bluuebunny/crossref_metadata_2025 huggingface dataset:
Three-dimensional transient simulation of CO2 laser tissue vaporization and experimental evaluation with a hydrogel phantom
Authors: Sudo Kazuma , Shimojo Yu , Nishimura Takahiro , Awazu Kunio
Published: March 2022

A quantitative analysis method of CO2 laser treatments promotes laser treatment performance and rapid clinical application of novel treatment devices. The in silico clinical trial approach, which is based on computational simulation of light-tissue interactions using the mathematical model, can provide quantitative data. Although several simulation methods of laser tissue vaporization with CO2 laser treatments have been proposed, validations of the CO2 laser wavelength have been insufficient. In this study, we demonstrated a tissue vaporization simulation using a CO2 laser and performed the experimental validation using a hydrogel phantom with constant physical parameters to evaluate the simulation accuracy of the vaporization process. The laser tissue vaporization simulation consists of the calculation of light transport, photothermal conversion, thermal diffusion, and phase change in the tissue. The vaporization width, depth, and area with CO2 laser irradiation to a tissue model were simulated. The simulated results differed from the actual vaporization width and depth by approximately 20% and 30%, respectively, because of the solubilization of the hydrogel phantom. Alternatively, the simulation vaporization area for all light irradiation parameters, which is related to the vaporization amount, agreed well with the actual vaporization values. These results suggest that the computational simulation can be used to evaluate the amount of tissue vaporization in the safety and effectiveness analysis of CO2 laser treatments.
Machine learning in finance: Major applications, issues, metrics, and future trends
Authors: Almaskati Nawaf
Published: March 2022

This paper provides a summary of the current literature related to applying machine learning algorithms in the field of finance with a focus on three main areas: asset pricing, bankruptcy prediction and detection of financial reporting anomalies. The paper also briefly discusses the most popular machine learning techniques used in finance and provides a general overview of some important concepts such as generalization and over- and under-fitting as well as a discussion of potential remedies. Last, the paper summarizes the various indicators and metrics available to evaluate and compare the performance of regression and classification machine learning models before discussing general research trends and potential future research.
Securing Low-Power Blockchain-enabled IoT Devices against Energy Depletion Attack
Authors: Alsirhani Amjad , Khan Muhammad Ali , Alomari Abdullah , Maryam Sauda , Younas Aiman , Iqbal Muddesar , Siqqidi Muhammad Hameed , Ali Amjad
Published: March 2022

Blockchain-enabled Internet of Things (IoT) envisions a world with rapid development and implementations to change our everyday lives based on smart devices. These devices are attached to the internet that can communicate with each other without human interference. A well-known wireless network in blockchain-enabled IoT frameworks is the Low Power and Lossy Network (LLN) that uses a novel protocol known as Routing protocol for low power and lossy networks (RPL) to provide effective and energy-efficient routing. LLNs that run on RPL are inherently prone to multiple Denial of Service (DoS) attacks due to the low cost, shared medium, and resource-constrained nature of blockchain-enabled IoT devices. A Spam DODAG Information Solicitation (DIS) attack is one of the novel attacks that drains the energy source of legitimate nodes and ends up causing the legitimate nodes to suffer from DoS. To address this problem, a mitigation scheme named DIS Spam Attack Mitigation (DISAM) is proposed. The proposed scheme effectively mitigates the effects of the Spam DIS attack on the network’s performance. The experimental results show that DISAM detects and mitigates the attack quickly and efficiently.
Evolutionarily conserved transcription factors as regulators of longevity and targets for geroprotection
Authors: Fischer Fabian , Grigolon Giovanna , Benner Christoph , Ristow Michael
Published: March 2022

Aging is the single largest risk factor for many debilitating conditions, including heart diseases, stroke, cancer, diabetes, and neurodegenerative disorders. Although far from understood in its full complexity, it is scientifically well established that aging is influenced by genetic and environmental factors and can be modulated by various interventions. One of aging’s early hallmarks is aberrations in transcriptional networks, controlling for example metabolic homeostasis or the response to stress. Evidence in different model organisms abounds that a number of evolutionarily conserved transcription factors, which control such networks, can affect life span and health span across species. These transcription factors thus potentially represent conserved regulators of longevity and are emerging as important targets in the challenging quest to develop treatments to mitigate age-related diseases, and possibly even to slow aging itself. This review provides an overview of evolutionarily conserved transcription factors that impact longevity or age-related diseases in at least one multicellular model organism (nematodes, flies, or mice) and/or are tentatively linked to human aging. Discussed is the general evidence for transcriptional regulation of aging and disease, followed by a more detailed look at selected transcription factor families, the common metabolic pathways involved, and the targeting of transcription factors as a strategy for geroprotective interventions.
Modelling the Transition Process from Higher Education to Employment: The Case of Undergraduates from Debre Markos University
Authors: Merie Hayimro Edemealem , Dessie Askalemariam Adamu , Bizuneh Mesfin Tura
Published: March 2022

