mdpi-ai/citelens huggingface dataset:
STEM education institutional change projects: examining enacted approaches through the lens of the Four Categories of Change Strategies Model
Field: EDUCATION

Background: Enacting STEM education reform is a complex task and there are a variety of approaches that might be selected by change agents. When working on an institutional change project to impact multiple parts of the STEM education system, teams of change agents may select multiple strategies and tactics to enact at one time and over multiple years of a project. However, the literature lacks studies which document and analyze strategies and tactics used by change project teams in a way that can be useful for other change agents. The current study seeks to fill this gap by investigating National Science Foundation-funded change initiatives at three public research universities focused on encouraging the adoption of evidenced-based instructional practices by STEM faculty in order to understand the strategies used within and across projects.Results: Qualitative framework analysis using the lens of the Henderson et al. (Journal of Research in Science Teaching 48(8): 952–984, 2011. Four Categories of Change Strategies Model showed that institutional projects enact a wide range of tactics that span the four strategies represented in the four categories of the model both across institutions and within each institution. The analysis documents a number of change tactics not previously described by the model and offers expanded definitions of the change processes that operate within each category in the context of institutional change projects.Conclusion: This descriptive work advances our understanding of the breadth and depth of actions taken by institutional change initiatives and provides insights into types of variations that might be observed based on different institutional contexts. The current analysis both affirms the value of the original model and identifies expanded ways to think about the four categories within the context of institutional change projects.
Diffuse Gliomas with FGFR3-TACC3 Fusions: Oncogenic Mechanisms, Hallmarks, and Therapeutic Perspectives
Field: ONCOLOGY

In 2012, whole-transcriptome sequencing analysis led to the discovery of recurrent fusions involving the FGFR3 and TACC3 genes as the main oncological driver in a subset of human glioblastomas. Since then, FGFR3-TACC3 fusions have been identified in several other solid cancers. Further studies dissected the oncogenic mechanisms of the fusion protein and its complex interplay with cancer cell metabolism. FGFR3-TACC3 fusion-driven gliomas emerged as a defined subgroup with specific clinical, histological, and molecular features. Several FGFR inhibitors were tested in FGFR3-TACC3 fusion-positive gliomas and proved some efficacy, although inferior to the results seen in other FGFR3-TACC3 fusion-driven cancers. In this review, we summarize and discuss the state-of-the-art knowledge resulting from a 10-year research effort in the field, its clinical implications for glioma patients, the potential reasons for targeted therapy failures, and the perspective of emerging treatments.
RETRACTED ARTICLE: Dual Siamese Anchor Points Adaptive Tracker with Transformer for RGBT Tracking
Field: AI

Due to environmental conditions, such as rainy days, foggy days, and dim lighting, objects in visible light images are not prominently displayed, leading to an easy loss of targets during tracking. In recent years, many RGB visible light trackers have achieved significant success in addressing visual tracking challenges. However, these trackers perform poorly when tracking targets under special conditions, such as occlusions and low-light scenarios. In contrast, objects in thermal infrared images are more distinct in poor lighting conditions. Given this characteristic, researchers have shown increased interest in the development of trackers that combine thermal infrared and visible light imagery. However, some mainstream RGBT (red–green–blue and thermal) algorithms, such as MANET and ADNET, are based on the anchor-based theory, requiring consideration of anchor box sizes and introducing a substantial number of hyperparameters. This can lead to suboptimal performance when tracking dynamically changing targets. Moreover, these models rely on convolutional neural networks for feature extraction, which have limitations in capturing global features. In this paper, we introduce a novel training network model called DAPAT, which combines the anchor-free concept with Transformer theory. DAPAT differs from previous models in several ways. Specifically, we have designed a straightforward model to extract precise global features from template and search images. We have also incorporated two enhancement modules into the model to improve template and search images of different sizes while suppressing the impact of non-target images. We employ a dual-stream feature fusion network to reduce the loss of image feature information due to feature correlation operations. Finally, we compare the performance of the tracking model proposed in this paper with some advanced RGBT trackers on three data sets (RGBT234, RGBT210, and GTOT). The test results demonstrate that our tracker exhibits improvements in robustness and success rate, among other performance aspects.
Treatment and Outcomes of Radiation-Induced Soft Tissue Sarcomas of the Extremities and Trunk—A Systematic Review of the Literature
Field: ONCOLOGY

