Incubation Period and Serial Interval of Mpox in 2022 Global Outbreak Compared with Historical Estimates.

Understanding changes in the transmission dynamics of mpox requires comparing recent estimates of key epidemiologic parameters with historical data. We derived historical estimates for the incubation period and serial interval for mpox and contrasted them with pooled estimates from the 2022 outbreak. Our findings show the pooled mean infection-to-onset incubation period was 8.1 days for the 2022 outbreak and 8.2 days historically, indicating the incubation periods remained relatively consistent over time, despite a shift in the major mode of transmission. However, we estimated the onset-to-onset serial interval at 8.7 days using 2022 data, compared with 14.2 days using historical data. Although the reason for this shortening of the serial interval is unclear, it may be because of increased public health interventions or a shift in the mode of transmission. Recognizing such temporal shifts is essential for informed response strategies, and public health measures remain crucial for controlling mpox and similar future outbreaks.

Measurement Modeling and Performance Analysis of a Bionic Polarimetric Imaging Navigation Sensor Using Rayleigh Scattering to Generate Scattered Sunlight.

The bionic polarimetric imaging navigation sensor (BPINS) is a navigation sensor that provides absolute heading, and it is of practical engineering significance to model the measurement error of BPINS. The existing BPINSs are still modeled using photodiode-based measurements rather than imaging measurements and are not modeled systematically enough. This paper proposes a measurement performance analysis method of BPINS that takes into account the geometric and polarization errors of the optical system. Firstly, the key error factors affecting the overall measurement performance of BPINS are investigated, and the Stokes vector-based measurement error model of BPINS is introduced. Secondly, based on its measurement error model, the effect of the error source on the measurement performance of BPINS is quantitatively analyzed using Rayleigh scattering to generate scattered sunlight as a known incident light source. The numerical results show that in angle of E-vector (AoE) measurement, the coordinate deviation of the principal point has a greater impact, followed by grayscale response inconsistency of CMOS and integration angle error of micro-polarization array, and finally lens attenuation; in degree of linear polarization (DoLP) measurement, the grayscale response inconsistency of CMOS has a more significant impact. This finding can accurately guide the subsequent calibration of BPINS, and the quantitative results provide an important theoretical reference for its optimal design.

Underwater Target Detection Utilizing Polarization Image Fusion Algorithm Based on Unsupervised Learning and Attention Mechanism.

Since light propagation in water bodies is subject to absorption and scattering effects, underwater images using only conventional intensity cameras will suffer from low brightness, blurred images, and loss of details. In this paper, a deep fusion network is applied to underwater polarization images; that is, the underwater polarization images are fused with intensity images using the deep learning method. To construct a training dataset, we establish an experimental setup to obtain underwater polarization images and perform appropriate transformations to expand the dataset. Next, an end-to-end learning framework based on unsupervised learning and guided by an attention mechanism is constructed for fusing polarization and light intensity images. The loss function and weight parameters are elaborated. The produced dataset is used to train the network under different loss weight parameters, and the fused images are evaluated based on different image evaluation metrics. The results show that the fused underwater images are more detailed. Compared with light intensity images, the information entropy and standard deviation of the proposed method increase by 24.48% and 139%. The image processing results are better than other fusion-based methods. In addition, the improved U-net network structure is used to extract features for image segmentation. The results show that the target segmentation based on the proposed method is feasible under turbid water. The proposed method does not require manual adjustment of weight parameters, has faster operation speed, and has strong robustness and self-adaptability, which is important for research in vision fields, such as ocean detection and underwater target recognition.

Transmission Dynamics and Effectiveness of Control Measures during COVID-19 Surge, Taiwan, April-August 2021.

