bluuebunny/crossref_metadata_2025 huggingface dataset:
Impact of COVID-19 lockdowns on hospital presentations and admissions in the context of low community transmission: evidence from time series analysis in Melbourne, Australia
Authors: Collyer Taya A , Athanasopoulos George , Srikanth Velandai , Tiruvoipati Ravindranath , Matthews Chris , Mcinnes Nicholas , Menon Shyaman , Dowling Jonathan , Braun Gary , Krivitsky Timur A , Cooper Helen , Andrew Nadine E
Published: November 2021

Background

Melbourne, Australia, successfully halted exponential transmission of COVID-19 via two strict lockdowns during 2020. The impact of such restrictions on healthcare-seeking behaviour is not comprehensively understood, but is of global importance. We explore the impact of the COVID-19 pandemic on acute, subacute and emergency department (ED) presentations/admissions within a tertiary, metropolitan health service in Melbourne, Australia, over two waves of community transmission (1 March to 20 September 2020).

Methods

We used 4 years of historical data and novel forecasting methods to predict counterfactual hospital activity for 2020, assuming absence of COVID-19. Observed activity was compared with forecasts overall, by age, triage category and for myocardial infarction and stroke. Data were analysed for all patients residing in the health service catchment area presenting between 4 January 2016 and 20 September 2020.

Results

ED presentations (n=401 805), acute admissions (n=371 723) and subacute admissions (n=15 676) were analysed. Substantial departures from forecasted presentation levels were observed during both waves in the ED and acute settings, and during the second wave in subacute. Reductions were most marked among those aged >80 and <18 years. Presentations persisted at expected levels for urgent conditions, and ED triage categories 1 and 5, with clear reductions in categories 2–4.

Conclusions

Our analyses suggest citizens were willing and able to present with life-threatening conditions during Melbourne’s lockdowns, and that switching to telemedicine did not cause widespread spill-over from primary care into ED. During a pandemic, lockdowns may not inhibit appropriate hospital attendance where rates of infectious disease are low.
Genotype-phenotype correlation in clubfoot (talipes equinovarus)
Authors: Hordyjewska-Kowalczyk Ewa , Nowosad Karol , Jamsheer Aleksander , Tylzanowski Przemko
Published: November 2021

Clubfoot (talipes equinovarus) is a congenital malformation affecting muscles, bones, connective tissue and vascular or neurological structures in limbs. It has a complex aetiology, both genetic and environmental. To date, the most important findings in clubfoot genetics involve PITX1 variants, which were linked to clubfoot phenotype in mice and humans. Additionally, copy number variations encompassing TBX4 or single nucleotide variants in HOXC11, the molecular targets of the PITX1 transcription factor, were linked to the clubfoot phenotype. In general, genes of cytoskeleton and muscle contractile apparatus, as well as components of the extracellular matrix and connective tissue, are frequently linked with clubfoot aetiology. Last but not least, an equally important element, that brings us closer to a better understanding of the clubfoot genotype/phenotype correlation, are studies on the two known animal models of clubfoot—the pma or EphA4 mice. This review will summarise the current state of knowledge of the molecular basis of this congenital malformation.
Swiss cost-effectiveness analysis of universal screening for Lynch syndrome of patients with colorectal cancer followed by cascade genetic testing of relatives
Authors: Salikhanov Islam , Heinimann Karl , Chappuis Pierre , Buerki Nicole , Graffeo Rossella , Heinzelmann Viola , Rabaglio Manuela , Taborelli Monica , Wieser Simon , Katapodi Maria C.
Published: November 2021

Background

We estimated the cost-effectiveness of universal DNA screening for Lynch syndrome (LS) among newly diagnosed patients with colorectal cancer (CRC) followed by cascade screening of relatives from the Swiss healthcare system perspective.

Methods

We integrated decision trees with Markov models to calculate incremental cost per quality-adjusted life-year saved by screening all patients with CRC (alternative strategy) compared with CRC tumour-based testing followed by DNA sequencing (current strategy).

Results

The alternative strategy has an incremental cost-effectiveness ratio of CHF65 058 compared with the current strategy, which is cost-effective according to Swiss standards. Based on annual incidence of CRC in Switzerland, universal DNA screening correctly identifies all 123 patients with CRC with LS, prevents 17 LS deaths and avoids 19 CRC cases, while the current strategy leads to 32 false negative results and 253 LS cases lost to follow-up. One way and probabilistic sensitivity analyses showed that universal DNA testing is cost-effective in around 80% of scenarios, and that the cost of DNA testing and the number of invited relatives per LS case determine the cost-effectiveness ratio.

