bluuebunny/crossref_metadata_2025 huggingface dataset:
Impact of chronic kidney disease on case ascertainment for hospitalised acute myocardial infarction: an English cohort study
Authors: Bidulka Patrick , Scott Jemima , Taylor Dominic M , Udayaraj Udaya , Caskey Fergus , Teece Lucy , Sweeting Michael , Deanfield John , de Belder Mark , Denaxas Spiros , Weston Clive , Adlam David , Nitsch Dorothea
Published: March 2022

Objectives

Acute myocardial infarction (AMI) case ascertainment improves for the UK general population using linked health data sets. Because care pathways for people with chronic kidney disease (CKD) change based on disease severity, AMI case ascertainment for these people may differ compared with the general population. We aimed to determine the association between CKD severity and AMI case ascertainment in two secondary care data sets, and the agreement in estimated glomerular filtration rate (eGFR) between the same data sets.

Methods

We used a cohort study design. Primary care records for people with CKD or risk factors for CKD, identified using the National CKD Audit (2015–2017), were linked to the Myocardial Ischaemia National Audit Project (MINAP, 2007–2017) and Hospital Episode Statistics (HES, 2007–2017) secondary care registries. People with an AMI recorded in either MINAP, HES or both were included in the study cohort. CKD status was defined using eGFR, derived from the most recent serum creatinine value recorded in primary care. Moderate–severe CKD was defined as eGFR <60 mL/min/1.73 m2, and mild CKD or at risk of CKD was defined as eGFR ≥60 mL/min/1.73 m2or eGFR missing. CKD stages were grouped as (1) At risk of CKD and Stages 1–2 (eGFR missing or ≥60 mL/min/1.73 m2), (2) Stage 3a (eGFR 45–59 mL/min/1.73 m2), (3) Stage 3b (eGFR 30–44 mL/min/1.73 m2) and (4) Stages 4–5 (eGFR <30 mL/min/1.73 m2).

Results

We identified 6748 AMIs: 23% were recorded in both MINAP and HES, 66% in HES only and 11% in MINAP only. Compared with people at risk of CKD or with mild CKD, AMIs in people with moderate–severe CKD were more likely to be recorded in both MINAP and HES (42% vs 11%, respectively), or MINAP only (22% vs 5%), and less likely to be recorded in HES only (36% vs 84%). People with AMIs recorded in HES only or MINAP only had increased odds of death during hospitalisation compared with those recorded in both (adjusted OR 1.61, 95% CI 1.32 to 1.96 and OR 1.60, 95% CI 1.26 to 2.04, respectively). Agreement between eGFR at AMI admission (MINAP) and in primary care was poor (kappa (K) 0.42, SE 0.012).

Conclusions

AMI case ascertainment is incomplete in both MINAP and HES, and is associated with CKD severity.
Improving ondansetron use and oral rehydration instructions for pediatric acute gastroenteritis
Authors: Patel Perseus Vistasp , Wallach Thomas , Rosenbluth Glenn , Heyman Mel , Verstraete Sofia
Published: March 2022

In paediatric patients with acute gastroenteritis (AGE), ondansetron use decreases the need for intravenous fluids, reduces hospitalisations and shortens illness duration. Oral rehydration is also known to have excellent outcomes for mild to moderate dehydration secondary to AGE. Although these interventions are recommended in guidelines from international professional societies, baseline data at our clinic showed that <2% of these patients were offered ondansetron, and that few patients received appropriately detailed rehydration instructions. Therefore, we engaged residents and fellows as teachers and leaders in our university clinic’s quality improvement programme to promote evidence-based practice for paediatric AGE. Our gap analysis identified opportunities for interventions including educating paediatricians and paediatrics residents on the safety and utility of the medication. We created standardised oral rehydration after-visit instructions and implemented a trainee-led educational approach that encouraged appropriate medication use. We used a follow-up survey to uncover provider concerns and tailor future interventions. The process metrics included: proportion of paediatric patients appropriately treated with ondansetron (goal of 80%), and proportion of patients given appropriate oral rehydration instructions. The outcome metric was 7-day representation rates. To achieve sustainability, we restructured our process to have senior residents take ownership of teaching and data collection. Trainee-driven interventions increased ondansetron prescription rates to a median of 66.6%. Patients prescribed ondansetron were less likely to represent to care, although representation rate was low overall. Postintervention data suggests that prescription rates decreased without continued interventions and additional systems redesign may help sustain impact.
Behavioural survey and street-based HIV and HCV rapid testing programme among transgender sex workers
Authors: Lapadula Giuseppe , Soria Alessandro , Modesti Massimo , Vecchi Arianna , Sabbatini Francesca , Monopoli Antonia , Squillace Nicola , Lungu Eugenia , Coloma Jessenia , Columpsi Paola , Cristiano Vincenzo , Bonfanti Paolo
Published: March 2022

Background

Transgender women sex workers (TGW-SW) are disproportionally affected by HIV and have reduced access to testing. Moreover, information regarding their behaviours and health needs is scarce.

Methods

A behavioural survey and a targeted testing programme in prostitution sites were conducted in Milan and Monza areas. The non-profit organisation ‘ALA Milano Onlus’ and ‘San Gerardo’ Hospital (Monza) implemented a mobile HIV testing unit involving a TGW peer educator, four physicians, a counsellor, a psychologist and a cultural mediator. All TGW-SW were offered anonymous HIV and hepatitis C virus (HCV) oral testing and asked to fill a questionnaire on sexual habits, drug abuse, and knowledge and attitudes towards HIV and STDs.

Results

Between May and July 2017, 130 TGW-SW, predominantly migrants, were contacted during 15 street visits; among them, 78 (60%) were interviewed. HIV and HCV testing were accepted by 53 (42%) and 67 (52%) TGW-SW, respectively. Twenty-five (19.8%) subjects who reported already established HIV infection were not retested. Seven patients received a new diagnosis of HIV, while nobody tested positive for HCV. Overall, HIV prevalence was 13.2% (25% including those with already known HIV infection). Recent arrival in Italy and young age were associated with risk of undiagnosed HIV infection. Inconsistent condom use was commonly reported during commercial sex (27%) and with non-commercial partners (64%). Alcohol and cocaine abuse were common problems which facilitated risky behaviours.

Conclusions

Oral rapid HIV and HCV testing for TGW-SW in outreach settings were feasible and acceptable and led to a considerable number of new diagnoses. Interventions tailored to TGW-SW, focused on HIV prevention, testing and engagement in care, are fundamental.
Anisotropy in constraint 4D Gauss–Bonnet gravity
Authors: Shahidi Shahab , Khosravi Nima
Published: March 2022

Recently a new 4D Einstein–Gauss–Bonnet theory has been introduced (Glavan and Lin in Phys Rev Lett 124: 081301, 2020) with a serious debate that it does not possess a covariant equation of motion in 4D. This feature, makes impossible to consider non-symetric space-times in this model, such as anisotropic cosmology. In this note, we will present a new proposal to make this happen, by introducing a Lagrange multiplier to the action which eliminates the higher dimensional term from the equation of motion. The theory has then a covariant 4D equation of motion which is useful to study the less symmetric metrics. On top of FRW universe, the constraint theory is equivalent to the original 4D Einstein–Gauss–Bonnet gravity. We will then consider the anisotropic cosmology of the model and compare the theory with observational data. We will see that the theory becomes non-conservative and the matter density abundance falls more rapidly at larger redshifts compared to the conservative matter sources.
Taming modeling uncertainties with mass unspecific supervised tagging
Authors: Aguilar-Saavedra J. A.
Published: March 2022

We address the modeling dependence of jet taggers built using the method of mass unspecific supervised tagging, by using two different parton showering and hadronisation schemes. We find that the modeling dependence of the results – estimated by using different schemes in the design of the taggers and applying them to the same type of data – is rather small, even if the jet substructure varies significantly between the two schemes. These results add great value to the use of generic supervised taggers for new physics searches.
The β-function of supersymmetric theories from vacuum supergraphs: A three-loop example
Authors: Aleshin S. S. , Goriachuk I. O. , Kolupaev D. S. , Stepanyantz K. V.
Published: March 2022

In this paper, we verify a method which allows to obtain the [Formula: see text]-function of supersymmetric theories regularized by higher covariant derivatives by calculating only specially modified vacuum supergraphs. With the help of this method for a general renormalizable [Formula: see text] supersymmetric gauge theory, a part of the three-loop [Formula: see text]-function depending on the Yukawa couplings is constructed in the general [Formula: see text]-gauge. The result is written in the form of an integral of double total derivatives with respect to the loop momenta. It is demonstrated that all gauge-dependent terms cancel each other in agreement with the general statements. The result in the Feynman gauge (found earlier) coincides with the one obtained by the standard technique which requires to calculate the sum of superdiagrams with two external gauge lines. This fact confirms the correctness of the considered method by a highly nontrivial multiloop calculation.
Optical vortex array generation of intense high-order harmonics
Authors: Zhang Hao , Yu Haobiao , Li Guicun , Zhang Shuo , Wen Kairui , Mu Jinzhen , Liu Zongming
Published: March 2022

An ultra-short and ultra-intense optical vortex array (OVA) is generated when two intense counter-propagating Laguerre–Gaussian (LG) pulses with [Formula: see text]-polarization impinge on two solid thin targets. In this paper, we propose a method for generating an OVA of high intensity, short wavelength, and high-order mode. Three-dimensional (3D) particle-in-cell (PIC) simulation results demonstrated that the generated high-order vortex harmonics interfered with each other in the transmitted area. These interference patterns of the fundamental and high-order harmonics exhibit an OVA distribution. Furthermore, vortex oscillating mirror (VOM) and classic wave interference models are then used to explain the simulation results.
PFCMVO: Political fractional competitive multi-verse optimization enabled deep neuro fuzzy network for student performance estimation in spark environment
Authors: Jyoti Baruah Amlan , Baruah Siddhartha
Published: March 2022

Student performance calculation is an essential process in online learning scheme, which intends to afford students along with admittance to active learning. Student performance forecast is most concerning problem in education and training field, particularly in educational data mining (EDM). The prediction process provisions the students to choose courses and intend suitable training strategies for themselves. Furthermore, student performance calculation permits lecturers and educational supervisors to designate which students should be observed and maintained to finish their plans with finest outcomes. These provisions can decrease the official notices and exclusions from universities because of students’ poor performance. In this paper, Political Fractional Competitive Multi-verse Optimization enabled Deep Neuro fuzzy network (PFCMVO enabled DNFN) uses spark framework for student performance calculation. Moreover, Yeo–Johnson transformation is applied for transforming the input data for effectual student performance prediction. In addition, Damerau–Levenshtein (DL) distance is applied to select appropriate features. The DNFN classifier is utilized to execute student performance prediction where the classifier is trained by PFCMVO algorithm. The developed student performance prediction model outperforms than the other existing techniques with respect to Precision, Recall, [Formula: see text]-measure, and Prediction accuracy of 0.9259, 0.9321, 0.9290, and 0.9372 for dataset-1 and 0.9126, 0.9271, 0.9198, and 0.9248 for dataset-2, respectively.
Intelligent question and answer analysis model of power ICT based on BI-LSTM-CRF
Authors: Feifei Hu , Shibo Zeng , Danke Hong , Situo Zhang , Yongwei Song , Cheng Xu
Published: March 2022

With the deepening of the country’s digital transformation and the development of big data artificial intelligence software definition and other technologies, customers’ demand for IT services is constantly increasing. In the electric power industry, the front-end knowledge base for traditional customer service lacks self-learning ability, the accumulated customer service data in the middle cannot be analyzed and utilized effectively, the back-end quality inspection was not fully covered and lacked emotion recognition, etc. Apply artificial intelligence, information and communication technology and big data technology to power ICT customer service. It is proposed to construct a network model by combining conditional random airport with the long- and short-term memory neural network, and to realize the intelligent customer service model combined with human–computer interaction by constructing an intelligent knowledge base and the information system fault association analysis model, so as to improve the extraction performance of context information and establish a knowledge base question answering system in the field of power service. Through the comparative analysis of experimental data, the proposed model has achieved good results and can be effectively applied to the power industry to improve customer service satisfaction.
Localizing Submerged Acoustic Sources Under Adverse Conditions
Authors: Collins Michael D. , Fialkowski Laurie T. , Lingevitch Joseph F.
Published: March 2022

This paper reviews various approaches for localizing submerged acoustic sources under adverse conditions. It is essential to obtain data of the highest possible quality when there are adverse conditions, such as uncertainties in the environment, source motion, and low signal-to-noise ratio. Focalization is an approach in which the source location and environmental parameters are treated as unknowns. Due to a parameter hierarchy in which source location outranks environmental parameters, there may be many realizations of the environment that bring the source into focus; the ambiguity in the environment can be an advantage if the primary objective is to localize the source. Environmental uncertainty is often associated with environmental complexity, which can be an advantage by reducing the ambiguity of the source location. Obtaining a high-quality estimate of the covariance matrix may be difficult when there is source motion, but the complexity of the received field from a moving source is another factor that can reduce ambiguity. It may be possible to localize a source that is buried in noise when an estimate of the noise covariance matrix is available. During the development of approaches for localizing submerged sources, much of the focus has been on one-dimensional vertical arrays. An extension of the multi-valued Bartlett processor to the case of a rectangular array was designed to take advantage of the extra dimension of the array and appears to have the potential to be the most powerful combination of hardware and signal processing to date.
Modalities and Parametric Adjoints
Authors: Gratzer Daniel , Cavallo Evan , Kavvos G. A. , Guatto Adrien , Birkedal Lars
Published: March 2022

Birkedal et al. recently introduced dependent right adjoints as an important class of (non-fibered) modalities in type theory. We observe that several aspects of their calculus are left underdeveloped and that it cannot serve as an internal language. We resolve these problems by assuming that the modal context operator is a parametric right adjoint. We show that this hitherto unrecognized structure is common. Based on these discoveries we present a new well-behaved Fitch-style multimodal type theory, which can be used as an internal language. Finally, we apply this syntax to guarded recursion and parametricity.
TF-Coder: Program Synthesis for Tensor Manipulations
Authors: Shi Kensen , Bieber David , Singh Rishabh
Published: March 2022

The success and popularity of deep learning is on the rise, partially due to powerful deep learning frameworks such as TensorFlow and PyTorch, which make it easier to develop deep learning models. However, these libraries also come with steep learning curves, since programming in these frameworks is quite different from traditional imperative programming with explicit loops and conditionals. In this work, we present a tool called TF-Coder for programming by example in TensorFlow. TF-Coder uses a bottom-up weighted enumerative search, with value-based pruning of equivalent expressions and flexible type- and value-based filtering to ensure that expressions adhere to various requirements imposed by the TensorFlow library. We train models to predict TensorFlow operations from features of the input and output tensors and natural language descriptions of tasks to prioritize relevant operations during search. TF-Coder solves 63 of 70 real-world tasks within 5 minutes, sometimes finding simpler solutions in less time compared to experienced human programmers.
Parameterized Complexity of Elimination Distance to First-Order Logic Properties
Authors: Fomin Fedor V. , Golovach Petr A. , Thilikos Dimitrios M.
Published: March 2022

The
elimination distance
to some target graph property
P
is a general graph modification parameter introduced by Bulian and Dawar. We initiate the study of elimination distances to graph properties expressible in first-order logic. We delimit the problem’s fixed-parameter tractability by identifying sufficient and necessary conditions on the structure of prefixes of first-order logic formulas. Our main result is the following meta-theorem: For every graph property
P
expressible by a first order-logic formula

