Miracle-dz/UniFAR-FTUs huggingface dataset:
Operational Differences between Product Development Partnership, Pharmaceutical Industry, and Investigator Initiated Clinical Trials

Medicine development is a lengthy endeavour. Increasing regulatory stringency and trial complexity might lead to reduced efficiency, dwindled output, and elevated costs. However, alternative models are possible. We compared the operational differences between pharmaceutical industry sponsored trials, product development partnership trials, and investigator-initiated trials to identify key drivers of inefficiency in clinical research. We conducted an exploratory mixed-methods study with stakeholders, including clinical trial sponsors, contract research organisations, and investigators. The qualitative component included 40 semi-structured interviews, document reviews of 12 studies and observations through work shadowing in research institutions in Burkina Faso, Mali, and Switzerland. The findings were triangulated with an online survey polling clinical research professionals. The operational differences were grouped under five categories: (i) trial start-up differences including governance and management structure; (ii) study complexity; (iii) site structural and organisational differences; (iv) study conduct, quality approaches, and standard operating procedures; and (v) site capacity strengthening and collaboration. Early involvement of sites in the planning and tailored quality approaches were considered critical for clinical operations performance. Differences between the types of trials reviewed pertained to planning, operational complexities, quality approaches, and support to the sites. Integration of quality-by-design components has the potential to alleviate unnecessary process burden.
Prx1 Expressing Cells Are Required for Periodontal Regeneration of the Mouse Incisor

Previous studies have shown that post-natal skeletal stem cells expressing Paired-related homeobox 1 (PRX1 or PRRX1) are present in the periosteum of long bones where they contribute to post-natal bone development and regeneration. Our group also identified post-natal PRX1 expressing cells (pnPRX1+ cells) in mouse calvarial synarthroses (sutures) and showed that these cells are required for calvarial bone regeneration. Since calvarial synarthroses are similar to dentoalveolar gomphosis (periodontium) and since there is no information available on the presence or function of pnPRX1+ cells in the periodontium, the present study aimed at identifying and characterizing pnPRX1+ cells within the mouse periodontium and assess their contribution to periodontal development and regeneration. Here we demonstrated that pnPRX1+ cells are present within the periodontal ligament (PDL) of the mouse molars and of the continuously regenerating mouse incisor. By means of diphtheria toxin (DTA)-mediated conditional ablation of pnPRX1+ cells, we show that pnPRX1+ cells contribute to post-natal periodontal development of the molars and the incisor, as ablation of pnPRX1+ cells in 3-days old mice resulted in a significant enlargement of the PDL space after 18 days. The contribution of pnPRX1+ cells to periodontal regeneration was assessed by developing a novel non-critical size periodontal defect model. Outcomes showed that DTA-mediated post-natal ablation of pnPRX1+ cells results in lack of regeneration in periodontal non-critical size defects in the regeneration competent mouse incisors. Importantly, gene expression analysis of these cells shows a profile typical of quiescent cells, while gene expression analysis of human samples of periodontal stem cells (PDLSC) confirmed that Prx1 is highly expressed in human periodontium. In conclusion, pnPRX1+ cells are present within the continuously regenerating PDL of the mouse incisor, and at such location they contribute to post-natal periodontal development and regeneration. Since this study further reports the presence of PRX1 expressing cells within human periodontal ligament, we suggest that studying the mouse periodontal pnPRX1+ cells may provide significant information for the development of novel and more effective periodontal regenerative therapies in humans.
Canine Mammary Tumors: Classification, Biomarkers, Traditional and Personalized Therapies

