mdpi-ai/citelens huggingface dataset:
Predicting Biomass and Yield in a Tomato Phenotyping Experiment Using UAV Imagery and Random Forest
Field: AI

Biomass and yield are key variables for assessing the production and performance of agricultural systems. Modeling and predicting the biomass and yield of individual plants at the farm scale represents a major challenge in precision agriculture, particularly when salinity and other abiotic stresses may play a role. Here, we evaluate a diversity panel of the wild tomato species (Solanum pimpinellifolium) through both field and unmanned aerial vehicle (UAV)-based phenotyping of 600 control and 600 salt-treated plants. The study objective was to predict fresh shoot mass, tomato fruit numbers, and yield mass at harvest based on a range of variables derived from the UAV imagery. UAV-based red–green–blue (RGB) imageries collected 1, 2, 4, 6, 7, and 8 weeks before harvest were also used to determine if prediction accuracies varied between control and salt-treated plants. Multispectral UAV-based imagery was also collected 1 and 2 weeks prior to harvest to further explore predictive insights. In order to estimate the end of season biomass and yield, a random forest machine learning approach was implemented using UAV-imagery-derived predictors as input variables. Shape features derived from the UAV, such as plant area, border length, width, and length, were found to have the highest importance in the predictions, followed by vegetation indices and the entropy texture measure. The multispectral UAV imagery collected 2 weeks prior to harvest produced the highest explained variances for fresh shoot mass (87.95%), fruit numbers (63.88%), and yield mass per plant (66.51%). The RGB UAV imagery produced very similar results to those of the multispectral UAV dataset, with the explained variance reducing as a function of increasing time to harvest. The results showed that predicting the yield of salt-stressed plants produced higher accuracies when the models excluded control plants, whereas predicting the yield of control plants was not affected by the inclusion of salt-stressed plants within the models. This research demonstrates that it is possible to predict the average biomass and yield up to 8 weeks prior to harvest within 4.23% of field-based measurements and up to 4 weeks prior to harvest at the individual plant level. Results from this work may be useful in providing guidance for yield forecasting of healthy and salt-stressed tomato plants, which in turn may inform growing practices, logistical planning, and sales operations.
On Consequentialism and Fairness
Field: AI

Recent work on fairness in machine learning has primarily emphasized how to define, quantify, and encourage “fair” outcomes. Less attention has been paid, however, to the ethical foundations which underlie such efforts. Among the ethical perspectives that should be taken into consideration is consequentialism, the position that, roughly speaking, outcomes are all that matter. Although consequentialism is not free from difficulties, and although it does not necessarily provide a tractable way of choosing actions (because of the combined problems of uncertainty, subjectivity, and aggregation), it nevertheless provides a powerful foundation from which to critique the existing literature on machine learning fairness. Moreover, it brings to the fore some of the tradeoffs involved, including the problem of who counts, the pros and cons of using a policy, and the relative value of the distant future. In this paper we provide a consequentialist critique of common definitions of fairness within machine learning, as well as a machine learning perspective on consequentialism. We conclude with a broader discussion of the issues of learning and randomization, which have important implications for the ethics of automated decision making systems.
The Impact of Learning Strategies and Future Orientation on Academic Success: The Moderating Role of Academic Self-Efficacy among Italian Undergraduate Students
Field: EDUCATION

Promoting academic success among undergraduate students is crucial for tackling the need to foster employability competencies. Low levels of academic attainment in higher education, along with the increasing number of persons participating in tertiary education, represent crucial trends, which need to be studied in order to develop efficient retention practices. The current study aimed to investigate the relationship between relevant factors that can foster academic success: learning strategies, future orientation, and academic self-efficacy. To this purpose, a longitudinal study was performed on a sample of N = 87 undergraduate students from one of the largest Italian universities (63.4% males, 74.2% enrolled in the first year). Participants filled in an online questionnaire at two different time points, with a time lag of 12 months. Results of a moderated mediation model indicated that the relationship between learning strategies at Time 1 (T1) and Grade Point Average (GPA) at Time 2 (T2) was mediated by students’ future orientation. Moreover, this association was moderated by T1 academic self-efficacy. These results suggest that learning strategies positively influence GPA through an enhanced future orientation, in particular when students report high or medium levels of self-efficacy. The current findings invite a thorough review of training interventions for improving academic achievement.
Prognostic significance of VEGF and components of the plasminogen activator system in endometrial cancer
Field: ONCOLOGY