Introduction: Radiation-induced soft tissue sarcomas (RISs) are rare secondary malignancies with a dire prognosis. The literature on the management of these tumors remains scarce due to their low incidence. Our systematic review sought to assess the treatment alternatives and outcomes of patients with RIS. Methods: A systematic review was conducted following the PRISMA guidelines. Our study was registered in PROSPERO (ID: CRD42023438415). Quality assessment was performed using the STROBE checklist. Weighted means for both continuous and categorical values were calculated. Results: Twenty-one studies comprising 1371 patients with RIS were included. The mean latency period from radiation to RIS diagnosis was 14 years, and the mean radiation dose delivered to the primary malignancy was 29.2 Gy. The most common histological type was undifferentiated pleomorphic sarcoma (42.2%), and 64% of all tumors were high-grade. The trunk was the most common location (59%), followed by extremities (21%) and pelvis (11%). Surgery was performed in 68% of patients and, among those with an appendicular tumor, the majority (74%) underwent limb-salvage surgery. Negative margins were attained in 58% of patients. Chemotherapy and radiotherapy were administered in 29% and 15% of patients, respectively. The mean 5-year overall survival was 45%, and the local recurrence and metastasis rates were 39% and 27%, respectively. Conclusions: In our study, the most common treatment was surgical resection, with RT and chemotherapy being administered in less than one third of patients. Patients with RIS exhibited poor oncologic outcomes. Future studies should compare RIS with de novo STS while controlling for confounders.
Successful School Leadership in New Zealand: A Scoping Review
Field: EDUCATION

This article examines the available evidence on what it means to be a successful school leader within the current educational landscape in Aotearoa, New Zealand. It provides a nuanced understanding of common success factors and the contribution of the school principal’s leadership to that success in context. A set of factors that shaped their behaviour and actions is presented that draws attention to cultural relationships and contextual awareness, emphasising not only students’ academic success, but also students’ cultures as vehicles for learning and social change. Findings from this article provide insights into how successful school leaders consider their entire school as a complex system with interconnected parts and build social infrastructures that nurture partnerships with multiple stakeholders.
Nomophobia and Its Association with Depression, Anxiety and Stress (DASS Scale), among Young Adults in Greece
Field: PSYCHOLOGY

Smartphones with their numerous applications have become essential daily equipment, prompting scientific research to deal with the impact of their use on psychosocial health. Under this spectrum, the aim of the present cross-sectional study was to examine the association between nomophobia and the negative emotional states of depression, anxiety, and stress, in relation to self-esteem and sociodemographic data, among the young adult population. The study sample consisted of 1408 young adults aged 18–25 years, participating on a voluntary basis with an online anonymous questionnaire. Data were collected through the “Nomophobia Questionnaire (NMP-Q)”, “Depression Anxiety Stress Scales—short form (DASS-21)”, and Rosenberg Self-Esteem Scale (RSES). The questionnaire also included socio-demographic characteristics and smartphone use variables. Data analysis showed that women were identified with severe depression and stress to a greater extent than men (63.3% vs. 55.1% for depression and 18.1% vs. 13.8% for stress scale). With respect to nomophobia, participants with severe levels of nomophobia also exhibited severe levels of negative emotional states in all DASS components, i.e., 40.6% in depression, 73.7% in anxiety, and 32.7% in stress (all p values < 0.001). Participants with severe levels of depression and anxiety were very often checking their phone and used it in all daily activities. Moreover, correlation analysis revealed that self-esteem had a moderating effect on the relationship between nomophobia and DASS, a fact that modifies the association between the involved variables: stronger relationships appeared between nomophobia and DASS components in individuals with normal/high self-esteem than in individuals with low self-esteem.
SNP array genomic analysis of matched pairs of brain and liver metastases in primary colorectal cancer
Field: ONCOLOGY

Purpose Brain metastasis formation is a rare and late event in colorectal cancer (CRC) patients and associated with poor survival. In contrast to other metastatic sites, the knowledge on chromosomal aberrations in brain metastases is very limited. Methods Therefore, we carried out single nucleotide polymorphism (SNP) array analyses on matched primary CRC and brain metastases of four patients as well as on liver metastases of three patients. Results Brain metastases showed more chromosomal aberrations than primary tumors or liver metastases. Commonly occurring aberrations were gain of 8q11.1-q24.3 (primary CRC), gain of 13q12.13-q12.3 (liver metastases), and gain of 20q11.1-q13.33 (brain metastases). Furthermore, we found one copy-neutral loss of heterozygosity (cn-LOH) region on chromosome 3 in primary CRC, three cn-LOH regions in liver metastases and 23 cn-LOH regions in brain metastases, comprising 26 previously undescribed sites. Conclusion The more frequent occurrence of cn-LOHs and subsequently affected genes in brain metastases shed light on the pathophysiology of brain metastasis formation. Further pairwise genetic analyses between primary tumors and their metastases will help to define the role of affected genes in cn-LOH regions.
Models of classroom assessment for course-based research experiences
Field: EDUCATION

