Development of a Fiber Optic Sensor for Online Monitoring of Thin Coatings

 The thickness measurement of gas, liquid and solid layers is not only important for the basic research on nanoscience but equally valuable in contemporary applied biomedical research. Here, we have developed an optical spectroscopy based technique for the online monitoring of thin films (coatings). A low cost light emitting diode (LED) source combined with a fiber optic bundle and grating based spectrograph have been used to generate white light interferogram. We have monitored online change of refractive index of an air film (~4 μm thickness) with temperature following the change in the intensity profile of the interferogram. A thin film of water between two cover slips (thin glass plates) has also been monitored. We have proposed a schematic for further lowering the cost of the developed instrument for the online monitoring of the coating thickness (semitransparent liquid/gas/solid films) during manufacturing/processing. A brief theoretical analysis on the detection limit of the developed technique has also been discussed in the paper

Polycystic ovary syndrome, blood group & diet: A correlative study in South Indian females

Aim: To find out the co-relation between polycystic ovary syndrome (PCOS) with blood group & diet in South Indian females, between the age-group of (20-30) years. Objectives: Correlative analysis of ABO & Rh system, dietary habits & alcohol consumption with PCOS. Materials & Methods: 100 patients between (20-30) years, diagnosed with PCOS were selected. A standard PCOS questionnaire was given. Blood group & dietary status data were collected. Patients were grouped according to ABO & Rh system considering their diet & alcohol intake (p≤0.05 significant). Result: Our data revealed that the highest risk of PCOS was observed in females with blood group ‘O’ positive followed by ‘B’ positive who were on mixed diet & used to consume alcohol. Our study also suggests that Rh negative individuals didn’t show any association with PCOS. Conclusion: The results of our study suggest that ‘O’ positive females, are more prone to PCOS. Though the relative frequency of B positive individuals are more in India, females with blood group O positive are more susceptible to PCOS, contributing factors being mixed diet & alcohol intake. So, early screening of ‘O’ positive &‘B’ positive females of reproductive age-group in South-India, could be used as a measure for timely diagnosis of PCOS, better management &also prevention of complications. However, further research should be done to investigate the multifaceted mechanisms triggering these effects

Cell-autonomous immune dysfunction driven by disrupted autophagy in C9orf72-ALS iPSC-derived microglia contributes to neurodegeneration

Although microglial activation is widely found in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), the underlying mechanism(s) are poorly understood. Here, using human-induced pluripotent stem cell-derived microglia-like cells (hiPSC-MG) harboring the most common ALS/FTD mutation (C9orf72, mC9-MG), gene-corrected isogenic controls (isoC9-MG), and C9orf72 knockout hiPSC-MG (C9KO-MG), we show that reduced C9ORF72 protein is associated with impaired phagocytosis and an exaggerated immune response upon stimulation with lipopolysaccharide. Analysis of the C9ORF72 interactome revealed that C9ORF72 interacts with regulators of autophagy and functional studies showed impaired initiation of autophagy in mC9-MG and C9KO-MG. Coculture studies with motor neurons (MNs) demonstrated that the autophagy deficit in mC9-MG drives increased vulnerability of mC9-MNs to excitotoxic stimulus. Pharmacological activation of autophagy ameliorated both cell-autonomous functional deficits in hiPSC-MG and MN death in MG-MN coculture. Together, these findings reveal an important role for C9ORF72 in regulating immune homeostasis and identify dysregulation in myeloid cells as a contributor to neurodegeneration in ALS/FTD.</p

Givenness Hierarchy Theoretic Referential Choice in Situated Contexts

We present a computational cognitive model of referential choice that models and explains the choice between a wide variety of referring forms using a small set of features important to situated contexts. By combining explainable machine learning techniques, data collected in situated contexts, and recent computational models of cognitive status, we produce an accurate and explainable model of referential choice that provides an intuitive pragmatic account of this process in humans, and an intuitive method for computationally enabling this capability in robots and other autonomous agents