bluuebunny/crossref_metadata_2025 huggingface dataset:
Achieving gains in state and local child safety systems and workforce development: application of the framework for quality improvement and innovation in child safety
Authors: Leonardo Jennifer B , Ali Bina , Stern-Carusone Jenny , Katradis Maria
Published: May 2022

Objective

This study investigated the application of the Children’s Safety Network (CSN) Framework for Quality Improvement and Innovation in Child Safety through the Child Safety Learning Collaborative (CSLC).

Methods

The CSN Framework was used by 26 state/jurisdiction teams that participated in cohort 1 of the CSLC, from November 2018 to April 2020. The aim was to strengthen child safety systems and the workforce to spread child safety evidence-based and evidence-informed strategies and programmes for children and adolescents ages <1–19 years.

Procedures

Participating teams’ child safety system development, workforce development, engagement in the CSLC, challenges encountered and overall satisfaction with the CSLC were assessed through ongoing CSLC participation records and an end-of-cohort survey (survey response rate: 73.1%).

Results

Teams showed an average change of 2.4-fold increase in the spread of evidence-based and evidence-informed child safety strategies and programmes, indicating improvement in child safety systems. Knowledge development on CSLC tools and strategies was reported by 77.8% of teams, with 55.5% reporting CSLC tools and strategies contributed to workforce development. Over two-thirds (70.6%) reported being satisfied or very satisfied with the CSLC, but identified some challenges, including staff turnover and the need to strengthen partnerships. All teams demonstrated engagement in the CSLC, based on participation in a virtual meeting, learning session or a monthly report submission.

Conclusions

Despite challenges, teams continued to participate in the CSLC, recognising the importance of collaborative learning. The CSN Framework is helpful for state/jurisdiction teams to improve child safety systems and develop their workforce.
Implementation of the updated NICE haematological cancers (NG47) improving outcomes guidelines across Specialist Integrated Haematological Malignancy Diagnostic Services (SIHMDS) in England: a UK NEQAS LI survey
Authors: Cartwright Ashley , Snowden John A , Whitehouse Helen , Scott Stuart , Whitby Liam
Published: May 2022

Aims

Haematological malignancies represent a diverse group of diseases with complex diagnostic requirements. National Institute for Health and Care Excellence (NICE) Haematological Cancer: Improving Outcomes Guidance was published in 2003 and updated in 2016 (NG47), providing recommendations for service delivery including Specialist Integrated Haematological Malignancy Diagnostic Services (SIHMDSs). This survey assessed the implementation of NG47 guidelines, with a specific focus on implementation in relation to laboratory SIHMDS delivery.

Methods

A survey was issued to the 17 SIHMDSs identified in England. The questionnaire covered laboratory configuration, information systems, integrated reporting and multidisciplinary team (MDT) working recommendations.

Results

In the 10 responding SIHMDS, full implementation of recommendations was not achieved. Higher levels of implementation were reported in ‘colocated’ services compared with ‘networked’ SIHMDS. Increased guideline implementation was reported with longer duration since initial establishment of a SIHMDS and for laboratory based as opposed to clinical (MDT) reporting recommendations.

Conclusions

Our survey highlights variable implementation of NICE guidance across SIHMDS, with likely inequity of access, standardisation and quality in haemato-oncology diagnostics. Provision of a more structured framework for guideline implementation could assist in increasing compliance to meet the goals of quality and equity of access to harmonised haemato-oncology diagnostics across the NHS in England. This would provide a basis for evaluating the clinical benefits and health economic impact of the SIHMDS model on patient care and outcomes.
Charge parity violation parameters in neutral beauty meson decays into charmonium and strange kaon mesons
Authors: Mohammadi Behnam , Amirkhanlou Elnaz
Published: May 2022

The most precise measurement of decay-time-dependent CP violation in the decays of [Formula: see text] and [Formula: see text] was reported by LHCb collaboration with the values of [Formula: see text] and [Formula: see text] [Formula: see text]. In this study, the parameters of CP violation in these decay modes have been calculated under the factorization approach. In these decays, CP violation in the interference of mixing and decay has has arisen from decay-time-dependent asymmetry. This type of CP violation has been characterized by [Formula: see text]. We have estimated the parameters of [Formula: see text] and [Formula: see text] for the [Formula: see text] decay and [Formula: see text] and [Formula: see text] correspond to the [Formula: see text] decay which are compatible with the experimental values approved by the LHCb collaboration. In the following, we have obtained the value of [Formula: see text]. Finally, we have calculated the total amplitude and obtained comparable results with the experimental values for the branching ratios of both decays.
The large-scale structure formation in an expanding universe
Authors: Hameeda Mir , Pourhassan Behnam , Masood Syed , Faizal Mir , Wang Li-Gang , Abass Shohaib
Published: May 2022

In this paper, we analyze the effects of expansion on large-scale structure formation in our universe. We do that by incorporating a cosmological constant term in the gravitational partition function. This gravitational partition function with a cosmological constant is used for analyzing the thermodynamics of this system. We analyze the virial expansion for this system, and obtain its equation of state. It is observed that the generalization of this equation of state is like the Van der Waals equation. We also analyze a gravitational phase transition in this system using the mean-field theory. We construct the cosmic energy equation for this system of galaxies, and discuss its consequences. We obtain and analyze the distribution function for this system, using the gravitational partition function. We also compare the results obtained in this paper with the observational data.
ANALYSIS OF THERMAL CONDUCTIVITY OF DAMAGED TREE-LIKE BIFURCATION NETWORK WITH FRACTAL ROUGHENED SURFACES
Authors: XIAO BOQI , FANG JING , LONG GONGBO , TAO YUANZHANG , HUANG ZIJUN
Published: May 2022

