Literatura académica sobre el tema "University of Pittsburgh. Knowledge Availability Systems Center"

Burkart Holzner is Distinguished Service Professor of International Studies, Professor of Sociology and of Public and International Affairs at the University of Pittsburgh. For two decades he was the Director of the University Center for International Studies at the University of Pittsburgh. He wrote about knowledge systems in society, and about the roles of knowledge use in modernity. His recent work is on international studies and global change. Currently he works with Leslie Holzner on a long-term project to explain the causes and consequences of the rise of transparency in global change. Dr Holzner is a member of the World Society for Ekistics (WSE). Leslie Holzner is a sociologist who has worked for the past several years with her husband on the issue of transparency and other global phenomena. She spent 30 years at the University of Pittsburgh where she was Assistant Director of the Learning Research and Development Center. Her research and development activity has centered on planned change, and restructuring organizations, with an emphasis on educational institutions. The text that follows is a slightly edited and revised version of a paper prepared for the WSE Symposion "Defining Success of the City in the 21st Century," Berlin, 24-28 October, 2001.

The present study investigated the current status of integrating ICT into teaching and learning at Sultan Qaboos University (SQU). A sample of 220 faculty members from six different colleges and four administrators from the Center of Educational Technology (CET) and the Center for Information Systems (CIS) at SQU in Oman were chosen, and quantitative qualitative design using a semi-structured questionnaire, interviews and checklists was employed. The findings show that SQU had a high availability of ICT infrastructure in terms of hardware, software and support services, as well as adequate computer labs for educational purposes. However, the results also indicated that, although SQU provided a series of professional development workshops related to using ICT in teaching, few faculty members were interested. Furthermore, the finding indicated that, the degree of ICT integration into teaching at SQU was at a medium level. Given the importance of ICT integration in teaching and learning, it is recommended that SQU should recognise and acknowledge the faculty members’ role in ICT integration. Therefore, it should provide ways to support faculty members in developing their ICT skills and knowledge and increasing capability in performing their role.

Purpose – Drawing on the scientific literature, the purpose of this paper is to elucidate the harmful effects of toxic stress on the developing brain. It explains how severe, chronic adversity during development, in the absence of responsive caregiving, can impair brain architecture. It also outlines policy implications for preventing or mitigating the effects of toxic stress in early childhood. Design/methodology/approach – The National Scientific Council on the Developing Child, based at the Center on the Developing Child at Harvard University, is a multidisciplinary, multiuniversity panel of scholars that seeks to bring science to bear on public decision making. Council members selected excessive stress as a topic meriting translation for a general audience and conducted extensive peer review in drafting the paper's key scientific concepts. Findings – The paper discusses how healthy development can be derailed by excessive or prolonged activation of the biological stress response systems and how that increases lifetime risk for certain behavioural and physiological disorders. It finds that supportive relationships with caregivers can help buffer the negative consequences of toxic stress. Social implications – The paper calls for improvements to family support programmes, mental health services, and the quality and availability of early care and education. Originality/value – This paper describes an original taxonomy of positive, tolerable, and toxic stress and demonstrates the need to translate scientific knowledge about the developing brain into actionable strategies for the prevention and treatment of the effects of adverse childhood experiences.

