bluuebunny/crossref_metadata_2025 huggingface dataset:
Unmet needs in non-metastatic castration-resistant prostate cancer from the Japanese patient perspective: a discrete choice experiment
Authors: Uemura Hiroji , Matsushima Hisashi , Yokomizo Akira , Kobayashi Kazuki , Arai Gaku , Satoh Takefumi , Grillo Vince , Chen Yirong , Singh Shikha , Ledesma Dianne Athene
Published: August 2021

Objectives

With novel antiandrogen treatments of varying clinical benefits and risks becoming available, this study investigates how patients with castration-resistant prostate cancer (CRPC) value differences in treatment characteristics.

Design

Cross-sectional observational study.

Setting

A discrete choice experiment was conducted. Patients chose between two hypothetical non-metastatic CRPC (nmCRPC) treatments defined by six attributes: risk of fatigue, falls or fracture, cognitive impairment, hypertension, rashes as side effects to treatment and extension of time until cancer-related pain occurs.

Participants

A total of 137 adult male patients with CRPC with no prior experience with chemotherapy and with Eastern Cooperative Oncology Group status 0–1 were recruited. Patients were excluded if they participated in an investigational programme outside of routine clinical practice, had a clinically relevant medical or psychiatric condition, or diagnosis of visceral/other metastases not related to the prostate, or were otherwise deemed ineligible by the referring physician.

Primary outcome measures

Relative preference weights and relative importance of the attributes was estimated by hierarchical Bayesian logistic regression.

Results

Among the treatment attributes, ‘risk of cognitive impairment as a side effect of treatment’ was the most important attribute (relative importance (RI) (95% CI): 27.47% (24.80% to 30.14%)), followed by ‘extension of time until cancer-related pain occurs’ (RI (95% CI): 17.87% (15.49% to 20.25%)) and the ‘risk of falls or fracture’ (RI (95% CI): 15.99% (14.73% to 17.25%)). The ‘risk of hypertension as a side effect of treatment’ (RI (95% CI): 13.77% (12.73% to 14.81%)) had similar RI as ‘risk of rashes as a side effect of treatment’ (RI (95% CI): 13.17% (12.15% to 14.19%)), followed by the ‘risk of fatigue as a side effect of treatment’ (RI (95% CI): 11.74% (10.75% to 12.73%)).

Conclusions

Patients consider the risk of cognitive impairment as a side effect of treatment as the most important attribute in nmCRPC, followed by the extension of time until cancer-related pain occurs, and the risk of falls and fracture. These features should be considered in treatment decision making for nmCRPC in Japan.
Pentoxifylline in diabetic kidney disease (VA PTXRx): protocol for a pragmatic randomised controlled trial
Authors: Leehey David J , Carlson Kimberly , Reda Domenic J , Craig Ian , Clise Christina , Conner Todd A , Agarwal Rajiv , Kaufman James S , Anderson Robert J , Lammie Douglas , Huminik Jeffrey , Polzin Linda , McBurney Conor , Huang Grant D , Emanuele Nicholas V
Published: August 2021

Introduction

Diabetic kidney disease (DKD) is the most frequent cause of end-stage renal disease (ESRD) in the USA and worldwide. Recent experimental and clinical data suggest that the non-specific phosphodiesterase inhibitor pentoxifylline (PTX) may decrease progression of chronic kidney disease. However, a large-scale randomised clinical trial is needed to determine whether PTX can reduce ESRD and death in DKD.

Methods and analysis

Veterans Affairs (VA) PTXRx is a pragmatic, randomised, placebo-controlled multicentre VA Cooperative Study to test the hypothesis that PTX, when added to usual care, leads to a reduction in the time to ESRD or death in patients with type 2 diabetes with DKD when compared with usual care plus placebo. The study aims to enrol 2510 patients over a 4-year period with an additional up to 5-year follow-up to generate a total of 646 primary events. The primary objective of this study is to compare the time until ESRD or death (all-cause mortality) between participants randomised to PTX or placebo. Secondary endpoints will be: (1) health-related quality of life, (2) time to doubling of serum creatinine, (3) incidence of hospitalisations for congestive heart failure, (4) incidence of a three-point major adverse cardiovascular events composite (cardiovascular death, non-fatal myocardial infarction, non-fatal stroke), (5) incidence of peripheral vascular disease, (6) change in urinary albumin-to-creatinine ratio from baseline to 6 months and (7) rate of annual change in estimated glomerular filtration rate (eGFR) during the study period.

Ethics and dissemination

This study was approved by the VA Central Institutional Review Board (cIRB/18-36) and will be conducted in compliance with the Declaration of Helsinki and the Guidelines for Good Clinical Practice. The Hines Cooperative Studies Programme will finalise the study results, which will be published in accordance with the Consolidated Standards of Reporting Trials statement in a peer-reviewed scientific journal.

Trial registration number

NCT03625648.
Analysis of outcome of 6-month readmissions after percutaneous left atrial appendage occlusion
Authors: Morita Sae , Malik Aaqib H , Kuno Toshiki , Ando Tomo , Kaul Risheek , Yandrapalli Srikanth , Briasoulis Alexandros
Published: August 2021

Objective

Percutaneous left atrial appendage occlusion (LAAO) is an alternative strategy for prevention of thromboembolic events in patients with atrial fibrillation and unsuitable for long-term oral anticoagulation. The study aimed to evaluate the causes and timing of readmissions within 6 months following percutaneous LAAO in a real-world setting.

