Research Field Research Articles

Recombinant Antibody Fragments for Neurological Disorders: An Update.

Recombinant antibody fragments are promising alternatives to full-length immunoglobulins, creating big opportunities for the pharmaceutical industry. Nowadays, antibody fragments such as antigen-binding fragments (Fab), single-chain fragment variable (scFv), single-domain antibodies (sdAbs), and bispecific antibodies (bsAbs) are being evaluated as diagnostics or therapeutics in preclinical models and in clinical trials. Immunotherapy approaches, including passive transfer of protective antibodies, have shown therapeutic efficacy in several animal models of Alzheimer's disease (AD), Parkinson's disease (PD), frontotemporal dementia (FTD), Huntington's disease (HD), transmissible spongiform encephalopathies (TSEs) and multiple sclerosis (MS). There are various antibodies approved by the Food and Drug Administration (FDA) for treating multiple sclerosis and two amyloid beta-specific humanized antibodies, Aducanumab and Lecanemab, for AD. Our previous review summarized data on recombinant antibodies evaluated in pre-clinical models for immunotherapy of neurodegenerative diseases. Here, we explore recent studies in this fascinating research field, give an update on new preventive and therapeutic applications of recombinant antibody fragments for neurological disorders and discuss the potential of antibody fragments for developing novel approaches for crossing the blood-brain barrier (BBB) and targeting cells and molecules of interest in the brain.

Using health evidence to influence healthier urban development: a qualitative evaluation of a researcher–practitioner collaboration

ABSTRACT Despite evidence showing health impacts associated with environmental features such as housing, air quality, transport, and greenspaces, urban development decisions often result in less healthy environments. To address research-practice gaps we explore a collaboration involving an embedded researcher who bridged between urban development practitioners and researchers on a UK local government-led regeneration project. This facilitated development and implementation of a new health economic model that demonstrated the health impacts of changes to the built environment. We shared evidence at multiple timepoints to influence decision-making for a regeneration framework. Evaluation of this approach involved analysis of semi-structured interviews with key practitioners, alongside project meeting notes and embedded researcher field notes. We found that the academic-practitioner partnership, enabled by the embedded researcher, helped academics understand a complex system, and provided contextually relevant evidence for practitioners to highlight problems; support good/aspirational solutions; and consider trade-offs. Academic-practitioner collaborations can help develop and implement impactful interventions to tackle important, and complex, health challenges associated with the built environment. Understanding how evidence may be useful for different purposes can help to implement its use more effectively, and translate it for different audiences.

Immediate shock and residual stress: Unmasking the dual dynamics of covid-19 on the mental health of health professionals in China.

The pandemic of 2019 has created large scale disruption around the globe. It has posed extraordinary hurdles for Chinese health workers, affecting their mental health with respect to situations in the present as well as the future. Recognising the dynamic features of these mental health issues among health professionals is critical for developing effective support systems. This study aims to explore the dual impact of COVID-19 on the mental health of health professionals in China, both during and after the pandemic; to explore the probable underlying factors causing these mental health issues; to identify both adaptive and maladaptive coping behaviours employed by the health personnel; and to assess the organisational and social support to mitigate the effect of the pandemic on the mental health of the Chinese health professionals. To address these objectives an explorative qualitative research design has been adopted involving analysis of previous researches in the relevant field. The study confirms anxiety, depression, sleep disorders, PTSD, and burnout among medical personnel as immediate shock and residual stress, along with its demographic variations. Few maladaptive coping behaviours have been identified that can lead to delayed psychological effects even after a pandemic. The key findings underscore the complicated interaction of immediate and delayed mental health challenges among health professionals, advocating for the deployment of tailored treatments and policy modifications to address the ever changing needs of this vital workforce.

Applications of Silver Nanoparticles in Patent Research.

