Brain rhythms in small and large networks of neurons

I studied two neuronal networks, one small to investigate the interaction of brain rhythms and one large, to investigate the effects of multiple connectivity types on resonance in a target network. Theta (4 − 8 Hz) and gamma (30 − 80 Hz) rhythms are commonly associated with memory and learning. The precision of co-firing between neurons and incoming inputs is critical in these cognitive functions. To understand the interaction of the two rhythms, I considered a single model neuron with an inhibitory autapse and M- current, under forcing from gamma pulses and a sinusoidal current of theta frequency. The M-current has a long time constant (~90 ms) and generates resonance at theta frequencies. I found that this slow M-current contributes to the precise co-firing between the network and fast gamma pulses in the presence of a slow sinusoidal forcing. This current expands the range of phase-locking frequency to the gamma input, counteracts the slow theta forcing, and admits bistability in some parameter range. The effects of the M-current balancing the theta forcing are reduced if the sinusoidal current is faster than the theta frequency band. For these results I used averaging methods, geometric singular perturbation theory, and bifurcation analysis. Beta rhythms (10 − 30 Hz) are associated with motor functions; patients with Parkinson’s Disease display prominent pathological beta rhythms in the basal ganglia. Research has suggested that a sub-circuit of the basal ganglia, subthalamic nucleus- globus pallidus externus (STN-GPe), is a potential generator of beta rhythms. The anatomical structure of STN-GPe also suggests that it may act as an amplifier of incoming rhythms. I considered a model of this sub-circuit based on the work of Kumar et al. (2011) and studied the mechanism of its intrinsic oscillation and how it might amplify inputs from the striatum. Through parameter sweeps, I found that the network manifests a robust intrinsic beta oscillation, not changeable by moderate parameter variation. Surprisingly, this STN-GPe network only amplifies rhythms of or close to the intrinsic oscillatory frequency, regardless of three different connection structures simulated. However, introducing heterogeneity into the network can make the network amplify rhythms of a wide range of frequencies

Retail Internationalisation process: a case study of Tesco in China

In the past several decades, there are volumes of researches on the international retailing process, focusing on different perspectives, such as scale, the motivations, entry modes, international retail learning, specific markets and unique frameworks. Due to insufficient analysis in the UK retailer, Tesco, as well as the possibility of developing a new integrated framework, this paper has developed a new comprehensive framework for the retail internationalisation process. Further to this, this paper analyses and discusses Tesc

Propozycja standardu ekologicznej kompensacji dla obszarowych zanieczyszczeń z rolnictwa

Non-point source water pollution mainly comes from farmland chemical fertilizers which has become an obstacle of agricultural sustainability and ecological health. As a public policy tool for assessing global ecological crisis and environmental pollution, ecological compensation is important for regional agricultural sustainability. Ecological compensation that farmers receive from governments is based on their reduction of fertilizer application at optimal ecological and economic levels. In this study we estimated the ecological compensation standards for nitrogen non-point pollution in Yixng city with contingent valuation method and cost-benefit method.  Results showed that the range of theoretical values of ecological compensation of nitrogen in Yixing City depended upon its optimal ecological and economic nitrogen application levels. The willingness of farmers to accept the compensation was positively correlated with their farming experience and education. There were about half of farmers willing to accept the compensation. Based on the present study, we found Yixing’s ecological compensation standard for controlling nitrogen non-point pollution was 620.0 yuan/hm2 at the current economic development level.Obszarowe zanieczyszczeń wód z rolnictwa pochodzą ze stosowania nawozów sztucznych, stanowiących przeszkodę na drodze do osiągnięcia rolniczej zrównoważoności i równowagi ekologicznej. W tym kontekście ekologiczna kompensacja, stanowiąca narzędzie polityczne do oceny kryzysu ekologicznego i ogólnego poziomu zanieczyszczenia środowiska, okazuje się także ważna w wymiarze lokalnej zrównoważoności rolniczej. Wysokość świadczeń, które rolniczy dostają od władz, jest uwarunkowana poziomem redukcji stosowania nawozów, którego celem jest osiągnięcie poziomu optymalnego zarówno zer strony ekologicznej, jak i ekonomicznej. W tym artykule, przy pomocy  Metoda wyceny warunkowej i metody kosztów i korzyści, ustaliliśmy standardy ekologicznej kompensacji dla miasta Yixng. Otrzymane rezultaty pozwalają na stwierdzenie, że zakres teoretycznych wartości ekologicznej kompensacji dla azotu w Yixing zależy od ustalenia optymalnych ekologicznych i ekonomicznych pozimów stosowania azotu. Zainteresowanie rolników otrzymaniem odszkodowania okazało się być pozytywnie skorelowane z ich doświadczeniem rolniczym i poziomem wykształcenia. Chęć jego otrzymania zgłosiła połowa z nich. Ustaliliśmy ponadto, że standard ekologicznej kompensacji dla Yixing odnoszący się kontrolowania obszarowych zanieczyszczeń związanych z nawozami azotowymi wynosi 620.0 yuan/hm2 , przy założeniu obecnego poziomu rozwoju ekonomicznego

Transcriptional response of Desulfatibacillum alkenivorans AK-01 to growth on alkanes: insights from RT-qPCR and microarray analyses.

