The influence of color vision deficiency on vessel visibility during colorectal endoscopic submucosal dissection and the potential advantage of red dichromatic imaging to achieve color vision barrier-free.

Objectives: Although color information is important in gastrointestinal endoscopy, there are limited studies on how endoscopic images are viewed by people with color vision deficiency. We aimed to investigate the differences in the visibility of blood vessels during endoscopic submucosal dissection (ESD) among people with different color vision characteristics and to examine the effect of red dichromatic imaging (RDI) on blood vessel visibility. Methods: Seventy-seven pairs of endoscopic images of white light imaging (WLI) and RDI of the same site were obtained during colorectal ESD. The original images were set as type C (WLI-C and RDI-C), a common color vision. These images were computationally converted to simulate images perceived by people with color vision deficiency protanope (Type P) or deutanope (Type D) and denoted as WLI-P and RDI-P or WLI-D and RDI-D. Blood vessels and background submucosa that needed to be identified during ESD were selected in each image, and the color differences between these two objects were measured using the color difference (ΔE 00) to assess the visibility of blood vessels. Results: ΔE 00 between a blood vessel and the submucosa was greater under RDI (RDI-C/P/D: 24.05 ± 0.64/22.85 ± 0.66/22.61 ± 0.64) than under WLI (WLI-C/P/D: 22.26 ± 0.60/5.19 ± 0.30/8.62 ± 0.42), regardless of color vision characteristics. This improvement was more pronounced in Type P and Type D and approached Type C in RDI. Conclusions: Color vision characteristics affect the visibility of blood vessels during ESD, and RDI improves blood vessel visibility regardless of color vision characteristics.

A machine vision tool for multi-color H2O2 sensing by MoOx nanoparticles with oxygen vacancies.

Improving the ease of operation and portability of hydrogen peroxide (H2O2) detection in daily production and life holds significant application value. However, it remains a challenge to achieve rapid colorimetric detection of H2O2 and color change quantification. In this study, we achieved rapid and visual detection of H2O2 by MoOx (2 ≤ x ≤ 3) nanoparticles with rich oxygen vacancies using machine vision. As the concentration of H2O2 increases, the detection system exhibited a visible multi-color change from blue to green and then yellow and the absorption peak near 680 nm measured by the UV-visible spectrophotometer gradually decreased. With excellent sensitivity, a wide linear range of 0.1-600 µmol/L, concentrations as low as 0.1 µmol/L can be detected with good selectivity towards H2O2. The sensing mechanism of detecting H2O2 by the change of oxygen vacancies in MoOx was revealed through characterization methods such as XPS, EPR, and DFT. In addition, the Hue, Saturation, Value (HSV) visual analysis system based on MoOx was constructed to assist in the rapid, portable, and sensitive monitoring of H2O2 in practical application scenarios. This work offers an easy-to operate, low cost, and convenience for achieving rapid colorimetric determination of H2O2 and has broad application prospects in daily life and industrial production.

Efficacy of anti-suppression therapy in improving binocular vision in children with small-angle Esotropia

Purpose: To evaluate the efficacy of anti-suppression exercises in children with small-angle esotropia in achieving binocular vision. Methods: A retrospective review of patients aged 3–8 years who underwent anti-suppression exercises for either monocular or alternate suppression between January 2016 and December 2021 was conducted. Patients with esotropia less than 15 prism diopters (PD) and visual acuity ≥ 6/12 were included. Patients with previous intra-ocular surgery or less than three-month follow-up were excluded. Success was defined as the development of binocular single vision (BSV) for distance, near, or both (measured clinically with either the 4 prism base out test or Worth four dot test) and maintained at two consecutive visits. Qualified success was defined as the presence of diplopia response for both distance and near. Additionally, improvement in near stereo acuity was measured using the Stereo Fly test. Results: Eighteen patients with a mean age of 5.4 ± 1.38 years (range 3–8 years) at the time of initiation of exercises were included in the study. The male female ratio was 10:8. The mean best corrected visual acuity was 0.18 LogMAR unit(s) and the mean spherical equivalent was +3.8 ± 0.14 diopters (D). The etiology of the esotropia was fully accommodative refractive esotropia (8), microtropia (1), post–operative infantile esotropia (4), partially accommodative esotropia (1), and post-operative partially accommodative esotropia (4). Patients received either office-based, home-based, or both modes of treatment for an average duration of 4.8 months (range 3–8). After therapy, BSV was achieved for either distance or near in 66.6 % of patients (95 % CI = 40.03–93.31 %). Binocular single vision for both distance and near was seen in 50 % of children. Qualified success was observed in 38.46% of patients. Persistence of suppression was observed in one patient (5.5 %)... (AU)

