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Leading Towards Racial Justice: Counterstories of TK-12 Latinx Men Administrators
Get PDFMore than half of all students in public schools identify as Students of Color. However, there is an underrepresentation of PreKindergarten (PK)-12 Administrators of Color in public schools, including Latinx men administrators. Furthermore, the literature on the experiences and leadership practices of PK-12 Administrators of Color has mostly been on Women of Color and has utilized a phenomenology or case study research design. Therefore, this study sought to address these gaps in the literature by examining the experiences and leadership practices of Transitional Kindergarten (TK)-12 Latinx Men Administrators by centering their voices. This study utilized a Latinx Critical Race Theory framework, a Critical Race CounterStorytelling, and Narrative Inquiry methodology as a lens to examine the educational, life experiences, and leadership practices of TK-12 Latinx Men Administrators in an in-depth and meaningful way. The counterstories of this study derived from individual, semi-structured, open-ended interviews and revealed the participants’ perseverance through discrimination as Men and Educators of Color as well as their culturally responsive leadership practices. The findings of this study also suggested implications for practice for TK-12 public school districts and higher education institutions
Use of a Novel Combination of Multiplex PCR and DNA Barcoding in Assessing Authenticity of Ginseng Products
Get PDFGinseng (Panax sp.) is a medicinal plant used for its purported health benefits, primarily in East Asian countries. The COVID-19 pandemic led to a significant increase in sales of dietary supplements, including ginseng supplements, to purportedly support immune health and provide other health benefits. However, this heightened demand has subsequently increased the risk of adulteration in these dietary supplements. This study aimed to determine the efficacy of a novel combination of DNA barcoding and multiplex PCR to identify species in ginseng supplements. A total of 50 commercial ginseng supplements containing Panax ginseng, Panax quinquefolius, or Panax notoginseng were obtained from an online vendor. Two composite samples per product were subjected to DNA extraction followed by multiplex PCR and DNA barcoding with three genetic targets (i.e., rbcL, matK, and ITS2). Of the three DNA barcoding markers, ITS2 had the highest amplification success at 74%, and matK had the highest sequencing success at 60%. DNA barcoding alone identified species in 68% of products, while multiplex PCR had an overall 60% identification rate. The combination of both methods resulted in a 72% species identification rate. In addition to improving the overall identification rate, the combination of both methods allowed for greater species resolution and the detection of undeclared plant species. Future research should explore combining DNA-based methods with chemical-based approaches to improve ginseng detection capabilities and enable the quantification of undeclared adulterants
Impact of a Feedback Strategy in a Series of Communication-Focused Patient Care Simulations
Get PDFIntroduction
Patient care simulations (PCS) and objective structured clinical examinations (OSCE) allow pharmacy students to practice communication. Feedback can help improve communication, but the impact over time is not well understood. Objective
This study investigated the impact of a feedback strategy on pharmacy students\u27 communication skills over three PCS. It also evaluated the alignment between students\u27 self-scoring and faculty scoring. Methods
Pharmacy students participated in three sessions (PCS1, OSCE, and PCS3) that were focused on the affective domain. Individualized numerical and narrative feedback was provided to students on their performance after PCS1. Students\u27 communication was scored by faculty graders out of an 18-point validated rubric. Students self-scored their communication with the same rubric. Faculty and student scores were compared using a linear mixed effects model, and an intraclass correlation coefficient was used to measure agreement. Results
In PCS1, 82 students scored an average of 15.41 ± 2.14 for faculty scores and 16.06 ± 1.55 for self-graded scores (0.36, p \u3c 0.001). In the OSCE, 81 students had an average of 15.93 ± 1.86 for faculty scores and 16.45 ± 1.35 for self-graded scores (0.1, p = 0.18). In PCS3, 74 students scored an average of 15.22 ± 2.15 for faculty scores and 16.25 ± 1.44 for self-graded scores (0.14, p = 0.08). A correlation between faculty and student scores was seen for PCS1. Over the three sessions, no significant differences were found between student self-graded scores (p = 0.08), but faculty scores did differ, with the OSCE having higher scores than PCS3 (p \u3c 0.01). Many students with faculty-graded scores greater than 1 standard deviation below the mean scored themselves higher than faculty did. Conclusion
