Integrating Remote Sensing Data into Fuzzy Control System for Variable Rate Irrigation Estimates

Variable rate irrigation (VRI) is the capacity to vary the depth of water application in a field spatially. Developing precise management zones is necessary to efficient variable rate irrigation technologies. Intelligent fuzzy inference system based on precision irrigation knowledge, i.e., a system capable of creating prescriptive maps to control the rotation speed of the central pivot. Based on the VRI-prescribed map created by the intelligent system of decision-making, the pivot can increase or decrease its speed, reaching the desired depth of application in a certain irrigation zone. Therefore, this strategy of speed control is more realistic compared to traditional methods. Results indicate that data from the edaphoclimatic variables, when well fitted to the fuzzy logic, can solve uncertainties and non-linearities of an irrigation system and establish a control model for high-precision irrigation. Because remote sensing provides quick measurements and easy access to crop information for large irrigation areas, images will be used as inputs. The developed fuzzy system for pivot control is original and innovative. Furthermore, the artificial intelligent systems can be applied widely in agricultural areas, so the results were favorable to the continuity of studies on precision irrigation and application of the fuzzy logic in precision agriculture

Biotechnologically produced chitosans with nonrandom acetylation patterns differ from conventional chitosans in properties and activities

Chitosans are versatile biopolymers with multiple biological activities and potential applications. They are linear copolymers of glucosamine and N-acetylglucosamine defined by their degree of polymerisation (DP), fraction of acetylation (FA), and pattern of acetylation (PA). Technical chitosans produced chemically from chitin possess defined DP and FA but random PA, while enzymatically produced natural chitosans probably have non-random PA. This natural process has not been replicated using biotechnology because chitin de-N-acetylases do not efficiently deacetylate crystalline chitin. Here, we show that such enzymes can partially N-acetylate fully deacetylated chitosan in the presence of excess acetate, yielding chitosans with FA up to 0.7 and an enzyme-dependent non-random PA. The biotech chitosans differ from technical chitosans both in terms of physicochemical and nanoscale solution properties and biological activities. As with synthetic block co-polymers, controlling the distribution of building blocks within the biopolymer chain will open a new dimension of chitosan research and exploitation

Genome-wide, high-content siRNA screening identifies the Alzheimer's genetic risk factor FERMT2 as a major modulator of APP metabolism

Genome-wide association studies (GWASs) have identified 19 susceptibility loci for Alzheimer’s disease (AD). However, understanding how these genes are involved in the pathophysiology of AD is one of the main challenges of the “post-GWAS” era. At least 123 genes are located within the 19 susceptibility loci; hence, a conventional approach (studying the genes one by one) would not be time- and cost-effective. We therefore developed a genome-wide, high-content siRNA screening approach and used it to assess the functional impact of gene under-expression on APP metabolism. We found that 832 genes modulated APP metabolism. Eight of these genes were located within AD susceptibility loci. Only FERMT2 (a β3-integrin co-activator) was also significantly associated with a variation in cerebrospinal fluid Aβ peptide levels in 2886 AD cases. Lastly, we showed that the under-expression of FERMT2 increases Aβ peptide production by raising levels of mature APP at the cell surface and facilitating its recycling. Taken as a whole, our data suggest that FERMT2 modulates the AD risk by regulating APP metabolism and Aβ peptide production

NCAM (CD56) Expression in keratin-producing odontogenic cysts: aberrant expression in KCOT

Background: Keratin-producing odontogenic cysts (KPOCs) are a group of cystic lesions that are often aggressive, with high rates of recurrence and multifocality. KPOCs included orthokeratinised odontogenic cyst (OOC) and parakeratotic odontogenic cysts, which are now considered true tumours denominated keratocystic odontogenic tumours (KCOTs). GLUT1 is a protein transporter that is involved in the active uptake of glucose across cell membranes and that is overexpressed in tumours in close correlation with the proliferation rate and positron emission tomography (PET) imaging results. Methods: A series of 58 keratin-producing odontogenic cysts was evaluated histologically and immunohistochemically in terms of GLUT1 expression. Different data were correlated using the beta regression model in relation to histological type and immunohistochemical expression of GLUT1, which was quantified using two different morphological methods. Results: KPOC cases comprised 12 OOCs and 46 KCOTs, the latter corresponding to 6 syndromic and 40 sporadic KCOTs. GLUT1 expression was very low in OOC cases compared with KCOT cases, with statistical significant differences when quantification was considered. Different GLUT1 localisation patterns were revealed by immunostaining, with the parabasal cells showing higher reactivity in KCOTs. However, among KCOTs cases, GLUT1 expression was unable to establish differences between syndromic and sporadic cases. Conclusions: GLUT1 expression differentiated between OOC and KCOT cases, with significantly higher expression in KCOTs, but did not differentiate between syndromic and sporadic KCOT cases. However, given the structural characteristics of KCOTs, we hypothesised that PET imaging methodology is probably not a useful diagnostic tool for KCOTs. Further studies of GLUT1 expression and PET examination in KCOT series are needed to confirm this last hypothesis. Keywords: Glucose transporter protein, Immunohistochemistry, Keratin-producing odontogenic cyst, Keratocystic odontogenic tumour, Orthokeratinised odontogenic cyst, Positron emission tomograph