Visual function correlates of self-reported vision-related nighttime driving difficulties / La función visual se correlaciona con las dificultades de conducción nocturna relacionadas con la visión autoinformadas

Purpose: To investigate the visual function correlates of self-reported vision-related night driving difficulties among drivers. Methods: One hundred and seven drivers (age: 46.06 ± 8.24, visual acuity [VA] of 0.2logMAR or better) were included in the study. A standard vision and night driving questionnaire (VND-Q) was administered. VA and contrast sensitivity were measured under photopic and mesopic conditions. Mesopic VA was remeasured after introducing a peripheral glare source into the participants' field of view to enable computation of disability glare index. Regression analyses were used to assess the associations between VND-Q scores, and visual function measures. Results: The mean VND-Q score was -3.96±1.95 logit (interval scale score: 2.46±1.28). Simple linear regression models for photopic contrast sensitivity, mesopic VA, mesopic contrast sensitivity, and disability index significantly predicted VND-Q score (P<0.05), with mesopic VA and disability glare index accounting for the greatest variation (21 %) in VND-Q scores followed by photopic contrast sensitivity (19 %), and mesopic contrast sensitivity (15 %). A multiple regression model to determine the association between the predictors (photopic contrast sensitivity, mesopic VA, mesopic contrast sensitivity, and disability index) and VND-Q score yielded significant results, F (4, 102) = 8.58, P < 0.001, adj. R2 = 0.2224. Seeing dark-colored cars was the most challenging vision task. Conclusion: Changes in mesopic visual acuity, photopic and mesopic contrast sensitivity, as well as disability glare index are associated with and explain night driving-related visual difficulties. It is recommended to incorporate measurement of these visual functions into assessments related to driving performance.(AU)

Assessing the role of visual dysfunctions in the association between visual symptomatology and the use of digital devices / Evaluación del papel de las disfunciones visuales en la asociación entre la sintomatología visual y el uso de dispositivos digitales

Purpose: To evaluate the association between visual symptoms and use of digital devices considering the presence of visual dysfunctions. Methods: An optometric examination was conducted in a clinical sample of 346 patients to diagnose any type of visual anomaly. Visual symptoms were collected using the validated SQVD questionnaire. A threshold of 6 hours per day was used to quantify the effects of digital device usage and patients were divided into two groups: under and above of 35 years old. A multivariate logistic regression was employed to investigate the association between digital device use and symptoms, with visual dysfunctions considered as a confounding variable. Crude and the adjusted odds ratio (OR) were calculated for each variable. Results: 57.02 % of the subjects reported visual symptoms, and 65.02% exhibited some form of visual dysfunction. For patients under 35 years old, an association was found between having visual symptoms and digital device use (OR = 2.10, p = 0.01). However, after adjusting for visual dysfunctions, this association disappeared (OR = 1.44, p = 0.27) and the association was instead between symptoms and refractive dysfunction (OR = 6.52, p < 0.001), accommodative (OR = 10.47, p < 0.001), binocular (OR = 6.68, p < 0.001) and accommodative plus binocular dysfunctions (OR = 46.84, p < 0.001). Among patients over 35 years old, no association was found between symptoms and the use of digital devices (OR = 1.27, p = 0.49) but there was an association between symptoms and refractive dysfunction (OR = 3.54, p = 0.001). Conclusions: Visual symptoms are not dependent on the duration of digital device use but rather on the presence of any type of visual dysfunction: refractive, accommodative and/or binocular one, which should be diagnosed.(AU)

Study of stereopsis using a depth sensation detection platform equipped with computer vision technology (DALE3D) / Estudio de estereopsis mediante una plataforma de detección de sensaciones de profundidad equipada con tecnología de visión por ordenador (DALE3D)

