bluuebunny/crossref_metadata_2025 huggingface dataset:
Designing mindfulness information for interaction in social media: The role of information framing, health risk perception and lay theories of health
Authors: Wang Xiaomei , Zhu Bin , Guo Qing , Wang Wenyu , Zhao Ruoxuan
Published: February 2023

In the post-pandemic era, our health is facing unprecedented challenges, and people are more willing to obtain health-related information or interact with each other than ever before. In this context, people’s interest in mindfulness information is also growing. However, not enough attention has been paid to the relationship between mindfulness information design and information interaction. The purpose of this study is to assess the impact of information design based on the gain and loss framework on people’s willingness to interact with mindfulness information, and to identify the framework for achieving better results. Through two experimental studies, we find that information design based on the framework of gains and losses can produce different effects. Specifically, the findings of the first experiment (N = 282) shows the individuals are more willing to interact mindfulness information when they are exposed to gain-framed information rather than loss-framed. In the second experiment (N = 308), we find that loss framing, compared with gain framing, led to greater health risk perception, which in turn make participants more likely to interact with mindfulness information with others. Additionally, our results show that the lay theories of health plays a moderating role in the direct effect of information framework on willingness to interact with mindfulness information in social media. When individuals hold incremental lay theories, they are more willing to interact with mindfulness information under the gain-framed information condition compared with the loss-framed information condition. However, when individuals are in entity condition, there is no significant difference in the willingness to interact with mindfulness information between the gain-framed and loss-framed information. Our studies of integrating information framework into designing mindfulness information suggest a promising strategy of health information interaction in social media.
Factors influencing smart device addiction among preschool children: An extended protection-risk model perspective
Authors: Cheng Lu , Cao Junwei
Published: February 2023

Today, users of smart devices are from increasingly younger generations, and their use is very common among preschoolers. The problem of smart device addiction in preschool children has attracted widespread attention, due to which this study explores the influencing factors in children aged 2–5 years. Based on the protection–risk model, 236 Chinese parents were surveyed, and the data was analyzed using partial least squares structural equation modeling. The findings show that parental emotion regulation significantly and negatively influences children’s depression and social withdrawal symptoms, whereas it has a significant and positive influence on parental self-control as well as outdoor intention. Also, children’s depression and social withdrawal symptoms have a significant and positive influence on their smart device addiction, whereas parental self-control and outdoor intentions have no significant influence on it. Moreover, children’s social withdrawal and depression have a mediating effect between parental emotion regulation and children’s smart device addiction, but parental self-control and outdoor intention have no mediating effect between the two. This study identifies the factors influencing children’s smart device addiction from a new perspective, providing theoretical support to address this problem of addiction.
Do you reap what you sow? Driving mechanism of supply chain transparency on consumers' indirect reciprocity
Authors: He Shaohua
Published: February 2023

Introduction

To maintain sustainable poverty alleviation in the post-pandemic world, China encourages firms to participate in the “Social Commerce Helping Farmers Project.” This study aims to explore the phenomenon of indirect reciprocity between firms, consumers, and farmers in the supply chain. This study explores how supply chain transparency stimulates indirect reciprocity among consumers through competence trust, goodwill trust, and integrity trust. Furthermore, we explore the impact of compassion and the need for social status on the model.

Methods

We fit a partial least square analysis structural equation modeling (PLS-SEM) using data from an online random vignette-based experiment questionnaire survey.

Results

Supply chain transparency of social responsibility practices asymmetrically affects three dimensions of consumer trust by improving perceived information quality. And the three dimensions of trust asymmetrically contribute to indirect reciprocity. Furthermore, compassion has a positive moderating effect on the relationship between perceived information quality and trust. However, the moderating effect of the need for social status on the relationship between the three dimensions of trust and indirect reciprocity differed significantly.

Discussion

Our findings indicate that supply chain transparency improves consumer trust, making consumers more responsive and rewarding companies that assist vulnerable groups in their supply chains. Faced with a trust crisis, companies can take different measures to achieve their goals based on each dimension of trust. At the same time, companies need to consider differences in the responses of consumers with different personality traits (e.g., compassion and the need for social status) when revealing their corporate social responsibility practices to consumers.
The sociolinguistic problems of English medium instruction in the Middle East and North Africa: Implications for epistemic access
Authors: Ntombela Berrington Xolani Siphosakhe
Published: February 2023