Conclusion

Results can inform policymakers, healthcare providers and insurance companies about the costs and benefits associated with universal screening for LS and cascade genetic testing of relatives.
Prevalence of dementia in ischaemic or mixed stroke populations: systematic review and meta-analysis
Authors: Craig Louise , Hoo Zhi Liang , Yan Toh Zeng , Wardlaw Joanna , Quinn Terence J
Published: November 2021

An understanding of the epidemiology of poststroke dementia (PSD) is necessary to inform research, practice and policy. With increasing primary studies, a contemporary review of PSD could allow for analyses of incidence and prevalence trends. Databases were searched using a prespecified search strategy. Eligible studies described an ischaemic or mixed stroke cohort with prospective clinical assessment for dementia. Pooled prevalence of dementia was calculated using random-effects models at any time after stroke (primary outcome) and at 1 year (range: 6–18 months), stratified for inclusion of prestroke dementia. Meta-regression explored the effect of year of study. Sensitivity analyses removed low-quality or outlier studies. Of 12 505 titles assessed, 44 studies were included in the quantitative analyses. At any time point after stroke, the prevalence of PSD was 16.5% (95% CI 10.4% to 25.1%) excluding prestroke dementia and 22.3% (95% CI 18.8% to 26.2%) including prestroke dementia. At 1 year, the prevalence of PSD was 18.4% (95% CI 7.4% to 38.7%) and 20.4% (95% CI 14.2% to 28.2%) with prestroke dementia included. In studies including prestroke dementia there was a negative association between dementia prevalence and year of study (slope coefficient=−0.05 (SD: 0.01), p<0.0001). Estimates were robust to sensitivity analyses. Dementia is common following stroke. At any point following stroke, more than one in five people will have dementia, although a proportion of this dementia predates the stroke. Declining prevalence of prestroke dementia may explain apparent reduction in PSD over time. Risk of dementia following stroke remains substantial and front-loaded, with high prevalence at 1 year post event.
Informed consent in pragmatic trials: results from a survey of trials published 2014–2019
Authors: Zhang Jennifer Zhe , Nicholls Stuart G , Carroll Kelly , Nix Hayden Peter , Goldstein Cory E , Hey Spencer Phillips , Brehaut Jamie C , McLean Paul C , Weijer Charles , Fergusson Dean A , Taljaard Monica
Published: November 2021

Objectives

To describe reporting of informed consent in pragmatic trials, justifications for waivers of consent and reporting of alternative approaches to standard written consent. To identify factors associated with (1) not reporting and (2) not obtaining consent.

Methods

Survey of primary trial reports, published 2014–2019, identified using an electronic search filter for pragmatic trials implemented in MEDLINE, and registered in ClinicalTrials.gov.

Results

Among 1988 trials, 132 (6.6%) did not include a statement about participant consent, 1691 (85.0%) reported consent had been obtained, 139 (7.0%) reported a waiver and 26 (1.3%) reported consent for one aspect (eg, data collection) but a waiver for another (eg, intervention). Of the 165 trials reporting a waiver, 76 (46.1%) provided a justification. Few (53, 2.9%) explicitly reported use of alternative approaches to consent. In multivariable logistic regression analyses, lower journal impact factor (p=0.001) and cluster randomisation (p<0.0001) were significantly associated with not reporting on consent, while trial recency, cluster randomisation, higher-income country settings, health services research and explicit labelling as pragmatic were significantly associated with not obtaining consent (all p<0.0001).

Discussion

Not obtaining consent seems to be increasing and is associated with the use of cluster randomisation and pragmatic aims, but neither cluster randomisation nor pragmatism are currently accepted justifications for waivers of consent. Rather than considering either standard written informed consent or waivers of consent, researchers and research ethics committees could consider alternative consent approaches that may facilitate the conduct of pragmatic trials while preserving patient autonomy and the public’s trust in research.
Transition from Anti-coherence resonance to coherence resonance for mixed-mode oscillations and period-1 firing of nervous system
Authors: Liu Yifan , Lu Bo , Zhang Wanqin , Gu Huaguang
Published: November 2021

Identification of dynamics of the mixed-mode oscillations (MMOs), which exhibit transition between oscillations with large and small amplitudes, is very important for nonlinear physics. In this paper, the MMOs with transition between subthreshold oscillations and spikes are investigated in a neuron model. In the absence of noise, the MMOs appear between the resting state and period-1 firing with increasing depolarization current. After introducing white noise, coherence resonance (CR) is evoked from the resting state and non-CR is induced from period-1 firing far from the MMOs, which is consistent with the traditional viewpoint. However, an interesting result that a transition from anti-CR to CR is evoked by noise from both the MMOs and the period-1 firing near the MMOs is acquired, which is characterized by the increase, decrease and increase again of the coefficient of variations of interspike intervals (ISIs) with increasing noise intensity. At small noise intensity, more subthreshold oscillations are evoked by noise to reduce the firing frequency, resulting in faster increase of standard deviation (SD) of ISIs than that of mean value of ISIs, which is the cause for the anti-CR. The decrease of SD is faster for middle noise intensity and is lower for strong noise intensity, which is the cause for the CR. The different stochastic responses of MMOs and period-1 firing nearby at different levels of noise insanity are the dynamical mechanism for the transition from anti-CR to CR. Such results present potential functions of the MMOs and period-1 firing on information processing in the nervous system with noise and extend the conditions for the CR and anti-CR phenomena, which enriches the contents of nonlinear dynamics.
Optimization of Pipe Network Simulation Algorithm for Tree-Shaped Water Injection System in Large-Scale Oilfield
Authors: Ruan Yan , Zhang Xuliang , Chen Jiaona
Published: November 2021

