Cotton disease identification method based on pruning

Deep convolutional neural networks (DCNN) have shown promising performance in plant disease recognition. However, these networks cannot be deployed on resource-limited smart devices due to their vast parameters and computations. To address the issue of deployability when developing cotton disease identification applications for mobile/smart devices, we compress the disease recognition models employing the pruning algorithm. The algorithm uses the γ coefficient in the Batch Normalization layer to prune the channels to realize the compression of DCNN. To further improve the accuracy of the model, we suggest two strategies in combination with transfer learning: compression after transfer learning or transfer learning after compression. In our experiments, the source dataset is famous PlantVillage while the target dataset is the cotton disease image set which contains images collected from the Internet and taken from the fields. We select VGG16, ResNet164 and DenseNet40 as compressed models for comparison. The experimental results show that transfer learning after compression overall surpass its counterpart. When compression rate is set to 80% the accuracies of compressed version of VGG16, ResNet164 and DenseNet40 are 90.77%, 96.31% and 97.23%, respectively, and the parameters are only 0.30M, 0.43M and 0.26M, respectively. Among the compressed models, DenseNet40 has the highest accuracy and the smallest parameters. The best model (DenseNet40-80%-T) is pruned 75.70% of the parameters and cut off 65.52% of the computations, with the model size being only 2.2 MB. Compared with the version of compression after transfer learning, the accuracy of the model is improved by 0.74%. We further develop a cotton disease recognition APP on the Android platform based on the model and on the test phone, the average time to identify a single image is just 87ms

TGLI1 transcription factor mediates breast cancer brain metastasis via activating metastasis-initiating cancer stem cells and astrocytes in the tumor microenvironment

Mechanisms for breast cancer metastasis remain unclear. Whether truncated glioma-associated oncogene homolog 1 (TGLI1), a transcription factor known to promote angiogenesis, migration and invasion, plays any role in metastasis of any tumor type has never been investigated. In this study, results of two mouse models of breast cancer metastasis showed that ectopic expression of TGLI1, but not GLI1, promoted preferential metastasis to the brain. Conversely, selective TGLI1 knockdown using antisense oligonucleotides led to decreased breast cancer brain metastasis (BCBM) in vivo. Immunohistochemical staining showed that TGLI1, but not GLI1, was increased in lymph node metastases compared to matched primary tumors, and that TGLI1 was expressed at higher levels in BCBM specimens compared to primary tumors. TGLI1 activation is associated with a shortened time to develop BCBM and enriched in HER2-enriched and triple-negative breast cancers. Radioresistant BCBM cell lines and specimens expressed higher levels of TGLI1, but not GLI1, than radiosensitive counterparts. Since cancer stem cells (CSCs) are radioresistant and metastasis-initiating cells, we examined TGLI1 for its involvement in breast CSCs and found TGLI1 to transcriptionally activate stemness genes CD44, Nanog, Sox2, and OCT4 leading to CSC renewal, and TGLI1 outcompetes with GLI1 for binding to target promoters. We next examined whether astrocyte-priming underlies TGLI1-mediated brain tropism and found that TGLI1-positive CSCs strongly activated and interacted with astrocytes in vitro and in vivo. These findings demonstrate, for the first time, that TGLI1 mediates breast cancer metastasis to the brain, in part, through promoting metastasis-initiating CSCs and activating astrocytes in BCBM microenvironment

Mapping the Distribution of Water Resource Security in the Beijing-Tianjin-Hebei Region at the County Level under a Changing Context

The Beijing-Tianjin-Hebei (Jingjinji) region is the most densely populated region in China and suffers from severe water resource shortage, with considerable water-related issues emerging under a changing context such as construction of water diversion projects (WDP), regional synergistic development, and climate change. To this end, this paper develops a framework to examine the water resource security for 200 counties in the Jingjinji region under these changes. Thus, county-level water resource security is assessed in terms of the long-term annual mean and selected typical years (i.e., dry, normal, and wet years), with and without the WDP, and under the current and projected future (i.e., regional synergistic development and climate change). The outcomes of such scenarios are assessed based on two water-crowding indicators, two use-to-availability indicators, and one composite indicator. Results indicate first that the water resources are distributed unevenly, relatively more abundant in the northeastern counties and extremely limited in the other counties. The water resources are very limited at the regional level, with the water availability per capita and per unit gross domestic product (GDP) being only 279/290 m3 and 46/18 m3 in the current and projected future scenarios, respectively, even when considering the WDP. Second, the population carrying capacity is currently the dominant influence, while economic development will be the controlling factor in the future for most middle and southern counties. This suggests that significant improvement in water-saving technologies, vigorous replacement of industries from high to low water consumption, as well as water from other supplies for large-scale applications are greatly needed. Third, the research identifies those counties most at risk to water scarcity and shows that most of them can be greatly relieved after supplementation by the planned WDP. Finally, more attention should be paid to the southern counties because their water resources are not only limited but also much more sensitive and vulnerable to climate change. This work should benefit water resource management and allocation decisions in the Jingjinji region, and the proposed assessment framework can be applied to other similar problems.This study is supported by the National Key Research and Development Program of China (2016YFC0401401) and the National Natural Science Foundation of China (51609256, 51609122, 51522907, 51739011, and 51569026). Partial support is also from the Young Elite Scientists Sponsorship Program by the China Association for Science and Technology (2017QNRC001

Over 20% PCE perovskite solar cells with superior stability achieved by novel and low-cost hole-transporting materials

The exploration of alternative low-cost molecular hole-transporting materials (HTMs) for both highly efficient and stable perovskite solar cells (PSCs) is a relatively new research area. Two novel HTMs using the thiophene core were designed and synthesized (Z25 and Z26). The perovskite solar cells based on Z26 exhibited a remarkable overall power conversion efficiency (PCE) of 20.1%, which is comparable to 20.6% obtained with spiroOMeTAD. Importantly, the devices based-on Z26 show better stability compared to devices based on Z25 and spiroOMeTAD when aged under ambient air of 30% or 85% relative humidity in the dark and under continuous full sun illumination at maximum power point tracking respectively. The presented results demonstrate a simple strategy by introducing double bonds to design hole-transporting materials for highly efficient and stable perovskite solar cells with low cost, which is important for commercial application

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure