thus provides a practical yet straightforward approach to assess whether any 3D compound can be exfoliated into 2D layers.
‘Flux+Mutability’: a conditional generative approach to one-class classification and anomaly detection
Authors: Fanelli C , Giroux J , Papandreou Z 
Published: October 2022

Abstract
Anomaly Detection is becoming increasingly popular within the experimental physics community. At experiments such as the Large Hadron Collider, anomaly detection is growing in interest for finding new physics beyond the Standard Model. This paper details the implementation of a novel Machine Learning architecture, called Flux+Mutability, which combines cutting-edge conditional generative models with clustering algorithms. In the ‘flux’ stage we learn the distribution of a reference class. The ‘mutability’ stage at inference addresses if data significantly deviates from the reference class. We demonstrate the validity of our approach and its connection to multiple problems spanning from one-class classification to anomaly detection. In particular, we apply our method to the isolation of neutral showers in an electromagnetic calorimeter and show its performance in detecting anomalous dijets events from standard QCD background. This approach limits assumptions on the reference sample and remains agnostic to the complementary class of objects of a given problem. We describe the possibility of dynamically generating a reference population and defining selection criteria via quantile cuts. Remarkably this flexible architecture can be deployed for a wide range of problems, and applications like multi-class classification or data quality control are left for further exploration.
Coefficient multipliers in the Hardy space associated with Jacobi expansions
Authors: Shi Yehao , Li Zhongkai 
Published: October 2022

In this paper a multiplier theorem in the Hardy space

H
1

(

T

)

H^1(\mathbb {T})

associated with Jacobi expansions of exponential type is proved, that is, a bilateral sequence

{

λ
n

}

n
=
−
∞

∞

\left \{\lambda _n\right \}_{n=-\infty }^{\infty }

is a multiplier from

H
1

(

T

)

H^1(\mathbb {T})

into the sequence space

ℓ
1

(

Z

)

\ell ^1(\mathbb {Z})

associated with Jacobi expansions of exponential type, if
\[

sup
N

∑

k
=
1

∞

(

∑

k
N
>

|

j

|

≤
(
k
+
1
)
N

|

λ
j

|

)

2

>
∞
.

\sup _N\sum _{k=1}^{\infty }\left (\sum _{kN>|j|\le (k+1)N}|\lambda _j|\right )^2>\infty .

\]
This is a generalization of a multiplier theorem on usual Fourier expansions in the Hardy space

H
1

(

T

)

H^1(\mathbb {T})

, and for

λ
n

=
(

|

n

|

+
1

)

−
1

\lambda _n=(|n|+1)^{-1}

, a Hardy type inequality for Jacobi expansions is immediate which has ever been proved by Kanjin and Sato [Math. Inequal. Appl. 7 (2004), pp. 551–555].
The Effect of Automatic Text Simplification on L2 Readers’ Text Comprehension
Authors: Murphy Odo Dennis 
Published: October 2022

Abstract
Texts used in L2 classrooms have traditionally been simplified manually, but recent technological advances allow us to investigate whether automatic text simplification (ATS) software can help L2 learners comprehend texts in second and foreign languages. Participants were divided into low and high L2 reading proficiency groups and assigned to read either the authentic or automatically simplified version of a text and completed a free recall task and MC comprehension test. The results did not show any significant correlations among the variables of topic knowledge, topic interest, and MC comprehension, but there were correlations among L2 reading comprehension, MC comprehension, and free recall results. Results also showed that the automatically simplified text facilitated the comprehension of the more proficient readers but not the less proficient readers according to their performance on the free recall assessment. Implications are that L2 teachers cannot blindly use whatever text they want with ATS, and ATS software designers may need to reconsider the current conservative approach to simplification that many ATS tools use.
Identification of drug-side effect association via restricted Boltzmann machines with penalized term
Authors: Qian Yuqing , Ding Yijie , Zou Quan , Guo Fei 
Published: October 2022