The global spread and dominance of English in higher education has reached alarming heights. While there has been a drive to argue for the relevance and importance of education through local languages, English has snatched the biggest slice of the cake by subtly imposing itself as the sole global language of education. This paper interrogates the sociolinguistic problems posed by the hegemony of English language. It argues that globalization and internationalization work in tandem with neo-colonial and neoliberal operations to create a class of global citizens that must support the economic aspirations of English imperial expansion and sustenance. The arguments are drawn from the experiences of Middle East and North Africa as well as lessons from Eastern and Southern Africa. The paper adopts a critical approach in order to place urgency against the onslaught of English medium of instruction in global higher education. This is done by problematizing the rhetoric of globalized and internationalized education. The paper then draws conclusions on epistemic access in the context of burgeoning knowledge economies. It argues that English medium of instruction is implicated in stalling access to knowledge for the great majority in order to cater for and safeguard the economic dominance of the elite minority.
Psychosocial predictors in upper-extremity vascularized composite allotransplantation: A qualitative study of multidimensional experiences including patients, healthcare professionals, and close relatives
Authors: Hummel Nikolas R. , Zuo Kevin J. , Talbot Simon , Zimmerman Zoe E. , Katz Jeffrey N. , Kinsley Sarah E. , Kumnig Martin
Published: February 2023

Background

The impact of patient-specific psychosocial factors on functional outcomes after upper-extremity (UE) vascularized composite allotransplantation (VCA) is poorly understood. The objective of this study was to identify relevant psychosocial predictors for success or failure of UE VCA in an Austrian cohort.

Methods

A qualitative study was undertaken consisting of semi-structured interviews with UE VCA staff, transplanted patients, and close relatives. Participants were asked about their perceptions of factors that either favored or hindered a successful transplant outcome, including functional status before surgery, preparation for transplant, decision-making, rehabilitation and functional outcome after surgery, and family and social support. Interviews were conducted online and recorded with the consent of interviewees.

Results

Four bilateral UE VCA patients, 7 healthcare professionals, and a sister of a patient participated in the study. Thematic analysis revealed the importance of an expert interdisciplinary team with adequate resources for patient selection. Psychosocial aspects of prospective candidates are crucial to evaluate as they contribute to success. Both patients and providers may be impacted by public perceptions of UE VCA. Functional outcomes are optimized with a life-long commitment to rehabilitation as well as close, ongoing provider involvement.

Conclusion

Psychosocial factors are important elements in the assessment and follow-up care for UE VCA. To best capture psychosocial elements of care, protocols must be individualized, patient-centered, and interdisciplinary. Investigating psychosocial predictors and collecting outcomes is, thus, critical to justifying UE VCA as a medical intervention and to providing accurate and salient information to prospective candidates.
Managing the negative impact of workforce diversity: The important roles of inclusive HRM and employee learning-oriented behaviors
Authors: Liu Jiakun , Zhu Yufeng , Wang Huatian
Published: February 2023

The workforce has become more diverse than it used to be. Although organizations actively capitalize on workforce diversity to enhance team innovation and organizational performance, it is found that workforce diversity also has potential risks, among which interpersonal conflict is the most salient one. However, we still know relatively less about why workforce diversity may link to higher interpersonal conflict and, more importantly, how to mitigate the negative impact of workforce diversity. Based on the workplace diversity theories (e.g., the categorization-elaboration model), this study examined how workforce diversity was positively related to interpersonal conflict through impacting one’s affective states, and to what extent this indirect effect can be weakened by organization-initiated practices (i.e., the inclusive human resources management (HRM) practices) and employee-initiated behaviors (i.e., employee learning-oriented behaviors). Using two-wave surveys from 203 employees from various organizations in China, we confirmed our hypotheses. Our results showed that perceived workforce diversity was positively related to interpersonal conflict through increasing negative affect (after we controlled for the objective diversity level calculated by the Blau index), and this indirect effect was weakened when the levels of inclusive HRM practices and employee learning-oriented behaviors were high. Our study suggests that it is important for organizations to be aware of the detrimental impact of workforce diversity. In addition, it is essential to use both the top-down (e.g., inclusive HRM practices) and bottom-up (e.g., employee learning-oriented behaviors) approaches to managing the challenges presented by diversity so as to unlock more potential of diversity in the workplace.
Cannabis-assisted psychotherapy for complex dissociative posttraumatic stress disorder: A case report
Authors: Ragnhildstveit Anya , Kaiyo Miriam , Snyder Matthew Brian , Jackson Laura Kate , Lopez Alex , Mayo Chasity , Miranda Alyssa Claire , August River Jude , Seli Paul , Robison Reid , Averill Lynnette Astrid
Published: February 2023

Background

A dissociative subtype of posttraumatic stress disorder, known as “D-PTSD”, has been included in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. In addition to meeting criteria for PTSD, patients endorse prominent dissociative symptoms, namely depersonalization and derealization, or detachment from one's self and surroundings. At present, this population is supported by a highly heterogeneous and undeveloped literature. Targeted interventions are therefore lacking, and those indicated for PTSD are limited by poor efficacy, delayed onset of action, and low patient engagement. Here, we introduce cannabis-assisted psychotherapy (CAP) as a novel treatment for D-PTSD, drawing parallels to psychedelic therapy.