\( \varphi \in \Sigma _3 \)

, that is, of the form

\( \begin{equation*} \varphi =\exists x_1\exists x_2\cdots \exists x_r\ \ \forall y_{1}\forall y_{2}\cdots \forall y_{s}\ \ \exists z_1\exists z_2\cdots \exists z_t~~ \psi ,\end{equation*} \)

where

\( \psi \)

is a quantifier-free first-order formula, checking whether the elimination distance of a graph to
P
does not exceed

\( k \)

, is
fixed-parameter tractable
parameterized by

\( k \)

. Properties of graphs expressible by formulas from

\( \Sigma _3 \)

include being of bounded degree, excluding a forbidden subgraph, or containing a bounded dominating set. We complement this theorem by showing that such a general statement does not hold for formulas with even slightly more expressive prefix structure: There are formulas

\( \varphi \in \Pi _3 \)

, for which computing elimination distance is

\( {\sf W}[2] \)

-hard.
BayesBeat
Authors: Das Sarkar Snigdha Sarathi , Shanto Subangkar Karmaker , Rahman Masum , Islam Md Saiful , Rahman Atif Hasan , Masud Mohammad M. , Ali Mohammed Eunus
Published: March 2022

Smartwatches or fitness trackers have garnered a lot of popularity as potential health tracking devices due to their affordable and longitudinal monitoring capabilities. To further widen their health tracking capabilities, in recent years researchers have started to look into the possibility of Atrial Fibrillation (AF) detection in real-time leveraging photoplethysmography (PPG) data, an inexpensive sensor widely available in almost all smartwatches. A significant challenge in AF detection from PPG signals comes from the inherent noise in the smartwatch PPG signals. In this paper, we propose a novel deep learning based approach, BayesBeat that leverages the power of Bayesian deep learning to accurately infer AF risks from noisy PPG signals, and at the same time provides an uncertainty estimate of the prediction. Extensive experiments on two publicly available dataset reveal that our proposed method BayesBeat outperforms the existing state-of-the-art methods. Moreover, BayesBeat is substantially more efficient having 40--200X fewer parameters than state-of-the-art baseline approaches making it suitable for deployment in resource constrained wearable devices.
Learning Disentangled Behaviour Patterns for Wearable-based Human Activity Recognition
Authors: Su Jie , Wen Zhenyu , Lin Tao , Guan Yu
Published: March 2022

In wearable-based human activity recognition (HAR) research, one of the major challenges is the large intra-class variability problem. The collected activity signal is often, if not always, coupled with noises or bias caused by personal, environmental, or other factors, making it difficult to learn effective features for HAR tasks, especially when with inadequate data. To address this issue, in this work, we proposed a Behaviour Pattern Disentanglement (BPD) framework, which can disentangle the behavior patterns from the irrelevant noises such as personal styles or environmental noises, etc. Based on a disentanglement network, we designed several loss functions and used an adversarial training strategy for optimization, which can disentangle activity signals from the irrelevant noises with the least dependency (between them) in the feature space. Our BPD framework is flexible, and it can be used on top of existing deep learning (DL) approaches for feature refinement. Extensive experiments were conducted on four public HAR datasets, and the promising results of our proposed BPD scheme suggest its flexibility and effectiveness. This is an open-source project, and the code can be found at http://github.com/Jie-su/BPD
MotorBeat
Authors: Wang Weiguo , Li Jinming , He Yuan , Guo Xiuzhen , Liu Yunhao
Published: March 2022

More and more home appliances are now connected to the Internet, thus enabling various smart home applications. However, a critical problem that may impede the further development of smart home is overlooked: Small appliances account for the majority of home appliances, but they receive little attention and most of them are cut off from the Internet. To fill this gap, we propose MotorBeat, an acoustic communication approach that connects small appliances to a smart speaker. Our key idea is to exploit direct current (DC) motors, which are common components of small appliances, to transmit acoustic messages. We design a novel scheme named Variable Pulse Width Modulation (V-PWM) to drive DC motors. MotorBeat achieves the following 3C goals: (1) Comfortable to hear, (2) Compatible with multiple motor modes, and (3) Concurrent transmission. We implement MotorBeat with commercial devices and evaluate its performance on three small appliances and ten DC motors. The results show that the communication range can be up to 10 m.
On Privacy Risks of Watching YouTube over Cellular Networks with Carrier Aggregation
Authors: Lakshmanan Nitya , Bentaleb Abdelhak , Choi Byoungjun , Zimmermann Roger , Han Jun , Kang Min Suk
Published: March 2022