Abstract
In the entire life cycle of drug development, the side effect is one of the major failure factors. Severe side effects of drugs that go undetected until the post-marketing stage leads to around two million patient morbidities every year in the United States. Therefore, there is an urgent need for a method to predict side effects of approved drugs and new drugs. Following this need, we present a new predictor for finding side effects of drugs. Firstly, multiple similarity matrices are constructed based on the association profile feature and drug chemical structure information. Secondly, these similarity matrices are integrated by Centered Kernel Alignment-based Multiple Kernel Learning algorithm. Then, Weighted K nearest known neighbors is utilized to complement the adjacency matrix. Next, we construct Restricted Boltzmann machines (RBM) in drug space and side effect space, respectively, and apply a penalized maximum likelihood approach to train model. At last, the average decision rule was adopted to integrate predictions from RBMs. Comparison results and case studies demonstrate, with four benchmark datasets, that our method can give a more accurate and reliable prediction result.
ATLIGATOR: editing protein interactions with an atlas-based approach
Authors: Kynast Josef Paul , Schwägerl Felix , Höcker Birte 
Published: October 2022

Abstract

Motivation
Recognition of specific molecules by proteins is a fundamental cellular mechanism and relevant for many applications. Being able to modify binding is a key interest and can be achieved by repurposing established interaction motifs. We were specifically interested in a methodology for the design of peptide binding modules. By leveraging interaction data from known protein structures, we plan to accelerate the design of novel protein or peptide binders.

Results
We developed ATLIGATOR—a computational method to support the analysis and design of a protein’s interaction with a single side chain. Our program enables the building of interaction atlases based on structures from the PDB. From these atlases pocket definitions are extracted that can be searched for frequent interactions. These searches can reveal similarities in unrelated proteins as we show here for one example. Such frequent interactions can then be grafted onto a new protein scaffold as a starting point of the design process. The ATLIGATOR tool is made accessible through a python API as well as a CLI with python scripts.

Availability and implementation
Source code can be downloaded at github (https://www.github.com/Hoecker-Lab/atligator), installed from PyPI (‘atligator’) and is implemented in Python 3.
Cognitive Impairment in Tuberculous Meningitis
Authors: Davis Angharad G , Dreyer Anna J , Albertyn Christine , Maxebengula Mpumi , Stek Cari , Wasserman Sean , Marais Suzaan , Bateman Kathleen , Solms Mark , Joska John , Wilkinson Robert J , Nightingale Sam 
Published: October 2022

AbstractBackgroundCognitive impairment is reported as a common complication in adult tuberculous meningitis (TBM), yet few studies have systematically assessed the frequency and nature of impairment. Moreover, the impact of impairment on functioning and medication adherence has not been described.MethodsA cognitive test battery (10 measures assessing 7 cognitive domains) was administered to 34 participants with human immunodeficiency virus (HIV)–associated TBM 6 months after diagnosis. Cognitive performance was compared with that a comparator group of 66 people with HIV without a history of tuberculosis. A secondary comparison was made between participants with TBM and 26 participants with HIV 6 months after diagnosis of tuberculosis outside the central nervous system (CNS). Impact on functioning was evaluated, including through assessment of medication adherence.ResultsOf 34 participants with TBM, 16 (47%) had low performance on cognitive testing. Cognition was impaired across all domains. Global cognitive performance was significantly lower in participants with TBM than in people with HIV (mean T score, 41 vs 48, respectively; P &lt; .001). These participants also had lower global cognition scores than those with non-CNS tuberculosis (mean global T score, 41 vs 46; P = .02). Functional outcomes were not significantly correlated with cognitive performance in the subgroup of participants in whom this was assessed (n = 19).ConclusionsLow cognitive performance following HIV-associated TBM is common. This effect is independent of, and additional to, effects of HIV and non-CNS tuberculosis disease. Further studies are needed to understand longer-term outcomes, clarify the association with treatment adherence, a key predictor of outcome in TBM, and develop context-specific tools to identify individuals with cognitive difficulties in order to improve outcomes in TBM.
Randomized Trials With Repeatedly Measured Outcomes: Handling Irregular and Potentially Informative Assessment Times
Authors: Pullenayegum Eleanor M , Scharfstein Daniel O 
Published: October 2022

