Regis College

Mild cognitive impairment (MCI) is a clinical condition characterized by a decline in cognitive ability and progression of cognitive impairment. It is often considered a transitional stage between normal aging and Alzheimer's disease (AD). This study aimed to compare deep learning (DL) and traditional machine learning (ML) methods in predicting MCI using plasma proteomic biomarkers. A total of 239 adults were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort along with a pool of 146 plasma proteomic biomarkers. We evaluated seven traditional ML models (support vector machines (SVMs), logistic regression (LR), naïve Bayes (NB), random forest (RF), k-nearest neighbor (KNN), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost)) and six variations of a deep neural network (DNN) model-the DL model in the H2O package. Least Absolute Shrinkage and Selection Operator (LASSO) selected 35 proteomic biomarkers from the pool. Based on grid search, the DNN model with an activation function of "Rectifier With Dropout" with 2 layers and 32 of 35 selected proteomic biomarkers revealed the best model with the highest accuracy of 0.995 and an F1 Score of 0.996, while among seven traditional ML methods, XGBoost was the best with an accuracy of 0.986 and an F1 Score of 0.985. Several biomarkers were correlated with the APOE-ε4 genotype, polygenic hazard score (PHS), and three clinical cerebrospinal fluid biomarkers (Aβ42, tTau, and pTau). Bioinformatics analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed several molecular functions and pathways associated with the selected biomarkers, including cytokine-cytokine receptor interaction, cholesterol metabolism, and regulation of lipid localization. The results showed that the DL model may represent a promising tool in the prediction of MCI. These plasma proteomic biomarkers may help with early diagnosis, prognostic risk stratification, and early treatment interventions for individuals at risk for MCI.

Myocardial infarction (MI) is a significant cardiovascular disease that restricts blood flow, resulting in massive cell death and leading to stiff and noncontractile fibrotic scar tissue formation. Recently, sustained oxygen release in the MI area has shown regeneration ability; however, improving its therapeutic efficiency for regenerative medicine remains challenging. Here, a combinatorial strategy for cardiac repair by developing cardioprotective and oxygenating hybrid hydrogels that locally sustain the release of stromal cell-derived factor-1 alpha (SDF) and oxygen for simultaneous activation of neovascularization at the infarct area is presented. A sustained release of oxygen and SDF from injectable, mechanically robust, and tissue-adhesive silk-based hybrid hydrogels is achieved. Enhanced endothelialization under normoxia and anoxia is observed. Furthermore, there is a marked improvement in vascularization that leads to an increment in cardiomyocyte survival by ≈30% and a reduction of the fibrotic scar formation in an MI animal rodent model. Improved left ventricular systolic and diastolic functions by ≈10% and 20%, respectively, with a ≈25% higher ejection fraction on day 7 are also observed. Therefore, local delivery of therapeutic oxygenating and cardioprotective hydrogels demonstrates beneficial effects on cardiac functional recovery for reparative therapy.

During orthodontic tooth movement (OTM), osteocytes, the most mechanosensitive cells in alveolar bone, suffer the heavy orthodontic force and initiate alveolar bone resorption on the compression side. However, the inherent mechanisms of compressive force-induced osteocyte death are not fully understood. In this study, we established an OTM model on Sprague-Dawley rats by inserting coil springs to investigate osteocyte damage on the compression side of alveolar bone. We then applied compressive force on the MLO-Y4 osteocyte-like cell line in vitro to explore whether the reactive oxygen species (ROS)-mediated endoplasmic reticulum stress (ERS) pathway is involved in compressive force-induced osteocyte death. We found that the orthodontic force caused apparent alveolar bone loss, osteocyte death, and elevated serum sclerostin and receptor activator of NF-κB ligand (RANKL) levels in rats. In vitro, compressive force inhibited cell viability but increased the LDH leakage and loss of mitochondrial membrane potential in MLO-Y4 cells. Simultaneously, it activated protein kinase RNA-like endoplasmic reticulum kinase (PERK), eukaryotic translation initiation factor 2 (eIF2α), and their downstream pro-apoptotic ERS signaling proteins and caused significant osteocyte apoptosis, which can be blocked by ERS inhibitor salubrinal. Moreover, the compressive force elevated intracellular ROS levels, while the ROS scavenger N-acetyl-L-cysteine (NAC) alleviated ERS and apoptosis in loaded osteocytes. These results suggest that the orthodontic compressive force induced osteocyte apoptosis via the ROS-mediated ERS pathway. This study first proposes the ERS pathway as a new potential pathway for regulating the rate of OTM based on osteocyte death. RESEARCH HIGHLIGHTS: The orthodontic force increases osteocyte death in rat alveolar bone. The compressive force causes osteocyte apoptosis by the endoplasmic reticulum stress (ERS) pathway in vitro. The ROS scavenger NAC blocked compressive force-induced ERS and osteocyte apoptosis.

