bluuebunny/crossref_metadata_2025 huggingface dataset:
MRI-Based Digital Models Forecast Patient-Specific Treatment Responses to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer
Authors: Wu Chengyue , Jarrett Angela M. , Zhou Zijian , Elshafeey Nabil , Adrada Beatriz E. , Candelaria Rosalind P. , Mohamed Rania M.M. , Boge Medine , Huo Lei , White Jason B. , Tripathy Debu , Valero Vicente , Litton Jennifer K. , Yam Clinton , Son Jong Bum , Ma Jingfei , Rauch Gaiane M. , Yankeelov Thomas E.
Published: August 2022

Triple-negative breast cancer (TNBC) is persistently refractory to therapy, and methods to improve targeting and evaluation of responses to therapy in this disease are needed. Here, we integrate quantitative MRI data with biologically based mathematical modeling to accurately predict the response of TNBC to neoadjuvant systemic therapy (NAST) on an individual basis. Specifically, 56 patients with TNBC enrolled in the ARTEMIS trial (NCT02276443) underwent standard-of-care doxorubicin/cyclophosphamide (A/C) and then paclitaxel for NAST, where dynamic contrast-enhanced MRI and diffusion-weighted MRI were acquired before treatment and after two and four cycles of A/C. A biologically based model was established to characterize tumor cell movement, proliferation, and treatment-induced cell death. Two evaluation frameworks were investigated using: (i) images acquired before and after two cycles of A/C for calibration and predicting tumor status after A/C, and (ii) images acquired before, after two cycles, and after four cycles of A/C for calibration and predicting response following NAST. For Framework 1, the concordance correlation coefficients between the predicted and measured patient-specific, post-A/C changes in tumor cellularity and volume were 0.95 and 0.94, respectively. For Framework 2, the biologically based model achieved an area under the receiver operator characteristic curve of 0.89 (sensitivity/specificity = 0.72/0.95) for differentiating pathological complete response (pCR) from non-pCR, which is statistically superior (P < 0.05) to the value of 0.78 (sensitivity/specificity = 0.72/0.79) achieved by tumor volume measured after four cycles of A/C. Overall, this model successfully captured patient-specific, spatiotemporal dynamics of TNBC response to NAST, providing highly accurate predictions of NAST response.

Significance:

Integrating MRI data with biologically based mathematical modeling successfully predicts breast cancer response to chemotherapy, suggesting digital twins could facilitate a paradigm shift from simply assessing response to predicting and optimizing therapeutic efficacy.
Impact of a Multi-Level, Multi-Component, System Intervention on HPV Vaccination in a Federally Qualified Health Center
Authors: Glenn Beth A. , Nonzee Narissa J. , Herrmann Alison K. , Crespi Catherine M. , Haroutunian G. Greg , Sundin Phillip , Chang L. Cindy , Singhal Rita , Taylor Victoria M. , Bastani Roshan
Published: August 2022

Background:

Human papillomavirus (HPV) vaccines can significantly reduce the burden of HPV-associated cancers, but remain underutilized. We evaluated a multi-component, system-level intervention to improve HPV vaccination in a large Federally Qualified Health Center (FQHC) that serves a primarily low income Latino population.

Methods:

From January 2015 through March 2017, we evaluated the effectiveness of a multi-component, system-level intervention to improve HPV vaccination rates in eight clinics randomly assigned to study condition (four intervention, four usual care). The intervention included parent reminders for HPV vaccine series completion, provider training, clinic-level audit and feedback, and workflow modifications to reduce missed opportunities for vaccination. Using a difference-in-differences approach, we compared HPV vaccination rates among patients, ages 11 to 17 during a 12-month preintervention period and a 15-month intervention period. Linear mixed models were used to estimate intervention effects on vaccine initiation and completion.

Results:

The sample included approximately 15,000 adolescents each quarter (range 14,773–15,571; mean age 14 years; 51% female, 88% Latino). A significantly greater quarterly increase in HPV vaccine initiation was observed for intervention compared with usual care clinics (0.75 percentage point greater increase, P < 0.001), corresponding to 114 additional adolescents vaccinated per quarter. The intervention led to a greater increase in HPV vaccine completion rates among boys (0.65 percentage point greater increase, P < 0.001), but not girls.

Conclusions:

Our system-level intervention was associated with modest improvements in HPV vaccine initiation overall and completion among boys.

Impact:

Study findings have implications for reducing HPV-related cancers in safety net populations.
Loss of PTEN Expression, PIK3CA Mutations, and Breast Cancer Survival in the Nurses’ Health Studies
Authors: Wang Tengteng , Heng Yujing J. , Baker Gabrielle M. , Bret-Mounet Vanessa C. , Quintana Liza M. , Frueh Lisa , Hankinson Susan E. , Holmes Michelle D. , Chen Wendy Y. , Willett Walter C. , Rosner Bernard , Tamimi Rulla M. , Eliassen A. Heather
Published: August 2022

Background:

The relationships between PTEN loss and/or PIK3CA mutation and breast cancer prognosis remain controversial. We aim to examine the associations in large epidemiologic cohorts.