Tree bifurcation network has always been a hot issue concerning scientists all over the world. However, the research on the heat conduction properties of damaged tree-like bifurcation networks is insufficient, and many issues remain unsolved. Therefore, this paper systematically studies the heat conduction phenomenon of a new damaged tree-like bifurcation network with fractal roughened surfaces (FRS), and derives the equivalent thermal conductivity of damaged network. According to the study, the dimensionless thermal conductivity (DTC) shows a downward trend with an increase in roughness. In addition, it can be seen that an increase in the number of damaged channels leads to a downward trend in the optimal thermal conductivity, but a slow upward trend in the critical diameter. Another interesting phenomenon is that the factors leading to the change in the optimal thermal conductivity do not include the total number of bifurcation levels, bifurcation number or the fractal dimension of length distribution. The model established in this paper does not contain any empirical constants to ensure that each parameter has its physical significance, revealing the heat transfer mechanism of tree-like bifurcation network more profoundly.
AVERAGE FERMAT DISTANCES OF TWO-COLORED SUBSTITUTION NETWORKS
Authors: LEI LEI , JIA QI , XI LIFENG
Published: May 2022

The substitution network is a model of evolving self-similar networks. Using the technique of finite pattern, we obtain the asymptotic formula of the average Fermat distances on two-colored substitution networks.
INFORMATION FRACTAL DIMENSION OF MASS FUNCTION
Authors: QIANG CHENHUI , DENG YONG , CHEONG KANG HAO
Published: May 2022

Fractals play an important role in nonlinear science. The most important parameter when modeling a fractal is the fractal dimension. Existing information dimension can calculate the dimension of probability distribution. However, calculating the fractal dimension given a mass function, which is the generalization of probability, is still an open problem of immense interest. The main contribution of this work is to propose an information fractal dimension of mass function. Numerical examples are given to show the effectiveness of our proposed dimension. We discover an important property in that the dimension of mass function with the maximum Deng entropy is [Formula: see text], which is the well-known fractal dimension of Sierpiski triangle. The application in complexity analysis of time series illustrates the effectiveness of our method.
Coherent states on a circle: The Higgs-like approach
Authors: Mahdifar A. , Amooghorban E.
Published: May 2022

In this paper, the Higgs-like approach is used to analyze the quantum dynamics of a harmonic oscillator constrained on a circle. We obtain the Hamiltonian of this system as a function of the Cartesian coordinate of the tangent line through the gnomonic projection and then quantize it in the standard way. We then recast the Hamiltonian in a shape-invariant form and derive the spectrum energy of the confined harmonic oscillator on the circle. With help of the f-deformed oscillator algebra, we construct the coherent states on the circle and investigate their quantum statistical properties. We find that such states show nonclassical features like squeezing and sub-Poissonian statistics even in small curvatures of the circle.
Departure to pure de Sitter geometry based on Planck data
Authors: Suratgar K. , Mohsenzadeh M. , Yusofi E. , Tagizadeh-Farahmand F.
Published: May 2022

In this paper, we will obtain the basic parameters of primordial inflation such as power spectrum, scalar spectral index, and tensor-to-scalar ratio as a function of Hankel index [Formula: see text] for asymptotic-de Sitter inflation and then, based on the observational constraints on these parameters from Planck data, we obtain the allowable range for index [Formula: see text]. We will show that only the non-phantom phase is acceptable for quasi-de Sitter inflation at early universe. As a significant result, both of the primordial and late accelerating expansion are always in a quasi-vacuum phase (with [Formula: see text]) and it has never been in a pure-vacuum phase (with [Formula: see text]), i.e. cosmological constant. Therefore, based on our recent finding [E. Yusofi, M. Khanpour, B. Khanpour, M. A. Ramzanpour and M. Mohsenzadeh, Surface tension of cosmic voids as a possible source for dark energy, Mon. Not. R. Astron. Soc. 511 (2022) L82], we offer the surface tension of cosmic voids as a suitable alternative to the cosmological constant [Formula: see text], for accelerating universe.
A Deep Dive Inside DREBIN: An Explorative Analysis beyond Android Malware Detection Scores
Authors: Daoudi Nadia , Allix Kevin , Bissyandé Tegawendé François , Klein Jacques
Published: May 2022

