mdpi-ai/citelens huggingface dataset:
PECLIDES Neuro: A Personalisable Clinical Decision Support System for Neurological Diseases
Field: AI

Neurodegenerative diseases such as Alzheimer's and Parkinson's impact millions of people worldwide. Early diagnosis has proven to greatly increase the chances of slowing down the diseases' progression. Correct diagnosis often relies on the analysis of large amounts of patient data, and thus lends itself well to support from machine learning algorithms, which are able to learn from past diagnosis and see clearly through the complex interactions of a patient's symptoms and data. Unfortunately, many contemporary machine learning techniques fail to reveal details about how they reach their conclusions, a property considered fundamental when providing a diagnosis. Here we introduce our Personalisable Clinical Decision Support System (PECLIDES), an algorithmic process formulated to address this specific fault in diagnosis detection. PECLIDES provides a clear insight into the decision-making process leading to a diagnosis, making it a gray box model. Our algorithm enriches the fundamental work of Masheyekhi and Gras in data integration, personal medicine, usability, visualization, and interactivity. Our decision support system is an operation of translational medicine. It is based on random forests, is personalisable and allows a clear insight into the decision-making process. A well-structured rule set is created and every rule of the decision-making process can be observed by the user (physician). Furthermore, the user has an impact on the creation of the final rule set and the algorithm allows the comparison of different diseases as well as regional differences in the same disease. The algorithm is applicable to various decision problems. In this paper we will evaluate it on diagnosing neurological diseases and therefore refer to the algorithm as PECLIDES Neuro1.
The MARquette Visualization Lab (MARVL): An Immersive Virtual Environment for Research, Teaching and Collaboration
Field: EDUCATION

The MARquette Visualization Lab (MARVL) is a large-scale immersive virtual environment for research, teaching, collaboration and outreach at our mid-sized liberal arts university. MARVL consists of multiple display surfaces including an extra wide front wall and floor, and two side walls. This resource includes stereoscopic viewing, motion tracking and space for a large audience. MARVL’s versatile configuration facilitates viewing of content by 30 people, while also projecting on the entire width of the floor. This feature uniquely facilitates comparative or separate content visible simultaneously via “split mode” operation (two 3-sided environments), as well as detailed motion for applications such as gait analysis and performing arts. Since establishing the lab, its members have received numerous queries and requests pertaining to how system attributes and applications were determined, suggesting these and related decisions remain a challenge nearly three decades since the first CAVE was constructed. This paper provides an overview of MARVL including the processes used in identifying a diverse group of cross campus users, understanding their collective vision for potential use, and synthesizing this information to create the resource described above. The subsequent design, qualitative and quantitative approaches to vendor selection, and software decisions are then discussed. Steps implemented for dealing with simulator sickness and latency are presented along with current approaches being implemented for project development with end users. Finally, we present results from the use of MARVL by several end users identified in the early planning stage, and recent upgrades to the system.
Factors influencing participation of underrepresented students in STEM fields: matched mentors and mindsets
Field: EDUCATION

Background: Women and ethnic minorities remain underrepresented in science, technology, engineering, and math (STEM) fields. The goal of this pilot study is to better understand the beliefs and experiences of underrepresented US students pursuing STEM. Our focus was to gain insights into their mentorship experiences and preferences regarding having mentors who are gender and ethnicity matched. Environmental and psychological factors associated with participants’ decision to pursue STEM, such as family influences, academic mindsets, and attitudes towards STEM, were also studied. Methods: We developed a survey tool based on published literature and established instruments, including measures of STEM belonging, science identity, and growth mindset, as well as measures assessing students’ views on their STEM participation. We surveyed members of a STEM-focused non-profit who were in college, graduate school, or were recent graduates. Results: Forty-eight adults currently pursuing STEM responded to the survey. The majority (71%) were female and nearly all (96%) identified as an ethnic minority. Most reported knowing someone of their same gender (68%) or ethnicity (66%) with a STEM career who served as a role model. The majority (54%) stated that meeting a STEM professional of their own gender and ethnicity would be effective encouragement to pursue STEM. A similar percentage (56%) believed that media exposure to gender- and ethnicity-matched STEM professionals would be effective encouragement. Most (73%) demonstrated a growth mindset and had strong family support to pursue STEM (68%). Only two-thirds (66%) felt they belonged in STEM careers, and 30% agreed that people in their STEM classes are a lot like them. Conclusion: This study contributes additional information on the views and experiences of diverse students actively pursuing STEM. Most participants indicated the importance of meeting and being mentored in STEM by those of their same gender and ethnicity, either in person or through media. Future educational efforts to increase STEM diversity should consider students’ mentorship preferences and facilitate interactions with matched-background mentors accordingly, with consideration given to the use of media. Educators should focus on inclusive learning by highlighting the accomplishments of diverse STEM professionals, to help strengthen feelings of STEM belonging.
“I Invest by Following Lead Investors!” The Role of Lead Investors in Fundraising Performance of Equity Crowdfunding
Field: PSYCHOLOGY