In recent years, many studies have focused their attention on the dog as a proper animal model for human cancer. In dogs, mammary tumors develop spontaneously, involving a complex interplay between tumor cells and the immune system and revealing several molecular and clinical similarities to human breast cancer. In this review, we summarized the major features of canine mammary tumor, risk factors, and the most important biomarkers used for diagnosis and treatment. Traditional therapy of mammary tumors in dogs includes surgery, which is the first choice, followed by chemotherapy, radiotherapy, or hormonal therapy. However, these therapeutic strategies may not always be sufficient on their own; advancements in understanding cancer mechanisms and the development of innovative treatments offer hope for improved outcomes for oncologic patients. There is still a growing interest in the use of personalized medicine, which should play an irreplaceable role in the research not only in human cancer therapy, but also in veterinary oncology. Moreover, immunotherapy may represent a novel and promising therapeutic option in canine mammary cancers. The study of novel therapeutic approaches is essential for future research in both human and veterinary oncology.
Effect of Polyethylene Glycol Induced Water Stress on Germination and Seedling Growth of Wheat ( Triticum aestivum )

The performance of twenty wheat genotypes under Polyethylene Glycol (PEG) induced water stress during germination and early seedling growth stages were tested under three levels of water potential i) Control (Tap water), ii) -2 bars and iii) -4 bar at the Crop Physiology and Ecology Laboratory of Hajee Mohammad Danesh Science and Technology University, Dinajpur during September 2014 to October 2014. Rate of germination and vigor index of all wheat genotypes were delayed with the increment of water stress induced by PEG. Shoot and root lengths and seedling dry weight of 10 days old seedlings were found to be reduced due to the increment of water stress. However, the degree of reduction of these parameters with the increment of water stress was not similar for all wheat genotypes. Stress tolerance index (STI) based on seedling dry weight indicated a wide difference in stress tolerance among the wheat genotypes. At moderate water deficit stress, BARI Gom 25, E 34, E 28 and BAW 1170 showed more stress tolerance and the wheat genotypes- Sourav, E 23 and BAW 1140 showed greater stress sensitivity than the other wheat genotypes. At higher water deficit stress, BARI Gom 25, BARI Gom 28, E 28 and BAW 1170 showed more stress tolerance and the wheat genotypes- Satabdi, Sourav, BARI Gom 26, E 23, E 38, E 24, BAW 1163, BAW 1140 and BAW 1151 showed greater stress sensitivity than the others. Considering both moderate and high water deficit stress, BARI Gom 25, E 28 and BAW 1170 were found as tolerant and Sourav, E 23 and BAW 1140 were found as water deficit stress sensitive wheat genotypes. The Agriculturists 2017; 15(1) 81-91
How Firm Innovation Affect Competitive Advantage Concurrently with Leagile Strategy: An Empirical Analysis of Construction Companies

This research embarked on establishing the extent to which construction companies’ supply chains in Nairobi City County-Kenya realized competitive advantage as a result of the implementation of leagile strategy, and firm innovation strategies. A total of 260 construction companies in Nairobi City County-Kenya were surveyed vide structured questionnaires. Data was evaluated vide various diagnostic tests and regression analysis to determine the relationships between the study variables. The intervening effects of firm innovation was further tested vide a stepwise approach proposed by Baron and Kenny in 1986. The results revealed that leagile strategy has a significant positive influence on competitive advantage. Equally, firm innovation has a positive significant influence on competitive advantage. Further tests showed that leagile strategy has a significant influence on firm innovation, which in turn significantly impact on competitive advantage of construction companies’ supply chains. It was concluded from this study that firm innovation had a positive significant intervening influence on the association concerning leagile strategy, and competitive advantage in the construction companies’ supply chains. The study outcome validates that leagile strategy, and firm innovation are strategic enablers of competitive advantage in the construction companies’ supply chains. The leagile strategy, and firm innovation model is a key measure that might help policymakers, practitioners, as well as researchers in understanding achievement of competitive advantage in construction companies in developing economies like Kenya. The study had limitations emanating from the effects of COVID-19 Pandemic, which was a challenge during the data collection. Further studies are recommended to be conducted in other supply chains different from those of construction companies. Additionally, a similar study could be conducted to incorporate transport, and distribution companies in the construction companies’ supply chains in Nairobi City County-Kenya.
Advancements and Challenges in IoT Simulators: A Comprehensive Review