AbstractRandomized trials are often designed to collect outcomes at fixed points in time after randomization. In practice, the number and timing of outcome assessments can vary among participants (i.e., irregular assessment). In fact, the timing of assessments may be associated with the outcome of interest (i.e., informative assessment). For example, in a trial evaluating the effectiveness of treatments for major depressive disorder, not only did the timings of outcome assessments vary among participants but symptom scores were associated with assessment frequency. This type of informative observation requires appropriate statistical analysis. Although analytic methods have been developed, they are rarely used. In this article, we review the literature on irregular assessments with a view toward developing recommendations for analyzing trials with irregular and potentially informative assessment times. We show how the choice of analytic approach hinges on assumptions about the relationship between the assessment and outcome processes. We argue that irregular assessment should be treated with the same care as missing data, and we propose that trialists adopt strategies to minimize the extent of irregularity; describe the extent of irregularity in assessment times; make their assumptions about the relationships between assessment times and outcomes explicit; adopt analytic techniques that are appropriate to their assumptions; and assess the sensitivity of trial results to their assumptions.
INITIAL ELECTROCARDIOGRAPHIC PROFILE OF THE POPULATION OF TANGIERS – MOROCCO INFECTED WITH SARS CoV2: ABOUT 1087 CASES
Authors: Ouafdel Ousama 
Published: October 2022

Abstract

Introduction
Coronavirus disease 2019 (COVID-19) is a pathology which initially affects the respiratory system, but also many other noble organs due to his inflammatory phase (Cytokine Storm), especially the cardiovascular system.
The cardiovascular risk factors present in a population infected with SARS-COV 2 are of particular interest.
The ECG is a fast available means to get an idea about the cardiac status.
This permits not only to diagnose an unknown pre-existing pathology but also to detect a killing cardiac complication of the SARS COV-2 infection.

Methods
We performed a cross-sectional study on COVID-19 patients passing through Tangiers sorting center. Clinical characteristics and ECG variables were manually abstracted from the electronic health record and first ECG. Our aim was to describe the electrocardiographic features of Covid-19 Patients in Morocco.

Results
1087 patients who presented to the COVID 19 triage center in Tangier-Morocco were systematically given a pre-treatment ECG before deciding on the type of management: outpatient, ward, intensive care.The mean age was 42.78 years, 30.5% of the cases had at least one cardiovascular risk factor, 8.6% of the patients were followed for hypertension, 8.2% for type 1 or 2 diabetes, 10.9% of the patients were obese with a BMI greater than 30, and 26.4% of the patients were either men over 50 or women over 60.
The mean heart rate was 80.89 bpm ± 15.58; The mean PR interval was 149.51 ± 21.70; The mean QRS duration was 98.59 ms ± 11.71; The mean QTc interval was 406.62 ms ± 24.32.
We found: A conduction disorder in 11.1% of patients, 1.6% of 1st degree BAV, 2 of incomplete right bundle branch block, 5.7% of complete BBD, 0.9% of BBG and 1.4% of left anterior hemiblock.
A rhythm disorder in 5.1% of our patients, 2.1% of ventricular extrasystole, 1.6% of supraventricular extrasystole, 1.1% of atrial fibrillation, wolf- parkinson-white syndrome in 0.3% of patients and 0.5% of Brugada pattern.
A repolarization disorder in 5.2% of our population. 3.6% of patients had negative T waves and 1.6% had ST segment abnormalities. We also found a significant association between the presence of cardiovascular risk factors and electrical abnormality.

Conclusion
We found that the majority of EKG were normal and all critically ill patients had a large spectrum of EKG abnormalities. Pending the results, we still believe that we can potentially establish a severity score including EKG to predict inpatient mortality in Moroccan covid-19 patients.

Additional Content
An author video to accompany this abstract is available on https://academic.oup.com/eurheartjsupp. Please click on the arrow next to ‘More Content’ and then click on ‘Author videos’.
Long Term Results of LAD Reconstruction with LIMA Patch in Patients with Multiple Significant LAD disease
Authors: Leon Khaled N , Soliman Rafik 
Published: October 2022

Abstract

Background
Diffusely diseased left anterior descending (LAD) coronary artery with multiple significant stenoses is commonly encountered and is a surgically challenging condition.

Aim
This study aimed to evaluate the long-term patency of LAD reconstruction with a left internal mammary artery (LIMA) patch in patients with multiple significant LAD stenoses.