This study aimed to identify the factors that influence Debre Markos University bachelor degree graduates’ transition duration from higher education to employment. A retrospective study design and a total of 1448 undergraduates were considered in this study. Kaplan-Meier, Cox proportional hazard, and parametric accelerated failure time survival analysis were used to identify the factors that influence the duration of the job search of graduates. The best parametric model that could describe the duration of the job search to obtain the first job was chosen using the Akaike information criterion. As per the study, 50% of graduates were able to get their first employment 18 months after graduation. The Weibull parametric accelerated failure time model performed better in predicting the time it takes for graduates to find their first job. College, cumulative grade point average, father education, graduate region, public advertisement, starting own business, and doing an internship during the study were found to be statistically significant indicators of the transition duration of the graduates to obtain the first job. Debre Markos University 2019 graduates had a longer period of unemployment. The institution should have a fully functioning career service office that provides job search training and connects students with employers.
Dynamic Propagation Characteristics of a Mode-III Interfacial Crack in Piezoelectric Bimaterials
Authors: Zhang Yani , Li Junlin , Xie Xiufeng
Published: March 2022

This article presents the dynamic behavior of a semi-infinite interfacial crack in piezoelectric bimaterials under impact loading. With the help of the transform methods (the Laplace transforms and Fourier transforms), the problem is studied with the Wiener–Hopf technique. This strict proof guarantees the feasibility of this approach. The dynamic stress intensity factor and dynamic electric displacement intensity factor of the interfacial crack propagation characteristics are expressed. Finally, several classic numerical examples are mentioned and discussed to demonstrate that the theoretical deduction is highly accurate for interfacial crack analysis of piezoelectric bimaterials. The results show that the crack propagation is affected by the electromechanical coupling coefficient. In addition, if the velocity of the dynamic crack propagation reaches the generalized Raleigh wave speed, the dynamic stress intensity factor will disappear. Furthermore, for a given time, the ratio of the dynamic stress intensity factor to load increases with the electromechanical coupling coefficient decreasing. Numerical examples are presented to highlight the result.
Application of Health Education Model Based on Theory of Behavior Change in Nursing Care of Patients with Chronic Hepatitis B
Authors: Luo Ying , Dong Zhenhua , Qi Suying
Published: March 2022

Chronic hepatitis B (CHB) is a lifelong disease that harms human health. Bad lifestyle can induce and aggravate liver dysfunction of patients with CHB and even cause liver cirrhosis and liver cancer. Therefore, it is very important to help patients with CHB establish a good lifestyle to control their liver function deterioration. In this study, the theory of behavior change was applied to the nursing process of patients with CHB. The results showed that this method could help to improve liver function of patients with CHB, improve patients’ compliance behavior, and promote the development of self‐management behavior ability and then improve patient satisfaction, which was worthy of promotion.
Intelligent Wearable Devices Enabled Automatic Vehicle Detection and Tracking System with Video-Enabled UAV Networks Using Deep Convolutional Neural Network and IoT Surveillance
Authors: JayaSudha A. R , Dadheech Pankaj , Prasad K. Ramalingeswara , Hemalatha S. , Sharma Meghna , Jamal Sajjad Shaukat , Krah Daniel
Published: March 2022

The discipline of computer vision is becoming more popular as a research subject. In a surveillance-based computer vision application, item identification and tracking are the core procedures. They consist of segmenting and tracking an object of interest from a sequence of video frames, and they are both performed using computer vision algorithms. In situations when the camera is fixed and the backdrop remains constant, it is possible to detect items in the background using more straightforward methods. Aerial surveillance, on the other hand, is characterized by the fact that the target, as well as the background and video camera, are all constantly moving. It is feasible to recognize targets in the video data captured by an unmanned aerial vehicle (UAV) using the mean shift tracking technique in combination with a deep convolutional neural network (DCNN). It is critical that the target detection algorithm maintains its accuracy even in the presence of changing lighting conditions, dynamic clutter, and changes in the scene environment. Even though there are several approaches for identifying moving objects in the video, background reduction is the one that is most often used. An adaptive background model is used to create a mean shift tracking technique, which is shown and implemented in this work. In this situation, the background model is provided and updated frame-by-frame, and therefore, the problem of occlusion is fully eliminated from the equation. The target tracking algorithm is fed the same video stream that was used for the target identification algorithm to work with. In MATLAB, the works are simulated, and their performance is evaluated using image-based and video-based metrics to establish how well they operate in the real world.
Persistent Müllerian Duct Syndrome: Understanding the Challenges
Authors: Chua Irene , Samnakay Naeem
Published: March 2022

Persistent Müllerian duct syndrome (PMDS) is a rare autosomal recessive condition defined by the presence of Müllerian duct-derived structures in an otherwise normally masculinized phenotypical and genotypical (46,XY) male. We describe the case of an infant diagnosed with PMDS, managed and followed up for 7 years. The diagnosis of PMDS was made at laparoscopy at 6 months of age for investigation and management of bilateral impalpable testes. A Müllerian structure resembling a uterus with bilateral fallopian tube-like structures was seen in the pelvis, along with bilateral intra-abdominal testes. Gonadal biopsy confirmed normal testicular tissue. The child underwent successful bilateral two-stage Fowler-Stephens orchidopexies. The Müllerian remnant was preserved to maintain testicular vascularity. At the most recent follow-up, the testes are intrascrotal and normal on palpation. There have been no clinical symptoms or concerns with the Müllerian remnant during surveillance with ultrasound and MRI. To date, there are less than 300 cases described in the medical literature, with limited consensus on management. We reflect on challenges the condition poses, including fertility preservation in PMDS, testicular and Müllerian malignancy risk in PMDS, and optimal management and surveillance of PMDS.
Defending against Deep-Learning-Based Flow Correlation Attacks with Adversarial Examples
Authors: Zhang Ziwei , Ye Dengpan
Published: March 2022

