Synthesis, crystal structures, hydrogen bonding graph-sets and theoretical studies of nickel (+II) co-ordinations with pyridine-2,6-dicarboxamide oxime

The pyridine-2,6-dicarboxamide oxime, C7H9N5O2, was Synthesis and  characterises with 1H NMR and FTIR spectroscopy . The reaction of this ligand with nickel (II) perchlorate yielded green crystals of formula  [Ni(C<sub>7</sub>H<sub>9</sub>N<sub>5</sub>O<sub>2</sub>)<sub>2</sub>]<sup>2+</sup>,2[ClO<sub>4</sub>]-, which crystallized in the monoclinic space group C2/c with a = 14.915(2), b = 0.895(2), c = 8.205(1) Å, β = 114.69(1), and Z = 4. The complex consists of discrete cations (+II) and one perchlorate anion, the  cations existing in a slightly distorted octahedral  complex with bonding through the heterocyclic and oxime nitrogen atoms. The structure is held together through N-H…O, O-H…O and C-H...O hydrogen bonds occurring  between the coordinated oxime  molecules and the perchlorate counter-ion. Computational investigations of nickel(II) complex are done by using M062X method with 6-31+G(d)(LANL2DZ) basis set in vacuo.Keywords: Oxime complexe; Crystal structure; Hydrogen-bonding graph-set; DFT; M062X method; 6-31+G(d)(LANL2DZ) basis

Synthesis, quantum chemical computations and x-ray crystallographic studies of a new complex based of manganese (+II)

The ligand oxime, C7H9N5O2, was Synthesis and characterises with different characterization methods such as 1H NMR and FTIR spectroscopy. The complexation of this ligand with manganese (II) perchlorate yielded pink crystals of formula [Mn (C7H9N5O2)2]2+, 2[ClO4]-, which crystallized in the monoclinic space group P21/n with a = 12.824(3), b=13.799(2), c=15.441(4)Å, β = 100.17(2), and Z = 4. The complex consists of cations (+II) and two perchlorate anions, the cations part existing in a slightly distorted octahedral complex. Computational investigations of manganese (II) complex are done by using the DFTmethod with B3LYP functional in conjunction with the 6-31G(d,p) and lanl2dz basis sets in the gas phase imposing the C1 and C2v symmetries.Keywords: Manganese complex; Crystal structure; DFT method; B3LYP functional; 6-31G(d,p) and (LANL2DZ) basi

Fecal microbiota and bile acid interactions with systemic and adipose tissue metabolism in diet-induced weight loss of obese postmenopausal women

Microbiota and bile acids in the gastrointestinal tract profoundly alter systemic metabolic processes. In obese subjects, gradual weight loss ameliorates adipose tissue inflammation and related systemic changes. We assessed how rapid weight loss due to a very low calorie diet (VLCD) affects the fecal microbiome and fecal bile acid composition, and their interactions with the plasma metabolome and subcutaneous adipose tissue inflammation in obesity. We performed a prospective cohort study of VLCD-induced weight loss of 10% in ten grades 2-3 obese postmenopausal women in a metabolic unit. Baseline and post weight loss evaluation included fasting plasma analyzed by mass spectrometry, adipose tissue transcription by RNA sequencing, stool 16S rRNA sequencing for fecal microbiota, fecal bile acids by mass spectrometry, and urinary metabolic phenotyping by H-NMR spectroscopy. Outcome measures included mixed model correlations between changes in fecal microbiota and bile acid composition with changes in plasma metabolite and adipose tissue gene expression pathways. Alterations in the urinary metabolic phenotype following VLCD-induced weight loss were consistent with starvation ketosis, protein sparing, and disruptions to the functional status of the gut microbiota. We show that the core microbiome was preserved during VLCD-induced weight loss, but with changes in several groups of bacterial taxa with functional implications. UniFrac analysis showed overall parallel shifts in community structure, corresponding to reduced abundance of the genus Roseburia and increased Christensenellaceae;g__ (unknown genus). Imputed microbial functions showed changes in fat and carbohydrate metabolism. A significant fall in fecal total bile acid concentration and reduced deconjugation and 7-α-dihydroxylation were accompanied by significant changes in several bacterial taxa. Individual bile acids in feces correlated with amino acid, purine, and lipid metabolic pathways in plasma. Furthermore, several fecal bile acids and bacterial species correlated with altered gene expression pathways in adipose tissue. VLCD dietary intervention in obese women changed the composition of several fecal microbial populations while preserving the core fecal microbiome. Changes in individual microbial taxa and their functions correlated with variations in the plasma metabolome, fecal bile acid composition, and adipose tissue transcriptome

