MIDDLE-AGED FEMALE DEPRESSION IN PERIMENOPAUSAL PERIOD AND SQUARE DANCE INTERVENTION

Background: Depression is one of the most common psychiatric illnesses among perimenopausal women. Currently, drug treatments for the disorder tend to have higher risks than other forms of treatment. On the contrary, aerobic exercise can effectively relieve menopausal syndrome among perimenopausal women. Square dance, a kind of aerobic exercise favored by middle-aged women in China, could be a beneficial intervention for perimenopausal depression. Subjects and methods: A total of 321 women in perimenopause were chosen from Nangang Community, DaoLi Community, and Daowai Community in Harbin, Heilongjiang Province, from September 2015 through April 2016. Of the women with depressive symptoms, 60 did not participate in square dance. The subjects were randomly assigned to the intervention group (n=26) and control group (n=24). Intervention group patients participated in guided square dance exercise 60-90 min at least 5 times per week at a regular time for 3 months. The women in the control group received no intervention. Results: Of the screened subjects, 72 women (22.4%) suffered mild to severe depression; younger, working married women who square danced regularly presented a low depression rate (p<0.05). The depression index score of the intervention group after three months was 0.43±0.09, a statistically significant decrease (t=5.658, p<0.001). The difference in the depression index changes of the intervention and control group was also significant (t=5.407, p<0.001). Conclusion: The depression rate among women in perimenopause is high. Some intervention measures, such as promoting female employment, organizing collective activities for retired or unemployed women, and stabilizing family ties can prevent or improve the depression of women in perimenopause. Square dance can effectively reduce the depression levels of women in perimenopause

PIN-mediated polar auxin transport regulations in plant tropic responses

Tropisms, growth responses to environmental stimuli such as light or gravity, are spectacular examples of adaptive plant development. The plant hormone auxin serves as a major coordinative signal. The PIN auxin exporters, through their dynamic polar subcellular localizations, redirect auxin fluxes in response to environmental stimuli and the resulting auxin gradients across organs underly differential cell elongation and bending. In this review, we discuss recent advances concerning regulations of PIN polarity during tropisms, focusing on PIN phosphorylation and trafficking. We also cover how environmental cues regulate PIN actions during tropisms, and a crucial role of auxin feedback on PIN polarity during bending termination. Finally, the interactions between different tropisms are reviewed to understand plant adaptive growth in the natural environment

An Optimized Real-Time PCR to Avoid Species-/Tissue-Associated Inhibition for H5N1 Detection in Ferret and Monkey Tissues

The real-time PCR diagnostics for avian influenza virus H5N1 in tissue specimens are often suboptimal, since naturally occurring PCR inhibitors present in samples, or unanticipated match of primer to unsequenced species' genome. With the principal aim of optimizing the SYBR Green real-time PCR method for detecting H5N1 in ferret and monkey (Chinese rhesus macaque) tissue specimens, we screened various H5N1 gene-specific primer pairs and tested their ability to sensitively and specifically detect H5N1 transcripts in the infected animal tissues, then we assessed RNA yield and quality by comparing Ct values obtained from the standard Trizol method, and four commonly used RNA isolation kits with small modifications, including Roche High Pure, Ambion RNAqueous, BioMIGA EZgene, and Qiagen RNeasy. The results indicated that a single primer pair exhibited high specificity and sensitivity for H5N1 transcripts in ferret and monkey tissues. Each of the four kits and Trizol reagent produced high-quality RNA and removed all or nearly all PCR inhibitors. No statistically significant differences were found between the Ct values from the isolation methods. So the optimized SYBR Green real-time PCR could avoid species- or tissue-associated PCR inhibition in detecting H5N1 in ferret and monkey tissues, including lung and small intestine

Towards a deep-learning-based framework of sentinel-2 imagery for automated active fire detection

This paper proposes an automated active fire detection framework using Sentinel-2 imagery. The framework is made up of three basic parts including data collection and preprocessing, deep-learning-based active fire detection, and final product generation modules. The active fire detection module is developed on a specifically designed dual-domain channel-position attention (DCPA)+HRNetV2 model and a dataset with semi-manually annotated active fire samples is constructed over wildfires that commenced on the east coast of Australia and the west coast of the United States in 2019-2020 for the training process. This dataset can be used as a benchmark for other deep-learning-based algorithms to improve active fire detection accuracy. The performance of active fire detection is evaluated regarding the detection accuracy of deep-learning-based models and the processing efficiency of the whole framework. Results indicate that the DCPA and HRNetV2 combination surpasses DeepLabV3 and HRNetV2 models for active fire detection. In addition, the automated framework can deliver active fire detection results of Sentinel-2 inputs with coverage of about 12,000 km(2) (including data download) in less than 6 min, where average intersections over union (IoUs) of 70.4% and 71.9% were achieved in tests over Australia and the United States, respectively. Concepts in this framework can be further applied to other remote sensing sensors with data acquisitions in SWIR-NIR-Red ranges and can serve as a powerful tool to deal with large volumes of high-resolution data used in future fire monitoring systems and as a cost-efficient resource in support of governments and fire service agencies that need timely, optimized firefighting plans

Missile-Borne SAR Raw Signal Simulation for Maneuvering Target

SAR raw signal simulation under the case of maneuver and high-speed has been a challenging and urgent work recently. In this paper, a new method based on one-dimensional fast Fourier transform (1DFFT) algorithm is presented for raw signal simulation of maneuvering target for missile-borne SAR. Firstly, SAR time-domain raw signal model is given and an effective Range Frequency Azimuth Time (RFAT) algorithm based on 1DFFT is derived. In this algorithm, the “Stop and Go” (SaG) model is adopted and the wide radar scattering characteristic of target is taken into account. Furthermore, the “Inner Pulse Motion” (IPM) model is employed to deal with high-speed case. This new RFAT method can handle the maneuvering cases, high-speed cases, and bistatic radar cases, which are all possible in the missile-borne SAR. Besides, this raw signal simulation adopts the electromagnetic scattering calculation so that we do not need a scattering rate distribution map as the simulation input. Thus, the multiple electromagnetic reflections can be considered. Simulation examples prove the effectiveness of our method

Transmission of H7N9 influenza virus in mice by different infective routes.

BackgroundOn 19 February 2013, the first patient infected with a novel influenza A H7N9 virus from an avian source showed symptoms of sickness. More than 349 laboratory-confirmed cases and 109 deaths have been reported in mainland China since then. Laboratory-confirmed, human-to-human H7N9 virus transmission has not been documented between individuals having close contact; however, this transmission route could not be excluded for three families. To control the spread of the avian influenza H7N9 virus, we must better understand its pathogenesis, transmissibility, and transmission routes in mammals. Studies have shown that this particular virus is transmitted by aerosols among ferrets.MethodsTo study potential transmission routes in animals with direct or close contact to other animals, we investigated these factors in a murine model.ResultsViable H7N9 avian influenza virus was detected in the upper and lower respiratory tracts, intestine, and brain of model mice. The virus was transmissible between mice in close contact, with a higher concentration of virus found in pharyngeal and ocular secretions, and feces. All these biological materials were contagious for naïve mice.ConclusionsOur results suggest that the possible transmission routes for the H7N9 influenza virus were through mucosal secretions and feces