Background and objectives: The invention described herein is a prototype based on computer vision technology that measures depth perception and is intended for the early examination of stereopsis. Materials and methods: The prototype (software and hardware) is a depth perception measurement system that consists on: (a) a screen showing stereoscopic models with a guide point that the subject must point to; (b) a camera capturing the distance between the screen and the subject's finger; and (c) a unit for recording, processing and storing the captured measurements. For test validation, the reproducibility and reliability of the platform were calculated by comparing results with standard stereoscopic tests. A demographic study of depth perception by subgroup analysis is shown. Subjective comparison of the different tests was carried out by means of a satisfaction survey. Results: We included 94 subjects, 25 children and 69 adults, with a mean age of 34.2 ± 18.9 years; 36.2 % were men and 63.8 % were women. The DALE3D platform obtained good repeatability with an interclass correlation coefficient (ICC) between 0.94 and 0.87, and coefficient of variation (CV) between 0.1 and 0.26. Threshold determining optimal and suboptimal results was calculated for Randot and DALE3D test. Spearman's correlation coefficient, between thresholds was not statistically significant (p value > 0.05). The test was considered more visually appealing and easier to use by the participants (90 % maximum score). Conclusions: The DALE3D platform is a potentially useful tool for measuring depth perception with optimal reproducibility rates. Its innovative design makes it a more intuitive tool for children than current stereoscopic tests. Nevertheless, further studies will be needed to assess whether the depth perception measured by the DALE3D platform is a sufficiently reliable parameter to assess stereopsis.(AU)

Binocular vision parameters and visual performance in bilateral keratoconus corrected with spectacles versus rigid gas-permeable contact lenses / Parámetros de visión binocular y rendimiento visual en queratocono bilateral corregido con gafas versus lentes de contacto rígidos permeables al gas

Purpose: To analyze binocular vision of individuals aged 18 to 35 years diagnosed with keratoconus, utilizing spectacles and rigid gas-permeable (RGP) contact lenses. Research was led by the Universidad Autónoma de Aguascalientes, México and Fundación Universitaria del Área Andina Pereira, Colombia. Methods: A single center, prospective non-randomized, comparative, interventional, open-label study, in which the differences in binocular vision performance with both spectacles and RGP contact lenses was carried out from December 2018 to December 2019. Sampling was performed according to consecutive cases with keratoconus that met the inclusion criteria until the proposed sample size was reached. Results: Rigid gas-permeable (RGP) contact lenses notably enhanced distance and near visual acuity in keratoconus patients compared to spectacles. Visual alignment analysis shows exophoria at both distances and is slightly higher with RGP contact lenses. The difference was statistically significant (p<0.05), with 82.5 % presenting compensated phoria with spectacles and pnly 42.50% with RGP contact lenses. Stereoscopic vision improved while wearing RGP contact lenses (42.59 %), although accommodation and accommodative flexibility remained within normal ranges. Conclusions: Patients with keratoconus fitted with RGP contact lenses have improved binocular vision skills such as visual acuity, stereopsis, and accommodative flexibility. However, even when the vergence and motor system is decompensated with respect to normal ranges, the range between break and recovery points for both fusional reserves and the near point of convergence (NPC) improves with the use of RGP contact lenses, giving indications of an adaptive condition of the motor system from the medium to the long term.(AU)

Deep learning models for the early detection of maize streak virus and maize lethal necrosis diseases in Tanzania.

Agriculture is considered the backbone of Tanzania's economy, with more than 60% of the residents depending on it for survival. Maize is the country's dominant and primary food crop, accounting for 45% of all farmland production. However, its productivity is challenged by the limitation to detect maize diseases early enough. Maize streak virus (MSV) and maize lethal necrosis virus (MLN) are common diseases often detected too late by farmers. This has led to the need to develop a method for the early detection of these diseases so that they can be treated on time. This study investigated the potential of developing deep-learning models for the early detection of maize diseases in Tanzania. The regions where data was collected are Arusha, Kilimanjaro, and Manyara. Data was collected through observation by a plant. The study proposed convolutional neural network (CNN) and vision transformer (ViT) models. Four classes of imagery data were used to train both models: MLN, Healthy, MSV, and WRONG. The results revealed that the ViT model surpassed the CNN model, with 93.1 and 90.96% accuracies, respectively. Further studies should focus on mobile app development and deployment of the model with greater precision for early detection of the diseases mentioned above in real life.