The Internet of Things (IoT) has emerged as an important concept, bridging the physical and digital worlds through interconnected devices. Although the idea of interconnected devices predates the term “Internet of Things”, which was coined in 1999 by Kevin Ashton, the vision of a seamlessly integrated world of devices has been accelerated by advancements in wireless technologies, cost-effective computing, and the ubiquity of mobile devices. This study aims to provide an in-depth review of existing and emerging IoT simulators focusing on their capabilities and real-world applications, and discuss the current challenges and future trends in the IoT simulation area. Despite substantial research in the IoT simulation domain, many studies have a narrow focus, leaving a gap in comprehensive reviews that consider broader IoT development metrics, such as device mobility, energy models, Software-Defined Networking (SDN), and scalability. Notably, there is a lack of literature examining IoT simulators’ capabilities in supporting renewable energy sources and their integration with Vehicular Ad-hoc Network (VANET) simulations. Our review seeks to address this gap, evaluating the ability of IoT simulators to simulate complex, large-scale IoT scenarios and meet specific developmental requirements, as well as examining the current challenges and future trends in the field of IoT simulation. Our systematic analysis has identified several significant gaps in the current literature. A primary concern is the lack of a generic simulator capable of effectively simulating various scenarios across different domains within the IoT environment. As a result, a comprehensive and versatile simulator is required to simulate the diverse scenarios occurring in IoT applications. Additionally, there is a notable gap in simulators that address specific security concerns, particularly battery depletion attacks, which are increasingly relevant in IoT systems. Furthermore, there is a need for further investigation and study regarding the integration of IoT simulators with traffic simulation for VANET environments. In addition, it is noteworthy that renewable energy sources are underrepresented in IoT simulations, despite an increasing global emphasis on environmental sustainability. As a result of these identified gaps, it is imperative to develop more advanced and adaptable IoT simulation tools that are designed to meet the multifaceted challenges and opportunities of the IoT domain.
Pneumonia Detection Using Chest Radiographs With Novel EfficientNetV2L Model

Pneumonia is a potentially life-threatening infectious disease that is typically diagnosed through physical examinations and diagnostic imaging techniques such as chest X-rays, ultrasounds or lung biopsies. Accurate diagnosis is crucial as wrong diagnosis, inadequate treatment or lack of treatment can cause serious consequences for patients and may become fatal. The advancements in deep learning have significantly contributed to aiding medical experts in diagnosing pneumonia by assisting in their decision-making process. By leveraging deep learning models, healthcare professionals can enhance diagnostic accuracy and make informed treatment decisions for patients suspected of having pneumonia. In this study, six deep learning models including CNN, InceptionResNetV2, Xception, VGG16, ResNet50 and EfficientNetV2L are implemented and evaluated. The study also incorporates the Adam optimizer, which effectively adjusts the epoch for all the models. The models are trained on a dataset of 5856 chest X-ray images and show 87.78%, 88.94%, 90.7%, 91.66%, 87.98% and 94.02% accuracy for CNN, InceptionResNetV2, Xception, VGG16, ResNet50 and EfficientNetV2L, respectively. Notably, EfficientNetV2L demonstrates the highest accuracy and proves its robustness for pneumonia detection. These findings highlight the potential of deep learning models in accurately detecting and predicting pneumonia based on chest X-ray images, providing valuable support in clinical decision-making and improving patient treatment.
Sensitivity of the WRF-Chem v4.4 simulations of ozone and formaldehyde and their precursors to multiple bottom-up emission inventories over East Asia during the KORUS-AQ 2016 field campaign

