Miracle-dz/UniFAR-FTUs huggingface dataset:
Melatonin‐mediated malic enzyme 2 orchestrates mitochondrial fusion and respiratory functions to promote odontoblastic differentiation during tooth development

Tooth development is a complex process that is tightly controlled by circadian rhythm. Melatonin (MT) is a major hormonal regulator of the circadian rhythm, and influences dentin formation and odontoblastic differentiation during tooth development; however, the underlying mechanism remains elusive. This study investigated how MT regulates odontoblastic differentiation, with a special focus on its regulation of mitochondrial dynamics. In rat dental papilla cells (DPCs), we found that MT promotes odontoblastic differentiation concurrently with enhanced mitochondrial fusion, while disruption of mitochondrial fusion by depleting optic atrophy 1 (OPA1) impairs MT‐mediated differentiation and mitochondrial respiratory functions. Through RNA sequencing, we discovered that MT significantly upregulated malic enzyme 2 (ME2), a mitochondrial NAD(P)+‐dependent enzyme, and identified ME2 as a critical MT downstream effector that orchestrates odontoblastic differentiation, mitochondrial fusion, and respiration functions. By detecting the spatiotemporal expression of ME2 in developing tooth germs, and using tooth germ reconstituted organoids, we also provided in vivo and ex vivo evidence that ME2 promotes dentin formation, indicating a possible involvement of ME2 in MT‐modulated tooth development. Collectively, our findings offer novel understandings regarding the molecular mechanism by which MT affects cell differentiation and organogenesis, meanwhile, the critical role of ME2 in MT‐regulated mitochondrial functions is also highlighted.
Target recognitions in multiple-camera closed-circuit television using color constancy

Abstract. People tracking in crowded scenes from closed-circuit television (CCTV) footage has been a popular and challenging task in computer vision. Due to the limited spatial resolution in the CCTV footage, the color of people’s dress may offer an alternative feature for their recognition and tracking. However, there are many factors, such as variable illumination conditions, viewing angles, and camera calibration, that may induce illusive modification of intrinsic color signatures of the target. Our objective is to recognize and track targets in multiple camera views using color as the detection feature, and to understand if a color constancy (CC) approach may help to reduce these color illusions due to illumination and camera artifacts and thereby improve target recognition performance. We have tested a number of CC algorithms using various color descriptors to assess the efficiency of target recognition from a real multicamera Imagery Library for Intelligent Detection Systems (i-LIDS) data set. Various classifiers have been used for target detection, and the figure of merit to assess the efficiency of target recognition is achieved through the area under the receiver operating characteristics (AUROC). We have proposed two modifications of luminance-based CC algorithms: one with a color transfer mechanism and the other using a pixel-wise sigmoid function for an adaptive dynamic range compression, a method termed enhanced luminance reflectance CC (ELRCC). We found that both algorithms improve the efficiency of target recognitions substantially better than that of the raw data without CC treatment, and in some cases the ELRCC improves target tracking by over 100% within the AUROC assessment metric. The performance of the ELRCC has been assessed over 10 selected targets from three different camera views of the i-LIDS footage, and the averaged target recognition efficiency over all these targets is found to be improved by about 54% in AUROC after the data are processed by the proposed ELRCC algorithm. This amount of improvement represents a reduction of probability of false alarm by about a factor of 5 at the probability of detection of 0.5. Our study concerns mainly the detection of colored targets; and issues for the recognition of white or gray targets will be addressed in a forthcoming study.
Safety and efficacy of anti-hyperglycemic agents in patients with type 2 diabetes mellitus (T2DM): Protocol for an overview of systematic reviews based on network meta-analysis

Type 2 diabetes mellitus (T2DM) has caused a huge clinical and economic burden worldwide. The management strategy of T2DM has been mentioned in many guidelines. However, controversy still exists in the recommendation of anti-hyperglycemic agents. To this end, this protocol has been written according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P). We will make an overview of systematic reviews based-on network meta-analysis firstly that report on safety and efficacy of different category of anti-hyperglycemic agents for T2DM patients. We will identify network meta-analysis by applying a robust and standardized search strategy within Embase, PubMed, Web of Science, and Cochrane Database of Systematic Reviews. Hemoglobin A1c (HbA1c) and fasting plasma glucose (FPG) will be defined as the primary outcomes. We will assess the methodological quality of included reviews by applying the A MeaSurement Tool to Assess Systematic Reviews (AMSTAR-2) tool, and quality of evidence for all outcomes will be judged by using the Grading of Recommendations Assessment, Development and Evaluation (GRADE). This will provide an accessible narrative synthesis to clinicians, patients, policy makers, and developers of clinical guidelines based on published high-quality network meta-analysis. We will submit our results for peer-review publication and presentation at domestic and international conferences. We will also disseminate our results through established clinical networks and consumer networks, using pamphlet where appropriate. Ethics approval is not required for this overview as we will analysis published network meta-analysis only. Trial registration number: INPLASY202070118.
Fast and effective algorithm for radar-to-optical scene matching based on the knowledge of object region