23 Background: Advances have been made in elevating cancer survivorship as a public health priority and defining elements needed to deliver high-quality follow-up care to survivors. However, a lack of research on how best to care for survivors and the most effective and efficient strategies for delivering survivorship care in the community setting still exists. We report our assessment of the current state of practice, knowledge and professional development, and plan to increase access to care of urban and rural practices across the state of Kansas. Methods: In 2014, the Midwest Cancer Alliance (MCA), a membership-based outreach arm of The University of Kansas Cancer Center, convened an educational summit and survey to assess the survivorship landscape in Kansas. Post-summit, individual interviews were conducted. Survey and interviews included questions regarding health records, treatment summaries, survivorship care plans (SCP), availability of survivorship programs and resources, access to primary care and specialists, distress screening, community support, and educational needs. Results: Ten MCA member health systems were invited to participate and 7 indicated interest in participating in the project. Only one organization provided an SCP to survivors. Barriers included lack of an integrated approach and knowledge. A majority of survivorship care could be delivered close to home, however, services including fertility preservation, genetic counseling, oncology rehab, sexual health, and second opinions, required travel of more than 50 miles. Identified educational needs focused on comprehensive survivorship care across the health care team. Conclusions: Survivorship care remains fragmented across the state of Kansas. Based on this project, we have secured a CDC survivorship grant that will facilitate clinical and technical assistance related to process improvement and electronic health record integration focused on survivorship care and delivery of an SCP. Next steps include engaging primary care providers and survivors to assure the SCP meets the needs of stakeholders. This work will focus on a translational process to meet the growing needs of the survivors and complex health care organizations.

Background: Sickle cell disease (SCD) is the most common inherited hemoglobinopathy worldwide. The pathophysiology of the disease results in end organ damage which leads to morbidity and mortality. In a subset of patients, SCD-related complications have resulted in prolonged hospitalizations and increased frequency of 30-day hospital readmissions. In the era of value-based health care, hospital quality metrics and reimbursements are generated based on strategic health care utilization. Therefore, being able to identify early unplanned hospital readmissions is critical in managing health care expenditure. Objective: To develop machine learning algorithms for predicting the 30-day unplanned readmission risk of SCD patients and to compare the predictive power of machine learning models against standard hospital readmission scoring systems. Methods: We analyzed retrospective real-world electronic health records (EHR) data for patients with SCD at our institution from January 1, 2013- November 1, 2018. The raw data set contained 2824 unique SCD patients from across 5 hospitals within the University of Pittsburgh Medical Center. After preprocessing using our inclusion criteria, our cohort included 3299 admissions comprising of 446 adult SCD patients. Features extracted from the EHR data were reduced and regrouped using both data-driven methods and clinical knowledge, resulting in 486 unique features. Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) were applied to predict for 30-day unplanned hospital readmissions in SCD. Prediction performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity and specificity. We compared our results against standard hospital readmission prediction tools such as LACE and HOSPITAL indices. Results: We randomly selected the inpatient admissions incurred by 30 percent of the 195 return patients and 251 nonreturn patients to be included in the testing set (n = 134); the training set contained the inpatient admissions incurred by the remaining 211 patients. Thus, our training and testing sets contained similar demographic information, predictors, and outcomes. The average number of admissions was 7.40 (12.90) for the 446 patients, and 14.47 (16.97) for the 195 patients who had 30-day readmissions. Since the number of samples in our study is comparatively small, our results might be sensitive to the training and testing splits. To address this problem, we performed 100 different training and testing splits and averaged the resulting 100 AUCs. Figure 1 summarizes the two performance metrics of each model. The two benchmark prediction tools, LACE and HOSPITAL, have AUCs of 0.56 (95%CI 0.52-0.60) and 0.63 (95%CI 0.59-0.67), respectively. Notably, all three machine learning algorithms outperformed both benchmarks. The RF was the best machine learning model in prediction of hospital readmissions, as reported in similar machine learning studies (Deschepper et al. 2019), with an AUC of 0.73 (95%CI 0.69-0.76). Table 1 summarizes the sensitivity and specificity of our RF model. Conclusion: Machine learning algorithms outperformed the standard hospital readmission risk scoring systems, LACE and HOSPITAL, by a large margin in a real world data set of SCD patients at a single institution. In particular, machine learning algorithms were able to identify important variables that are underrepresented in the traditional risk scoring systems (Figure 2). The use of machine learning algorithms can be a powerful tool in providing valuable insight towards health care expenditure and resource allocation in high risk patient groups. Disclosures No relevant conflicts of interest to declare.