Methods:

We followed women with invasive breast cancer from the Nurses’ Health Studies with available data on tumor PTEN expression (n = 4,111) and PIK3CA mutation (n = 2,930). PTEN expression was evaluated by IHC and digitally scored (0%–100%). Pyrosequencing of six hotspot mutations of PIK3CA was performed.

Results:

We found loss of PTEN expression (≤10%) occurred in 17% of cases, and PIK3CA mutations were detected in 11% of cases. After adjusting for clinical and lifestyle factors, PTEN loss was not associated with worse breast cancer-specific mortality among all samples [HR, 0.85; 95% confidence intervals (CI), 0.71–1.03] or among estrogen receptor (ER)-positive tumors (HR, 0.99; 95% CI, 0.79–1.24). However, among ER-negative tumors, PTEN loss was associated with lower breast cancer-specific mortality (HR, 0.68; 95% CI, 0.48–0.95). PIK3CA mutation was not strongly associated with breast cancer-specific mortality (HR, 0.89; 95% CI, 0.67–1.17). Compared with tumors without PTEN loss and without PIK3CA mutation, those with alterations (n = 540) were not at higher risk (HR, 1.07; 95% CI, 0.86–1.34). However, women with both PTEN loss and PIK3CA mutation (n = 38) were at an increased risk of breast cancer-specific mortality (HR, 1.65; 95% CI, 0.83–3.26).

Conclusions:

In this large epidemiologic study, the PTEN-mortality association was more pronounced for ER-negative tumors, and the joint PTEN loss and PIK3CA mutation may be associated with worse prognosis.

Impact:

Further studies with a larger sample of ER-negative tumors are needed to replicate our findings and elucidate underlying mechanisms.
Cancer Prevention Prioritized at AACR Annual Meeting and a New Working Group
Authors: Albini Adriana
Published: August 2022

Scientific advances in the late 19th century set the stage for progress in understanding and treating cancer, a disease that was previously considered almost hopeless. One hundred years later, cancer prevention is becoming an increasingly important focus for oncology research. New tools and ideas bring to the field some extremely promising molecular, organizational, social, and political approaches, which were a focus of the American Association for Cancer Research 2022 Annual Meeting and of the newly launched AACR Cancer Prevention Working Group (CPWG). We are moving toward precision prevention, better tools for early detection and for risk assessment, the use of a Precancer Atlas, unveiling of new biomarkers. Besides improving lifestyle, by avoiding risk factors such as tobacco use, excessive UV exposure, infectious agents, as well as poor dietary habits, lack of exercise, overweight, and obesity, many other factors can impact cancer risk, which is a warning to consider a multifaceted molecular but also social approach. Gender, ethnicity, geographic, and economic lines are associated with disparities in prevention, which we want to overcome. Here we summarize some challenges and priorities in cancer prevention emerging from the work of AACR and CPWG.
Optimal siting and sizing of unified power flow controller using sensitivity constrained differential evolution algorithm
Authors: Kumar Reddy Karri Manoz , Rao Kailasa A. , Rao Rayapudi Srinivasa
Published: August 2022

This paper presents Sensitivity constrained placement of unified power flow controller (UPFC) considering active-power flow sensitive index (APFSI) and static voltage stability index (STATIC-VSI) to minimize active-power losses and to improve power transmission capacity. The sensitive factors are derived with respect to voltage, phase angle and current to formulate APFSI. Transmission line impedance parameters along with active and reactivepower flow measurements are considered to formulate static-VSI. Sensitivity constrained differential evolutionary (SCDE) algorithm is proposed for parameter setting through which power control and minimization of losses in system can be achieved. Testing is performed on IEEE-5, 14 and 30-bus networks in MATLAB and results indicate that SCDE is robust optimization technique compared to conventional method and genetic algorithm (GA)
Sensor evaluation for hand grip strength
Authors: Biju Soly Mathew , Sheikh Hashir Zahid
Published: August 2022

This paper discusses the evaluation of the sensors used in the hand grip strength glove. The glove comprises of flex and force resisting sensors. Force resisting sensor determines the force applied by various parts of the palm, while the flex sensor determines the flexion of the fingers. These sensors are placed in a specific position on the glove to obtain correct data when the glove is used. The glove has two modes, which are pencil grip mode and object grip mode. The sensors determine which mode the glove is in depending on the gesture made. The glove is examined using a pencil and a cylindrical object to evaluate the strength of the grip. After gripping the object or pencil, the system evaluates the force applied using the sensors. This data is transferred to a computer for further analysis using a trained model. The model was able to achieve an accuracy of 90.8%.
A computationally efficient learning model to classify audio signal attributes
Authors: Prasad Kantipud Maha Veera Vara , Kumar Satish
Published: August 2022

