Innovative Cases of Organic Agriculture in Asia

Under a 10-year cooperation agreement between Xichong County, PR China and IFOAM Asia, the “Xichong International Organic Innovation Summit” is to be held every two years while the Asia Organic Agriculture Technology, Research & Development Xichong Center is responsible for collection, dissemination and publication of innovations in organic agriculture. To this end, the Xichong Organic Innovation Committee has been formed consisting of 9 experts from different Asian countries or regions. After the efforts made by the experts of the committee as well as the assistance of the IFOAM Asia head office staff, the first collection of the innovation technologies and cases have been compiled for publication and dissemination

Synthesis and Characterization of ZnO Nanowire–CdO Composite Nanostructures

ZnO nanowire–CdO composite nanostructures were fabricated by a simple two-step process involving ammonia solution method and thermal evaporation. First, ZnO nanowires (NWs) were grown on Si substrate by aqueous ammonia solution method and then CdO was deposited on these ZnO NWs by thermal evaporation of cadmium chloride powder. The surface morphology and structure of the synthesized composite structures were analyzed by scanning electron microscopy, X-ray diffraction and transmission electron microscopy. The optical absorbance spectrum showed that ZnO NW–CdO composites can absorb light up to 550 nm. The photoluminescence spectrum of the composite structure does not show any CdO-related emission peak and also there was no band gap modification of ZnO due to CdO. The photocurrent measurements showed that ZnO NW–CdO composite structures have better photocurrent when compared with the bare ZnO NWs

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Peripheral Vision Transformer

Human vision possesses a special type of visual processing systems called peripheral vision. Partitioning the entire visual field into multiple contour regions based on the distance to the center of our gaze, the peripheral vision provides us the ability to perceive various visual features at different regions. In this work, we take a biologically inspired approach and explore to model peripheral vision in deep neural networks for visual recognition. We propose to incorporate peripheral position encoding to the multi-head self-attention layers to let the network learn to partition the visual field into diverse peripheral regions given training data. We evaluate the proposed network, dubbed PerViT, on the large-scale ImageNet dataset and systematically investigate the inner workings of the model for machine perception, showing that the network learns to perceive visual data similarly to the way that human vision does. The state-of-the-art performance in image classification task across various model sizes demonstrates the efficacy of the proposed method.Comment: Technical repor

Association of MTHFR C677T polymorphism and type 2 diabetes mellitus (T2DM) susceptibility

Abstract Introduction Methylenetetrahydrofolate reductase (MTHFR) is essential in mediating folate metabolism, and thus plays an important role in diabetes and diabetic complications. MTHFR C677T (rs1801133 C>T) polymorphism has been proposed to be linked with type 2 diabetes mellitus (T2DM) susceptibility. However, the conclusions are inconsistent. Therefore, we rechecked their linkage aiming to obtain a more reliable estimation by performing an updated meta‐analysis. Methods We searched electronic databases PubMed, EMBASE, CNKI, and Wanfang to obtain studies updated to October 2019. Results After carefully screening, we finally incorporated 68 studies with 10,812 cases and 8,745 controls. The genotype frequency of C677T polymorphism was analyzed pooled to generate odds ratios (ORs) and 95% confidence intervals (CIs). Pooled results presented that MTHFR C677T polymorphism was significantly associated with T2DM under homozygous (OR = 1.64, 95% CI = 1.39–1.94), heterozygous (OR = 1.38, 95% CI = 1.20–1.59), recessive (OR = 1.41, 95% CI = 1.23–1.61), dominant (OR = 1.47, 95% CI = 1.27–1.70), and allele (OR = 1.37, 95% CI = 1.23–1.52) genetic models. Stratified analysis demonstrated that C677T genotype was associated with T2DM in Asian populations, but not Caucasian and African populations. Conclusion Our results indicated that MTHFR C677T polymorphism confers to T2DM, especially in Asian populations. Much more large‐scale case–control studies are needed to strengthen such conclusion in the future

Prediction models constructed for Hashimoto’s thyroiditis risk based on clinical and laboratory factors