Pollinator response to yellow UV-patterned versus white UV-patternless flower dimorphism in Anemone palmata.

Flower colour polymorphisms are uncommon but widespread among angiosperms and can be maintained by a variety of balancing selection mechanisms. Anemone palmata is mostly yellow-flowered, but white-flowered plants coexist in some populations. We analysed the distribution of colour morphs of A. palmata across its range. We also characterised their colours and compared their vegetative and sexual reproductive traits, pollinator attention and fitness. The range of A. palmata is limited to the Western Mediterranean, while white-flowered plants are restricted to Portugal and SW Spain, where they occur at low proportions. Yellow flowers have a characteristic UV pattern, with a UV-absorbing centre and UV-reflecting periphery, which is absent in the white morph. Colour features of both morphs were highly delineated, making it easy for pollinators to distinguish them. Both morphs were protogynous, with the same duration of sexual stages, and the main floral traits related to pollinator attraction, apart from flower colour, were similar. Hymenoptera and Diptera were the main pollinators, showing preference for the yellow morph, clear partitioning of pollinator groups between the two colour morphs and a marked constancy to flower colour during foraging. Both morphs combined clonal propagation with sexual reproduction, but sexual reproductive potential was lower in white-flowered plants. Finally, female fitness was higher in the yellow morph. Pollinator partitioning and colour constancy could maintain this polymorphism, despite the lower visitation rate and fitness of white-flowered plants, which could facilitate their clonal propagation.

The Normalization of Vaping on TikTok Using Computer Vision, Natural Language Processing, and Qualitative Thematic Analysis: Mixed Methods Study.

BACKGROUND: Social media posts that portray vaping in positive social contexts shape people's perceptions and serve to normalize vaping. Despite restrictions on depicting or promoting controlled substances, vape-related content is easily accessible on TikTok. There is a need to understand strategies used in promoting vaping on TikTok, especially among susceptible youth audiences. OBJECTIVE: This study seeks to comprehensively describe direct (ie, explicit promotional efforts) and indirect (ie, subtler strategies) themes promoting vaping on TikTok using a mixture of computational and qualitative thematic analyses of social media posts. In addition, we aim to describe how these themes might play a role in normalizing vaping behavior on TikTok for youth audiences, thereby informing public health communication and regulatory policies regarding vaping endorsements on TikTok. METHODS: We collected 14,002 unique TikTok posts using 50 vape-related hashtags (eg, #vapetok and #boxmod). Using the k-means unsupervised machine learning algorithm, we identified clusters and then categorized posts qualitatively based on themes. Next, we organized all videos from the posts thematically and extracted the visual features of each theme using 3 machine learning-based model architectures: residual network (ResNet) with 50 layers (ResNet50), Visual Geometry Group model with 16 layers, and vision transformer. We chose the best-performing model, ResNet50, to thoroughly analyze the image clustering output. To assess clustering accuracy, we examined 4.01% (441/10,990) of the samples from each video cluster. Finally, we randomly selected 50 videos (5% of the total videos) from each theme, which were qualitatively coded and compared with the machine-derived classification for validation. RESULTS: We successfully identified 5 major themes from the TikTok posts. Vape product marketing (1160/10,990, 8.28%) reflected direct marketing, while the other 4 themes reflected indirect marketing: TikTok influencer (3775/14,002, 26.96%), general vape (2741/14,002, 19.58%), vape brands (2042/14,002, 14.58%), and vaping cessation (1272/14,002, 9.08%). The ResNet50 model successfully classified clusters based on image features, achieving an average F1-score of 0.97, the highest among the 3 models. Qualitative content analyses indicated that vaping was depicted as a normal, routine part of daily life, with TikTok influencers subtly incorporating vaping into popular culture (eg, gaming, skateboarding, and tattooing) and social practices (eg, shopping sprees, driving, and grocery shopping). CONCLUSIONS: The results from both computational and qualitative analyses of text and visual data reveal that vaping is normalized on TikTok. Our identified themes underscore how everyday conversations, promotional content, and the influence of popular figures collectively contribute to depicting vaping as a normal and accepted aspect of daily life on TikTok. Our study provides valuable insights for regulatory policies and public health initiatives aimed at tackling the normalization of vaping on social media platforms.