Silver nanoparticles (AgNPs) have been widely applied in research and industrial fields, finding applications in nanomedicine, drug delivery, biomedical devices, electronics, the energy sector, and environmental protection. Patents provide information on the industrial viability of product technologies, and the number of patent documents provides an estimate of the evolution of a specific technological field. The present work aims to describe the current trends in AgNPs patent applications. In addition, a retrospective study of published patents in Brazil is presented. Analyses of AgNPs-related patents were conducted using the free platform for patent search Lens® in 2010-2019 and articles published in same period using the Scholar® base. The patent applications and their evolution over time, major depositors and holders, and the main technological areas associated with AgNP applications have been described. China and United States are the major patent applicants for nanotechnologies. The worldwide distribution of publications of journal articles shows that China, India, and the United States are the leading countries in the total number of articles published, in that order. Our study of patent applications and published articles confirmed the growing global increase in new technologies involving NPs and AgNPs, particularly in the biotechnology area, in the fields of medicine and agriculture.

Robust-EQA: Robust Learning for Embodied Question Answering With Noisy Labels.

Embodied question answering (EQA) is a recently emerged research field in which an agent is asked to answer the user's questions by exploring the environment and collecting visual information. Plenty of researchers turn their attention to the EQA field due to its broad potential application areas, such as in-home robots, self-driven mobile, and personal assistants. High-level visual tasks, such as EQA, are susceptible to noisy inputs, because they have complex reasoning processes. Before the profits of the EQA field can be applied to practical applications, good robustness against label noise needs to be equipped. To tackle this problem, we propose a novel label noise-robust learning algorithm for the EQA task. First, a joint training co-regularization noise-robust learning method is proposed for noisy filtering of the visual question answering (VQA) module, which trains two parallel network branches by one loss function. Then, a two-stage hierarchical robust learning algorithm is proposed to filter out noisy navigation labels in both trajectory level and action level. Finally, by taking purified labels as inputs, a joint robust learning mechanism is given to coordinate the work of the whole EQA system. Empirical results demonstrate that, under extremely noisy environments (45% of noisy labels) and low-level noisy environments (20% of noisy labels), the robustness of deep learning models trained by our algorithm is superior to the existing EQA models in noisy environments.

Development of a Case Based Learning (CBL) Model Based on a Knowledge Management System to Improve Students' Critical Thinking Skills

The current era changes the educational situation. There is an urgent need to integrate advanced information technology with innovative teaching models to improve students' analytical thinking skills.It was employed to Vocational High School teachers and students as a learning innovation in the current era.The selection of Vocational High School teachers and students for research is because the researcher focuses on research in the field of vocational education and it is relevant with the researcher's field of knowledge. This study aims to develop and validate a Case Based Learning model integrated with a Knowledge Management System, designed to enhance critical thinking skills among vocational high school students. The paper applies Research and Development with theADDIE scheme. The latest concept of the model, namely the Case Based Learning Model, can be collaborated with the Knowledge Management System to improveCritical Thinking Skills student as a competency needed in the current era. It is expected to have a positive impact and change practices and learning outcomes in the context of the industrial revolution currently taking place.

Nano-particles size measurement based on semantic segmentation via convolution neural network

Automatic recognition and size measurement of nanoparticles in SEM images have been an evolving field of study within the material informatics domain. These methods have been typically assessed using traditional image processing techniques. This paper presents a robust and fully automated pipeline for all researchers, particularly those in chemical engineering, based on image processing techniques and convolution neural networks. We evaluated our method on a dataset of 1,701 2D particle images, employing a three-fold split for training, validation, and testing. The proposed approach integrates semantic segmentation models (U-Net, LinkNet) with size measurement algorithms, achieving high accuracy even in the presence of noise or blurriness. Our results demonstrate the method effectiveness in precisely estimating particle sizes, with a Pearson correlation coefficient of 0.9633 and mean absolute error of 1.287e-06. The method’s robustness against poor-quality images and its fully automated procedures make it a valuable tool for researches in SEM images field.