Objective The plasminogen activator system (PAS) and vascular endothelial growth factor (VEGF) are important in the carcinogenesis and play a key role in cancer invasion and mediating metastasis of carcinomas. The aim of the study was to evaluate the correlation of serum levels of VEGF and components of the PAS with clinicopathological risk factors and outcome in patients with endometrial cancer (EC). Methods Preoperative blood was collected from 173 patients treated for EC between 1999 and 2009. Serum concentrations of VEGF, urokinase plasminogen activator (uPA) tissue plasminogen activator (tPA), plasminogen activator inhibitor type-1 (PAI-1) and -2 (PAI-2) were assessed by enzyme-linked immunosorbent assays (ELISA). Results Serum levels of VEGF and components of the PAS were significantly associated with stage of the disease, tumor histology, tumor grade, myometrial invasion (MI), presence of lymphovascular space invasion (LVSI) and lymph node metastases (LNM). Preoperative serum levels of PAI-1 and -2 and tPA were higher in patients who experienced a recurrence than in patients who remained disease free (p < 0.01). PAI-1 and -2 and tPA were significantly independent prognostic factors for DFS with a HR of 3.85 (95% CI 1.84–8.07), 3.90 (95% CI 1.75–8.66) and 2.53 (95% CI 1.16–5.55), respectively. PAI-1 and tPA turned out to be independent prognostic factors for OS, with a HR of 2.09 (95% CI 1.08–4.05) and 2.16 (95% CI 1.06–4.44), respectively. Conclusion Serum levels of VEGF and components of the PAS at primary diagnosis were associated with well-known clinicopathological risk factors such as; FIGO stage, tumor histology, tumor grade, MI, LVSI and LNM. High concentrations of PAI-1 and-2 and tPA are independent factors for poor prognosis in patients with endometrial cancer.
Tales from within: Gifted Students’ Lived Experiences with Teaching Practices in Regular Classrooms
Field: EDUCATION

Gifted students in regular classrooms have fewer opportunities to develop activities that are based on their characteristics as learners and address their needs; however, many of them spend most of their school time in these classrooms. The results presented here were part of a 2-year qualitative project that analyzed 12 Chilean gifted students’ lived experiences in regular classrooms by exploring the factors that foster and hinder their learning through the use of photos, focus groups, and interviews. The results showed students’ discontent with the national curriculum and teaching practices related to rigidity, lack of meaning, and unchallenging assessments. Nevertheless, positive experiences were reported related to teaching strategies, especially when they add novelty and move away from traditional approaches. Waiting experiences were common, but were often seen by students as opportunities for creative production. Methods for engaging gifted students in their learning are highlighted.
Cities of the Future? The Potential Impact of Artificial Intelligence
Field: AI

Artificial intelligence (AI), like many revolutionary technologies in human history, will have a profound impact on societies. From this viewpoint, we analyze the combined effects of AI to raise important questions about the future form and function of cities. Combining knowledge from computer science, urban planning, and economics while reflecting on academic and business perspectives, we propose that the future of cities is far from being a determined one and cities may evolve into ghost towns if the deployment of AI is not carefully controlled. This viewpoint presents a fundamentally different argument, because it expresses a real concern over the future of cities in contrast to the many publications who exclusively assume city populations will increase predicated on the neoliberal urban growth paradigm that has for centuries attracted humans to cities in search of work.
Reducing withdrawal and failure rates in introductory programming with subgoal labeled worked examples
Field: EDUCATION

Background: Programming a computer is an increasingly valuable skill, but dropout and failure rates in introductory programming courses are regularly as high as 50%. Like many fields, programming requires students to learn complex problem-solving procedures from instructors who tend to have tacit knowledge about low-level procedures that they have automatized. The subgoal learning framework has been used in programming and other fields to breakdown procedural problem solving into smaller pieces that novices can grasp more easily, but it has only been used in short-term interventions. In this study, the subgoal learning framework was implemented throughout a semester-long introductory programming course to explore its longitudinal effects. Of 265 students in multiple sections of the course, half received subgoal-oriented instruction while the other half received typical instruction. Results: Learning subgoals consistently improved performance on quizzes, which were formative and given within a week of learning a new procedure, but not on exams, which were summative. While exam performance was not statistically better, the subgoal group had lower variance in exam scores and fewer students dropped or failed the course than in the control group. To better understand the learning process, we examined students’ responses to open-ended questions that asked them to explain the problem-solving process. Furthermore, we explored characteristics of learners to determine how subgoal learning affected students at risk of dropout or failure. Conclusions: Students in an introductory programming course performed better on initial assessments when they received instructions that used our intervention, subgoal labels. Though the students did not perform better than the control group on exams on average, they were less likely to get failing grades or to drop the course. Overall, subgoal labels seemed especially effective for students who might otherwise struggle to pass or complete the course.
Integration of Digital Pathologic and Transcriptomic Analyses Connects Tumor-Infiltrating Lymphocyte Spatial Density With Clinical Response to BRAF Inhibitors
Field: ONCOLOGY

