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1. Facial expressions of emotions by people with visual impairment and blindness via video conferencing

   https://doi.org/10.1177/02646196231163491  

   Nam_Kim, Hyung (April 2023, British Journal of Visual Impairment)

   Many people including those with visual impairment and blindness take advantage of video conferencing tools to meet people. Video conferencing tools enable them to share facial expressions that are considered as one of the most important aspects of human communication. This study aims to advance knowledge of how those with visual impairment and blindness share their facial expressions of emotions virtually. This study invited a convenience sample of 28 adults with visual impairment and blindness to Zoom video conferencing. The participants were instructed to pose facial expressions of basic human emotions (anger, fear, disgust, happiness, surprise, neutrality, calmness, and sadness), which were video recorded. The facial expressions were analyzed using the Facial Action Coding System (FACS) that encodes the movement of specific facial muscles called Action Units (AUs). This study found that there was a particular set of AUs significantly engaged in expressing each emotion, except for sadness. Individual differences were also found in AUs influenced by the participants’ visual acuity levels and emotional characteristics such as valence and arousal levels. The research findings are anticipated to serve as the foundation of knowledge, contributing to developing emotion-sensing technologies for those with visual impairment and blindness. 

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2. The International Tree‐Ring Data Bank ( ITRDB ) revisited: Data availability and global ecological representativity

   https://doi.org/10.1111/jbi.13488  

   Zhao, Shoudong; Pederson, Neil; D'Orangeville, Loïc; HilleRisLambers, Janneke; Boose, Emery; Penone, Caterina; Bauer, Bruce; Jiang, Yuan; Manzanedo, Rubén D. (December 2018, Journal of Biogeography)

   Abstract AimThe International Tree‐Ring Data Bank (ITRDB) is the most comprehensive database of tree growth. To evaluate its usefulness and improve its accessibility to the broad scientific community, we aimed to: (a) quantify its biases, (b) assess how well it represents global forests, (c) develop tools to identify priority areas to improve its representativity, and d) make available the corrected database. LocationWorldwide. Time periodContributed datasets between 1974 and 2017. Major taxa studiedTrees. MethodsWe identified and corrected formatting issues in all individual datasets of theITRDB. We then calculated the representativity of theITRDBwith respect to species, spatial coverage, climatic regions, elevations, need for data update, climatic limitations on growth, vascular plant diversity, and associated animal diversity. We combined these metrics into a global Priority Sampling Index (PSI) to highlight ways to improveITRDBrepresentativity. ResultsOur refined dataset provides access to a network of >52 million growth data points worldwide. We found, however, that the database is dominated by trees from forests with low diversity, in semi‐arid climates, coniferous species, and in western North America. Conifers represented 81% of theITRDBand even in well‐sampled areas, broadleaves were poorly represented. OurPSIstressed the need to increase the database diversity in terms of broadleaf species and identified poorly represented regions that require scientific attention. Great gains will be made by increasing research and data sharing in African, Asian, and South American forests. Main conclusionsThe extensive data and coverage of theITRDBshow great promise to address macroecological questions. To achieve this, however, we have to overcome the significant gaps in the representativity of theITRDB. A strategic and organized group effort is required, and we hope the tools and data provided here can guide the efforts to improve this invaluable database. 

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3. FACIAL GRID TRANSFORMATION: A NOVEL FACE REGISTRATION APPROACH FOR IMPROVING FACIAL ACTION UNIT RECOGNITION

   https://doi.org/10.1109/ICIP.2014.7025283  

   Han, Shizhong; Meng, Zibo; Liu, Ping; Tong, Yan (October 2014, Proceedings - International Conference on Image Processing)

   Face registration is a major and critical step for face analysis. Existing facial activity recognition systems often employ coarse face alignment based on a few fiducial points such as eyes and extract features from equal-sized grid. Such extracted features are susceptible to variations in face pose, facial deformation, and person-specific geometry. In this work, we propose a novel face registration method named facial grid transformation to improve feature extraction for recognizing facial Action Units (AUs). Based on the transformed grid, novel grid edge features are developed to capture local facial motions related to AUs. Extensive experiments on two wellknown AU-coded databases have demonstrated that the proposed method yields significant improvements over the methods based on equal-sized grid on both posed and more importantly, spontaneous facial displays. Furthermore, the proposed method also outperforms the state-of-the-art methods using either coarse alignment or mesh-based face registration. 