This paper discusses the problem of radar to optical scene matching. Because of the different imaging principles, there exists poor similarity between radar images and optical images, and for region features, only exist the relative stable common characteristics: large scale regions. This paper presents a fast and effective matching method to reach the demand of the fast orientation, which makes use of region segmentation technique to extract large scale regions in a radar image, and recognizes the large scale object in it according to the knowledge of the object region in the corresponding optical image to perform coarse location, finally using an additional template matching processing to perform fine location.
Adaptive anisotropic diffusion filter for speckle noise reduction for ultrasound images

In image processing noise is possibly the most annoying problem. Ultrasound image has been used for effective diagnosis for its non-invasive, harmless, accurate and cost effectiveness. Unfortunately, ultrasound images are degraded by an intrinsic artefact called speckle. Eliminating such speckle noise is an important preprocessing task. For acquiring a better performance for denoising, an adaptive anisotropic diffusion technique for ultrasound images is presented in this paper. It is a new efficient method for denoising the image without blurring the frontiers between different regions. The proposed noise detection-oriented method is based on the differentiation of the diffusion in the direction of the gradient and the adjacent pixels of entire image. The performance of different metrics shows the proposed method exhibit better result to existing Perona-Malik anisotropic diffusion method.
A Synopsis of Introduced and Native Seaweeds from 14 Open Coastal and Estuarine Sites within Southern Maine and New Hampshire, U.S.A.

ABSTRACT Floristic studies of introduced and native seaweed populations from 14 open coastal and estuarine sites within southern Maine and New Hampshire were documented between 1965 and 2017. A total of 186 seaweed taxa were recorded, including 15 introduced (8.1%) and 171 native (91.9%) species. The highest species diversity (123 taxa) occurred at the open coastal Seapoint Beach site in Kittery, Maine, and an estuarine tidal rapid site at Dover Point, New Hampshire, with 106 taxa. The numbers of introduced species per site ranged from 2–11. The percent occurrence patterns for the 15 introduced seaweeds were highly variable, with Codium fragile subsp. fragile and Ulonema rhizophorum being restricted to single sites (7%), Melanothamnus harveyi occurring at 12 sites (86%), and Agarophyton vermiculophyllum and Dasysiphonia japonica at 13 sites (93%). The geographical origins and initial collection dates for the 15 introduced taxa were highly variable, with their initial collections ranging from 1848 (M. harveyi) to 2007 (D. japonica). Codium fragile subsp. fragile had the most protracted period between its initial occurrence at Orient Point, New York, in 1957 and in northern New England 40 years later (i.e., 1997). By contrast, several other introduced taxa had rapid geographic expansions within five years. The numbers and abundance of introduced species in the Gulf of Maine have increased dramatically between 1986 and 2017, with nine recorded in 1986 and 15 in 2017. Two native warm-water red algae (Agardhiella subulata and Gelidium crinale) showed sudden northward expansions during 2016 and 2017, respectively. Their presence may be associated with global warming, which is presently occurring at an alarming rate within the Gulf of Maine and exceeds many areas within the world.
Learner Ideas and Interests Expressed in Open-ended Projects in a Middle School Computer Science Curriculum

Ensuring that computer science curricula connect to learners' home culture, interests, and lived experiences is one approach to making the field more equitable. A central feature of the Scratch Encore Curriculum is to provide many opportunities for learners to plan and implement open-ended programming projects that invite them to draw on their prior knowledge, experiences, and cultural resources. To date, relatively little research has been done to analyze how learners respond to such curricular invitations, specifically with respect to what aspects of themselves and their interests they choose to express in their resulting projects. In this work, we investigate what people, places, interests, and experiences learners draw from as they plan and program open-ended Scratch projects. We analyzed the planning documents and final projects of 101 4th-7th grade (9-13 years) learners from the first two modules of our curriculum. The results show that when given the chance, learners incorporate aspects (such as Home and Family, and Hobbies and Leisure) of themselves into open-ended projects. Some learners drew from many areas of their lives while others focused on specific events, people, or interests. Our findings also indicate that the activities and Scratch are conducive to having learners of this age group express themselves, even early in the curriculum when they are still learning basic Scratch and CS concepts. This work contributes to our understanding of the impact of culturally responsive curricula and how it shapes the way learners engage with and express themselves in computing curricula.
Reducing Procrastination Without Sacrificing Students' Autonomy Through Optional Weekly Presentations of Student-Generated Content

