Microbiota and Nutrient Portraits of European Roe Deer (Capreolus capreolus) Rumen Contents in Characteristic Southern German Habitats.

Roe deer (Capreolus capreolus) are found in various habitats, from pure forest cultures to agricultural areas and mountains. In adapting to the geographically and seasonally differentiating food supply, they depend, above all, on an adapted microbiome. However, knowledge about the microbiome of wild ruminants still needs to be improved. There are only a few publications for individual species with a low number of samples. This study aims to identify a core microbiota for Bavarian roe deer and present nutrient and microbiota portraits of the individual habitat types. This study investigated the roe deer's rumen (reticulorumen) content from seven different characteristic Bavarian habitat types. The focus was on the composition of nutrients, fermentation products, and the rumen bacterial community. A total of 311 roe deer samples were analysed, with the most even possible distribution per habitat, season, age class, and gender. Significant differences in nutrient concentrations and microbial composition were identified for the factors habitat, season, and age class. The highest crude protein content (plant protein and microbial) in the rumen was determined in the purely agricultural habitat (AG), the highest value of non-fibre carbohydrates in the alpine mountain forest, and the highest fibre content (neutral detergent fibre, NDF) in the pine forest habitat. Maximum values for fibre content go up to 70% NDF. The proportion of metabolites (ammonia, lactate, total volatile fatty acids) was highest in the Agriculture-Beech-Forest habitat (ABF). Correlations can be identified between adaptations in the microbiota and specific nutrient concentrations, as well as in strong fluctuations in ingested forage. In addition, a core bacterial community comprising five genera could be identified across all habitats, up to 44% of total relative abundance. As with all wild ruminants, many microbial genera remain largely unclassified at various taxonomic levels. This study provides a more in-depth insight into the diversity and complexity of the roe deer rumen microbiota. It highlights the key microorganisms responsible for converting naturally available nutrients of different botanical origins.

Real-time forecasting of COVID-19 spread according to protective behavior and vaccination: autoregressive integrated moving average models.

BACKGROUND: Mathematical and statistical models are used to predict trends in epidemic spread and determine the effectiveness of control measures. Automatic regressive integrated moving average (ARIMA) models are used for time-series forecasting, but only few models of the 2019 coronavirus disease (COVID-19) pandemic have incorporated protective behaviors or vaccination, known to be effective for pandemic control. METHODS: To improve the accuracy of prediction, we applied newly developed ARIMA models with predictors (mask wearing, avoiding going out, and vaccination) to forecast weekly COVID-19 case growth rates in Canada, France, Italy, and Israel between January 2021 and March 2022. The open-source data was sourced from the YouGov survey and Our World in Data. Prediction performance was evaluated using the root mean square error (RMSE) and the corrected Akaike information criterion (AICc). RESULTS: A model with mask wearing and vaccination variables performed best for the pandemic period in which the Alpha and Delta viral variants were predominant (before November 2021). A model using only past case growth rates as autoregressive predictors performed best for the Omicron period (after December 2021). The models suggested that protective behaviors and vaccination are associated with the reduction of COVID-19 case growth rates, with booster vaccine coverage playing a particularly vital role during the Omicron period. For example, each unit increase in mask wearing and avoiding going out significantly reduced the case growth rate during the Alpha/Delta period in Canada (-0.81 and -0.54, respectively; both p < 0.05). In the Omicron period, each unit increase in the number of booster doses resulted in a significant reduction of the case growth rate in Canada (-0.03), Israel (-0.12), Italy (-0.02), and France (-0.03); all p < 0.05. CONCLUSIONS: The key findings of this study are incorporating behavior and vaccination as predictors led to accurate predictions and highlighted their significant role in controlling the pandemic. These models are easily interpretable and can be embedded in a "real-time" schedule with weekly data updates. They can support timely decision making about policies to control dynamically changing epidemics.

Evaluation of registered nurses' interprofessional emergency care competence through the gamification of cardiopulmonary resuscitation training: a cross-sectional study.

