mdpi-ai/citelens huggingface dataset:
Guidelines for Supporting a Community of Inquiry through Graded Online Discussion Forums in Higher Education
Field: EDUCATION

Graded online discussion forums allow students to interact with course content, peers, and instructors. These discussions have the potential to enhance students’ learning experiences significantly. By adding graded online discussions to an online structured Master’s program in Education, it was necessary to determine the value of these discussions and their contribution to creating an online community. The purpose of this study, therefore, was to determine how a community of inquiry could support graded online discussions. The study used the Community of Inquiry theoretical framework as its basis. A qualitative exploratory case study design was used, involving eleven purposefully selected participants who were enrolled for a structured master’s program in Education. Data were collected from two sources: feedback from students on their experiences of the online discussions, and the actual online discussions. The data were analyzed using the six-phase thematic analysis approach following a deductive approach. This study revealed that these discussions supported students’ learning and created an online learning community promoting social, cognitive, and teaching presences. These findings have implications for practice. Firstly, fostering social presence is essential for online discussions because it leads to increased engagement, motivation, a sense of belonging, and collaboration. Secondly, online discussions need to be designed with clear guidelines, structured questions, and discussion opportunities. Lastly, online discussions designed to promote cognitive presence challenge students, encourage debate, and assist them in gaining the needed knowledge and higher order thinking skills. Based on these findings, the unique contribution of this study is to provide guidelines for fostering discussion forum participation within the Community of Inquiry (COI) framework. The suggested guidelines can serve as a resource to facilitate effective graded discussion forums in higher education contexts.
Supporting Emergent Writing in Preschool Classrooms: Results of a Professional Development Program
Field: EDUCATION

Emergent writing is a key component of early literacy development and contributes to later school success, yet it receives little attention in most preschool classrooms. This paper presents results of a quasi-experimental study of a teacher professional development package that included writing as one of four focal areas. The study was conducted in 15 Head Start classrooms located in the U.S. state of Hawaiʻi. The participants were 39 lead and assistant teachers and 240 children. Intervention teachers had higher quality writing environments and overall classroom environments, while intervention children showed better outcomes on emergent reading and upper case letter knowledge. Emergent writing was assessed only in the intervention group, where children showed large gains along with changes in code-related skills needed for invented spelling. Results are discussed in terms of recommended practices for early writing instruction and teacher professional development.
Neo-Adjuvant Treatment in Primary Resectable Pancreatic Cancer: A Systematic Review and PRISMA-Compliant Updated Metanalysis of Oncological Outcomes
Field: ONCOLOGY

Background: Despite advances in treatment, the prognosis of resectable pancreatic adenocarcinoma remains poor. Neoadjuvant therapy (NAT) has gained great interest in hopes of improving survival. However, the results of available studies based on different treatment approaches, such as chemotherapy and chemoradiotherapy, showed contrasting results. The aim of this systematic review and meta-analysis is to clarify the benefit of NAT compared to upfront surgery (US) in primarily resectable pancreatic adenocarcinoma. Methods: A PRISMA literature review identified 139 studies, of which 15 were finally included in the systematic review and meta-analysis. All data from eligible articles was summarized in a systematic summary and then used for the meta-analysis. Specifically, we used HR for OS and DFS and risk estimates (odds ratios) for the R0 resection rate and the N+ rate. The risk of bias was correctly assessed according to the nature of the studies included. Results: From the pooled HRs, OS for NAT patients was better, with an HR for death of 0.80 (95% CI: 0.72–0.90) at a significance level of less than 1%. In the sub-group analysis, no difference was found between patients treated with chemoradiotherapy or chemotherapy exclusively. The meta-analysis of seven studies that reported DFS for NAT resulted in a pooled HR for progression of 0.66 (95% CI: 0.56–0.79) with a significance level of less than 1%. A significantly lower risk of positive lymph nodes (OR: 0.45; 95% CI: 0.32–0.63) and an improved R0 resection rate (OR: 1.70; 95% CI: 1.23–2.36) were also found in patients treated with NAT, despite high heterogeneity. Conclusions: NAT is associated with improved survival for patients with resectable pancreatic adenocarcinoma; however, the optimal treatment strategy has yet to be defined, and further studies are required.
Glucocorticoid Receptor Activation in Lobular Breast Cancer Is Associated with Reduced Cell Proliferation and Promotion of Metastases
Field: ONCOLOGY

