Development Efforts Research Articles

Disease coverage of human genome-wide association studies and pharmaceutical research and development

BackgroundDespite the growing interest in the use of human genomic data for drug target identification and validation, the extent to which the spectrum of human disease has been addressed by genome-wide association studies (GWAS), or by drug development, and the degree to which these efforts overlap remain unclear.MethodsIn this study we harmonize and integrate different data sources to create a sample space of all the human drug targets and diseases and identify points of convergence or divergence of GWAS and drug development efforts.ResultsWe show that only 612 of 11,158 diseases listed in Human Disease Ontology have an approved drug treatment in at least one region of the world. Of the 1414 diseases that are the subject of preclinical or clinical phase drug development, only 666 have been investigated in GWAS. Conversely, of the 1914 human diseases that have been the subject of GWAS, 1121 have yet to be investigated in drug development.ConclusionsWe produce target-disease indication lists to help the pharmaceutical industry to prioritize future drug development efforts based on genetic evidence, academia to prioritize future GWAS for diseases without effective treatments, and both sectors to harness genetic evidence to expand the indications for licensed drugs or to identify repurposing opportunities for clinical candidates that failed in their originally intended indication.

CFD modeling of DME synthesis based on methanol dehydration process by an adiabatic reactor: Characterization

Today, Dimethyl ether (DME) can be used as a fuel in various applications, including as a substitute for diesel fuel. Despite its advantages, there are some challenges associated with DME as a fuel. One of the main challenges is the limited availability of DME refueling stations and the need for dedicated storage facilities due to their different properties compared to traditional fuels. DME shows promise as a clean-burning alternative fuel with the potential to reduce emissions and dependence on fossil fuels, especially in the transportation and power generation sectors. Ongoing research and development efforts are focused on improving production methods, expanding infrastructure, and exploring new applications for DME as a fuel. DME is a colorless gas that can be used as a fuel or as a precursor for the production of other chemicals. The most common method for DME synthesis involves the dehydration of methanol. Methanol is first converted to dimethyl ether by removing a molecule of water. This reaction is typically catalyzed by acidic materials such as alumina or zeolites. In this work, to study the physical properties of the methanol dehydration reaction, CFD modeling of the adiabatic reactor has been accomplished. The researchers showed that the use of an alumina catalyst, which has selective mechanical properties and unique surface properties, is a suitable choice as a catalyst for DME synthesis. The results of this study indicate that laboratory data such as pressure, energy, and velocity in the adiabatic reactor satisfy the reaction conditions well. Also, temperature profile diagrams with changes in temperature, pressure, and velocity show that the reaction of methanol dehydration is strongly dependent on environmental factors and presents different results under the influence of other conditions.

Exploring the Scope and Impact of Vocational Education and Skill Development Programs on Women’s Self-Help Groups

Women's self-help groups (WSHGs) have arisen as an important tool for women's empowerment and socioeconomic development in many nations, particularly in developing areas. Skill development and vocational education programs aimed at women in these categories have received attention as possible catalysts for increasing their economic independence, social status, and general well-being. The purpose of this article is to look at the influence of skill development and vocational education efforts on women's self-help groups, with a particular emphasis on economic empowerment, community development, and female agency. Drawing on empirical evidence and theoretical frameworks, this study investigates the mechanisms by which skill development and vocational education programs contribute to women's empowerment in self-help groups, as well as the broader implications for gender equality and sustainable development.

Penerapan Digital Marketing sebagai Upaya Peningkatan UMKM Keripik Pepaya di Desa Lubuk Enau

The ever-evolving technological transformation can produce innovations that attract public attention over time. Digital marketing is a form of effort that uses digital media, such as the internet, to promote and market a brand. However, the lack of understanding of village communities regarding sales in the market is a problem that needs to be understood. Lubuk Enau Village is a place for community service activities. This social service activity was carried out by KKN-Thematic students of the Faculty of Economics, Universitas Indo Global Mandiri, in the context of Sustainable Entrepreneurship Development Efforts in Lubuk Enau Village. This guidance is expected to make it easier for MSME economic actors to create a marketplace that aims to sell processed culinary products such as Instagram, Facebook, WhatsApp, and other e-commerce. Increasing online sales promotions aims to expand the marketing of papaya chips so that it can increase the economy of business actors.