As intelligence technology develops, there is a higher requirement for computing speed and accuracy of water injection system simulation. In this paper, aiming at the tree-shaped water injection pipe network system of large-scale oilfields, based on the energy equation for calculating the pressure drop [Formula: see text] of pipe section, a mathematical model of the pipeline unit and the node unit is established, and finally, a mathematical model of pipe network for the entire water injection system is established; then, the improved iterative algorithm is used to solve the simulation model of water injection system. In this way, we determine the boundary calculation conditions, take the water injection station as reference node, and use the maximum pressure of water injection well as the initial value of the reference node for calculation, which reduces the number of iterations in model calculation; by comparing the simulation results of different iteration steps, 0.01 is selected as the iteration step size due to its higher calculation accuracy; and the calculation process has also been optimized. The process of solving the characteristic matrix [Formula: see text] is combined with the process of calculating the pressure drop [Formula: see text] of pipe section, and placed outside the algorithm loop, thereby shortening the calculation time of a single cycle and reducing the calculation amount of the algorithm. The application cases show that the proposed optimization algorithm for water injection system pipe network simulation can be used as an effective method to improve the solution speed and calculation accuracy of the simulation algorithm of tree-shaped water injection system in large-scale oilfields.
Methods on Detecting Closely Related Topics and Spatial Events
Authors: Yu Zehao
Published: November 2021

Topic detection is a hot issue that many researchers are interested in. The previous researches focused on the single data stream, they did not consider the topic detection from different data streams in a harmonious way, so they cannot detect closely related topics from different data streams. Recently, Twitter, along with other SNS such as Weibo, and Yelp, began backing position services in their texts. Previous approaches are either complex to be conducted or oversimplified that cannot achieve better performance on detecting spatial topics. In our paper, we introduce a probabilistic method which can precisely detect closely related bursty topics and their bursty periods across different data streams in a unified way. We also introduce a probabilistic method called Latent Spatial Events Model (LSEM) that can find areas as well as to detect the spatial events, it can also predict positions of the texts. We evaluate LSEM on different datasets and reflect that our approach outperforms other baseline approaches in different indexes such as perplexity, entropy of topic and KL-divergence, range error. Evaluation of our first proposed approach on different datasets shows that it can detect closely related topics and meaningful bursty time periods from different datasets.
On the Use of Generative Deep Learning Approaches for Generating Hidden Test Scripts
Authors: Oz Mert , Kaya Caner , Olmezogullari Erdi , Aktas Mehmet S.
Published: November 2021

With the advent of web 2.0, web application architectures have been evolved, and their complexity has grown enormously. Due to the complexity, testing of web applications is getting time-consuming and intensive process. In today’s web applications, users can achieve the same goal by performing different actions. To ensure that the entire system is safe and robust, developers try to test all possible user action sequences in the testing phase. Since the space of all the possibilities is enormous, covering all user action sequences can be impossible. To automate the test script generation task and reduce the space of the possible user action sequences, we propose a novel method based on long short-term memory (LSTM) network for generating test scripts from user clickstream data. The experiment results clearly show that generated hidden test sequences are user-like sequences, and the process of generating test scripts with the proposed model is less time-consuming than writing them manually.
The potential of family-free rearrangements towards gene orthology inference
Authors: Rubert Diego P. , Doerr Daniel , Braga Marília D. V.
Published: November 2021

Recently, we proposed an efficient ILP formulation [Rubert DP, Martinez FV, Braga MDV, Natural family-free genomic distance, Algorithms Mol Biol 16:4, 2021] for exactly computing the rearrangement distance of two genomes in a family-free setting. In such a setting, neither prior classification of genes into families, nor further restrictions on the genomes are imposed. Given two genomes, the mentioned ILP computes an optimal matching of the genes taking into account simultaneously local mutations, given by gene similarities, and large-scale genome rearrangements. Here, we explore the potential of using this ILP for inferring groups of orthologs across several species. More precisely, given a set of genomes, our method first computes all pairwise optimal gene matchings, which are then integrated into gene families in the second step. Our approach is implemented into a pipeline incorporating the pre-computation of gene similarities. It can be downloaded from gitlab.ub.uni-bielefeld.de/gi/FFGC. We obtained promising results with experiments on both simulated and real data.
Interacting Rényi holographic dark energy with parametrization on the interaction term
Authors: Sharma Umesh Kumar , Dubey Vipin Chandra
Published: November 2021

In this work, we study the Rényi holographic dark energy (RHDE) model in a flat FRW Universe where the infrared cut-off is taken care by the Hubble horizon and also by taking three different parametrizations of the interaction term between the dark matter and the dark energy. Analyzing graphically, the behavior of some cosmological parameters in particular deceleration parameter, equation of state (EoS) parameter, energy density parameter and squared speed of sound, in the process of the cosmic evolution, is found to be leading towards the late-time accelerated expansion of the RHDE model. Also, we find the departure for the derived models from the standard [Formula: see text]CDM model according to the evolution of jerk parameter. Moreover, we compare the model parameters by considering the observational Hubble data which consist of 51 points in the redshift range [Formula: see text].
The effect of interference time in a predator–prey–nonprey system
Authors: Mukherjee Debasis
Published: November 2021