BACKGROUNDS: Cardiopulmonary resuscitation (CPR) training is generally led by instructors in a classroom; thus, conventional teaching materials used in CPR training are often constrained by spatiotemporal factors, limiting learners' interest and sense of achievement in learning and preventing them from effectively applying what they learn in practice. For greater effectiveness and more flexible application, clinical nursing education has increasingly emphasized contextualization, individualization, and interprofessional learning. This study determined the self-assessed emergency care competencies of nurses who received gamified emergency care training and explored the factors associated with those competencies. METHODS: Quota sampling of nurses working at a certain regional hospital in central Taiwan was conducted, and a structured questionnaire was administered to the recruited nurses. A total of 194 valid responses were collected. The research tool was a scale measuring the participants' emergency care competencies after they received gamified emergency care training. The data were analyzed using descriptive and inferential statistics and multiple regression. RESULTS: Of the recruited participants, 50.52% were ≤ 30 years old; 48.45% worked in the internal medicine department; 54.64% graduated from 2-year university technical programs; 54.12% were N2 registered nurses; 35.57% and 21.13% had ≥ 10 and 1-3 years of work experience, respectively; and 48.45% worked in general wards. User need (r = 0.52, p = 0.000), perceived usefulness (r = 0.54, p = 0.000), perceived ease of use (r = 0.51, p = 0.000), and usage attitude (r = 0.41, p = 0.000) were positively correlated with emergency care competencies. Furthermore, the multiple regression analysis revealed that perceived usefulness was the primary factor associated with the participants' emergency care competencies. CONCLUSIONS: The results of this study may serve as a reference for acute care facility authorities in designing advanced nursing competency standards and emergency care training programs for nurses.

Use of likelihood estimates for variances for the design and evaluation of multiregional clinical trials with heterogeneous variances.

Globalized drug development studies, such as multiregional clinical trials (MRCTs), have attracted much attention due to their ability to expedite drug development and shorten the time lag of drug release. While observing the overall effect of a new drug, the region-specific effects to support drug registration in constituent regions can also be evaluated. Several challenges arise in conducting MRCTs, such as the heterogeneity in the variability of the primary endpoint across regions. However, most of the existing statistical methods assume a common variability, which may not be valid in practice due to differences across regions (eg, diversities in ethnicity or disparities in medical culture/practice). We present a statistical method for the design and evaluation of MRCTs to consider the heterogeneous variability across regions. We assessed the overall sample size requirement and addressed the region-specific sample size determination to establish the consistency of treatment effects between the specific region and the entire group. We demonstrate the proposed approach with numerical examples.

Untargeted LC-MS/MS-Based Multi-Informative Molecular Networking for Targeting the Antiproliferative Ingredients in Tetradium ruticarpum Fruit.

The fruit of Tetradium ruticarpum (TR) is commonly used in Chinese herbal medicine and it has known antiproliferative and antitumor activities, which can serve as a good source of functional ingredients. Although some antiproliferative compounds are reported to be present in TR fruit, most studies only focused on a limited range of metabolites. Therefore, in this study, the antiproliferative activity of different extracts of TR fruit was examined, and the potentially antiproliferative compounds were highlighted by applying an untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based multi-informative molecular networking strategy. The results showed that among different extracts of TR fruit, the EtOAc fraction F2-3 possessed the most potent antiproliferative activity against HL-60, T24, and LX-2 human cell lines. Through computational tool-aided structure prediction and integrating various data (sample taxonomy, antiproliferative activity, and compound identity) into a molecular network, a total of 11 indole alkaloids and 47 types of quinolone alkaloids were successfully annotated and visualized into three targeted bioactive molecular families. Within these families, up to 25 types of quinolone alkaloids were found that were previously unreported in TR fruit. Four indole alkaloids and five types of quinolone alkaloids were targeted as potentially antiproliferative compounds in the EtOAc fraction F2-3, and three (evodiamine, dehydroevodiamine, and schinifoline) of these targeted alkaloids can serve as marker compounds of F2-3. Evodiamine was verified to be one of the major antiproliferative compounds, and its structural analogues discovered in the molecular network were found to be promising antitumor agents. These results exemplify the application of an LC-MS/MS-based multi-informative molecular networking strategy in the discovery and annotation of bioactive compounds from complex mixtures of potential functional food ingredients.

