STUDY OF SALIVARY PH IN PATIENTS WITH THE PREVALENCE OF PERIODONTITIS WITH OR WITHOUT DIABETES MELLITUS

ABSTRACTObjective: To assess the relationship between salivary pH and periodontitis in subjects with and without diabetes mellitus. The relationship betweendiabetes mellitus and salivary pH has been assessed. The relationship between periodontitis and salivary pH has been assessed.But the relationshipbetween diabetes mellitus, periodontitis and salivary pH among adults has received less attention.Methods: A total of sixty subjects were evaluated in the study with no history of diabetes mellitus and periodontitis, with periodontitis but no historyof diabetes mellitus, with periodontitis and diabetes mellitusas twenty in each group.pH strips were used to measure the salivary pH.Results: The results show that a decreased salivary pHwas seen in subjects having periodontitis with diabetes as compared to subjects havingperiodontitis without diabetes.Conclusion: Thus diabetes mellitus have a direct effect on salivary pH, reducing it from normal levels.Keywords: Periodontitis, Diabetes mellitus, Salivary pH

Exploring the Metabolic Landscape of AML: From Haematopoietic Stem Cells to Myeloblasts and Leukaemic Stem Cells

Despite intensive chemotherapy regimens, up to 60% of adults with acute myeloid leukaemia (AML) will relapse and eventually succumb to their disease. Recent studies suggest that leukaemic stem cells (LSCs) drive AML relapse by residing in the bone marrow niche and adapting their metabolic profile. Metabolic adaptation and LSC plasticity are novel hallmarks of leukemogenesis that provide important biological processes required for tumour initiation, progression and therapeutic responses. These findings highlight the importance of targeting metabolic pathways in leukaemia biology which might serve as the Achilles’ heel for the treatment of AML relapse. In this review, we highlight the metabolic differences between normal haematopoietic cells, bulk AML cells and LSCs. Specifically, we focus on four major metabolic pathways dysregulated in AML; (i) glycolysis; (ii) mitochondrial metabolism; (iii) amino acid metabolism; and (iv) lipid metabolism. We then outline established and emerging drug interventions that exploit metabolic dependencies of leukaemic cells in the treatment of AML. The metabolic signature of AML cells alters during different biological conditions such as chemotherapy and quiescence. Therefore, targeting the metabolic vulnerabilities of these cells might selectively eradicate them and improve the overall survival of patients with AML

ERBlox: Combining Matching Dependencies with Machine Learning for Entity Resolution

Entity resolution (ER), an important and common data cleaning problem, is about detecting data duplicate representations for the same external entities, and merging them into single representations. Relatively recently, declarative rules called matching dependencies (MDs) have been proposed for specifying similarity conditions under which attribute values in database records are merged. In this work we show the process and the benefits of integrating three components of ER: (a) Classifiers for duplicate/non-duplicate record pairs built using machine learning (ML) techniques, (b) MDs for supporting both the blocking phase of ML and the merge itself; and (c) The use of the declarative language LogiQL -an extended form of Datalog supported by the LogicBlox platform- for data processing, and the specification and enforcement of MDs.Comment: To appear in Proc. SUM, 201

Targeting Versican as a Potential Immunotherapeutic Strategy in the Treatment of Cancer.

A growing body of literature links events associated with the progression and severity of immunity and inflammatory disease with the composition of the tissue extracellular matrix as defined by the matrisome. One protein in the matrisome that is common to many inflammatory diseases is the large proteoglycan versican, whose varied function is achieved through multiple isoforms and post-translational modifications of glycosaminoglycan structures. In cancer, increased levels of versican are associated with immune cell phenotype, disease prognosis and failure to respond to treatment. Whether these associations between versican expression and tumour immunity are the result of a direct role in the pathogenesis of tumours is not clear. In this review, we have focused on the role of versican in the immune response as it relates to tumour progression, with the aim of determining whether our current understanding of the immunobiology of versican warrants further study as a cancer immunotherapy target

Dynamic Power Quality Disturbance Classification in Grid-Integrated PV Systems: Leveraging Clark Transformed Modal Voltage and Subspace Weighted KNN

AuthorsThis study focuses on detecting Power Quality Disturbance Events (PQDE) in microgrids integrated with a Solar Energy Conversion System (SECS). The research proposes a novel signal reduction technique called Clark Transformed Modal, which reduces three-phase voltage to a single unit signal, optimizing memory utilization and reducing computational load during feature extraction. A total of 16 features are extracted from the proposed modal signal by performing multi-resolution analysis through Maximum Overlap Discrete Wavelet Transform (MODWT). Various disturbances, including sag, swell, transients, notches, and flicker, are intentionally simulated in a PV-grid tied MATLAB/Simulink model to obtain a dataset of 10800 samples. Further, the dataset is randomly divided into training-testing subsets to verify the classification ability of a novel ensemble classifier called subspace weighted k-nearest Neighbor (SWKNN). In addition to that the optimum mother wavelet (dmay) is identified to even further boost the classifier performance. The results demonstrate the superior classification capabilities of the proposed MODWT-SWKNN classifier in terms of various performance metrics like precision, recall and F1-score. It also outperformed when compared with several competitive PQ classification models based on PV-integrated systems both under ideal and noisy conditions. Additionally, the disturbance detection system is validated in an OPAL-RT real-time environment to demonstrate its efficiency in terms of detection time. The accuracy of detection is found to be 99.74% in ideal case and fall back to no more than 3% regulation i.e., 97.28% even in dense noise of 20dB with as low as 8 WKNN subspaces. Further, average detection time with 500 trails is found to be 0.0285 seconds. The efficacy of the proposed PQ detection algorithm is also tested in a large PV integrated IEEE 13-bus system