Case presentation

A 28-year-old female presented with complex D-PTSD. In a naturalistic setting, she underwent 10 sessions of CAP, scheduled twice monthly over 5 months, coupled with integrative cognitive behavioral therapy. An autonomic and relational approach to CAP was leveraged, specifically psychedelic somatic interactional psychotherapy. Acute effects included oceanic boundlessness, ego dissolution, and emotional breakthrough. From baseline to post-treatment, the patient showed a 98.5% reduction in pathological dissociation, as measured by the Multidimensional Inventory of Dissociation, no longer meeting criteria for D-PTSD. This was accompanied by decreased cognitive distractibility and emotional suffering, as well as increased psychosocial functioning. Anecdotally, the patient has sustained improvements for over 2 years to date.

Conclusions

There is urgency to identify treatments for D-PTSD. The present case, while inherently limited, underscores the potential of CAP as a therapeutic option, leading to robust and sustained improvement. Subjective effects were comparable to those produced by classic and non-classic psychedelics, such as psilocybin and ketamine. Further research is warranted to explore, establish, and optimize CAP in D-PTSD, and to characterize its role in the pharmacological landscape.
Addressing COVID-19 vaccine hesitancy in rural communities: A case study in engaging trusted messengers to pivot and plan
Authors: Reichelt Melhaney , Cullen John Patrick , Mayer-Fried Sara , Russell Holly Ann , Bennett Nancy M. , Yousefi-Nooraie Reza
Published: February 2023

The pandemic declaration of COVID-19 in 2020 presented unique challenges, lessons, and opportunities for public health practice in the United States. Despite clear evidence of COVID-19 vaccine effectiveness, vaccine uptake and vaccine confidence remained low in many regions. Vaccine holdouts, or those who are vaccine hesitant, have been an increasingly difficult population to reach. Several factors influence vaccine hesitancy and behavior in rural areas, including health care access challenges, misinformation, political loyalties, and concerns regarding the perceived lack of trustworthy evidence and knowledge of long-term effects. In March 2021, the Finger Lakes Rural Immunization Initiative (FLRII) engaged stakeholders to address vaccine hesitancy in a nine-county region of rural New York known as the Finger Lakes. Driven by data collected from community partners, physicians, and local health departments regarding their biggest barriers and greatest needs, the FLRII team created an interactive program for trusted messengers (TMs) including a stakeholder panel, called the Trusted Messenger Forum (TMF). The TMF met every 2 weeks from August 2021- August 2022 to engage local TMs and disseminate up-to-date knowledge in real time. During forum sessions, TMs shared detailed accounts of their experiences combating vaccine hesitancy in their communities and supported one another in their efforts through positive interaction and reaffirming conversations. Collaborations between community stakeholders can form a scaffolding to support a rapid response to a variety of public health problems and result in impactful change. For researchers implementing community-based research projects, modeling stakeholder panels after trusted messenger forums can be effective for diversifying the scope of the project and reacting to emergent problems in real-time.
Comprehensive AI-assisted tool for ankylosing spondylitis based on multicenter research outperforms human experts
Authors: Li Hao , Tao Xiang , Liang Tuo , Jiang Jie , Zhu Jichong , Wu Shaofeng , Chen Liyi , Zhang Zide , Zhou Chenxing , Sun Xuhua , Huang Shengsheng , Chen Jiarui , Chen Tianyou , Ye Zhen , Chen Wuhua , Guo Hao , Yao Yuanlin , Liao Shian , Yu Chaojie , Fan Binguang , Liu Yihong , Lu Chunai , Hu Junnan , Xie Qinghong , Wei Xiao , Fang Cairen , Liu Huijiang , Huang Chengqian , Pan Shixin , Zhan Xinli , Liu Chong
Published: February 2023

Introduction

The diagnosis and treatment of ankylosing spondylitis (AS) is a difficult task, especially in less developed countries without access to experts. To address this issue, a comprehensive artificial intelligence (AI) tool was created to help diagnose and predict the course of AS.

Methods

In this retrospective study, a dataset of 5389 pelvic radiographs (PXRs) from patients treated at a single medical center between March 2014 and April 2022 was used to create an ensemble deep learning (DL) model for diagnosing AS. The model was then tested on an additional 583 images from three other medical centers, and its performance was evaluated using the area under the receiver operating characteristic curve analysis, accuracy, precision, recall, and F1 scores. Furthermore, clinical prediction models for identifying high-risk patients and triaging patients were developed and validated using clinical data from 356 patients.

Results

The ensemble DL model demonstrated impressive performance in a multicenter external test set, with precision, recall, and area under the receiver operating characteristic curve values of 0.90, 0.89, and 0.96, respectively. This performance surpassed that of human experts, and the model also significantly improved the experts' diagnostic accuracy. Furthermore, the model's diagnosis results based on smartphone-captured images were comparable to those of human experts. Additionally, a clinical prediction model was established that accurately categorizes patients with AS into high-and low-risk groups with distinct clinical trajectories. This provides a strong foundation for individualized care.