An unprecedented surge of COVID-19 cases in Taiwan in May 2021 led the government to implement strict nationwide control measures beginning May 15. During the surge, the government was able to bring the epidemic under control without a complete lockdown despite the cumulative case count reaching >14,400 and >780 deaths. We investigated the effectiveness of the public health and social measures instituted by the Taiwan government by quantifying the change in the effective reproduction number, which is a summary measure of the ability of the pathogen to spread through the population. The control measures that were instituted reduced the effective reproduction number from 2.0-3.3 to 0.6-0.7. This decrease was correlated with changes in mobility patterns in Taiwan, demonstrating that public compliance, active case finding, and contact tracing were effective measures in preventing further spread of the disease.

Early Prompt Response to COVID-19 in Taiwan: Comprehensive surveillance, decisive border control, and information technology support.

Since the COVID-19 outbreak was detected in Wuhan in December 2019 by the event-based surveillance of Taiwan Centers for Disease Control, Taiwan has been aligning risk management to policy planning with the assistance of comprehensive surveillance and regular rapid risk assessments. Taiwan Central Epidemic Command Center (CECC) promptly initiated stepwise border control for major cities and provinces in China, European and American countries, and eventually expanded it to the whole world in March 2020. With stringent quarantine measures, the early response not only successfully blocked the first wave of imported cases, but also slowed down subsequent large local outbreaks. Digital technologies including digital fencing and government database linkage were adopted to facilitate the application of public health interventions and data collection. The experience of Taiwan's prompt and comprehensive response at the early stage may contribute to the preparedness for the next disease X outbreak.

Ocean Wind Observation Based on the Mean Square Slope Using a Self-Developed Miniature Wave Buoy.

Real-time, continuous, and long-term marine monitoring data benefits ocean research. This study developed a low-cost, multi-parameter, miniature wave buoy. High spatial and temporal resolution of sea surface parameters, including wind, waves, and current, can be obtained at low cost through the deployment of numerous buoys, thus forming an observation array. Tested in the laboratory water tank, the relative error of water surface slope measurement of the buoy was approximately 5.6% when the slope angle was less than 15°. For frequencies between 0.1 and 1.0 Hz, the measurement of slope spectrum was almost identical to that of the wave gauge. The buoy underestimated the slope spectrum between 1.0−1.56 Hz. A good relationship (r2 = 0.75) was obtained between wind speed at 10 m above sea surface (U10) and the low-pass-filtered mean square slope (LPMSS). After incorporating the wave age into the U10 inversion process, the root mean square error (RMSE) and BIAS were reduced to 1.15 m/s and 0.02 m/s, respectively. The 2D distribution of buoy-measured slope components was used to detect the wind direction, with an RMSE of 23.7°. The spectral tail slope steepened with increasing wind speed at low wind speeds (<7 m/s). A technical flow chart of the miniature wave buoy is proposed to observe the sea surface parameters. This miniature buoy will play an essential complementary role in the growing demand for sea state monitoring, especially in nearshore oceans.

Impacts of the COVID-19 lockdown on building energy consumption and indoor environment: A case study in Dalian, China.

Restricting social distancing is an effective means of controlling the COVID-19 pandemic, resulting in a sharp drop in the utilization of commercial buildings. However, the specific changes in the operating parameters are not clear. This study aims to quantify the impact of COVID-19 lockdowns on commercial building energy consumption and the indoor environment, including correlation analysis. A large green commercial building in Dalian, China's only country to experience five lockdowns, has been chosen. We compared the performance during the lockdown to the same period last year. The study found that the first lockdown caused a maximum 63.5% drop in monthly energy consumption, and the second lockdown was 55.2%. The energy consumption per unit area in 2020 dropped by 55.4% compared with 2019. In addition, during the lockdown, the compliance rate of indoor thermal environment increased by 34.7%, and indoor air quality was 9.5%. These findings could partly explain the short-term and far-reaching effects of the lockdown on the operating parameters of large commercial buildings. Humans are likely to coexist with COVID-19 for a long time, and commercial buildings have to adapt to new energy and health demands. Effective management strategies need to be developed.

Proactive and blended approach for COVID-19 control in Taiwan.