Metastatic melanoma is one of the most immunogenic malignancies due to its high rate of mutations and neoantigen formation. Response to BRAF inhibitors (BRAFi) may be determined by intratumoral immune activation within melanoma metastases. To evaluate whether CD8+ T cell infiltration and distribution within melanoma metastases can predict clinical response to BRAFi, we developed a methodology to integrate immunohistochemistry with automated image analysis of CD8+ T cell position. CD8+ distribution patterns were correlated with gene expression data to identify and quantify “hot” areas within a tumor. Furthermore, the relative activation of CD8+cells, based on transcriptomic analysis, and their relationship to other CD8+ T cells and non-CD8+ cells within the tumor suggested a less crowded distribution of cells around activated CD8+ T cells. Furthermore, the relative activation of these CD8+ T cells was associated with improved clinical outcomes and decreased tumor cell proliferation. This study demonstrates the potential of digital pathomics to incorporate immune cell spatial distribution within metastases and RNAseq analysis to predict clinical response to BRAF inhibition in metastatic melanoma.
Bringing Big Data to Bear in Environmental Public Health: Challenges and Recommendations
Field: AI

Understanding the role that the environment plays in influencing public health often involves collecting and studying large, complex data sets. There have been a number of private and public efforts to gather sufficient information and confront significant unknowns in the field of environmental public health, yet there is a persistent and largely unmet need for findable, accessible, interoperable, and reusable (FAIR) data. Even when data are readily available, the ability to create, analyze, and draw conclusions from these data using emerging computational tools, such as augmented and artificial intelligence (AI) and machine learning, requires technical skills not currently implemented on a programmatic level across research hubs and academic institutions. We argue that collaborative efforts in data curation and storage, scientific computing, and training are of paramount importance to empower researchers within environmental sciences and the broader public health community to apply AI approaches and fully realize their potential. Leaders in the field were asked to prioritize challenges in incorporating big data in environmental public health research: inconsistent implementation of FAIR principles in data collection and sharing, a lack of skilled data scientists and appropriate cyber-infrastructures, and limited understanding of possibilities and communication of benefits were among those identified. These issues are discussed, and actionable recommendations are provided.
Preclinical Efficacy and Involvement of AKT, mTOR, and ERK Kinases in the Mechanism of Sulforaphane against Endometrial Cancer
Field: ONCOLOGY

Sulforaphane exerts anti-cancer activity against multiple cancer types. Our objective was to evaluate utility of sulforaphane for endometrial cancer therapy. Sulforaphane reduced viability of endometrial cancer cell lines in association with the G2/M cell cycle arrest and cell division cycle protein 2 (Cdc2) phosphorylation, and intrinsic apoptosis. Inhibition of anchorage-independent growth, invasion, and migration of the cell lines was associated with sulforaphane-induced alterations in epithelial-to-mesenchymal transition (EMT) markers of increased E-cadherin and decreased N-cadherin and vimentin expression. Proteomic analysis identified alterations in AKT, mTOR, and ERK kinases in the networks of sulforaphane effects in the Ishikawa endometrial cancer cell line. Western blots confirmed sulforaphane inhibition of AKT, mTOR, and induction of ERK with alterations in downstream signaling. AKT and mTOR inhibitors reduced endometrial cancer cell line viability and prevented further reduction by sulforaphane. Accumulation of nuclear phosphorylated ERK was associated with reduced sensitivity to the ERK inhibitor and its interference with sulforaphane activity. Sulforaphane induced apoptosis-associated growth inhibition of Ishikawa xenograft tumors to a greater extent than paclitaxel, with no evidence of toxicity. These results verify sulforaphane’s potential as a non-toxic treatment candidate for endometrial cancer and identify AKT, mTOR, and ERK kinases in the mechanism of action with interference in the mechanism by nuclear phosphorylated ERK.
Measuring university students’ interest in biology: evaluation of an instrument targeting Hidi and Renninger’s individual interest
Field: EDUCATION