Japanese encephalitis virus: An overview.

Japanese encephalitis (JE) is a mosquito-borne infectious disease caused by the Japanese encephalitis virus (JEV), posing a substantial threat to human health and property safety. Until now, there has been a lack of specific therapeutic options for treating JEV infections. In this review article, we provide a comprehensive discussion of JEV's characteristics, diagnostic methodologies, vaccine development efforts, and potential anti-JEV pharmaceuticals to provide insights and references that could be used to inform and enhance strategies for the prevention and control of Japanese encephalitis.

In-field performance evaluation of robotic arm developed for harvesting cotton bolls

Robotic harvesting of cotton bolls combines the advantages of manual picking and mechanical harvesting, including selective picking and reduced labor dependency. Thus, we designed, developed, and tested a cotton-picking robotic arm equipped with a depth camera, a 4-DoF manipulator, and a vacuum-type end-effector. Convolutional neural networks were implemented for cotton boll recognition, while an artificial neural network-assisted particle swarm optimization-based model was utilized for solving inverse kinematics. Linear actuators, controlled by a microcontroller and relays, positioned the end-effector for cotton boll harvesting. Laboratory testing resulted in picking 44–49 cotton bolls per hour, taking approximately 73.0 s per boll. During field testing, the robotic arm picked 40–42 bolls per hour with an average time of 86.0 s per boll. Picking efficiency ranged from 76.19% to 85.71% in the lab and 66.32% to 72.04% in the field. The complex structure of cotton plants sometimes hindered the end-effector’s path, resulting in lower picking efficiency during field testing. As observed in present study, manual pickers achieved a significantly higher picking rate, harvesting 1.381 kg of cotton per hour, nearly 4.7 times more than the robotic arm. Manual pickers achieved 98.04% picking efficiency, significantly higher than the robotic arm’s 68.25%. This significant difference in efficiency is due to the agility, adaptability, and decision-making abilities of human pickers. The developed robotic arm demonstrates competitive efficiency in the domain of harvesting robots, contributing to advancements in robotic agriculture. Future research and development efforts in this field hold the potential to enhance the efficiency and productivity of robotic cotton harvesters.

Türkiye’s Vision for Africa: Humanitarian Diplomacy and Development Cooperation

This article aims to analyze Türkiye’s humanitarian diplomacy and development cooperation strategies toward Africa within a theoretical and conceptual framework, assessing their impact and significance on the continent. The research problem addresses how Türkiye’s unique approach, which integrates historical ties, cultural interactions, and development efforts, distinguishes it from other global and regional actors. The arguments presented highlight that Türkiye’s humanitarian diplomacy strategy, based on mutual benefit and sustainable development goals, has effectively enhanced its positive image and influence in Africa. The hypothesis posits that Türkiye’s humanitarian diplomacy and development cooperation efforts have strengthened its position as a humanitarian actor on the continent and contributed to achieving sustainable development goals. The findings suggest that the country’s activities in healthcare, education, agriculture, and socio-cultural fields have created significant impacts, improving local communities’ living conditions and fostering sustainable development. Furthermore, Türkiye’s distinct approach, unlike traditional Western policies, is characterized by an emphasis on mutual interdependence and cooperation, positioning it as a reliable partner in Africa.

Has the Development of Broadband Infrastructure Improved Household Energy Consumption in Rural China?

Digital infrastructure plays a crucial role in shaping household energy consumption behaviors, especially in the context of achieving carbon peaking and carbon neutrality goals in China. In this study, we explored the impact of broadband infrastructure on household energy consumption and clean fuel choices, using data from the China Health and Retirement Longitudinal Study (CHARLS). In a difference-in-differences (DID) framework, our findings indicate that the promotion of broadband infrastructure significantly increases rural household energy consumption and facilitates a transition to cleaner energy sources. Notably, households with better socioeconomic status, particularly those with higher education and income levels, benefit more from enhanced broadband internet access. Mechanism analysis reveals that the “Broadband China” policy can positively influence household consumption through several potential channels: fostering technological innovation, enhancing energy efficiency in cities, and improving overall environmental awareness. These findings contribute to our understanding of the role of broadband infrastructure in shaping household energy consumption patterns and have important implications for policymakers, particularly in the context of sustainable development and energy transition efforts.