Insect Cell-Based Quadrivalent Seasonal Influenza Virus-like Particles Vaccine Elicits Potent Immune Responses in Mice.

Influenza viruses can cause highly infectious respiratory diseases, posing noteworthy epidemic and pandemic threats. Vaccination is the most cost-effective intervention to prevent influenza and its complications. However, reliance on embryonic chicken eggs for commercial influenza vaccine production presents potential risks, including reductions in efficacy due to HA gene mutations and supply delays due to scalability challenges. Thus, alternative platforms are needed urgently to replace egg-based methods and efficiently meet the increasing demand for vaccines. In this study, we employed a baculovirus expression vector system to engineer HA, NA, and M1 genes from seasonal influenza strains A/H1N1, A/H3N2, B/Yamagata, and B/Victoria, generating virus-like particle (VLP) vaccine antigens, H1N1-VLP, H3N2-VLP, Yamagata-VLP, and Victoria-VLP. We then assessed their functional and antigenic characteristics, including hemagglutination assay, protein composition, morphology, stability, and immunogenicity. We found that recombinant VLPs displayed functional activity, resembling influenza virions in morphology and size while maintaining structural integrity. Comparative immunogenicity assessments in mice showed that our quadrivalent VLPs were consistent in inducing hemagglutination inhibition and neutralizing antibody titers against homologous viruses compared to both commercial recombinant HA and egg-based vaccines (Vaxigrip). The findings highlight insect cell-based VLP vaccines as promising candidates for quadrivalent seasonal influenza vaccines. Further studies are worth conducting.

Suppression of the alpha, delta, and omicron variants of SARS-Cov-2 in Taiwan.

BACKGROUND: Taiwan was a coronavirus disease 2019 (COVID-19) outlier, with an extraordinarily long transmission-free record: 253 days without locally transmitted infections while the rest of the world battled wave after wave of infection. The appearance of the alpha variant in May 2021, closely followed by the delta variant, disrupted this transmission-free streak. However, despite low vaccination coverage (<1%), outbreaks were well-controlled. METHODS: This study analyzed the time to border closure and conducted one-sample t test to compare between Taiwan and Non-Taiwan countries prior to vaccine introduction. The study also collected case data to observe the dynamics of omicron transmission. Time-varying reproduction number,Rt, was calculated and was used to reflect infection impact at specified time points and model trends of future incidence. RESULTS: The study analyzed and compare the time to border closure in Taiwan and non-Taiwan countries. The mean times to any border closure from the first domestic case within each country were -21 and 5.98 days, respectively (P < .0001). The Taiwanese government invested in quick and effective contact tracing with a precise quarantine strategy in lieu of a strict lockdown. Residents followed recommendations based on self-discipline and unity. The self-discipline in action is evidenced in Google mobility reports. The central and local governments worked together to enact non-pharmaceutical interventions (NPIs), including universal masking, social distancing, limited unnecessary gatherings, systematic contact tracing, and enhanced quarantine measures. The people cooperated actively with pandemic-prevention regulations, including vaccination and preventive NPIs. CONCLUSIONS: This article describes four key factors underlying Taiwan's success in controlling COVID-19 transmission: quick responses; effective control measures with new technologies and rolling knowledge updates; unity and cooperation among Taiwanese government agencies, private companies and organizations, and individual citizens; and Taiwanese self-discipline.

Unlocked evanescent waves in periodic structures.

We predict the existence of evanescent modes with unlocked phases in two-dimensional (2D) dielectric periodic structures. Contrary to what is known for one-dimensional structures, where evanescent fields lock to the host modulation, we show that in 2D systems a new class of evanescent modes exists with an unlocked real part of the wave vector. Hence, beams constructed from such unlocked evanescent waves can exhibit spatial effects. A significant focalization of a beam propagating within the band gap of a flat photonic crystal slab is also shown. The predicted phenomenon is expected to be generic for spatially modulated materials.

IgG4-Related Disease Mimicking Unilateral Urothelial Carcinoma: A Rare Case Report and Literature Review.

