Enhanced deep learning models for automatic fish species identification in underwater imagery.

Underwater cameras are crucial in marine ecology, but their data management needs automatic species identification. This study proposes a two-stage deep learning approach. First, the Unsharp Mask Filter (UMF) preprocesses images. Then, an enhanced region-based fully convolutional network (R-FCN) detects fish using two-order integrals for position-sensitive score maps and precise region of interest (PS-Pr-RoI) pooling for accuracy. The second stage integrates ShuffleNetV2 with the Squeeze and Excitation (SE) module, forming the Improved ShuffleNetV2 model, enhancing classification focus. Hyperparameters are optimized with the Enhanced Northern Goshawk Optimization Algorithm (ENGO). The improved R-FCN model achieves 99.94 % accuracy, 99.58 % precision and recall, and a 99.27 % F-measure on the Fish4knowledge dataset. Similarly, the ENGO-based ShuffleNetV2 model, evaluated on the same dataset, shows 99.93 % accuracy, 99.19 % precision, 98.29 % recall, and a 98.71 % F-measure, highlighting its superior classification accuracy.

Unmasking anxiety: a head-to-head comparison of open and closed masks in head and neck cancer radiotherapy.

Background: Facemasks accurately immobilise patients with head and neck cancer (HNC) receiving radiotherapy (RT). However, such masks are associated with treatment related distress, a prognostic factor for poorer survival. Open masks offer increased comfort and patient satisfaction. We investigated whether open masks could immobilise patients without affecting treatment accuracy. Materials and methods: Over an 18-month period, all HNC RT patients with anxiety were offered open masks. Once 30 patients had completed treatment, set-up data was compared to patients in closed masks. The mean displacement and one-dimensional standard deviations (SD) of the mean, systematic and random set-up errors were calculated for translational directions: anterior-posterior (x), superior-inferior (y), medial-lateral (z). The mean and SD of the mean was calculated for rotational displacements. Mann-Whitney U was used to determine any significant differences between set-up data. Results: Sixty patients were included (30 open & 30 closed masks). There was no statistically significant difference found in the x (p = 0.701), y (p = 0.246) or z (p = 0.535) direction for the SD of the mean displacements between both masks. No statistically significant difference was found in the SD of means for rotational displacements. The calculated planning target volume (PTV) margin requirements were minimally less for the closed masks 3.5, 2.6, and 2.7 mm (x, y, z, respectively) versus 4.2, 3.2, and 3.7 mm, respectively, for open masks. Conclusion: Our study demonstrates that open masks maintain accuracy at levels comparable to closed masks in patients with anxiety. The minor difference in the calculated PTV margin could be rectified with daily on-line imaging or surface guided imaging.

Face Mask Therapy and Comprehensive Orthodontic Treatment for Skeletal Class III Malocclusion: A Case Report.

Aim: This article aims to report a case of face mask therapy and comprehensive orthodontic treatment for skeletal class III malocclusion in a 16-year-old girl. Background: Treating skeletal class III malocclusion in a growing patient is crucial, as it can help avoid the need for additional surgery. Early treatment also lessens the negative impacts of the patient's facial abnormality on their social life because surgery is only done later. Case description: In this case report, a 14-year-old female patient presented with skeletal class III malocclusion with primary complaints of anterior crossbite. There was no relevant medical history. Face mask therapy and fixed appliance therapy were components of the treatment approach that successfully corrected the malocclusion. The total period of treatment was 20 months. Conclusion: The treatment resulted in a harmonious face, a well-aligned smile arch, stable dental and skeletal relationships, and significant esthetic improvements, including improved facial symmetry and profile. Significance: A growing teen who has a skeletal class III malocclusion and a maxillary deficit may be helped by a combination of face mask therapy and thorough orthodontic treatment. This case report outlines the use of the aforementioned technique to successfully treat a 14-year-old child with class III malocclusion and maxillary deficiencies.Early management of skeletal class III malocclusion in developing adolescents is vital as it can potentially eliminate the necessity for future surgical intervention, leading to improved treatment outcomes.Careful case selection, patient cooperation, and long-term stability enable a successful, stable, and esthetically pleasing treatment outcome. How to cite this article: Le LN, Do TT, P Le KV. Face Mask Therapy and Comprehensive Orthodontic Treatment for Skeletal Class III Malocclusion: A Case Report. Int J Clin Pediatr Dent 2024;17(3):368-376.