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4. Exploring Multidimensional Measurements for Pain Evaluation using Facial Action Units

   https://doi.org/10.1109/FG47880.2020.00087  

   Xu, Xiaojing; de Sa, Virginia R. (January 2020, 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020))

   Although pain is widely recognized to be a multidimensional experience, it is typically measured by unidimensional patient self-reported visual analog scale (VAS). However, self-reported pain is subjective, difficult to interpret and sometimes impossible to obtain. Machine learning models have been developed to automatically recognize pain at both the frame level and sequence (or video) level. Many methods use or learn facial action units (AUs) defined by the Facial Action Coding System (FACS) for describing facial expressions with muscle movement. In this paper, we analyze the relationship between sequence-level multidimensional pain measurements and frame-level AUs and an AU derived pain-related measure, the Prkachin and Solomon Pain Intensity (PSPI). We study methods that learn sequence-level metrics from frame-level metrics. Specifically, we explore an extended multitask learning model to predict VAS from human-labeled AUs with the help of other sequence-level pain measurements during training. This model consists of two parts: a multitask learning neural network model to predict multidimensional pain scores, and an ensemble learning model to linearly combine the multidimensional pain scores to best approximate VAS. Starting from human-labeled AUs, the model achieves a mean absolute error (MAE) on VAS of 1.73. It outperforms provided human sequence-level estimates which have an MAE of 1.76. Combining our machine learning model with the human estimates gives the best performance of MAE on VAS of 1.48. 

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5. Effects of water turbidity on the survival of Staphylococcus aureus in environmental fresh and brackish waters

   https://doi.org/10.1002/wer.10923  

   Steadmon, Maria; Ngiraklang, Kebang; Nagata, Macy; Masga, Keanu; Frank, Kiana L. (September 2023, Water Environment Research)

   Abstract Staphylococcus aureusis an opportunistic pathogen frequently detected in environmental waters and commonly causes skin infections to water users.S. aureusconcentrations in fresh, brackish, and marine waters are positively correlated with water turbidity. To reduce the risk ofS. aureusinfections from environmental waters,S. aureussurvival (stability and multiplication) in turbid waters needs to be investigated. The aim of this study was to measureS. aureusin turbid fresh and brackish water samples and compare the concentrations over time to determine which conditions are associated with enhancedS. aureussurvival. Eighteen samples were collected from fresh and brackish water sources from two different sites on the east side of Oʻahu, Hawaiʻi.S. aureuswas detected in microcosms for up to 71 days with standard microbial culturing techniques. On average, the greatest environmental concentrations ofS. aureuswere in high turbidity fresh waters followed by high turbidity brackish waters. Models demonstrate that salinity and turbidity significantly predict environmentalS. aureusconcentrations.S. aureuspersistence over the extent of the experiment was the greatest in high turbidity microcosms with T₉₀'s of 147.8 days in brackish waters and 80.8 days in freshwaters. This study indicates that saline, turbid waters, in the absence of sunlight, provides suitable conditions for enhanced persistence ofS. aureuscommunities that may increase the risk of exposure in environmental waters. Practitioner PointsStaphylococcus aureusconcentrations, survival, and persistence were assessed in environmental fresh and brackish waters.Experimental design preserved in situ conditions to measureS. aureussurvival.Higher initialS. aureusconcentrations were observed in fresh waters with elevated turbidity, while sustained persistence was greater in brackish waters.Water turbidity and salinity were both positively associated withS. aureusconcentrations and persistence.Climate change leads to more intense rainfall events which increase water turbidity and pathogen loading, heightening the exposure risk toS. aureus. 

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