Systemic Therapy in Thyroid Cancer

The standard treatment for patients with differentiated thyroid cancer (DTC) is a combination of surgery, radioactive iodine (RAI), and long-term thyroid hormone–suppression therapy. Treatment of patients whose diseases persist, recur, or metastasize remains a challenge. The role of cytotoxic chemotherapy in the treatment of thyroid cancer is limited. The key signaling pathways involved in the pathogenesis of thyroid cancers are the RAS/RAF/MEK & PI3K/Akt/mTOR pathways. Systemic therapy in thyroid cancer involves the use of tyrosine kinase inhibitors targeting the above mentioned pathways which are often both effective in controlling disease and have manageable toxicity. Sorafenib and lenvatinib are approved for advanced radioiodine refractory and poorly differentiated thyroid cancers and vandetanib and cabozantinib for recurrent or metastatic medullary thyroid cancers. Cabozantinib is also approved for the treatment of locally advanced or metastatic radioactive iodine–refractory differentiated thyroid cancer that has progressed after prior VEGF-targeted therapy. The combination of dabrafenib (BRAF inhibitor) and trametinib (MEK inhibitor) is approved for BRAF V600E mutated unresectable locally advanced anaplastic thyroid cancer. Selpercatinib, RET kinase inhibitor is used for advanced and metastatic RET mutated medullary thyroid cancers and advanced and metastatic RET fusion-positive thyroid cancers of any histologic type. Various clinical trials using newer molecules targeting the aforementioned pathways are ongoing

Challenges and opportunities in mixed method data collection on mental health issues of health care workers during COVID-19 pandemic in India

Background: The present paper describes the key challenges and opportunities of mixed method telephonic data collection for mental health research using field notes and the experiences of the investigators in a multicenter study in ten sites of India. The study was conducted in public and private hospitals to understand the mental health status, social stigma and coping strategies of different healthcare personnel during the COVID-19 pandemic in India.Methods: Qualitative and quantitative interviews were conducted telephonically. The experiences of data collection were noted as a field notes/diary by the data collectors and principal investigators.Results: The interviewers reported challenges such as network issues, lack of transfer of visual cues and sensitive content of data. Although the telephonic interviews present various challenges in mixed method data collection, it can be used as an alternative to face-to-face data collection using available technology.Conclusions: It is important that the investigators are well trained keeping these challenges in mind so that their capacity is built to deal with these challenges and good quality data is obtained

On the Observability of a Linear System With a Sparse Initial State

In this letter, we address the problem of observability of a linear dynamical system from compressive measurements and the knowledge of its external inputs. Observability of a high dimensional system state may require a large number of measurements in general, but we show that if the initial state vector admits a sparse representation, the number of measurements can be significantly reduced by using random projections for obtaining the measurements. We derive guarantees for the observability of the system using tools from probability theory and compressed sensing. Our analysis uses properties of the transfer matrix and random measurementmatrices to derive concentration ofmeasure bounds, which lead to sufficient conditions for the restricted isometry property of the observability matrix to hold. Hence, under the derived conditions, the initial state can be recovered by solving a computationally tractable convex optimization problem

Reconstruction of a Gaussian Random Field with Application to Spectrum Cartography

We consider the problem of estimating the intensity map of a spatially random phenomenon over a geographical area observed by a sensor network. The spatial phenomenon of interest is modeled using a Gaussian random field specified by its nonlinear mean and covariance functions. Our proposed algorithm includes two stages: a novel greedy sparse recovery algorithm to estimate the parameters of the mean function, and a spatial interpolation stage using an algorithm called simple kriging. Further, we study the application of the proposed algorithm to radio spectrum cartography, and show that it offers a significant advantage in terms of accuracy of map reconstruction compared to existing methods

A Noniterative Online Bayesian Algorithm for the Recovery of Temporally Correlated Sparse Vectors

In this paper, we address the problem of online (sequential) recovery of temporally correlated sparse vectors sharing a common support, from noisy underdetermined linear measurements. The temporal correlation of the sparse vectors is modeled using a first-order autoregressive process. The online algorithm is formulated using the sparse Bayesian learning framework and is implemented using a sequential expectation-maximization procedure. Our algorithm is noniterative in nature, and requires less computational and memory resources compared to offline processing. We analyze the convergence of the algorithm in the case when the sparse vectors are uncorrelated, using tools from stochastic approximation theory. We show that the sequence of the covariance estimates converge either to the global minimum of the offline equivalent cost function or to the all zero vector, regardless of the sparsity level of the signal. Through numerical results, we demonstrate the efficacy of the proposed online algorithm and compare it with its offline counterpart as well as with existing online sparse vector recovery algorithms. We also illustrate the performance of the algorithm in the context of sparse orthogonal frequency division multiplexing channel estimation

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Not AvailableNethravathi river basin, located in Dakshina Kannada district of Karnataka experiences from water scarcity during summer, severe runoff and soil loss during rainy season. In the present study, an attempt was made to predict runoff from Nethravathi river basin using SWAT model for 36 Years (1970-2005). SWAT-CUP (SWAT-Calibration and Uncertainty Programs) was used to ascertain the model sensitivity, calibration and validation by Sequential Uncertainty Fitting (SUFI-2) technique. Both monthly and daily discharge data calibration was performed for the period from 1995 to 1999, and then validated for the period 2000–2005 using Central Water Commission (CWC) discharge data recorded at Bantwal station. Modeling results indicated that monthly time step yield better results than that for the daily time step during both calibration and validation. For monthly calibration, the R2 and NS values were 0.96 and 0.94 and for validation it was 0.91 each. On the other hand, for daily calibration, the R2 and NS values were 0.88 and 0.84 and for validation, it was 0.8 and 0.79 respectively. A simulation that exactly corresponds to observed data would be described by a P-factor of 1. The value of simulated results indicated that p-factor and r-factors during monthly and daily calibrations were satisfactory. The estimated average annual runoff is equivalent to 30 % of average annual rainfall of the entire river basin. The runoff varied spatially from 774 mm to 1527 mm. The average annual runoff resulted from different land use, land cover patterns inferred that minimum runoff (1068 mm) was observed in the evergreen forest land and the maximum was in orchard and agricultural crop area (1394.1 mm). From the results of estimated runoff during the above normal, normal and drought years, it can be suggested that appropriate soil and water conservation structures are needed for the sustainable management of the study areaNot Availabl