Abstract. In this study, the WRF-Chem v4.4 model was utilized to evaluate the sensitivity of O3 simulations with three bottom-up emission inventories (EDGAR-HTAP v2 and v3 and KORUS v5) using surface and aircraft data in East Asia during the Korea-United States Air Quality (KORUS-AQ) campaign period in 2016. All emission inventories were found to reproduce the diurnal variations of O3 and its main precursor NO2 as compared to the surface monitor data. However, the spatial distributions of the daily maximum 8 h average (MDA8) O3 in the model do not completely align with the observations. The model MDA8 O3 had a negative (positive) bias north (south) of 30° N over China. All simulations underestimated the observed CO by 50 %–60 % over China and South Korea. In the Seoul Metropolitan Area (SMA), EDGAR-HTAP v2 and v3 and KORUS v5 simulated the vertical shapes and diurnal patterns of O3 and other precursors effectively, but the model underestimated the observed O3, CO, and HCHO concentrations. Notably, the model aromatic volatile organic compounds (VOCs) were significantly underestimated with the three bottom-up emission inventories, although the KORUS v5 shows improvements. The model isoprene estimations had a positive bias relative to the observations, suggesting that the Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 2.04 overestimated isoprene emissions. Additional model simulations were conducted by doubling CO and VOC emissions over China and South Korea to investigate the causes of the model O3 biases and the effects of the long-range transport on the O3 over South Korea. The doubled CO and VOC emission simulations improved the model O3 simulations for the local-emission-dominant case but led to the model O3 overestimations for the transport-dominant case, which emphasizes the need for accurate representations of the local VOC emissions over South Korea.
Interpersonal behaviors in sports context: Spanish adaptation and measurement invariance

Introduction The interpersonal behavior questionnaire (IBQ) is an instrument that measures support and thwarting interpersonal behaviors based on the self-determination theory (SDT). The aim of this work was to adapt the IBQ to the Spanish spoken in Mexico and to examine its psychometric properties (structural validity, discriminant validity, composite reliability, factorial invariance, and nomological validity) in a sample of athletes. Methods For this purpose, 472 athletes (average age 17.15 years; SD = 1.47) completed a question booklet. Results and discussion Confirmatory factor analysis supported the structure of six related factors, three factors of behaviors that support autonomy, competence, and relatedness, and three factors of behaviors that thwarting them. The internal consistency of each factor was also supported, as well as the average variance extracted. However, the discriminant validity between the factors of competence and relatedness in their dimensions of support, on the one hand, and thwarting, on the other, is questioned. Factorial invariance was confirmed across gender (men and women) and sport type (individual and team). Nomological validity is in accordance with theory and empirical literature. More studies of the IBQ in sport are necessary to see if these results are a fortuitous product or if they manifest themselves consistently.
Study of The Feasibility of Minimarkets in Pontianak Cities Based on the Microbiological Quality of Air In Relation to the Physical Condition of the Room

Indoor air quality affects human health and productivity. The presence of microorganisms in indoor air is influenced by physical parameters of the space, activities within the space, building factors, ventilation systems, and human maintenance and care. This study aims to assess air quality and analyze the difference in the number of bacteria between minimarkets and the relationship test with their influencing factors. The study used a cross sectional method with 4 minimarket samples, 2 AC and 2 non-AC. Airborne bacterial colony counts were measured using a passive method with blood agar media, a contact time of 15 minutes, and 3 days of repetition. The average value of airborne bacterial colony counts in non-air-conditioned minimarkets (294.3 CFU/) was lower than in air-conditioned minimarkets (531.8 CFU/). The Rank Spearman correlation test results indicated that there was no significant relationship between airborne bacterial colony counts and temperature (p=0.498), humidity (p=0.089), light intensity (p=0.948), and visitor count (p=0.481). All studied minimarkets met the microbiological air quality, population density, and air circulation standards, but not all met the standards for room temperature, humidity, and light intensity based on the quality standards of the Indonesian Ministry of Health Number 1405/MENKES/SK/XI/2002.
Injectable Micro-Hydrogel for DNA Delivery: A Promising Therapeutic Platform