Tor is vulnerable to flow correlation attacks, adversaries who can observe the traffic metadata (e.g., packet timing, size, etc.) between client to entry relay and exit relay to the server will deanonymize users by calculating the degree of association. A recent study has shown that deep-learning-based approach called DeepCorr provides a high flow correlation accuracy of over 96%. The escalating threat of this attack requires timely and effective countermeasures. In this paper, we propose a novel defense mechanism that injects dummy packets into flow traces by precomputing adversarial examples, successfully breaks the flow pattern that CNNs model has learned, and achieves a high protection success rate of over 97%. Moreover, our defense only requires 20% bandwidth overhead, which outperforms the state-of-the-art defense. We further consider implementing our defense in the real world. We find that, unlike traditional scenarios, the traffic flows are “fixed” only when they are coming, which means we must know the next packet’s feature. In addition, the websites are not immutable, and the characteristics of the transmitted packets will change irregularly and lead to the inefficiency of adversarial samples. To solve these problems, we design a system to adapt our defense in the real world and further reduce bandwidth overhead.
Augmentation-Consistent Clustering Network for Diabetic Retinopathy Grading with Fewer Annotations
Authors: Zhang Guanghua , Li Keran , Chen Zhixian , Sun Li , zhang Jianwei , Pan Xueping
Published: March 2022

Diabetic retinopathy (DR) is currently one of the severe complications leading to blindness, and computer-aided, diagnosis technology-assisted DR grading has become a popular research trend especially for the development of deep learning methods. However, most deep learning-based DR grading models require a large number of annotations to provide data guidance, and it is laborious for experts to find subtle lesion areas from fundus images, making accurate annotation more expensive than other vision tasks. In contrast, large-scale unlabeled data are easily accessible, becoming a potential solution to reduce the annotating workload in DR grading. Thus, this paper explores the internal correlations from unknown fundus images assisted by limited labeled fundus images to solve the semisupervised DR grading problem and proposes an augmentation-consistent clustering network (ACCN) to address the above-mentioned challenges. Specifically, the augmentation provides an efficient cue for the similarity information of unlabeled fundus images, assisting the supervision from the labeled data. By mining the consistent correlations from augmentation and raw images, the ACCN can discover subtle lesion features by clustering with fewer annotations. Experiments on Messidor and APTOS 2019 datasets show that the ACCN surpasses many state-of-the-art methods in a semisupervised manner.
Analysis of the Driving Factors of the Extended Producer Responsibility System in China’s Tire Industry
Authors: Houbao Hu , Qiang Chen , Junjun Liu
Published: March 2022

The effective treatment of wrap tires could mitigate black pollution, reduce carbon emission, and supply risks. The Extended Producer Responsibility (EPR) system is an effective way to deal with the problems aroused from wrap tires in some countries. In order to analyze the driving factors for the EPR system in the Chinese tire industry, this study first identifies the key driving factors based on literature analysis, expert interviews, and field research. And then, using the grey decision-making and trial evaluation laboratory (Grey-DEMATEL) method, the interrelationships among these factors were quantified analyzed. We find that the causal factor is to “strengthen the service function of dealers and service providers and improve the satisfaction of sales channels.” Finally, sensitivity analysis was performed to ensure the robustness of the results. Conclusions are summarized below. If the implementation of the EPR system for tire companies can promote the sales of new tires, achieving the economic and environmental benefits must be the most critical factor. The effect factor is to fulfill social responsibility and achieve the brand effect. This result is in line with the original intention of tire companies to fulfill social responsibility and realize resource recycling. The key factors are related to policy guidance and regulations at the national level. And managerial implications are suggested for promoting the EPR system in China tire industry.
Unifications of Continuous and Discrete Fractional Inequalities of the Hermite–Hadamard–Jensen–Mercer Type via Majorization
Authors: Faisal Shah , Adil Khan Muhammad , Khan Tahir Ullah , Saeed Tareq , Mohammad Mahdi Sayed Zaid Mohammmad
Published: March 2022

The main objective of the paper is to develop an innovative idea of bringing continuous and discrete inequalities into a unified form. The desired objective is thus obtained by embedding majorization theory with the existing notion of continuous inequalities. These notions are applied to the latest generalized form of the inequalities, popularly known as the Hermite–Hadamard–Jensen–Mercer inequalities. Moreover, the frequently-used Caputo fractional operators are employed, which are rightly considered critical, especially for applied problems. Both weighted and unweighted forms of the developed results are discussed. In addition to this, some bounds are also provided for the absolute difference between the left- and right-sides of the main results.
Clinical Application of Artificial Intelligence: Auto-Discerning the Effectiveness of Lidocaine Concentration Levels in Osteosarcoma Femoral Tumor Segment Resection
Authors: Ni Shuqin , Li Xin , Yi Xiuna
Published: March 2022