Perceptual-Cognitive Skills of Basketball Referees: On-The-Court Visual Search Behavior.

Perceptual-cognitive skills are crucial in successfully managing information and decision-making in sports, particularly in high-pressure environments. We examined 16 basketball referees' on-the-court visual search behavior by comparing referees of different experience levels (experienced, n = 8; and novice, n = 8) and different court positions. Participants' visual search behavior was analyzed during 20 live gameplay situations using eye-tracking technology. Dependent variables were the number of eye fixations, mean fixation time, and total fixation time on selected areas of interest; and independent variables were the referees' experience and visual angles (lead and trail referee positions). Experienced referees exhibited significantly lower total fixation time than novice referees (p = .009). Referees in the trail position showed more fixations of shorter duration and a greater focus on the basket than those in the lead position. Our findings suggest that the visual search behavior of basketball referees varies with their court position and experience. These data provide valuable insights into referees' complex visual search patterns in the real-game context, and they highlight the importance of considering viewing angle and experience in future research.

Computer vision quickly identifies radio signals with unlimited accuracy.

A seminal component of systems thinking is the application of an advanced technology in one domain to solve a challenging problem in a different domain. This article introduces a method of using advanced computer vision to solve the challenging signal processing problem of specific emitter identification. A one-dimensional signal is sampled; those samples are transformed into to two-dimensional images by computing a bispectrum; those images are evaluated using advanced computer vision; and the results are statistically combined until any user-selected level of classification accuracy is obtained. In testing on a published DARPA challenge dataset, for every eight additional signal samples taken from a candidate signal (out of many thousands), classification error decreases by an entire order of magnitude.

Advancing forest carbon stocks' mapping using a hierarchical approach with machine learning and satellite imagery.

Remote sensing of forests is a powerful tool for monitoring the biodiversity of ecosystems, maintaining general planning, and accounting for resources. Various sensors bring together heterogeneous data, and advanced machine learning methods enable their automatic handling in wide territories. Key forest properties usually under consideration in environmental studies include dominant species, tree age, height, basal area and timber stock. Being proxies of stand productivity, they can be utilized for forest carbon stock estimation to analyze forests' status and proper climate change mitigation measures on a global scale. In this study, we aim to develop an effective machine learning-based pipeline for automatic carbon stock estimation using solely freely available and regularly updated satellite observations. We employed multispectral Sentinel-2 remote sensing data to predict forest structure characteristics and produce their detailed spatial maps. Using the Extreme Gradient Boosting (XGBoost) algorithm in classification and regression settings and management-level inventory data as reference measurements, we achieved quality of predictions of species equal to 0.75 according to the F1-score, and for stand age, height, and basal area, we achieved an accuracy of 0.75, 0.58 and 0.56, respectively, according to the R2. We focused on the growing stock volume as the main proxy to estimate forest carbon stocks on the example of the stem pool. We explored two approaches: a direct approach and a hierarchical approach. The direct approach leverages the remote sensing data to create the target maps, and the hierarchical approach calculates the target forest properties using predicted inventory characteristics and conversion equations. We estimated stem carbon stock based on the same approach: from Earth observation imagery directly and using biomass and conversion factors developed for the northern regions. Thus, our study proposes an end-to-end solution for carbon stock estimations based on the complexation of inventory data at the forest stand level, Earth observation imagery, machine learning predictions and conversion equations for the region. The presented approach enables more robust and accurate large-scale assessments using limited annotated datasets.

A scarce dataset for ancient Arabic handwritten text recognition.

Developing Deep Learning Optical Character Recognition is an active area of research, where models based on deep neural networks are trained on data to eventually extract text within an image. Even though many advances are currently being made in this area in general, the Arabic OCR domain notably lacks a dataset for ancient manuscripts. Here, we fill this gap by providing both the image and textual ground truth for a collection of ancient Arabic manuscripts. This scarce dataset is collected from the central library of the Islamic University of Madinah, and it encompasses rich text spanning different geographies across centuries. Specifically, eight ancient books with a total of forty pages, both images and text, transcribed by the experts, are present in this dataset. Particularly, this dataset holds a significant value due to the unavailability of such data publicly, which conspicuously contributes to the deep learning models development/augmenting, validation, testing, and generalization by researchers and practitioners, both for the tasks of Arabic OCR and Arabic text correction.