Discussion

In this study, an exceptionally comprehensive AI tool was developed for the diagnosis and management of AS in complex clinical scenarios, especially in underdeveloped or rural areas that lack access to experts. This tool is highly beneficial in providing an efficient and effective system of diagnosis and management.
Football training as a non-pharmacological treatment of the global aging population—A topical review
Authors: Mohr Magni , Fatouros Ioannis G. , Asghar Muhammad , Buono Pasqualina , Nassis George P. , Krustrup Peter
Published: February 2023

In the present topical mini-review, the beneficial impact of small-sided game football training for the increasing elderly global population is presented. As a multicomponent type of physical activity, football training executed on small pitched with 4–6 players in each team is targeting a myriad of physiological systems and causes positive adaptations of relevance for several non-communicable diseases, of which the incidence increases with advancing age. There is strong scientific evidence that this type of football training promotes cardiovascular, metabolic and musculo-skeletal health in elderly individuals. These positive adaptations can prevent cardiovascular disease, type 2 diabetes, sarcopenia and osteoporosis, and lower the risk of falls. Also, football training has been proven an efficient part of the treatment of several patient groups including men with prostate cancer and women after breast cancer. Finally, regular football training has an anti-inflammatory effect and may slow the biological aging. Overall, there is a growing body of evidence suggesting that recreational football training can promote health in the elderly.
Unsupervised domain adaptation for the detection of cardiomegaly in cross-domain chest X-ray images
Authors: Thiam Patrick , Lausser Ludwig , Kloth Christopher , Blaich Daniel , Liebold Andreas , Beer Meinrad , Kestler Hans A.
Published: February 2023

In recent years, several deep learning approaches have been successfully applied in the field of medical image analysis. More specifically, different deep neural network architectures have been proposed and assessed for the detection of various pathologies based on chest X-ray images. While the performed assessments have shown very promising results, most of them consist in training and evaluating the performance of the proposed approaches on a single data set. However, the generalization of such models is quite limited in a cross-domain setting, since a significant performance degradation can be observed when these models are evaluated on data sets stemming from different medical centers or recorded under different protocols. The performance degradation is mostly caused by the domain shift between the training set and the evaluation set. To alleviate this problem, different unsupervised domain adaptation approaches are proposed and evaluated in the current work, for the detection of cardiomegaly based on chest X-ray images, in a cross-domain setting. The proposed approaches generate domain invariant feature representations by adapting the parameters of a model optimized on a large set of labeled samples, to a set of unlabeled images stemming from a different data set. The performed evaluation points to the effectiveness of the proposed approaches, since the adapted models outperform optimized models which are directly applied to the evaluation sets without any form of domain adaptation.
Digital technologies in local agri-food systems: Opportunities for a more interoperable digital farmgate sector
Authors: Glaros Alesandros , Thomas David , Nost Eric , Nelson Erin , Schumilas Theresa
Published: February 2023

Agriculture e-commerce technologies are transforming how small and medium-scale farmers distribute food, consumers access local food, and market vendors negotiate sales. However, most of the social scientific literature exploring digital agriculture concentrates on big data analytics in the context of commodity farming systems and conventional supply chains. In this paper we review the social scientific literature on agriculture e-commerce technologies and situate this literature within broader debates over digital agriculture and its uneven social and economic dynamics. We find that most social scientific literature does not include agriculture e-commerce in its definition of digital agriculture, instead defining it predominantly in terms of production (e.g., variable-rate technology) or verification (e.g., blockchain) technologies. We contextualize this review with results from a series of focus groups exploring the challenges faced by Ontario's “digital farmgate sector”—the suite of agriculture e-commerce platforms that organize local food sales for hubs, farmers' markets, and small- and medium-scale farmers—related to lack of platform interoperability. We find that local food systems actors are increasingly adopting e-commerce platforms, particularly in the context of the pandemic, and observing substantial business-related benefits to their adoption. Yet, there are common frustrations with digital tools due to market fragmentation and lack of platform interoperability. We recommend the collaborative development of an open standard for e-commerce platforms that allows for the cross-platform sale of local food and farming products.
Effects of organic fertilizers produced from fish pond sediment on growth performances and yield of Malabar and Amaranthus vegetables
Authors: Thanh Dang Trung , Ty Nguyen Minh , Hien Nguyen Vinh , Berg Håkan , Nguyen Thi Kieu Oanh , Vu Pham Thanh , Minh Vo Quang , Da Chau Thi
Published: February 2023