Estrogen receptor-positive (ER+) invasive lobular breast cancer (ILC) comprises about ~15% of breast cancer. ILC’s unique genotypic (loss of wild type E-cadherin expression) and phenotypic (small individual round cancer cells that grow in discontinuous nests) are thought to contribute to a distinctive pattern of metastases to serosal membranes. Unlike invasive ductal carcinoma (IDC), ILC metastases often intercalate into the mesothelial layer of the peritoneum and other serosal surfaces. While ER activity is a known driver of ILC proliferation, very little is known about how additional nuclear receptors contribute to ILC’s distinctive biology. In ER+ IDC, we showed previously that glucocorticoid receptor (GR) activity inhibits pro-proliferative gene expression and cell proliferation. Here we examined ER+ ILC models and found that GR activation similarly reduces S-phase entry gene expression and ILC proliferation. While slowing tumor growth rate, our data also suggest that GR activation results in an enhanced metastatic phenotype through increasing integrin-encoding gene expression, extracellular matrix protein adhesion, and mesothelial cell clearance. Moreover, in an intraductal mouse mammary gland model of ILC, we found that GR expression is associated with increased bone metastases despite slowed primary mammary tumor growth. Taken together, our findings suggest GR-mediated gene expression may contribute to the unusual characteristics of ILC biology.
A Single-Center Study of the Impact of the COVID-19 Pandemic on the Organization of Healthcare Service Delivery to Patients with Head and Neck Cancer
Field: ONCOLOGY

The aim of this study was to identify and assess the impact of the COVID-19 pandemic on the diagnosis and treatment of head and neck cancer (HNC) patients of the Department of Otolaryngology, Head and Neck Surgery of the 4th Military Teaching Hospital in Wroclaw for whom oncological treatment was planned by a cancer case board between March 2018 and February 2022. We analysed the medical records of 625 patients. In order to verify whether the relationships between the analysed features were statistically significant, the chi-square test of independence and the Student’s t-test for independent samples were used (p < 0.05). Our analysis showed that the impact of the pandemic on the organization of health service delivery to HNC patients was not uniform. The largest difference in the number of formulated treatment plans was observed at the beginning of the pandemic (22.1% reduction compared with the year before the pandemic). During the pandemic, the proportion of patients admitted on the basis of a DILO (diagnosis and oncological treatment) card issued by a primary care physician, instead of a regular referral to hospital, issued also by a primary care physician, was significantly higher compared with the that during the pre-pandemic period. The majority of cancer patients with a oncological treatment planned during the pandemic lived in urban areas. During the pandemic, the number of patients with more-advanced-stage cancer, assessed on the basis of the type of planned treatment (radical vs. palliative), did not increase compared with that during the pre-pandemic period. However, our follow-up period was quite short. It is necessary to intensify activities aimed at promoting health and increasing health awareness in people living in rural areas and setting long-term priorities and objectives for health policies at the national, regional and local levels, with particular focus on this group of people.
Self-Supervised Learning for Industrial Image Anomaly Detection by Simulating Anomalous Samples
Field: AI

Industrial image anomaly detection (AD) is a critical issue that has been investigated in different research areas. Many works have attempted to detect anomalies by simulating anomalous samples. However, how to simulate abnormal samples remains a significant challenge. In this study, a method for simulating anomalous samples is designed. First, for the object category, patch extraction and patch paste are designed to ensure that the extracted image patches come from the objects and are pasted to the objects in the image. Second, based on the statistical analysis of various anomalies’ presence, a combination of data augmentation is proposed to cover various anomalies as much as possible. The method is evaluated on MVTec AD and BTAD datasets; the experimental results demonstrate that our method achieves an overall detection AUC of 97.6% in MVTec AD datasets, outperforming the baseline by 1.5%, and the improvement over VT-ADL method is 4.3% on the BTAD datasets, demonstrating our method’s effectiveness and generalization.
Enhancing prognostic accuracy: a SEER-based analysis for overall and cancer-specific survival prediction in cervical adenocarcinoma patients
Field: ONCOLOGY

Background: Cervical adenocarcinoma (CA) is the second most prevalent histological subtype of cervical cancer, following cervical squamous cell carcinoma (CSCC). As stated in the guidelines provided by the National Comprehensive Cancer Network, they are staged and treated similarly. However, compared with CSCC patients, CA patients are more prone to lymph node metastasis and recurrence with a poorer prognosis. The objective of this research was to discover prognostic indicators and develop nomograms that can be utilized to anticipate the overall survival (OS) and cancer-specific survival (CSS) of patients diagnosed with CA. Methods: Using the Surveillance, Epidemiology, and End Result (SEER) database, individuals with CA who received their diagnosis between 2004 and 2015 were identified. A total cohort (n = 4485) was randomly classified into two separate groups in a 3:2 ratio, to form a training cohort (n = 2679) and a testing cohort (n = 1806). Overall survival (OS) was the primary outcome measure and cancer-specific survival (CSS) was the secondary outcome measure. Univariate and multivariate Cox analyses were employed to select significant independent factors and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis was utilized to develop predictive nomogram models. The predictive accuracy and discriminatory ability of the nomogram were assessed by employing metrics such as the calibration curve, receiver operating characteristic (ROC) curve, and the concordance index (C-index). Results: Age, Tumor Node Metastasis stages (T, N, and M), SEER stage, grade, and tumor size were assessed as common independent predictors of both OS and CSS. The C-index value of the nomograms for predicting OS was 0.832 (95% CI 0.817–0.847) in the training cohort and 0.823 (95% CI 0.805–0.841) in the testing cohort. Conclusion: We developed and verified nomogram models for predicting 1-, 3- and 5-year OS and CSS among patients with cervical adenocarcinoma. These models exhibited excellent performance in prognostic prediction, providing support and assisting clinicians in assessing survival prognosis and devising personalized treatments for CA patients.
Clinical characteristics of sarcoma patients: a population-based data analysis from a German clinical cancer registry
Field: ONCOLOGY