Immunoglobulin G4-related disease (IgG4-RD) is an autoimmune-mediated disorder with heterogeneous multiorgan manifestations. Early identification and treatment of IgG4-RD are crucial for organ function recovery. Rarely, IgG4-RD manifests as a unilateral renal pelvic soft tissue mass that may be misdiagnosed as urothelial malignancy, resulting in invasive surgical intervention and organ damage. Here we present a 73-year-old man who had a right ureteropelvic mass with hydronephrosis detected by enhanced computed tomography. Right upper tract urothelial carcinoma and lymph node metastasis was highly suggested based on the image findings. However, IgG4-RD was suspected due to his past history of bilateral submandibular lymphadenopathy, nasolacrimal duct obstruction, as well as a high serum IgG4 level of 861 mg/dL. The ureteroscopy with tissue biopsy showed no evidence of urothelial malignancy. His lesions and symptoms improved after glucocorticoid treatment. Hence, a diagnosis of IgG4-RD was made, with the phenotype of classic Mikulicz syndrome with systemic involvement. The manifestation of IgG4-RD as a unilateral renal pelvic mass is rare and should be kept in mind. A ureteroscopic biopsy and serum IgG4 level measurement can help in the diagnosis of IgG4-RD in patients with a unilateral renal pelvic lesion.

Mechanical transmission of dengue virus by Aedes aegypti may influence disease transmission dynamics during outbreaks.

BACKGROUND: Dengue virus outbreaks are increasing in number and severity worldwide. Viral transmission is assumed to require a minimum time period of viral replication within the mosquito midgut. It is unknown if alternative transmission periods not requiring replication are possible. METHODS: We used a mouse model of dengue virus transmission to investigate the potential of mechanical transmission of dengue virus. We investigated minimal viral titres necessary for development of symptoms in bitten mice and used resulting parameters to inform a new model of dengue virus transmission within a susceptible population. FINDINGS: Naïve mice bitten by mosquitoes immediately after they took partial blood meals from dengue infected mice showed symptoms of dengue virus, followed by mortality. Incorporation of mechanical transmission into mathematical models of dengue virus transmission suggest that this supplemental transmission route could result in larger outbreaks which peak sooner. INTERPRETATION: The potential of dengue transmission routes independent of midgut viral replication has implications for vector control strategies that target mosquito lifespan and suggest the possibility of similar mechanical transmission routes in other disease-carrying mosquitoes. FUNDING: This study was funded by grants from the National Health Research Institutes, Taiwan (04D2-MMMOST02), the Human Frontier Science Program (RGP0033/2021), the National Institutes of Health (1R01AI143698-01A1, R01AI151004 and DP2AI152071) and the Ministry of Science and Technology, Taiwan (MOST104-2321-B-400-016).

A comparative study of Colles' fractures in patients between fifty and seventy years of age: percutaneous K-wiring versus volar locking plating.

PURPOSE: This retrospective study was to compare the clinical outcomes of volar locking plating (VLP) and percutaneous Kirschner wiring (PKW) for the management of displaced Colles type distal radius fractures in patients between 50 and 70 years old. METHODS: There were 31 elderly patients with displaced Colles' fractures treated by VLP. We compared them to 31 match-paired patients treated by PKW. The patients were matched according to age (within five years) and sex. All patients were followed up retrospectively for at least 12 months. The functional outcomes and radiological results were compared between the two groups. RESULTS: All fractures healed within three months. There were two complications (6.5%) in the PKW group and one complication (3.2%) in the VLP group. At final follow-up, wrist flexion, extension, and ulnar deviation were significantly better in the VLP group compared with the PKW group (all p values<0.05). According to modified Green and O'Brien criteria, the VLP group showed a trend towards increased rate of satisfactory outcome compared with the PKW group (p = 0.09). CONCLUSION: For the treatment of displaced Colles' fractures in patients between 50 and 70 years old, both groups had high union rate and low complication rate. However, better functional results can be expected in association with open reduction and volar locking plating.

Echinocystic Acid Ameliorates Arthritis in SKG Mice by Suppressing Th17 Cell Differentiation and Human Rheumatoid Arthritis Fibroblast-Like Synoviocytes Inflammation.

