Effective conservation of rare carnivores requires reliable estimates of population density for prioritizing investments and assessing the effectiveness of conservation interventions. We used camera traps and capture-recapture analysis to provide the first reliable abundance and density estimates for the common leopard Panthera pardus and clouded leopard Neofelis nebulosa in Manas National Park, India. In 57 days of camera trapping, with a total of 4,275 camera-trap days, we photo-captured 27 individually identified common leopards (11 males, 13 females and three unidentified), and 16 clouded leopards (four males, five females and seven unidentified). The abundance estimates using the M h jackknife and Pledger model M h were 47.0 and 35.6, respectively, for the common leopard, and 21.0 and 25.0, respectively, for the clouded leopard. Density estimates using maximum likelihood spatiallyexplicit capture-recapture were 3.4 ± SE 0.82 and 4.73 ± SE 1.43 per 100 km 2 for the common and clouded leopards, respectively. Spatially-explicit capture-recapture provided more realistic density estimates compared with those obtained from conventional methods. Our data indicates that camera trapping using a capture-recapture framework is an effective tool for assessing population sizes of cryptic and elusive carnivores such as the common and clouded leopards. The study has established a baseline for the longterm monitoring programme for large carnivores in Manas National Park.

Background and Purpose: The T1-weighted dynamic contrast enhanced (DCE)-MRI is an imaging technique that provides a quantitative measure of pharmacokinetic (PK) parameters characterizing microvasculature of tissues. For the present study, we propose a new machine learning (ML) based approach to directly estimate the PK parameters from the acquired DCE-MRI image-time series that is both more robust and faster than conventional model fitting.Materials and Methods: We specifically utilize deep convolutional neural networks (CNNs) to learn the mapping between the image-time series and corresponding PK parameters. DCE-MRI datasets acquired from 15 patients with clinically evident mild ischaemic stroke were used in the experiments. Training and testing were carried out based on leave-one-patient-out cross- validation. The parameter estimates obtained by the proposed CNN model were compared against the two tracer kinetic models: (1) Patlak model, (2) Extended Tofts model, where the estimation of model parameters is done via voxelwise linear and nonlinear least squares fitting respectively.Results: The trained CNN model is able to yield PK parameters which can better discriminate different brain tissues, including stroke regions. The results also demonstrate that the model generalizes well to new cases even if a subject specific arterial input function (AIF) is not available for the new data.Conclusion: A ML-based model can be used for direct inference of the PK parameters from DCE image series. This method may allow fast and robust parameter inference in population DCE studies. Parameter inference on a 3D volume-time series takes only a few seconds on a GPU machine, which is significantly faster compared to conventional non-linear least squares fitting.

Magnetic Resonance Spectroscopic Imaging (MRSI) is a clinical imaging modality for measuring tissue metabolite levels in-vivo. An accurate estimation of spectral parameters allows for better assessment of spectral quality and metabolite concentration levels. The current gold standard quantification method is the LCModel -a commercial fitting tool. However, this fails for spectra having poor signal-to-noise ratio (SNR) or a large number of artifacts. This paper introduces a framework based on random forest regression for accurate estimation of the output parameters of a model based analysis of MR spectroscopy data. The goal of our proposed framework is to learn the spectral features from a training set comprising of different variations of both simulated and in-vivo brain spectra and then use this learning for the subsequent metabolite quantification. Experiments involve training and testing on simulated and in-vivo human brain spectra. We estimate parameters such as concentration of metabolites and compare our results with that from the LCModel.