Nitrated benzothiadiazole-based D-A electrochromic polymers with tunable spectral properties

Nitrated benzothiadiazole (BT) is the promising acceptor units in constructing D-A-D electrochromic polymers, but its researches in the electrochromic field was very limited so far. In this work, four kinds of nitrated polymer precursors (Th-NO2-BT, EDOT-NO2-BT, Th-2NO2-BT, and EDOT-2NO2-BT) were synthesized by Stille coupling reaction with EDOT and thiophene units as the end groups (electron donor units) and mono-nitrated benzothiadiazole and di-nitrated benzothiadiazole as the core groups (electron acceptor units). The chemical structure and optical properties of the precursor were investigated by means of 1H NMR, UV–vis absorption and fluorescence spectra; the corresponding mono-nitrated polymer films were obtained in CH2Cl2-Bu4NPF6 system by electrochemical deposition method. The optical band gap of di-nitrated polymer precursor is obviously larger than the corresponding mono-nitrated polymer precursor (both ultraviolet and fluorescence spectra are obviously blue-shifted), accompanied with the obvious increased initial oxidation potential. Due to the fluorescence quenching effect of -NO2 group, their absolute quantum yields are all very low (0.0006–0.015). The spectroelectrochemistry and electrochromic kinetics of mono-nitrated polymer films (P(Th-NO2-BT) and P(EDOT-NO2-BT)) were studied. It was found that P(EDOT-NO2-BT) can show better electrochromic performance than P(Th-NO2-BT), with dark green in the neutral state and more obvious electrochromic performance in the near-infrared region (optical contrast: 38.4 %, coloration efficiency: 129.1 cm2/C, and fast response time: 1.4 s). This systematically study about the effects of nitro substitution effect on the properties of D-A electrochromic polymers, which will further expand the selection of the new acceptor units and the application prospect of nitrated electrochromic materials.

Is the publishing landscape of bioprinting research going to change?

Bioprinting as a new and emerging research field is now gaining momentum. Figure 1 was generated from Scopus by using bioprinting as the search keyword. It shows the trend of the number of published docu-ments (article or review) per year. There are 361 documents in total and 103 are published in the most recent year.

Metabolomics: Recent Advances and Future Prospects Unveiled

Abstract: In the era of genomics, fueled by advanced technologies and analytical tools, metabolomics has become a vital component in biomedical research. Its significance spans various domains, encompassing biomarker identification, uncovering underlying mechanisms and pathways, as well as the exploration of new drug targets and precision medicine. This article presents a comprehensive overview of the latest developments in metabolomics techniques, emphasizing their wide-ranging applications across diverse research fields and underscoring their immense potential for future advancements.

Highlights from the Top 100 Most Influential Articles Regarding the Nuclear Receptor PPAR-γ: A Bibliometric Analysis.

PPAR-γ is one of three members of the PPAR group of the nuclear receptor superfamily and plays an important regulatory role as a ligand-dependent transcription factor. This study aimed to identify the top 100 most influential articles in the field of PPAR-γ. We hypothesized that a bibliometric and scientometric analysis of the PPAR-γ research field could render trends that provide researchers and funding agencies valuable insight into the history of the field, and potential future directions. A literature search of publications was carried out using the Web of Science (WOS) and Scopus database based on specific subject words on September 11, 2023. Articles were listed in descending order of the number of citations. Statistical analysis was performed on the data of the top 100 cited articles in terms of year of publication, journal, research direction, institution, author, and country. Meanwhile, co-authorship networks and co-citation networks were constructed by using VOSviewer software, and keywords were analyzed for co-occurrence. A total of 9,456 articles regarding PPAR-γ were identified and analyzed based on the WOS database, and the top 100 cited articles in the field of PPAR-γ were ranked by citation. The most cited article was published in 1998, with 2,571 citations and a density of 102.80 citations/ year. Of the 100 articles, Harvard University was the institution with the highest number of articles published. Spiegelman, B. M. was the author with the highest number of articles published. Using the VOSviewer software, we found that the most used keywords were geneexpression, activated receptor-gamma, and adipocyte differentiation. PPAR-γ, one of the most widely studied transcription factors, is an important drug target for many diseases. Therefore, screening for small molecule compounds targeting PPAR-γ remains of great value. The present study identified the top 100 most influential articles in the field of PPAR-γ, which help global researchers to better understand research perspectives and develop future research directions of PPAR-γ.

