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

Low temperature fabrication of hydrangea-like NiCo2S4 as electrode materials for high performance supercapacitors

Hydrangea-like NiCo2S4 as electrode materials for high performance supercapacitors was synthesized by using a facile low temperature (90 °C) two-step hydrothermal technique without surfactant or template. The special hydrangea-like structure and large specific surface area (74.8 m2/g) provided plenty of electro active sites which were beneficial to superior pseudocapacitive performance of NiCo2S4. The supercapacitors performance of NiCo2S4 was investigated by a three-electrode system. NiCo2S4 exhibited high specific capacitance with 1475 F g−1 at a current density of 3 A g−1, and a fairly high rate capacity with 1152 F g−1 at 20 A g−1. These results indicate that low temperature hydrothermal is a very promising method to prepare electrode materials for supercapacitors

Spatial Patterns and Drivers of Microbial Taxa in a Karst Broadleaf Forest

Spatial patterns and drivers of soil microbial communities have not yet been well documented. Here, we used geostatistical modeling and Illumina sequencing of 16S rRNA genes to explore how the main microbial taxa at the phyla level are spatially distributed in a 25-ha karst broadleaf forest in southwest China. Proteobacteria, dominated by Alpha- and Deltaproteobacteria, was the most abundant phylum (34.51%) in the karst forest soils. Other dominating phyla were Actinobacteria (30.73%), and Acidobacteria (12.24%). Soil microbial taxa showed spatial dependence with an autocorrelation range of 44.4–883.0 m, most of them within the scope of the study plots (500 m). An increasing trend was observed for Alphaproteobacteria, Deltaproteobacteria, and Chloroflexi from north to south in the study area, but an opposite trend for Actinobacteria, Acidobacteira, and Firmicutes was observed. Thaumarchaeota, Bacteroidetes, Gemmatimonadetes, and Verrucomicrobia had patchy patterns, Nitrospirae had a unimodal pattern, and Latescibacteria had an intermittent pattern with low and high value strips. Location, soil total phosphorus, elevation, and plant density were significantly correlated with main soil bacterial taxa in the karst forest. Moreover, the total variation in soil microbial communities better explained by spatial factors than environmental variables. Furthermore, a large part of variation (76.8%) was unexplained in the study. Therefore, our results suggested that dispersal limitation was the primary driver of spatial pattern of soil microbial taxa in broadleaved forest in karst areas, and other environmental variables (i.e., soil porosity and temperature) should be taken into consideration

Seasonal Changes and Vertical Distribution of Fine Root Biomass During Vegetation Restoration in a Karst Area, Southwest China

In karst ecosystems, plants absorbing smaller amounts of nutrients, owing to shallow soil, show limited growth. In addition, fine roots (diameter < 2 mm) contribute to the regulation of nutrient cycles in terrestrial ecosystems. However, the spatial and temporal variations of fine root biomass in different vegetation types of the karst region remains poorly understood. In this study, we investigated the seasonal and vertical variation in biomass, necromass, and total mass of fine roots using sequential soil coring under different stages of vegetation restoration (grassland, shrubland, secondary forest, and primary forest) in Southwest China. The results showed that the fine root biomass and necromass ranged from 136.99 to 216.18 g m−2 and 47.34 to 86.94 g m−2, respectively. The total mass of fine roots and their production ranged from 187.00 to 303.11 g m−2 and 55.74 to 100.84 g m−2 year−1, respectively. They showed a single peak across the vegetation restoration gradient. The fine root biomass and total fine root mass also showed a single peak with seasonal change. In autumn, the fine root biomass was high, whereas the necromass was low. Most of the fine roots were concentrated in the surface soil layer (0–10 cm), which accounted more than 57% root biomass, and decreased with increasing soil depth. In addition, fine root production showed a similar vertical pattern of variation with biomass. Overall, our results suggested that fine roots show clear seasonal and vertical changes with vegetation succession. Moreover, there was a higher seasonal fluctuation and a greater vertical decreasing trend in late-successional stages than in the early-successional stages. The conversion of degraded land to forest could improve the productivity of underground ecosystems and vegetation restoration projects in the fragile karst region should, therefore, continue

Influence of Fe-rich phases and precipitates on the mechanical behaviour of Al-Cu-Mn-Fe-Sc-Zr alloys studied by synchrotron X-ray and neutron