Emerging trends in gait recognition based on deep learning: a survey.

Gait recognition, a biometric identification method, has garnered significant attention due to its unique attributes, including non-invasiveness, long-distance capture, and resistance to impersonation. Gait recognition has undergone a revolution driven by the remarkable capacity of deep learning to extract complicated features from data. An overview of the current developments in deep learning-based gait identification methods is provided in this work. We explore and analyze the development of gait recognition and highlight its uses in forensics, security, and criminal investigations. The article delves into the challenges associated with gait recognition, such as variations in walking conditions, viewing angles, and clothing as well. We discuss about the effectiveness of deep neural networks in addressing these challenges by providing a comprehensive analysis of state-of-the-art architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention mechanisms. Diverse neural network-based gait recognition models, such as Gate Controlled and Shared Attention ICDNet (GA-ICDNet), Multi-Scale Temporal Feature Extractor (MSTFE), GaitNet, and various CNN-based approaches, demonstrate impressive accuracy across different walking conditions, showcasing the effectiveness of these models in capturing unique gait patterns. GaitNet achieved an exceptional identification accuracy of 99.7%, whereas GA-ICDNet showed high precision with an equal error rate of 0.67% in verification tasks. GaitGraph (ResGCN+2D CNN) achieved rank-1 accuracies ranging from 66.3% to 87.7%, whereas a Fully Connected Network with Koopman Operator achieved an average rank-1 accuracy of 74.7% for OU-MVLP across various conditions. However, GCPFP (GCN with Graph Convolution-Based Part Feature Polling) utilizing graph convolutional network (GCN) and GaitSet achieves the lowest average rank-1 accuracy of 62.4% for CASIA-B, while MFINet (Multiple Factor Inference Network) exhibits the lowest accuracy range of 11.72% to 19.32% under clothing variation conditions on CASIA-B. In addition to an across-the-board analysis of recent breakthroughs in gait recognition, the scope for potential future research direction is also assessed.

New insights on the degradation of polystyrene and polypropylene by larvae of the superworm Zophobas atratus and gut bacterial consortium enrichments obtained under different culture conditions.

This study aims to deepen knowledge of the biodegradation of plastics, focusing on polypropylene (PP) fabric from surgical masks and polystyrene (PS) by larvae of Zophobas atratus as well as of specialized bacterial consortia from their gut, which were obtained in different enrichment conditions (aerobic, anaerobic, presence or absence of combined nitrogen). Plastics ingested by larvae obtained in Spain did not show any signs of oxidation but only limited depolymerization, preferably from the lowest molecular weight chains. Gut microbiota composition changed as an effect of plastic feeding. Such differences were more evident in bacterial enrichment cultures, where the polymer type influenced the composition more than by culture conditions, with an increase in the presence of nitrogen-fixers in anaerobic conditions. PS and PP degradation by different enrichment cultures was confirmed under aerobic and anaerobic conditions by respirometry tests, with anaerobic conditions favouring a more active plastic degradation. In addition, exposure to selected bacterial consortia in aerobiosis induced limited surface oxidation of PS. This possibly indicates that different biochemical routes are being utilized in the anaerobic gut and in aerobic conditions to degrade the polymer.

Mathematical assessment of control strategies against the spread of MERS-CoV in humans and camels in Saudi Arabia.