Selective ligand recognition and activation of somatostatin receptors SSTR1 and SSTR3

Somatostatin receptors (SSTRs) exert critical biological functions such as negatively regulating hormone release and cell proliferation, making them popular targets for developing therapeutics to treat endocrine disorders, especially neuroendocrine tumors. Although several panagonists mimicking the endogenous ligand somatostatin are available, the development of more effective and safer somatostatinergic therapies is limited due to a lack of molecular understanding of the ligand recognition and regulation of divergent SSTR subtypes. Here, we report four cryoelectron microscopy structures of Gi-coupled SSTR1 and SSTR3 activated by distinct agonists, including the FDA-approved panagonist pasireotide as well as their selective small molecule agonists L-797591 and L-796778. Our structures reveal a conserved recognition pattern of pasireotide in SSTRs attributed to the binding with a conserved extended binding pocket, distinct from SST14, octreotide, and lanreotide. Together with mutagenesis analyses, our structures further reveal the dynamic feature of ligand binding pockets in SSTR1 and SSTR3 to accommodate divergent agonists, the key determinants of ligand selectivity lying across the orthosteric pocket of different SSTR subtypes, as well as the molecular mechanism underlying diversity and conservation of receptor activation. Our work provides a framework for rational design of subtype-selective SSTR ligands and may facilitate drug development efforts targeting SSTRs with improved therapeutic efficacy and reduced side effects.

Population structure analysis of Phlebotomus papatasi populations using transcriptome microsatellites: possible implications for leishmaniasis control and vaccine development

BackgroundPhlebotomus papatasi is considered the primary vector of Leishmania major parasites that cause zoonotic cutaneous leishmaniasis (ZCL) in the Middle East and North Africa. Phlebotomus papatasi populations have been studied extensively, revealing the existence of different genetic populations and subpopulations over its large distribution range. Genetic diversity and population structure analysis using transcriptome microsatellite markers is important to uncover the vector distribution dynamics, essential for controlling ZCL in endemic areas.MethodsIn this study, we investigated the level of genetic variation using expressed sequence tag-derived simple sequence repeats (EST-SSRs) among field and colony P. papatasi samples collected from 25 different locations in 11 countries. A total of 302 P. papatasi sand fly individuals were analyzed, including at least 10 flies from each region.ResultsThe analysis revealed a high-level population structure expressed by five distinct populations A through E, with moderate genetic differentiation among all populations. These genetic differences in expressed genes may enable P. papatasi to adapt to different environmental conditions along its distribution range and likely affect dispersal.ConclusionsElucidating the population structuring of P. papatasi is essential to L. major containment efforts in endemic countries. Moreover, the level of genetic variation among these populations may improve our understanding of Leishmania–sand fly interactions and contribute to the efforts of vaccine development based on P. papatasi salivary proteins.Graphical

Green Communication in IoT for Enabling Next-Generation Wireless Systems

Recent developments and the widespread use of IoT-enabled technologies has led to the Research and Development (R&D) efforts in green communication. Traditional dynamic-source routing is one of the well-known protocols that was suggested to solve the information dissemination problem in an IoT environment. However, this protocol suffers from a high level of energy consumption in sensor-enabled device-to-device and device-to-base station communications. As a result, new information dissemination protocols should be developed to overcome the challenge of dynamic-source routing, and other similar protocols regarding green communication. In this context, a new energy-efficient routing protocol (EFRP) is proposed using the hybrid adopted heuristic techniques. In the densely deployed sensor-enabled IoT environment, an optimal information dissemination path for device-to-device and device-to-base station communication was identified using a hybrid genetic algorithm (GA) and the antlion optimization (ALO) algorithms. An objective function is formulated focusing on energy consumption-centric cost minimization. The evaluation results demonstrate that the proposed protocol outperforms the Greedy approach and the DSR protocol in terms of a range of green communication metrics. It was noticed that the number of alive sensor nodes in the experimental network increased by more than 26% compared to the other approaches and lessened energy consumption by about 33%. This leads to a prolonged IoT network lifetime, increased by about 25%. It is evident that the proposed scheme greatly improves the information dissemination efficiency of the IoT network, significantly increasing the network’s throughput.