In this paper, we propose a three-species model consisting of two competing (prey and nonprey) species and a predator species. Here, nonprey species are not included in the predator’s food choice. The competition process follows Holling type II competitive response to interference time. Basic results include the stability of the system. First, it is established that an increasing number of interference time stabilizes the system. Second, it is shown that the interference time has an impact on the predator equilibrium density. Third, we develop the criterion of persistence of all the species. It is also shown that the system may not be persistent when multiple steady states appear. We examine the global stability of the coexistence equilibrium point. Numerical experiments are carried out to understand the analytical outcomes.
Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data
Authors: Alizadehsani Roohallah , Sharifrazi Danial , Izadi Navid Hoseini , Joloudari Javad Hassannataj , Shoeibi Afshin , Gorriz Juan M. , Hussain Sadiq , Arco Juan E. , Sani Zahra Alizadeh , Khozeimeh Fahime , Khosravi Abbas , Nahavandi Saeid , Islam Sheikh Mohammed Shariful , Acharya U. Rajendra
Published: November 2021

The new coronavirus has caused more than one million deaths and continues to spread rapidly. This virus targets the lungs, causing respiratory distress which can be mild or severe. The X-ray or
computed tomography
(
CT
) images of lungs can reveal whether the patient is infected with COVID-19 or not. Many researchers are trying to improve COVID-19 detection using artificial intelligence. Our motivation is to develop an automatic method that can cope with scenarios in which preparing labeled data is time consuming or expensive. In this article, we propose a
Semi-supervised Classification using Limited Labeled Data
(
SCLLD
) relying on Sobel edge detection and
Generative Adversarial Networks
(
GANs
) to automate the COVID-19 diagnosis. The GAN discriminator output is a probabilistic value which is used for classification in this work. The proposed system is trained using 10,000 CT scans collected from Omid Hospital, whereas a public dataset is also used for validating our system. The proposed method is compared with other state-of-the-art supervised methods such as Gaussian processes. To the best of our knowledge, this is the first time a semi-supervised method for COVID-19 detection is presented. Our system is capable of learning from a mixture of limited labeled and unlabeled data where supervised learners fail due to a lack of sufficient amount of labeled data. Thus, our semi-supervised training method significantly outperforms the supervised training of
Convolutional Neural Network
(
CNN
) when labeled training data is scarce. The 95% confidence intervals for our method in terms of accuracy, sensitivity, and specificity are 99.56 ± 0.20%, 99.88 ± 0.24%, and 99.40 ± 0.18%, respectively, whereas intervals for the CNN (trained supervised) are 68.34 ± 4.11%, 91.2 ± 6.15%, and 46.40 ± 5.21%.
Learning to Fool the Speaker Recognition
Authors: Li Jiguo , Zhang Xinfeng , Xu Jizheng , Ma Siwei , Gao Wen
Published: November 2021

Due to the widespread deployment of fingerprint/face/speaker recognition systems, the risk in these systems, especially the adversarial attack, has drawn increasing attention in recent years. Previous researches mainly studied the adversarial attack to the vision-based systems, such as fingerprint and face recognition. While the attack for speech-based systems has not been well studied yet, although it has been widely used in our daily life. In this article, we attempt to fool the state-of-the-art speaker recognition model and present
speaker recognition attacker
, a lightweight multi-layer convolutional neural network to fool the well-trained state-of-the-art speaker recognition model by adding imperceptible perturbations onto the raw speech waveform. We find that the speaker recognition system is vulnerable to the adversarial attack, and achieve a high success rate on both the non-targeted attack and targeted attack. Besides, we present an effective method by leveraging a pretrained phoneme recognition model to optimize the speaker recognition attacker to obtain a tradeoff between the attack success rate and the perceptual quality. Experimental results on the TIMIT and LibriSpeech datasets demonstrate the effectiveness and efficiency of our proposed model. And the experiments for frequency analysis indicate that high-frequency attack is more effective than low-frequency attack, which is different from the conclusion drawn in previous image-based works. Additionally, the ablation study gives more insights into our model.
Precise No-Reference Image Quality Evaluation Based on Distortion Identification
Authors: Yan Chenggang , Teng Tong , Liu Yutao , Zhang Yongbing , Wang Haoqian , Ji Xiangyang
Published: November 2021