A new mathematical model for the transmission dynamics and control of the Middle Eastern respiratory syndrome (MERS), a respiratory virus caused by MERS-CoV coronavirus (and primarily spread to humans by dromedary camels) that first emerged out of the Kingdom of Saudi Arabia (KSA) in 2012, was designed and used to study the transmission dynamics of the disease in a human-camel population within the KSA. Rigorous analysis of the model, which was fitted and cross-validated using the observed MERS-CoV data for the KSA, showed that its disease-free equilibrium was locally asymptotically stable whenever its reproduction number (denoted by $ {\mathbb R}_{0M} $) was less than unity. Using the fixed and estimated parameters of the model, the value of $ {\mathbb R}_{0M} $ for the KSA was estimated to be 0.84, suggesting that the prospects for MERS-CoV elimination are highly promising. The model was extended to allow for the assessment of public health intervention strategies, notably the potential use of vaccines for both humans and camels and the use of face masks by humans in public or when in close proximity with camels. Simulations of the extended model showed that the use of the face mask by humans who come in close proximity with camels, as a sole public health intervention strategy, significantly reduced human-to-camel and camel-to-human transmission of the disease, and this reduction depends on the efficacy and coverage of the mask type used in the community. For instance, if surgical masks are prioritized, the disease can be eliminated in both the human and camel population if at least 45% of individuals who have close contact with camels wear them consistently. The simulations further showed that while vaccinating humans as a sole intervention strategy only had marginal impact in reducing the disease burden in the human population, an intervention strategy based on vaccinating camels only resulted in a significant reduction in the disease burden in camels (and, consequently, in humans as well). Thus, this study suggests that attention should be focused on effectively combating the disease in the camel population, rather than in the human population. Furthermore, the extended model was used to simulate a hybrid strategy, which combined vaccination of both humans and camels as well as the use of face masks by humans. This simulation showed a marked reduction of the disease burden in both humans and camels, with an increasing effectiveness level of this intervention, in comparison to the baseline scenario or any of the aforementioned sole vaccination scenarios. In summary, this study showed that the prospect of the elimination of MERS-CoV-2 in the Kingdom of Saudi Arabia is promising using pharmaceutical (vaccination) and nonpharmaceutical (mask) intervention strategies, implemented in isolation or (preferably) in combination, that are focused on reducing the disease burden in the camel population.

Predicting the Airborne Transmission of Measles: Impact of Indoor Carbon Dioxide (CO2) Levels and Mitigation Strategies.

BACKGROUND: Measles is a highly contagious cause of febrile illness typically seen in young children. It is transmitted primarily through respiratory droplets and small-particle aerosols and can remain viable in the air. Despite the availability of an effective vaccine, measles remains a major global issue, particularly in regions with low vaccination rates. AIM: This study aimed to quantify the airborne transmission risk of the measles virus in various indoor environments. METHODS: Using indoor carbon dioxide (CO2) levels, we estimated the probability of airborne transmission and the basic reproduction number (Ro) in four hypothetical indoor scenarios, including restaurants, mass gathering events, homes, and business meetings, based on the modified Wells-Riley model. RESULTS: The relationship between airborne transmission rates and indoor CO2 concentrations was visualized, with and without mask usage. Without masks, at an indoor CO2 concentration of 1,000 ppm, the airborne transmission rates were high in homes (100.0%) and business meetings (100.0%) and moderate in restaurants (45.6%) and live events (30.6%). By contrast, the Ro was high in audience-participatory live events (60.9%) and restaurants (13.2%), indicating a higher risk of cluster infections. DISCUSSION AND CONCLUSION: In all indoor environmental scenarios, a positive linear relationship was found between the risk of airborne transmission and indoor CO2 levels. The risk of airborne transmission varied significantly across scenarios, which was influenced by various parameters, such as mask usage, quality of ventilation, conversation, and exposure duration. This model suggests that the risk of airborne transmission of measles can be easily predicted using a CO2 meter.

Functionalized N95 Face Mask with a Chemical-Free Paper-Based Collector for Exhaled Breath Analysis: SARS-CoV-2 Detection with a Printed Immunosensor as a Case Study.