Purpose: Sarcomas are a heterogeneous group of malignant neoplasms with a wide range of histological types and occur in almost any anatomic site and side. This study evaluated the prognostic factors in sarcoma patients based on German clinical cancer registry data. Methods: The German clinical cancer register of Saxony-Anhalt was used for all data analyses. Sarcoma cases of all clinical or pathological T-stages (T1a–T4c), all N-stages (N0-3) and M-stages (0–1b) corresponding to the Union for International Cancer Control (UICC) stages I to IVB were considered. In our analyses, 787 cases diagnosed between 2005 and 2022 were included. Further, we assessed the association of cancer-related parameters with mortality and hazard ratios (HR) from the Cox proportional hazard models. We included sex, age at diagnosis, histological grade, T-, N- and M-stages, tumor size, tumor localization and tumor side as parameters in our regression models. Results: The majority of sarcoma patients were diagnosed with leiomyosarcoma (12%), liposarcoma (11%), angiosarcoma (5.3%) and myxofibrosarcoma (2.7%). In our univariate regression models, tumors localized in more than one location, head, face and neck region as well as the pelvis and lower extremity were associated with increased mortality risk (more than one location: HR 7.10, 95% CI 2.20–22.9; head, face and neck: HR 1.35, 95% CI 0.89–2.06; pelvis: HR 1.27, 95% CI 0.86–1.89; lower extremity: HR 1.44, 95% CI 1.05–1.96). Higher histological grades, UICC-grades and TNM-stages were related to a higher mortality risk. Differing histological subtypes had significant influence on overall survival and progression-free survival. Patients diagnosed with fibromyxoid sarcoma, rhabdomyosarcoma and angiosarcoma were related to higher mortality risk compared to other histological subtypes (fibromyxoid sarcoma: HR 5.2, 95% CI 0.71–38.1; rhabdomyosarcoma: HR 2.93, 95% CI 1.44–6.00; angiosarcoma: HR 1.07, 95% CI 0.53–2.18). Conclusions: Histological grade, tumor size, nodal and distant metastasis, tumor localization and histological subtype were determined as prognostic factors in terms of survival.
Gender gap in STEM pathways: the role of secondary curricula in a highly differentiated school system—the case of Chile
Field: EDUCATION

Background: STEM fields are instrumental in increasing the technological and innovative capacity of the economy. As women are underrepresented in the STEM workforce, diverse strategies have been implemented to boost their preparedness and interest in these fields, including early exposure to academic and vocational STEM courses. Using the case of Chile’s highly differentiated school system, this paper examines the role of secondary curricula on students’ enrollment and persistence in STEM programs offered by vocational postsecondary institutions and universities. In doing so, we seek to identify whether exposure to STEM courses within the academic or vocational tracks translates into fewer gender differences in STEM higher education.Results: Our results reveal that upper-secondary tracks connected to STEM courses are positively associated with enrollment in STEM higher education and, to some degree, persistence. More specifically, exposure to STEM courses in the academic track is the most effective path to boost chances of enrolling in STEM university programs but has no connection to later persistence. In contrast, applied STEM courses within the vocational tracks perform better in the case of STEM programs in postsecondary vocational institutions both in enrollment and persistence. However, this STEM pipeline significantly amplifies gender gaps as males benefit more than women from early exposure to applied STEM courses. We also found that other indirect routes, such as enrolling in STEM university programs from the vocational track with applied STEM courses, boost female participation in these programs, helping reduce gender gaps.Conclusions: While secondary STEM courses attract more female students to STEM higher education, they alone are insufficient to achieve gender equality in STEM fields as gender gaps widen in the more effective routes. In highly differentiated school systems, policymakers and high school leaders should offer increased support to women interested in STEM studies and careers across all secondary tracks to boost female participation in STEM fields. At the same time, all high school students should be able to select both academic and applied STEM courses as a part of their non-mandatory curriculum.
Unveiling the Transparency of Prediction Models for Spatial PM2.5 over Singapore: Comparison of Different Machine Learning Approaches with eXplainable Artificial Intelligence
Field: AI