The era of machine learning has opened up groundbreaking realities and opportunities in the field of medical diagnosis. However, it is also observed that faster and proper diagnosis of any diseases/medical conditions require proper analysis and classification of digital signal data. It indicates the proper identification of tumors in the brain. Brain magnetic resonance imaging (MRI) data has to be appropriately classified, and similarly, pulse signal analysis is required to evaluate the human heart operating condition. Several studies have used machine learning (ML) modeling to classify speech signals, but very few studies have explored the classification of audio signal attributes in the context of intelligent healthcare monitoring. The study thereby aims to introduce novel mathematical modeling to analyze and classify synthetic pulse audio signal attributes with cost-effective computation. The numerical modeling is composed of several functional blocks where deep neural network-based learning (DNNL) plays a crucial role during the training phase, and also it is further combined with a recurrent structure of long-short term memory (R-LSTM) feedback connections (FCs). The design approaches further experiment in a numerical computing environment in terms of accuracy and computational aspects. The classification outcome of the proposed approach shows that it attains approximately 85% accuracy, which is comparable to the baseline approaches and execution time.
Feature selection of unbalanced breast cancer data using particle swarm optimization
Authors: Elnawasany Amal , Makhlouf Mohamed Abd Allah , Tawfik BenBella , Nassar Hamed
Published: August 2022

Breast cancer is one of the significant deaths causing diseases of women around the globe. Therefore, high accuracy in cancer prediction models is vital to improving patients’ treatment quality and survivability rate. In this work, we presented a new method namely improved balancing particle swarm optimization (IBPSO) algorithm to predict the stage of breast cancer using unbalanced surveillance epidemiology and end result (USEER) data. The work contributes in two directions. First, design and implement an improved particle swarm optimization (IPSO) algorithm to avoid the local minima while reducing USEER data’s dimensionality. The improvement comes primarily through employing the cross-over ability of the genetic algorithm as a fitness function while using the correlation-based function to guide the selection task to a minimal feature subset of USEER sufficiently to describe the universe. Second, develop an improved synthetic minority over-sampling technique (ISMOTE) that avoid overfitting problem while efficiently balance USEER. ISMOTE generates the new objects based on the average of the two objects with the smallest and largest distance from the centroid object of the minority class. The experiments and analysis show that the proposed IBPSO is feasible and effective, outperforms other state-of-the-art methods; in minimizing the features with an accuracy of 98.45%.
On the applicability of some existing tropospheric scintillation prediction models for Ikeja and Abuja, Nigeria
Authors: Adeyemo Zachaeus Kayode , Yusuf Ridwan Olalekana , Lasisi Hammed Oyebamiji , Ojo Samson Iyanda , Omowaiye Rebecca Olufunmilayoa , Adedeji Adebukola Ayoade
Published: August 2022

Tropospheric scintillation which causes rapid fluctuation of signal due to the turbulence in the atmosphere is of significance in satellite link budget design. Existing models that predict the intensity of scintillation such as Karasawa, International Telecommunication Union Radiocommunication (ITU-R) Sector, Otung, Van de Kamp and Ortgies are not appropriate for predicting scintillation intensity (SI) in Nigeria due to localization of the models to particular regions. The monthly average air temperature and relative humidity for Ikeja and Abuja, Nigeria, retrieved from the Nigerian Meteorological Centre data bank were used for the investigation. An elevation angle of 5° was used as look angle, antenna diameter of 0.3 m, 40 GHz and 45 GHz frequencies were considered in this study. The existing scintillation model values were then compared with the measured values for the two locations to determine the best performance model. The SI results at 40 GHz and 45 GHz were obtained for each of the existing models. It could be deduced from the results obtained that orgies model values were the most closet to the measured values. Therefore, Orgies-N model was the most appropriate for SI prediction in Ikeja and Abuja.
Impact of carrier frequency offset and in-phase and quadrature imbalance on the performance of wireless precoded orthogonal frequency division multiplexing
Authors: Suyoto Suyoto , Subekti Agus , Satyawan Arief Suryadi , Armi Nasrullah , Ali Wael Chaeriah Bin , Nurkahfi Galih Nugraha
Published: August 2022

Precoding in orthogonal frequency division multiplexing (OFDM) system proved to reduce the peak-to-average power ratio (PAPR), so that it improves BER. However, from the existing literature, the effect of carrier frequency offset (CFO), in-phase and quadrature (IQ) imbalance on precoded wireless OFDM systems has not been carried out. Therefore, this study evaluated the precoded OFDM (P-OFDM) system performance by considering the impact of CFO and IQ imbalance. P-OFDM performance evaluation is expressed in signal-to-interference noise ratio (SINR) and bit error rates (BER). The communication channels used are the additive white Gaussian noise (AWGN) channel and the frequency-selective Rayleigh fading (FSRF) channel, while the channel equalization process is considered perfect. The results of the analysis and simulation show that P-OFDM is greater affected by the presence of CFO and IQ imbalance than conventional OFDM system. In AWGN channel, P-OFDM experiences different SINR for each subcarrier. This is different from conventional OFDM system, where all SINRs are the same for all subcarriers. In the FSRF channel, both the POFDM system and the OFDM system experience different SINR for each subcarrier, where the SINRs fluctuation in the P-OFDM system is much larger than in the OFDM system.
Automated information retrieval and services of graduate school using chatbot system
Authors: Rukhiran Meennapa , Netinant Paniti
Published: August 2022