Methods

We conducted a retrospective cohort study of percutaneous LAAO performed in the USA between January and June of 2016–2018 using the Nationwide Readmissions Database.

Results

Overall, 12 446 patients who underwent LAAO were included in the analyses and 3477 patients (28%) were readmitted within 6 months following the interventions. Readmitted patients were more often women (p=0.001). The index hospitalisation was characterised by longer duration of hospital stay (p<0.001) and complicated with acute kidney injury (p<0.001) among readmitted patients compared with those without readmissions. Readmissions within 6 months following the index intervention were mainly due to heart failure (13%) and gastrointestinal bleeding (12%). Characteristics associated with readmissions due to heart failure included previously known heart failure (HR 2.39; 95% CI 1.70 to 3.37), valvular heart disease (HR 1.39; 95% CI 1.05 to 1.84) and chronic kidney disease (HR 1.42; 95% CI 1.03 to 1.94). Readmissions due to gastrointestinal bleeding were associated with diabetes mellitus (HR 1.78; 95% CI 1.25 to 2.53), chronic kidney disease (HR 1.86; 95% CI 1.23 to 2.81) and previous anaemia (HR 2.41; 95% CI 1.54 to 3.77).

Conclusions

After percutaneous LAAO, over a quarter of the patients in the USA required rehospitalisation within 6 months, mainly due to heart failure and gastrointestinal bleeding.
Natural history of mental health competence from childhood to adolescence
Authors: O'Connor Meredith , Arnup Sarah J , Mensah Fiona , Olsson Craig , Goldfeld Sharon , Viner Russell M , Hope Steven
Published: August 2021

Background

Mental health competence (MHC) involves psychosocial capabilities such as regulating emotions, interacting well with peers and caring for others, and predicts a range of health and social outcomes. This study examines the course of MHC from childhood to adolescence and patterning by gender and disadvantage, in Australian and UK contexts.

Methods

Data: Longitudinal Study of Australian Children (n=4983) and the Millennium Cohort Study (n=18 296). Measures: A measure capturing key aspects of MHC was derived summing items from the parent-reported Strengths and Difficulties Questionnaire, assessed at 4–5 years, 6–7 years, 10–11 years and 14–15 years. Analysis: Proportions of children with high MHC (scores ≥23 of range 8–24) were estimated by age and country. Random-effects models were used to define MHC trajectories according to baseline MHC and change over time. Sociodemographic patterns were described.

Results

The prevalence of high MHC steadily increased from 4 years to 15 years (from 13.6% to 15.8% and 20.6% to 26.2% in Australia and the UK, respectively). Examination of trajectories revealed that pathways of some children diverge from this normative MHC progression. For example, 7% and 9% of children in Australia and the UK, respectively, had a low starting point and decreased further in MHC by mid-adolescence. At all ages, and over time, MHC was lower for boys compared with girls and for children from disadvantaged compared with advantaged family backgrounds.

Conclusions

Approaches to promoting MHC require a sustained focus from the early years through to adolescence, with more intensive approaches likely needed to support disadvantaged groups and boys.
Tracing X-ray-induced formation of warm dense gold with Boltzmann kinetic equations
Authors: Ziaja Beata , Bekx John Jasper , Masek Martin , Medvedev Nikita , Piekarz Przemyslaw , Saxena Vikrant , Stransky Michal , Toleikis Sven
Published: August 2021

In this paper, we report on the Boltzmann kinetic equation approach adapted for simulations of warm dense matter created by irradiation of bulk gold with intense ultrashort X-ray pulses. X-rays can excite inner-shell electrons, which triggers creation of deep-lying core holes. Their relaxation, especially in heavier elements such as gold (atomic number $$Z= 79$$

Z
=
79

) takes complicated pathways, involving collisional processes, and leading through a large number of active configurations. This number can be so high that solving a set of evolution equations for each configuration becomes computationally inefficient, and another modeling approach should be used instead. Here, we use the earlier introduced ’predominant excitation and relaxation path’ approach. It still uses true atomic configurations but limits their number by restricting material relaxation to a selected set of predominant pathways for material excitation and relaxation. With that, we obtain time-resolved predictions for excitation and relaxation in X-ray irradiated bulk of gold, including the respective change of gold optical properties. We compare the predictions with the available data from high-energy-density experiments. Their good agreement indicates ability of the Boltzmann kinetic equation approach to describe warm dense matter created from high-Z materials after their irradiation with X rays, which can be validated in future experiments.

Graphic Abstract
An Ultra-Low-Power, Self-Start-Up DC-DC Boost Converter for Self-Powered IoT Node
Authors: Komatla Krishna Reddy , Patri Sreehari Rao
Published: August 2021

This paper presents an ultra-low-power boost converter for self-powered IoT applications to self-start and power-up IoT devices from scratch without any requirement of an external start-up. The proposed converter and its clock generator operate in sub-threshold utilizing bulk-driven technique for low-power operation. The bulk-driven technique improves charge transfer switches for effective switching using auxiliary transistors. This approach enables a MOSFET to operate on supplies lower than its threshold voltage with a significant reduction in the reverse charge transfer and switching loss while increasing the voltage conversion efficiency and output voltage. To validate the performance of the proposed architecture, the post-layout simulation is carried out in standard CMOS 0.18[Formula: see text][Formula: see text]m technology. Under low-voltage supply of 0.4[Formula: see text]V, the simulated transient output voltage takes 110[Formula: see text][Formula: see text]s to reach 1.92[Formula: see text]V with 0.15[Formula: see text] output voltage ripple, while consuming the power of 772[Formula: see text]nW.
NUMERICAL SOLUTION OF NONLINEAR FRACTAL–FRACTIONAL DYNAMICAL MODEL OF INTERPERSONAL RELATIONSHIPS WITH POWER, EXPONENTIAL AND MITTAG-LEFFLER LAWS
Authors: JENA RAJARAMA MOHAN , CHAKRAVERTY SNEHASHISH , ZENG SHENGDA
Published: August 2021