<p class="p0">Housing and business centers are growing rapidly in the urban area. This causes agricultural land and other green lands increasingly narrow due to land conversion. One area affected by land conversion is Lengkong Kulon Village, Tangerang Regency, where most of the agricultural land has been turned into a residential area and business center. This condition has an impact on food availability which is getting lower and environmental quality is declining. To overcome these problems, Surya University, supported by the Ministry of Research, Technology, and Higher Education (Kemenristekdikti), organized community service activities by pioneering sustainable green villages and adequate nutrition through vertical farming. This program was implemented by the workshop and participatory community. The number of participants involved in this activity was more than 25 people from youth groups concerned about the environment, PKK members, and university students. This activity consisted of three stages: Community awareness building, Vertical and organic farming training, and Providing vertical farming equipment assistance packages. The result of this community service showed that participants knowledge about organic farming and vertical farming increased from 26% to 86% and from 11% to 72%, respectively. In addition, the level of community skills to apply organic farming systems also increased from 15% to 80%, and for vertical farming increased from 7% to 60%. This activity is expected to improve environmental conditions as well as food access for target communities through consumption of sufficients vegetables and fruits grown using the vertical farming system. </p><p class="p0"><strong>Bahasa Indonesia Abstrak</strong>: Abstrak Pesatnya pembangunan komplek perumahan dan pusat bisnis di perkotaan telah menyebabkan semakin sempitnya lahan pertanian dan lahan hijau lainnya akibat alih fungsi lahan. Salah satu wilayah yang terkena dampak alih fungsi lahan adalah Kampung Lengkong Kulon, Desa Lengkong Kulon, Kabupaten Tangerang di mana sebagian besar lahan pertaniannya telah berubah menjadi kawasan perumahan dan pusat bisnis. Kondisi ini telah berimplikasi pada ketersediaan pangan yang semakin rendah serta kualitas lingkungan yang menurun. Untuk mengatasi permasalahan tersebut, Universitas Surya yang didukung oleh Kementristekdikti menyelenggarakan kegiatan pengabdian masyarakat dengan tema rintisan kampung hijau lestari dan cukup gizi melalui vertical farming. Program ini dilaksanakan menggunakan model workshop dan participatory community. Jumlah peserta yang terlibat dalam kegiatan ini yaitu lebih dari 25 orang, yang berasal dari kelompok pemuda peduli lingkungan, ibu-ibu PKK, anggota posyandu, serta kelompok mahasiswa. Tahapan kegiatan ini meliputi pembangunan kesadaran masyarakat (community awareness), pelatihan vertical farming dan pertanian organik, serta pemberian paket bantuan peralatan vertical farming. Hasil evaluasi pelatihan menunjukkan bahwa terjadi peningkatan pengetahuan masyarakat tentang pertanian organik dari semula hanya 26% menjadi 86%, sedangkan untuk vertical farming dari semula 11% meningkat menjadi 72%. Tingkat keterampilan masyarakat untuk menerapkan sistem pertanian organik juga meningkat dari semula hanya 15% menjadi 80%, sementara untuk untuk vertical farming dari semula 7% meningkat menjadi 60%. Kegiatan ini diharapkan dapat memperbaiki kondisi lingkungan serta adanya peningkatan akses pangan bagi masyarakat sasaran melalui konsumsi sayuran dan buahbuahan yang cukup, yang ditanam menggunakan sistem vertical farming.</p>

Abstract Overview of Proposed Research. Head and neck cancers are a deadly cancer that ranks among the six most common cancers worldwide. (American Cancer Society, 2021). Studies have shown greater disease burden among minority populations for head and neck cancer, higher mortality rates and lower oral cancer knowledge, but limited evidence has defined the underlying causes of late stage diagnoses and access to the healthcare system (Suzuki et al, 2019). More specifically, in comparison to the national rates, South Carolina is among the top ten for head and neck cancers (Community Outreach, Hollings Cancer Center, MUSC, 2019). Dental and primary care play a critical role delivering quality patient education, prevention, risk reduction and treatment regimes. However, there is a paucity of information published on the value of designing and implementing a health literacy program to prioritize head and neck cancer screening and prevention. Rural communities face unique challenges to achieving optimal oral health, impacted mainly by geographic location and socioeconomic status (IOM&NRC, 2011). More specifically, rural southern states such as SC continues to have a tremendous shortage of primary care providers. SC currently has 44 of 46 counties designated as geographic Primary Care and Dental Health Professional Shortage Areas and approximately 25% of SC citizenry are living in rural areas (HRSA, 2018). Because the vast majority of the state has