Professional Standards and Educational Leadership: Higher Secondary Teachers’ Behavioral Intention Towards Adopting New Teaching Technologies

Integrating new teaching technologies in education has transformed the traditional classroom environment, offering innovative methods for teaching and learning. With the rapid advancement in digital tools and platforms, educational institutions are increasingly adopting these technologies to enhance instructional practices, engage students, and improve learning outcomes. Therefore, this research aimed to investigate the higher secondary teachers’ behavioral intention towards adopting new teaching technologies in the context of professional standards and educational leadership. The study adopted a descriptive research design and utilized a quantitative research approach. A standardized questionnaire and a web-based purposive sampling method were used to collect 350 data from higher secondary teachers in Bangladesh. The data was assessed, and the hypotheses were analyzed using a process called partial least squares structural equation modeling (PLS-SEM). The PLS-SEM analysis identified a notable link among perceived ease of use, enjoyment, usefulness, and behavioral intention in the context of professional standards and educational leadership. This study is highly significant for educational institutions and policymakers that aim to enhance teacher job satisfaction and teachers’ quality. Educational technology should feature an interface that is easy to use, engage the user’s interest, and fulfill its intended function efficiently. Furthermore, it is imperative to tailor training programs to aid teachers’ assimilation of the technology. School administrators must enact professional development efforts, encourage the effective use of technology, establish support networks, and provide adaptable solutions to meet the unique needs of instructors. These variables can potentially increase the rate of acceptance and satisfaction with technology in educational institutions.

A novel Real-time Control System for next generation gravitational-wave detectors

This contribution presents the INFN Pisa group research and development efforts aimed at defining and implementing a novel Real-time Control System (RCS) for next-generation interferometers, focusing on the initial phase of the Einstein Telescope (ET) gravitational wave detector. The RCS handles complex closed-loop systems, managing data collection from sensing elements, processing and actuator control within well defined time constraints. It integrates multiple spatially distributed control units, facilitating communications among them, with external systems as well as with users. Two distinct hardware approaches are explored: “Katane” and “Zancle”. These systems primarily differ in their core processing elements. The “Katane” system is built on powerful DSP processor boards, based on the Super-Attenuator control system, developed at INFN Pisa for Advanced Virgo detector. It uses the MicroTCA.4 architecture standard, ensuring flexibility, easy maintenance, and fast data exchange among the various custom modules, including several types of Front-End data converter boards, FPGA-based pre-processing boards, DSPs and standard processors. On the other hand, ”Zancle” explores GPU-based processing, aiming to use cost-effective consumer market systems and to leverage substantial computational power of these devices, also incorporating machine learning algorithms.

Urbanization alters sandy beach scavenging assemblages and dogs suppress ecosystem function

AbstractUrbanization is rapidly transforming coastal landscapes around the world, altering the structure and function of marine, intertidal, and terrestrial ecosystems. In tandem, coastal areas are hotspots for human recreation, leading to shifts in wildlife behavior and activity patterns. Together, urban development and recreational use of wildlife habitats can shape wildlife behavior, abundance, and ecosystem dynamics at different spatial and temporal scales. In this study, we explore the impact of urbanization and human and domestic dog activity on the structure of vertebrate scavenging assemblages and the ecosystem functions they provide in sandy beach ecosystems across 40 km of the central California coast, USA. We surveyed vertebrate scavenging assemblages using baited camera traps on 17 beaches spanning a gradient of coastal urbanization. We found that urbanization extent within small spatial scales (i.e., 1 or 3 km radii of each site) and the rate of beach visitation by domestic dogs or humans were the best additive predictors of assemblage structure. We identified pronounced urbanization‐associated shifts in the composition of vertebrate scavenger guilds but found that the rate of carrion processing was more strongly influenced by domestic dog habitat use and diel period. Scavenging activity was substantially lower on beaches with more domestic dogs, suggesting that dogs interfere with critical scavenging ecosystem functions on sandy beaches. Our results underscore the pervasive and nuanced effects of urbanization and recreation on the dynamics of land–sea connectivity and suggest a need for comprehensive consideration of cross‐ecosystem linkages in ongoing shoreline conservation and development efforts.