FruitSeg30_Segmentation dataset & mask annotations: A novel dataset for diverse fruit segmentation and classification.

Fruits are mature ovaries of flowering plants that are integral to human diets, providing essential nutrients such as vitamins, minerals, fiber and antioxidants that are crucial for health and disease prevention. Accurate classification and segmentation of fruits are crucial in the agricultural sector for enhancing the efficiency of sorting and quality control processes, which significantly benefit automated systems by reducing labor costs and improving product consistency. This paper introduces the "FruitSeg30_Segmentation Dataset & Mask Annotations", a novel dataset designed to advance the capability of deep learning models in fruit segmentation and classification. Comprising 1969 high-quality images across 30 distinct fruit classes, this dataset provides diverse visuals essential for a robust model. Utilizing a U-Net architecture, the model trained on this dataset achieved training accuracy of 94.72 %, validation accuracy of 92.57 %, precision of 94 %, recall of 91 %, f1-score of 92.5 %, IoU score of 86 %, and maximum dice score of 0.9472, demonstrating superior performance in segmentation tasks. The FruitSeg30 dataset fills a critical gap and sets new standards in dataset quality and diversity, enhancing agricultural technology and food industry applications.

Development of deep learning model for diagnosing muscle-invasive bladder cancer on MRI with vision transformer.

Rationale and objectives: To develop and validate a deep learning (DL) model to automatically diagnose muscle-invasive bladder cancer (MIBC) on MRI with Vision Transformer (ViT). Materials and methods: This multicenter retrospective study included patients with BC who reported to two institutions between January 2016 and June 2020 (training dataset) and a third institution between May 2017 and May 2022 (test dataset). The diagnostic model for MIBC and the segmentation model for BC on MRI were developed using the training dataset with 5-fold cross-validation. ViT- and convolutional neural network (CNN)-based diagnostic models were developed and compared for diagnostic performance using the area under the curve (AUC). The performance of the diagnostic model with manual and auto-generated regions of interest (ROImanual and ROIauto, respectively) was validated on the test dataset and compared to that of radiologists (three senior and three junior radiologists) using Vesical Imaging Reporting and Data System scoring. Results: The training and test datasets included 170 and 53 patients, respectively. Mean AUC of the top 10 ViT-based models with 5-fold cross-validation outperformed those of the CNN-based models (0.831 ± 0.003 vs. 0.713 ± 0.007-0.812 ± 0.006, p < .001). The diagnostic model with ROImanual achieved AUC of 0.872 (95 % CI: 0.777, 0.968), which was comparable to that of junior radiologists (AUC = 0.862, 0.873, and 0.930). Semi-automated diagnosis with the diagnostic model with ROIauto achieved AUC of 0.815 (95 % CI: 0.696, 0.935). Conclusion: The DL model effectively diagnosed MIBC. The ViT-based model outperformed CNN-based models, highlighting its utility in medical image analysis.

FEVR combined with macular heterotopia in children presenting as pseudo-exotropia: a case report and literature review.

Familial exudative retinopathy (FEVR) is a hereditary disease involving abnormal retinal vascular development in which macular heterotopia (MH) caused by mechanical-like pulling of the vitreous may lead to pseudo-strabismus. We describe the case of a 12-year-old male patient from China who presented to our hospital with a request for surgical correction of exotropia. Examination revealed that the strabismic appearance was due to MH, and dilated pupil examination of the peripheral fundus revealed that the blood vessels of the left eye and the macula were displaced toward the temporal retina by pulling, and further FFA examination was performed to diagnose FEVR. With good binocular vision and stereoscopic distance vision, corrective surgery for strabismus in this patient would have resulted in a hard-to-resolve diplopia. Therefore, it is important to identify FEVR combined with MH in clinical practice to avoid wrong diagnostic and treatment options.

Automated quantification of SARS-CoV-2 pneumonia with large vision model knowledge adaptation.