Automated information retrieval and servicing systems are a priority demand system in today's businesses to ensure instantaneous customer satisfaction. The chatbot system is an incredible technological application that enables communication channels to automatically respond to end-users in real-time and 24 hours a day. By providing effective services for retrieving information and electronic documents continuously and automating the information service system, the coronavirus disease (COVID-19) is challenging to promote graduate school programs, update news, and retrieve student information in this era. This article discusses automated information retrieval and services based on the architecture, components, technology, and experiment of chatbots. The chatbot system's primary functions are to deliver the course and contact information, answer frequency questions, and provide a link menu to apply for our online course platform. We manage the entire functional process of gathering course information and submitting an application for a course online. The final results compare end users' perceptions of chatbot system usage to onsite services to ensure that the chatbot system can be integrated into the university's information system, supporting university-related questions and answers. We may expand our chatbot system's connection to the university's server to provide information services to students in various informative areas for future research.
Traffic light control design approaches: a systematic literature review
Authors: Mohamed Nazar Elfadil , Radwan Intisar Ibrahim
Published: August 2022

To assess different approaches to traffic light control design, a systematic literature review was conducted, covering publications from 2006 to 2020. The review’s aim was to gather and examine all studies that looked at road traffic and congestion issues. As well, it aims to extract and analyze protruding techniques from selected research articles in order to provide researchers and practitioners with recommendations and solutions. The research approach has placed a strong emphasis on planning, performing the analysis, and reporting the results. According to the results of the study, there has yet to be developed a specific design that senses road traffic and provides intelligent solutions. Dynamic time intervals, learning capability, emergency priority management, and intelligent functionality are all missing from the conventional design approach. While learning skills in the adaptive self-organization strategy were missed. Nonetheless, the vast majority of intelligent design approach papers lacked intelligent fear tires and learning abilities.
Provenance and social network analysis for recommender systems: a literature review
Authors: Rrmoku Korab , Selimi Besnik , Ahmedi Lule
Published: August 2022

Recommender systems (RS) and their scientific approach have become very important because they help scientists find suitable publications and approaches, customers find adequate items, tourists find their preferred points of interest, and many more recommendations on domains. This work will present a literature review of approaches and the influence that social network analysis (SNA) and data provenance has on RS. The aim is to analyze differences and similarities using several dimensions, public datasets for assessing their impacts and limitations, evaluations of methods and metrics along with their challenges by identifying the most efficient approaches, the most appropriate assessment data sets, and the most appropriate assessment methods and metrics. Hence, by correlating these three fields, the system will be able to improve the recommendation of certain items, by being able to choose the recommendations that are made from the most trusted nodes/resources within a social network. We have found that content-based filtering techniques, combined with term frequency-inverse document frequency (TF-IDF) features are the most feasible approaches when combined with provenance since our focus is to recommend the most trusted items, where trust, distrust, and ignorance are calculated as weight in terms of the relationship between nodes on a network.
Neural network training for serial multisensor of autonomous vehicle system
Authors: Budisusila Eka Nuryanto , Prasetyowati Sri Arttini Dwi , Suprapto Bhakti Yudho , Nawawi Zainuddin
Published: August 2022

This study aims to find the best artificial neural network weight values to be applied to the autonomous vehicle system with ultrasonic multisensor. The implementation of neural network in the system required long time process due to its training process. Therefore, this research is using offline training before implementing to online training by embedding the best network weight values to obtain the outputs faster according to desired targets. Simulink were used to train the system offline. Eight ultrasonic sensors are used on all sides of the vehicle and arranged in a serial multisensory configuration as inputs of neural network. With eight inputs, one sixteen-depth hidden layer, and five outputs, it was trained using the back-propagation algorithm of artificial neural network. By 100000 iterations, the output values and the target values are almost the same, indicating its convergency with minimum of errors. The result of this training is the best weights of the networks. These weight values can be implemented as fixed-weight in online training.
Optimal k-means clustering using artificial bee colony algorithm with variable food sources length
Authors: Raheem Sabreen Fawzi , Alabbas Maytham
Published: August 2022