Removal of hexavalent chromium using chitosan prepared from shrimp shells

Contamination of the aqueous environment by heavy metals and due to the discharge of metal containing effluents into the water bodies is one of the environmental issues of the century. Thus, in this work, the main concern has been the preparation of chitin and chitosan from the raw materials of shrimp shells and the characterization of the prepared chitosan by field emission scanning electron microscopy (FESEM) and Fourier transform infrared spectroscopy (FTIR). The work was then shifted to investigate the potentiality of Cr+6 adsorption with the prepared chitosan. The controlled parameters of adsorption process were studied. The percentage of Cr+6 removal using the shrimp chitosan was 64.29%.Keywords: Shrimp shells, chitosan, adsorption, chiti

Influence of mine drainage on water quality along River Nyaba in Enugu South-Eastern Nigeria

Major and Trace elements concentration were measured in water samples collected in and around Okpara coal mine in Enugu southeastern Nigeria to investigate the influence of mine drainage on the quality of water. The cations and trace elements were determined by ICP- MS while the anions were measured by spectrophotometer and titration methods. Field parameters such as pH, temperature and conductivity were determined in the field using standard equipment. The results show that the water is acidic to moderately acidic (pH 2.84 to 6.69) with pH increasing along the flow direction. The mean values of pH (4.66 (dry), 4.22 (wet), Color (334.34 TCU (dry), 153.11 TCU (wet) and turbidity (53,67 NTU (dry), 17.43 NTU (wet) as well as iron (6.35 mg/L(dry), 5.14 mg/L(wet), aluminum(1.14 mg/L(dry), 4.30 mg/L(wet), manganese (1.43 mg/L(dry), 5.36 mg/L (wet) and nickel, 0.053 mg/L (wet) recorded in the dry and wet seasons are above levels recommended by WHO for drinking water and other domestic purposes. Mean levels of fluoride (5.4 mg/L) with ranged of 0.00 to 45 mg/L, potassium (12 mg/L) with ranged of 1.17 to 27.85 mg/L and nickel (53.10 ìg/L) with ranged of 1.50 to 309.30 ìg/L, as well as maximum levels of chromium (100 ìg/L) with ranged of 0.05 to 100 ìg/L , chloride(400 mg/L) with ranged of 40 to 400 mg/L, nitrate(1012 mg/L) with ranged of 158 to 1012 mg/L and sulphate (517 mg/L) with ranged of 10 to 512 mg/L obtained in the wet season are above the WHO maximum permissible level. Generally, the levels of the elements decrease with distance away from the mine waste except for nitrate and fluoride. Thus the quality of the water is most probably influenced by acidic mine drainage and it impact on human health and the environment could be severe. Microbial assessment and element speciation are recommended for further quality assessment in the study area

Einstein metrics in projective geometry

It is well known that pseudo-Riemannian metrics in the projective class of a given torsion free affine connection can be obtained from (and are equivalent to) the solutions of a certain overdetermined projectively invariant differential equation. This equation is a special case of a so-called first BGG equation. The general theory of such equations singles out a subclass of so-called normal solutions. We prove that non-degerate normal solutions are equivalent to pseudo-Riemannian Einstein metrics in the projective class and observe that this connects to natural projective extensions of the Einstein condition.Comment: 10 pages. Adapted to published version. In addition corrected a minor sign erro

Advanced Multilevel Node Separator Algorithms

A node separator of a graph is a subset S of the nodes such that removing S and its incident edges divides the graph into two disconnected components of about equal size. In this work, we introduce novel algorithms to find small node separators in large graphs. With focus on solution quality, we introduce novel flow-based local search algorithms which are integrated in a multilevel framework. In addition, we transfer techniques successfully used in the graph partitioning field. This includes the usage of edge ratings tailored to our problem to guide the graph coarsening algorithm as well as highly localized local search and iterated multilevel cycles to improve solution quality even further. Experiments indicate that flow-based local search algorithms on its own in a multilevel framework are already highly competitive in terms of separator quality. Adding additional local search algorithms further improves solution quality. Our strongest configuration almost always outperforms competing systems while on average computing 10% and 62% smaller separators than Metis and Scotch, respectively

Asymptotic behaviour of estimators of the parameters of nearly unstable INAR(1) models

A sequence of first-order integer-valued autoregressive type (INAR(1)) processes is investigated, where the autoregressive type coefficients converge to 1. It is shown that the limiting distribution of the joint conditional least squares estimators for this coefficient and for the mean of the innovation is normal. Consequences for sequences of Galton{Watson branching processes with unobservable immigration, where the mean of the offspring distribution converges to 1 (which is the critical value), are discussed