challenges with availability of primary care and dental providers, this potentially exacerbates access to care inequities for rural and underserved minority populations. To enhance the quality and equity of oral cancer prevention in rural SC, it is critical that we prioritize strategies to elevate the significance of head and neck cancer risks. Therefore, the need to design and deliver innovative strategies to increase opportunities that intersect the healthcare system and community is inevitable. In this regard, we propose PULL A-HEAD, Pursuing Leadership in Literacy to Ameliorate HEAd and neck cancer Disparities. PULL A-HEAD is aimed to develop and implement a community-centered approach design for increasing the health literacy and efficacy of navigating the healthcare system for early detection of head and neck cancers. In this presentation we will share outcome and impact results of a community-centered, health literacy program in collaboration with our Regional Medical Library - Region 2 (RML2), National Library of Medicine partners at the Medical University of South Carolina. The program (i.e. PULL A-HEAD) will emphasize the delivery of health literacy education and health systems navigation tools to improve the early detection of head and neck cancers. Citation Format: Joni D. Nelson, Irene M. Lubker. Pursuing leadership in literacy to ameliorate head and neck cancer disparities [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 998.

Background The introduction of health information technology (HIT) has drastically changed health care organizations and the way health care professionals work. Some health care professionals have trouble coping efficiently with the demands of HIT and the personal and professional changes it requires. Lagging in digital knowledge and skills hampers health care professionals from adhering to professional standards regarding the use of HIT and may cause professional performance problems, especially in the older professional population. It is important to gain more insight into the reasons and motivations behind the technology issues experienced by these professionals, as well as to explore what could be done to solve them. Objective Our primary research objective was to identify factors that influence the adoption of HIT in a sample of nurses who describe themselves as digitally lagging behind the majority of their colleagues in their workplaces. Furthermore, we aimed to formulate recommendations for practice and leadership on how to help and guide these nurses through ongoing digital transformations in their health care work settings. Methods In a Dutch university medical center, 10 face-to-face semi-structured interviews were performed with registered nurses (RN). Ammenwerth’s FITT-framework (fit between the Individual, Task, and Technology) was used to guide the interview topic list and to formulate themes to explore. Thematic analysis was used to analyze the interview data. The FITT-framework was also used to further interpret and clarify the interview findings. Results Analyses of the interview data uncovered 5 main categories and 12 subthemes. The main categories were: (1) experience with digital working, (2) perception and meaning, (3) barriers, (4) facilitators, and (5) future perspectives. All participants used electronic devices and digital systems, including the electronic health record. The latter was experienced by some as user-unfriendly, time-consuming, and not supportive in daily professional practice. Most of the interviewees described digital working as “no fun at all,” “working in a fake world,” “stressful,” and “annoying.” There was a lack of general digital knowledge and little or no formal basic digital training or education. A negative attitude toward computer use and a lack of digital skills contributed to feelings of increased incompetency and postponement or avoidance of the use of HIT, both privately and professionally. Learning conditions of digital training and education did not meet personal learning needs and learning styles. A positive impact was seen in the work environment when colleagues and nurse managers were aware and sensitive to the difficulties participants experienced in developing digital skills, and when there was continuous training on the job and peer support from digitally savvy colleagues. The availability of a digital play environment combined with learning on the job and support of knowledgeable peers was experienced as helpful and motivating by participants. Conclusions Nurses who are digitally lagging often have had insufficient and ineffective digital education. This leads to stress, frustration, feelings of incompetency, and postponement or avoidance of HIT use. A digital training approach tailored to the learning needs and styles of these nurses is needed, as well as an on-the-job training structure and adequate peer support. Hospital management and nurse leadership should be informed about the importance of the fit between technology, task, and the individual for adequate adoption of HIT.