Exhaled breath electrochemical sensing is a promising biomedical technology owing to its portability, painlessness, cost-effectiveness, and user-friendliness. Here, we present a novel approach for target analysis in exhaled breath by integrating a comfortable paper-based collector into an N95 face mask, providing a universal solution for analyzing several biomarkers. As a model analyte, we detected SARS-CoV-2 spike protein from the exhaled breath by sampling the target analyte into the collector, followed by its detection out of the N95 face mask using a magnetic bead-based electrochemical immunosensor. This approach was designed to avoid any contact between humans and the chemicals. To simulate human exhaled breath, untreated saliva samples were nebulized on the paper collector, revealing a detection limit of 1 ng/mL and a wide linear range of 3.7-10,000 ng/mL. Additionally, the developed immunosensor exhibited high selectivity toward the SARS-CoV-2 spike protein, compared to other airborne microorganisms, and the SARS-CoV-2 nucleocapsid protein. Accuracy assessments were conducted by analyzing the simulated breath samples spiked with varying concentrations of SARS-CoV-2 spike protein, resulting in satisfactory recovery values (ranging from 97 ± 4 to 118 ± 1%). Finally, the paper-based hybrid immunosensor was successfully applied for the detection of SARS-CoV-2 in real human exhaled breath samples. The position of the collector in the N95 mask was evaluated as well as the ability of this paper-based analytical tool to identify the positive patient.

Constraint based Bayesian optimization of bioink precursor: a machine learning framework.

Current research practice for optimizing bioink involves exhaustive experimentation with multi-material composition for determining the printability, shape fidelity and biocompatibility. Predicting bioink properties can be beneficial to the research community but is a challenging task due to the non-Newtonian behavior in complex composition. Existing models such as Cross model become inadequate for predicting the viscosity for heterogeneous composition of bioinks. In this paper, we utilize a machine learning framework to accurately predict the viscosity of heterogeneous bioink compositions, aiming to enhance extrusion-based bioprinting techniques. Utilizing Bayesian optimization (BO), our strategy leverages a limited dataset to inform our model. This is a technique especially useful of the typically sparse data in this domain. Moreover, we have also developed a mask technique that can handle complex constraints, informed by domain expertise, to define the feasible parameter space for the components of the bioink and their interactions. Our proposed method is focused on predicting the intrinsic factor (e.g., viscosity) of the bioink precursor which is tied to the extrinsic property (e.g., cell viability) through the mask function. Through the optimization of the hyperparameter, we strike a balance between exploration of new possibilities and exploitation of known data, a balance crucial for refining our acquisition function. This function then guides the selection of subsequent sampling points within the defined viable space and the process continues until convergence is achieved, indicating that the model has sufficiently explored the parameter space and identified the optimal or near-optimal solutions. Employing this AI-guided BO framework, we have developed, tested, and validated a surrogate model for determining the viscosity of heterogeneous bioink compositions. This data-driven approach significantly reduces the experimental workload required to identify bioink compositions conducive to functional tissue growth. It not only streamlines the process of finding the optimal bioink compositions from a vast array of heterogeneous options but also offers a promising avenue for accelerating advancements in tissue engineering by minimizing the need for extensive experimental trials.

Validity of machine learning algorithms for automatically extract growing rod length on radiographs in children with early-onset scoliosis.

The magnetically controlled growing rod technique is an effective surgical treatment for children who have early-onset scoliosis. The length of the instrumented growing rods is adjusted regularly to compensate for the normal growth of these patients. Manual measurement of rod length on posteroanterior spine radiographs is subjective and time-consuming. A machine learning (ML) system using a deep learning approach was developed to automatically measure the adjusted rod length. Three ML models-rod model, 58 mm model, and head-piece model-were developed to extract the rod length from radiographs. Three-hundred and eighty-seven radiographs were used for model development, and 60 radiographs with 118 rods were separated for final testing. The average precision (AP), the mean absolute difference (MAD) ± standard deviation (SD), and the inter-method correlation coefficient (ICC[2,1]) between the manual and artificial intelligence (AI) adjustment measurements were used to evaluate the developed method. The AP of the 3 models were 67.6%, 94.8%, and 86.3%, respectively. The MAD ± SD of the rod length change was 0.98 ± 0.88 mm, and the ICC[2,1] was 0.90. The average time to output a single rod measurement was 6.1 s. The developed AI provided an accurate and reliable method to detect the rod length automatically.