Artificial Intelligence in Drug Discovery: A Bibliometric Analysis and Literature Review.

Drug discovery is a complex and iterative process, making it ideal for using artificial intelligence (AI). This paper uses a bibliometric approach to reveal AI's trend and underlying structure in drug discovery (AIDD). A total of 4310 journal articles and reviews indexed in Scopus were analyzed, revealing that AIDD has been rapidly growing over the past two decades, with a significant increase after 2017. The United States, China, and the United Kingdom were the leading countries in research output, with academic institutions, particularly the Chinese Academy of Sciences and the University of Cambridge, being the most productive. In addition, industrial companies, including both pharmaceutical and high-tech ones, also made significant contributions. Additionally, this paper thoroughly discussed the evolution and research frontiers of AIDD, which were uncovered through co-occurrence analyses of keywords using VOSviewer. Our findings highlight that AIDD is an interdisciplinary and promising research field that has the potential to revolutionize drug discovery. The comprehensive overview provided here will be of significant interest to researchers, practitioners, and policy-makers in related fields. The results emphasize the need for continued investment and collaboration in AIDD to accelerate drug discovery, reduce costs, and improve patient outcomes.

Epistemic practices in conceptions of computer science

ABSTRACT Background and context We address the question of what computer science students take the discipline to be. How students conceive the discipline can influence whether a student pursues computer science, what particular area within computer science they focus on and whether they persist in the discipline. In this paper, we examine the epistemic practices – how knowledge is created and warranted – of students and teacher as a way to understand what computer science is, as practiced by the participants. Objective The goal of this study is to provide an account for how the students and teacher make and legitimize knowledge in the classroom and computer lab. Method In this case study, we use a combination of ethnographic and multimodal methods. The data collected include audio and video recordings and researcher field notes of classroom and lab sessions. The analysis of the data considers speech, marks on the blackboard and embodied actions including gesture and gaze orientation. Findings In both of these learning environments, CS knowledge in the form of general principles and programming practice are discussed by students and the instructor. However, the way in which such knowledge is warranted in these two settings is different. In the classroom, the instructor is the authority that warrants knowledge claims, whereas in the computer lab, the computer plays a key authority role to determine “what works”. Implications This study provides insight into how instructors can design learning environments based on ways they intend knowledge to be created and warranted.

“I need more help, but everyone expects me to know it”: perceived mentoring needs of contemporary second-year early career teachers

ABSTRACT While there has been theoretical and empirical address of the changing characteristics of teachers as they progress through the early years of their professional careers, much less research has considered how this influences the changing ways in which early career teachers (ECTs) may prefer to experience mentoring during this time. Most ECT mentoring research and in-school mentoring programmes focus on teachers in their first year of the profession, or ECTs in very broad terms. Consequently, the specific mentoring needs and preferred approaches of second-year teachers, in particular, remain unclear. In this paper, we report on data collected via researcher field notes, an online survey, and professional learning activities with 15 mentors and ECTs in their second year of teaching in Queensland, Australia. Using a combination of descriptive statistics and directed qualitative content analysis, findings indicated that second-year teachers identify as having specific mentoring needs that they perceive require a unique combination of directive and co-exploratory mentoring approaches. Critically drawing on theories of teacher development, these findings have implications for schools and systems both in Australia and internationally in the development of policies and programmes for mentoring beyond the first year of teaching.