Coronavirus disease 2019 (COVID-19) has become the greatest threat to human society in a century. To better devise control strategies, policymakers should adjust policies based on scientific evidence in hand. Several countries have limited the epidemics of COVID-19 by prioritizing containment strategies to mitigate the impacts on public health and healthcare systems. However, asymptomatic/pre-symptomatic transmission of COVID-19 complicated traditional symptom-based approaches for disease control. In addition, drastic population-based interventions usually have significant societal and economic impacts. Therefore, in Taiwan, the containment strategies consisted of the more extended case-based interventions (e.g., case detection with enhanced surveillance and contact tracing with active monitoring and quarantine of close contacts) and more targeted population-based interventions (e.g., face mask use in recommended settings and risk-oriented border control with corresponding quarantine requirement). The success of the blended approach emphasizes not only the importance of evidence-supported policymaking but also the coordinated efforts between the government and the people.

Underwater image recovery utilizing polarimetric imaging based on neural networks.

Underwater imaging faces challenges due to complex optical properties in water. Our purpose is to explore the application of polarimetric imaging in image recovery under turbid water based on deep learning. A polarization camera is used to capture the polarization images of objects under water as datasets. The method used in our study aims to explore a structure and loss function that is suitable for the model. In terms of the model structure, four pairs of models consisting of polarized version and gray version based on the idea of dense U-Net and information flow were proposed. In the aspect of loss function, the method of combining weighted mean squared error with perceptual loss was proposed and a proper set of loss weights was selected through comparison experiments. Comparing the model outputs, it is found that adding polarized information along with the light intensity information to the model at the very front of the model structure brings about better recovering image. The model structure proposed can be used for image recovery in turbid water or other scattering environments. Since the polarization characteristics are considered, the recovered image has more detailed features than that where only intensity is considered. The results of comparison with other methods show the effectiveness of the proposed method.

Taiwan's COVID-19 response: Timely case detection and quarantine, January to June 2020.

The coronavirus disease 2019 (COVID-19) pandemic has become severe threats to economic, societal, and healthcare systems. To analyze the epidemiological characteristics of the COVID-19 outbreak in Taiwan and evaluate the key interventions, we conducted a retrospective cohort study during January 17-June 30, 2020. As of June 30, the COVID-19 outbreak, including 447 laboratory-confirmed cases, was eliminated by mixed approaches: border control, enhanced surveillance, case detection with contact tracing, quarantine, and population-based interventions like face mask use. The improvement of median time from disease onset to notification (5 days [range -3 to 27] before March 1 to 1 day [range -8 to 22] after March 1) suggested the timeliness and comprehensiveness of surveillance and contact tracing. Travel restrictions with quarantine, resulting in fewer clusters, were also complementary to minimize disease spread. Under combined interventions, Taiwan successfully contained the COVID-19 spread within the country and minimized its impact on the society.

Applying Machine Learning Models with An Ensemble Approach for Accurate Real-Time Influenza Forecasting in Taiwan: Development and Validation Study.