One's core values, personality, and social status may be reflected in the watch history of online video streaming services such as YouTube. Unfortunately, several prior research work demonstrates that man-in-the-middle or malware-assisted attackers can accurately infer the titles of encrypted streaming videos by exploiting the inherent correlation between the encoded video contents and the traffic rate changes. In this paper, we present a novel video-inference attack called Moba that further exacerbates the problem by only requiring the adversary to simply eavesdrop the broadcast messages of a primary cell of a targeted user's cellular phone. Our attack utilizes a side channel in modern cellular networks that leaks the number of actively transmitting cells for each user. We show that this seemingly harmless system information leakage can be used to achieve practical video-inference attacks. To design effective video-inference attacks, we augment the coarse-grained side-channel measurements with precise timing information and estimate the traffic bursts of encrypted video contents. The Moba attack considers an adversary-chosen set of suspect YouTube videos, from which a targeted user may watch some videos during the attack. We confirm the feasibility of Moba in identifying the exact YouTube video title (if it is from the suspect set) via our over-the-air experiments conducted in LTE-Advanced networks in two countries. Moba can be effective in verifying whether a targeted user watches any of the suspect videos or not; e.g., precision of 0.98 is achieved after observing six-minutes of a single video play. When further allowed to observe multiple video plays, Moba adversary is able to identify whether the targeted user frequently watches the suspect videos with a probability close to one and a near-zero false positive rate. Finally, we present a simple padding-based countermeasure that significantly reduces the attack effectiveness without sacrificing any cellular radio resources.
Investigating Cross-Modal Approaches for Evaluating Error Acceptability of a Recognition-Based Input Technique
Authors: Henderson Jay , Jonker Tanya R. , Lank Edward , Wigdor Daniel , Lafreniere Ben
Published: March 2022

Emerging input techniques that rely on sensing and recognition can misinterpret a user's intention, resulting in errors and, potentially, a negative user experience. To enhance the development of such input techniques, it is valuable to understand implications of these errors, but they can very costly to simulate. Through two controlled experiments, this work explores various low-cost methods for evaluating error acceptability of freehand mid-air gestural input in virtual reality. Using a gesture-driven game and a drawing application, the first experiment elicited error characteristics through text descriptions, video demonstrations, and a touchscreen-based interactive simulation. The results revealed that video effectively conveyed the dynamics of errors, whereas the interactive modalities effectively reproduced the user experience of effort and frustration. The second experiment contrasts the interactive touchscreen simulation with the target modality - a full VR simulation - and highlights the relative costs and benefits for assessment in an alternative, but still interactive, modality. These findings introduce a spectrum of low-cost methods for evaluating recognition-based errors in VR and a series of characteristics that can be understood in each.
Back-Analysis of Parameters of Jointed Surrounding Rock of Metro Station Based on Random Forest Algorithm Optimized by Cuckoo Search Algorithm
Authors: Guo Xinping , Jiang Annan , Liu Xiang
Published: March 2022

The efficiency and accuracy in determining mechanical parameters of joint of rock mass have significant influences on the safety of construction. In this paper, a new back-analysis method of joint parameters of the ubiquitous-joint model is proposed. This study combines the ubiquitous-joint model, random forest algorithm (RF), and cuckoo search algorithm (CS) to construct the parameters identification method of a jointed rock mass. The parameter determination is transformed into a global optimization problem. In this method, the mean square error between the measured displacement (stress) and the calculated displacement (stress) is taken as the objective function, and the joint parameters are taken as the decision variables. The method is applied to the project of Qingniwaqiao Metro Station of Dalian Metro Line 5. This method can be used for the back-analysis of parameters of a jointed rock mass in underground engineering construction.
Clinical Efficacy Evaluation of Psychological Nursing Intervention Combined with Drugs Treatment of Children with ADHD under Artificial Intelligence
Authors: Guo Ying , Wang Jinping , Yan Shuyan , Sui Shujie
Published: March 2022

ADHD in children is one of the most common neurodevelopmental disorders. It is manifested as inattention, hyperactivity, impulsiveness, and other symptoms that are inconsistent with the developmental level in different occasions, accompanied by functional impairment in social, academic, and occupational aspects. At present, the treatment for children with ADHD is mainly based on psychological nursing intervention combined with drug therapy. Therefore, the actual efficacy evaluation of this treatment regimen is very important. Neural networks are widely used in smart medical care. This work combines artificial intelligence with the evaluation of clinical treatment effects of ADHD children and designs an intelligent model based on neural networks for evaluating the clinical efficacy of psychological nursing intervention combined with drug treatment of children with ADHD. The main research is that, for the evaluation of clinical treatment effect of ADHD in children, this paper proposes a 1D Parallel Multichannel Network (1DPMN), which is a convolutional neural network. The results show that network models can extract different data features through different channels and can achieve high accuracy evaluation of clinical efficacy of ADHD in children. On the basis of the model, performance is improved through the study of Adam optimizer to speed up the model convergence, adopts batch normalization algorithm to improve stability, and uses Dropout to improve the generalization ability of the network. Aiming at the problem of too many parameters, the 1DPMN is optimized through the principle of local sparseness, and the model parameters are greatly reduced.
Severe Quantitative Scale of Acanthosis Nigricans in Neck is Associated with Abdominal Obesity, HOMA‐IR, and Hyperlipidemia in Obese Children from Mexico City: A Cross‐Sectional Study
Authors: Burguete-García Ana I. , Ramírez Valverde Alan Gilberto , Espinoza-León Meztli , Vázquez Isaac Sánchez , Estrada Ramírez Evelyn Yazmín , Maldonado-López Itzel , Martínez Alfredo Lagunas , Diaz Benítez Cinthya Estefhany , Araujo Roberto Karam , Fernández-Madinaveitia Diana , Anides Fonseca Adriana E. , Cruz Miguel , de Jesús Peralta Romero José
Published: March 2022