Clustering is a robust machine learning task that involves dividing data points into a set of groups with similar traits. One of the widely used methods in this regard is the k-means clustering algorithm due to its simplicity and effectiveness. However, this algorithm suffers from the problem of predicting the number and coordinates of the initial clustering centers. In this paper, a method based on the first artificial bee colony algorithm with variable-length individuals is proposed to overcome the limitations of the k-means algorithm. Therefore, the proposed technique will automatically predict the clusters number (the value of k) and determine the most suitable coordinates for the initial centers of clustering instead of manually presetting them. The results were encouraging compared with the traditional k-means algorithm on three real-life clustering datasets. The proposed algorithm outperforms the traditional k-means algorithm for all tested real-life datasets.
Review on effectiveness of deep learning approach in digital forensics
Authors: Ekhande Sonali , Patil Uttam , Kulhalli Kshama Vishwanath
Published: August 2022

Cyber forensics is use of scientific methods for definite description of cybercrime activities. It deals with collecting, processing and interpreting digital evidence for cybercrime analysis. Cyber forensic analysis plays very important role in criminal investigations. Although lot of research has been done in cyber forensics, it is still expected to face new challenges in near future. Analysis of digital media specifically photographic images, audio and video recordings are very crucial in forensics This paper specifically focus on digital forensics. There are several methods for digital forensic analysis. Currently deep learning (DL), mainly convolutional neural network (CNN) has proved very promising in classification of digital images and sound analysis techniques. This paper presents a compendious study of recent research and methods in forensic areas based on CNN, with a view to guide the researchers working in this area. We first, defined and explained preliminary models of DL. In the next section, out of several DL models we have focused on CNN and its usage in areas of digital forensic. Finally, conclusion and future work are discussed. The review shows that CNN has proved good in most of the forensic domains and still promise to be better.
Stress detection during job interview using physiological signal
Authors: Mand Ali Afzalian , Sayeed Md. Shohel , Hossen Md. Jakir , Bin Zuber Muhammad Amer Ridzuan
Published: August 2022

A job interview can be challenging and stressful even when one has gone through it many times. Failure to handle the stress may lead to unsuccessful delivery of their best throughout the interview session. Therefore, an alternative method which is preparing a video resume and interview before the actual interview could reduce the level of stress. An intelligent stress detection is proposed to classify individuals with different stress levels by understanding the physiological signal through electrocardiogram (ECG) signals. The Augsburg biosignal toolbox (AUBT) dataset was used to obtain the state-of-art results. Only five selected features are significant to the stress level were fed into neural network multi-layer perceptron (MLP) as the optimum classifier. This stress detection achieved an accuracy of 92.93% when tested over the video interview dataset of 10 male subjects who were recording the video resume for the analysis purposes.
Design and implementation of smart guided glass for visually impaired people
Authors: Islam Md. Tobibul , Rashid Mohd Abdur , Ahmad Mohiuddin , Kuwana Anna , Kobayashi Haruo
Published: August 2022

The objective of this paper is to develop an innovative microprocessor-based sensible glass for those who are square measure visually impaired. Among all existing devices in the market, one can help blind people by giving a buzzer sound when detecting an object. There are no devices that can provide object, hole, and barrier information associated with distance, family member, and safety information in a single device. Our proposed guiding glass provides all that necessary information to the blind person’s ears as audio instructions. The proposed system relies on Raspberry pi three model B, Pi camera, and NEO-6M global positioning system (GPS) module. We use TensorFlow and faster region-based convolutional neural network (R-CNN) approach for detection of objects and recognition of family members of the blind man. This system provides voice information through headphones to the ears of the blind person, and facile the blind individual to gain independence and freedom within the indoor and outdoor atmosphere.
Sentiment analysis on Bangla conversation using machine learning approach
Authors: Hassan Mahmudul , Shakil Shahriar , Moon Nazmun Nessa , Islam Mohammad Monirul , Hossain Refath Ara , Mariam Asma , Nur Fernaz Narin
Published: August 2022

Nowadays, online communication is more convenient and popular than face-to-face conversation. Therefore, people prefer online communication over face-to-face meetings. Enormous people use online chatting systems to speak with their loved ones at any given time throughout the world. People create massive quantities of conversation every second because of their online engagement. People's feelings during the conversation period can be gleaned as useful information from these conversations. Text analysis and conclusion of any material as summarization can be done using sentiment analysis by natural language processing. The use of communication for customer service portals in various e-commerce platforms and crime investigations based on digital evidence is increasing the need for sentiment analysis of a conversation. Other languages, such as English, have well-developed libraries and resources for natural language processing, yet there are few studies conducted on Bangla. It is more challenging to extract sentiments from Bangla conversational data due to the language's grammatical complexity. As a result, it opens vast study opportunities. So, support vector machine, multinomial naïve Bayes, k-nearest neighbors, logistic regression, decision tree, and random forest was used. From the dataset, extracted information was labeled as positive and negative.
A combined computing framework for load balancing in multi-tenant cloud eco-system
Authors: Chandrashekhar Amith Shekhar , Gunakimath Suryakanth Sharvani
Published: August 2022