The term fractional differentiation has recently been merged with the term fractal differentiation to create a new fractional differentiation operator. Several kernels were used to explore these new operators, including the power-law, exponential decay, and Mittag-Leffler functions. In this study, we analyze three forms of interpersonal relationships model. The numerical solution of the fractal–fractional interpersonal relationships model based on different kernels has been investigated. The new operators contain two parameters: one is for fractional order [Formula: see text], and the other is for fractal dimension [Formula: see text]. We use Lagrangian polynomial interpolation along with numerical method and the concept of fractional theory to solve these three forms of the titled model. All three forms of the numerical computation are compared with the solutions of the other existing method when [Formula: see text] that leads to a good agreement. The existence and uniqueness of the models have been studied using the Picard–Lindelöf theorem. To understand how the effects of fractal dimension and fractional order influence the model, we have illustrated various plots taking different values of [Formula: see text] and [Formula: see text].
PARAMETER ESTIMATION OF THE CLASSICAL FRACTAL MAP BASED ON A GIVEN JULIA SET’S SHAPE
Authors: WANG DA , ZHAO SHICUN , CHEN KE , LIU SHUTANG
Published: August 2021

Julia set is one of the most important branches in fractal theory, and has achieved much progress on both theoretical analysis and application research. Nevertheless, most of the existing investigations relied on the systems with known parameters. Few works have been done on the inverse problem about how to determine the system parameter based on a given Julia set’s shape. This work aims to address this issue by starting with the most classical polynomial map. First, by constructing a proper fitness function measuring the Julia sets’ area error, the Julia sets’ parameter estimation is formulated as a kind of optimization problem. According to the known conditions of Julia set to be estimated, the optimization problem is classified into two cases. For one case, when both the shape and size of Julia set are given, we only need to estimate the system’s own parameter [Formula: see text]. For the other case, when the Julia set has only a shape, we redesign the problem into three dimensions by adding a new scaling factor parameter [Formula: see text] and applying the partial coverage principle. Second, a particle swarm optimization (PSO) approach including dynamically adjustment strategy is adopted to solve the proposed optimization problem. At last, we present seven groups of simulations in which both randomly generated Julia sets and those in existing literature (or on the internet) are included. The simulation results verify the effectiveness of the proposed parameter estimation scheme.
FRACTAL-BASED PRODUCTION ANALYSIS FOR SHALE RESERVOIR CONSIDERING VERTICAL CROSS-FLOW
Authors: SU YULIANG , LI MENG , WANG WENDONG , DONG MINGZHE
Published: August 2021

Multi-fractured horizontal wells have been widely used in shale oil development. The induced fracture network around the hydraulic fractures has significant impacts on pressure propagation and fluid migration. In shale formations, the effectiveness of reservoir stimulation is vertical heterogeneous due to the various sedimentary environment. In the horizontal direction, the permeability/porosity and density of the induced fractures are self-similar. No solutions have been achieved combing both the vertical and the horizontal heterogeneities of the stimulated reservoir volume (SRV). In this work, a novel semi-analytical model is proposed to characterize the SRV heterogeneities in both vertical and horizontal directions. The SRV is divided into several layers to characterize the vertical heterogeneities of shale formation. The fractal permeability/porosity and density of induced fractures are introduced to describe the horizontal heterogeneities of each layer. The fractal vertical cross-flow between adjacent layers is considered to depict the flux transfer caused by the vertical heterogeneities. The proposed model is solved in Laplace domain and inversed to the real domain numerically. Then, the model validation and sensitivity studies are conducted. The results show that the fractal vertical cross-flow causes the flow behavior in shale formation transforms from a no-cross-flow system to a vertical homogeneous system. The larger the fractal dimension, the earlier the transformation occurs, and the larger the reservoir seepage capacity. The effects of fractal parameters are obvious at the early and middle stages. This paper provides a more realistic representation of the flow behavior of shale oil in multi-fractured horizontal wells. The new model is beneficial for understanding the flow mechanisms and improving production forecasting of shale oil.
HAUSDORFF FRACTAL NEW COUPLED NONLINEAR SCHRÖDINGER MODEL AND ITS NOVEL SOLITARY WAVE SOLUTION
Authors: KHAN Y. , FARAZ N.
Published: August 2021

This paper uses the Hausdorff fractal derivative to convert the new Schrödinger nonlinear coupled equation into a novel fractal coupled nonlinear Schrödinger model. By applying the variational principle, a plethora of new soliton solutions are retrieved from the developed framework. The conditions of constraints are set for the presence of appropriate solitons. The 3D, 2D, and contour graphs of the reported solutions are depicted under the collection of appropriate parameter values. Moreover, it is noted that the variational principle built on the Hausdorff derivative for the proposed fractal model delivers a direct convenient and efficient mathematical tool for solving nonlinear partial differential equations in the solitary wave theory.
Determinant Factors of Crowdfunding Success and Its Influence on SMEs in West African Countries
Authors: Edem Djimesah Isaac , Zhao Hongjiang , Dedei Okine Agnes Naa , Elijah Duah
Published: August 2021