Modern technologies and various domains of human activities increasingly rely on data science to develop smarter and autonomous systems. This trend has already changed the whole landscape of the global economy becoming more AI-driven. Massive production of data by humans and machines, its availability for feasible processing with advent of deep learning infrastructures, combined with advancements in reliable information transfer capacities, open unbounded horizons for societal progress in close future. Quite naturally, this brings also new challenges for science and industry. In that context, Internet of things (IoT) is an enormously huge factory of monitoring and data generation. It enables countless devices to act as sensors which record and manipulate data, while requiring efficient algorithms to derive actionable knowledge. Billions of end-users equipped with smart mobile phones are also producing immensely large volumes of data, being it about user interaction or indirect telemetry such as location coordinates. Social networks represent another kind of data-intensive sources, with both structured and unstructured components, containing valuable information about world&rsquo;s connectivity, dynamism, and more. Last but not least, to help businesses run smoothly, today&rsquo;s cloud computing infrastructures and applications are also serviced and managed through measuring huge amounts of data to leverage in various predictive and automation tasks for healthy performance and permanent availability. Therefore, all these technology areas, experts and practitioners, are facing innovation challenges on building novel methodologies, accurate models, and systems for respective data-driven solutions which are effective and efficient. In view of the complexity of contemporary neural network architectures and models with millions of parameters they derive, one of such challenges is related to the concept of explainability of the machine learning models. It refers to the ability of the model to give information which can be interpreted by humans about the reasons for the decision made or recommendation released. These challenges can only be met with a mix of basic research, process modeling and simulation under uncertainty using qualitative and quantitative methods from the involved sciences, and taking into account international standards and adequate evaluation methods. Based on a successful funded collaboration between the American University of Armenia, the University of Duisburg-Essen and the University of Chile, in previous years a network was built, and in September 2020 a group of researchers gathered (although virtually) for the 2nd CODASSCA workshop on &ldquo;Collaborative Technologies and Data Science in Smart City Applications&rdquo;. This event has attracted 25 paper submissions which deal with the problems and challenges mentioned above. The studies are in specialized areas and disclose novel solutions and approaches based on existing theories suitably applied. The authors of the best papers published in the conference proceedings on Collaborative Technologies and Data Science in Artificial Intelligence Applications by Logos edition Berlin were invited to submit significantly extended and improved versions of their contributions to be considered for a journal special issue of J.UCS. There was also a J.UCS open call so that any author could submit papers on the highlighted subject. For this volume, we selected those dealing with more theoretical issues which were rigorously reviewed in three rounds and 6 papers nominated to be published. The editors would like to express their gratitude to J.UCS foundation for accepting the special issues in their journal, to the German Research Foundation (DFG), the German Academic Exchange Service (DAAD) and the universities and sponsors involved for funding the common activities and thank the editors of the CODASSCA2020 proceedings for their ongoing encouragement and support, the authors for their contributions, and the anonymous reviewers for their invaluable support. The paper &ldquo;Incident Management for Explainable and Automated Root Cause Analysis in Cloud Data Centers&rdquo; by Arnak Poghosyan, Ashot Harutyunyan, Naira Grigoryan, and Nicholas Kushmerick addresses an increasingly important problem towards autonomous or self-X systems, intelligent management of modern cloud environments with an emphasis on explainable AI. It demonstrates techniques and methods that greatly help in automated discovery of explicit conditions leading to data center incidents. The paper &ldquo;Temporal Accelerators: Unleashing the Potential of Embedded FPGAs&rdquo; by Christopher Cichiwskyj and Gregor Schiele presents an approach for executing computational tasks that can be split into sequential sub-tasks. It divides accelerators into multiple, smaller parts and uses the reconfiguration capabilities of the FPGA to execute the parts according to a task graph. That improves the energy consumption and the cost of using FPGAs in IoT devices. The paper &ldquo;On Recurrent Neural Network based Theorem Prover for First Order Minimal Logic&rdquo; by Ashot Baghdasaryan and Hovhannes Bolibekyan investigates using recurrent neural networks to determine the order of proof search in a sequent calculus for first-order minimal logic with a history mechanism. It demonstrates reduced durations in automated theorem proving systems.