Psychological factors play a critical role in affecting investor decisions. This study explores how lead investors influence following investors psychologically, thus affecting fundraising performance of equity crowdfunding. We draw upon the signaling theory and observational learning theory to argue that following investors could be induced to invest in a project if they observe the proportion of funding by lead investors in the funding target to be high, that the lead investors have rich investment experience, and that the lead investors can offer help to the projects. To test our hypotheses, we analyze a sample of 215 projects from a Chinese equity crowdfunding platform. The results reveal that the proportion of lead investor investment in the funding target and their investment experience are positively related to fundraising performance. However, the help offered by lead investors toward the projects has no impact on funding performance. Theoretical and practical implications are discussed.
Correlations between modes of student cognitive engagement and instructional practices in undergraduate STEM courses
Field: EDUCATION

Background: Within STEM education, research on instructional practices has focused on ways to increase student engagement and thereby reap the associated benefits of increased learning, persistence, and academic success. These meaningful-learning goals have been tied most specifically to cognitive engagement, a construct that is often difficult for instructors to assess on their own. While it has been shown that certain instructional practices are tied to higher cognitive engagement in students, tools to measure instructional practices and student engagement have remained largely isolated in their development and use. Results: This research uses previously developed instruments to simultaneously assess modes of cognitive engagement in students (Student Course Cognitive Engagement Instrument [SCCEI]) and instructional practices (Postsecondary Instructional Practices Survey [PIPS]) within a course. A sample of 19 STEM courses was recruited to participate in this study, with instructors and students each self-reporting data. Results from the instructor and students in each course were scored, and ANOVA and partial correlation analysis were conducted on the sample. ANOVA indicated the significance of and classroom structure on student engagement. From the correlation analysis, a significant relationship was found between four student-reported modes of cognitive engagement and instructor-reported teaching practices. Conclusions: With an understanding of student engagement response to classroom structure, instructors may consider their teaching environment when implementing instructional practices. Moreover, Interactivity with Peers, the deepest mode of cognitive engagement suggested by previous research, was correlated with instructional practices in our study, suggesting that instructors may be able to shape their students’ learning by encouraging collaboration in the classroom. We also found that assessment played a role in students’ cognitive engagement; this indicates that instructors may wish to thoughtfully consider their methods of assessment to facilitate modes of cognitive engagement associated with deeper learning of course material. By understanding factor correlations, the PIPS and SCCEI can be used in tandem to understand impacts of instructional practices on student cognitive engagement within a course. We conclude that there is a need for ongoing research to study the interplay of instructional practices and student cognitive engagement as instruments are developed to measure such phenomena.
Deep Learning Based Wildfire Event Object Detection from 4K Aerial Images Acquired by UAS
Field: AI

Unmanned Aerial Systems, hereafter referred to as UAS, are of great use in hazard events such as wildfire due to their ability to provide high-resolution video imagery over areas deemed too dangerous for manned aircraft and ground crews. This aerial perspective allows for identification of ground-based hazards such as spot fires and fire lines, and to communicate this information with fire fighting crews. Current technology relies on visual interpretation of UAS imagery, with little to no computer-assisted automatic detection. With the help of big labeled data and the significant increase of computing power, deep learning has seen great successes on object detection with fixed patterns, such as people and vehicles. However, little has been done for objects, such as spot fires, with amorphous and irregular shapes. Additional challenges arise when data are collected via UAS as high-resolution aerial images or videos; an ample solution must provide reasonable accuracy with low delays. In this paper, we examined 4K ( 3840 × 2160 ) videos collected by UAS from a controlled burn and created a set of labeled video sets to be shared for public use. We introduce a coarse-to-fine framework to auto-detect wildfires that are sparse, small, and irregularly-shaped. The coarse detector adaptively selects the sub-regions that are likely to contain the objects of interest while the fine detector passes only the details of the sub-regions, rather than the entire 4K region, for further scrutiny. The proposed two-phase learning therefore greatly reduced time overhead and is capable of maintaining high accuracy. Compared against the real-time one-stage object backbone of YoloV3, the proposed methods improved the mean average precision(mAP) from 0 . 29 to 0 . 67 , with an average inference speed of 7.44 frames per second. Limitations and future work are discussed with regard to the design and the experiment results.
Learning to Play the Chess Variant Crazyhouse Above World Champion Level With Deep Neural Networks and Human Data
Field: AI