The increasing intensification of aquaculture production requires the development of strategies to reduce its environmental impacts such as the pollution caused by the discharge of nutrient-rich sediments into local water bodies. This research was undertaken to investigate and evaluate the effect of using organic fertilizers produced from the pond sludge of freshwater snakehead fish (Channa striata) composted with organic amendments of peanut shells and coir fiber on growth performance indices and yields of Malabar spinach (Basella albaL.) andAmaranthus cruentus(AmaranthusL.) vegetables in the dry and wet seasons. An organic fertilizer quality experiment showed that the richest nutrient contents of the produced organic fertilizer were achieved when using 30% sludge mixed with 70% organic amendments (50% peanut shells + 50% coir fiber). This was selected and used for a vegetable cultivation experiment. For the reference treatment, only chemical fertilizer was applied, while in the other four treatments, 25, 50, 75, and 100% of the chemical fertilizer were substituted with the organic fertilizer. A 25–50% reduction in the chemical fertilizer application resulted in better growth performance indices and final yields than the other treatments, including the reference treatment, for both crops. The highest yields of Malabar spinach andAmaranthus cruentusvegetables were found in Treatment 3 (50% chemical fertilizer combined with 50% organic fertilizer), followed by Treatment 2 (25% organic fertilizer combined with 75% inorganic fertilizer) (P< 0.05). The results show that the reuse of sludge from snakehead fish ponds mixed with agricultural by-products as organic fertilizer for vegetables not only improves vegetable productivity but also reduces the costs of chemical fertilizer and decreases environmental pollution.
Greater trochanteric osteotomy and subtrochanteric osteotomy in primary/revision total hip arthroplasty
Authors: Pan Yuqi , Chen Yunsu
Published: February 2023

Osteotomy of the femur is necessary in some cases of primary/revision total hip arthroplasty (THA) procedure. There are two mainly used femur osteotomy methods in THA: greater trochanteric osteotomy and subtrochanteric osteotomy. Greater trochanteric osteotomy can improve hip exposure, provide greater stability against dislocation and favorably influence the abductor moment arm. Whether in the primary or revision THA, greater trochanteric osteotomy has its unique position. Subtrochanteric osteotomy adjusts the degree of femoral de-rotation and corrects the leg length. It is widely used in hip preservation and arthroplasty surgery. All osteotomy methods have specific indications, while nonunion is the commonest complication. In this paper, we analyze the greater trochanteric osteotomy and the subtrochanteric osteotomy in primary/revision THA and summarize the characteristics of different osteotomy methods.
Alignment options and robotics in total knee arthroplasty
Authors: Aflatooni Justin O. , Wininger Austin E. , Park Kwan J. , Incavo Stephen J.
Published: February 2023

Total knee arthroplasty is one of the most widely performed surgical procedures today. Its widespread popularity has helped drive innovation and improvement in the field. Different schools of thought have developed regarding the best way to perform this operation. Specifically, there are controversaries regarding the best alignment philosophy for the femoral and tibial components to optimize implant stability and longevity. Traditionally, neutral mechanical alignment has been the preferred alignment target. More recently, some surgeons advocate for alignment matching the patient's pre-arthritic anatomic alignment (“physiologic” varus or valgus), which has been described as kinematic alignment. Functional alignment is a hybrid technique that focuses on the coronal plane minimizing soft tissue releases. To date, there is no evidence demonstrating superiority of one method over another. There is growing popularity of robotic surgical techniques to improve accuracy of implant position and alignment. The choice of alignment philosophy is an important aspect of robotic assisted TKA surgery and has the potential to clarify the optimal alignment technique.
Application of perturbation gene expression profiles in drug discovery—From mechanism of action to quantitative modelling
Authors: Szalai Bence , Veres Dániel V.
Published: February 2023

High dimensional characterization of drug targets, compound effects and disease phenotypes are crucial for increased efficiency of drug discovery. High-throughput gene expression measurements are one of the most frequently used data acquisition methods for such a systems level analysis of biological phenotypes. RNA sequencing allows genome wide quantification of transcript abundances, recently even on the level of single cells. However, the correct, mechanistic interpretation of transcriptomic measurements is complicated by the fact that gene expression changes can be both the cause and the consequence of altered phenotype. Perturbation gene expression profiles, where gene expression is measured after a genetic or chemical perturbation, can help to overcome these problems by directly connecting the causal perturbations to their gene expression consequences. In this Review, we discuss the main large scale perturbation gene expression profile datasets, and their application in the drug discovery process, covering mechanisms of action identification, drug repurposing, pathway activity analysis and quantitative modelling.
3D bioprinting and the revolution in experimental cancer model systems—A review of developing new models and experiences with in vitro 3D bioprinted breast cancer tissue-mimetic structures
Authors: Sztankovics Dániel , Moldvai Dorottya , Petővári Gábor , Gelencsér Rebeka , Krencz Ildikó , Raffay Regina , Dankó Titanilla , Sebestyén Anna
Published: February 2023