The success rate of crowdfunding has boosted SME growth in developed countries, while the developing nations have not taken advantage of it. This study formulated five hypotheses from some determinants of crowdfunding success, and the outcome demonstrated that all the constructs were supported. The results provided R2values of the constructs with repercussions on the research proposed model signifying 84.1% variance of crowdfunding success rate for SMEs in the West Africa Region. Using the Important-Performance Matrix Analysis to examine further revealed crowdfunding platform availability had the highest total effect value of 0.890. However, SMEs’ initial capital requirement and duration involved in raising the required funds also affect crowdfunding success rate in terms of performance with an index value of 72.692.
SURFACE ROUGHNESS ANALYSIS OF CRYO-TREATED AND TEMPERED STEELS IN CYLINDRICAL GRINDING OPERATION
Authors: KAM MENDERES , KABASAKALOĞLU UFUK
Published: August 2021

Cylindrical grinding operation is an important metal cutting process used as a finish process to achieve the surface quality and dimensional stability of the products. In this context, experimental work and statistical analysis in researches contribute to improve product quality of manufactured parts. Tempered steels are widely used for automotive components and manufacturing applications. The objective of this study is to analyze the surface roughness (Ra) values of cryogenically (cryo) treated and tempered steels in cylindrical grinding operation. According to the grinding experiments created by the Taguchi method, grinding wheels (Al2O3 and SiC), heat treated steel samples (HT, CT24, and CT36) and depth of cut (50, 100, and 150[Formula: see text][Formula: see text]m) were selected to determine the optimum surface roughness values of these steels. The results showed that significant improvements in Ra values of cryo-treated and tempered steels were observed. The lowest Ra values were obtained in cryo-treated sample (CT36) with SiC grinding wheel and depth of cut (50[Formula: see text][Formula: see text]m).
Linear permutations and their compositional inverses over 𝔽qn
Authors: Bastos Gustavo Terra
Published: August 2021

The use of permutation polynomials over finite fields has appeared, along with their compositional inverses, as a good choice in the implementation of cryptographic systems. As a particular case, the construction of involutions is highly desired since their compositional inverses are themselves. In this work, we present an effective way of how to construct several linear permutation polynomials over [Formula: see text] as well as their compositional inverses using a decomposition of [Formula: see text] based on its primitive idempotents. As a consequence, involutions are also constructed.
Discrete Social Spider Algorithm for Solving Traveling Salesman Problem
Authors: Khosravanian Asieh , Rahmanimanesh Mohammad , Keshavarzi Parviz
Published: August 2021

The Social Spider Algorithm (SSA) was introduced based on the information-sharing foraging strategy of spiders to solve the continuous optimization problems. SSA was shown to have better performance than the other state-of-the-art meta-heuristic algorithms in terms of best-achieved fitness values, scalability, reliability, and convergence speed. By preserving all strengths and outstanding performance of SSA, we propose a novel algorithm named Discrete Social Spider Algorithm (DSSA), for solving discrete optimization problems by making some modifications to the calculation of distance function, construction of follow position, the movement method, and the fitness function of the original SSA. DSSA is employed to solve the symmetric and asymmetric traveling salesman problems. To prove the effectiveness of DSSA, TSPLIB benchmarks are used, and the results have been compared to the results obtained by six different optimization methods: discrete bat algorithm (IBA), genetic algorithm (GA), an island-based distributed genetic algorithm (IDGA), evolutionary simulated annealing (ESA), discrete imperialist competitive algorithm (DICA) and a discrete firefly algorithm (DFA). The simulation results demonstrate that DSSA outperforms the other techniques. The experimental results show that our method is better than other evolutionary algorithms for solving the TSP problems. DSSA can also be used for any other discrete optimization problem, such as routing problems.
A QPTAS for ɛ-Envy-Free Profit-Maximizing Pricing on Line Graphs
Authors: Elbassioni Khaled
Published: August 2021

We consider the problem of pricing edges of a line graph so as to maximize the profit made from selling intervals to single-minded customers. An instance is given by a set
E
of
n
edges with a limited supply for each edge, and a set of
m
clients, where each client specifies one interval of
E
she is interested in and a budget
B

j

which is the maximum price she is willing to pay for that interval. An
envy-free
pricing is one in which every customer is allocated an (possibly empty) interval maximizing her utility. Grandoni and Rothvoss (SIAM J. Comput. 2016) proposed a
polynomial-time approximation scheme
(
PTAS
) for the unlimited supply case with running time (
nm
)

O
((1/ɛ)
1/ɛ
)

, which was extended to the limited supply case by Grandoni and Wiese (ESA 2019). By utilizing the known
hierarchical decomposition
of
doubling metrics
, we give a PTAS with running time (
nm
)

O
(1/ ɛ
2
)

for the unlimited supply case. We then consider the limited supply case, and the notion of ɛ-envy-free pricing in which a customer gets an allocation maximizing her utility within an
additive error
of ɛ. For this case, we develop an approximation scheme with running time (
nm
)

O
(log
5/2
max

e

H

e

/ɛ
3
)

, where
H

e

=
B
max
(
e
)/
B
min
(
e
) is the maximum ratio of the budgets of any two customers demanding edge
e
. This yields a PTAS in the
uniform budget
case, and a quasi-PTAS for the general case. The best approximation known, in both cases, for the exact envy-free pricing version is
O
(log
c
max
), where
c
max
is the maximum item supply. Our method is based on the known hierarchical decomposition of doubling metrics, and can be applied to other problems, such as the
maximum feasible subsystem
problem with interval matrices.
Weighted Envy-freeness in Indivisible Item Allocation
Authors: Chakraborty Mithun , Igarashi Ayumi , Suksompong Warut , Zick Yair
Published: August 2021