A multiscale methodology using scanning and transmission electron microscope, synchrotron X-ray nano-tomography and micro-tomography, small angle neutron scattering, and in situ synchrotron X-ray diffraction has been used, to reveal the effect of Fe-rich phases and precipitates on the mechanical behaviour of an Al-Cu-Mn-Fe-Sc-Zr alloy. The α-Al grains size is reduced from 185.1 μm (0 MPa) and 114.3 μm (75 MPa) by applied pressure. Moreover, it has been demonstrated that suitable heat treatments modify the 3D morphology of Fe-rich phases from interconnected to a disaggregated structure that improves the mechanical properties of the alloy. The size and morphology evolution of fine precipitates under different ageing temperature and time are revealed. At ageing temperature of 160 °C, the precipitates change from GP zones to θ' (around 75 nm in length) with ageing time increasing from 1 h to 24 h; the Vickers hardness increases from 72.0 HV to 110.7HV. The high ductility of the Sc, Zr modified Al-Cu alloy is related to the complex shape and the loss of interconnectivity of the Fe-rich particles due to the heat treatment. The evolution of the crystal lattice strains in α-Al, and β-Fe calculated during tensile test using in-situ synchrotron X-ray diffraction corroborates the influence of the microstructure in the ductility of the modified alloy.This work was financially supported by the Natural Science Foundation of China (Nos. 52104373 and 51901042), the Basic and Applied Basic Foundation of Guangdong Province, China (Nos. 2020B1515120065 and 2021B1515140028); the Guangdong Province Office of Education, China (No. 2018KQNCX256). We also would like to thank the WL13HB beamline and WL14B1 beamline of Shanghai Synchrotron Radiation Facility, SSRF, China; 4W1A beamline of Beijing Synchrotron Radiation Facility, BSRF, China for provision of synchrotron radiation beamtime; and Small Angle Neutron Scattering (SANS) Beamline in China Spallation Neutron Source (CSNS, Dongguan, China) for providing neutron beamtime

Screening biomarkers for Sjogren’s Syndrome by computer analysis and evaluating the expression correlations with the levels of immune cells

BackgroundSjögren’s syndrome (SS) is a systemic autoimmune disease that affects about 0.04-0.1% of the general population. SS diagnosis depends on symptoms, clinical signs, autoimmune serology, and even invasive histopathological examination. This study explored biomarkers for SS diagnosis.MethodsWe downloaded three datasets of SS patients’ and healthy pepole’s whole blood (GSE51092, GSE66795, and GSE140161) from the Gene Expression Omnibus (GEO) database. We used machine learning algorithm to mine possible diagnostic biomarkers for SS patients. Additionally, we assessed the biomarkers’ diagnostic value using the receiver operating characteristic (ROC) curve. Moreover, we confirmed the expression of the biomarkers through the reverse transcription quantitative polymerase chain reaction (RT-qPCR) using our own Chinese cohort. Eventually, the proportions of 22 immune cells in SS patients were calculated by CIBERSORT, and connections between the expression of the biomarkers and immune cell ratios were studied.ResultsWe obtained 43 DEGs that were mainly involved in immune-related pathways. Next, 11 candidate biomarkers were selected and validated by the validation cohort data set. Besides, the area under curves (AUC) of XAF1, STAT1, IFI27, HES4, TTC21A, and OTOF in the discovery and validation datasets were 0.903 and 0.877, respectively. Subsequently, eight genes, including HES4, IFI27, LY6E, OTOF, STAT1, TTC21A, XAF1, and ZCCHC2, were selected as prospective biomarkers and verified by RT-qPCR. Finally, we revealed the most relevant immune cells with the expression of HES4, IFI27, LY6E, OTOF, TTC21A, XAF1, and ZCCHC2.ConclusionIn this paper, we identified seven key biomarkers that have potential value for diagnosing Chinese SS patients

Construction of porous hierarchical NiCo2S4 toward high rate performance supercapacitor