Since the world is getting digitalized, cloud computing has become a core part of it. Massive data on a daily basis is processed, stored, and transferred over the internet. Cloud computing has become quite popular because of its superlative quality and enhanced capability to improvise data management, offering better computing resources and data to its user bases (UBs). However, there are many issues in the existing cloud traffic management approaches and how to manage data during service execution. The study introduces two distinct research models: data center virtualization framework under multi-tenant cloud-ecosystem (DCVF-MT) and collaborative workflow of multi-tenant load balancing (CW-MTLB) with analytical research modeling. The sequence of execution flow considers a set of algorithms for both models that address the core problem of load balancing and resource allocation in the cloud computing (CC) ecosystem. The research outcome illustrates that DCVF-MT, outperforms the one-to-one approach by approximately 24.778% performance improvement in traffic scheduling. It also yields a 40.33% performance improvement in managing cloudlet handling time. Moreover, it attains an overall 8.5133% performance improvement in resource cost optimization, which is significant to ensure the adaptability of the frameworks into futuristic cloud applications where adequate virtualization and resource mapping will be required.
Restoration of Cardiomyogenesis in Aged Mouse Hearts by Voluntary Exercise
Authors: Lerchenmüller Carolin , Vujic Ana , Mittag Sonja , Wang Annie , Rabolli Charles P. , Heß Chiara , Betge Fynn , Rangrez Ashraf Y. , Chaklader Malay , Guillermier Christelle , Gyngard Frank , Roh Jason D. , Li Haobo , Steinhauser Matthew L. , Frey Norbert , Rothermel Beverly , Dieterich Christoph , Rosenzweig Anthony , Lee Richard T.
Published: August 2022

Background:

The human heart has limited capacity to generate new cardiomyocytes and this capacity declines with age. Because loss of cardiomyocytes may contribute to heart failure, it is crucial to explore stimuli of endogenous cardiac regeneration to favorably shift the balance between loss of cardiomyocytes and the birth of new cardiomyocytes in the aged heart. We have previously shown that cardiomyogenesis can be activated by exercise in the young adult mouse heart. Whether exercise also induces cardiomyogenesis in aged hearts, however, is still unknown. Here, we aim to investigate the effect of exercise on the generation of new cardiomyocytes in the aged heart.

Methods:

Aged (20-month-old) mice were subjected to an 8-week voluntary running protocol, and age-matched sedentary animals served as controls. Cardiomyogenesis in aged hearts was assessed on the basis of
15
N-thymidine incorporation and multi-isotope imaging mass spectrometry. We analyzed 1793 cardiomyocytes from 5 aged sedentary mice and compared these with 2002 cardiomyocytes from 5 aged exercised mice, followed by advanced histology and imaging to account for ploidy and nucleation status of the cell. RNA sequencing and subsequent bioinformatic analyses were performed to investigate transcriptional changes induced by exercise specifically in aged hearts in comparison with young hearts.

Results:

Cardiomyogenesis was observed at a significantly higher frequency in exercised compared with sedentary aged hearts on the basis of the detection of mononucleated/diploid
15
N-thymidine–labeled cardiomyocytes. No mononucleated/diploid
15
N-thymidine–labeled cardiomyocyte was detected in sedentary aged mice. The annual rate of mononucleated/diploid
15
N-thymidine–labeled cardiomyocytes in aged exercised mice was 2.3% per year. This compares with our previously reported annual rate of 7.5% in young exercised mice and 1.63% in young sedentary mice. Transcriptional profiling of young and aged exercised murine hearts and their sedentary controls revealed that exercise induces pathways related to circadian rhythm, irrespective of age. One known oscillating transcript, however, that was exclusively upregulated in aged exercised hearts, was isoform 1.4 of regulator of calcineurin, whose regulation and functional role were explored further.

Conclusions:

Our data demonstrate that voluntary running in part restores cardiomyogenesis in aged mice and suggest that pathways associated with circadian rhythm may play a role in physiologically stimulated cardiomyogenesis.
The Role of Early Superficial Temporal to Middle Cerebral Artery Bypass Revascularization Surgery in Middle Cerebral Artery‐Territory Strokes: A Systematic Review Study
Authors: Sabouri Masih , Aminmansour Bahram , Sourani Arman , Sharafi Mohammad , Sahraei Ahmad , Rahimi Arvin , Saghaei Saeid , Baradaran Mahdavi Sadegh , Foroughi Mina
Published: August 2022

Background

To systematically summarize and critically review the available literature regarding the functional outcome of the urgent superficial temporal artery to the middle cerebral artery (MCA) bypass surgery in stroke patients associated with MCA lesions.