We introduce and analyze new envy-based fairness concepts for agents with
weights
that quantify their entitlements in the allocation of indivisible items. We propose two variants of weighted envy-freeness up to one item (WEF1):
strong
, where envy can be eliminated by removing an item from the envied agent’s bundle, and
weak
, where envy can be eliminated either by removing an item (as in the strong version) or by replicating an item from the envied agent’s bundle in the envying agent’s bundle. We show that for additive valuations, an allocation that is both Pareto optimal and strongly WEF1 always exists and can be computed in pseudo-polynomial time; moreover, an allocation that maximizes the weighted Nash social welfare may not be strongly WEF1, but it always satisfies the weak version of the property. Moreover, we establish that a generalization of the round-robin picking sequence algorithm produces in polynomial time a strongly WEF1 allocation for an arbitrary number of agents; for two agents, we can efficiently achieve both strong WEF1 and Pareto optimality by adapting the adjusted winner procedure. Our work highlights several aspects in which weighted fair division is richer and more challenging than its unweighted counterpart.
A Federated Learning Approach to Anomaly Detection in Smart Buildings
Authors: Sater Raed Abdel , Hamza A. Ben
Published: August 2021

Internet of Things (IoT) sensors in smart buildings are becoming increasingly ubiquitous, making buildings more livable, energy efficient, and sustainable. These devices sense the environment and generate multivariate temporal data of paramount importance for detecting anomalies and improving the prediction of energy usage in smart buildings. However, detecting these anomalies in centralized systems is often plagued by a huge delay in response time. To overcome this issue, we formulate the anomaly detection problem in a federated learning setting by leveraging the multi-task learning paradigm, which aims at solving multiple tasks simultaneously while taking advantage of the similarities and differences across tasks. We propose a novel privacy-by-design federated learning model using a stacked long short-time memory (LSTM) model, and we demonstrate that it is more than twice as fast during training convergence compared to the centralized LSTM. The effectiveness of our federated learning approach is demonstrated on three real-world datasets generated by the IoT production system at General Electric Current smart building, achieving state-of-the-art performance compared to baseline methods in both classification and regression tasks. Our experimental results demonstrate the effectiveness of the proposed framework in reducing the overall training cost without compromising the prediction performance.
Influence of Supporting Electrolyte on Hydroxide Exchange Membrane Water Electrolysis Performance: Anolyte
Authors: Kiessling Aleksandr , Fornaciari Julie C. , Anderson Grace , Peng Xiong , Gerstmayr Andreas , Gerhardt Michael R. , McKinney Samuel , Serov Alexey , Kim Yu Seung , Zulevi Barr , Weber Adam Z. , Danilovic Nemanja
Published: August 2021

Hydroxide-exchange-membrane water electrolysis (HEMWE) is an emerging hydrogen-production pathway that combines many advantages of incumbent alkaline water electrolysis (AWE) and proton-exchange-membrane water electrolysis (PEMWE). Advancement in HEMWE has been accelerated with the development of stable and conductive hydroxide exchange membranes (HEMs) and a more comprehensive understanding of alkaline gas-evolving kinetics. However, performance and durability without supporting electrolytes (SELs) remain inferior to PEMWE and AWE and little is known about the role and impact of the SELs. This study investigates the effects of SELs used as anolyte solutions in HEMWEs including cation-type, anion-type, SEL conductivity and pH, presence of carbonates and increased cation/OH− ratios on cell voltage and stability. We report our findings that (i) cell potential and high-frequency resistance did not correlate with anolyte SEL conductivity, (ii) cation-type influences cell voltage at low current densities (<50 mA cm−2) as predicted by half-cell measurements, (iii) increased cation/OH− ratio causes increased overpotentials, and (iv) carbonates are exchanged in the HEM but removed via self-purging at high current density. Overall, this study concludes that concentrated KOH is still the best SEL.
Classification Framework for Healthy Hairs and Alopecia Areata: A Machine Learning (ML) Approach
Authors: Shakeel Choudhary Sobhan , Khan Saad Jawaid , Chaudhry Beenish , Aijaz Syeda Fatima , Hassan Umer
Published: August 2021

Alopecia areata is defined as an autoimmune disorder that results in hair loss. The latest worldwide statistics have exhibited that alopecia areata has a prevalence of 1 in 1000 and has an incidence of 2%. Machine learning techniques have demonstrated potential in different areas of dermatology and may play a significant role in classifying alopecia areata for better prediction and diagnosis. We propose a framework pertaining to the classification of healthy hairs and alopecia areata. We used 200 images of healthy hairs from the Figaro1k dataset and 68 hair images of alopecia areata from the Dermnet dataset to undergo image preprocessing including enhancement and segmentation. This was followed by feature extraction including texture, shape, and color. Two classification techniques, i.e., support vector machine (SVM) and

k

-nearest neighbor (KNN), are then applied to train a machine learning model with 70% of the images. The remaining image set was used for the testing phase. With a 10-fold cross-validation, the reported accuracies of SVM and KNN are 91.4% and 88.9%, respectively. Paired sample