Utilizing the immune system as a strategy for disease prevention and treatment is promising, especially with dendritic cells (DCs) playing a central role in adaptive immune responses. The unique properties of DCs drive interest in developing materials for cell-based therapy and immune modulation. Injectable systems require syringe-compatible scaffolds, while hydrogels, like alginate, known for their programmability and biocompatibility, offer a versatile platform for immune medicine enhancement through easy preparation and room-temperature cross-linking. In this study, we synthesized alginate balls loaded with DCs or cytosine–phosphorothioate–guanine deoxyribonucleotide (CpG DNA) microparticles, aiming for long-term immune cell culture with potential immune stimulation effects. Encapsulated DCs exhibited proliferation within the alginate balls for up to 7 days, and CpG MPs were uniformly dispersed, which can facilitate uptake by DCs. This was supported by the result that DCs effectively phagocytosed CpG microparticles in a 2D environment. After the uptake of CpG MPs, the alginate balls with CpG-MP-uptaken DCs were synthesized successfully. The injectable properties of the alginate balls were easily modulated by adjusting the syringe needle gauges. This innovative strategy holds substantial promise for advancing medical treatments, offering effective and comfortable solutions for controlled immune modulation.
Linking Work Events with Work Engagement: Mediating Role of Emotions and Moderating Role of Psychological Capital

We examined the role of work-related emotions and personal resources operationalised as psychological capital (PsyCap) in the relationship between events occurring at work and employees’ work engagement. Using affective events theory and broaden-and-build theory as theoretical frameworks, we theorise that the perceived frequency of positive and negative events at work and work engagement is mediated by positive and negative work-related emotions and moderated by PsyCap. The results of path analysis on a sample of US and Polish employees showed that PsyCap moderated the relationship between the perceived frequency of negative work events and negative work-related emotions, however, we also found culture-specific effects of PsyCap. Our study contributes to the human resource development (HRD) literature by providing evidence of the role of personal resources in the event–emotion–engagement process in the workplace. Also, our findings deepen the understanding of HR developers in multinational organisations and provide suggestions on how they can implement PsyCap trainings based on culture-specific work environment.
Towards Generalizable Crop-Agnostic Plant Disease Recognition: An Unsupervised-Learning Approach

In recent years, machine learning techniques have been incorporated in the agricultural industry to automate plant disease recognition. However, most existing frameworks are constrained to the recognition of certain species’ diseases, such as those of tomatoes, due to the severe imbalance in the published dataset. These methods tend to exhibit a strong bias towards specific plants, which require extensive retraining if the model were to accurately classify the disease of other plants. To ensure the system functions in a more realistic context, where images of various plants would be given, it is crucial to develop a generalized system that can recognize a diverse set of plants. To address the aforementioned problem, in this research paper, I propose a novel crop-agnostic plant detection framework. The method leverages contrastive learning, a type of unsupervised learning, to extract discriminative features from plant images regardless of their species. Moreover, the generalizable solution is capable of successfully distinguishing diseases from a dataset with imbalanced class and category. The method achieved an accuracy of 85.88% in the plant village dataset. Through experimental results, it has been demonstrated that the proposed method outperforms existing state-of-the-art methods by a significant margin.
Exploring Vernacular Perceptions of Spatial Entities: Using Twitter Data and R for Delimiting Vague, Dinformal Neighbourhoods in Inner London, UK

The informal nature of how citizens discuss and conceive geographic entities, such as neighbourhoods, has traditionally be difficult to capture. Social media services offer researchers an opportunity to collect large amounts of geo-referenced information concerning vernacular geography. Twitter data was harvested and analysed in R statistical software in order demonstrate whether geodata from social media is a feasible method for spatially defining vague, vernacular neighbourhood units in Inner London, UK. The results suggest that social media data can be a valuable source for capturing vernacular geography from which vernacular neighbourhoods could be delimited. The study also revealed factors which may have contributed to vernacular neighbourhood unit demarcation. Twitter data was seen to both mirror the physical form of the underlying topography and reflect the social character of the city’s land use. This work builds upon previous attempts to investigate vernacular geography which used more traditional methods, such as sketch maps and interviews. It also examines how qualitative coding can improve data quality and how R statistical software can be used to capture, analyse and present geospatial data.