Methods

The current systematic review was conducted and reported using the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis statement. Medline (PubMed), ISI Web of Science, Scopus, and google scholar (manually searches) were systematically searched up to August 2021. The eligibility criteria were defined as all the patients who had MCA‐territory strokes due to MCA/intradural internal carotid artery lesions that underwent same‐admission superficial temporal artery‐MCA bypass surgery. The strokes with cervical internal carotid artery and anterior cerebral artery lesions/strokes were excluded. The checklists produced by the US National Heart Lung and Blood Institute were used for quality assessment. Demographic variables, clinical findings, National Institutes of Health Stroke Scale scores, the time interval between symptoms to surgery, imaging results, endovascular treatments, surgical notes, mortality rate, follow‐up findings, modified Rankin scale scores, and all reported medical records were extracted and analyzed with SPSS software (ver. 23).
P
values ≤0.05 were considered meaningful.

Results

The initial search yielded 3050 records, and 13 articles met the inclusion criteria and entered this systematic review. A total of 165 patients were pooled for data analysis. Nine studies had data regarding neurological deficits before and after surgery. Except for 5 patients, the National Institutes of Health Stroke Scale score after the operation was the same or better than the preoperative score. Eleven out of 17 patients in 3 studies showed early neurological improvement (a decrease in National Institutes of Health Stroke Scale score of ≥4 points at 3 days) after bypass surgery. Functional outcome was stated in 10 studies. Overall, a good functional outcome (modified Rankin scale=0–2) was achieved in 9 out of 21 (42.1%) patients at discharge and in 44 out of 59 (74.5%) patients at 3 months follow‐up. Lower age was associated with favorable outcomes. Long‐term graft patency was excellent in 99.5% of patients. Postoperative hematoma and mortality (all noncranial) rates were 3% and 3.6%, respectively.

Conclusion

The current study suggests that early superficial temporal artery‐MCA revascularization should be considered an available option in younger patients (<60‐year‐old) with MCA territory strokes who fail medical treatment.
The Phi Angle: A Theoretical Essay on Sense of Presence, Human Factors, and Performance in Virtual Reality
Authors: Maneuvrier Arthur , Westermann Hannes
Published: August 2022

The question of the relationship between the sense of presence and performance in virtual reality is fundamental for anyone wishing to use the tool methodologically. Indeed, if the sense of presence can modify performance per se, then individual factors affecting the human–computer interaction might have repercussions on performance, despite being unrelated to it. After a discussion on the sense of presence and the particularities it provokes, this work studies the psychophysiology of virtual reality. This in virtuo experience is understood according to a constitutive and reciprocal relationship with the subject's cognitive profile, made up of all the human, contextual, and motivational factors impacting the processing of immersion. The role and importance of performance in virtual reality is described in this framework in such a way as to be studied methodologically. The presence–performance relationship is discussed based on previous works and analyzed in terms of attentional resources. Finally, the degree of ecological validity of the performance is described as the factor modulating the relationship between the sense of presence and performance (the Phi Angle). Limitations, applications, and test hypotheses of the model are presented. This work not only aims to help explain the conceptualization of virtual reality, but also to improve its methodological framework.
Fact Checking with Insufficient Evidence
Authors: Atanasova Pepa , Simonsen Jakob Grue , Lioma Christina , Augenstein Isabelle
Published: August 2022

Automating the fact checking (FC) process relies on information obtained from external sources. In this work, we posit that it is crucial for FC models to make veracity predictions only when there is sufficient evidence and otherwise indicate when it is not enough. To this end, we are the first to study what information FC models consider sufficient by introducing a novel task and advancing it with three main contributions. First, we conduct an in-depth empirical analysis of the task with a new fluency-preserving method for omitting information from the evidence at the constituent and sentence level. We identify when models consider the remaining evidence (in)sufficient for FC, based on three trained models with different Transformer architectures and three FC datasets. Second, we ask annotators whether the omitted evidence was important for FC, resulting in a novel diagnostic dataset, SufficientFacts1, for FC with omitted evidence. We find that models are least successful in detecting missing evidence when adverbial modifiers are omitted (21% accuracy), whereas it is easiest for omitted date modifiers (63% accuracy). Finally, we propose a novel data augmentation strategy for contrastive self-learning of missing evidence by employing the proposed omission method combined with tri-training. It improves performance for Evidence Sufficiency Prediction by up to 17.8 F1 score, which in turn improves FC performance by up to 2.6 F1 score.
Formal Language Recognition by Hard Attention Transformers: Perspectives from Circuit Complexity
Authors: Hao Yiding , Angluin Dana , Frank Robert
Published: August 2022

This paper analyzes three formal models of Transformer encoders that differ in the form of their self-attention mechanism: unique hard attention (UHAT); generalized unique hard attention (GUHAT), which generalizes UHAT; and averaging hard attention (AHAT). We show that UHAT and GUHAT Transformers, viewed as string acceptors, can only recognize formal languages in the complexity class AC0, the class of languages recognizable by families of Boolean circuits of constant depth and polynomial size. This upper bound subsumes Hahn’s (2020) results that GUHAT cannot recognize the DYCK languages or the PARITY language, since those languages are outside AC0 (Furst et al., 1984). In contrast, the non-AC0 languages MAJORITY and DYCK-1 are recognizable by AHAT networks, implying that AHAT can recognize languages that UHAT and GUHAT cannot.
High Quality Rather than High Model Probability: Minimum Bayes Risk Decoding with Neural Metrics
Authors: Freitag Markus , Grangier David , Tan Qijun , Liang Bowen
Published: August 2022