Adolescents and children worldwide are threatened by osteosarcoma, a tumor that predominantly affects the long bone epiphysis. Osteosarcoma is the most common and highly malignant bone tumor in youngsters. Early tumor detection is the key to effective treatment of this disease. The discovery of biomarkers and the growing understanding of molecules and their complex interactions have improved the outcome of clinical trials in osteosarcoma. This article describes biomarkers of osteosarcoma with the aim of positively influencing the progress of clinical treatment of osteosarcoma. Femoral bone tumor is a typical condition of osteosarcoma. Due to the wide range of femoral stem types, complexities in the distal femur, and tumors in the rotor part of femur, physicians following the traditional clinical approach face difficulties in removing the lesion and fixing the femur with resection of the tumor segment. In this paper, the effect of small doses of different concentrations of lidocaine anesthesia in patients undergoing lumpectomy for osteosarcoma femoral tumor segments is investigated. A computer-based artificial intelligence method for automated determination of different concentration levels of lidocaine anesthesia and amputation of osteosarcoma femoral tumor segment is proposed. Statistical analysis is carried on the empirical data including intraoperative bleeding, intraoperative and postoperative pain scores, surgical operation time, postoperative complications, patient satisfaction, and local anesthetic dose. The results showed that the patients in the study group had low intraoperative bleeding, short operation time, low postoperative hematoma formation rate, high patient satisfaction, higher dosage of anesthetic solution, and low dosage of lidocaine. Results revealed that mean arterial pressure and heart rate in extubating and intubating were significantly lower in the observation group than in the control group, and a significant difference (

P
<
0.05

) was observed between the two groups. This proves that the proposed algorithm can adequately reduce bleeding, alleviate postoperative pain, shorten operation time, reduce complications, accelerate recovery, and ensure better treatment results.
Graph-Based Link Prediction between Human Phenotypes and Genes
Authors: Patel Rushabh , Guo Yanhui , Alhudhaif Adi , Alenezi Fayadh , Althubiti Sara A , Polat Kemal
Published: March 2022

Deep phenotyping is defined as learning about genotype-phenotype associations and the history of human illness by analyzing phenotypic anomalies. It is significant to investigate the association between phenotype and genotype. Machine learning approaches are good at predicting the associations between abnormal human phenotypes and genes. A novel framework based on machine learning is proposed to estimate the links between human phenotype ontology (HPO) and genes. The Orphanet’s annotation parses the human phenotype-gene associations. An algorithm node2vec generates the embeddings for the nodes (HPO and genes). It performs node sampling on the graph using random walks and learns features on these sampled nodes for embedding. These embeddings were used downstream to predict the link between these nodes by supervised classifiers. Results show the gradient boosting decision tree model (LightGBM) has achieved an optimal AUROC of 0.904 and an AUCPR of 0.784, an optimal weighted F1 score of 0.87. LightGBM can detect more accurate interactions and links between human phenotypes and gene pairs.
Physical Exercise Monitoring System Based on Computer Somatosensory Technology
Authors: Li Huajian , Lei Wang , Zhao Zhiqiang
Published: March 2022

Aiming at the problems of poor convergence, slow acceleration, and low throughput in traditional physical exercise monitoring systems, a physical exercise monitoring system based on computer somatosensory technology is proposed and designed. The whole structure of the physical activity monitoring system was created from the hardware and software, according to the research. Human body posture monitoring sensor module, communication connection module, power supply module, main control unit module, sensor module, and other pieces make up the hardware section. To improve detection performance, computer somatosensory technology is used in the software design to gather physical activity posture data. Also, for experimental comparison and verification, we employ system convergence, acceleration, and throughput as indicators. Experimental results show that the improved method can effectively improve system convergence, increase system acceleration, and increase system throughput, which has certain advantages in use.
Dark Web Data Classification Using Neural Network
Authors: Rajawat Anand Singh , Bedi Pradeep , Goyal S. B. , Kautish Sandeep , Xihua Zhang , Aljuaid Hanan , Mohamed Ali Wagdy
Published: March 2022

There are several issues associated with Dark Web Structural Patterns mining (including many redundant and irrelevant information), which increases the numerous types of cybercrime like illegal trade, forums, terrorist activity, and illegal online shopping. Understanding online criminal behavior is challenging because the data is available in a vast amount. To require an approach for learning the criminal behavior to check the recent request for improving the labeled data as a user profiling, Dark Web Structural Patterns mining in the case of multidimensional data sets gives uncertain results. Uncertain classification results cause a problem of not being able to predict user behavior. Since data of multidimensional nature has feature mixes, it has an adverse influence on classification. The data associated with Dark Web inundation has restricted us from giving the appropriate solution according to the need. In the research design, a Fusion NN (Neural network)-S3VM for Criminal Network activity prediction model is proposed based on the neural network; NN- S3VM can improve the prediction.
A Prospective Comparative Study between Implantable Phakic Intraocular Contact Lens and Implantable Collamer Lens in Treatment of Myopia in Adults
Authors: Rateb Mahmoud , Gad Ahmed A.M. , Tohamy Dalia , Elmohamady Mohamed Nagy
Published: March 2022