Growing evidence propagates those alternative technologies (relevant human cell-based—e.g., organ-on-chips or biofabricated models—or artificial intelligence-combined technologies) that could helpin vitrotest and predict human response and toxicity in medical research more accurately.In vitrodisease model developments have great efforts to create and serve the need of reducing and replacing animal experiments and establishing human cell-basedin vitrotest systems for research use, innovations, and drug tests. We need human cell-based test systems for disease models and experimental cancer research; therefore,in vitrothree-dimensional (3D) models have a renaissance, and the rediscovery and development of these technologies are growing ever faster. This recent paper summarises the early history of cell biology/cellular pathology, cell-, tissue culturing, and cancer research models. In addition, we highlight the results of the increasing use of 3D model systems and the 3D bioprinted/biofabricated model developments. Moreover, we present our newly established 3D bioprinted luminal B type breast cancer model system, and the advantages ofin vitro3D models, especially the bioprinted ones. Based on our results and the reviewed developments ofin vitrobreast cancer models, the heterogeneity and the realin vivosituation of cancer tissues can be represented better by using 3D bioprinted, biofabricated models. However, standardising the 3D bioprinting methods is necessary for future applications in different high-throughput drug tests and patient-derived tumour models. Applying these standardised new models can lead to the point that cancer drug developments will be more successful, efficient, and consequently cost-effective in the near future.
Tolerance Induction in Vascularized Composite Allotransplantation—A Brief Review of Preclinical Models
Authors: Huelsboemer Lioba , Kauke-Navarro Martin , Reuter Stefan , Stoegner Viola A. , Feldmann Jan , Hirsch Tobias , Kueckelhaus Maximilian , Dermietzel Alexander
Published: February 2023

Pre-clinical studies are an obligatory tool to develop and translate novel therapeutic strategies into clinical practice. Acute and chronic rejection mediated by the recipient’s immune system remains an important limiting factor for the (long-term) survival of vascularized composite allografts (VCA). Furthermore, high intensity immunosuppressive (IS) protocols are needed to mitigate the immediate and long-term effects of rejection. These IS regiments can have significant side-effects such as predisposing transplant recipients to infections, organ dysfunction and malignancies. To overcome these problems, tolerance induction has been proposed as one strategy to reduce the intensity of IS protocols and to thereby mitigate long-term effects of allograft rejection. In this review article, we provide an overview about animal models and strategies that have been used to induce tolerance. The induction of donor-specific tolerance was achieved in preclinical animal models and clinical translation may help improve short and long-term outcomes in VCAs in the future.
A Novel State Estimation Approach for Suspension System with Time-Varying and Unknown Noise Covariance
Authors: Li Qiangqiang , Chen Zhiyong , Shi Wenku
Published: February 2023

In this paper, a novel state estimation approach based on the variational Bayesian adaptive Kalman filter (VBAKF) and road classification is proposed for a suspension system with time-varying and unknown noise covariance. Using the VB approach, the time-varying noise covariance can be inferred from the inverse-Wishart distribution and then optimized state estimation by the finite sampling posterior probability distribution function (PDF) of noise covariance and backward Kalman smoothing. In addition, a new road classification algorithm based on multi-objective optimization and the linear classifier is proposed to identify the unknown noise covariance. Simulation results for a suspension model with time-varying and unknown noise covariance show that the proposed approach has a higher performance in state estimation accuracy than other filters.
Evaluation Metrics Research for Explainable Artificial Intelligence Global Methods Using Synthetic Data
Authors: Oblizanov Alexandr , Shevskaya Natalya , Kazak Anatoliy , Rudenko Marina , Dorofeeva Anna
Published: February 2023

In recent years, artificial intelligence technologies have been developing more and more rapidly, and a lot of research is aimed at solving the problem of explainable artificial intelligence. Various XAI methods are being developed to allow the user to understand the logic of how machine learning models work, and in order to compare the methods, it is necessary to evaluate them. The paper analyzes various approaches to the evaluation of XAI methods, defines the requirements for the evaluation system and suggests metrics to determine the various technical characteristics of the methods. A study was conducted, using these metrics, which determined the degradation in the explanation quality of the SHAP and LIME methods with increasing correlation in the input data. Recommendations are also given for further research in the field of practical implementation of metrics, expanding the scope of their use.
A Review of DC Fast Chargers with BESS for Electric Vehicles: Topology, Battery, Reliability Oriented Control and Cooling Perspectives
Authors: Polat Hakan , Hosseinabadi Farzad , Hasan Md. Mahamudul , Chakraborty Sajib , Geury Thomas , El Baghdadi Mohamed , Wilkins Steven , Hegazy Omar
Published: February 2023

The global promotion of electric vehicles (EVs) through various incentives has led to a significant increase in their sales. However, the prolonged charging duration remains a significant hindrance to the widespread adoption of these vehicles and the broader electrification of transportation. While DC-fast chargers have the potential to significantly reduce charging time, they also result in high power demands on the grid, which can lead to power quality issues and congestion. One solution to this problem is the integration of a battery energy storage system (BESS) to decrease peak power demand on the grid. This paper presents a review of the state-of-the-art use of DC-fast chargers coupled with a BESS. The focus of the paper is on industrial charger architectures and topologies. Additionally, this paper presents various reliability-oriented design methods, prognostic health monitoring techniques, and low-level/system-level control methods. Special emphasis is placed on strategies that can increase the lifetime of these systems. Finally, the paper concludes by discussing various cooling methods for power electronics and stationary/EV batteries.
rAAV Manufacturing: The Challenges of Soft Sensing during Upstream Processing
Authors: Iglesias Cristovão Freitas , Ristovski Milica , Bolic Miodrag , Cuperlovic-Culf Miroslava
Published: February 2023