Course-based research pedagogy involves positioning students as contributors to authentic research projects as part of an engaging educational experience that promotes their learning and persistence in science. To develop a model for assessing and grading students engaged in this type of learning experience, the assessment aims and practices of a community of experienced course-based research instructors were collected and analyzed. This approach defines four aims of course-based research assessment—(1) Assessing Laboratory Work and Scientific Thinking; (2) Evaluating Mastery of Concepts, Quantitative Thinking and Skills; (3) Appraising Forms of Scientific Communication; and (4) Metacognition of Learning—along with a set of practices for each aim. These aims and practices of assessment were then integrated with previously developed models of course-based research instruction to reveal an assessment program in which instructors provide extensive feedback to support productive student engagement in research while grading those aspects of research that are necessary for the student to succeed. Assessment conducted in this way delicately balances the need to facilitate students’ ongoing research with the requirement of a final grade without undercutting the important aims of a CRE education.
Towards Cancer Nanoradiopharmaceuticals—Radioisotope Nanocarrier System for Prostate Cancer Theranostics Based on Radiation-Synthesized Polymer Nanogels
Field: ONCOLOGY

Despite the tremendous development of oncology, prostate cancer remains a debilitating malignancy. One of the most promising approaches to addressing this issue is to exploit the advancements of nanomedicine in combination with well-established nuclear medicine and radiotherapy. Following this idea, we have developed a radioisotope nanocarrier platform of electron-beam-synthesized nanogels based on poly(acrylic acid). We have developed a functionalization protocol, showing the very high (>97%) efficiency of the conjugation in targeting a ligand–bombesin derivative. This engineered peptide can bind gastrin-releasing peptide receptors overexpressed in prostate cancer cells; moreover, it bears a radioisotope-chelating moiety. Our nanoplatform exhibits very promising performance in vitro; the radiolabeled nanocarriers maintained high radiochemical purity of >90% in both the labeling buffer and human serum for up to 14 days. The application of the targeted nanocarrier allowed also effective and specific uptake in PC-3 prostate cancer cells, up to almost 30% after 4 h, which is a statistically significant improvement in comparison to carrier-free radiolabeled peptides. Although our system requires further studies for more promising results in vivo, our study represents a vital advancement in radionanomedicine—one of many steps that will lead to effective therapy for castration-resistant prostate cancer.
Closing the Gap: Automated Distractor Generation in Japanese Language Testing
Field: EDUCATION

Recent advances in natural language processing have increased interest in automatic question generation, particularly in education (e.g., math, biology, law, medicine, and languages) due to its efficiency in assessing comprehension. Specifically, multiple-choice questions have become popular, especially in standardized language proficiency tests. However, manually creating high-quality tests is time-consuming and challenging. Distractor generation, a critical aspect of multiple-choice question creation, is often overlooked, yet it plays a crucial role in test quality. Generating appropriate distractors requires ensuring they are incorrect but related to the correct answer (semantically or contextually), are grammatically correct, and of similar length to the target word. While various languages have seen research in automatic distractor generation, Japanese has received limited attention. This paper addresses this gap by automatically generating cloze tests, including distractors, for Japanese language proficiency tests, evaluating the generated questions’ quality, difficulty, and preferred distractor types, and comparing them to human-made questions through automatic and manual evaluations.
Group-Fusion One-Dimensional Convolutional Neural Network for Ballistic Target High-Resolution Range Profile Recognition with Layer-Wise Auxiliary Classifiers
Field: AI

Ballistic missile defense systems require accurate target recognition technology. Effective feature extraction is crucial for this purpose. The deep convolutional neural network (CNN) has proven to be an effective method for recognizing high-resolution range profiles (HRRPs) of ballistic targets. It excels in perceiving local features and extracting robust features. However, the standard CNN's fully connected manner results in high computational complexity, which is unsuitable for deployment in real-time missile defense systems with stringent performance requirements. To address the issue of computational complexity in HRRP recognition based on the standard one-dimensional CNN (1DCNN), we propose a lightweight network called group-fusion 1DCNN with layer-wise auxiliary classifiers (GFAC-1DCNN). GFAC-1DCNN employs group convolution (G-Conv) instead of standard convolution to effectively reduce model complexity. Simply using G-Conv, however, may decrease model recognition accuracy due to the lack of information flow between feature maps generated by each G-Conv. To overcome this limitation, we introduce a linear fusion layer to combine the output features of G-Convs, thereby improving recognition accuracy. Additionally, besides the main classifier at the deepest layer, we construct layer-wise auxiliary classifiers for different hierarchical features. The results from all classifiers are then fused for comprehensive target recognition. Extensive experiments demonstrate that GFAC-1DCNN with such simple and effective techniques achieves higher overall testing accuracy than state-of-the-art ballistic target HRRP recognition models, while significantly reducing model complexity. It also exhibits a higher recall rate for warhead recognition compared to other methods. Based on these compelling results, we believe this work is valuable in reducing workload and enhancing missile interception rates in missile defense systems.
Prevention of Work Absence Due to Back Pain: A Network Meta-Analysis
Field: PSYCHOLOGY