T

-test showed significant differences between the two accuracies with a

p
<
0.001

. SVM generated higher accuracy (91.4%) as compared to KNN (88.9%). The findings of our study demonstrate potential for better prediction in the field of dermatology.
SDF-1α/OPF/BP Composites Enhance the Migrating and Osteogenic Abilities of Mesenchymal Stem Cells
Authors: Li Linli , Liu Xifeng , Gaihre Bipin , Park Sungjo , Li Yong , Terzic Andre , Lu Lichun
Published: August 2021

In situ cell recruitment is a promising regenerative medicine strategy with the purpose of tissue regeneration without stem cell transplantation. This chemotaxis-based strategy is aimed at ensuring a restorative environment through the release of chemokines that promote site-specific migration of healing cell populations. Stromal cell-derived factor-1α (SDF-1α) is a critical chemokine that can regulate the migration of mesenchymal stem cells (MSCs). Accordingly, here, SDF-1α-loaded microporous oligo[poly(ethylene glycol) fumarate]/bis[2-(methacryloyloxy)ethyl] phosphate composites (SDF-1α/OPF/BP) were engineered and probed. SDF-1α/OPF/BP composites were loaded with escalating SDF-1α concentrations, namely, 0 ng/ml, 50 ng/ml, 100 ng/ml, and 200 ng/ml, and were cocultured with MSC. Scratching assay, Transwell assay, and three-dimensional migration model were utilized to assess the migration response of MSCs. Immunofluorescence staining of Runx2 and osteopontin (OPN), ELISA assay of osteocalcin (OCN) and alkaline phosphatase (ALP), and Alizarin Red S staining were conducted to assess the osteogenesis of MSCs. All SDF-1α/OPF/BP composites engendered a release of SDF-1α (>80%) during the first four days. SDF-1α released from the composites significantly promoted migration and osteogenic differentiation of MSCs documented by upregulated expression of osteogenic-related proteins, ALP, Runx2, OCN, and OPN. SDF-1α at 100 ng/ml was optimal for enhanced migration and osteogenic proficiency. Thus, designed SDF-1α/OPF/BP composites were competent in promoting the homing and osteogenesis of MSCs and thus offer a promising bioactive scaffold candidate for on-demand bone tissue regeneration.
A Novel High-Content Screening-Based Method for Anti-Trypanosoma cruzi Drug Discovery Using Human-Induced Pluripotent Stem Cell-Derived Cardiomyocytes
Authors: Portella Diogo Crispim Nascimento , Rossi Erik Aranha , Paredes Bruno Diaz , Bastos Tanira Matutino , Meira Cássio Santana , Nonaka Carolina Vasques Kymie , Silva Daniela Nascimento , Improta-Caria Alex , Moreira Diogo Rodrigo Magalhaes , Leite Ana Cristina Lima , de Oliveira Filho Gevanio Bezerra , Filho José Maria Barbosa , dos Santos Ricardo Ribeiro , Soares Milena Botelho Pereira , Souza Bruno Solano de Freita
Published: August 2021

Chagas disease is caused by Trypanosoma cruzi infection and remains a relevant cause of chronic heart failure in Latin America. The pharmacological arsenal for Chagas disease is limited, and the available anti-T. cruzi drugs are not effective when administered during the chronic phase. Cardiomyocytes derived from human-induced pluripotent stem cells (hiPSC-CMs) have the potential to accelerate the process of drug discovery for Chagas disease, through predictive preclinical assays in target human cells. Here, we aimed to establish a novel high-content screening- (HCS-) based method using hiPSC-CMs to simultaneously evaluate anti-T. cruzi activity and cardiotoxicity of chemical compounds. To provide proof-of-concept data, the reference drug benznidazole and three compounds with known anti-T. cruzi activity (a betulinic acid derivative named BA5 and two thiazolidinone compounds named GT5A and GT5B) were evaluated in the assay. hiPSC-CMs were infected with T. cruzi and incubated for 48 h with serial dilutions of the compounds for determination of EC50 and CC50 values. Automated multiparametric analyses were performed using an automated high-content imaging system. Sublethal toxicity measurements were evaluated through morphological measurements related to the integrity of the cytoskeleton by phalloidin staining, nuclear score by Hoechst 33342 staining, mitochondria score following MitoTracker staining, and quantification of NT-pro-BNP, a peptide released upon mechanical myocardial stress. The compounds showed EC50 values for anti-T. cruzi activity similar to those previously described for other cell types, and GT5B showed a pronounced trypanocidal activity in hiPSC-CMs. Sublethal changes in cytoskeletal and nucleus scores correlated with NT-pro-BNP levels in the culture supernatant. Mitochondrial score changes were associated with increased cytotoxicity. The assay was feasible and allowed rapid assessment of anti-T. cruzi action of the compounds, in addition to cardiotoxicity parameters. The utilization of hiPSC-CMs in the drug development workflow for Chagas disease may help in the identification of novel compounds.
RNA-Binding Motif Protein 11 (RBM11) Serves as a Prognostic Biomarker and Promotes Ovarian Cancer Progression
Authors: Fu Chunhong , Yuan Ming , Sun Jie , Liu Gang , Zhao Xiaojuan , Chang Wei , Ma Zhongling
Published: August 2021

Ovarian cancer is one of the most lethal gynecologic malignancies for women. Due to the lack of efficient target therapy, the overall survival rate for patients with advanced ovarian cancer is still low. Illustrating the molecular mechanisms dictating ovarian cancer progression is critically important to develop novel therapeutic agents. Here, we found that RNA-binding motif protein 11 (RBM11) was highly elevated in ovarian cancer tissues compared with normal ovary, while RBM11 depletion in ovarian cancer cells resulted in impaired cell growth and invasion. Moreover, knockdown of RBM11 also retarded tumor growth in the A2780 ovarian cancer xenograft model. Mechanically, we found that RBM11 positively regulated Akt/mTOR signaling pathway activation in ovarian cancer cells. Thus, these results identify RBM11 is a novel oncogenic protein and prognostic biomarker for ovarian cancers.
Research on the Reform and Innovation of Preschool Education Informatization under the Background of Wireless Communication and Virtual Reality
Authors: Li Juan
Published: August 2021