Deep neural networks have been successfully applied in learning the board games Go, chess, and shogi without prior knowledge by making use of reinforcement learning. Although starting from zero knowledge has been shown to yield impressive results, it is associated with high computationally costs especially for complex games. With this paper, we present CrazyAra which is a neural network based engine solely trained in supervised manner for the chess variant crazyhouse. Crazyhouse is a game with a higher branching factor than chess and there is only limited data of lower quality available compared to AlphaGo. Therefore, we focus on improving efficiency in multiple aspects while relying on low computational resources. These improvements include modifications in the neural network design and training configuration, the introduction of a data normalization step and a more sample efficient Monte-Carlo tree search which has a lower chance to blunder. After training on 569537 human games for 1.5 days we achieve a move prediction accuracy of 60.4%. During development, versions of CrazyAra played professional human players. Most notably, CrazyAra achieved a four to one win over 2017 crazyhouse world champion Justin Tan (aka LM Jann Lee) who is more than 400 Elo higher rated compared to the average player in our training set. Furthermore, we test the playing strength of CrazyAra on CPU against all participants of the second Crazyhouse Computer Championships 2017, winning against twelve of the thirteen participants. Finally, for CrazyAraFish we continue training our model on generated engine games. In 10 long-time control matches playing Stockfish 10, CrazyAraFish wins three games and draws one out of 10 matches.
The Role of miRNA for the Treatment of MGMT Unmethylated Glioblastoma Multiforme
Field: ONCOLOGY

Glioblastoma multiforme (GBM) is the most common high-grade intracranial tumor in adults. It is characterized by uncontrolled proliferation, diffuse infiltration due to high invasive and migratory capacities, as well as intense resistance to chemo- and radiotherapy. With a five-year survival of less than 3% and an average survival rate of 12 months after diagnosis, GBM has become a focus of current research to urgently develop new therapeutic approaches in order to prolong survival of GBM patients. The methylation status of the promoter region of the O6-methylguanine–DNA methyltransferase (MGMT) is nowadays routinely analyzed since a methylated promoter region is beneficial for an effective response to temozolomide-based chemotherapy. Furthermore, several miRNAs were identified regulating MGMT expression, apart from promoter methylation, by degrading MGMT mRNA before protein translation. These miRNAs could be a promising innovative treatment approach to enhance Temozolomide (TMZ) sensitivity in MGMT unmethylated patients and to increase progression-free survival as well as long-term survival. In this review, the relevant miRNAs are systematically reviewed.
More Than a Metabolic Enzyme: MTHFD2 as a Novel Target for Anticancer Therapy?
Field: ONCOLOGY

The bifunctional methylenetetrahydrofolate dehydrogenase/cyclohydrolase (MTHFD2) is a mitochondrial one-carbon folate metabolic enzyme whose role in cancer was not known until recently. MTHFD2 is highly expressed in embryos and a wide range of tumors but has low or absent expression in most adult differentiated tissues. Elevated MTHFD2 expression is associated with poor prognosis in both hematological and solid malignancy. Its depletion leads to suppression of multiple malignant phenotypes including proliferation, invasion, migration, and induction of cancer cell death. The non-metabolic functions of this enzyme, especially in cancers, have thus generated considerable research interests. This review summarizes current knowledge on both the metabolic functions and non-enzymatic roles of MTHFD2. Its expression, potential functions, and regulatory mechanism in cancers are highlighted. The development of MTHFD2 inhibitors and their implications in pre-clinical models are also discussed.
Shaping Up the Tumor Microenvironment With Cellular Fibronectin
Field: ONCOLOGY