Comparative effectiveness of frame-based and mask-based Gamma Knife stereotactic radiosurgery in brain metastases: A 509 patient meta-analysis.

PURPOSE: Stereotactic Radiosurgery (SRS) is the primary treatment for patients with limited numbers of small brain metastases. Head fixation is usually performed with framed-based (FB) fixation; however, mask-based (MB) fixation has emerged as a less invasive alternative. A comparative meta-analysis between both approaches has not been performed. METHODS: Databases were searched until August 28th, 2023, to identify studies comparing MB and FB SRS in the treatment of brain metastases. Our outcomes of interest included local tumor control (LTC), radiation necrosis (RN), mortality, and treatment time (TT). Mean difference (MD), risk ratio (RR), and hazard ratio (HR) were used for statistical comparisons. RESULTS: From 295 articles initially identified, six studies (1 clinical trial) involving 509 patients were included. LTC revealed comparable RR at 6-months (RR = 0.95[95%CI = 0.89-1.01], p = 0.12) and a marginal benefit in FB SRS at 1-year (RR = 0.87[95%CI = 0.78-0.96], p = 0.005). However, in oligometastases exclusively treated with single-fraction SRS, LTC was similar among groups (RR = 0.92 [95%CI = 0.89-1.0], p = 0.30). Similarly, in patients with oligometastases treated with single-fraction SRS, RN (HR = 1.69; 95%CI = 0.72-3.97, p = 0.22), TT (MD = -29.64; 95%CI = -80.38-21.10, p = 0.25), and mortality were similar among groups (RR = 0.62; 95%CI = 0.22-1.76, p = 0.37). CONCLUSION: Our findings suggest that FB and MB SRS, particularly oligometastases treated with single-fraction, are comparable in terms of LTC, RN, TT, and mortality. Further research is essential to draw definitive conclusions.

Pipeline multi-type high consequence area identification based on mask R-CNN with fused attention mechanisms.

Identification of high consequence areas is an important task in pipeline integrity management. However, traditional identification methods are generally characterized by low efficiency, high cost and low accuracy. For this reason, this paper proposes a recognition method based on the improved algorithm Mask Region-based Convolutional Neural Network. Coordinate attention mechanism module is introduced into the traditional Mask R-CNN algorithm to improve the recognition accuracy and reduce the training time. For the identification results, GIS tools are utilized to establish high consequence zones along both sides of the pipeline, and the grade and scope of the high consequence zones are determined according to relevant specifications.In this paper, this method is used to identify the high-consequence area of a pipeline section in Guangdong Province, the results show that: 1, the improved algorithm in the identification of densely populated, geologic hazards, flammable and explosive high consequence zones of the average accuracy of the identification of 1.7%, 3.4%, 3.9%. 2, The method in this paper identifies 8 more building elements and 0.311 more kilometers of pipeline mileage compared to traditional identification methods. The method of this paper can provide a reference for the early identification of high consequence areas of pipelines.

Longitudinal assessment of the impact of COVID-19 infection on mask-wearing behaviors.

BACKGROUND: Wearing a mask was a crucial component in slowing the COVID-19 pandemic. However, little is known about the intersectionality between mask usage, risk perception, and infection. The purpose of this study was to investigate whether risk perceptions and masking behaviors are associated with contracting SARS-CoV-2 and how contracting SARS-CoV-2 subsequently changes masking behaviors in specific situations. METHODS: This cohort study utilized survey data from the UC San Diego ZAP COVID-19 study (n = 1,230) to evaluate the risk of contracting SARS-CoV-2 in relation to baseline risk perceptions and masking behaviors in various situations and how contracting SARS-CoV-2 affects subsequent masking behavior. RESULTS: We found that more consistent self-reported mask use in indoor public spaces (p = 0.03) and in other people's houses (p = 0.002) was associated with remaining free of SARS-CoV-2 infection. We also found that contracting SARS-CoV-2 was associated with a subsequent increase in mask use in other people's houses (p = 0.01). CONCLUSIONS: Our findings suggest that consistent mask use is correlated with decreased infection and that contracting SARS-CoV-2 may modify mask use behaviors in high-risk situations. These findings may help inform future public health messaging for infectious disease prevention. TRIAL REGISTRATION: This study has not been previously registered as it is an observational study. There was no pre-registration of the analytic plan for the present study.