The difficulty of no-reference image quality assessment (NR IQA) often lies in the lack of knowledge about the distortion in the image, which makes quality assessment blind and thus inefficient. To tackle such issue, in this article, we propose a novel scheme for precise NR IQA, which includes two successive steps, i.e., distortion identification and targeted quality evaluation. In the first step, we employ the well-known Inception-ResNet-v2 neural network to train a classifier that classifies the possible distortion in the image into the four most common distortion types, i.e., Gaussian white noise (WN), Gaussian blur (GB), jpeg compression (JPEG), and jpeg2000 compression (JP2K). Specifically, the deep neural network is trained on the large-scale Waterloo Exploration database, which ensures the robustness and high performance of distortion classification. In the second step, after determining the distortion type of the image, we then design a specific approach to quantify the image distortion level, which can estimate the image quality specially and more precisely. Extensive experiments performed on LIVE, TID2013, CSIQ, and Waterloo Exploration databases demonstrate that (1) the accuracy of our distortion classification is higher than that of the state-of-the-art distortion classification methods, and (2) the proposed NR IQA method outperforms the state-of-the-art NR IQA methods in quantifying the image quality.
Towards a Contemplative Research Framework for Training Self-Observation in HCI: A Study of Compassion Cultivation
Authors: Mah Kristina , Loke Lian , Hespanhol Luke
Published: November 2021

With the emergence in human–computer interaction (HCI) of researching contemplative practices, authentic descriptions of first-person lived experience informing design are few. Most researchers in HCI are not trained in observing the mind. We draw on learnings from neurophenomenology, inspired by well-established Buddhist techniques for mind-training. We present a self-observation ofTonglen, a Buddhist meditation technique for compassion, conducted over 12 weeks. We found that to keenly observe and document the practice, it is important to go through preparatory stages of stabilising attention and observing the mind. For the practitioner-cum-researcher, the technique should be embedded into a framework training self-observation and developing meta-awareness, supported by documentation of somatic snapshots and reflective journal writing. The first-person method of self-enquiry and account of self-evidence offer insight and directions for refining first-person approaches for future HCI research in body and mind cultivation, and design implications for interactive technologies supporting any practice with a contemplative component.
Timely Reporting of Heavy Hitters Using External Memory
Authors: Singh Shikha , Pandey Prashant , Bender Michael A. , Berry Jonathan W. , Farach-Colton Martín , Johnson Rob , Kroeger Thomas M. , Phillips Cynthia A.
Published: November 2021

Given an input stream
S
of size
N
, a

ɸ-heavy hitter

is an item that occurs at least
ɸN
times in
S
. The problem of finding heavy-hitters is extensively studied in the database literature.

We study a real-time heavy-hitters variant in which an element must be reported shortly after we see its T = ɸ N-th occurrence (and hence it becomes a heavy hitter). We call this the Timely Event Detection (
TED
) Problem. The
TED
problem models the needs of many real-world monitoring systems, which demand accurate (i.e., no false negatives) and timely reporting of all events from large, high-speed streams with a low reporting threshold (high sensitivity).

Like the classic heavy-hitters problem, solving the
TED
problem without false-positives requires large space (Ω (N) words). Thus in-RAM heavy-hitters algorithms typically sacrifice accuracy (i.e., allow false positives), sensitivity, or timeliness (i.e., use multiple passes).

We show how to adapt heavy-hitters algorithms to external memory to solve the
TED
problem on large high-speed streams while guaranteeing accuracy, sensitivity, and timeliness. Our data structures are limited only by I/O-bandwidth (not latency) and support a tunable tradeoff between reporting delay and I/O overhead. With a small bounded reporting delay, our algorithms incur only a logarithmic I/O overhead.

We implement and validate our data structures empirically using the Firehose streaming benchmark. Multi-threaded versions of our structures can scale to process 11M observations per second before becoming CPU bound. In comparison, a naive adaptation of the standard heavy-hitters algorithm to external memory would be limited by the storage device’s random I/O throughput, i.e., ≈100K observations per second.
A Learning-based
Innovized
Progress Operator for Faster Convergence in Evolutionary Multi-objective Optimization
Authors: Mittal Sukrit , Saxena Dhish Kumar , Deb Kalyanmoy , Goodman Erik D.
Published: November 2021

Learning effective problem information from already explored search space in an optimization run, and utilizing it to improve the convergence of subsequent solutions, have represented important directions in Evolutionary Multi-objective Optimization (EMO) research. In this article, a machine learning (ML)-assisted approach is proposed that: (a)
maps
the solutions from earlier generations of an EMO run to the current non-dominated solutions
in the decision space
; (b) learns the salient patterns in the
mapping
using an ML method, here an artificial neural network (ANN); and (c) uses the learned ML model to
advance
some of the subsequent offspring solutions in an adaptive manner. Such a multi-pronged approach, quite different from the popular
surrogate-modeling
methods, leads to what is here referred to as the
Innovized Progress
(IP) operator. On several test and engineering problems involving two and three objectives, with and without constraints, it is shown that an EMO algorithm assisted by the IP operator offers faster convergence behavior, compared to its base version independent of the IP operator. The results are encouraging, pave a new path for the performance improvement of EMO algorithms, and set the motivation for further exploration on more challenging problems.
Toward Understanding and Evaluating Structural Node Embeddings
Authors: Jin Junchen , Heimann Mark , Jin Di , Koutra Danai
Published: November 2021

While most network embedding techniques model the proximity between nodes in a network, recently there has been significant interest in
structural embeddings
that are based on node
equivalences
, a notion rooted in sociology: equivalences or positions are collections of nodes that have similar roles—i.e., similar functions, ties or interactions with nodes in other positions—irrespective of their distance or reachability in the network. Unlike the proximity-based methods that are rigorously evaluated in the literature, the evaluation of structural embeddings is less mature. It relies on small synthetic or real networks with labels that are not perfectly defined, and its connection to sociological equivalences has hitherto been vague and tenuous. With new node embedding methods being developed at a breakneck pace,
proper evaluation, and systematic characterization of existing approaches will be essential to progress.