Machine learning advances have been extensively explored for implementing large-scale malware detection. When reported in the literature, performance evaluation of machine learning based detectors generally focuses on highlighting the ratio of samples that are correctly or incorrectly classified, overlooking essential questions on why/how the learned models can be demonstrated as reliable. In the Android ecosystem, several recent studies have highlighted how evaluation setups can carry biases related to datasets or evaluation methodologies. Nevertheless, there is little work attempting to dissect the produced model to provide some understanding of its intrinsic characteristics. In this work, we fill this gap by performing a comprehensive analysis of a state-of-the-art Android malware detector, namely DREBIN, which constitutes today a key reference in the literature. Our study mainly targets an in-depth understanding of the classifier characteristics in terms of (1) which features actually matter among the hundreds of thousands that DREBIN extracts, (2) whether the high scores of the classifier are dependent on the dataset age, and (3) whether DREBIN’s explanations are consistent within malware families, among others. Overall, our tentative analysis provides insights into the discriminatory power of the feature set used by DREBIN to detect malware. We expect our findings to bring about a systematisation of knowledge for the community.
Real-Time Hair Filtering with Convolutional Neural Networks
Authors: Currius Roc R. , Assarsson Ulf , Sintorn Erik
Published: May 2022

Rendering of realistic-looking hair is in general still too costly to do in real-time applications, from simulating the physics to rendering the fine details required for it to look natural, including self-shadowing.

We show how an autoencoder network, that can be evaluated in real time, can be trained to filter an image of few stochastic samples, including self-shadowing, to produce a much more detailed image that takes into account real hair thickness and transparency.
Real-Time Ray-Traced Soft Shadows of Environmental Lighting by Conical Ray Culling
Authors: Xu Yang , Jiang Yuanfa , Zhang Junbo , Li Kang , Geng Guohua
Published: May 2022

Soft shadows of environmental lighting provide important visual cues in realistic rendering. However, rendering of soft shadows of environmental lighting in real-time is difficult because evaluating the visibility function is challenging. In this work, we present a method to render soft shadows of environmental lighting at real-time frame rates based on hardware-accelerated ray tracing. We assume that the scene contains both static and dynamic objects. To composite the soft shadows cast by dynamic objects with the precomputed lighting of static objects, the incident irradiance occluded by dynamic objects, which is obtained by accumulating the occluded incident radiances over the hemisphere using ray tracing, is subtracted from the precomputed incident irradiance. Conical ray culling is proposed to exclude the rays that cannot intersect dynamic objects, which significantly improves rendering efficiency. Rendering results demonstrate that our proposed method can achieve real-time rendering of soft shadows of environmental lighting cast by dynamic objects.
Streaming Data Preprocessing via Online Tensor Recovery for Large Environmental Sensor Networks
Authors: Hu Yue , Qu Ao , Wang Yanbing , Work Daniel B.
Published: May 2022

Measuring the built and natural environment at a fine-grained scale is now possible with low-cost urban environmental sensor networks. However, fine-grained city-scale data analysis is complicated by tedious data cleaning including removing outliers and imputing missing data. While many methods exist to automatically correct anomalies and impute missing entries, challenges still exist on data with large spatial-temporal scales and shifting patterns. To address these challenges, we propose an online robust tensor recovery (OLRTR) method to preprocess streaming high-dimensional urban environmental datasets. A small-sized dictionary that captures the underlying patterns of the data is computed and constantly updated with new data. OLRTR enables online recovery for large-scale sensor networks that provide continuous data streams, with a lower computational memory usage compared to offline batch counterparts. In addition, we formulate the objective function so that OLRTR can detect structured outliers, such as faulty readings over a long period of time. We validate OLRTR on a synthetically degraded National Oceanic and Atmospheric Administration temperature dataset, and apply it to the Array of Things city-scale sensor network in Chicago, IL, showing superior results compared with several established online and batch-based low-rank decomposition methods.
HW-Forest: Deep Forest with Hashing Screening and Window Screening
Authors: Ma Pengfei , Wu Youxi , Li Yan , Guo Lei , Jiang He , Zhu Xingquan , Wu Xindong
Published: May 2022

As a novel deep learning model, gcForest has been widely used in various applications. However, current multi-grained scanning of gcForest produces many redundant feature vectors, and this increases the time cost of the model. To screen out redundant feature vectors, we introduce a hashing screening mechanism for multi-grained scanning and propose a model called HW-Forest which adopts two strategies: hashing screening and window screening. HW-Forest employs perceptual hashing algorithm to calculate the similarity between feature vectors in hashing screening strategy, which is used to remove the redundant feature vectors produced by multi-grained scanning and can significantly decrease the time cost and memory consumption. Furthermore, we adopt a self-adaptive instance screening strategy called window screening to improve the performance of our approach, which can achieve higher accuracy without hyperparameter tuning on different datasets. Our experimental results show that HW-Forest has higher accuracy than other models, and the time cost is also reduced.
Online Application Guidance for Heterogeneous Memory Systems
Authors: Olson M. Ben , Kammerdiener Brandon , Jantz Michael R. , Doshi Kshitij A. , Jones Terry
Published: May 2022

As scaling of conventional memory devices has stalled, many high-end computing systems have begun to incorporate alternative memory technologies to meet performance goals. Since these technologies present distinct advantages and tradeoffs compared to conventional DDR* SDRAM, such as higher bandwidth with lower capacity or vice versa, they are typically packaged alongside conventional SDRAM in a heterogeneous memory architecture. To utilize the different types of memory efficiently, new data management strategies are needed to match application usage to the best available memory technology. However, current proposals for managing heterogeneous memories are limited, because they either (1) do not consider high-level application behavior when assigning data to different types of memory or (2) require separate program execution (with a representative input) to collect information about how the application uses memory resources.