Ramped versus sniffing position for Ambu® AuraGain™ insertion in patients with obesity: a randomized controlled study.

Background: The ramped position facilitates mask ventilation and endotracheal intubation in patients with obesity. This study aimed to determine whether the ramped position improves supraglottic airway (SGA) insertion in patients with obesity. Methods: In this prospective, randomized, single-center trial, 48 obese patients undergoing elective surgery were randomized into either ramped or sniffing position groups. The Ambu® AuraGain™ (Ambu A/S, Ballerup, Denmark), a second-generation SGA, was used. The primary outcome was the time required for the AuraGain insertion. Secondary outcomes included ease and number of insertion attempts, oropharyngeal leak pressure (OLP), and complications. The number needed to treat (NNT) was calculated to ensure ease of insertion. Results: The time required for the AuraGain insertion was significantly shorter in the ramped group than in the sniffing group (13.0 [11.0-16.0] vs. 24.0 [21.0-28.0], P < 0.001). The insertion was easier in the ramped group than in the sniffing group (23 / 24 vs. 13 / 24, NNT = 2.4 [95% CI, 1.6-5.0], P = 0.003). The first-attempt success rate was higher in the ramped group than in the sniffing group, although the difference was not statistically significant (22 / 24 vs. 18 / 24, P = 0.319). The OLP and postoperative complication rates were not significantly different between the groups. Conclusions: The ramped position reduced the time required for the AuraGain insertion in obese patients while providing comparable airway sealing without increasing adverse events. Therefore, a ramped position may be a more suitable option for SGA insertion in this population.

Effect of Lidocaine-prilocaine Cream on Using CPAP Mask Amongst Patients Undergone PAPtitration Study for Obstructive Sleep Apnea: A Randomized Controlled Trial Study.

Background/objective: Despite the high efficacy of using Continuous positive airway pressure (CPAP) in reversing upper airway obstruction in obstructive sleep apnea (OSA), the efficiency of this treatment is limited due to the low adherence. Mask pressure is suggested to play a significant role in adherence. In this study, we intend to investigate the effect of Lidocaine-prilocaine cream CPAP mask on pressure sensation. Methods: In this study, 75 patients referred to CPAP titration were divided into three groups. In groups one and two, Lidocaine-prilocaine cream and Petroleum jelly were used respectively. The third group had no intervention. Results: VAS discomfort immediately (VAS0), after 15 min (VAS15), and the next day (VAS all) in three groups were compared. VAS0, VAS15, and VAS all were not significantly different among the three groups (P > 0.05). Among participants with VAS0 above 5, VAS15 was significantly lower in intervention groups than the control group (P < 0.05). Conclusion: This study shows that both Petroleum jelly and Lidocaine-prilocaine cream can be used for decreasing pressure sensation during CPAP titration among patients who suffer excessive facial discomfort immediately after putting on a CPAP mask.

Enhancing prostate cancer segmentation on multiparametric magnetic resonance imaging with background information and gland masks.