Team-optimal distributed MMSE estimation in general and tree networks

We construct team-optimal estimation algorithms over distributed networks for state estimation in the finite-horizon mean-square error (MSE) sense. Here, we have a distributed collection of agents with processing and cooperation capabilities. These agents observe noisy samples of a desired state through a linear model and seek to learn this state by interacting with each other. Although this problem has attracted significant attention and been studied extensively in fields including machine learning and signal processing, all the well-known strategies do not achieve team-optimal learning performance in the finite-horizon MSE sense. To this end, we formulate the finite-horizon distributed minimum MSE (MMSE) when there is no restriction on the size of the disclosed information, i.e., oracle performance, over an arbitrary network topology. Subsequently, we show that exchange of local estimates is sufficient to achieve the oracle performance only over certain network topologies. By inspecting these network structures, we propose recursive algorithms achieving the oracle performance through the disclosure of local estimates. For practical implementations we also provide approaches to reduce the complexity of the algorithms through the time-windowing of the observations. Finally, in the numerical examples, we demonstrate the superior performance of the introduced algorithms in the finite-horizon MSE sense due to optimal estimation. © 2017 Elsevier Inc

Loss of Nrf2 abrogates the protective effect of Keap1 down regulation in a preclinical model of cutaneous squamous cell carcinoma

Cutaneous squamous cell carcinomas (cSCC) are the most common and highly mutated human malignancies, challenging identification of driver mutations and targeted therapies. Transcription factor NF-E2 p45-related factor 2 (Nrf2) orchestrates a cytoprotective inducible program, which counteracts the damaging effects of solar UV radiation, the main etiological factor in cSCC development. Downregulation of Kelch-like ECH-associated protein 1 (Keap1), a Cullin-3/Rbx1 ubiquitin ligase substrate adaptor protein, which mediates the ubiquitination and proteasomal degradation of Nrf2, has a strong protective effect in a preclinical model of cSCC. However, in addition to Nrf2, Keap1 affects ubiquitination of other proteins in the carcinogenesis process, including proteins involved in inflammation and DNA damage repair. Here, we generated Keap1(flox/flox) SKH-1 hairless mice in which Nrf2 is disrupted (Keap1(flox/flox)/Nrf2(−/−)) and subjected them chronically to solar-simulated UV radiation. We found that the incidence, multiplicity and burden of cSCC that form in Keap1(flox/flox)/Nrf2(−/−) mice are much greater than in their Keap1(flox/flox)/Nrf2(+/+) counterparts, establishing Nrf2 activation as the protection mediator. Our findings further imply that inhibition of Nrf2 globally, a strategy proposed for cancer treatment, is unlikely to be beneficial

Why Are Outcomes Different for Registry Patients Enrolled Prospectively and Retrospectively? Insights from the Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF).

Background: Retrospective and prospective observational studies are designed to reflect real-world evidence on clinical practice, but can yield conflicting results. The GARFIELD-AF Registry includes both methods of enrolment and allows analysis of differences in patient characteristics and outcomes that may result. Methods and Results: Patients with atrial fibrillation (AF) and ≥1 risk factor for stroke at diagnosis of AF were recruited either retrospectively (n = 5069) or prospectively (n = 5501) from 19 countries and then followed prospectively. The retrospectively enrolled cohort comprised patients with established AF (for a least 6, and up to 24 months before enrolment), who were identified retrospectively (and baseline and partial follow-up data were collected from the emedical records) and then followed prospectively between 0-18 months (such that the total time of follow-up was 24 months; data collection Dec-2009 and Oct-2010). In the prospectively enrolled cohort, patients with newly diagnosed AF (≤6 weeks after diagnosis) were recruited between Mar-2010 and Oct-2011 and were followed for 24 months after enrolment. Differences between the cohorts were observed in clinical characteristics, including type of AF, stroke prevention strategies, and event rates. More patients in the retrospectively identified cohort received vitamin K antagonists (62.1% vs. 53.2%) and fewer received non-vitamin K oral anticoagulants (1.8% vs . 4.2%). All-cause mortality rates per 100 person-years during the prospective follow-up (starting the first study visit up to 1 year) were significantly lower in the retrospective than prospectively identified cohort (3.04 [95% CI 2.51 to 3.67] vs . 4.05 [95% CI 3.53 to 4.63]; p = 0.016). Conclusions: Interpretations of data from registries that aim to evaluate the characteristics and outcomes of patients with AF must take account of differences in registry design and the impact of recall bias and survivorship bias that is incurred with retrospective enrolment. Clinical Trial Registration: - URL: http://www.clinicaltrials.gov . Unique identifier for GARFIELD-AF (NCT01090362)