This work presents a new data management toolset to address the limitations of existing approaches for managing complex memories. It extends the application runtime layer with automated monitoring and management routines that assign application data to the best tier of memory based on previous usage, without any need for source code modification or a separate profiling run. It evaluates this approach on a state-of-the-art server platform with both conventional DDR4 SDRAM and non-volatile Intel Optane DC memory, using both memory-intensive high-performance computing (HPC) applications as well as standard benchmarks. Overall, the results show that this approach improves program performance significantly compared to a standard unguided approach across a variety of workloads and system configurations. The HPC applications exhibit the largest benefits, with speedups ranging from 1.4× to 7× in the best cases. Additionally, we show that this approach achieves similar performance as a comparable offline profiling-based approach after a short startup period, without requiring separate program execution or offline analysis steps.
Integration of Protein Structure and Population-Scale DNA Sequence Data for Disease Gene Discovery and Variant Interpretation
Authors: Li Bian , Jin Bowen , Capra John A. , Bush William S.
Published: May 2022

The experimental and computational techniques for capturing information about protein structures and genetic variation within the human genome have advanced dramatically in the past 20 years, generating extensive new data resources. In this review, we discuss these advances, along with new approaches for determining the impact a genetic variant has on protein function. We focus on the potential of new methods that integrate human genetic variation into protein structures to discover relationships to disease, including the discovery of mutational hotspots in cancer-related proteins, the localization of protein-altering variants within protein regions for common complex diseases, and the assessment of variants of unknown significance for Mendelian traits. We expect that approaches that integratethese data sources will play increasingly important roles in disease gene discovery and variant interpretation.
The Affordable Care Act After a Decade: Its Impact on the Labor Market and the Macro Economy
Authors: Fang Hanming , Krueger Dirk
Published: May 2022

The Affordable Care Act (ACA) is one of the most important reforms of the US health insurance system since the introduction of Medicare. Because employment is a main source of health insurance for the working-age population in the USA, this sweeping health insurance reform has also had important implications for the labor market and the macro economy. In this article, we review the prototype models that are used in the macro and labor literature, extended to integrate health and health insurance, to study the short- and long-run consequences of the ACA. We also suggest open areas for future research.
The UK Biobank: A Shining Example of Genome-Wide Association Study Science with the Power to Detect the Murky Complications of Real-World Epidemiology
Authors: Tan Vanessa Y. , Timpson Nicholas J.
Published: May 2022

Genome-wide association studies (GWASs) have successfully identified thousands of genetic variants that are reliably associated with human traits. Although GWASs are restricted to certain variant frequencies, they have improved our understanding of the genetic architecture of complex traits and diseases. The UK Biobank (UKBB) has brought substantial analytical opportunity and performance to association studies. The dramatic expansion of many GWAS sample sizes afforded by the inclusion of UKBB data has improved the power of estimation of effect sizes but, critically, has done so in a context where phenotypic depth and precision enable outcome dissection and the application of epidemiological approaches. However, at the same time, the availability of such a large, well-curated, and deeply measured population-based collection has the capacity to increase our exposure to the many complications and inferential complexities associated with GWASs and other analyses. In this review, we discuss the impact that UKBB has had in the GWAS era, some of the opportunities that it brings, and exemplar challenges that illustrate the reality of using data from this world-leading resource.
Synaptic Mechanisms Regulating Mood State Transitions in Depression
Authors: Parekh Puja K. , Johnson Shane B. , Liston Conor
Published: May 2022

Depression is an episodic form of mental illness characterized by mood state transitions with poorly understood neurobiological mechanisms. Antidepressants reverse the effects of stress and depression on synapse function, enhancing neurotransmission, increasing plasticity, and generating new synapses in stress-sensitive brain regions. These properties are shared to varying degrees by all known antidepressants, suggesting that synaptic remodeling could play a key role in depression pathophysiology and antidepressant function. Still, it is unclear whether and precisely how synaptogenesis contributes to mood state transitions. Here, we review evidence supporting an emerging model in which depression is defined by a distinct brain state distributed across multiple stress-sensitive circuits, with neurons assuming altered functional properties, synapse configurations, and, importantly, a reduced capacity for plasticity and adaptation. Antidepressants act initially by facilitating plasticity and enabling a functional reconfiguration of this brain state. Subsequently, synaptogenesis plays a specific role in sustaining these changes over time.
The role of mitochondria in the pathophysiology and treatment of common metabolic diseases in humans
Authors: Georgiev Asen , Granata Cesare , Roden Michael
Published: May 2022

Common metabolic diseases such as obesity, type 2 diabetes mellitus, and nonalcoholic fatty liver disease significantly contribute to morbidity and mortality worldwide. They are frequently associated with insulin resistance and altered mitochondrial functionality. Insulin-responsive tissues can show changes in mitochondrial features such as oxidative capacity, mitochondrial content, and turnover, which do not necessarily reflect abnormalities but rather adaptation to a certain metabolic condition. Lifestyle modifications and classic or novel drugs can modify these alterations and help to treat these metabolic diseases. This review addresses the role of mitochondria in human metabolic diseases and discusses potential future research directions.
Prevalence of spasticity in humans with spinal cord injury with different injury severity
Authors: Sangari Sina , Perez Monica A.
Published: May 2022