Echinocystic acid (EA), a pentacyclic triterpene, exhibits anti-inflammatory, antioxidant, and analgesic activities to counteract pathological effects in various diseases. Here, we aimed to determine the immunomodulatory effect of EA on zymosan-induced arthritis in SKG mice and how it would influence Th17 differentiation and human rheumatoid arthritis fibroblast-like synoviocytes inflammation. Our results showed that EA (10 and 25 mg/kg) attenuated arthritis symptoms, including high arthritis scores, infiltrating inflammatory cells, synovial hyperplasia, bone erosion, and the high levels of proinflammatory cytokines, such as TNF-α, interleukin (IL)-6, and IL-1ß in paw tissues, and reduced the number of splenic Th17 cells. Mechanistically, we found that in vitro treatment of EA inhibited both IL-6- and transforming growth factor-ß (TGF-ß)-induced Th17 cell differentiation by suppressing the phosphorylation of signal transducers and transcriptional activators, especially STAT3. In line with the in vivo result, EA significantly reduced the protein and mRNA expression of IL-6 and IL-1ß in human RA-FLA cells, MH7A cells. Furthermore, the production of both cytokines was confirmed with the downregulation of mitogen-activated protein kinases (MAPK) and nuclear factor-κB (NF-κB) signaling pathways under the stimulation of TNF-α. In conclusion, these findings revealed that EA was capable of amelioration of arthritic disorders in SKG mice through inhibiting Th17 cell differentiation and synovial fibroblast inflammation, supporting that EA is a promising therapeutic candidate for treating RA patients.

Design of Reflective Polarization Rotator in Silicon Waveguide.

In this work, we investigate theoretically the reflective polarization rotator in a silicon waveguide formed by periodically arranged rectangular air holes. The etched air holes generate the large birefringence for the waveguide. The effective refractive index of the non-etched waveguide is isotropic. The structure can be regarded as a stack of alternating birefringent waveplates and isotropic material similar to the folded Solc filter. The band structure of the stack of birefringent waveplates with isotropic background is calculated to confirm the fact that high reflection peaks in the reflection spectra of the waveguide result from the photonic bandgap. The polarization extinction ratio for the reflected light is 15.8 dB. The highest reflectivity of the device is 93.1%, and the device length is 9.21 µm. An ultra-wide operation bandwidth from 1450.3 to 1621.8 nm can be achieved.

A Precision Health Service for Chronic Diseases: Development and Cohort Study Using Wearable Device, Machine Learning, and Deep Learning.

This paper presents an integrated and scalable precision health service for health promotion and chronic disease prevention. Continuous real-time monitoring of lifestyle and environmental factors is implemented by integrating wearable devices, open environmental data, indoor air quality sensing devices, a location-based smartphone app, and an AI-assisted telecare platform. The AI-assisted telecare platform provided comprehensive insight into patients' clinical, lifestyle, and environmental data, and generated reliable predictions of future acute exacerbation events. All data from 1,667 patients were collected prospectively during a 24-month follow-up period, resulting in the detection of 386 abnormal episodes. Machine learning algorithms and deep learning algorithms were used to train modular chronic disease models. The modular chronic disease prediction models that have passed external validation include obesity, panic disorder, and chronic obstructive pulmonary disease, with an average accuracy of 88.46%, a sensitivity of 75.6%, a specificity of 93.0%, and an F1 score of 79.8%. Compared with previous studies, we establish an effective way to collect lifestyle, life trajectory, and symptom records, as well as environmental factors, and improve the performance of the prediction model by adding objective comprehensive data and feature selection. Our results also demonstrate that lifestyle and environmental factors are highly correlated with patient health and have the potential to predict future abnormal events better than using only questionnaire data. Furthermore, we have constructed a cost-effective model that needs only a few features to support the prediction task, which is helpful for deploying real-world modular prediction models.

Contribution of Testing Strategies and Contact Tracing towards COVID-19 Outbreaks Control: A Mathematical Modeling Study.