To Enhance the Performance of CI Engine with Using of Additives Based Hybrid Bio Fuel—A Review

AbstractThe growing demand of sustainable and environment friendly energy sources has spurred research and development efforts towards the integration of biofuels in various applications, particularly in internal combustion engines. Microemulsion technique is a new and innovative alternative to traditional fossil fuel that has gained a significant attention in recent years. This type of bio fuel is made by blending of small amount of biofuel and large amount of conventional diesel fuel. This review paper explores the potential of enhancing the performance of Compression ignition (CI) engines by incorporating additives into hybrid biofuels. The study focuses on the synergistic effects of combining traditional fossil fuels with bio‐derived components, such as ethanol, biodiesel, and other bio‐based additives. This review paper aim is to explore the performance of a 4‐stroke, single‐cylinder, direct injection Compression Ignition (CI) engine at different loads using different fuels blends like CNWEDB, BFNP150, PPNP150, and ME Diesel/Colza oil. The review outlines recent advancements in biofuel technology, including novel production methods and feedstock options, aiming to overcome limitations associated with conventional biofuels. The article aims to investigate the potential of vegetable oil in formulating MHBF, analyzing its performance and emissions in CI engines. The engine performance parameters viz., brake thermal efficiency brake specific fuel consumption have been reviewed and found that these values are comparable to the biodiesel blends with pure petrodiesel. At 60%, 80%, and full load condition the BTE of microemulsion diesel/colza oil is increasing while at 40%, 80%, and full load the BSFC is decreasing. Emissions reported by the various researchers, however, have a positive attribute with respect to NOx, CO, and UHC. At full load CO is decreasing and at all load conditions the value of UHC is decreasing. The paper concludes with a discussion on future research directions, emphasizing the need for continued innovation to address emerging issues and optimize the performance of CI engines for a sustainable and energy‐efficient future.

Detection, Counting, and Maturity Assessment of Blueberries in Canopy Images using YOLOv8 and YOLOv9

Blueberry growers currently rely on manual or subjective approaches to harvest decision-making and yield estimation. Canopy image-based, pre-harvest blueberry detection provides a promising means for automated harvest maturity assessment, fruit counting, and yield estimation to optimize crop management and reduce labor costs. Data-driven deep-learning-based object detectors, especially the YOLO (You Only Look Once) series models, have emerged as a powerful tool for a diversity of computer vision tasks in agriculture. However, the detection of such small, densely populated objects as blueberries in unstructured scenes can pose great challenges, especially given the lack of dedicated datasets for model development. This study, therefore, presents the first publicly available dataset of blueberry canopy images with 17,955 ripe and unripe blueberries, which were captured in diverse orchard conditions, and performance evaluation of YOLOv8l (large) and YOLOv9-c (compact) with comparable complexity for blueberry detection and whereby fruit counting and ripe fruit percentage estimation. At the input image resolution of 2560 × 2560 pixels, both models performed similarly in terms of detection accuracy with around 91% mAP@50, except that YOLOv9-c was far more time-consuming to train. Trained with the input of higher resolution images of 3520 × 3520 pixels, YOLOv8l achieved an overall mAP@50 of nearly 93%, and an RMSEs (root-mean-square errors) of 10.4 in fruit counting and 3.62% in estimating the percentage of ripe fruit of each image. Both the dataset and software programs11https://github.com/vicdxxx/BlueberryDetectionAndCounting of this study have been or will be made publicly available, which are expected to be useful for promoting greater community efforts for further algorithm development.