Management of congestive heart failure: a gender gap may still exist. Observations from a contemporary cohort

BACKGROUND: Unlike other cardiovascular diseases the incidence and prevalence of congestive heart failure (CHF) continues to increase. While gender differences in coronary artery disease have been well described, to date, there has been a relative paucity of similar data in patients with CHF. We conducted a pilot study to evaluate the profile and management of patients with CHF at a tertiary care centre to determine if a gender difference exists. METHODS: A chart review was performed at a tertiary care centre on consecutive patients admitted with a primary diagnosis of CHF between June 1997 and 1998. Co-morbidity, diagnostic investigations, and management of CHF were recorded. Comparisons between male and female patients were conducted. RESULTS: One hundred and forty five patients were reviewed. There were 80 male (M) and 65 female (F) patients of similar age [71.6 vs. 71.3 (M vs. F), p = NS]. Male patients were more likely to have had a previous myocardial infarction (66% vs. 35%, p < 0.01) and revascularization (41% vs. 20%, p < 0.05), and had worse left ventricular ejection fraction (LVEF) than women, [median LVEF 3 vs. 2 (M vs. F), p < 0.01]. Male patients were more likely to have a non-invasive assessment of left ventricular (LV) function [85% vs. 69%, (M vs. F), p < 0.05]. A logistic regression analysis suggests that amongst those without coronary disease, males were more likely to receive non-invasive testing. There were no differences in the use of prescribed medications, in this cohort. CONCLUSIONS: This pilot study demonstrated that there seem to be important gender differences in the profile and management of patients with CHF. Importantly women were less likely to have an evaluation of LV function. As assessment of LV function has significant implications on patient management, this data justifies the need for larger studies to assess gender differences in CHF profile and treatment

DREAM4: Combining Genetic and Dynamic Information to Identify Biological Networks and Dynamical Models

Current technologies have lead to the availability of multiple genomic data types in sufficient quantity and quality to serve as a basis for automatic global network inference. Accordingly, there are currently a large variety of network inference methods that learn regulatory networks to varying degrees of detail. These methods have different strengths and weaknesses and thus can be complementary. However, combining different methods in a mutually reinforcing manner remains a challenge.We investigate how three scalable methods can be combined into a useful network inference pipeline. The first is a novel t-test-based method that relies on a comprehensive steady-state knock-out dataset to rank regulatory interactions. The remaining two are previously published mutual information and ordinary differential equation based methods (tlCLR and Inferelator 1.0, respectively) that use both time-series and steady-state data to rank regulatory interactions; the latter has the added advantage of also inferring dynamic models of gene regulation which can be used to predict the system's response to new perturbations.Our t-test based method proved powerful at ranking regulatory interactions, tying for first out of methods in the DREAM4 100-gene in-silico network inference challenge. We demonstrate complementarity between this method and the two methods that take advantage of time-series data by combining the three into a pipeline whose ability to rank regulatory interactions is markedly improved compared to either method alone. Moreover, the pipeline is able to accurately predict the response of the system to new conditions (in this case new double knock-out genetic perturbations). Our evaluation of the performance of multiple methods for network inference suggests avenues for future methods development and provides simple considerations for genomic experimental design. Our code is publicly available at http://err.bio.nyu.edu/inferelator/

Hub-Centered Gene Network Reconstruction Using Automatic Relevance Determination

Network inference deals with the reconstruction of biological networks from experimental data. A variety of different reverse engineering techniques are available; they differ in the underlying assumptions and mathematical models used. One common problem for all approaches stems from the complexity of the task, due to the combinatorial explosion of different network topologies for increasing network size. To handle this problem, constraints are frequently used, for example on the node degree, number of edges, or constraints on regulation functions between network components. We propose to exploit topological considerations in the inference of gene regulatory networks. Such systems are often controlled by a small number of hub genes, while most other genes have only limited influence on the network's dynamic. We model gene regulation using a Bayesian network with discrete, Boolean nodes. A hierarchical prior is employed to identify hub genes. The first layer of the prior is used to regularize weights on edges emanating from one specific node. A second prior on hyperparameters controls the magnitude of the former regularization for different nodes. The net effect is that central nodes tend to form in reconstructed networks. Network reconstruction is then performed by maximization of or sampling from the posterior distribution. We evaluate our approach on simulated and real experimental data, indicating that we can reconstruct main regulatory interactions from the data. We furthermore compare our approach to other state-of-the art methods, showing superior performance in identifying hubs. Using a large publicly available dataset of over 800 cell cycle regulated genes, we are able to identify several main hub genes. Our method may thus provide a valuable tool to identify interesting candidate genes for further study. Furthermore, the approach presented may stimulate further developments in regularization methods for network reconstruction from data