&nbsp; The paper &ldquo;Incremental Autoencoders for Text Streams Clustering in Social Networks&rdquo; by Amal Rekik and Salma Jamoussi proposes a deep learning method to identify trending topics in a social network. It is built on detecting changes in streams of tweets. The method is experimentally validated to outperform relevant data stream algorithms in identifying &ldquo;hot&rdquo; topics. The paper &ldquo;E-Capacity&ndash;Equivocation Region of Wiretap Channel&rdquo; by Mariam Haroutunian studies a secure communication problem over the wiretap channel, where information transfer from the source to a legitimate receiver needs to be realized maximally secretly for an eavesdropper. This is an information-theoretic research which generalizes the capacity-equivocation region and secrecy-capacity function of the wiretap channel subject to error exponent criterion, thus deriving new and extended fundamental limits in reliable and secure communication in presence of a wiretapper. The paper &ldquo;Leveraging Multifaceted Proximity Measures among Developers in Predicting Future Collaborations to Improve the Social Capital of Software Projects&rdquo; by Amit Kumar and Sonali Agarwal targets improving the social capital of individual software developers and projects using machine learning. Authors&rsquo; approach applies network proximity and developer activity features to build a classifier for predicting the future collaborations among developers and generating relevant recommendations.

Translational Medicine and Exercise Prescription (TMEP): Advancing the Era of Exercise Medicine Sulin Cheng1,2,3,4, Moritz Schumann()2,4 and Wilhelm Bloch4 1Exercise, Health and Technology Center, Department of Physical Education, Shanghai Jiao Tong University, Shanghai, China 2Exercise Translational Medicine Center, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China 3Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland 4Department of Molecular and Cellular Sport Medicine, German Sport University, Cologne, Germany © The Authors "Exercise is medicine" has gained popularity worldwide after the American Medical Association and the American College of Sports Medicine co-launched their ground-breaking health initiative in 20071 . This initiative was aimed at improving population health and well-being, mainly by raising the awareness of healthcare providers to regard performing physical activity (PA) as one of the vital signs. Ever since, this concept has spread worldwide and PA has been proposed as an essential part of treatment for chronic diseases2 . In their pioneering work, Pedersen and Saltin provided compelling evidence for the role of exercise as the first- or second-line therapy for at least 26 diseases2 . These findings are further corroborated by meta-epidemiological data, indicating exercise interventions to be as effective as drug interventions, such as during rehabilitation after stroke and for the treatment of heart failure3 . However, the overall effect appears to be strongly correlated with important determinants of the exercise program performed (i.e. dose of training [frequency, volume, intensity], type of exercise and adherence to the training program) and disease-related specifics4 , thus requiring not only clinical expertise but also an in-depth understanding of exercise physiology and biology. In this new journal-Translational Medicine and Exercise Prescription (TMEP), we are aiming to bring together the fields of exercise physiology and biology, sports medicine and the science of physical training and testing to bridge the gap between mechanistic research and clinical practice. The journal covers nine sections, including obesity, diabetes, cancer, cardiovascular diseases, neurological and psychiatric diseases, pulmonary diseases, musculoskeletal diseases, endocrine disorders as well as advanced exercise prescription and health maintenance. In this first issue of TMEP, we are delighted to present seven papers from different areas of translational research performed in humans on specific themes related to the treatment and prevention of chronic diseases. Appropriate selection of primary and secondary endpoints is critical for successfully designing translational studies. In the first paper of this issue, TMEP Section Editor, Jörn Rittweger provides his thoughts