To fill in this gap, we set out to understand
what
types of equivalences structural embeddings capture. We are the first to contribute rigorous intrinsic and extrinsic evaluation methodology for structural embeddings, along with carefully-designed, diverse datasets of varying sizes. We observe a number of different evaluation variables that can lead to different results (e.g., choice of similarity measure, classifier, and label definitions). We find that degree distributions within nodes’ local neighborhoods can lead to simple yet effective baselines in their own right and guide the future development of structural embedding. We hope that our findings can influence the design of further node embedding methods and also pave the way for more comprehensive and fair evaluation of structural embedding methods.
On Directed Densest Subgraph Discovery
Authors: Ma Chenhao , Fang Yixiang , Cheng Reynold , Lakshmanan Laks V. S. , Zhang Wenjie , Lin Xuemin
Published: November 2021

Given a directed graph
G
, the directed densest subgraph (DDS) problem refers to the finding of a subgraph from
G
, whose density is the highest among all the subgraphs of
G
. The DDS problem is fundamental to a wide range of applications, such as fraud detection, community mining, and graph compression. However, existing DDS solutions suffer from efficiency and scalability problems: on a 3,000-edge graph, it takes three days for one of the best exact algorithms to complete. In this article, we develop an efficient and scalable DDS solution. We introduce the notion of [
x
,
y
]-core, which is a dense subgraph for
G
, and show that the densest subgraph can be accurately located through the [
x
,
y
]-core with theoretical guarantees. Based on the [
x
,
y
]-core, we develop exact and approximation algorithms. We further study the problems of maintaining the DDS over dynamic directed graphs and finding the weighted DDS on weighted directed graphs, and we develop efficient non-trivial algorithms to solve these two problems by extending our DDS algorithms. We have performed an extensive evaluation of our approaches on 15 real large datasets. The results show that our proposed solutions are up to six orders of magnitude faster than the state-of-the-art.
IoT-Fog-Cloud Centric Earthquake Monitoring and Prediction
Authors: Saini Kanika , Kalra Sheetal , Sood Sandeep K.
Published: November 2021

Earthquakes are among the most inevitable natural catastrophes. The uncertainty about the severity of the earthquake has a profound effect on the burden of disaster and causes massive economic and societal losses. Although unpredictable, it can be expected to ameliorate damage and fatalities, such as monitoring and predicting earthquakes using the Internet of Things (IoT). With the resurgence of the IoT, an emerging innovative approach is to integrate IoT technology with Fog and Cloud Computing to augment the effectiveness and accuracy of earthquake monitoring and prediction. In this study, the integrated IoT-Fog-Cloud layered framework is proposed to predict earthquakes using seismic signal information. The proposed model is composed of three layers: (i) at sensor layer, seismic data are acquired, (ii) fog layer incorporates pre-processing, feature extraction using fast Walsh–Hadamard transform (FWHT), selection of relevant features by applying High Order Spectral Analysis (HOSA) to FWHT coefficients, and seismic event classification by K-means accompanied by real-time alert generation, (iii) at cloud layer, an artificial neural network (ANN) is employed to forecast the magnitude of an earthquake. For performance evaluation, K-means classification algorithm is collated with other well-known classification algorithms from the perspective of accuracy and execution duration. Implementation statistics indicate that with chosen HOS features, we have been able to attain high accuracy, precision, specificity, and sensitivity values of 93.30%, 96.65%, 90.54%, and 92.75%, respectively. In addition, the ANN provides an average correct magnitude prediction of 75%. The findings ensured that the proposed framework has the potency to classify seismic signals and predict earthquakes and could therefore further enhance the detection of seismic activities. Moreover, the generation of real-time alerts further amplifies the effectiveness of the proposed model and makes it more real-time compatible.
Towards a Consistent Interpretation of AIOps Models
Authors: Lyu Yingzhe , Rajbahadur Gopi Krishnan , Lin Dayi , Chen Boyuan , Jiang Zhen Ming (Jack)
Published: November 2021

Artificial Intelligence for IT Operations (AIOps) has been adopted in organizations in various tasks, including interpreting models to identify indicators of service failures. To avoid misleading practitioners, AIOps model interpretations should be consistent (i.e., different AIOps models on the same task agree with one another on feature importance). However, many AIOps studies violate established practices in the machine learning community when deriving interpretations, such as interpreting models with suboptimal performance, though the impact of such violations on the interpretation consistency has not been studied.