Purpose. To compare implantable collamer lenses (ICLs) and acrylic implantable phakic contact lenses (IPCLs) in the treatment of myopia in adults, as regards refractive outcome and adverse effects. Methods. Prospective, randomized comparative study with phakic intraocular lenses (IOLs) was carried out for treatment of myopia. Patients were randomized into two groups: one for ICL and the other for IPCL. Preoperative assessments included a full examination, pentacam, endothelial cell count, and biometry. We compared the adverse effects and refractive outcomes between both groups. The study was registered in clinical trials and the registration number is NCT04624035. Results. Sixty eyes of sixty patients (28 in the ICL group and 32 in the IPCL group) with a follow-up period of 12 months. The mean preoperative spherical equivalent was −12.7 ± 3.4 D and −13.6 ± 4.4 D in the ICL and IPCL groups, respectively (

P
=
0.37

). The mean postoperative spherical equivalent value was ±0.4 ± 0.2 D and ±0.6 ± 0.1 D in the ICL and IPCL groups, respectively. Uncorrected visual acuity (UCVA) has improved from 1.3 ± 0.06 to 0.15 ± 0.02 Log MAR in the ICL group (

P
<
0.001

) and from 1.3 ± 0.02 to 0.15 ± 0.01 Log MAR in the IPCL group (

P
<
0.001

). The mean endothelial cell count was reduced by 3.3% in the IPCL group and by 3.2% in the IPCL group. Conclusion. Both ICL and IPCL are effective methods to correct high myopia in adults with no statistically significant differences between the two lenses as regarding adverse effects.
The Effects of Using K-Wire or Titanium Elastic Nails on Clinical and Radiological Outcomes in the Métaizeau Closed Reduction Technique
Authors: Aydin Mahmud , Surucu Serkan
Published: March 2022

Aim. The purpose of this study was to compare the effects of using Kirschner-wire (K-wire) or titanium elastic nails (TEN) on the functional results of patients treated with the Métaizeau closed reduction technique, which is a popular treatment modality for pediatric radial neck fractures (PRNF). Methods. Patients who underwent the Métaizeau closed reduction technique for PRNF between May 2017 and May 2019 were assessed retrospectively. The Judet classification was used for the PRNF classification. The study consisted of patients who underwent the Métaizeau technique for Judet Type IV PRNFs and had at least 12 months of follow-up. Results. This study enrolled a total of 43 patients, 22 females and 21 males. Their mean age was 8.69 ± 2.3 at the time of surgery. The mean MEPS of patients undergoing K-wire surgery was 89.50, while the mean MEPS of patients undergoing TEN surgery was 92.83. There was no statistically significant difference between the two groups in terms of MEPS. When the complication rates of the two groups were evaluated, there was no statistically significant difference between them. The cost per patient for TEN was $40. The cost per patient for the K-wire was $1. Conclusion. The effect of K-wire or TEN used in the Métaizeau closed reduction technique on clinical outcomes is similar, and K-wire is more cost-effective.
Predicting Stenosis Aggravation in Follow-Up High-Resolution Magnetic Resonance Images of Patients with Intracranial Atherosclerosis
Authors: Lee Hyung-Soo , Jung Jin-Man , Yang Hwa-Been , Lee Sang-Hun
Published: March 2022

Background: High-resolution magnetic resonance imaging (HRMRI) can provide information on the histopathological characteristics of intracranial atherosclerotic lesions causing arterial stenosis; however, its clinical application in intracranial atherosclerosis lacks standardization for predicting stenosis. Therefore, this study investigated the characteristics of HRMRI that can predict progression based on comparisons of follow-up HRMRI. Methods: We retrospectively enrolled patients who underwent HRMRI within 7 days of symptom onset to evaluate the characteristics associated with intracranial stenotic lesions. Among them, patients diagnosed with severe stenosis due to atherosclerosis and who underwent follow-up HRMRI 12–24 months after initial HRMRI were included in the final study. We analyzed distinct features, such as stenosis aggravation, the presence of initial plaque enhancement, increment of plaque enhancement, the existence of both eccentric and concentric plaques, and the presence of initial intraplaque hematoma on initial and follow-up HRMRI. Results: Among 442 patients who underwent HRMRI for severe stenosis due to atherosclerosis, 35 underwent follow-up HRMRI 12–24 months later. Patients with stenosis aggravation showed a higher incidence of plaque enhancement (87.5% vs. 3.7%, p < 0.001) and the presence of both concentric and eccentric plaques (75.0% vs. 11.1%; p = 0.001). The area under the curve for the increment of plaque enhancement was 0.92 (95% confidence interval [CI] 0.78–1.00, p ≤ 0.001), while that for the presence of both concentric and eccentric plaques was 0.82 (95% CI 0.63–1.00, p < 0.007). Conclusions: The presence of both concentric and eccentric plaques and an increase in plaque enhancement were the strongest predictors of aggravation of intracranial artery stenosis.
Long-Term Outcome Analysis and Technical Refinements after Autologous Breast Reconstruction with PAP Flap: What We Have Learnt
Authors: Morandi Evi M. , Winkelmann Selina , Pülzl Petra , Augustin Angela , Wachter Tanja , Bauer Thomas , Egle Daniel , Brunner Christine , Wolfram Dolores
Published: March 2022

