Pharmacotherapeutic Options for Visceral Leishmaniasis—Current Scenario

Visceral leishmaniasis (VL) or Kala-azar is a protozoal disease, which was previously regarded as one of the most neglected tropical diseases. Management of this disease is quite difficult, because it is said to affect the poorest of the poor. Previously Sodium Stibogluconate (SSG) was regarded as the gold standard treatment for VL. But due to the increasing unresponsiveness, to this drug various other drugs were tried and are still being tried. Pentamidine is very toxic and has been discarded of late. Amphotericin B and its lipid formulations are very effective but require hospital admission and monitoring. Oral drugs like Miltefosine have already been launched. An amino glycoside Paromomycin and another oral drug Sitamaquine are in the pipe line. Interferon gamma has been used with discouraging results

PosSAM: Panoptic Open-vocabulary Segment Anything

In this paper, we introduce an open-vocabulary panoptic segmentation model that effectively unifies the strengths of the Segment Anything Model (SAM) with the vision-language CLIP model in an end-to-end framework. While SAM excels in generating spatially-aware masks, it's decoder falls short in recognizing object class information and tends to oversegment without additional guidance. Existing approaches address this limitation by using multi-stage techniques and employing separate models to generate class-aware prompts, such as bounding boxes or segmentation masks. Our proposed method, PosSAM is an end-to-end model which leverages SAM's spatially rich features to produce instance-aware masks and harnesses CLIP's semantically discriminative features for effective instance classification. Specifically, we address the limitations of SAM and propose a novel Local Discriminative Pooling (LDP) module leveraging class-agnostic SAM and class-aware CLIP features for unbiased open-vocabulary classification. Furthermore, we introduce a Mask-Aware Selective Ensembling (MASE) algorithm that adaptively enhances the quality of generated masks and boosts the performance of open-vocabulary classification during inference for each image. We conducted extensive experiments to demonstrate our methods strong generalization properties across multiple datasets, achieving state-of-the-art performance with substantial improvements over SOTA open-vocabulary panoptic segmentation methods. In both COCO to ADE20K and ADE20K to COCO settings, PosSAM outperforms the previous state-of-the-art methods by a large margin, 2.4 PQ and 4.6 PQ, respectively. Project Website: https://vibashan.github.io/possam-web/

Magnetic properties of hematite revealed by an ab initio parameterized spin model

Hematite is a canted antiferromagnetic insulator, promising for applications in spintronics. Here, we present ab initio calculations of the tensorial exchange interactions of hematite and use them to understand its magnetic properties by parameterizing a semiclassical Heisenberg spin model. Using atomistic spin dynamics simulations, we calculate the equilibrium properties and phase transitions of hematite, most notably the Morin transition. The computed isotropic and Dzyaloshinskii--Moriya interactions result in a N\'eel temperature and weak ferromagnetic canting angle that are in good agreement with experimental measurements. Our simulations show how dipole-dipole interactions act in a delicate balance with first and higher-order on-site anisotropies to determine the material's magnetic phase. Comparison with spin-Hall magnetoresistance measurements on a hematite single-crystal reveals deviations of the critical behavior at low temperatures. Based on a mean-field model, we argue that these differences result from the quantum nature of the fluctuations that drive the phase transitions.Comment: 11 pages, 10 figure