Nowadays, when various information technologies are constantly being updated, preschool children will inevitably need to use new technologies for knowledge learning. At present, preschool education informatization has begun to deploy new technologies in various colleges and universities, but there are still deficiencies. This article analyzes the significance of the combination of wireless communication and VR technology applied to preschool education for children and explores the status quo of children’s participation in the use of information technology in kindergarten classrooms. Based on the problems existing in the status quo, try to use wireless communication and VR technology to improve preschool education in our country. Informatization of education proposes innovative strategies and analyzes the advantages and challenges of applying wireless communication and VR technology to the informatization of preschool education.
Boundedness for Commutators of Rough
p
-Adic Hardy Operator on
p
-Adic Central Morrey Spaces
Authors: Sarfraz Naqash , Aslam Muhammad , Jarad Fahd
Published: August 2021

In the present article we obtain the boundedness for commutators of rough

p

-adic Hardy operator on

p

-adic central Morrey spaces. Furthermore, we also acquire the boundedness of rough

p

-adic Hardy operator on Lebesgue spaces.
Fuzzy‐Based Mobile Edge Orchestrators in Heterogeneous IoT Environments: An Online Workload Balancing Approach
Authors: Khanh Tran Trong , Nguyen VanDung , Huh Eui-Nam
Published: August 2021

Online workload balancing guarantees that the incoming workloads are processed to the appropriate servers in real time without any knowledge of future resource requests. Currently, by matching the characteristics of incoming Internet of Things (IoT) applications to the current state of computing and networking resources, a mobile edge orchestrator (MEO) provides high‐quality service while temporally and spatially changing the incoming workload. Moreover, a fuzzy‐based MEO is used to handle the multicriteria decision‐making process by considering multiple parameters within the same framework in order to make an offloading decision for an incoming task of an IoT application. In a fuzzy‐based MEO, the fuzzy‐based offloading strategy leads to unbalanced loads among edge servers. Therefore, the fuzzy‐based MEO needs to scale its capacity when it comes to a large number of devices in order to avoid task failures and to reduce service times. In this paper, we investigate and propose an online workload balancing algorithm, which we call the fuzzy‐based (FuB) algorithm, for a fuzzy‐based MEO. By considering user configuration requirements, server geographic locations, and available resource capacities for achieving an online algorithm, our proposal allocates the proximate server for each incoming task in real time at the MEO. A simulation was conducted in augmented reality, healthcare, compute‐intensive, and infotainment applications. Compared to two benchmark schemes that use the fuzzy logic approach for an MEO in IoT environments, the simulation results (using EdgeCloudSim) show that our proposal outperforms the existing algorithms in terms of service time, the number of failed tasks, and processing times when the system is overloaded.
A Novel Aerial Manipulator with Front Cutting Effector: Modeling, Control, and Evaluation
Authors: Xu Hao , Yang Zhong , Zhou Guoxing , Liao Luwei , Xu Changliang , Wu Jiying , Zhang Qiuyan , Zhang Chi
Published: August 2021

This paper proposes a novel aerial manipulator with front cutting effector (AMFCE) to address the aerial physical interaction (APhI) problem. First, the system uncertainty and external disturbance during the system movement and contact operation are estimated by modeling the entire robot and contact position. Next, based on the established model, the nonlinear disturbance observer (NDO) is used to estimate and compensate the unknown external disturbance of the system and the uncertainty of the model parameters in real time. Then, the nonsingular terminal synovial membrane control method is used to suppress the part that is difficult to estimate. Finally, a controller which is suitable for the movement and operation of the entire system is designed. The controller’s performance is verified through experiments, and the results show that the design, modeling, and control of the entire system can achieve the APhI.
Neurological Presentations of COVID‐19: Characteristic Features in a Case Series of Hospitalized Patients from Abu Dhabi, UAE
Authors: Deeb Asma , Kumar Palat Chirakkara , Sakrani Nida , Trehan Ravi Kumar , Papinenei Vijay Ram
Published: August 2021