Introduction: The profunda artery perforator (PAP) flap represents a valuable alternative to the deep inferior epigastric artery perforator flap which, nowadays, is considered the golden standard for autologous breast reconstruction. The goal of this study was to evaluate the long-term satisfaction, functional outcomes of the donor site following PAP flap-based breast reconstruction and to present our personal learning experience along with suggestions for technique refinements. Methods: In this prospective single-center appraisal, 18 patients who underwent PAP flap-based breast reconstruction between January 2016 and November 2019 were enrolled. The Patient and Observer Scar Assessment Scale (POSAS) and the Breast-Q questionnaire were employed to evaluate the results 12 months postoperative. Data were analyzed with the Q-Score program. Complications were recorded in the medical database and classified with the Clavien-Dindo classification. Results: In the questionable time frame, 164 female patients underwent free flap breast reconstruction. Of those, 18 patients that received PAP flaps (9 bilateral) were included in this study. We recorded one flap loss because of venous failure. Most complications concerned the donor site, including hematoma, seroma, and wound healing problems. Patients’ satisfaction was high at 12 months post-surgery, despite critical evaluation of the donor site scar. Conclusion: The PAP flap serves as an excellent option for breast reconstruction in patients who do not have abundant abdominal tissue. The overall clinical outcome was good and patients’ evaluation showed high satisfaction after 12 months despite high complication rates. Modifications in planning and flap harvesting might improve the donor site outcome and the overall complication rate.
Tissue‐Specific Roles for the Slit–Robo Pathway During Heart, Caval Vein, and Diaphragm Development
Authors: Zhao Juanjuan , Bruche Susann , Potts Helen G. , Davies Benjamin , Mommersteeg Mathilda T. M.
Published: March 2022

Background

Binding of Slit ligands to their Robo receptors regulates signaling pathways that are important for heart development. Genetic variants in
ROBO1
and
ROBO4
have been linked to congenital heart defects in humans. These defects are recapitulated in mouse models with ubiquitous deletions of the Slit ligands or Robo receptors and include additional heart defects not currently linked to SLIT or ROBO mutations in humans. Given the broad expression patterns of these genes, the question remains open which tissue‐specific ligand‐receptor interactions are important for the correct development of different cardiac structures.

Methods and Results

We used tissue‐specific knockout mouse models of
Robo1
/
Robo2
,
Robo4
,
Slit2
and
Slit3
and scored cardiac developmental defects in perinatal mice. Knockout of
Robo2
in either the whole heart, endocardium and its derivatives, or the neural crest in ubiquitous
Robo1
knockout background resulted in ventricular septal defects. Neural crest‐specific removal of
Robo2
in
Robo1
knockouts showed fully penetrant bicuspid aortic valves (BAV). Endocardial knock‐out of either
Slit2
or
Robo4
caused low penetrant BAV. In contrast, endocardial knockout of
Slit3
using a newly generated line resulted in fully penetrant BAV, while removal from smooth muscle cells also resulted in BAV. Caval vein and diaphragm defects observed in ubiquitous
Slit3
mutants were recapitulated in the tissue‐specific knockouts.

Conclusions

Our data will help understand defects observed in patients with variants in
ROBO1
and
ROBO4
. The results strongly indicate interaction between endocardial
Slit3
and neural crest
Robo2
in the development of BAV, highlighting the need for further studies of this connection.
Blood Pressure and Stroke: A Review of Sex- and Ethnic/Racial-Specific Attributes to the Epidemiology, Pathophysiology, and Management of Raised Blood Pressure
Authors: Wang Xia , Carcel Cheryl , Woodward Mark , Schutte Aletta E.
Published: March 2022

Raised blood pressure (BP) is the leading cause of death and disability worldwide, and its particular strong association with stroke is well established. Although systolic BP increases with age in both sexes, raised BP is more prevalent in males in early adulthood, overtaken by females at middle age, consistently across all ethnicities/races. However, there are clear regional differences on when females overtake males. Higher BP among males is observed until the seventh decade of life in high-income countries, compared with almost 3 decades earlier in low- and middle-income countries. Females and males tend to have different cardiovascular disease risk profiles, and many lifestyles also influence BP and cardiovascular disease in a sex-specific manner. Although no hypertension guidelines distinguish between sexes in BP thresholds to define or treat hypertension, observational evidence suggests that in terms of stroke risk, females would benefit from lower BP thresholds to the magnitude of 10 to 20 mm Hg. More randomized evidence is needed to determine if females have greater cardiovascular benefits from lowering BP and whether optimal BP is lower in females. Since 1990, the number of people with hypertension worldwide has doubled, with most of the increase occurring in low- and-middle-income countries where the greatest population growth was also seen. Sub-Saharan Africa, Oceania, and South Asia have the lowest detection, treatment, and control rates. High BP has a more significant effect on the burden of stroke among Black and Asian individuals than Whites, possibly attributable to differences in lifestyle, socioeconomic status, and health system resources. Although pharmacological therapy is recommended differently in local guidelines, recommendations on lifestyle modification are often very similar (salt restriction, increased potassium intake, reducing weight and alcohol, smoking cessation). This overall enhanced understanding of the sex- and ethnic/racial-specific attributes to BP motivates further scientific discovery to develop more effective prevention and treatment strategies to prevent stroke in high-risk populations.
Direct Discriminative Decoder Models for Analysis of High-Dimensional Dynamical Neural Data
Authors: Rezaei Mohammad R. , Hadjinicolaou Alex E. , Cash Sydney S. , Eden Uri T. , Yousefi Ali
Published: March 2022