Accuracy of K-Nearest Neighbors Algorithm Classification For Archiving Research Publications

The Archives and Research Publication Information System plays an important role in managing academic research and scientific publications efficiently. With the increasing volume of research and publications carried out each year by university researchers, the Research Archives and Publications Information System is essential for organizing and processing these materials. However, managing large amounts of data poses challenges, including the need to accurately classify a researcher's field of study. To overcome these challenges, this research focuses on implementing the K-Nearest Neighbors classification algorithm in the Archives and Research Publications Information System application. This research aims to improve the accuracy of classification systems and facilitate better decision-making in the management of academic research. This research method is systematic involving data acquisition, pre-processing, algorithm implementation, and evaluation. The results of this research show that integrating Chi-Square feature selection significantly improves K-Nearest Neighbors performance, achieving 86% precision, 84.3% recall, 89.2% F1 Score, and 93.3% accuracy. This research contributes to increasing the efficiency of the Archives and Research Publication Information System in managing research and academic publications.

Communities of practice and the elevation of urban elementary teacher discourse about critical pedagogy of place

Children who live in under-resourced communities and attend under-resourced schools deserve access to high-quality teachers and educational opportunities to support their success and well-being. This study emerged from a professional development (PD) for urban teachers working in such schools, to expand educational opportunities for elementary students through outdoor science teaching. Engaging frameworks of communities of practice (CoP) and critical pedagogy of place (CPP), this critical ethnographic study investigates how urban elementary teachers engage in discourse about critical issues of place. Additionally, the investigation seeks to understand how a CoP supports such discourse. The primary data for this study were multiple sets of researcher field notes collected from participant teachers during virtual spring and in-person summer PD. Over the course of the PD, participants shifted from viewing their outdoor teaching spaces with a deficit perspective to an asset-focused one. As they visited one another’s teaching sites, the CoP the teachers were a part of created opportunity for discourse about social justice linked to issues of place within their particular school neighborhoods. The ability of urban elementary teachers to connect social justice to issues of place and to the teaching of science has implications for countering the injustice that characterizes many urban communities in the USA and elsewhere.

Spatio-Temporal Contextual Learning for Single Object Tracking on Point Clouds.

Single object tracking (SOT) is one of the most active research directions in the field of computer vision. Compared with the 2-D image-based SOT which has already been well-studied, SOT on 3-D point clouds is a relatively emerging research field. In this article, a novel approach, namely, the contextual-aware tracker (CAT), is investigated to achieve a superior 3-D SOT through spatially and temporally contextual learning from the LiDAR sequence. More precisely, in contrast to the previous 3-D SOT methods merely exploiting point clouds in the target bounding box as the template, CAT generates templates by adaptively including the surroundings outside the target box to use available ambient cues. This template generation strategy is more effective and rational than the previous area-fixed one, especially when the object has only a small number of points. Moreover, it is deduced that LiDAR point clouds in 3-D scenes are often incomplete and significantly vary from frame to another, which makes the learning process more difficult. To this end, a novel cross-frame aggregation (CFA) module is proposed to enhance the feature representation of the template by aggregating the features from a historical reference frame. Leveraging such schemes enables CAT to achieve a robust performance, even in the case of extremely sparse point clouds. The experiments confirm that the proposed CAT outperforms the state-of-the-art methods on both the KITTI and NuScenes benchmarks, achieving 3.9% and 5.6% improvements in terms of precision.

Busworld Website Development To Improve Business Vocabulary Achievement Of Vocational School Marketing Students