Background. Acanthosis nigricans (AN) is a clinical sign that commonly occurs in obesity; however, its specificity and sensitivity have been controversial. It is unknown if AN severity degree can be a useful marker for cardiometabolic disorders screening. We suggest that the stratified analysis of AN severity degree in neck by Burke’s scale could be a useful tool in the screening of cardiometabolic alterations in obese children. Objective. The aim of this study was the association of AN severity degree in neck by Burke’s scale with anthropometric, biochemical, and inflammatory parameters in obese school‐age children from Mexico City. Methods. A cross‐sectional study was conducted, including 95 obese school‐age children stratified by AN severity degree in neck by Burke’s scale. Anthropometric and fasting biochemical measurements were determined. Variables were compared by x2 test for frequencies and one‐way ANOVA with Bonferroni posttest for continuous variables. Linear regression analysis adjusted by gender, BMI, and age was performed to evaluate the association between AN severity degree and cardiometabolic alterations. Statistical significance was set at p < 0.05. Results. As AN severity degree in neck by Burke’s scale increased, diastolic blood pressure (p = 0.001) and triglycerides (p = 0.02) significantly increased and adiponectin significantly decreased (p = 0.02). Positive associations between grade 3 AN and waist circumference, HOMA‐IR, triglycerides, total cholesterol, and LDL cholesterol were observed. Conclusion. Our findings could be used to identify an easier clinical tool to prevent obesity progression and its complications in pediatrics. There are no similar studies.
A Risk-Scoring Model Based on Evaluation of Ferroptosis-Related Genes in Osteosarcoma
Authors: Jiang Mingyang , Wang Zifan , He Xiaoyu , Hu Yang , Xie Mingjing , Jike Yiji , Bo Zhandong , Qin Wentao
Published: March 2022

Background. Osteosarcoma (OS) is a bone malignancy frequently seen in pediatrics and has high mortality and incidence. Ferroptosis is an important cell death process in regulating the apoptosis and invasion of tumor cells, so constructing the risk-scoring model based on OS ferroptosis-related genes (FRGs) will benefit the evaluation of both treatment and prognosis. Methods. The OS dataset was screened from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database, and OS-related FRGs were found through the Ferroptosis Database (FerrDb) using a multivariate Cox regression model, followed by the generation of the risk scores and a risk-scoring prediction model. Further systematical exploration for immune cell infiltration and assessing the prediction of response to targeted drugs was conducted. Results. Based on OS-related FRGs, a risk-scoring model of FRGs in OS was constructed. The six FRGs played a role in the carbon metabolism, glutathione metabolism, and pentose phosphate pathways. Results from targeted drug sensitivity analyses were concordant to pathway analyses. The response to targeted drugs statistically differed between the two groups with different risks, and the high-risk group presented a high sensitivity to targeted drugs. Conclusions. We identified a 6-ferroptosis-gene-based prognostic signature in OS and created and verified a risk-scoring model to predict the prognosis of OS at 1, 3, and 5 years for OS patients independently.
The Application of Minority Music Style Recognition Based on Deep Convolution Loop Neural Network
Authors: Fan Qiaozhen
Published: March 2022

In recent years, with the development of Internet and digital audio technology, music information retrieval has gradually become a research hotspot. Due to the rise of deep learning and machine learning in recent years, as well as the rapid improvement of computer software and hardware performance, it has laid a good foundation for identifying different genres of music. Among them, the application of minority music style recognition is also an important research direction. At present, the application performance of minority music style recognition based on deep convolution loop neural network is poor. Because convolution loop neural networks (CNNs) have strong ability to capture information features, this paper uses CNN to extract various features from music signals and classify them. Firstly, the original music signal spectrum is separated into time characteristic harmonic component and frequency characteristic impact component by using the harmonic/percussive sound separation (HPSS) algorithm. Combined with the original spectra as the input of CNN, the network structure of CNN is designed, and the influence of different parameters in the network structure on the recognition rate is studied. Experiments on minority music data sets, compared with other scholars’ music recognition methods, it shows that this method can effectively improve the recognition of minority music styles using a single feature.
Aspirin Suppressed PD-L1 Expression through Suppressing KAT5 and Subsequently Inhibited PD-1 and PD-L1 Signaling to Attenuate OC Development
Authors: Xiao Xiyun , Zeng Saitian , Li Yanying , Li Lingling , Zhang Jiyan
Published: March 2022