BackgroundHashimoto's thyroiditis (HT) frequently occurs among autoimmune diseases and may simultaneously appear with thyroid cancer. However, it is difficult to diagnose HT at an early stage just by clinical symptoms. Thus, it is urgent to integrate multiple clinical and laboratory factors for the early diagnosis and risk prediction of HT.MethodsWe recruited 1,303 participants, including 866 non-HT controls and 437 diagnosed HT patients. 44 HT patients also had thyroid cancer. Firstly, we compared the difference in thyroid goiter degrees between controls and patients. Secondly, we collected 15 factors and analyzed their significant differences between controls and HT patients, including age, body mass index, gender, history of diabetes, degrees of thyroid goiter, UIC, 25-(OH)D, FT3, FT4, TSH, TAG, TC, FPG, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol. Thirdly, logistic regression analysis demonstrated the risk factors for HT. For machine learning modeling of HT and thyroid cancer, we conducted the establishment and evaluation of six models in training and test sets.ResultsThe degrees of thyroid goiter were significantly different among controls, HT patients without cancer (HT-C), and HT patients with thyroid cancer (HT+C). Most factors had significant differences between controls and patients. Logistic regression analysis confirmed diabetes, UIC, FT3, and TSH as important risk factors for HT. The AUC scores of XGBoost, LR, SVM, and MLP models indicated appropriate predictive power for HT. The features were arranged by their importance, among which, 25-(OH)D, FT4, and TSH were the top three high-ranking factors.ConclusionsWe firstly analyzed comprehensive factors of HT patients. The proposed machine learning modeling, combined with multiple factors, are efficient for thyroid diagnosis. These discoveries will extensively promote precise diagnosis, personalized therapies, and reduce unnecessary cost for thyroid diseases

Precise Layer Control and Electronic State Modulation of a Transition Metal Dichalcogenide via Phase-Transition-Induced Growth

Wafer-scale growth of transition metal dichalcogenides with precise control over the number of layers, and hence the electronic state is an essential technology for expanding the practical application of 2D materials. Herein, a new growth method, phase-transition-induced growth (PTG), is proposed for the precisely controlled growth of molybdenum disulfide (MoS2) films consisting of one to eleven layers with spatial uniformity on a 2 in. wafer. In this method, an energetically unstable amorphous MoSxOy (a-MoSxOy) phase is effectively converted to a thermodynamically stable crystalline MoS2 film. The number of MoS2 layers is readily controlled layer-by-layer by controlling the amount of Mo atoms in a-MoSxOy, which is also applicable for the growth of heteroatom-inserted MoS2. The electronic states of intrinsic and Nb-inserted MoS2 with one and four layers grown by PTGare are analyzed based on their work functions. The work function of monolayer MoS2 effectively increases with the substitution of Nb for Mo. As the number of layers increases to four, charge screening becomes weaker, dopant ionization becomes easier, and ultimately the work function increases further. Thus, better electronic state modulation is achieved in a thicker layer, and in this respect, PTG has the advantage of enabling precise control over the film thickness

An exceptionally facile method to produce layered double hydroxides on a conducting substrate and their application for solar water splitting without an external bias

An exceptionally facile process is presented for in situ formation of zinc chromium layered double hydroxide (ZnCr:LDH) nanosheets on a conducting substrate. Thus, ZnCr:LDH nanosheets were synthesized from a metallic Zn film/fluorine-doped tin oxide (FTO) glass by simply dipping into a Cr nitrate solution for only one minute at room temperature. Then, ZnCr:LDHs were converted into zinc chromium mixed metal oxide (ZnCr:MMO) nanoparticles by calcination. Under visible light irradiation (?? > 420 nm), the in situ synthesized ZnCr:MMO photoanode exhibited a stable and an order-of-magnitude higher activity for photoelectrochemical water splitting than that of a ZnCr:MMO film fabricated ex situ by electrophoretic deposition of already-synthesized ZnCr:MMO powders. More significant was that it generated anodic photocurrents even without an externally applied bias potential, which is an unprecedented result for an oxide photoanode-driven PEC system working under visible light.close3