The prevalence of spasticity in humans with SCI remains poorly understood. Using kinematic and clinical outcomes, we examined spasticity in individuals with subacute and chronic injuries of different severity. We found that spasticity in the quadriceps femoris muscle was more prevalent among individuals with subacute motor incomplete than in those with motor complete injuries. However, in a different group of individuals with chronic injuries, no differences were found in the prevalence of spasticity across injury severity.
Detection Anomaly in Video Based on Deep Support Vector Data Description
Authors: Wang Bokun , Yang Caiqian , Chen Yaojing
Published: May 2022

Video surveillance systems have been widely deployed in public places such as shopping malls, hospitals, banks, and streets to improve the safety of public life and assets. In most cases, how to detect video abnormal events in a timely and accurate manner is the main goal of social public safety risk prevention and control. Due to the ambiguity of anomaly definition, the scarcity of anomalous data, as well as the complex environmental background and human behavior, video anomaly detection is a major problem in the field of computer vision. Existing anomaly detection methods based on deep learning often use trained networks to extract features. These methods are based on existing network structures, instead of designing networks for the goal of anomaly detection. This paper proposed a method based on Deep Support Vector Data Description (DSVDD). By learning a deep neural network, the input normal sample space can be mapped to the smallest hypersphere. Through DSVDD, not only can the smallest size data hypersphere be found to establish SVDD but also useful data feature representations and normal models can be learned. In the test, the samples mapped inside the hypersphere are judged as normal, while the samples mapped outside the hypersphere are judged as abnormal. The proposed method achieves 86.84% and 73.2% frame-level AUC on the CUHK Avenue and ShanghaiTech Campus datasets, respectively. By comparison, the detection results achieved by the proposed method are better than those achieved by the existing state-of-the-art methods.
Prevalence and Associated Risk Factors of Intestinal Parasites among Diarrheic Under-Five Children Attending Bahir Dar and Han Health Centers, Northwest Ethiopia: A Cross-Sectional Study
Authors: Mohammed Jemal , Shiferaw Alemtsehay , Zeleke Abaynesh , Eshetu Yemsrach , Gebeyehu Zenash , Ayehu Animen , Adem Yesuf
Published: May 2022

Background. Intestinal parasitic infection is one of the major public health problems in developing countries including Ethiopia. The problem is highly pronounced in children under five years of age who are not able to adhere to prevention and control precautions. Objective. To assess the prevalence of intestinal parasitic infections (IPIs) and associated factors among diarrheic children under five years of age attending Bahir Dar and Han Health Centers, Bahir Dar, Northwest Ethiopia. Methods. An institution-based cross-sectional study was conducted among diarrheic children less than five years of age at Bahir Dar and Han Health Centers, Northwest Ethiopia, 2020. A total of 221 diarrheic children less than five years of age were recruited using the convenience sampling technique. Data were collected using a pretested structured Amharic version questionnaire after obtaining informed consent from children’s parents or guardians. Approximately 1 mL of fresh fecal specimen was collected and subjected to direct saline wet mount microscopy. All data were entered into Epi Info version 7 and transferred to SPSS statistical software version 20 for analysis. Logistic regression was employed to assess risk factors associated with increased prevalence of intestinal parasitic infection in diarrheic children under five years of age, and a

P

value < 0.05 was taken as statistically significant. Results. The overall prevalence of intestinal parasites was 19% (

95
%
CI
=
14
–
24.6

). The most prevalent intestinal parasites were Entamoeba histolytica/Entamoeba dispar (24 (10.86%)), Giardia lamblia (12 (5.43%)), and Ascaris lumbricoides (2 (0.9%)). Children’s mother/guardian washed their hands sometimes after the toilet (

AOR
=
2.98

, 95% CI: 1.09-8.18), children who always eat unwashed fruits and vegetables (

AOR
=
4.63

, 95% CI: 1.09–19.75), and children’s mother/guardian who had no knowledge about the mode of transmission (

AOR
=
4.03

, 95% CI: 1.04–15.64) were risk factors significantly associated with increased intestinal parasitic infections. Conclusion. The prevalence of intestinal parasitic infections was found low compared to the population prevalence reported by WHO. However, strengthening health education about food and personal hygiene of both children and their mothers/guardians is crucial to limit the transmission of IPIs.
A Wearable Real-Time Character Recognition System Based on Edge Computing-Enabled Deep Learning for Air-Writing
Authors: Zhang Hongyu , Chen Lichang , Zhang Yunhao , Hu Renjie , He Chunjuan , Tan Yaqing , Zhang Jiajin
Published: May 2022

Nowadays, wearable sensors play a vital role in the detection of human motions, innovating an alternate and intuitive form in human–computer interaction (HCI). In this study, we present a novel real-time wearable system for finger air-writing recognition in three-dimensional (3D) space based on the Arduino Nano 33 BLE Sense as an edge device, which can run TensorFlow Lite to realize recognition and classification on the device. This system enables users to have the freedom and flexibility to write characters (10 digits and 26 English lower-case letters) in free space by moving fingers and uses a deep learning algorithm to recognize 36 characters from the motion data captured by inertial measurement units (IMUs) and processed by a microcontroller, which are both embedded in an Arduino Nano 33 BLE Sense. We prepared 63000 air-writing stroke data samples of 35 subjects containing 18 males and 17 females for convolutional neural network (CNN) training and achieved a high recognition accuracy at 97.95%.
Hippocampal Mitochondrial Abnormalities Induced the Dendritic Complexity Reduction and Cognitive Decline in a Rat Model of Spinal Cord Injury
Authors: Hu Xvlei , Wu Liang , Wen Yujun , Liu Juan , Li Hailiang , Zhang Yifan , Wang Zhihua , Ding Jiangwei , Zeng Zhong , Xia Hechun
Published: May 2022