Ovarian cancer (OC) is a frequently occurred malignancy with high incidence and poor survival worldwide. In recent years, immune checkpoint inhibition that targets PD-1/PD-L1 axis has become an efficient and popular therapy for cancers. Aspirin (ASP), an anti-inflammatory drug, exhibits a wide spectrum of biological functions including anticancer property. However, the role of ASP treatment in ovarian cancer treatment remains unclear. In this work, we explored the role of ASP in modulating PD-L1 signaling during OC development. Notably, in vitro experiments showed that ASP treatment caused repressed proliferation of OC cells. The results from in vivo xenograft model suggested suppressed tumor growth and tumor weight under ASP treatment. ASP treatment also caused downregulated PD-L1 and Ki-67 levels in mice tumors. Moreover, the IFN-γ-caused PD-L1 accumulation was inhibited by ASP treatment. The administration of ASP decreased the expression of PD-L1 of OC cells in a coculture system with activated T cell or unstimulated PBMCs, along with decreased expression of PD-1 by activated T cells. ASP reversed PD-L1 expression caused by coculture with activated T cells and abolished the suppressed T cells activation and proliferation. Analysis on molecular mechanisms revealed that KAT5 bonded to the promoter region of PD-L1 and upregulated its expression via enhancing histone H3 lysine 27 acetylation (H3K27ac), whereas ASP downregulated KAT5 expression and blocked this phenomenon. Moreover, ASP enhanced the effect of antiPD-L1 therapy in the in vivo tumor model. Hence, we proposed that ASP decreased expression of PD-L1 protein via inhibiting the epigenetic regulation by KAT5 and suppressed the PD-1/PD-L1 signaling to attenuate tumor growth. ASP may be a promising adjuvant in OC immunotherapy.
The Association between Energy-Adjusted Dietary Inflammatory Index, Body Composition, and Anthropometric Indices in COVID-19-Infected Patients: A Case-Control Study in Shiraz, Iran
Authors: Firoozi Donya , Masoumi Seyed Jalil , Ranjbar Sara , Shivappa Nitin , Hebert James R. , Zare Morteza , Poustchi Hossein , Hoseini Faeze Sadat
Published: March 2022

Background and Aims. Inflammation is strongly associated with the severity and mortality rate of SARS-CoV-2 disease (COVID-19). Dietary factors have a crucial role in preventing chronic and systemic inflammation. This study aimed to evaluate the association between energy-adjusted dietary inflammatory index (E-DII) scores and body composition parameters in COVID-19-infected patients compared to noninfected controls. Methods. A total of 133 COVID-19-infected patients and 322 noninfected controls were selected and enrolled from the Cohort Study of Employees of Shiraz University of Medical Sciences. E-DII score was calculated based on a validated food frequency questionnaire (FFQ) and body composition was measured using In-Body 770 equipment. Logistic regression models were utilized to estimate the odds ratio (OR). Results. In the control group, the mean E-DII score was significantly lower than the case group (−2.05 vs. −0.30,

P
≤
0.001

), indicating that the diet of COVID-19-infected subjects was more proinflammatory than the controls. For every 1 unit increase in E-DII score, the odds of infection with COVID-19 was nearly triple (OR: 2.86, CI: 2.30, 3.35,

P
≤
0.001

). Moreover, for each unit increase in body mass index (BMI), the odds of infection to COVID-19 increased by 7% (OR: 1.07, CI: 1.01, 1.13, P = 0.02). No significant difference was observed for other anthropometric parameters. Conclusion. The findings revealed that obese people and those consuming a more proinflammatory diet were more susceptible to coronavirus infection. Therefore, maintaining ideal body weight and consuming a more anti-inflammatory diet can decrease the probability of COVID-19 infection.
Stellate Ganglion Block Combined with Dexmedetomidine Protects Obese Rats from Lipopolysaccharide-Induced Acute Lung Injury
Authors: Zhang Weiwei , Li Pengfei , Gao Xiang , Lv Zhigan
Published: March 2022

Objective. To investigate the effect and mechanism of combined stellate ganglion block (SGB) and dexmedetomidine (Dex) in obesity-related acute lung injury. Methods. Thirty-six 4-week-old male Wistar rats were randomly divided into 6 groups, each with 6 rats: blank group (Control), high-fat diet group (HFD), high-fat + lipopolysaccharide (LPS)-induced acute lung injury group (HFD + LPS), SGB group, Dex group, and SGB + Dex group. H&E staining detected the pathological structure of rat lung tissue. TUNEL staining was used to examine cell apoptosis in lung tissue. Oxidative factors were accessed by biochemical reagents. ELISA was employed to measure the levels of TNF-α, IL-1β, and MCP1 in rat alveolar lavage fluid. Western blot detected the protein expression of glucose-regulated Protein 78 (GRP78), C/EBP homologous protein (CHOP), protein kinase R-like endoplasmic reticulum kinase (PERK), and p-PERK in lung tissue. Results. The body weight of rats in the HFD group was higher than that in the control group. The use of SGB or Dex alone could significantly reduce the rate of pulmonary edema and lung cell apoptosis in HFD-induced obese rats and reduce MPO, TNF-α, IL-1β, and MCP1 levels, increasing the activity of SOD and GSH-Px. In addition, using SGB or Dex alone can also significantly reduce the protein expression levels of GRP78, CHOP, and p-PERK. The combined use of SGB and Dex can enhance the above effects. Conclusion. The combined use of SGB and Dex can protect against obesity-related acute lung injury and is more effective than using SGB or Dex alone.
On Elegant Labelling and Magic Labelling of Large‐Scale Graphs
Authors: Su Jing , Wang Hongyu , Yao Bing
Published: March 2022

In this paper, we deduce the equivalence relationship among strongly c‐elegant labelling, super‐edge magic total labelling, edge antimagic total labelling, and super (t, 1)‐magical labelling. We study some properties of the graph with a strongly c‐elegant labelling. On the basis of small‐scale graphs with strongly c‐elegant labelling, several types of large‐scale graphs are constructed through graph operations, and we further prove the existence of their strongly c‐elegant labelling. In addition, we also define a transformation of strongly c‐elegant labelling, which provides a method for the transformation between several strongly c‐elegant labellings of a graph.
Prediction about Effect of Span‐to‐Depth Ratio on Shear Capacity for FRP Bar Reinforced Concrete Beams without Web Reinforcement
Authors: Gao Danying , Wang Tao , Zhang Changhui , Li Cong
Published: March 2022