In this article, we investigate the consistency of AIOps model interpretation along three dimensions: internal consistency, external consistency, and time consistency. We conduct a case study on two AIOps tasks: predicting Google cluster job failures and Backblaze hard drive failures. We find that the randomness from learners, hyperparameter tuning, and data sampling should be controlled to generate consistent interpretations. AIOps models with AUCs greater than 0.75 yield more consistent interpretation compared to low-performing models. Finally, AIOps models that are constructed with the Sliding Window or Full History approaches have the most consistent interpretation with the trends presented in the entire datasets. Our study provides valuable guidelines for practitioners to derive consistent AIOps model interpretation.
The hyperthermic effect of central cholecystokinin is mediated by the cyclooxygenase-2 pathway
Authors: Keringer Patrik , Furedi Nora , Gaszner Balazs , Miko Alexandra , Pakai Eszter , Fekete Kata , Olah Emoke , Kelava Leonardo , Romanovsky Andrej A. , Rumbus Zoltan , Garami Andras
Published: November 2021

An association between central cholecystokinin signaling and the cyclooxygenase-prostaglandin E pathway has been proposed but remained poorly understood. We show that the hyperthermic response to the central administration of cholecystokinin alters the neuronal activity within efferent thermoeffector pathways and that these effects are fully blocked by the inhibition of cyclooxygenase. We also show that the activation of cyclooxygenase-2 is required for the hyperthermic effect of cholecystokinin and that cholecystokinin is a modulator of endotoxin-induced fever.
Diabetic ketoacidosis and COVID-19: what have we learned so far?
Authors: de Sá-Ferreira Caio Oliveira , da Costa Camila Helena Macedo , Guimarães João Campos Wiltgen , Sampaio Nathasha Souza , Silva Leticia de Moraes Lopes , de Mascarenhas Larissa Paula , Rodrigues Nicollas Garcia , dos Santos Talita Labonia , Campos Solange , Young Esther Cytrynbaum
Published: November 2021

In December 2019, a pandemic emerged due to a new coronavirus that imposed various uncertainties and discoveries. It has been reported that diabetes is a risk factor for worst outcomes of COVID-19 and also that SARS-CoV-2 infection was correlated with the occurrence of diabetic ketoacidosis (DKA) in patients. The aim of this work is to discuss this correlation emphasizing the main case reports from 2020 while exploring the management of DKA during the course of COVID-19. Web of Science, PubMed, and Scopus databases were searched using two sets of Medical Subject Heading (MeSH) search terms or Title/Abstract words: Coronavirus Infections (Coronavirus Infections, Middle East Respiratory Syndrome, COVID-19) and Diabetic Ketoacidosis (Diabetic Ketoacidosis, Diabetic Acidosis, Diabetic Ketosis). There is a clear correlation between COVID-19 and DKA. The SARS-Cov-2 infection may precipitate both a hyperglycemic state and ketoacidosis occurrence in patients with diabetes and nondiabetic patients, which may lead to fatal outcomes. DKA in patients with COVID-19 may increase risk and worse outcomes. Hence, the SARS-Cov-2 infection presents a new perspective toward the management of glycemia and acidosis in patients with diabetes and nondiabetic patients, highlighting the need for rapid interventions to minimize the complications from COVID-19 while reducing its spreading.
Bladder infection with uropathogenic Escherichia coli increases the excitability of afferent neurons
Authors: Montalbetti Nicolas , Dalghi Marianela G. , Bastacky Sheldon I. , Clayton Dennis R. , Ruiz Wily G. , Apodaca Gerard , Carattino Marcelo D.
Published: November 2021

Urinary tract infection (UTI) produced by uropathogenic Escherichia coli (UPEC) promotes sensitization of bladder afferent sensory neurons with tetrodotoxin-resistant and tetrodotoxin-sensitive action potentials. Lipopolysaccharide and other virulence factors produced by UPEC contribute to the sensitization of bladder afferents in UTI. In conclusion, sensitized afferents contribute to the voiding symptoms and pelvic pain present in mice bladder inoculated with UPEC.
Modeling and Optimization of Multiaction Dynamic Dispatching Problem for Shared Autonomous Electric Vehicles
Authors: Wang Ning , Guo Jiahui
Published: November 2021