This modeling study considers different screening strategies, contact tracing, and the severity of novel epidemic outbreaks for various population sizes, providing insight into multinational containment effectiveness of emerging infectious diseases, prior to vaccines development. During the period of the ancestral SARS-Cov-2 virus, contact tracing alone is insufficient to achieve outbreak control. Although universal testing is proposed in multiple nations, its effectiveness accompanied by other measures is rarely examined. Our research investigates the necessity of universal testing when contact tracing and symptomatic screening measures are implemented. We used a stochastic transmission model to simulate COVID-19 transmission, evaluating containment strategies via contact tracing, one-time high risk symptomatic testing, and universal testing. Despite universal testing having the potential to identify subclinical cases, which is crucial for non-pharmaceutical interventions, our model suggests that universal testing only reduces the total number of cases by 0.0009% for countries with low COVID-19 prevalence and 0.025% for countries with high COVID-19 prevalence when rigorous contact tracing and symptomatic screening are also implemented. These findings highlight the effectiveness of testing strategies and contact tracing in reducing COVID-19 cases by identifying subclinical cases.

Acute Exacerbation of a Chronic Obstructive Pulmonary Disease Prediction System Using Wearable Device Data, Machine Learning, and Deep Learning: Development and Cohort Study.

BACKGROUND: The World Health Organization has projected that by 2030, chronic obstructive pulmonary disease (COPD) will be the third-leading cause of mortality and the seventh-leading cause of morbidity worldwide. Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with an accelerated decline in lung function, diminished quality of life, and higher mortality. Accurate early detection of acute exacerbations will enable early management and reduce mortality. OBJECTIVE: The aim of this study was to develop a prediction system using lifestyle data, environmental factors, and patient symptoms for the early detection of AECOPD in the upcoming 7 days. METHODS: This prospective study was performed at National Taiwan University Hospital. Patients with COPD that did not have a pacemaker and were not pregnant were invited for enrollment. Data on lifestyle, temperature, humidity, and fine particulate matter were collected using wearable devices (Fitbit Versa), a home air quality-sensing device (EDIMAX Airbox), and a smartphone app. AECOPD episodes were evaluated via standardized questionnaires. With these input features, we evaluated the prediction performance of machine learning models, including random forest, decision trees, k-nearest neighbor, linear discriminant analysis, and adaptive boosting, and a deep neural network model. RESULTS: The continuous real-time monitoring of lifestyle and indoor environment factors was implemented by integrating home air quality-sensing devices, a smartphone app, and wearable devices. All data from 67 COPD patients were collected prospectively during a mean 4-month follow-up period, resulting in the detection of 25 AECOPD episodes. For 7-day AECOPD prediction, the proposed AECOPD predictive model achieved an accuracy of 92.1%, sensitivity of 94%, and specificity of 90.4%. Receiver operating characteristic curve analysis showed that the area under the curve of the model in predicting AECOPD was greater than 0.9. The most important variables in the model were daily steps walked, stairs climbed, and daily distance moved. CONCLUSIONS: Using wearable devices, home air quality-sensing devices, a smartphone app, and supervised prediction algorithms, we achieved excellent power to predict whether a patient would experience AECOPD within the upcoming 7 days. The AECOPD prediction system provided an effective way to collect lifestyle and environmental data, and yielded reliable predictions of future AECOPD events. Compared with previous studies, we have comprehensively improved the performance of the AECOPD prediction model by adding objective lifestyle and environmental data. This model could yield more accurate prediction results for COPD patients than using only questionnaire data.

Effect of Lactobacillus rhamnosus GG on Energy Metabolism, Leptin Resistance, and Gut Microbiota in Mice with Diet-Induced Obesity.

Obesity is closely associated with various metabolic disorders, including leptin resistance, which is characterized by high circulating leptin levels. Probiotics can decrease circulating leptin levels by alteration of the gut microbiota. Thus, they may have anti-obesogenic effects. In this study, the effects of administration of a probiotic bacterium, Lactobacillus rhamnosus GG (LGG), on gut microbiota and modulation of leptin resistance were evaluated in mice. Male Balb/C mice aged 7 weeks were fed either a normal diet (ND), high-fat diet (HFD), HFD supplemented with low-dose LGG (108 CFU/mouse/day), or HFD supplemented with high-dose LGG (1010 CFU/mouse/day) for 10 weeks. Significantly increased body weight, epididymal fat weight, and decreased leptin responsiveness to exogenous leptin treatment and ratio of villus height to crypt depth were observed in the HFD-fed mice compared to the ND-fed mice. Moreover, a remarkable increase in the proportion of Proteobacteria and ratio of Firmicutes/Bacteroidetes in the fecal microbiota were also observed in the HFD-fed mice. Supplementation of HFD with high-dose LGG restored exogenous leptin responsiveness, increased the ratio of villus height to crypt depth, and decreased the proportion of Proteobacteria in fecal microbiota. These findings suggest that LGG supplementation might alleviate leptin resistance caused by an HFD through the improvement of the digestive health of the host.