Does local government debt pressure hamper green transition? Evidence from China

The accumulated local government debt will change government fiscal behavior, which will further impact regional environment performance to a certain extent. However, the literature on how local government debt pressure (LGDP) affect green transition is scarce. By measuring the green transition by green total factor productivity (GTFP), we analyze the effect of LGDP on GTFP. We find that LGDP significantly reduces the GTFP. The negative effect acts through reducing green innovation and loosing environmental regulation. In addition, the negative impact will be more pronounced in cities with weak governance, development, and environmental protection efforts. Finally, enhancing marketization, lowering GDP growth target, and reducing tax burdens can alleviate the adverse effect of LGDP on GTFP. The conclusions of this study provide theoretical support for appropriate government debt governance and have important practical significance for green sustainable development in developing countries.

Toward Diverse Plant Proteins for Food Innovation.

This review highlights the development of plant proteins from a wide variety of sources, as most of the research and development efforts to date have been limited to a few sources including soy, chickpea, wheat, and pea. The native structure of plant proteins during production and their impact on food colloids including emulsions, foams, and gels are considered in relation to their fundamental properties, while highlighting the recent developments in the production and processing technologies with regard to their impacts on the molecular properties and aggregation of the proteins. The ability to quantify structural, morphological, and rheological properties can provide a better understanding of the roles of plant proteins in food systems. The applications of plant proteins as dairy and meat alternatives are discussed from the perspective of food structure formation. Future directions on the processing of plant proteins and potential applications are outlined to encourage the generation of more diverse plant-based products.

COVID-19 vaccination in patients with multiple sclerosis: what you need to know - a review.

The appearance of severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) initiated the COVID-19 pandemic, resulting in millions of confirmed cases and numerous fatalities. In response, rapid vaccine development efforts were launched to mitigate the pandemic's impact. Despite the high efficacy of COVID-19 vaccines, they are also associated with several common side effects/complications, some of them specific to the multiple sclerosis population. Our goal is to review various types of COVID-19 vaccines, assessing their efficacy, adverse events, their association with an MS relapse following vaccination, and the influence of disease modifying therapies (DMTs) on vaccines' efficacy. The review was based on a database search that included PubMed/Medline, Embase, Scopus, and the Web of Science conducted from January 2020 to July 2024 using the following MeSH terms: MS, COVID-19, COVID-19 vaccination, vaccine side effects, and vaccine hesitancy. Receiving any type of COVID-19 vaccine is a safer and more reliable approach to building immunity compared to becoming infected with the virus. Complications tend to be mild to moderate, occasionally severe. DMTs could affect the humoral response to the COVID-19 vaccine. Among all DMTs, a notable reduction in the humoral response has been observed in patients who received anti-CD20 and sphingosine-1-phosphate (S1P) receptor modulator drugs after their COVID-19 vaccination. Despite certain drawbacks, the benefits of the COVID-19 vaccine significantly outweigh the associated risks, making it a recommended course of action for people with multiple sclerosis (pwMS). However, physicians need to be mindful of potential complications especially in patients undergoing anti CD20 and manage them appropriately.

Exploring Benzo[b][1,4]Thiazine Derivatives: Multitarget Inhibition, Structure–Activity Relationship, Molecular Docking, and ADMET Analysis

AbstractA series of benzothiazine derivatives (1–17) were synthesized via an intermolecular cyclocondensation reaction involving 2‐aminothiophenol (i) and substituted phenacyl bromide (ii). Structural elucidation of these synthetic derivatives utilized EI–MS, HR‐EIMS, 1H NMR, and 13C NMR spectroscopic techniques. The synthesized analogs were evaluated against key enzyme targets (acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and α‐glucosidase (α‐Glu)) and tested for cytotoxicity against various cancer cell lines. Six compounds were selected based on their inhibition profiles, exhibiting significant inhibitory potential against enzymes. In silico studies corroborated the observed inhibitory activities, aligning closely with experimental outcomes. Additionally, an ADME/T study provided insights into pharmacokinetic and safety profiles, identifying promising candidates for future drug development efforts.