BACKGROUND: Changeful seasonal influenza activity in subtropical areas such as Taiwan causes problems in epidemic preparedness. The Taiwan Centers for Disease Control has maintained real-time national influenza surveillance systems since 2004. Except for timely monitoring, epidemic forecasting using the national influenza surveillance data can provide pivotal information for public health response. OBJECTIVE: We aimed to develop predictive models using machine learning to provide real-time influenza-like illness forecasts. METHODS: Using surveillance data of influenza-like illness visits from emergency departments (from the Real-Time Outbreak and Disease Surveillance System), outpatient departments (from the National Health Insurance database), and the records of patients with severe influenza with complications (from the National Notifiable Disease Surveillance System), we developed 4 machine learning models (autoregressive integrated moving average, random forest, support vector regression, and extreme gradient boosting) to produce weekly influenza-like illness predictions for a given week and 3 subsequent weeks. We established a framework of the machine learning models and used an ensemble approach called stacking to integrate these predictions. We trained the models using historical data from 2008-2014. We evaluated their predictive ability during 2015-2017 for each of the 4-week time periods using Pearson correlation, mean absolute percentage error (MAPE), and hit rate of trend prediction. A dashboard website was built to visualize the forecasts, and the results of real-world implementation of this forecasting framework in 2018 were evaluated using the same metrics. RESULTS: All models could accurately predict the timing and magnitudes of the seasonal peaks in the then-current week (nowcast) (ρ=0.802-0.965; MAPE: 5.2%-9.2%; hit rate: 0.577-0.756), 1-week (ρ=0.803-0.918; MAPE: 8.3%-11.8%; hit rate: 0.643-0.747), 2-week (ρ=0.783-0.867; MAPE: 10.1%-15.3%; hit rate: 0.669-0.734), and 3-week forecasts (ρ=0.676-0.801; MAPE: 12.0%-18.9%; hit rate: 0.643-0.786), especially the ensemble model. In real-world implementation in 2018, the forecasting performance was still accurate in nowcasts (ρ=0.875-0.969; MAPE: 5.3%-8.0%; hit rate: 0.582-0.782) and remained satisfactory in 3-week forecasts (ρ=0.721-0.908; MAPE: 7.6%-13.5%; hit rate: 0.596-0.904). CONCLUSIONS: This machine learning and ensemble approach can make accurate, real-time influenza-like illness forecasts for a 4-week period, and thus, facilitate decision making.

Ice-associated norovirus outbreak predominantly caused by GII.17 in Taiwan, 2015.

BACKGROUND: On 5 March 2015, Taiwan Centers for Disease Control was notified of more than 200 students with gastroenteritis at a senior high school during excursion to Kenting. We conducted an outbreak investigation to identify the causative agent and possible vehicle of the pathogen. METHODS: We conducted a retrospective cohort study by using a structured questionnaire to interview all students for consumed food items during their stay at the resort. Students were defined as a gastroenteritis case while having vomiting or diarrhea after the breakfast on 4 March. We inspected the environment to identify possible contamination route. We collected stool or vomitus samples from ill students, food handlers and environmental specimens for bacterial culture for common enteropathogens, reverse transcription polymerase chain reaction (RT-PCR) for norovirus and enzyme-linked immunosorbent assay (ELISA) for rotavirus. Norovirus PCR-positive products were then sequenced and genotyped. RESULTS: Of 267 students enrolled, 144 (54%) met our case definition. Regression analysis revealed elevated risk associated with iced tea, which was made from tea powder mixed with hot water and self-made ice (risk ratio 1.54, 95% confidence interval 1.22-1.98). Ice used for beverages, water before and after water filter of the ice machine and 16 stool and vomitus samples from ill students were tested positive for norovirus; Multiple genotypes were identified including GI.2, GI.4 and GII.17. GII.17 was the predominant genotype and phylogenetic analyses showed that noroviruses identified in ice, water and human samples were clustered into the same genotypes. Environmental investigation revealed the ice was made by inadequate-filtered and un-boiled water. CONCLUSIONS: We identified the ice made by norovirus-contaminated un-boiled water caused the outbreak and the predominant genotype was GII.17. Adequately filtered or boiled water should be strongly recommended for making ice to avoid possible contamination.

Using Novel Method to Detect Different Cancer-Cell Stages of Model Human Lung Carcinoma.

BACKGROUND: Synchrotron radiation infrared (SR-IR) microspectroscopy and SR-IR spectroscopic imaging are extremely valuable techniques for determining the molecular composition of biological and biomedical samples. In this work, SR-IR is applied in the study of the lung cancer cells in different cell cycles. METHODS: We use a novel synchrotron based radiation infrared system combined synchronized model human lung carcinoma to reveal its unique character pattern. RESULTS: After using SR-IR microspectroscopy, we discovered that the ratio of protein to lipid in G1 and G2 states is around 4.0 and 6.1, respectively. Moreover, for the DNA at the wavenumber position of 1225 cm(-1) , the intensity ratio of G2 state to G1 state is approximately 1.6. These data indicate that the cell in G1 state has more lipid composition to prepare for the DNA synthesis, but the cell in G2 state has more protein composition to prepare for the mitosis. The cell has larger DNA concentration in G2 state, which can be explained for the DNA synthesis. CONCLUSION: Through our research, we demonstrate that different growth state of cancer cell presenting unique functional groups concentration profiles and distribution via using SR-IR microspectrometry. These applications will provide another ways to improve modern cancer screening in the future.