The fusion of electricity, automation, and sharing is forming a new Autonomous Mobility-on-Demand (AMoD) system in current urban transportation, in which the Shared Autonomous Electric Vehicles (SAEVs) are a fleet to execute delivery, parking, recharging, and repositioning tasks automatically. To model the decision-making process of AMoD system and optimize multiaction dynamic dispatching of SAEVs over a long horizon, the dispatching problem of SAEVs is modeled according to Markov Decision Process (MDP) at first. Then two optimization models from short-sighted view and farsighted view based on combinatorial optimization theory are built, respectively. The former focuses on the instant and single-step reward, while the latter aims at the accumulative and multistep return. After that, the Kuhn–Munkres algorithm is set as the baseline method to solve the first model to achieve optimal multiaction allocation instructions for SAEVs, and the combination of deep Q-learning algorithm and Kuhn–Munkres algorithm is designed to solve the second model to realize the global optimization. Finally, a toy example, a macrosimulation of 1 month, and a microsimulation of 6 hours based on actual historical operation data are conducted. Results show that (1) the Kuhn–Munkres algorithm ensures the computational effectiveness in the large-scale real-time application of the AMoD system; (2) the second optimization model considering long-term return can decrease average user waiting time and achieve a 2.78% increase in total revenue compared with the first model; (3) and integrating combinatorial optimization theory with reinforcement learning theory is a perfect package for solving the multiaction dynamic dispatching problem of SAEVs.
Computing Gutman Connection Index of Thorn Graphs
Authors: Javaid Muhammad , Siddique Muhammad Khubab , Bonyah Ebenezer
Published: November 2021

Chemical structural formula can be represented by chemical graphs in which atoms are considered as vertices and bonds between them are considered as edges. A topological index is a real value that is numerically obtained from a chemical graph to predict its various physical and chemical properties. Thorn graphs are obtained by attaching pendant vertices to the different vertices of a graph under certain conditions. In this paper, a numerical relation between the Gutman connection (GC) index of a graph and its thorn graph is established. Moreover, the obtained result is also illustrated by computing the GC index for the particular families of the thorn graphs such as thorn paths, thorn rods, thorn stars, and thorn rings.
An Application of Sombor Index over a Special Class of Semigroup Graph
Authors: Oğuz Ünal Seda
Published: November 2021

Recently, Gutman introduced a class of novel topological invariants named Sombor index which is defined asSOG=∑uv∈EGdu2+dv2. In this study, the Sombor index of monogenic semigroup graphs, which is an important class of algebraic structures, is calculated.
Performance Evaluation and Site Application of a Hydrophobic Long-Chain Ester-Based CO2 Fracturing Fluid Thickener
Authors: Lu Jianqiang , Fu Meilong , Xu Liu , Huang Qian , Zheng Yan
Published: November 2021

Nowadays, there are a wide variety of thickeners developed for dry CO2 fracturing worldwide, but numerous problems remain during in situ testing. To address problems in CO2 fracturing fluid operation (high frictional drag, low viscosity, low proppant-carrying capacity, narrow reservoir fractures, etc.), the authors have synthesized the novel hydrophobic long-chain ester thickener, studied viscosity, frictional drag, and proppant-carrying capacity of CO2 fracturing fluid and core damage by CO2 fracturing fluid by varying the temperature, pressure, and level of injection of the novel thickener and explored the thickening mechanism for this thickener in CO2. Based on the study results, as the temperature, pressure, and amount of injected thickener increased, fracturing fluid viscosity increased steadily. In the case of shearing for 125 min under conditions of 170 S−1, 40°C, and 20 MPa, when the thickener level increased from 1% to 2%, fracturing fluid viscosity increased and then decreased, varying within 50–150 mPa·s, and the viscosity-enhancing effect was evident; under conditions of 20°C and 12 MPa, as the flow rate increased, drag reduction efficiency reached 78.3% and the minimal proppant settling speed was 0.09 m/s; under conditions of 40°C and 20 MPa, drag reduction efficiency reached 77.4% and the proppant settling speed was 0.08 m/s; with the increases in temperature, pressure, and injection amount, core damage rates of the thickener varied within 1.77%–2.88%, indicating that basically no damage occurred. This study is of significant importance to the development of CO2 viscosity enhancers and CO2 fracturing operation.
The Chronotropic Function of Propofol and the Underlying Mechanism in Rabbits
Authors: Cheng Wenjie , Sun Xiaohua , Liu Yanfang , Han Shiqi , Ren Wanlu
Published: November 2021

The report of bradycardia caused by propofol is increasing. In the experiment, we investigated the chronotropic function of propofol and the underlying mechanism. Rabbits of both sexes were randomly divided into 4 groups: propofol 5 mg/kg group, 10 mg/kg group, 15 mg/kg group, and sham group. Heart rate and frequency of vagal efferent discharge were recorded before the injection and 0, 0.5, 1, 2, and 10 min after the injection through intravenous mode. Then, their hearts were removed, and sinoatrial nodes were dissected. The action potentials of the sinus node pacemaker cells were recorded by the intracellular glass microelectrode technique, and the sinoatrial (SA) node was exposed to propofol 1, 3, 5, and 10 µM respectively. The action potentials were recorded after the sinoatrial nodes were exposed to each concentration of propofol for 15 min. Our results show that the heart rate significantly decreased, and the vagal efferent discharge was significantly increased at 0, 0.5, 1, and 2 min after the injection, respectively. Besides, as the dose increases, the magnitude of change shows a dose-dependent manner. Propofol exerts a negative chronotropic action on sinoatrial node pacemaker cells. The drug significantly decreased APA, VDD, RPF, and prolonged APD90 in a concentration-dependent manner. These effects may be the main mechanism of propofol-induced bradycardia in clinical study.