Collaborative question generation and mapping by students has been shown to improve students' active (engaged) and meaningful learning. In implementing these methods into a course that enabled high levels of autonomy (as recommended by Self-determination theory), we found student procrastination to be a persistent problem. Since student contribution tends to wane over the course of a semester, the efficacy of a curriculum reliant on content generation similarly wanes. We describe our efforts in reducing procrastination in a course focused on collaborative question generation and mapping, using an iterative design research methodology, over eight months of two semesters. To encourage students to create high-quality questions, we implemented a voting system that graded students based on the number of instructor up-votes given to questions they created. While this reduced procrastination early on, students found ways to take advantage of the autonomy provided by the course curriculum, leading them to resume procrastinating. To address this issue, we adjusted the grading schema for the second semester by scoring both students' and instructors' up-votes, while also allowing students to present their created content in optional weekly meetings. The introduction of optional weekly meetings had the greatest effect towards decreasing procrastination. End-of-semester surveys reported that: 1) while students enjoyed both semesters, they gave more positive feedback for the second semester; 2) student-led discussions for personally created content, when combined with the autonomy to choose both the topic and time to study, helped their learning and time management skills.
SENSC algorithm for object and scene categorization

So far, the most popular method for object & scene categorization (such as Vector Quantization (VQ), Sparse coding (SC)) transforms low-level descriptors (usually SIFT descriptors) into mid-level representations with more meaningful information. These methods have two key steps: (1) the building dictionary step, which provides a mechanism to map low-level descriptors into mid-level representations. (2) The coding step, which implements the map from low-level descriptors to mid-level representation for each image by the dictionary. In this paper, we proposed to use a stable and efficient nonnegative sparse coding (SENSC) algorithm for building dictionary and coding each image with it to develop an extension of Spatial Pyramid Matching (SPM) method. We also compare SENSC with SC (state-of-the-art performance) method and VQ method, analysis the drawbacks of SC and VQ for building dictionary, and show SENSC algorithm's performance. According to the experiments on three benchmarks (Caltech101, scene, and events), the method we proposed has shown a better performance than SC and VQ methods.
Introduction to Quantum Computing for Everyone: Experience Report

Quantum computing presents a paradigmatic shift in the field of computation, in which unintuitive properties of quantum mechanics can be harnessed to change the way we approach a wide range of problems. However, due to the mathematics and physics perspective through which quantum computing is traditionally presented, most resources are inaccessible to many undergraduate students, let alone the general public. It is thus imperative to develop resources and best-practices for quantum computing instruction accessible to students at all levels. In this paper, we describe the development and results of our Massive Open Online Course (MOOC) "Introduction to Quantum Computing for Everyone. " This course presents an introduction to quantum computing with few technical prerequisites. In the first half of the course, quantum computing concepts are introduced with a unique, purely visual representation, allowing students to develop conceptual understanding without the burden of learning new mathematical notation. In the second half, students are taught the formal notation for concepts and objects already introduced, reinforcing student understanding of these concepts and providing an applicable context for the technical material. Most notably, we find that introducing the math content in the curriculum's second stage led to no drops in engagement or student performance, suggesting that our curriculum's spiral structure eased the technical burden.
Proposed Conceptual Design of Artificial Intelligence Integration into a Pressure Oxidation Facility

Research on Artificial Intelligence has substantially increased in various disciplines to address safety and operation difficulties such as the mineral processing Industry. The control system of a Pressure Oxidation facility was evaluated to determine potential opportunities for the integration of Artificial Intelligence. Several challenges were identified during the start-up operations of the Autoclave including typographical errors, possible non-compliance with Standard Operating Procedures, and the need for expertise to manage the operations. These conditions satisfy the need to explore an AI system that can potentially minimize human intervention. Through related reviews, this study suggested a hybrid approach through incorporation of safe oxygen operation limits as part of supervised learning and to develop predictive models using historical data to predict the optimal oxygen requirement to achieve maximum oxidation results thereby making the POX process an ideal candidate for Artificial Intelligence system integration. A data flow diagram and user interface prototype were developed to illustrate the role of the proposed Artificial Intelligence system in controlling the Autoclave, which can significantly minimize human errors that can potentially improve safety and boost productivity.
Quartz enhanced photoacoustic spectroscopy (QEPAS) with antimonide compounds in very compact systems

