Skilled yet lost? Challenges of international medical graduates in psychiatry: Australian perspective

Abstract of presentation at the 67th Annual National Conference of the Indian Psychiatric Society (ANCIPS 2015), at Hyderabad, India, 8-11 January 2015

High Speed Machining for Enhancing the AZ91 Magnesium Alloy Surface Characteristics Influence and Optimisation of Machining Parameters

In this study, optimum machining parameters are evaluated for enhancing the surface roughness and hardness of AZ91 alloy using Taguchi design of experiments with Grey Relational Analysis. Dry face milling is performed using cutting conditions determined using Taguchi L9 design and Grey Relational Analysis has been used for the optimization of multiple objectives. Taguchi’s signal-to-noise ratio analysis is also performed individually for both characteristics and grey relational grade to identify the most influential machining parameter affecting them. Further, Analysis of Variance is carried to see the contribution of factors on both surface roughness and hardness. Finally, the predicted trends obtained from the signal-to-noise ratio are validated using confirmation experiments. The study showed the effectiveness of Taguchi design combined with Grey Relational Analysis for the multi-objective problems such as surface characteristics studies

NASGEM: Neural Architecture Search via Graph Embedding Method

Neural Architecture Search (NAS) automates and prospers the design of neural networks. Estimator-based NAS has been proposed recently to model the relationship between architectures and their performance to enable scalable and flexible search. However, existing estimator-based methods encode the architecture into a latent space without considering graph similarity. Ignoring graph similarity in node-based search space may induce a large inconsistency between similar graphs and their distance in the continuous encoding space, leading to inaccurate encoding representation and/or reduced representation capacity that can yield sub-optimal search results. To preserve graph correlation information in encoding, we propose NASGEM which stands for Neural Architecture Search via Graph Embedding Method. NASGEM is driven by a novel graph embedding method equipped with similarity measures to capture the graph topology information. By precisely estimating the graph distance and using an auxiliary Weisfeiler-Lehman kernel to guide the encoding, NASGEM can utilize additional structural information to get more accurate graph representation to improve the search efficiency. GEMNet, a set of networks discovered by NASGEM, consistently outperforms networks crafted by existing search methods in classification tasks, i.e., with 0.4%-3.6% higher accuracy while having 11%- 21% fewer Multiply-Accumulates. We further transfer GEMNet for COCO object detection. In both one-stage and twostage detectors, our GEMNet surpasses its manually-crafted and automatically-searched counterparts

ESSENS dyslipidemia: A placebo-controlled, randomized study of a nutritional supplement containing red yeast rice in subjects with newly diagnosed dyslipidemia

AbstractObjectiveEvidence suggests prolonged exposure to lower levels of low-density lipoprotein cholesterol (LDL-C), starting at a younger age, substantially lowers cardiovascular (CV) risk. Accordingly, the CV pandemic affecting younger population in low- to low-middle-income countries, where statin usage is poor even in secondary prevention, may benefit from lipid-lowering nutritional products, as nutritional intervention is generally preferred in these cultures. However, the safety and efficacy of such preparations have not been systematically tested.MethodsIn this multicenter, double-blind study, 191 statin-free subjects with newly-diagnosed hyperlipidemia (LDL-C >120 mg/dL, 3.11 mmol/L) and no evidence of CV disease were randomized to one capsule of a proprietary bioactive phytonutrient formulation containing red yeast rice, grape-seed, niacinamide, and folic acid (RYR-NS) or matched placebo twice daily, along with lifestyle modification, for 12 wk.ResultsMean baseline LDL-C levels were 148.5 ± 24.0 mg/dL (3.85 ± 0.62 mmol/L) and 148.6 ± 21.9 mg/dL (3.85 ± 0.57 mmol/L) in the RYR-NS and placebo groups respectively. Compared with placebo, RYR-NS resulted in a significant reduction in LDL-C (−29.4% versus −3.5%, P < 0.0001) and non–high-density lipoprotein cholesterol (non-HDL-C; −29.8% versus −10.3%, P < 0.0001) at 12 wk. With RYR-NS, 43.4% individuals attained desirable LDL-C levels and 55.4% desirable non-HDL-C levels by week 12, compared to only 0% and 1.1%, respectively, at baseline. No safety issues were observed.ConclusionThis study demonstrates the efficacy and safety of RYR-NS in lowering LDL-C and non-HDL-C after 12 wk, with magnitude of LDL-C reduction being comparable to that seen with moderate-intensity statin therapy. Further long-term studies are required to determine the impact of RYR-NS on treatment adherence and clinical outcomes

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Competency based psychiatry training: is it a reality or fantasy in India?

Abstract of a paper presented at the 65th Annual National Conference of Indian Psychiatric Society, Bangalor, 10-13 Jan, 2013