With the accelerated development of neural recording technology over the past few decades, research in integrative neuroscience has become increasingly reliant on data analysis methods that are scalable to high-dimensional recordings and computationally tractable. Latent process models have shown promising results in estimating the dynamics of cognitive processes using individual models for each neuron's receptive field. However, scaling these models to work on high-dimensional neural recordings remains challenging. Not only is it impractical to build receptive field models for individual neurons of a large neural population, but most neural data analyses based on individual receptive field models discard the local history of neural activity, which has been shown to be critical in the accurate inference of the underlying cognitive processes. Here, we propose a novel, scalable latent process model that can directly estimate cognitive process dynamics without requiring precise receptive field models of individual neurons or brain nodes. We call this the direct discriminative decoder (DDD) model. The DDD model consists of (1) a discriminative process that characterizes the conditional distribution of the signal to be estimated, or state, as a function of both the current neural activity and its local history, and (2) a state transition model that characterizes the evolution of the state over a longer time period.

While this modeling framework inherits advantages of existing latent process modeling methods, its computational cost is tractable. More important, the solution can incorporate any information from the history of neural activity at any timescale in computing the estimate of the state process. There are many choices in building the discriminative process, including deep neural networks or gaussian processes, which adds to the flexibility of the framework. We argue that these attributes of the proposed methodology, along with its applicability to different modalities of neural data, make it a powerful tool for high-dimensional neural data analysis. We also introduce an extension of these methods, called the discriminative-generative decoder (DGD). The DGD includes both discriminative and generative processes in characterizing observed data. As a result, we can combine physiological correlates like behavior with neural data to better estimate underlying cognitive processes. We illustrate the methods, including steps for inference and model identification, and demonstrate applications to multiple data analysis problems with high-dimensional neural recordings. The modeling results demonstrate the computational and modeling advantages of the DDD and DGD methods.
Ischnura elegans malikovae subspecies nova (Odonata, Coenagrionidae) from the Far East of Eurasia, with discussion of other possible subspecies
Authors: ONISHKO VLADIMIR V. , KOSTERIN OLEG E.
Published: March 2022

Ischnura elegans malikovae ssp. n. is described from the southern Far East of Russia (type locality: Russia, Primorskiy Kray, Pozharskiy District, Luchegorsk Town, the Luchegorsk Reservoir). Reconsideration of literature suggested it to broadly range in East Asia including Korea, Japan (Hokkaido and northernmost Honshu), north and north-east China and to be hitherto mistaken for Ischnura elegans elegans (Vander Linden, 1820) in Japan. The main difference of the new subspecies from I. e. elegans is the male paraprocts that are about 1.5 times shorter and scarcely divaricating in dorsal view and directed obliquely upward in lateral view. Ischnura elegans ebneri Schmidt, 1938 is reconsidered as the presumably Anterior Asian subspecies characterised by scarcely or not diverging male paraprocts in dorsal view, versus strongly diverging in I. e. elegans. Based on scarce information in the literature, I. elegans marquardti Schmidt, 1938 is supposed to be a junior synonym of I. elegans ordosi Bartenev, 1912 stat. rev., which is suggested to be an eastern Central Asian subspecies characterised by an incised prothoracic process in males.
Characteristics, Evolution, and Formation of Cold Air Outbreaks in the Great Plains of the United States
Authors: Millin Oliver T. , Furtado Jason C. , Basara Jeffrey B.
Published: March 2022

Wintertime cold air outbreaks (CAOs) in the Great Plains of the United States have significant socioeconomic, environmental, and infrastructural impacts; the events of December 1983 and February 2021 are key examples of this. Previous studies have investigated CAOs in other parts of North America, particularly the eastern United States, but the development of CAOs in the Great Plains and their potential subseasonal-to-seasonal (S2S) predictability have yet to be assessed. This study first identifies 37 large-scale CAOs in the Great Plains between 1950 and 2021, before examining their characteristics, evolution, and driving mechanisms. These events occur under two dominant weather regimes at event onset: one set associated with anomalous ridging over Alaska and the other set associated with anomalous pan-Arctic ridging. Alaskan ridge CAOs evolve quickly (i.e., on synoptic time scales) and involve stratospheric wave reflection. Conversely, Arctic high CAOs are preceded by weak stratospheric polar vortex conditions several weeks prior to the event. Both categories of CAOs feature anomalous upward wave activity flux from Siberia, with downward wave activity flux over Canada seen only in the Alaskan ridge CAOs. The rapid development of the Alaskan ridge CAOs, also linked with a North Pacific wave train and anomalous wave activity flux from the central Pacific, suggests that these events could be forced by tropical modes of variability. These findings present evidence that different forcing mechanisms, with contrasting time scales, may produce distinct sources of predictability for these CAOs on the S2S time scale.
How to Compete When Industries Digitize and Collide: An Ecosystem Development Framework
Authors: Jacobides Michael G.
Published: March 2022