Background: Large vision models (LVM) pretrained by large datasets have demonstrated their enormous capacity to understand visual patterns and capture semantic information from images. We proposed a novel method of knowledge domain adaptation with pretrained LVM for a low-cost artificial intelligence (AI) model to quantify the severity of SARS-CoV-2 pneumonia based on frontal chest X-ray (CXR) images. Methods: Our method used the pretrained LVMs as the primary feature extractor and self-supervised contrastive learning for domain adaptation. An encoder with a 2048-dimensional feature vector output was first trained by self-supervised learning for knowledge domain adaptation. Then a multi-layer perceptron (MLP) was trained for the final severity prediction. A dataset with 2599 CXR images was used for model training and evaluation. Results: The model based on the pretrained vision transformer (ViT) and self-supervised learning achieved the best performance in cross validation, with mean squared error (MSE) of 23.83 (95 % CI 22.67-25.00) and mean absolute error (MAE) of 3.64 (95 % CI 3.54-3.73). Its prediction correlation has the R 2 of 0.81 (95 % CI 0.79-0.82) and Spearman ρ of 0.80 (95 % CI 0.77-0.81), which are comparable to the current state-of-the-art (SOTA) methods trained by much larger CXR datasets. Conclusion: The proposed new method has achieved the SOTA performance to quantify the severity of SARS-CoV-2 pneumonia at a significantly lower cost. The method can be extended to other infectious disease detection or quantification to expedite the application of AI in medical research.

Fall risk prediction using temporal gait features and machine learning approaches.

Introduction: Falls have been acknowledged as a major public health issue around the world. Early detection of fall risk is pivotal for preventive measures. Traditional clinical assessments, although reliable, are resource-intensive and may not always be feasible. Methods: This study explores the efficacy of artificial intelligence (AI) in predicting fall risk, leveraging gait analysis through computer vision and machine learning techniques. Data was collected using the Timed Up and Go (TUG) test and JHFRAT assessment from MMU collaborators and augmented with a public dataset from Mendeley involving older adults. The study introduces a robust approach for extracting and analyzing gait features, such as stride time, step time, cadence, and stance time, to distinguish between fallers and non-fallers. Results: Two experimental setups were investigated: one considering separate gait features for each foot and another analyzing averaged features for both feet. Ultimately, the proposed solutions produce promising outcomes, greatly enhancing the model's ability to achieve high levels of accuracy. In particular, the LightGBM demonstrates a superior accuracy of 96% in the prediction task. Discussion: The findings demonstrate that simple machine learning models can successfully identify individuals at higher fall risk based on gait characteristics, with promising results that could potentially streamline fall risk assessment processes. However, several limitations were discovered throughout the experiment, including an insufficient dataset and data variation, limiting the model's generalizability. These issues are raised for future work consideration. Overall, this research contributes to the growing body of knowledge on fall risk prediction and underscores the potential of AI in enhancing public health strategies through the early identification of at-risk individuals.

PULSE Ambassadors program: empowering departments to transform STEM education for inclusion and student success.

The Partnership for Undergraduate Life Sciences Education (PULSE) is a non-profit educational organization committed to promoting the transformation of undergraduate STEM education by supporting departments in removing barriers to access, equity, and inclusion and in adopting evidence-based teaching and learning practices. The PULSE Ambassadors Campus Workshop program enables faculty and staff members of host departments to 1) develop communication, shared leadership, and inclusion skills for effective team learning; 2) implement facilitative leadership skills (e.g., empathic listening and collaboration); 3) create a shared vision and departmental action plan; and 4) integrate diversity, equity, and inclusion practices in the department and curriculum. From the first workshop in 2014, teams of trained Ambassadors conducted workshops at 58 institutions, including associate, bachelor, master, and doctoral institutions. In their workshop requests, departments cited several motivations: desire to revise and align their curriculum with Vision and Change recommendations, need for assistance with ongoing curricular reform, and wish for external assistance with planning processes and communication. Formative assessments during and immediately following workshops indicated that key outcomes were met. Post-workshop interviews of four departments confirm progress achieved on action items and development of individual department members as agents of change. The PULSE Ambassadors program continues to engage departments to improve undergraduate STEM education and prepare departments for the challenges and uncertainties of the changing higher education landscape.