Spinal cord injury (SCI) is a progressive neurodegenerative disease in addition to a traumatic event. Cognitive dysfunction following SCI has been widely reported in patients and animal models. However, the neuroanatomical changes affecting cognitive function after SCI, as well as the mechanisms behind these changes, have so far remained elusive. Herein, we found that SCI accelerates oxidative stress damage of hippocampal neuronal mitochondria. Then, for the first time, we presented a three-dimensional morphological atlas of rat hippocampal neurons generated using a fluorescence Micro-Optical Sectioning Tomography system, a method that accurately identifies the spatial localization of neurons and trace neurites. We showed that the number of dendritic branches and dendritic length was decreased in late stage of SCI. Western blot and transmission electron microscopy analyses also showed a decrease in synaptic communication. In addition, a battery of behavioral tests in these animals revealed hippocampal based cognitive dysfunction, which could be attributed to changes in the dendritic complexity of hippocampal neurons. Taken together, these results suggested that mitochondrial abnormalities in hippocampal neurons induced the dendritic complexity reduction and cognitive decline following SCI. Our study highlights the neuroanatomical basis and importance of mitochondria in brain degeneration following SCI, which might contribute to propose new therapeutic strategies.
Collision Avoidance Strategy for Multivehicle Conflict on Common Rail
Authors: Fang Bo , Qiu Cheng , Feng Ming , Liang Wei , Zhang Ximing
Published: May 2022

With the rapid expansion and application of bright logistics technology in the field of metallurgical production, in recent years, a type of intermediate granary storage and transportation system, signified by multiple child-parent rail car system, has gradually looked in steel plants, which effectively solves the drawbacks of crane craning in the intermediate warehouse area and improves the transportation competence of shop-level logistics. However, there are not many studies on conflict avoidance strategies for such multivehicle cooperative common rail transportation system in order to improve the ability of conflict detection and active avoidance in operation of such multivehicle transportation system. This paper draws on and improves the banker antideadlock conflict detection algorithm widely used in computer process management to detect conflicts in multivehicle on common rail system and effectively evades collision conflicts detected in the system by combining dynamic priority transfer with multiobjective time window offset. Finally, the validity of the improved banker algorithm conflict detection and priority transfer time window adjustment collision avoidance strategy is confirmed through an example analysis and comparison, providing a security guarantee for the efficient and intelligent operation of such common rail multivehicle logistics system, particularly for the exploration of the logistics common rail multivehicle collision avoidance strategy in metallurgical heavy-duty workshop.
Impact of Exercise on Susceptibility and Severity of COVID-19 in Patients with Cancer: A Retrospective Study
Authors: Bliss Joshua W. , Lavery Jessica A. , Underwood Whitney P. , Chun Su S. , Fickera Gina A. , Lee Catherine P. , Corcoran Stacie , Maloy Molly A. , Polubriaginof Fernanda C. , Kelly Daniel W. , Scott Jessica M. , Boutros Paul C. , Moskowitz Chaya S. , Jones Lee W.
Published: May 2022

Background:

Modifiable lifestyle-related factors heighten the risk and severity of coronavirus disease 2019 (COVID-19) in patients with cancer. Whether exercise lowers susceptibility or severity is not known.

Methods:

We identified 944 cancer patients from Memorial Sloan Kettering Cancer Center (mean age: 64; 85% female; 78% White) completing an exercise survey before receiving a confirmed positive or negative SARS-CoV-2 test. Exercise was defined as reporting moderate-intensity ≥5 days per week, ≥30 minutes/session or strenuous-intensity ≥3 days per week, ≥20 minutes/session. Multivariable logistic regression was used to determine the relationship between exercise and COVID-19 susceptibility and severity (i.e., composite of hospital admission or death events) with adjustment for clinical–epidemiologic covariates.

Results:

Twenty-four percent (230/944) of the overall cohort were diagnosed with COVID-19 and 35% (333/944) were exercisers. During a median follow-up of 10 months, 26% (156/611) of nonexercising patients were diagnosed with COVID-19 compared with 22% (74/333) of exercising patients. The adjusted OR for risk of COVID-19 was 0.65 [95% confidence interval (CI), 0.44–0.96, P = 0.03] for exercisers compared with nonexercisers. A total of 20% (47/230) of COVID-19 positive patients were hospitalized or died. No difference in the risk of severe COVID-19 as a function of exercise status was observed (P > 0.9).

Conclusions:

Exercise may reduce the risk of COVID-19 infection in patients with a history of cancer, but not its severity.