As industry boundaries dissolve and digitalization grows apace, ecosystems are becoming increasingly important. Yet for all the excitement and Big-Tech envy, there is little guidance for how to create ecosystems. How should a firm best engage? Should it become a partner to someone else’s ecosystem, or build its own? Should it focus on a broad range of digitally connected services, or narrow down? How should we think about ecosystem value proposition, governance, and complementor choice? And, what is the case for investment in ecosystems? Drawing on recent research and projects with leading firms, this article offers a framework for understanding, engaging in, and building business ecosystems.
Democratic representation and charter school governance: The case of KIPP
Authors: Wilson Garrett , Lubienski Christopher
Published: March 2022

The growth of school choice options such as charter schools is often understood as a way to meet the needs of under-represented communities, but concerns have been raised as to the diversity of those promoting and leading such options, as well as for the voices of those whose schooling is being reformed. Garrett Wilson and Christopher Lubienski examined governing boards in one of the most popular chains of charter schools, finding that public school boards are often smaller and more diverse with broader racial and female membership. They explore reasons for these patterns and discuss implications for democratic governance of public education.
Backtalk: Universities aren’t teaching the finance skills that K-12 leaders need
Authors: Roza Marguerite
Published: March 2022

School district leaders are responsible for managing million-dollar budgets, but many have received little or no training in education finance. Marguerite Roza discusses a recent analysis of administrator preparation programs that revealed startling gaps in content, including a tendency to focus on revenue in their finance education, instead of on spending, which district and leaders have more control over. She calls on higher education programs to increase their finance offerings and shares free resources from Georgetown University’s Edunomics Lab that can help them get started.
Associations of Self-Esteem With Attachment to Parents: A Meta-Analysis
Authors: Pinquart Martin
Published: March 2022

The main objective of the present meta-analysis was to analyze associations between security of attachment to parents and self-esteem. Studies were included if they assessed bivariate associations between self-esteem and attachment security with mothers and/or fathers, or with both parents in general. A systematic search in the electronic databases PSYCINFO, Web of Science, Google Scholar, and PSYNDEX identified 202 studies with 81,485 participants that were included in this multilevel meta-analysis. Criteria from the Mixed Methods Appraisal Tool were used for assessing the quality of the individual studies. Most studies assessed security with verbal measures (190 studies), and the mean age of participants was 16.5 years. We found a moderate positive concurrent correlation of attachment security to parents with self-esteem ( r = .34; 95% confidence interval [CI], .33-.36), with associations being stronger when assessing attachment to parents in general ( r = .37; CI, .35 to .40) rather than to mothers ( r = .33; CI, .31 to .35) or fathers ( r = .32; CI, .30 to .34) in particular. Cross-lagged effects indicate that higher initial attachment security predicts an increase in self-esteem over time ( r = .19; CI, .09 to .28), while initial self-esteem predicts change in security ( r = .08; CI, .02 to .14). Correlations of attachment security with self-esteem were weaker in older participants and stronger in studies with validated attachment measures. As most of the included studies have been conducted with adolescents and young adults, knowledge about associations of secure attachment to parents and self-esteem in the first years of life is still limited. Nonetheless, it is concluded that the available results support suggestions of attachment theory on the role of secure attachment for self-esteem, although causal effects could only be tested in experimental studies.
Rethinking the Infusion Approach to Teaching Critical Thinking in Reading in Chinese Tertiary English Classes
Authors: Zhang Hong , Yuan Rui
Published: March 2022

The teaching of critical thinking (CT) has become an important requirement in many English language classrooms at different educational levels, yet little is known about how teachers promote the learning of CT. This article reports on a qualitative study examining the ways in which two university English as a foreign language (EFL) teachers in China attempted to blend CT into their reading classrooms, and the factors that influenced their attempts at CT innovation. In-depth, semi-structured interviews, classroom observations, artefacts, and observation of eight joint discussion sessions between the two participants and the first author were analyzed to explain the participants’ implementation of CT innovation in their intensive reading courses and the influential factors. The findings indicate that the teachers implemented CT innovation in reading instruction through the infusion approach, which means explicitly highlighting CT during the course orientation of their reading courses, while also improving the students’ CT abilities and language competence through reflective and collaborative writing using reading journals. The study also revealed several factors that influenced their attempts at CT innovation. The study sheds light on how teachers can be supported to innovate conventional CT instruction in the reading classroom.