The correlation between the presence of viremia and clinical severity in patients with enterovirus 71 infection: a multi-center cohort study.

BACKGROUND: Enterovirus 71 (EV71) is a great disease burden across the whole world, particularly in Southeast Asia. However, in recent decades, the pathogenesis of severe EV71 infection was not well understood. This study was aimed to investigate the correlation between the presence of viremia and the clinical severity of EV71 infection. METHODS: We organized a prospective cohort study and enrolled laboratory-confirmed EV71 cases in six tertiary care hospitals in Taiwan during the EV71 epidemic from 2011 to 2012. Blood samples were collected once in the acute stage, on the first day of admission. We used real-time RT-PCR to detect EV71 viremia. Demographical and clinical data were collected and the clinical severity was categorized into four grades. Data analysis was performed to identify the risk factors of viremia and the correlation between viremia and clinical severity of EV71 infection. RESULTS: Of the total 224 enrolled patients, 59 (26%) patients were confirmed to have viremia. Two-thirds (68%) of viremic cases were detected within the first three days of infection. Viremia occurred more frequently in children under the age of one year old (odds ratios [OR] 4.82, p < 0.001) but the association between the presence of viremia and complicated EV71 infection was not found (OR 1.02, p = 0.96). In the viremia group, patients had significantly more severe complications if viremia was detected after the third day of disease onset (26% vs. 5%, p = 0.03). CONCLUSIONS: Viremia occurred more frequently in children under the age of one year and viremia detected beyond three days after the onset of disease correlated with more severe disease in EV71 patients.

Cathodically Coupled Electrolysis to Access Biheteroaryls.

An electrochemical approach to biheteroaryls through the coupling of diverse N-heteroarenes with heteroaryl phosphonium salts is reported. The reaction features pH and redox-neutral conditions and excellent regioselectivity, as well as exogenous air or moisture tolerance. Additionally, a one-pot, two-step protocol can be established to realize formal C-H/C-H coupling of heteroarenes, thereby greatly expanding the substrate availability. The utility of this method is demonstrated through late-stage functionalization, the total synthesis of nitraridine, and antifungal activity studies.

Thermoelectric active cooling for transient hot spots in microprocessors.

Modern microprocessor performance is limited by local hot spots induced at high frequency by busy integrated circuit elements such as the clock generator. Locally embedded thermoelectric devices (TEDs) are proposed to perform active cooling whereby thermoelectric effects enhance passive cooling by the Fourier law in removing heat from the hot spot to colder regions. To mitigate transient heating events and improve temperature stability, we propose a novel analytical solution that describes the temperature response of a periodically heated hot spot that is actively cooled by a TED driven electrically at the same frequency. The analytical solution that we present is validated by experimental data from frequency domain thermal reflectance (FDTR) measurements made directly on an actively cooled Si thermoelectric device where the pump laser replicates the transient hot spot. We herein demonstrate a practical method to actively cancel the transient temperature variations on circuit elements with TEDs. This result opens a new path to optimize the design of cooling systems for transient localized hot spots in integrated circuits.

Fluorosulfonyl Arylation of Alkynes via Electron Donor-Acceptor Photoactivation.

A radical fluorosulfonyl arylation of alkynes with sulfuryl chlorofluoride as the FSO2 radical precursor via electron donor-acceptor photoactivation driven by daylight or a blue light-emitting diode is disclosed. A series of valuable benzo-fused carbocycles and heterocycles have been produced with simple operation under mild conditions in the absence of any external catalysts or additives. The synthetic potential of this protocol has further demonstrated excellent scalability, as well as diverse postderivatizations, including SuFEx reactions and other useful cascade reactions.