BACKGROUND: The landscape of prostate cancer (PCa) segmentation within multiparametric magnetic resonance imaging (MP-MRI) was fragmented, with a noticeable lack of consensus on incorporating background details, culminating in inconsistent segmentation outputs. Given the complex and heterogeneous nature of PCa, conventional imaging segmentation algorithms frequently fell short, prompting the need for specialized research and refinement. PURPOSE: This study sought to dissect and compare various segmentation methods, emphasizing the role of background information and gland masks in achieving superior PCa segmentation. The goal was to systematically refine segmentation networks to ascertain the most efficacious approach. METHODS: A cohort of 232 patients (ages 61-73 years old, prostate-specific antigen: 3.4-45.6 ng/mL), who had undergone MP-MRI followed by prostate biopsies, was analyzed. An advanced segmentation model, namely Attention-Unet, which combines U-Net with attention gates, was employed for training and validation. The model was further enhanced through a multiscale module and a composite loss function, culminating in the development of Matt-Unet. Performance metrics included Dice Similarity Coefficient (DSC) and accuracy (ACC). RESULTS: The Matt-Unet model, which integrated background information and gland masks, outperformed the baseline U-Net model using raw images, yielding significant gains (DSC: 0.7215 vs. 0.6592; ACC: 0.8899 vs. 0.8601, p < 0.001). CONCLUSION: A targeted and practical PCa segmentation method was designed, which could significantly improve PCa segmentation on MP-MRI by combining background information and gland masks. The Matt-Unet model showcased promising capabilities for effectively delineating PCa, enhancing the precision of MP-MRI analysis.

EFFICACY OF NON-INVASIVE VENTILATION WITH EXPIRATORY WASHOUT IN STABLE COPD PATIENTS: A RANDOMIZED CROSS-OVER PILOT.

Background: A non-invasive ventilation (NIV) mask has been designed to deliver NIV with expiratory washout (EW) to improve efficacy of ventilation by optimizing clearance of expired gases from the anatomic dead-space. This study compared the performance and comfort of a novel investigational mask with EW with a conventional mask during NIV therapy.Methods: In this pilot cross-over study, participants with severe stable chronic obstructive pulmonary disease (COPD) attended a single visit to receive bi-level NIV through two masks; the investigational mask with EW, and a conventional mask. The order of mask use was randomly allocated, and each mask was used for 60-minutes with a 30-to-60-minute washout in between. The primary outcome was transcutaneous carbon dioxide (PtCO2) at 60 minutes. Other physiologic and NIV device variables were also assessed.Results: The mean difference [95% CI] in the PtCO2 between the investigational and conventional masks at 60 minutes, adjusted for baseline, was -0.74 mmHg [-2.81 to 1.33, P=0.45]. The investigational mask with EW elicited a lower tidal volume (-128.7 mL [-190.0 to -67.3], P<0.001) and minute ventilation (-2.28 L·min-1 [-3.12 to -1.43], P<0.001), and a higher leak (7.96 L·min-1 [4.39 to 11.54], P<0.001), than the conventional mask. There were no significant differences in other physiological responses or ratings of dyspnoea or comfort.Conclusions: NIV therapy delivered using a novel mask with EW was similarly effective at reducing PtCO2, while the delivered tidal volume and minute ventilation were significantly lower, when compared to a conventional mask in participants with severe COPD.

Real-Time Monitoring of Activated Sludge Flocs via Enhanced Mask Region-based Convolutional Neural Networks.

The functionality of activated sludge in wastewater treatment processes depends largely on the structural and microbial composition of its flocs, which are complex assemblages of microorganisms and their secretions. However, monitoring these flocs in real-time and consistently has been challenging due to the lack of suitable technologies and analytical methods. Here we present a laboratory setup capable of capturing instantaneous microscopic images of activated sludge, along with algorithms to interpret these images. To improve floc identification, an advanced Mask R-CNN-based segmentation that integrates a Dual Attention Network (DANet) with an enhanced Feature Pyramid Network (FPN) was used to enhance feature extraction and segmentation accuracy. Additionally, our novel PointRend module meticulously refines the contours of boundaries, significantly minimising pixel inaccuracies. Impressively, our approach achieved a floc detection accuracy of >95%. This development marks a significant advancement in real-time sludge monitoring, offering essential insights for optimising wastewater treatment operations proactively.

Linking Opinions Shared on Social Media About COVID-19 Public Health Measures to Adherence: Repeated Cross-Sectional Surveys of Twitter Use in Canada.