Microbial transformation of n-alkanes in anaerobic ecosystems plays a pivotal role in biogeochemical carbon cycling and bioremediation, but the requisite genetic machinery is not well elucidated.Desulfatibacillum alkenivorans AK-01 utilizes n-alkanes (C13 to C18) and contains two genomic loci encoding alkylsuccinate synthase (ASS) gene clusters. ASS catalyzes alkane addition to fumarate to form methylalkylsuccinic acids. We hypothesized that the genes in the two clusters would be differentially expressed depending on the alkane substrate utilized for growth. RT-qPCR was used to investigate ass-gene expression across AK-01's known substrate range, and microarray-based transcriptomic analysis served to investigate whole-cell responses to growth on n-hexadecane versus hexadecanoate. RT-qPCR revealed induction of ass gene cluster 1 during growth on all tested alkane substrates, and the transcriptional start sites in cluster 1 were determined via 5'RACE. Induction of ass gene cluster 2 was not observed under the tested conditions. Transcriptomic analysis indicated that the upregulation of genes potentially involved in methylalkylsuccinate metabolism, including methylmalonyl-CoA mutase and a putative carboxyl transferase. These findings provide new directions for studying the transcriptional regulation of genes involved in alkane addition to fumarate, fumarate recycling and the processing of methylalkylsuccinates with regard to isolates, enrichment cultures and ecological datasets

Recent progress in the synthesis of metal–organic frameworks

Metal–organic frameworks (MOFs) have attracted considerable attention for various applications due to their tunable structure, porosity and functionality. In general, MOFs have been synthesized from isolated metal ions and organic linkers under hydrothermal or solvothermal conditions via one-spot reactions. The emerging precursor approach and kinetically tuned dimensional augmentation strategy add more diversity to this field. In addition, to speed up the crystallization process and create uniform crystals with reduced size, many alternative synthesis routes have been explored. Recent advances in microwave-assisted synthesis and electrochemical synthesis are presented in this review. In recent years, post-synthetic approaches have been shown to be powerful tools to synthesize MOFs with modified functionality, which cannot be attained via de novo synthesis. In this review, some current accomplishments of post-synthetic modification (PSM) based on covalent transformations and coordinative interactions as well as post-synthetic exchange (PSE) in robust MOFs are provided

Causal Discovery in Radiographic Markers of Knee Osteoarthritis and Prediction for Knee Osteoarthritis Severity With Attention-Long Short-Term Memory.

The goal of this study is to build a prognostic model to predict the severity of radiographic knee osteoarthritis (KOA) and to identify long-term disease progression risk factors for early intervention and treatment. We designed a long short-term memory (LSTM) model with an attention mechanism to predict Kellgren/Lawrence (KL) grade for knee osteoarthritis patients. The attention scores reveal a time-associated impact of different variables on KL grades. We also employed a fast causal inference (FCI) algorithm to estimate the causal relation of key variables, which will aid in clinical interpretability. Based on the clinical information of current visits, we accurately predicted the KL grade of the patient\u27s next visits with 90% accuracy. We found that joint space narrowing was a major contributor to KOA progression. Furthermore, our causal structure model indicated that knee alignments may lead to joint space narrowing, while symptoms (swelling, grinding, catching, and limited mobility) have little impact on KOA progression. This study evaluated a broad spectrum of potential risk factors from clinical data, questionnaires, and radiographic markers that are rarely considered in previous studies. Using our statistical model, providers are able to predict the risk of the future progression of KOA, which will provide a basis for selecting proper interventions, such as proceeding to joint arthroplasty for patients. Our causal model suggests that knee alignment should be considered in the primary treatment and KOA progression was independent of clinical symptoms

ThumbNet: One Thumbnail Image Contains All You Need for Recognition

Although deep convolutional neural networks (CNNs) have achieved great success in computer vision tasks, its real-world application is still impeded by its voracious demand of computational resources. Current works mostly seek to compress the network by reducing its parameters or parameter-incurred computation, neglecting the influence of the input image on the system complexity. Based on the fact that input images of a CNN contain substantial redundancy, in this paper, we propose a unified framework, dubbed as ThumbNet, to simultaneously accelerate and compress CNN models by enabling them to infer on one thumbnail image. We provide three effective strategies to train ThumbNet. In doing so, ThumbNet learns an inference network that performs equally well on small images as the original-input network on large images. With ThumbNet, not only do we obtain the thumbnail-input inference network that can drastically reduce computation and memory requirements, but also we obtain an image downscaler that can generate thumbnail images for generic classification tasks. Extensive experiments show the effectiveness of ThumbNet, and demonstrate that the thumbnail-input inference network learned by ThumbNet can adequately retain the accuracy of the original-input network even when the input images are downscaled 16 times