Recombinant adeno-associated virus (rAAV) is the most effective viral vector technology for directly translating the genomic revolution into medicinal therapies. However, the manufacturing of rAAV viral vectors remains challenging in the upstream processing with low rAAV yield in large-scale production and high cost, limiting the generalization of rAAV-based treatments. This situation can be improved by real-time monitoring of critical process parameters (CPP) that affect critical quality attributes (CQA). To achieve this aim, soft sensing combined with predictive modeling is an important strategy that can be used for optimizing the upstream process of rAAV production by monitoring critical process variables in real time. However, the development of soft sensors for rAAV production as a fast and low-cost monitoring approach is not an easy task. This review article describes four challenges and critically discusses the possible solutions that can enable the application of soft sensors for rAAV production monitoring. The challenges from a data scientist’s perspective are (i) a predictor variable (soft-sensor inputs) set without AAV viral titer, (ii) multi-step forecasting, (iii) multiple process phases, and (iv) soft-sensor development composed of the mechanistic model.
Leveraging Scheme for Cross-Study Microbiome Machine Learning Prediction and Feature Evaluations
Authors: Song Kuncheng , Zhou Yi-Hui
Published: February 2023

The microbiota has proved to be one of the critical factors for many diseases, and researchers have been using microbiome data for disease prediction. However, models trained on one independent microbiome study may not be easily applicable to other independent studies due to the high level of variability in microbiome data. In this study, we developed a method for improving the generalizability and interpretability of machine learning models for predicting three different diseases (colorectal cancer, Crohn’s disease, and immunotherapy response) using nine independent microbiome datasets. Our method involves combining a smaller dataset with a larger dataset, and we found that using at least 25% of the target samples in the source data resulted in improved model performance. We determined random forest as our top model and employed feature selection to identify common and important taxa for disease prediction across the different studies. Our results suggest that this leveraging scheme is a promising approach for improving the accuracy and interpretability of machine learning models for predicting diseases based on microbiome data.
Artificial Neural Network Models for Accurate Predictions of Fat-Free and Fat Masses, Using Easy-to-Measure Anthropometric Parameters
Authors: Mitu Ivona , Dimitriu Cristina-Daniela , Mitu Ovidiu , Preda Cristina , Mitu Florin , Ciocoiu Manuela
Published: February 2023

Abdominal fat and fat-free masses report a close association with cardiometabolic risks, therefore this specific body compartment presents more interest than whole-body masses. This research aimed to develop accurate algorithms that predict body masses and specifically trunk fat and fat-free masses from easy to measure parameters in any setting. The study included 104 apparently healthy subjects, but with a higher-than-normal percent of adiposity or waist circumference. Multiple linear regression (MLR) and artificial neural network (ANN) models were built for predicting abdominal fat and fat-free masses in patients with relatively low cardiometabolic risks. The data were divided into training, validation and test sets, and this process was repeated 20 times per each model to reduce the bias of data division on model accuracy. The best performance models used a maximum number of five anthropometric inputs, with higher R2 values for ANN models than for MLR models (R2 = 0.96–0.98 vs. R2 = 0.80–0.94, p = 0.006). The root mean square error (RMSE) for all predicted parameters was significantly lower for ANN models than for MLR models, suggesting a higher accuracy for ANN models. From all body masses predicted, trunk fat mass and fat-free mass registered the best performance with ANN, allowing a possible error of 1.84 kg for predicting the correct trunk fat mass and 1.48 kg for predicting the correct trunk fat-free mass. The developed algorithms represent cost-effective prediction tools for the most relevant adipose and lean tissues involved in the physiopathology of cardiometabolic risks.
Development of a Sensitive Digital Droplet PCR Screening Assay for the Detection of GPR126 Non-Coding Mutations in Bladder Cancer Urine Liquid Biopsies
Authors: Jain Mark , Tivtikyan Alexander , Kamalov David , Avdonin Savva , Rakhmatullin Tagir , Pisarev Eduard , Zvereva Maria , Samokhodskaya Larisa , Kamalov Armais
Published: February 2023

