A Study on Working Capital Management and its Impact on Profitability of Selected Fertilizer Units of Gujarat State

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The effect of resting morphological lip shape during lip movement: A three-dimensional motion analysis study

Purpose The aim of this study was to determine the influence of different morphological lip shape during lip movement. Method A sample of 80 individuals with three-dimensional facial images at rest and during speech were recorded. Subjects were asked to pronounce four bilabial words in a relaxed manner and scanned using the 3dMDFace™ Dynamic System at 48 frames per second. Six lip landmarks were identified at rest and the landmark displacement vectors for the frame of maximal lip movement for all six visemes were recorded. Principal component analysis was applied to isolate relationship between lip traits and their registered coordinates. Eight specific resting morphological lip traits were identified for each individual. The principal component (PC) scores for each viseme were labelled by lip morphological trait and were graphically visualized as ellipses to discriminate any differences in lip movement. Results The first five PCs accounted for up to 95% of the total variance in lip shape during movement, with PC1 accounting for at least 38%. There was no clear discrimination between PC1, PC2 and PC3 for any of the resting morphological lip traits. Conclusion Lip shapes during movement are more uniform between individuals and resting morphological lip shape does not influence movement of the lips

Multilevel principal component analysis (mPCA) in shape analysis: a feasibility study in medical and dental imaging

Background and objective Methods used in image processing should reflect any multilevel structures inherent in the image dataset or they run the risk of functioning inadequately. We wish to test the feasibility of multilevel principal components analysis (PCA) to build active shape models (ASMs) for cases relevant to medical and dental imaging. Methods Multilevel PCA was used to carry out model fitting to sets of landmark points and it was compared to the results of “standard” (single-level) PCA. Proof of principle was tested by applying mPCA to model basic peri-oral expressions (happy, neutral, sad) approximated to the junction between the mouth/lips. Monte Carlo simulations were used to create this data which allowed exploration of practical implementation issues such as the number of landmark points, number of images, and number of groups (i.e., “expressions” for this example). To further test the robustness of the method, mPCA was subsequently applied to a dental imaging dataset utilising landmark points (placed by different clinicians) along the boundary of mandibular cortical bone in panoramic radiographs of the face. Results Changes of expression that varied between groups were modelled correctly at one level of the model and changes in lip width that varied within groups at another for the Monte Carlo dataset. Extreme cases in the test dataset were modelled adequately by mPCA but not by standard PCA. Similarly, variations in the shape of the cortical bone were modelled by one level of mPCA and variations between the experts at another for the panoramic radiographs dataset. Results for mPCA were found to be comparable to those of standard PCA for point-to-point errors via miss-one-out testing for this dataset. These errors reduce with increasing number of eigenvectors/values retained, as expected. Conclusions We have shown that mPCA can be used in shape models for dental and medical image processing. mPCA was found to provide more control and flexibility when compared to standard “single-level” PCA. Specifically, mPCA is preferable to “standard” PCA when multiple levels occur naturally in the dataset

Dendrimers: novel carriers for drug delivery

Dendrimers are highly branched, organic compounds with well-defined, symmetrical structure. From chemical point of view they are three-dimensional polymers, characterized by a globular shape. At the end of the arms are terminals, functional groups, which can be easily modified in order to change their chemical and physical properties. Dendrimers have nanoscopic particle size range from 1 to 100 nm. They are ideal drug delivery systems due to their feasible topology, functionality and dimensions, their size is very close to various important biological polymers and assemblies such as DNA and proteins. The structure of dendrimer molecules begins with a central atom or group of atoms labeled as the “core.” From this central structure, branches of other atoms called ‘dendrons.’ The continuous branching results in layers of branch structure called “generations.”Synthesis of dendrimers done by four methods. These are ‘Divergent’ Dendrimer Growth , ‘Convergent’ Dendrimer Growth ,‘Double Exponential’ and ‘Mixed’ Growth ,‘Click’ Synthesis (Hypercores and branched monomers growth). Mechanisms of drug loading onto dendrimer carriers by physical encapsulation of drug molecules and chemical conjugation of drug molecules. The conjugates show increased solubility, decreased systemic toxicity and selective accumulation in solid tumors. Various applications as pharmaceutical and non pharmaceuticals. Dendrimers may have toxicity mainly attributed to the interaction of the cationic dendrimers surface with negative biological load membranes damaging cellular membranes causing hemolytic toxicity and cytotoxicity also limitation that does not apply where the drug is solubilised with dendrimer and then released in the gut for absorption. Some Marketed products of dendrimers are available named as Starburst, Priostar, Stratus CS, Vivagel, Alert ticket, SuperFect, Taxotere