Impact:

This study provides the first data showing that exercise might lower the risk of COVID-19 in cancer patients, but further research is required.
Genetic Susceptibility in Understanding of Pancreatic Ductal Adenocarcinoma Risk: A Decade-Long Effort of the PANDORA Consortium
Authors: Vodickova Ludmila , Horak Josef , Vodicka Pavel
Published: May 2022

Pancreatic cancer, a complex disease, emerges as a severe health problem worldwide and it exhibits a poor prognosis and high mortality. Risk factors associated with sporadic pancreatic cancer remain poorly understood, even less is known about disease prognosis due to its rapid progression. The PANcreatic Disease ReseArch (PANDoRA) consortium, of which the authors are members, was established to coordinate the efforts of different research groups to uncover new genetic factors for pancreatic cancer risk, response to treatment, and patient survival. PANDoRA consortium has contributed to the identification of several low-penetrance risk loci for the disease both by candidate variants approach and genome-wide association studies, including those in cell-cycle and DNA damage response, telomere homeostasis, SCL and ABC transporters, ABO locus variability, mitochondrial metabolism and it participated on collaborative genome-wide association study approach and implementation of a search for functional-based pancreatic cancer risk loci and long noncoding RNAs. Complex studies covering genetic, environmental and microenvironmental factors in the pancreatic cancer onset, progression and its prognosis are warranted.
Inhibition of Oxidized Nucleotide Sanitation By TH1579 and Conventional Chemotherapy Cooperatively Enhance Oxidative DNA Damage and Survival in AML
Authors: Centio Anders , Estruch Montserrat , Reckzeh Kristian , Sanjiv Kumar , Vittori Camilla , Engelhard Sophia , Warpman Berglund Ulrika , Helleday Thomas , Theilgaard-Mönch Kim
Published: May 2022

Currently, the majority of patients with acute myeloid leukemia (AML) still die of their disease due to primary resistance or relapse toward conventional reactive oxygen species (ROS)- and DNA damage–inducing chemotherapy regimens. Herein, we explored the therapeutic potential to enhance chemotherapy response in AML, by targeting the ROS scavenger enzyme MutT homolog 1 (MTH1, NUDT1), which protects cellular integrity through prevention of fatal chemotherapy-induced oxidative DNA damage. We demonstrate that MTH1 is a potential druggable target expressed by the majority of patients with AML and the inv(16)/KITD816Y AML mouse model mimicking the genetics of patients with AML exhibiting poor response to standard chemotherapy (i.e., anthracycline & cytarabine). Strikingly, combinatorial treatment of inv(16)/KITD816Y AML cells with the MTH1 inhibitor TH1579 and ROS- and DNA damage-inducing standard chemotherapy induced growth arrest and incorporated oxidized nucleotides into DNA leading to significantly increased DNA damage. Consistently, TH1579 and chemotherapy synergistically inhibited growth of clonogenic inv(16)/KITD816Y AML cells without substantially inhibiting normal clonogenic bone marrow cells. In addition, combinatorial treatment of inv(16)/KITD816Y AML mice with TH1579 and chemotherapy significantly reduced AML burden and prolonged survival compared with untreated or single treated mice. In conclusion, our study provides a rationale for future clinical studies combining standard AML chemotherapy with TH1579 to boost standard chemotherapy response in patients with AML. Moreover, other cancer entities treated with ROS- and DNA damage–inducing chemo- or radiotherapies might benefit therapeutically from complementary treatment with TH1579.
Proteolytic pan-RAS Cleavage Leads to Tumor Regression in Patient-derived Pancreatic Cancer Xenografts
Authors: Vidimar Vania , Park Minyoung , Stubbs Caleb K. , Ingram Nana K. , Qiang Wenan , Zhang Shanshan , Gursel Demirkan , Melnyk Roman A. , Satchell Karla J.F.
Published: May 2022

The lack of effective RAS inhibition represents a major unmet medical need in the treatment of pancreatic ductal adenocarcinoma (PDAC). Here, we investigate the anticancer activity of RRSP-DTB, an engineered biologic that cleaves the Switch I of all RAS isoforms, in KRAS-mutant PDAC cell lines and patient-derived xenografts (PDX). We first demonstrate that RRSP-DTB effectively engages RAS and impacts downstream ERK signaling in multiple KRAS-mutant PDAC cell lines inhibiting cell proliferation at picomolar concentrations. We next tested RRSP-DTB in immunodeficient mice bearing KRAS-mutant PDAC PDXs. Treatment with RRSP-DTB led to ≥95% tumor regression after 29 days. Residual tumors exhibited disrupted tissue architecture, increased fibrosis and fewer proliferating cells compared with controls. Intratumoral levels of phospho-ERK were also significantly lower, indicating in vivo target engagement. Importantly, tumors that started to regrow without RRSP-DTB shrank when treatment resumed, demonstrating resistance to RRSP-DTB had not developed. Tracking persistence of the toxin activity following intraperitoneal injection showed that RRSP-DTB is active in sera from immunocompetent mice for at least 1 hour, but absent after 16 hours, justifying use of daily dosing. Overall, we report that RRSP-DTB strongly regresses hard-to-treat KRAS-mutant PDX models of pancreatic cancer, warranting further development of this pan-RAS biologic for the management of RAS-addicted tumors.
Therapeutic Targeting of EZH2 and BET BRD4 in Pediatric Rhabdoid Tumors
Authors: Ishi Yukitomo , Zhang Yongzhan , Zhang Ali , Sasaki Takahiro , Piunti Andrea , Suri Amreena , Watanabe Jun , Abe Kouki , He Xingyao , Katagi Hiroaki , Bhalla Pankaj , Natsumeda Manabu , Zou Lihua , Shilatifard Ali , Hashizume Rintaro
Published: May 2022

