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The inference of neuronal connectome from large-scale neuronal activity recordings, such as two-photon Calcium imaging, represents an active area of research in computational neuroscience. In this work, we developed FARCI (Fast and Robust Connectome Inference), a MATLAB package for neuronal connectome inference from high-dimensional two-photon Calcium fluorescence data. We employed partial correlations as a measure of the functional association strength between pairs of neurons to reconstruct a neuronal connectome. We demonstrated using in silico datasets from the Neural Connectomics Challenge (NCC) and those generated using the state-of-the-art simulator of Neural Anatomy and Optimal Microscopy (NAOMi) that FARCI provides an accurate connectome and its performance is robust to network sizes, missing neurons, and noise levels. Moreover, FARCI is computationally efficient and highly scalable to large networks. In comparison with the best performing connectome inference algorithm in the NCC, Generalized Transfer Entropy (GTE), and Fluorescence Single Neuron and Network Analysis Package (FluoroSNNAP), FARCI produces more accurate networks over different network sizes, while providing significantly better computational speed and scaling.

The accreting millisecond X-ray pulsar IGR J17498−2921 went into X-ray outburst on 2023 April 13–15, for the first time since its discovery on 2011 August 11. Here, we report on the first follow-up NuSTAR observation of the source, performed on 2023 April 23, around 10 d after the peak of the outburst. The NuSTAR spectrum of the persistent emission (3–60 keV band) is well described by an absorbed blackbody with a temperature of |$kT_{\mathrm{ bb}}=1.61\pm 0.04$|  keV, most likely arising from the NS surface and a Comptonization component with power-law index |$\Gamma =1.79\pm 0.02$|⁠ , arising from a hot corona at |$kT_{e}=16\pm 2$| keV. The X-ray spectrum of the source shows robust reflection features which have not been observed before. We use a couple of self-consistent reflection models, relxill and relxillCp , to fit the reflection features. We find an upper limit to the inner disc radius of |$6\: R_{\mathrm{ ISCO}}$| and |$9\: R_{\mathrm{ ISCO}}$| from relxill and relxillCp model, respectively. The inclination of the system is estimated to be |$\simeq 40^{\circ }$| from both reflection models. Assuming magnetic truncation of the accretion disc, the upper limit of magnetic field strength at the pole of the NS is found to be |$B\lesssim 1.8\times 10^{8}$| G. Furthermore, the NuSTAR observation revealed two type-I X-ray bursts and the burst spectroscopy confirms the thermonuclear nature of the burst. The blackbody temperature reaches nearly 2.2 keV at the peak of the burst. [ABSTRACT FROM AUTHOR]

Background: Volumetric modulated arc therapy (VMAT) machine parameter optimization (MPO) remains computationally expensive and sensitive to input dose objectives creating challenges for manual and automatic planning. Reinforcement learning (RL) involves machine learning through extensive trial‐and‐error, demonstrating performance exceeding humans, and existing algorithms in several domains. Purpose: To develop and evaluate an RL approach for VMAT MPO for localized prostate cancer to rapidly and automatically generate deliverable VMAT plans for a clinical linear accelerator (linac) and compare resultant dosimetry to clinical plans. Methods: We extended our previous RL approach to enable VMAT MPO of a 3D beam model for a clinical linac through a policy network. It accepts an input state describing the current control point and predicts continuous machine parameters for the next control point, which are used to update the input state, repeating until plan termination. RL training was conducted to minimize a dose‐based cost function for prescription of 60 Gy in 20 fractions using CT scans and contours from 136 retrospective localized prostate cancer patients, 20 of which had existing plans used to initialize training. Data augmentation was employed to mitigate over‐fitting, and parameter exploration was achieved using Gaussian perturbations. Following training, RL VMAT was applied to an independent cohort of 15 patients, and the resultant dosimetry was compared to clinical plans. We also combined the RL approach with our clinical treatment planning system (TPS) to automate final plan refinement, and creating the potential for manual review and edits as required for clinical use. Results: RL training was conducted for 5000 iterations, producing 40 000 plans during exploration. Mean ± SD execution time to produce deliverable VMAT plans in the test cohort was 3.3 ± 0.5 s which were automatically refined in the TPS taking an additional 77.4 ± 5.8 s. When normalized to provide equivalent target coverage, the RL+TPS plans provided a similar mean ± SD overall maximum dose of 63.2 ± 0.6 Gy and a lower mean rectum dose of 17.4 ± 7.4 compared to 63.9 ± 1.5 Gy (p = 0.061) and 21.0 ± 6.0 (p = 0.024) for the clinical plans. Conclusions: An approach for VMAT MPO using RL for a clinical linac model was developed and applied to automatically generate deliverable plans for localized prostate cancer patients, and when combined with the clinical TPS shows potential to rapidly generate high‐quality plans. The RL VMAT approach shows promise to discover advanced linac control policies through trial‐and‐error, and algorithm limitations and future directions are identified and discussed. [ABSTRACT FROM AUTHOR]

Analyzes the expression of genes for tyrosinase, tyrosinase-related protein 1, tyrosinase-related protein 2 and melanocortin-1 receptor using reverse transcription polymerase chain reaction. Result of the incubation of melanocyte-stimulating hormone/isobutylmethylxanthine; Correlation of the abundance and gel mobility pattern with changes in activity in hybrids and parental melanoma cells.