BACKGROUND: The effectiveness of public health measures (PHMs) depends on population adherence. Social media were suggested as a tool to assess adherence, but representativeness and accuracy issues have been raised. OBJECTIVE: The objectives of this repeated cross-sectional study were to compare self-reported PHM adherence and sociodemographic characteristics between people who used Twitter (subsequently rebranded X) and people who did not use Twitter. METHODS: Repeated Canada-wide web-based surveys were conducted every 14 days from September 2020 to March 2022. Weighted proportions were calculated for descriptive variables. Using Bayesian logistic regression models, we investigated associations between Twitter use, as well as opinions in tweets, and self-reported adherence with mask wearing and vaccination. RESULTS: Data from 40,230 respondents were analyzed. As self-reported, Twitter was used by 20.6% (95% CI 20.1%-21.2%) of Canadians, of whom 29.9% (95% CI 28.6%-31.3%) tweeted about COVID-19. The sociodemographic characteristics differed across categories of Twitter use and opinions. Overall, 11% (95% CI 10.6%-11.3%) of Canadians reported poor adherence to mask-wearing, and 10.8% (95% CI 10.4%-11.2%) to vaccination. Twitter users who tweeted about COVID-19 reported poorer adherence to mask wearing than nonusers, which was modified by the age of the respondents and their geographical region (odds ratio [OR] 0.79, 95% Bayesian credibility interval [BCI] 0.18-1.69 to OR 4.83, 95% BCI 3.13-6.86). The odds of poor adherence to vaccination of Twitter users who tweeted about COVID-19 were greater than those of nonusers (OR 1.76, 95% BCI 1.48-2.07). English- and French-speaking Twitter users who tweeted critically of PHMs were more likely (OR 4.07, 95% BCI 3.38-4.80 and OR 7.31, 95% BCI 4.26-11.03, respectively) to report poor adherence to mask wearing than non-Twitter users, and those who tweeted in support were less likely (OR 0.47, 95% BCI 0.31-0.64 and OR 0.96, 95% BCI 0.18-2.33, respectively) to report poor adherence to mask wearing than non-Twitter users. The OR of poor adherence to vaccination for those tweeting critically about PHMs and for those tweeting in support of PHMs were 4.10 (95% BCI 3.40-4.85) and 0.20 (95% BCI 0.10-0.32), respectively, compared to non-Twitter users. CONCLUSIONS: Opinions shared on Twitter can be useful to public health authorities, as they are associated with adherence to PHMs. However, the sociodemographics of social media users do not represent the general population, calling for caution when using tweets to assess general population-level behaviors.

Exploring the impact of parents' face-mask wearing on dyadic interactions in infants at higher likelihood for autism compared with general population.

Since the COVID-19 pandemic, both the public and researchers have raised questions regarding the potential impact of protective face-mask wearing on infants' development. Nevertheless, limited research has tested infants' response to protective face-mask wearing adults in real-life interactions and in neurodiverse populations. In addition, scarce attention was given to changes in interactive behavior of adults wearing a protective face-mask. The aims of the current study were (1) to examine differences in 12-month-old infants' behavioral response to an interactive parent wearing a protective face-mask during face-to-face interaction, (2) to investigate potential differences in infants at higher likelihood for autism (HL-ASD) as compared with general population (GP) counterparts, and (3) to explore significant differences in parents' behaviors while wearing or not wearing a protective face-mask. A total of 50 mother-infant dyads, consisting of 20 HL-ASD infants (siblings of individuals with autism) and 30 GP infants, participated in a 6-min face-to-face interaction. The interaction was videotaped through teleconferencing and comprised three 2-min episodes: (a) no mask, (b) mask, and (c) post-mask. Infants' emotionality and gaze direction, as well as mothers' vocal production and touching behaviors, were coded micro-analytically. Globally, GP infants exhibited more positive emotionality compared with their HL-ASD counterparts. Infants' negative emotionality and gaze avoidance did not differ statistically across episodes. Both groups of infants displayed a significant increase in looking time toward the caregiver during the mask episode. No statistically significant differences emerged in mothers' behaviors. These findings suggest that the use of protective face-masks might not negatively affect core dimensions of caregiver-infant interactions in GP and HL-ASD 12-month-old infants.