This study intends to ascertain the potential, issues, and requirements for learning English for marketing majors at vocational schools. It also seeks to evaluate the feasibility of the Busworld website, which was created as an alternate medium for this research. English communication abilities must be improved by vocational students. Issues that arise with marketing majors at vocational schools show that the same materials are still used in both general education and vocational education. A notable example of technology use in this study is the technology-based learning medium, which took the shape of a website centered around telecommunications. The Sugiyono Model is the model that is adapted for this research, which employs the Research and Development (R&D) technique. Finding Potential, Problems, and Data Analysis, Design Validation, Product Design, Product Testing, Product Revision, Final Product, and Publication are the steps that have been modified. The SMK Pemuda 1 Kesamben, SMK PGRI 2 Sutojayan, and SMKN 1 Kademangan served as the research's field sites. This study's data is quantitative in the form of teacher and student answers and product validation results, and qualitative in the form of observations, interviews, and documentation. The study's findings suggest that using the Busworld website as an alternate medium can help marketing students expand their vocabulary related to business. The results of the media validation and material validation, which were completed by three experts each, and which yielded a score of 98%, indicating high validity, serve as evidence for this. Additionally, 88.5% of the replies from thirty marketing students and three teachers were included in the results of the teacher and student surveys. In summary, the Busworld website, which offers resources such as 100 lists of verbs, 100 lists of nouns, 50 business abbreviations, and 50 conversations with business themes, is a valuable tool for improving the business vocabulary proficiency of marketing students at vocational schools.

Big data and artificial intelligence in post-stroke aphasia: A mapping review

BACKGROUND: Aphasia is an impairment of language as a result of brain damage which can affect individuals after a stroke. Recent research in aphasia has highlighted new technologies and techniques that fall under the umbrella of big data and artificial intelligence (AI). OBJECTIVES: This review aims to examine the extent, range and nature of available research on big data and AI relating to aphasia post stroke. METHODS: A mapping review is the most appropriate format for reviewing the evidence on a broad and emerging topic such as big data and AI in post-stroke aphasia. Following a systematic search of online databases and a two-stage screening process, data was extracted from the included studies. This analysis process included grouping the research into inductively created categories as the different areas within the research topic became apparent. RESULTS: Seventy-two studies were included in the review. The results showed an emergent body of research made up of meta-analyses and quasi-experimental studies falling into defined categories within big data and AI in post-stroke aphasia. The two largest categories were automation, including automated assessment and diagnosis as well as automatic speech recognition, and prediction and association, largely through symptom-lesion mapping and meta-analysis. CONCLUSIONS: The framework of categories within the research field of big data and AI in post-stroke aphasia suggest this broad topic has the potential to make an increasing contribution to aphasia research. Further research is needed to evaluate the specific areas within big data and AI in aphasia in terms of efficacy and accuracy within defined categories.

Refining ChatGPT-Generated Code: Characterizing and Mitigating Code Quality Issues

Since its introduction in November 2022, ChatGPT has rapidly gained popularity due to its remarkable ability in language understanding and human-like responses. ChatGPT, based on GPT-3.5 architecture, has shown great promise for revolutionizing various research fields, including code generation. However, the reliability and quality of code generated by ChatGPT remain unexplored, raising concerns about potential risks associated with the widespread use of ChatGPT-driven code generation. In this article, we systematically study the quality of 4,066 ChatGPT-generated programs of code implemented in two popular programming languages, i.e., Java and Python, for 2,033 programming tasks. The goal of this work is threefold. First, we analyze the correctness of ChatGPT on code generation tasks and uncover the factors that influence its effectiveness, including task difficulty, programming language, time that tasks are introduced, and program size. Second, we identify and characterize potential issues with the quality of ChatGPT-generated code. Last, we provide insights into how these issues can be mitigated. Experiments highlight that out of 4,066 programs generated by ChatGPT, 2,756 programs are deemed correct, 1,082 programs provide wrong outputs, and 177 programs contain compilation or runtime errors. Additionally, we further analyze other characteristics of the generated code through static analysis tools, such as code style and maintainability, and find that 1,930 ChatGPT-generated code snippets suffer from maintainability issues. Subsequently, we investigate ChatGPT’s self-repairing ability and its interaction with static analysis tools to fix the errors uncovered in the previous step. Experiments suggest that ChatGPT can partially address these challenges, improving code quality by more than 20%, but there are still limitations and opportunities for improvement. Overall, our study provides valuable insights into the current limitations of ChatGPT and offers a roadmap for future research and development efforts to enhance the code generation capabilities of artificial intelligence models such as ChatGPT.