Aberrant activity of the H3K27 modifiers EZH2 and BRD4 is an important oncogenic driver for atypical teratoid/rhabdoid tumor (AT/RT), and each is potentially a possible therapeutic target for treating AT/RT. We, therefore, determined whether targeting distinct histone modifier activities was an effective approach for treating AT/RT. The effects of EZH2 and BRD4 inhibition on histone modification, cell proliferation, and cell invasion were analyzed by immunoblotting, MTS assay, colony formation assay, and cell invasion assay. RNA- and chromatin immunoprecipitation-sequencing were used to determine transcriptional and epigenetic changes in AT/RT cells treated with EZH2 and BRD4 inhibitors. We treated mice bearing human AT/RT xenografts with EZH2 and BRD4 inhibitors. Intracranial tumor growth was monitored by bioluminescence imaging, and the therapeutic response was evaluated by animal survival. AT/RT cells showed elevated levels of H3K27 trimethylation (H3K27me3) and H3K27 acetylation (H3K27ac), with expression of EZH2 and BRD4, and lack of SMARCB1 proteins. Targeted inhibition of EZH2 and BRD4 activities reduced cell proliferation and invasiveness of AT/RT in association with decreasing H3K27me3 and H3K27ac. Differential genomic occupancy of H3K27me3 and H3K27ac regulated specific gene expression in response to EZH2 and BRD4 inhibitions. A combination of EZH2 and BRD4 inhibition increased the therapeutic benefit in vitro and in vivo, outperforming either monotherapy. Overall, histones H3K27me3 and H3K27ac were elevated in AT/RT cells and distributed in distinct chromatin regions to regulate specific gene expression and to promote AT/RT growth. Targeting EZH2 and BRD4 activity is, therefore, a potential combination therapy for AT/RT.
Gastrostomy Button Diameter and Length Variations after Percutaneous Endoscopic Gastrostomy: One Size Does Not Fit All
Authors: Macedo Silva Vítor , Freitas Marta , Sousa Magalhães Rui , Cúrdia Gonçalves Tiago , Boal Carvalho Pedro , Marinho Carla , Cotter José
Published: May 2022

Background: Percutaneous endoscopic gastrostomy (PEG) is a useful option for long-term enteral nutrition. Low-profile gastrostomy tubes (“buttons”) may afterward be placed in the stomach through the abdominal wall following maturation of the preexisting ostomy. Regular verification is essential since inadequate sizing is associated with accidental exteriorization or food leakage. We aimed to evaluate gastrostomy buttons diameter or length variations on the first year after their placement and possible factors associated with these variations. Methods: We analyzed consecutive PEGs between 2016 and 2018. A minimum follow-up of 12 after gastrostomy button placement was required. Diameter or length variations were assessed in a specialized PEG appointment during the follow-up period. Results: Final sample included 94 patients, from which 65 (69.1%) were women, and 29 (30.9%) were men, with a mean age of 76.9 ± 13.3 years. Measurements variations occurred in 44 (46.8%) patients. Diameter variation was significantly more frequent in patients living in a nursing home (OR = 5.43; 95% CI = 1.32–22.27; p = 0.019), patients with previous PEG tube dislodgement (OR = 3.84; 95% CI = 1.21–12.20; p = 0.023), and male patients (OR = 3.50; 95% CI = 1.06–11.49, p = 0.039). Length variation occurred more frequently in patients with a weight change during the follow-up period greater than 5 kg (OR = 3.71; 95% CI = 1.14–12.05; p = 0.029). Conclusions: A significant proportion of patients with gastrostomy buttons required a change in their measurements, especially if male, living in nursing homes, having significant weight changes, or accidental tube exteriorization. This emphasizes the importance of having a specialized PEG appointment to regularly assure the best fitted button for each patient and ultimately guarantee an adequate nutritional intake.
НОВІ ВІРУСНІ КЛІЩОВІ ІНФЕКЦІЇ В ХХІ СТ. ТА ЇХ ЕПІДЕМІЧНА ПОТЕНЦІЯ
Authors: Андрейчин М. А. , Завіднюк Н. Г. , Ничик Н. А. , Йосик Я. І.
Published: May 2022

На земній кулі як переносники збудників інфекційних хвороб людини кліщі посідають друге місце після комарів. У передачі вірусів роль кліщів як вектора відома вже понад 100 років. Час від часу вдається виявити нові патогенні збудники, а відомі поступово охоплюють все більші географічні території. На сьогодні взаємодія кліщ – вірус все ще перебуває на ранній стадії вивчення. Для деяких вірусів досі не відомий навіть головний кліщовий вектор. Цілком імовірно, що віруси, які переносяться кліщами, будуть продовжувати з’являтися і завдавати проблем громадському та ветеринарному здоров’ю у ХХІ столітті.

Наведено дані про деякі емерджентні хвороби та їх збудників, що передаються кліщами, які вдалося виявити в поточному столітті. Показана можливість занесення зоонозів, які були відкриті раніше, в нові регіони планети й вкорінення їх там. Наукові дослідження у цій ділянці можуть забезпечити розробку відповідного етіотропного лікування та ефективних профілактичних і протиепідемічних заходів.