In Neural Machine Translation, it is typically assumed that the sentence with the highest estimated probability should also be the translation with the highest quality as measured by humans. In this work, we question this assumption and show that model estimates and translation quality only vaguely correlate. We apply Minimum Bayes Risk (MBR) decoding on unbiased samples to optimize diverse automated metrics of translation quality as an alternative inference strategy to beam search. Instead of targeting the hypotheses with the highest model probability, MBR decoding extracts the hypotheses with the highest estimated quality. Our experiments show that the combination of a neural translation model with a neural reference-based metric, Bleurt, results in significant improvement in human evaluations. This improvement is obtained with translations different from classical beam-search output: These translations have much lower model likelihood and are less favored by surface metrics like Bleu.
The Gordian Knot
Authors: Keller Edmond J. , Omwami Edith Mukudi , Anena Catherine Pauline
Published: August 2022

This article is a preliminary critical assessment of Uganda’s current refugee policy, ReHOPE, in the north-western part of the country. The research was based on quantitative and qualitative data gathered over six weeks in 2018 and 2019. The article is centred on national efforts to implement the UN-mandated Sustainable Development Goal #5, “gender equity and empower all women and girls”. Here the focus is on the policy as it relates to formal education, technical training and livelihood improvements and the empowerment of female South Sudan refugees in two settlements in Adjumani District. ReHOPE is a very complex, ambitious and costly programme. The data from this study seems to indicate that the goals of refugee female empowerment and improved livelihoods are not being widely realised in the two Adjumani settlements accessed. However, it is suggested that future studies of ReHOPE policies should be more extensive than the present one and researchers, employing mixed methodologies, should spend more time on the ground than a few weeks.
Ex- and Post-Evangelicalism: Recent Developments in Brazil’s Changing Religious Landscape
Authors: Barreto Raimundo , Py Fábio
Published: August 2022

This article sheds light on a phenomenon that has been called ex- or post-evangelicalism, noticed first in the USA – especially since Donald Trump’s election in 2016 – and then in Brazil, more notably in connection with the rise of Bolsonarism. Based on a series of interviews, the article examines the reasons why a number of people formerly connected to evangelical churches are ceasing to name their evangelical affiliation, particularly as the connection between important evangelical leaders and the far-right government of Jair Bolsonaro in Brazil has gained prominence in the past few years. On top of such a connection, the article shows that among the motives that drive individuals and communities away from their former evangelical identity are the instances evangelical churches have taken against religious plurality, black and indigenous cultures, and LGBTQIA+ communities.
Springbok Escapers and Evaders in the Western Desert, 1941–1942
Authors: Kleynhans Evert , Gordon Will
Published: August 2022

During the Second World War, between 1941 and 1942, a large number of South African troops were made prisoners of war (POW) by the Axis forces in the Western Desert. These troops were first interned in POW transit camps in North Africa, before being shipped to more permanent camps in Italy and later Germany. A large number of the South African captives decided to accept their newfound fates and make their internment as ‘pleasant’ as possible. However, a small nucleus of South African servicemen either tried to evade capture altogether, or, when captured, actively tried to escape. The first large scale attempts of escape and evasion by South African servicemen therefore occurred in North Africa between 1941 and 1942. This article provides an exploratory investigation into the varied experiences of the South African soldiers that either evaded capture altogether or escaped from internment in North Africa.
Higher Education and Inherent Requirements: Beyond Inherency to Coherency
Authors: Corcoran Tim , Whitburn Ben , McCandless Trevor
Published: August 2022

This paper examines how inherency, established in statements universities produce asserting the core abilities necessary to enter and qualify for their degrees, connects with employment in accredited professions, creating barriers for people with disabilities and related conditions. There is no consistent definition of inherent requirements (ir s) across higher education internationally. To assist the ongoing development of ir s, our discussion is set out across three parts. We start by reviewing the origins of ir s highlighting an inconsistency in form and content across the sector. We then provide an analysis of two ir statements from actual institutions noting how they position disability, ability, and the competencies deemed inherent to teaching and learning. Finally, we examine areas where governance and policy, teaching and learning, and employability, can potentially change how ir s are deployed in future practice. Our goal is to shift academic and work-related requirements beyond inherent possessive limitations to coherent performative prospects.
Passiflora ketura, a New Species of Passiflora Section Decaloba (Passifloraceae) from Peru
Authors: MACDOUGAL JOHN M. , TILLETT STEPHEN S.
Published: August 2022

Passiflora ketura, a new species of Passiflora (Passifloraceae) endemic to the mountain forests in Amazonas Department, Northern Peru, is described and illustrated from herbarium material. It is compared to similar species and placed in subgenus Decaloba section Decaloba.