Management of orthodontic emergencies in primary care – self-reported confidence of general dental practitioners

Objective: To determine general dental practitioners’ (GDPs) confidence in managing orthodontic emergencies. Design: Cross-sectional study. Setting: Primary dental care. Subjects and methods: An online survey was distributed to dentists practicing in Wales. The survey collected basic demographic information and included descriptions of ten common orthodontic emergency scenarios. Main outcome measure Respondents’ self-reported confidence in managing the orthodontic emergency scenarios on a 5‑point Likert scale. Differences between the Likert responses and the demographic variables were investigated using chi-squared tests. Results: The median number of orthodontic emergencies encountered by respondents over the previous six months was 1. Overall, the self-reported confidence of respondents was high with 7 of the 10 scenarios presented scoring a median of 4 indicating that GDPs were ‘confident’ in their management. Statistical analysis revealed that GDPs who saw more orthodontic emergencies in the previous six months were more confident when managing the presented scenarios. Other variables such as age, gender, geographic location of practice and number of years practising dentistry were not associated with self reported confidence. Conclusions: Despite GDPs encountering very few orthodontic emergencies in primary care, they appear to be confident in dealing with commonly arising orthodontic emergency situations

MesoGraph: automatic profiling of mesothelioma subtypes from histological images

Mesothelioma is classified into three histological subtypes, epithelioid, sarcomatoid, and biphasic, according to the relative proportions of epithelioid and sarcomatoid tumor cells present. Current guidelines recommend that the sarcomatoid component of each mesothelioma is quantified, as a higher percentage of sarcomatoid pattern in biphasic mesothelioma shows poorer prognosis. In this work, we develop a dual-task graph neural network (GNN) architecture with ranking loss to learn a model capable of scoring regions of tissue down to cellular resolution. This allows quantitative profiling of a tumor sample according to the aggregate sarcomatoid association score. Tissue is represented by a cell graph with both cell-level morphological and regional features. We use an external multicentric test set from Mesobank, on which we demonstrate the predictive performance of our model. We additionally validate our model predictions through an analysis of the typical morphological features of cells according to their predicted score

Malignant Mesothelioma subtyping via sampling driven multiple instance prediction on tissue image and cell morphology data

Malignant Mesothelioma is a difficult to diagnose and highly lethal cancer usually associated with asbestos exposure. It can be broadly classified into three subtypes: Epithelioid, Sarcomatoid, and a hybrid Biphasic subtype in which significant components of both of the previous subtypes are present. Early diagnosis and identification of the subtype informs treatment and can help improve patient outcome. However, the subtyping of malignant mesothelioma, and specifically the recognition of transitional features from routine histology slides has a high level of inter-observer variability. In this work, we propose an end-to-end multiple instance learning (MIL) approach for malignant mesothelioma subtyping. This uses an adaptive instance-based sampling scheme for training deep convolutional neural networks on bags of image patches that allows learning on a wider range of relevant instances compared to max or top-N based MIL approaches. We also investigate augmenting the instance representation to include aggregate cellular morphology features from cell segmentation. The proposed MIL approach enables identification of malignant mesothelial subtypes of specific tissue regions. From this a continuous characterisation of a sample according to predominance of sarcomatoid vs epithelioid regions is possible, thus avoiding the arbitrary and highly subjective categorisation by currently used subtypes. Instance scoring also enables studying tumor heterogeneity and identifying patterns associated with different subtypes. We have evaluated the proposed method on a dataset of 234 tissue micro-array cores with an AUROC of 0.89±0.05 for this task. The dataset and developed methodology is available for the community at: https://github.com/measty/PINS

Social evolution in micro-organisms and a Trojan horse approach to medical intervention strategies

Medical science is typically pitted against the evolutionary forces acting upon infective populations of bacteria. As an alternative strategy, we could exploit our growing understanding of population dynamics of social traits in bacteria to help treat bacterial disease. In particular, population dynamics of social traits could be exploited to introduce less virulent strains of bacteria, or medically beneficial alleles into infective populations. We discuss how bacterial strains adopting different social strategies can invade a population of cooperative wild-type, considering public good cheats, cheats carrying medically beneficial alleles (Trojan horses) and cheats carrying allelopathic traits (anti-competitor chemical bacteriocins or temperate bacteriophage viruses). We suggest that exploitation of the ability of cheats to invade cooperative, wild-type populations is a potential new strategy for treating bacterial disease