Background: Boosting students’ disciplinary interest has long been considered an important mechanism to increase student success and retention in STEM education. Yet, interest is a complex construct and can mean different things to different people, and many of the existing interest questionnaires do not identify a specific theoretical framework underlying their items. To demonstrate that curricular interventions targeting students’ interest are effective, educators need a theoretically based instrument to measure interest. The aim of this study was to develop an instrument measuring undergraduate students’ interest in the discipline of biology and collect initial validity evidence supporting the proposed use. The instrument structure is based on Hidi and Renninger’s (Educational Psychologist 41:111–127, 2006) conceptualization of individual interest, and the intended use is to evaluate changes in the biology interests of the US undergraduate students pursuing STEM degrees. To provide evidence of validity, the instrument was completed by 446 biology majors and 489 non-biology majors at two R1 universities. Exploratory and confirmatory factor analyses were applied to evaluate the internal structure of the instrument.Results: The final three-factor instrument supported by these analyses includes 6 items representing positive feelings towards biology, 5 items representing personal value of biology, and 8 items representing reengagement in biology-related activities. Measurement invariance across biology and non-biology majors was established and subsequent comparisons of these populations demonstrated that biology majors report significantly higher positive feelings, personal value, and reengagement in biology-related activities compared to non-biology majors.Conclusions: The study findings support the use of the instrument to gain a broad understanding of students’ individual interest in biology. With minor adaptions, the instrument could also be evaluated for use in other STEM disciplines and for use by other populations.
Long Non-coding RNA AK025387 Promotes Cell Migration and Invasion of Gastric Cancer
Field: ONCOLOGY

Gastric cancer is one of the most common cancers in the world, and long non-coding RNAs (lncRNAs) play a crucial role in proliferation, metastasis, and invasion of gastric cancer. However, there are very few researches focusing on the effects of lncRNAs on metastatic gastric cancer. In this research, we identify one kind of lncRNA, called AK025387, which is highly expressed in metastatic gastric cancer samples compared with non-metastatic gastric cancer samples. The expression of AK025387 is significantly positively correlated with lymph node metastasis. The in situ hybridization demonstrates that AK025387 is located in both nucleus and cytoplasm, but mostly in cytoplasm. AK025387 promotes gastric cancer cells migratory and invasive ability, but it inhibits apoptosis in vitro. Furthermore, AK025387 regulates Raf-1, mitogen-activated protein kinase/extracellular signal-regulated kinase (MEK), and extracellular signal-regulated kinase (ERK) and is involved in mitogen-activated protein kinase (MAPK) signaling pathway to perform its biological functions. We conclude that AK025387 is highly expressed in metastatic gastric cancer, and its biological functions suggest the potential of AK025387 to be a biomarker of metastatic gastric cancer.
Development and Validation of a Comprehensive Multivariate Dosimetric Model for Predicting Late Genitourinary Toxicity Following Prostate Cancer Stereotactic Body Radiotherapy
Field: ONCOLOGY

Purpose: Dosimetric predictors of toxicity after Stereotactic Body Radiation Therapy (SBRT) are not well-established. We sought to develop a multivariate model that predicts Common Terminology Criteria for Adverse Events (CTCAE) late grade 2 or greater genitourinary (GU) toxicity by interrogating the entire dose-volume histogram (DVH) from a large cohort of prostate cancer patients treated with SBRT on prospective trials. Methods: Three hundred and thirty-nine patients with late CTCAE toxicity data treated with prostate SBRT were identified and analyzed. All patients received 40 Gy in five fractions, every other day, using volumetric modulated arc therapy. For each patient, we examined 910 candidate dosimetric features including maximum dose, volumes of each organ [CTV, organs at risk (OARs)], V100%, and other granular volumetric/dosimetric indices at varying volumetric/dosimetric values from the entire DVH as well as ADT use to model and predict toxicity from SBRT. Training and validation subsets were generated with 90 and 10% of the patients in our cohort, respectively. Predictive accuracy was assessed by calculating the area under the receiver operating curve (AROC). Univariate analysis with student t-test was first performed on each candidate DVH feature. We subsequently performed advanced machine-learning multivariate analyses including classification and regression tree (CART), random forest, boosted tree, and multilayer neural network. Results: Median follow-up time was 32.3 months (range 3–98.9 months). Late grade ≥2 GU toxicity occurred in 20.1% of patients in our series. No single dosimetric parameter had an AROC for predicting late grade ≥2 GU toxicity on univariate analysis that exceeded 0.599. Optimized CART modestly improved prediction accuracy, with an AROC of 0.601, whereas other machine learning approaches did not improve upon univariate analyses. Conclusions: CART-based machine learning multivariate analyses drawing from 910 dosimetric features and ADT use modestly improves upon clinical prediction of late GU toxicity alone, yielding an AROC of 0.601. Biologic predictors may enhance predictive models for identifying patients at risk for late toxicity after SBRT.