Our institution is heavily invested in improving the learning outcomes of our students, especially in core courses like statistics which is taken by majority of freshmen students. As such, we have been reviewing our existing pedagogy and exploring novel ways to improve students experience in statistics course. In this study we compare web-based vs paper-based traditional homework models and their impact on students’ performance in an undergraduate statistics course. In web-based model, students used MyStatLab to complete the homework assignments along with additional resources offered by this platform. On the other hand, in paper-based model students used traditional approach in completing the homework and was manually graded by the instructor. Our analysis indicated that there is no statistically significant difference in students’ final course grades between the two modalities. Additionally, we conducted a chi-square test of independence and found no statistically significant relationship between students’ final course grades and homework models. However, our analysis concluded that student performance and perceptions improved with online homework model compared to the traditional model. Keywords: Web-based online homework, Traditional homework, Student performance, Undergraduate Statistics DOI: 10.7176/JEP/13-27-09 Publication date: September 30 th 2022

Abstract The present study used an online behavioral instruction program to teach accurate American Psychological Association (APA) citation formatting to nine graduate students. The participants completed two self‐paced, online training modules targeting the correct formation of full reference and in‐text citations, which consisted of the three elements of behavioral instruction: multiple practice opportunities, mastery criteria, and automatic feedback provided contingent on response. Training occurred using a concurrent multiple baseline design across skills (for seven participants) or nonconcurrently across participants (for two participants). Most participants did not correctly use APA citation formatting when probed in the baseline condition. Following training, participants increased their accuracy in APA citations across trained and novel exemplars. Two participants’ data showed limited functional control due to increases in baseline. One participant required an additional visual checklist to reach the mastery criterion for one skill. Social validity was also assessed with overall confidence in using APA citation formatting increasing following completion of the modules. Participants reported overall satisfaction with the online modules stating that they were helpful in teaching correct citation implementation and were easy to use. Limitations to the current study and future uses of behavioral instruction as a technology are discussed.

The curr ent study aimed to evaluate the effects of asynchronous online instruction on correct fieldwork data entry for graduate students in behavior analysis preparing to sit for the BACB exam. Previous research has been conducted on using synchronous instructional methods to teach fieldwork data entry. To our knowledge, this is the first examination of a completely asynchronous approach to teaching the new Behavior Analysis Certification Board (BACB) fieldwork requirements (BACB, 2020a). Experimenters focused on the completion of daily fieldwork activities, as well as the completion of monthly fieldwork forms. Participants were 22 graduate students beginning their fieldwork experiences in pursuit of their board certified behavior analyst credential. Most participants did not reach the mastery criterion in baseline after only reviewing the fieldwork resources provided by the BACB for both phases. After undergoing training, all participants scored above the mastery criterion in their completion of both their daily fieldwork logs and monthly forms. • Fieldwork trainees taught to fill in Trackers and monthly forms. • Asynchronous online instruction used to teach data entry using mock fieldwork scenarios. • 18 of 18 participants in the Tracker Training improved from baseline. • 18 of 20 participants in the Monthly Forms Training improved from baseline. • Correct responding for 15 participants generalized to a novel scenario. Data suggest that asynchronous online instruction is an effective method to teach fieldwork data entry. • Social validity data suggest favorable views of the training.

The International Scientific Research Organization for Science, Engineering and Technology (ISROSET) is a Non-Profit Organization; The ISROSET is dedicated to improvement in academic sectors of Science (Chemistry, Bio-chemistry, Zoology, Botany, Biotechnology, Pharmaceutical Science, Bioscience, Bioinformatics, Biometrics, Biostatistics, Microbiology, Environmental Management, Medical Science, Forensic Science, Home Science, Library Science, Material Science Military Science, Physical Science, Physical Education Science, Educational Science, Fisheries, seed technology, Agriculture, Forestry Science, Mathematics, Physics, Statistics and Geology/Earth Science), Computer Science, Engineering and Information Technology, Commerce, Management, Economics Sociology and Social Science.