Normal tissue homeostasis and architecture restrain tumor growth. Thus for a tumor to develop and spread, malignant cells must overcome growth-repressive inputs from surrounding tissue and escape immune surveillance mechanisms that curb cancer progression. This is achieved by promoting the conversion of a physiological microenvironment to a pro-tumoral state and it requires a constant dialog between malignant cells and ostensibly normal cells of adjacent tissue. Pro-tumoral reprogramming of the stroma is accompanied by an upregulation of certain extracellular matrix (ECM) proteins and their cognate receptors. Fibronectin (FN) is one such component of the tumor matrisome. This large multidomain glycoprotein dimer expressed over a wide range of human cancers is assembled by cell-driven forces into a fibrillar array that provides an obligate scaffold for the deposition of other matrix proteins and binding sites for functionalization by soluble factors in the tumor microenvironment. Encoded by a single gene, FN regulates the proliferation, motile behavior and fate of multiple cell types, largely through mechanisms that involve integrin-mediated signaling. These processes are coordinated by distinct isoforms of FN, collectively known as cellular FN (as opposed to circulating plasma FN) that arise through alternative splicing of the FN1 gene. Cellular FN isoforms differ in their solubility, receptor binding ability and spatiotemporal expression, and that exert functions that have yet to be fully defined. FN induction at tumor sites constitutes an important step in the acquisition of biological capabilities required for several cancer hallmarks by sustaining proliferative signaling, promoting angiogenesis, facilitating invasion and metastasis, modulating growth suppressor activity and regulating anti-tumoral immunity. In this review, we will first provide an overview of ECM reprogramming through tumor-stroma crosstalk then focus on the role of cellular FN in tumor progression with respect to these hallmarks. Last, we will discuss the impact of dysregulated ECM on clinical efficacy of classical (radio-/chemo-) therapies and emerging treatments that target immune checkpoints and explore how our growing knowledge of the tumor ECM and the central role of FN can be leveraged for therapeutic benefit.
Molecular Signatures of JMJD10/MINA53 in Gastric Cancer
Field: ONCOLOGY

The JMJD10 gene and its encoded protein MYC-induced nuclear antigen (MINA53) are associated with multiple cancers. Besides having both an oncogenic and tumor suppressor function, the intricate role of JMJD10 in cancer is complex as it depends on the cancer type. In particular, the functional role of JMJD10/MINA53 in gastric cancer has been poorly understood. In this study, we have unraveled the molecular signatures and functional roles of JMJD10/MINA53 in gastric cancer by multiple approaches, i.e., multi-omics bioinformatics study, analysis of human gastric cancer tissues, and studies in vitro using knockdown or overexpression strategies in gastric cancer cell lines. The results indicated that the JMJD10 gene and MINA53 protein are commonly overexpressed in cancer patients. JMJD10/MINA53 is involved in the regulation of proliferation and survival of gastric cancer by controlling cell cycle gene expression. These processes are highly associated with MINA53 enzymatic activity in the regulation of H3K9me3 methylation status and controlling activation of AP-1 signaling pathways. This highlights the oncogenic role of JMJD10/MINA53 in gastric cancer and opens the opportunity to develop therapeutic targeting of JMJD10/MINA53 in gastric cancer.
Active HPV infection and its influence on survival in head and neck squamous-cell cancer
Field: ONCOLOGY

Purpose: HPV is involved in the development of some head and neck squamous-cell carcinomas (HNSCC). It was suggested that only transcriptionally active virus can induce carcinogenesis, therefore, the aim of our study was to analyze the frequency of active HPV infection, virus type, and its prognostic role in HNSCC patients.Methods: Status of active HPV infection was assessed for 155 HNSCC patients based on p16 expression and HPV DNA presence. Univariate and multivariate analyses with Cox proportional regression model were performed to select independent prognostic factors.Results: Active HPV infection was detected in 20.65% of patients. We identified 16.0, 40.9 and 1.7% of HPV positive oral cavity, oropharyngeal, and laryngeal cancer cases, respectively. HPV16 was dominant (81.25%) followed by HPV35 (9.38%) and double infections with HPV16 and 35 (6.25%) or HPV35 and 18 (3.12%). Patients with active HPV infection demonstrated significantly higher survival than HPV negative ones (OS 80.89% vs. 37.08%,p = 0.000; DFS 93.0% vs. 53.35%,p = 0.000, respectively). Longer OS and DFS were maintained for infected patients when oropharyngeal and non-oropharyngeal cases were analyzed separately. Interestingly, all patients infected with other than HPV16 types survived 5 years without cancer progression. In the analyzed group of 155 patients the strongest independent favourable prognostic factor for both OS and DFS was HPV presence.Conclusions: High prevalence of HPV-driven HNSCC (mostly within oropharynx) was detected, with HPV16 type the most frequent, followed by HPV35 and HPV18. The presence of active HPV infection improved survival of both oropharyngeal and non-oropharyngeal cancer patients and should be taken into account in treatment planning.