Developing high-performance supercapacitors is an effective way to satisfy the ever-increasing energy storage demand for emerging devices, but the inferior rate performance of battery-type supercapacitors limits their large-scale utilization. Herein, porous hierarchical nickel cobalt sulfide (NiCo2S4) was constructed by a novel strategy that the synthesized nickel cobalt oxide nanosheets as chemical template for hydrothermal method. Furthermore, the backbone of nickel cobalt oxide nanosheets can finally convert to NiCo2S4, which both plays the role of matrix to buffer the volume variation and enhances entire conductivity. Benefiting from high specific area (79.9 m2 g−1), suitable nanopores for KOH electrolyte, high conductivity, and multiple Co/Ni valence, the hierarchical NiCo2S4 electrode delivers a high specific capacity of 1035.1 F g−1 at the current density of 1 A g−1, and an ultrahigh rate performance of 80.9% capacitance retention at 20 A g−1 was obtained. The assembled asymmetric supercapacitor device could achieve the maximum capacity of 102.4 F g−1 at 5 mV s−1 and maintain at 80.5 F g−1 at 50 mV s−1, indicating its superior rate ability. In addition, the highest energy density of 35.4 Wh kg−1 can be obtained at a power density of 0.4 kW kg−1. These results indicate that the porous hierarchical NiCo2S4 could be served as high rate performance electrode materials for advanced supercapacitors

Loss-of-function mutations in TNFAIP3 leading to A20 haploinsufficiency cause an early-onset autoinflammatory disease

Systemic autoinflammatory diseases are driven by abnormal activation of innate immunity. Herein we describe a new disease caused by high-penetrance heterozygous germline mutations in TNFAIP3, which encodes the NF-B regulatory protein A20, in six unrelated families with early-onset systemic inflammation. The disorder resembles Behçet\u27s disease, which is typically considered a polygenic disorder with onset in early adulthood. A20 is a potent inhibitor of the NF-B signaling pathway. Mutant, truncated A20 proteins are likely to act through haploinsufficiency because they do not exert a dominant-negative effect in overexpression experiments. Patient-derived cells show increased degradation of IBα and nuclear translocation of the NF-B p65 subunit together with increased expression of NF-B-mediated proinflammatory cytokines. A20 restricts NF-B signals via its deubiquitinase activity. In cells expressing mutant A20 protein, there is defective removal of Lys63-linked ubiquitin from TRAF6, NEMO and RIP1 after stimulation with tumor necrosis factor (TNF). NF-B-dependent proinflammatory cytokines are potential therapeutic targets for the patients with this disease

SyD: A Middleware Testbed for Collaborative Applications over Small Heterogeneous Devices and Data Stores

Abstract. Currently, it is possible to develop a collaborative application running on a collection of heterogeneous, possibly mobile, devices, each potentially hosting data stores, using existing middleware technologies such as JXTA, BREW, compact.NET and J2ME. However, they require too many ad-hoc techniques as well as cumbersome and time-consuming programming. Our System on Mobile Devices (SyD) middleware, on the other hand, has a modular architecture that makes such application de-velopment very systematic and streamlined. The architecture supports transactions over mobile data stores, with a range of remote group invo-cation options and embedded interdependencies among such data store objects. The architecture further provides a persistent uniform object view, group transaction with Quality of Service (QoS) speci¯cations, and XML vocabulary for inter-device communication. This paper presents the basic SyD concepts, introduces the architecture and the design of the SyD middleware and its components. We also provide guidelines fo

Interfacial Embedding of Laser-Manufactured Fluorinated Gold Clusters Enabling Stable Perovskite Solar Cells with Efficiency Over 24%

Abstract Tackling the interfacial loss in emerged perovskite-based solar cells (PSCs) to address synchronously the carrier dynamics and the environmental stability, has been of fundamental and viable importance, while technological hurdles remain in not only creating such interfacial mediator, but the subsequent interfacial embedding in the active layer. This article reports a strategy of interfacial embedding of hydrophobic fluorinated-gold-clusters (FGCs) for highly efficient and stable PSCs. The p-type semiconducting feature enables the FGC efficient interfacial mediator to improve the carrier dynamics by reducing the interfacial carrier transfer barrier and boosting the charge extraction at grain boundaries. The hydrophobic tails of the gold clusters and the hydrogen bonding between fluorine groups and perovskite favor the enhancement of environmental stability. Benefiting from these merits, highly efficient formamidinium lead iodide PSCs (champion efficiency up to 24.02%) with enhanced phase stability under varied relative humidity (RH) from 40% to 95%, as well as highly efficient mixed-cation PSCs with moisture stability (RH of 75%) over 10 000 h are achieved. It is thus inspiring to advance the development of highly efficient and stable PSCs via interfacial embedding laser-generated additives for improved charge transfer/extraction and environmental stability