Kirigami-Based Light-Induced Shape-Morphing and Locomotion.

The development of stimuli-responsive soft actuators, a task largely undertaken by material scientists, has become a major driving force in pushing the frontiers of microrobotics. Devices made of soft active materials are oftentimes small in size, remotely and wirelessly powered/controlled, and capable of adapting themselves to unexpected hurdles. However, nowadays most soft microscale robots are rather simple in terms of design and architecture, and it remains a challenge to create complex 3D soft robots with stimuli-responsive properties. Here, it is suggested that kirigami-based techniques can be useful for fabricating complex 3D robotic structures that can be activated with light. External stress fields introduce out-of-plane deformation of kirigami film actuators made of liquid crystal networks. Such 2D-to-3D structural transformations can give rise to mechanical actuation upon light illumination, thus allowing the realization of kirigami-based light-fuelled robotics. A kirigami rolling robot is demonstrated, where a light beam controls the multigait motion and steers the moving direction in 2D. The device is able to navigate along different routes and moves up a ramp with a slope of 6°. The results demonstrate a facile technique to realize complex and flexible 3D structures with light-activated robotic functions.

The effect of preventing subclinical transmission on the containment of COVID-19: Mathematical modeling and experience in Taiwan.

The control strategies preventing subclinical transmission differed among countries. A stochastic transmission model was used to assess the potential effectiveness of control strategies at controlling the COVID-19 outbreak. Three strategies included lack of prevention of subclinical transmission (Strategy A), partial prevention using testing with different accuracy (Strategy B) and complete prevention by isolating all at-risk people (Strategy C, Taiwan policy). The high probability of containing COVID-19 in Strategy C is observed in different scenario, had varied in the number of initial cases (5, 20, and 40), the reproduction number (1.5, 2, 2.5, and 3.5), the proportion of at-risk people being investigated (40%, 60%, 80%, to 90%), the delay from symptom onset to isolation (long and short), and the proportion of transmission that occurred before symptom onset (<1%, 15%, and 30%). Strategy C achieved probability of 80% under advantageous scenario, such as low number of initial cases and high coverage of epidemiological investigation but Strategy B and C rarely achieved that of 60%. Considering the unsatisfactory accuracy of current testing and insufficient resources, isolation of all at-risk people, as adopted in Taiwan, could be an effective alternative.

Real-time dengue forecast for outbreak alerts in Southern Taiwan.

Dengue fever is a viral disease transmitted by mosquitoes. In recent decades, dengue fever has spread throughout the world. In 2014 and 2015, southern Taiwan experienced its most serious dengue outbreak in recent years. Some statistical models have been established in the past, however, these models may not be suitable for predicting huge outbreaks in 2014 and 2015. The control of dengue fever has become the primary task of local health agencies. This study attempts to predict the occurrence of dengue fever in order to achieve the purpose of timely warning. We applied a newly developed autoregressive model (AR model) to assess the association between daily weather variability and daily dengue case number in 2014 and 2015 in Kaohsiung, the largest city in southern Taiwan. This model also contained additional lagged weather predictors, and developed 5-day-ahead and 15-day-ahead predictive models. Our results indicate that numbers of dengue cases in Kaohsiung are associated with humidity and the biting rate (BR). Our model is simple, intuitive and easy to use. The developed model can be embedded in a "real-time" schedule, and the data (at present) can be updated daily or weekly based on the needs of public health workers. In this study, a simple model using only meteorological factors performed well. The proposed real-time forecast model can help health agencies take public health actions to mitigate the influences of the epidemic.