Recent whole-genome sequencing studies identified two novel recurrent mutations in the enhancer region of GPR126 in urothelial bladder cancer (UBC) tumor samples. This mutational hotspot is the second most common after the TERT promoter in UBC. The aim of the study was to develop a digital droplet PCR screening assay for the simultaneous detection of GPR126 mutations in a single tube. Its performance combined with TERT promoter mutation analysis was evaluated in urine of healthy volunteers (n = 50) and patients with cystitis (n = 22) and UBC (n = 70). The developed assay was validated using DNA constructs carrying the studied variants. None of the mutations were detected in control and cystitis group samples. GPR126 mutations were observed in the urine of 25/70 UBC patients (area under the ROC curve (AUC) of 0.679; mutant allele fraction (MAF) of 21.61 [8.30–44.52] %); TERT mutations–in 40/70 (AUC of 0.786; MAF = 28.29 [19.03–38.08] %); ≥1 mutation–in 47/70 (AUC of 0.836)). The simultaneous presence of GPR126 and TERT mutations was observed in 18/70 cases, with no difference in MAFs for the paired samples (31.96 [14.78–47.49] % vs. 27.13 [17.00–37.62] %, p = 0.349, respectively). The combined analysis of these common non-coding mutations in urine allows the sensitive and non-invasive detection of UBC.
Does the SARS-CoV-2 Spike Receptor-Binding Domain Hamper the Amyloid Transformation of Alpha-Synuclein after All?
Authors: Stroylova Yulia , Konstantinova Anastasiia , Stroylov Victor , Katrukha Ivan , Rozov Fedor , Muronetz Vladimir
Published: February 2023

Interactions of key amyloidogenic proteins with SARS-CoV-2 proteins may be one of the causes of expanding and delayed post-COVID-19 neurodegenerative processes. Furthermore, such abnormal effects can be caused by proteins and their fragments circulating in the body during vaccination. The aim of our work was to analyze the effect of the receptor-binding domain of the coronavirus S-protein domain (RBD) on alpha-synuclein amyloid aggregation. Molecular modeling showed that the predicted RBD complex with monomeric alpha-synuclein is stable over 100 ns of molecular dynamics. Analysis of the interactions of RBD with the amyloid form of alpha-synuclein showed that during molecular dynamics for 200 ns the number of contacts is markedly higher than that for the monomeric form. The formation of the RBD complex with the alpha-synuclein monomer was confirmed immunochemically by immobilization of RBD on its specific receptor ACE2. Changes in the spectral characteristics of the intrinsic tryptophans of RBD and hydrophobic dye ANS indicate an interaction between the monomeric proteins, but according to the data of circular dichroism spectra, this interaction does not lead to a change in their secondary structure. Data on the kinetics of amyloid fibril formation using several spectral approaches strongly suggest that RBD prevents the amyloid transformation of alpha-synuclein. Moreover, the fibrils obtained in the presence of RBD showed significantly less cytotoxicity on SH-SY5Y neuroblastoma cells.
Contribution of smFRET to Chromatin Research
Authors: Sengupta Bhaswati , Huynh Mai
Published: February 2023

Chromatins are structural components of chromosomes and consist of DNA and histone proteins. The structure, dynamics, and function of chromatins are important in regulating genetic processes. Several different experimental and theoretical tools have been employed to understand chromatins better. In this review, we will focus on the literatures engrossed in understanding of chromatins using single-molecule Förster resonance energy transfer (smFRET). smFRET is a single-molecule fluorescence microscopic technique that can furnish information regarding the distance between two points in space. This has been utilized to efficiently unveil the structural details of chromatins.
Energetic Valorization of Bio-Waste from Municipal Solid Waste in Porto Santo Island
Authors: Pereira Fabíola , Silva Carlos
Published: February 2023

The valorization of bio-waste as a resource for green energy production will be beneficial at a social, economic, and environmental level in different regions. The scope of this research is to develop the energetic valorization of the bio-waste fraction from municipal solid waste, to produce biogas from the anaerobic digestion process and electricity in a biogas CHP process, to increase the penetration of renewables in the electricity production system, with an application of these technologies on islands instead of these waste fractions being transported to other regions. The methodologies developed included: 1. Identification and mapping of resources; 2. State-of-the-art of bio-waste parameters and process solutions; 3. Pilot initiative for separation, collection, and analysis of food waste fractions; 4. Development of process solutions according to resources and needs; 5. Determination of investment, production costs, and revenues of the solution created. The case study selected was Porto Santo Island, with the potential to reduce maritime transportation costs of these undifferentiated waste fractions to Madeira Island and contribute to developing an innovative solution for the energetic valorization of bio-waste, including the participation of the local community. The results demonstrated a production of 272,221 m3 of biogas for use as fuel in a cogeneration unit to transform chemical energy into electrical and thermal energies. Furthermore, the self-consumption of the cogeneration unit is 25% of the total electricity produced and 29% of the total thermal energy produced. In conclusion, this research and solution is in compliance with PNEC, the EU Green Deal, and the European Directive 2018/851, which will make mandatory from 2024: the selective collection of bio-waste, 60% by weight of urban waste to be recycled by 2030, and only 10% of waste to be landfilled by 2035.