Background. COVID‐19 patients can present with neurological manifestations in the form of headache, dizziness, hyposmia, myalgia, peripheral neuropathy, acute cerebrovascular disease, and encephalopathy. Neurological involvement could be due to virus‐induced brain hypoxia, brain infection, or immune reaction. We aim to describe the neurological presentation of COVID‐19 patients and study their neuroimaging findings and disease outcome. Method. The study is a single‐centre, retrospective, observational study in Sheikh Shakhbout Medical City (SSMC), Abu Dhabi, UAE. Patients diagnosed with COVID‐19 between March and May 2020 who presented with neuropathological features with or without respiratory manifestations of COVID‐19 were enrolled. Electronic records were studied for age, sex, duration of hospitalization, detailed neurological presentation, history or documented concomitant fever and respiratory features of COVID‐19, inflammatory markers, neuroimaging, progress, and disease outcome. Results. Thirty‐three patients of 10 nationalities presented with neurological manifestations. Mean (range) age was 51.4 (21–86) years. Twenty‐four had comorbidities, and 18 had no prior or concomitant respiratory symptoms. Ten patients presented with encephalopathy and exhibited altered behavior/sensorium: 7 presented with myositis, 8 with stroke, and 4 with seizures, and 4 had peripheral and cranial nerve involvement. The mean (average) duration of hospital stay was 11.4 days (1‐38) with the longest observed in stroke patients. Fifteen patients (45%) died and 3 (9%) had residual weakness. Serum ferritin, CRP, and procalcitonin were higher in the severe disease group and correlated with risk of death. Twelve of 22 brain images showed abnormalities including haemorrhage, infarcts, small vessel ischemia, and oedema. Risk of death was higher in older age but did not differ based on the underlying neuropathology. Conclusion. COVID‐19 patients who present with neurological involvement have a higher risk of mortality which is aggravated by older age and higher inflammatory markers. The type of neurological pathology does not seem to influence the risk of mortality.
Adult Human Multipotent Neural Cells Could Be Distinguished from Other Cell Types by Proangiogenic Paracrine Effects via MCP-1 and GRO
Authors: Kim Sung Soo , Pyeon Hee-Jang , Bae Yoon Kyung , Nam Hyun , Kim Chung Kwon , Lee Sun-Ho , Joo Kyeung Min
Published: August 2021

Adult human multipotent neural cells (ahMNCs) are unique cells derived from adult human temporal lobes. They show multipotent differentiation potentials into neurons and astrocytes. In addition, they possess proangiogenic capacities. The objective of this study was to characterize ahMNCs in terms of expression of cell type-specific markers, in vitro differentiation potentials, and paracrine factors compared with several other cell types including fetal neural stem cells (fNSCs) to provide detailed molecular and functional features of ahMNCs. Interestingly, the expression of cell type-specific markers of ahMNCs could not be differentiated from those of pericytes, mesenchymal stem cells (MSCs), or fNSCs. In contrast, differentiation potentials of ahMNCs and fNSCs into neural cells were higher than those of other cell types. Compared with MSCs, ahMNCs showed lower differentiation capacities into osteogenic and adipogenic cells. Moreover, ahMNCs uniquely expressed higher levels of MCP-1 and GRO family paracrine factors than fNSCs and MSCs. These high levels of MCP-1 and GRO family mediated in vivo proangiogenic effects of ahMNCs. These results indicate that ahMNCs have their own distinct characteristics that could distinguish ahMNCs from other cell types. Characteristics of ahMNCs could be utilized further in the preclinical and clinical development of ahMNCs for regenerative medicine. They could also be used as experimental references for other cell types including fNSCs.
LC‐HRMS Profiling and Antidiabetic, Antioxidant, and Antibacterial Activities of Acacia catechu (L.f.) Willd
Authors: Aryal Babita , Adhikari Bikash , Aryal Niraj , Bhattarai Bibek Raj , Khadayat Karan , Parajuli Niranjan
Published: August 2021

Acacia catechu (L.f.) Willd is a profoundly used traditional medicinal plant in Asia. Previous studies conducted in this plant are more confined to extract level. Even though bioassay‐based studies indicated the true therapeutic potential of this plant, compound annotation was not performed extensively. This research is aimed at assessing the bioactivity of different solvent extracts of the plant followed by annotation of its phytoconstituents. Liquid chromatography equipped with high resolution mass spectrometry (LC‐HRMS) is deployed for the identification of secondary metabolites in various crude extracts. On activity level, its ethanolic extract showed the highest inhibition towards α-amylase and α‐glucosidase with an IC50 of 67.8 ± 1 μg/mL and 10.3 ± 0.1 μg/mL respectively, inspected through the substrate‐based method. On the other hand, the plant extract showed an antioxidant activity of 23.76 ± 1.57 μg/mL, measured through radical scavenging activity. Similarly, ethyl acetate and aqueous extracts of A. catechu showed significant inhibition against Staphylococcus aureus with a zone of inhibition (ZoI) of 13 and 14 mm, respectively. With the LC‐HRMS‐based dereplication strategy, we have identified 28 secondary metabolites belonging to flavonoid and phenolic categories. Identification of these metabolites from A. catechu and its biological implication also support the community‐based usage of this plant and its medicinal value.
Addressing Overfitting Problem in Deep Learning‐Based Solutions for Next Generation Data‐Driven Networks
Authors: Xiao Mansheng , Wu Yuezhong , Zuo Guocai , Fan Shuangnan , Yu Huijun , Shaikh Zeeshan Azmat , Wen Zhiqiang
Published: August 2021

Next‐generation networks are data‐driven by design but face uncertainty due to various changing user group patterns and the hybrid nature of infrastructures running these systems. Meanwhile, the amount of data gathered in the computer system is increasing. How to classify and process the massive data to reduce the amount of data transmission in the network is a very worthy problem. Recent research uses deep learning to propose solutions for these and related issues. However, deep learning faces problems like overfitting that may undermine the effectiveness of its applications in solving different network problems. This paper considers the overfitting problem of convolutional neural network (CNN) models in practical applications. An algorithm for maximum pooling dropout and weight attenuation is proposed to avoid overfitting. First, design the maximum value pooling dropout in the pooling layer of the model to sparse the neurons and then introduce the regularization based on weight attenuation to reduce the complexity of the model when the gradient of the loss function is calculated by backpropagation. Theoretical analysis and experiments show that the proposed method can effectively avoid overfitting and can reduce the error rate of data set classification by more than 10% on average than other methods. The proposed method can improve the quality of different deep learning‐based solutions designed for data management and processing in next‐generation networks.