Feedback after PCS1 did not significantly improve scores. Students with low faculty-graded scores frequently scored themselves higher indicating low self-awareness
Verbal Working Memory and Syntactic Comprehension Segregate into the Dorsal and Ventral Streams, Respectively
Get PDFSyntactic processing and verbal working memory are both essential components to sentence comprehension. Nonetheless, the separability of these systems in the brain remains unclear. To address this issue, we performed causal-inference analyses based on lesion and connectome network mapping using MRI and behavioural testing in two groups of individuals with chronic post-stroke aphasia. We employed a rhyme judgement task with heavy working memory load without articulatory confounds, controlling for the overall ability to match auditory words to pictures and to perform a metalinguistic rhyme judgement, isolating the effect of working memory load (103 individuals). We assessed non-canonical sentence comprehension, isolating syntactic processing by incorporating residual rhyme judgement performance as a covariate for working memory load (78 individuals). Voxel-based lesion analyses and structural connectome-based lesion symptom mapping controlling for total lesion volume were performed, with permutation testing to correct for multiple comparisons (4000 permutations). We observed that effects of working memory load localized to dorsal stream damage: posterior temporal-parietal lesions and frontal-parietal white matter disconnections. These effects were differentiated from syntactic comprehension deficits, which were primarily associated with ventral stream damage: lesions to temporal lobe and temporal-parietal white matter disconnections, particularly when incorporating the residual measure of working memory load as a covariate. Our results support the conclusion that working memory and syntactic processing are associated with distinct brain networks, largely loading onto dorsal and ventral streams, respectively
WIP: A Novel Learning Log Application for Classifying Learning Events Using Bloom’s Taxonomy
No full textLearning can be a daunting and challenging process, particularly in engineering. While cognitive models for learning such as Bloom\u27s taxonomy have been developed since the 1950s and evidenced to be useful in designing engineering courses, these models are not commonly explicitly taught in classrooms to help students manage and regulate their own learning. In highly demanding curriculum such as engineering, ineffective strategies can lead to poor academic performance that cascades throughout a student’s academic career. Feedback from traditional examinations often do not provide personalized and actionable changes to study habits (i.e., with suboptimal scores, students may know they need to study more, but whether “more” is effective is often unclear). There is a pressing need to bridge the gap between study practices and learning outcomes that enable students to regulate and improve their own learning strategies in engineering. This work in progress paper presents initial data from a novel “learning log” application that allows students to enter their studying activity (e.g., timed practice exam, redoing homework, reading the textbook, practice problems), and labels the cognition level (using Bloom\u27s taxonomy: remember, understand, apply, analyze, evaluate, create). In this work in progress, we present initial data from students’ logged studying activities using the application. The logging allows students to track their cognition distribution over time, providing data about how they engaged with course content
Multimodal Mixing Convolutional Neural Network and Transformer for Alzheimer’s Disease Recognition
No full textEarly recognition of Alzheimer’s disease (AD) and its precursor state, mild cognitive impairment (MCI), is pivotal in interrupting the progression of the disease and providing suitable treatment. Recent development in deep learning techniques has drawn great research attention for improving the efficacy of AD recognition. However, numerous current methods solely utilize data from a single auxiliary domain, limiting their ability to harness valuable intrinsic insights from multiple domains. To cope with the challenge, this paper is devoted to establishing an innovative multimodal medical data fusion model, termed as MMDF, to perform Alzheimer’s disease recognition. Multimodal data including clinical records and medical images are used by the proposed approach, and backbone models are constructed using various data modalities. Specifically, a vision transformer model, which is termed as MRI_ViT, is tailored to recognize AD using brain magnetic resonance imaging (MRI) data. In parallel, a novel multi-scale attention-embedded one-dimensional (1D) convolutional neural network (MA-1DCNN) is devised for analyzing clinical records. Subsequently, these basic models are combined for creating a new data fusion model to recognize Alzheimer’s disease. The experimental results reveal outstanding performance compared with state-of-the-art (SOTA) methods