Methods
We enrolled 50 patients who presented with multi-vessel disease and multiple significant LAD stenoses between January 2012 and January 2022. The LAD was opened all the way through leaving the most proximal lesion until probe 1.5 can pass smoothly through the proximal and distal LAD. Then, the LIMA was widely opened, 1.3 times the LAD opening, and they were anastomosed in the usual way with long length anastomosis. All patients were discharged on a combination of aspirin and clopidogrel for the first year and then aspirin alone for life. Postoperatively, patients were followed-up using coronary CT angiography at 5- and 10-years intervals.

Results
The mean age of the studied patients was 59.08±2.06, and the highest percent (72%) were males. Hypertension, dyslipidemia, and diabetes mellitus comprised the common comorbidities (84%, 68%, and 54%, respectively). the mean ejection fraction was 49.29±3.5 and the mean EuroScore II was 1.02±0.37. The number of grafts was either 5 (8%), 4 (70%), or 3 (22%). Follow-up revealed LAD patency in all the assessed patients at 5 years (49, 98%) and 10-years (46, 92%), while we lost follow-up of 1 (2%) patient at 5 years and 4 (8%) patients at 10-years.

Conclusion
Reconstruction with LIMA patch was effective in maintaining long-term patency of LAD in patients with multiple significant LAD stenosis.

Additional Content
An author video to accompany this abstract is available on https://academic.oup.com/eurheartjsupp. Please click on the arrow next to ‘More Content’ and then click on ‘Author videos’.
Proton pump inhibitors and myocardial infarction: an application of active comparators in a self-controlled case series
Authors: Chui Celine S L , Cheung Ka Shing , Brown Jeremy P , Douglas Ian J , Wong Ian C K , Chan Esther W , Wong Angel Y S 
Published: October 2022

Abstract

Background
Previous studies investigating potential cardiovascular adverse events of acid-suppressing drugs are susceptible to protopathic bias and confounding. We aimed to investigate the association between short-term risk of myocardial infarction (MI) and proton pump inhibitors (PPIs) using a self-controlled case series (SCCS) with an active comparator.

Methods
We conducted a SCCS using a population-wide database from Hong Kong from 2003–2014. Adult with ≥1 outpatient oral PPI prescription or H2 receptor antagonist (H2RA) and MI during the observation period were included. We used both simple ratio and effect modifier approaches to SCCS with active comparators to obtain comparator adjusted estimates.

Results
A total of 2802 and 1889 people with MI who had exposure to PPIs and H2RA were included respectively. We observed a higher risk of MI during days 1–14 following the start of PPI prescription (Incidence rate ratio (IRR): 2.30, 95% confidence interval (CI): 1.76–3.00) versus baseline. Similarly, we observed a higher risk of MI during days 1–14 following the start of H2RA prescription (IRR: 2.46, 95%CI: 1.92–3.16) versus baseline. In the novel SCCS analyses, comparator adjusted estimates were 0.93 (95%CI: 0.57–1.30) and 0.83 (95%CI: 0.58–1.20) during days 1–14 in simple ratio and effect modifier approach, respectively.

Conclusions
We observed no difference in risk of MI associated with PPIs compared with baseline using H2RA as the active comparator. The elevated risk of MI associated with PPIs is likely due to protopathic bias. More studies are required to explore the feasibility of using active comparators in SCCS to address protopathic bias in addition to confounding.
Priorities to accelerate workflow automation in health care
Authors: Zayas-Cabán Teresa , Okubo Tracy H , Posnack Steven 
Published: October 2022

Abstract
Inefficient workflows affect many health care stakeholders including patients, caregivers, clinicians, and staff. Widespread health information technology adoption and modern computing provide opportunities for more efficient health care workflows through automation. The Office of the National Coordinator for Health Information Technology (ONC) led a multidisciplinary effort with stakeholders across health care and experts in industrial engineering, computer science, and finance to explore opportunities for automation in health care. The effort included semistructured key informant interviews, a review of relevant literature, and a workshop to understand automation lessons across nonhealth care industries that could be applied to health care. In this article, we describe considerations for advancing workflow automation in health care that were identified through these activities. We also discuss a set of six priorities and related strategies developed through the ONC-led effort and highlight the role the informatics and research communities have in advancing each priority and the strategies.