Most of the prediction models for the shear capacity of FRP bar reinforced concrete beams without web reinforcement in current codes are reported to be conservative and do not consider the effect of span‐to‐depth ratio. Grey relational analysis (GRA) method was used in this paper to investigate the relevance of span‐to‐depth ratio with the shear capacity, and the results shown that the span‐to‐depth ratio greatly affects the shear capacity of FRP bar reinforced concrete beams without web reinforcement. A prediction model which considers the effect of span‐to‐depth ratio was proposed for the shear capacity of FRP bar reinforced concrete beams without web reinforcement by regression analysis, which is more accurate and reasonable in comparison with the current models.
Liver Fibrosis Scores and Hospitalization, Mechanical Ventilation, Severity, and Death in Patients with COVID-19: A Systematic Review and Dose-Response Meta-Analysis
Authors: Liu Menglu , Mei Kaibo , Tan Ziqi , Huang Shan , Liu Fuwei , Deng Chao , Ma Jianyong , Yu Peng , Liu Xiao
Published: March 2022

Background and Aim. The relationship between liver fibrosis scores and clinical outcomes in patients with COVID-19 is not compressively assessed. Methods. We identified relevant cohort studies that assessed the relationship between liver fibrosis scores (e.g., FIB-4, NAFLD fibrosis score (NFS), and aspartate aminotransferase to platelet ratio index (APRI)) and associated prognosis outcomes by searching the PubMed, EMBASE, and medRxiv databases. The potential dose-response effect was performed using a stage robust error meta-regression. Results. Sixteen studies with 8,736 hospitalized patients with COVID-19 were included. One-point score in FIB-4 increase was significantly associated with increased mechanical ventilation (RR: 2.23, 95% CI: 1.37–3.65,

P
=
0.001

), severe COVID-19 (RR: 1.82, 95% CI: 1.53–2.16,

P
<
0.001

), and death (RR: 1.47, 95% CI: 1.31–1.65,

P
<
0.001

), rather than hospitalization (RR: 1.35, 95% CI: 0.72–2.56,

P
=
0.35

). Furthermore, there is a significant positive linear relationship between FIB-4 and severe COVID-19 (

P

nonlinearity

=
0.12

) and mortality (

P

nonlinearity

=
0.18

). Regarding other liver scores, one unit elevation in APRI increased the risk of death by 178% (RR: 2.78, 95% CI: 1.10–6.99,

P
=
0.03

). Higher NFS (≥−1.5) and Forns index were associated with increased risk of severe COVID-19 and COVID-19-associated death. Conclusion. Our dose-response meta-analysis suggests high liver fibrosis scores are associated with worse prognosis in patients with COVID-19. For patients with COVID-19 at admission, especially for those with coexisting chronic liver diseases, assessment of liver fibrosis scores might be useful for identifying high risk of developing severe COVID-19 cases and worse outcomes.
Research on Big Data Traffic Operation Based on Intelligent Holographic 3D Image Vision
Authors: Fang Yufu
Published: March 2022

Aiming at the needs of refined and visualized management of transportation infrastructure, the 3D hologram acquisition technology integrating 3D laser scanning, tilt photogrammetry, and other technologies are used to acquire and integrate the 3D scene data of transportation infrastructure through indoor and outdoor and air and ground integration and build a 3D hologram spatial model that comprehensively reflects the three-dimensional information of transportation infrastructure. The technical solution for the acquisition, integration, and management of transport infrastructure data is formed. Through research and practice, it can be proved that the integrated indoor and outdoor 3D hologram data technology of transportation infrastructure can explore the technical basis for the comprehensive construction of 3D hologram data of transportation infrastructure and can effectively expand the data content of the transportation geographic information service platform and provide stronger technical support for the province's transportation industry management, operation monitoring, emergency disposal, and public services.
Comparative Efficacy of Water and Conventional Delivery during Labour: A Systematic Review and Meta-Analysis
Authors: Zhang Guanran , Yang Qiuhong
Published: March 2022

In many maternal settings, water delivery is widely available for women who do not have an increased risk of complications during childbirth. Soaking in water during labor has been associated with a number of maternal benefits. However, the situation of water birth is not well known, there is lack of hard evidence on safety, and little is known about the characteristics of women who give birth in water. In this paper, we have explored the effects of water delivery compared to the conventional delivery on the health of mothers and babies. For this purpose, clinical trials were conducted including women in labor, in which participants were treated with water labor or conventional labor, respectively, in the experimental and control group. In this analysis, we have selected 17 eligible studies which included 175654 participants. Compared to the conventional birth group, the risk of Apgar score <7 at 5 min of age in the water birth group dropped by 28% (OR = 0.72, 95% CI: 0.52–1.00, I2 = 25%,

P
=
0.05

). Also, the duration of labor was shorter the in water birth group whatever the labor stage was. The patients who underwent water birth showed an obviously lower rate of neonatal intensive care unit (NICU) admission (OR = 0.58, 95% CI: 0.39–0.86, I2 = 53%,

P
=
0.007

). In this meta-analysis, it was seen that water delivery has clinical significance in alleviating the pain of mothers, promoting the safety of mothers and infants, and reducing postpartum complications.