on "What Are Good Muscle Endpoints for Translational Studies?". In his important work, he highlights the importance of muscles for our health because of their size, their involvement in energy metabolism and their relevance for locomotion. He further suggests that at least eight different muscle functions are important to health. Well accepted methods exist for three relevant muscular endpoints, namely for power, strength and muscle mass, and these endpoints are utilized in clinical studies. However, such validated methods lack a number of additional muscle functions that are not yet fully scientifically explored. This applies foremost to not only the metabolic functions of muscles, but also to their role in storage and dissipation of mechanical energy. His work concludes by emphasizing how physiological knowledge can be an important base for the guidance of clinical diagnostics. Following this important message, the group of TMEP Section Editor Pieter de Lange shares their work entitled "Exercise with Energy Restriction as a Means of Losing Body Mass While Preserving Muscle Quality and Ameliorating Comorbidities: Towards A Therapy for Obesity?". Obesity and related comorbidities have reached pandemic proportions worldwide, particularly during the past decade. Therefore, finding effective intervention strategies not only requires scientific focus but these strategies are also of public interest. Based on both human and animal studies, this narrative review summarizes the effects of dietary and exercise-based programs on loss of different body mass components. Furthermore, both the gain and lack of loss of lean mass in view of muscle quality maintenance are discussed and data related to the mechanisms underlying the conservation of functional muscle mass provided. They also provide evidence of the interaction between energy restriction by diet and exercise-induced metabolic demands at the molecular level. This insight into the mechanisms underlines the relevance of translational considerations for personalized exercise prescription. Obesity and an unhealthy lifestyle are also known to be among the risk factors for type 2 diabetes (T2DM). The research group of TMEP Section Editor Thomas Yates highlights research opportunities and challenges for combining exercise and medical therapies by means of a narrative review entitled "Exercise, Pharmaceutical Therapies and Type 2 Diabetes: Looking beyond Glycemic Control to Whole Body Health and Function". The authors provide evidence on newer generations of glucose-lowering therapies that also induce concomitant weight loss, particularly on glucagon-like peptide-1 receptor agonists (GLP-1RAs) and sodium-glucose cotransporter 2 inhibitors (SGLT2is). Based on current knowledge, they stress the importance of investigating the interaction or synergy between exercise and other glucose-lowering or weight loss therapies, to make exercise a tailored therapy rather than a generic treatment in the management of T2DM. This review, therefore, clearly highlights the need of precise exercise prescription, originating from an in-depth mechanistic understanding of the effects of exercise. Cancer is another important metabolic and chronic inflammatory disease that may also be associated with obesity. TMEP Section Editor Jesper F Christensen and Associate Editor Ciaran M Fairman provide a very interesting viewpoint in their article,"Targeted Exercise Training for Cancer Patients: Moving beyond Generic Exercise Guidelines in Clinical Oncology". This paper aims to update the current knowledge and the clinical rationale for targeted exercise interventions in exercise oncology. Moreover, a framework for systematic guidance of the design and execution of targeted exercise interventions in oncology is presented. The authors hope that their framework can encourage further research into targeted exercise interventions in oncology and may also be used as a guideline for the design of future trials to increase quality and impact. The group of TMEP Section Editor Helen Dawes, shares their original data in the context of neurological and psychiatric diseases in the article entitled "Physical Activity and Fatigue in Multiple Sclerosis: Secondary Outcomes from a Double-blinded Randomized Controlled Trial of Cocoa Flavonoid Drinks". In this study, they performed intensive phenotyping of the inter-relationships of the time of day, physical activity levels and fatigue to determine exercise prescription in a group of people with multiple sclerosis (MS) participating in a six-week randomized controlled trial of morning flavonoid intake. It was found that fatigue levels increased during the day and higher levels of fatigue reduced physical activity; yet physical activity itself did not lead to increased fatigue. Additionally, morning cocoa intake reduced daytime fatigue and fatigue related to subsequent