This paper reviewed the most effective strategies for preventing work absence due to back pain (BP) and BP episodes (the number of people reporting back pain). We searched randomized controlled trials (RCTs) of prevention strategies for BP from previous meta-analyses, PubMed, CENTRAL, and Embase and conducted a network meta-analysis. Thirteen RCTs (2033 participants) were included. Low- to high-quality evidence showed that exercise combined with ergonomics, education, back belts, and education combined with ergonomics did not prevent sickness absenteeism or BP episodes. There was moderate-quality evidence that exercise, especially resistance exercise, was the best prevention strategy to reduce the number of people reporting absenteeism due to BP (risk ratio [RR] = 0.10; 95% CI: 0.01 to 0.69). Moderate-quality evidence suggested that resistance and stretching exercises combined with education was the best prevention strategy to reduce pain (RR = 0.80; 95% CI: 0.67 to 0.96) and the number of absenteeism days for BP (standardized mean difference [SMD] = −0.39; 95% CI: −0.77 to −0.02). In conclusion, exercise, especially resistance and stretching exercises, and exercise combined with education were ranked as the best interventions to prevent sickness absenteeism and BP episodes.
Therapeutic Targeting of Glioblastoma and the Interactions with Its Microenvironment
Field: ONCOLOGY

Glioblastoma (GBM) is the most common primary malignant brain tumour, and it confers a dismal prognosis despite intensive multimodal treatments. Whilst historically, research has focussed on the evolution of GBM tumour cells themselves, there is growing recognition of the importance of studying the tumour microenvironment (TME). Improved characterisation of the interaction between GBM cells and the TME has led to a better understanding of therapeutic resistance and the identification of potential targets to block these escape mechanisms. This review describes the network of cells within the TME and proposes treatment strategies for simultaneously targeting GBM cells, the surrounding immune cells, and the crosstalk between them.
Mental health professionals’ perspectives on the relevance of religion and spirituality to mental health care
Field: PSYCHOLOGY

Background: A large body of evidence indicates that spiritual and religious backgrounds, beliefs, and practices (SRBBPs) are related to better psychological health. Spirituality and religion (R/S) are also important aspects of multicultural diversity. There is evidence that clients would like to talk about their spirituality, and that including it in assessment and treatment planning can be beneficial. However, the extent to which practicing mental health professionals view SRBBPs as relevant to mental health and clinical practice is unclear. Methods: A survey examining several aspects of addressing SRBBPs in clinical practice was distributed to 894 professionals across mental health disciplines, including psychiatry, psychology, social work, marriage family therapy, licensed professional counselors, certified chemical dependency counselors, and psychiatric mental health nurses. Results: 89% of mental health professionals agreed that clinicians should receive training in R/S competencies. There were no differences between mental health disciplines in ratings of importance of such training. Younger individuals and those who identify as more spiritual were more likely to consider R/S training as important. Although 47.1% of professionals had not received much R/S training, many perceived themselves to be highly competent in R/S clinical integration practices (57.8% considered themselves able to display them very much or completely). In addition, respondents with more R/S training evaluated themselves as more proficient in R/S clinical integration. Nearly two-thirds (65.2%) of respondents reported encountering few to no barriers to engaging in R/S competent mental health care. Conclusions: There is a growing consensus among mental health care professionals that mental health professionals should be trained in R/S competencies. Strong agreement exists that basic R/S competencies include respect, empathy, examination of bias, and routine assessment of R/S in mental health care. Four in five of those surveyed agree that more active competencies, such as identifying and addressing religious and spiritual struggles and problems and helping clients explore and access R/S strengths and resources should be included, whereas one in five report less comfort with these competencies.
A Time Window Analysis for Time-Critical Decision Systems with Applications on Sports Climbing
Field: AI

Human monitoring systems are already utilized in various fields like assisted living, healthcare or sport and fitness. They are able to support in everyday life or act as a pre-warning system. We developed a system to monitor the ascent of a sport climber. It is integrated in a belay device. This paper presents the first time series analysis regarding the fall of a climber utilizing such a system. A Convolutional Neural Network handles the feature engineering part of the sensor information as well as the classification of the task at hand. In this way, the time is implicitly considered by the network. An analysis regarding the size of the time window was carried out with a focus on exploring the respective results. The neural network models were then tested against an already-existing principle based on a mechanical mechanism. We show that the size of the time window is a decisive factor in a time critical system. Depending on the size of the window, the mechanical principle was able to outperform the neural network. Nevertheless, most of our models outperformed the basic principle and returned promising results in predicting the fall of a climber within up to 91.8 ms.