Stochastic subgradient algorithms for strongly convex optimization over distributed networks

We study diffusion and consensus based optimization of a sum of unknown convex objective functions over distributed networks. The only access to these functions is through stochastic gradient oracles, each of which is only available at a different node; and a limited number of gradient oracle calls is allowed at each node. In this framework, we introduce a convex optimization algorithm based on stochastic subgradient descent (SSD) updates. We use a carefully designed time-dependent weighted averaging of the SSD iterates, which yields a convergence rate of O N ffiffiffi N p (1s)T after T gradient updates for each node on a network of N nodes, where 0 ≤ σ < 1 denotes the second largest singular value of the communication matrix. This rate of convergence matches the performance lower bound up to constant terms. Similar to the SSD algorithm, the computational complexity of the proposed algorithm also scales linearly with the dimensionality of the data. Furthermore, the communication load of the proposed method is the same as the communication load of the SSD algorithm. Thus, the proposed algorithm is highly efficient in terms of complexity and communication load. We illustrate the merits of the algorithm with respect to the state-of-art methods over benchmark real life data sets. © 2017 IEEE

Twice-universal piecewise linear regression via infinite depth context trees

We investigate the problem of sequential piecewise linear regression from a competitive framework. For an arbitrary and unknown data length n, we first introduce a method to partition the regressor space. Particularly, we present a recursive method that divides the regressor space into O(n) disjoint regions that can result in approximately 1.5n different piecewise linear models on the regressor space. For each region, we introduce a universal linear regressor whose performance is nearly as well as the best linear regressor whose parameters are set non-causally. We then use an infinite depth context tree to represent all piecewise linear models and introduce a universal algorithm to achieve the performance of the best piecewise linear model that can be selected in hindsight. In this sense, the introduced algorithm is twice-universal such that it sequentially achieves the performance of the best model that uses the optimal regression parameters. Our algorithm achieves this performance only with a computational complexity upper bounded by O(n) in the worst-case and O(log(n)) under certain regularity conditions. We provide the explicit description of the algorithm as well as the upper bounds on the regret with respect to the best nonlinear and piecewise linear models, and demonstrate the performance of the algorithm through simulations. © 2015 IEEE

Nutrition and lung cancer: a case control study in Iran

Background: Despite many prospective and retrospective studies about the association of dietary habit and lung cancer, the topic still remains controversial. So, this study aims to investigate the association of lung cancer with dietary factors. Method: In this study 242 lung cancer patients and their 484 matched controls on age, sex, and place of residence were enrolled between October 2002 to 2005. Trained physicians interviewed all participants with standardized questionnaires. The middle and upper third consumer groups were compared to the lower third according to the distribution in controls unless the linear trend was significant across exposure groups. Result: Conditional logistic regression was used to evaluate the association with lung cancer. In a multivariate analysis fruit (Ptrend < 0.0001), vegetable (P = 0.001) and sunflower oil (P = 0.006) remained as protective factors and rice (P = 0.008), bread (Ptrend = 0.04), liver (P = 0.004), butter (Ptrend = 0.04), white cheese (Ptrend < 0.0001), beef (Ptrend = 0.005), vegetable ghee (P < 0.0001) and, animal ghee (P = 0.015) remained as risk factors of lung cancer. Generally, we found positive trend between consumption of beef (P = 0.002), bread (P < 0.0001), and dairy products (P < 0.0001) with lung cancer. In contrast, only fruits were inversely related to lung cancer (P < 0.0001). Conclusion: It seems that vegetables, fruits, and sunflower oil could be protective factors and bread, rice, beef, liver, dairy products, vegetable ghee, and animal ghee found to be possible risk factors for the development of lung cancer in Iran

Communication efficient channel estimation over distributed networks

We study diffusion based channel estimation in distributed architectures suitable for various communication applications such as cognitive radios. Although the demand for distributed processing is steadily growing, these architectures require a substantial amount of communication among their nodes (or processing elements) causing significant energy consumption and increase in carbon footprint. Due to growing awareness of telecommunication industry's impact on the environment, the need to mitigate this problem is indisputable. To this end, we introduce algorithms significantly reducing the communication load between distributed nodes, which is the main cause in energy consumption, while providing outstanding performance. In this framework, after each node produces its local estimate of the communication channel, a single bit or a couple of bits of information is generated using certain random projections. This newly generated data is diffused and then used in neighboring nodes to recover the original full information, i.e., the channel estimate of the desired communication channel. We provide the complete state-space description of these algorithms and demonstrate the substantial gains through our experiments. © 2014 IEEE