We present in this paper measurements made by quartz enhanced photoacoustic spectroscopy (QEPAS) technique with antimonide laser diodes emitting at 2.3 μm and 3.3 μm. These measurements dedicated to environmental purposes allow us sensitive detection of ethylene and methane. Two experimental setups are reported: a laboratory and brand new compact benches. The detection limits are mentioned.
FalconCode: A Multiyear Dataset of Python Code Samples from an Introductory Computer Science Course

The lack of large and diverse datasets of student code samples limits some forms of computer science education research. To address this problem, we created FalconCode, a novel collection of over 1.5 million Python programs from over two thousand undergraduate students at the United States Air Force Academy. FalconCode captures over five semesters worth of code samples from our introduction to computing course, which is taken by every student regardless of their academic major. The dataset contains student code submissions for over 800 programming assignments, as well as additional metadata such as the prompt for each assignment, the testcase(s) used to evaluate student submissions, and the specific skills needed to solve each problem. In this paper, we describe the methodology used to create FalconCode and the steps taken to anonymize the data. We then describe FalconCode's data schema, and show how it can support a wide range of research---including those utilizing machine learning (ML) and artificial intelligence (AI). FalconCode is provided free-of-charge, and is available upon request for computer science education research.
Analysis of Novices' Web-Based Help-Seeking Behavior While Programming

Web-based help-seeking -- finding and utilizing websites to solve a problem -- is a critical skill during programming in both professional and academic settings. However, little work has explored how students, especially novices, engage in web-based help-seeking during programming, or what strategies they use and barriers they face. This study begins to investigate these questions through analysis of students' web-search behaviors during programming. We collected think-aloud, screen recording, and log data as students completed a challenging programming task. Students were encouraged to use the web for help when needed, as if in an internship. We then qualitatively analyzed the data to address three research questions: 1) What events motivate students to use web search? 2) What strategies do students employ to search for, select, and learn from web pages? 3) What barriers do students face in web search, and when do they arise? Our results suggest that that novices use a variety of web-search strategies -- some quite unexpected -- with varying degrees of success, suggesting that web search can be a challenging skill for novice programmers. We discuss how these results inform future research and pedagogy focused on how to support students in effective web search.
A Study on the Design of Pandora's Box Web Application Using Design Thinking approach: A study on a centralized web browser security extension design and planned realization

The prevailing cybersecurity problem continues to distract internet users from having a smooth and safe web browsing experience. With this issue encountered, the study aims to provide a product design that focuses on preventing and lessening cybersecurity threats through a browser security extension that is more reliable and convenient for users. The study will utilize the design thinking approach to determine the proposed product's design. The major findings of the study show that users are still facing issues with used and familiar available extensions, and a centralized all-in-one browser security extension that includes features such as Ad Manager, Cookies Manager, Website Blocker/Tracker, VPN, and a personalized dashboard would be beneficial to meet their security needs, however, further testing and feedback are still needed to provide improvements for the proposed product.
Centering Minoritized Students' Perspectives: What Makes CS Learning Consequential

Taking a justice-oriented approach to equity in Computer Science (CS) education, this paper questions the dominant discourse in CS education and asks what truly makes CS learning consequential from the perspective of youth. We define CS learning as consequential by focusing on its transformative impact on youth identity, agency, and perceptions of the world within and beyond CS classrooms, regardless of whether or not they pursue CS in the future. Our research-practice partnership used qualitative data, specifically longitudinal interview data with 30 students up to three years after they first experienced a high school CS class in a large public school district on the west coast serving majority Latinx, urban, low-income students. Our findings suggest that in order for CS learning to be meaningful and consequential for youth, learning must involve: 1) the freedom for youth to express their interests, passions, and concerns; 2) opportunities for youth to expand their views of CS and self in the world; and 3) teacher care for students, learning community, and subject matter. The findings have significant implications for the broader "CS for All" movement and future efforts to reform policy agendas aiming for a more justice-centered CS education.