physical activity. Therefore, combined prescription of morning exercise and dietary flavonoids may optimize the exercise and physical activity potential in people with MS. This study nicely demonstrates the importance of understanding and considering possible covariants such as nutrition and chronobiology to determine an optimal exercise prescription model. TMEP Section Editor Jonathan Myers and colleague Baruch Vainshelboim present a narrative review on "Resistance Training for Rehabilitation of Patients with Idiopathic Pulmonary Fibrosis". In this paper, the pathophysiology and clinical manifestations of Idiopathic Pulmonary Fibrosis (IPF) are summarized with an emphasis on the numerous health and clinical benefits of resistance training among older adults and patients with this respiratory disease. This article effectively explores the potential mechanisms by which systematic resistance training may help overcome exercise limitations in IPF, providing a therapeutic opportunity for rehabilitation. Furthermore, the authors provide important recommendations for pulmonary rehabilitation programs that are based on resistance training for patients with IPF. With this paper, the authors highlight that disease-adapted exercise prescription requires a pathophysiological understanding to justify the inclusion of specific exercise regimens. In the final paper of this special issue, the research group of Section Editor Anthony C Hackney shares their original data on the "Energy Availability and RED-S Risk Factors in Competitive, Non-elite Male Endurance Athletes". This paper particularly emphasizes the entire spectrum of TMEP, that is more than just the most common types of non-communicable diseases, but also includes chronic conditions that may be for example induced by athletic training. In this study, the authors assessed the associations of energy availability and risk factors of relative energy deficiency in sport (RED-S) in 60 competitive, recreationally trained male endurance athletes. They found that hormonal and bone biomarkers were within normal clinical ranges, even when the energy availability was low. The authors further state that athletes are considered at a high risk for RED-S if their energy availability is low (<30 kcal/kg FFM), which is based primarily upon research in women. However, the included recreationally trained male endurance athletes were below this criterion but displayed no RED-S symptomology. Thus, the authors suggest that the <30 kcal/kg FFM criterion may not be a valid categorization for a high risk of RED-S in non-elite male endurance athletes, highlighting the importance of using other criteria. While the purpose of this first issue is to stimulate further discussion on the topic of translational medicine and exercise prescription from different disciplines, we wish to highlight that these topics are not exhaustive and there are many more questions that must be addressed. With TMEP, we aim to introduce a platform for the transparent dissemination of research findings, where special consideration will be given to novel types of exercise prescriptions, including technology-based intervention approaches as well as mechanism-driven studies with translation to exercise prescription. Moreover, TMEP encourages submissions of "negative findings" and/or possible reports of harmful adverse events (e.g. if trials are terminated prematurely) as long as the quality of the study can be assured (i.e. indicated by relevance/novelty of the question, rigorous methodology, transparency and reproducibility). We strongly encourage submissions from all parts of the world to further improve our understanding on exercise prescription based on mechanistic approaches and by that commence a new era of evidence-based precision exercise medicine.

The systematic use of technologies in order to orchestrate learning has become widely used in the past years. Diverse technologies have been applied in a variety of teaching practices: for instance, learning tools that allow you to flip the classroom or monitor other active learning practices. E-learning is learning through the use of technology. It is growing at a rapid pace. Today more organizations are taking up e-learning. While e-learning technology has matured considerably since its inception, there are still many problems that practitioners face when implementing e-learning. This chapter aims to discuss the current challenges and future implications of e-learning systems and how an e-learning ecosystem can help higher education institutions on a large scale. The university is regarded as a center of such an ecosystem due to skilled knowledge providers and technical equipment availability. The ICT tools are very actively used in these initiatives aiming to develop highly demanded skills such as critical thinking, creativity, and complex problem solving.