Aerosols play a crucial role in the climate system due to direct and indirect effects, such as scattering and absorbing radiant energy. They also have adverse effects on visibility and human health. Humans are exposed to fine PM2.5, which has adverse health impacts related to cardiovascular and respiratory-related diseases. Long-term trends in PM concentrations are influenced by emissions and meteorological variations, while meteorological factors primarily drive short-term variations. Factors such as vegetation cover, relative humidity, temperature, and wind speed impact the divergence in the PM2.5 concentrations on the surface. Machine learning proved to be a good predictor of air quality. This study focuses on predicting PM2.5 with these parameters as input for spatial and temporal information. The work analyzes the in situ observations for PM2.5 over Singapore for seven years (2014–2021) at five locations, and these datasets are used for spatial prediction of PM2.5. The study aims to provide a novel framework based on temporal-based prediction using Random Forest (RF), Gradient Boosting (GB) regression, and Tree-based Pipeline Optimization Tool (TP) Auto ML works based on meta-heuristic via genetic algorithm. TP produced reasonable Global Performance Index values; 7.4 was the highest GPI value in August 2016, and the lowest was −0.6 in June 2019. This indicates the positive performance of the TP model; even the negative values are less than other models, denoting less pessimistic predictions. The outcomes are explained with the eXplainable Artificial Intelligence (XAI) techniques which help to investigate the fidelity of feature importance of the machine learning models to extract information regarding the rhythmic shift of the PM2.5 pattern.
A General Machine Learning Model for Assessing Fruit Quality Using Deep Image Features
Field: AI

Fruit quality is a critical factor in the produce industry, affecting producers, distributors, consumers, and the economy. High-quality fruits are more appealing, nutritious, and safe, boosting consumer satisfaction and revenue for producers. Artificial intelligence can aid in assessing the quality of fruit using images. This paper presents a general machine learning model for assessing fruit quality using deep image features. This model leverages the learning capabilities of the recent successful networks for image classification called vision transformers (ViT). The ViT model is built and trained with a combination of various fruit datasets and taught to distinguish between good and rotten fruit images based on their visual appearance and not predefined quality attributes. The general model demonstrated impressive results in accurately identifying the quality of various fruits, such as apples (with a 99.50% accuracy), cucumbers (99%), grapes (100%), kakis (99.50%), oranges (99.50%), papayas (98%), peaches (98%), tomatoes (99.50%), and watermelons (98%). However, it showed slightly lower performance in identifying guavas (97%), lemons (97%), limes (97.50%), mangoes (97.50%), pears (97%), and pomegranates (97%).
The association between social support provision, psychological capital, subjective well-being and sense of indebtedness among undergraduates with low socioeconomic status
Field: PSYCHOLOGY

Background: Social support consists of receipt and provision in the interpersonal exchange process. Many studies have explored and verified the effect of received social support. This study focuses on whether and when social support provision can benefit the providers’ positive psychological capital and subjective well-being. Methods: A sample of 732 Chinese undergraduates with low socioeconomic status completed questionnaires on social support provision, psychological capital, life satisfaction, positive affect, negative affect, and sense of indebtedness. Results: The correlation and regression analyses showed that impoverished college students’ social support provision was positively associated with life satisfaction, positive affect, and psychological capital and negatively associated with negative affect. The interaction between the sense of indebtedness and social support provision was negatively associated with life satisfaction, positive affect, and psychological capital, not significantly associated with negative affect. Conclusion: The results demonstrated that giving social support can be as beneficial as receiving social support, and the sense of indebtedness can limit the benefits. Individuals with a lower sense of indebtedness are more likely to benefit from social support provision. The findings have implications for marginalized groups’ subjective well-being and positive psychological capital and show the necessity of guiding individuals to provide social support while maintaining their autonomy.
Teacher Development for Equitable Mathematics Classrooms: Reflecting on Experience in the Context of Performativity
Field: EDUCATION

In this article, we chart the development of one of us—Sue Hough—from a teacher who wanted students to understand to one who gained new critical understandings of student thinking, pedagogy, and the very nature of mathematics. We comment on the role of research interventions and learning communities in this development, with a particular focus on Sue’s encounter with Realistic Mathematics Education and the connections it makes between informal and formal mathematics through the pedagogy of guided reinvention. Development towards teaching that enables all learners to make sense of mathematics requires fundamental changes in pedagogic practice and a reconceptualisation of progress. Bringing about such radical change relies on one further aspect of Sue’s story—the freedom to experiment and learn as a teacher. We note the remoteness of this possibility in a climate of performativity and marketised education, and we discuss the implications of Sue’s journey for our pedagogical responsibilities in professional development today.