FPCA-SETCN: A Novel Deep Learning Framework for Remaining Useful Life Prediction
Get PDFThe accurate prediction of remaining useful life (RUL) can serve as a reliable foundation for equipment maintenance, thereby effectively reducing the incidence of failure and maintenance costs. In this study, a novel deep learning (DL) framework that incorporates functional principal component analysis (FPCA) and enhanced temporal convolutional network (TCN) is proposed for RUL prediction. Precisely, FPCA is employed to capture the changing patterns in multistream degradation trajectories. Subsequently, the reconstructed signals from FPCA are fed into a convolutional block for extracting deep-level features. An enhanced squeeze-and-excitation (ESE) block is then incorporated into the network for adaptive feature recalibration, enhancing the network’s ability to focus on the most relevant information. The framework includes a TCN module augmented with hybrid attention mechanisms, comprising ESE and spatial attention (SA) blocks, to optimally capture forward and backward sequence information of the feature tensor. The efficiency and feasibility of the proposed approach are demonstrated through case studies on both the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) and Center for Advanced Life Cycle Engineering (CALCE) battery datasets. The proposed method achieves the lowest root-mean-square error (RMSE) of 15.56 on the C-MAPSS dataset and 0.03 on the CALCE dataset. The comparative studies highlight the superiority of the proposed network over existing DL algorithms
Deciphering Water Quality and Algal Dynamics in Clear Lake Through Hyperspectral Analysis Using Emit Data
Get PDFThis study evaluates the potential application of hyperspectral Earth Surface Mineral Dust Source Investigation (EMIT) remote sensing for monitoring harmful algal blooms (HABs) and water quality in Clear Lake, California. The research focuses on correlating the chlorophyll-a (Chl-a) concentrations with EMIT spectral signatures, using waterbody-wide statistical analysis of Chl-a and EMIT data sampling at various lake locations. Results demonstrate distinct spectral signatures associated with varying Chl-a levels, highlighting the potential of hyperspectral imaging in differentiating algae levels and assessing water quality variables. It also indicates the EMIT’s utility in filling data gaps and offering high-resolution monitoring. This study underscores the need for further research in hyperspectral imaging for aquatic ecosystems, especially under challenging atmospheric conditions, enhancing our understanding of water quality dynamics
Nonlinear Changes in Facial Affect and Posttraumatic Growth: Assessment of Ecological Momentary Assessment Video Data
Get PDFPosttraumatic Growth (PTG), characterized by newfound meaning, perspective, and purpose for trauma survivors, remains enigmatic in its nature. This state is thought to arise from the dynamic interplay of biopsychosocial factors; however, the nature of this interplay is unclear. This study aimed to investigate the intricate relationship between PTG and facial affect dynamics, shedding light on the complex interplay of biopsychosocial factors that underpin this transformative process. We conducted a comprehensive investigation involving 19 wildfire survivors who provided daily self-reported PTG ratings alongside smartphone videos analyzed using Automated Facial Affect Recognition (AFAR) software. Our findings revealed compelling evidence of self-organization within facial affect, as indicated by notably high mean R2 and shape parameter values (i.e., nonlinear indices indicative of structural integrity and flexibility). Further regression analyses unveiled a significant interaction between the degree of facial affect “burstiness” and coping self-efficacy (CSE) on PTG. This interaction suggested that PTG development was a nuanced process intricately linked to the coherence of emotion patterns exhibited by individuals. These insights illuminate the multifaceted dynamics at play in the emergence of PTG and contribute to a broader understanding of its biopsychosocial foundations
Incorporating Conditional Morality into Economic Decisions
No full textWe present a theoretical framework of individual-decision making that incorporates both moral motivations and social influence into the utility function. The main idea of the paper is that individuals face a trade-off between their material individual interests and their desire to follow moral obligation. In our model, we assume that moral motivation is weak or conditional in the sense that it may be influenced by others’ actions. Specifically, in our framework one’s moral obligation is a combination of two main components: an autonomous component and a social component that captures the influence of others. Our theoretical framework is able to explain many stylized results commonly observed in the literature and suggests a different mechanism to explain economic behavior