Structural modelling and comparative analysis of homologous, analogous and specific proteins from Trypanosoma cruzi versus Homo sapiens: putative drug targets for chagas' disease treatment

<p>Abstract</p> <p>Background</p> <p><it>Trypanosoma cruzi </it>is the etiological agent of Chagas' disease, an endemic infection that causes thousands of deaths every year in Latin America. Therapeutic options remain inefficient, demanding the search for new drugs and/or new molecular targets. Such efforts can focus on proteins that are specific to the parasite, but analogous enzymes and enzymes with a three-dimensional (3D) structure sufficiently different from the corresponding host proteins may represent equally interesting targets. In order to find these targets we used the workflows MHOLline and AnEnΠ obtaining 3D models from homologous, analogous and specific proteins of <it>Trypanosoma cruzi </it>versus <it>Homo sapiens</it>.</p> <p>Results</p> <p>We applied genome wide comparative modelling techniques to obtain 3D models for 3,286 predicted proteins of <it>T</it>. <it>cruzi</it>. In combination with comparative genome analysis to <it>Homo sapiens</it>, we were able to identify a subset of 397 enzyme sequences, of which 356 are homologous, 3 analogous and 38 specific to the parasite.</p> <p>Conclusions</p> <p>In this work, we present a set of 397 enzyme models of <it>T</it>. <it>cruzi </it>that can constitute potential structure-based drug targets to be investigated for the development of new strategies to fight Chagas' disease. The strategies presented here support the concept of structural analysis in conjunction with protein functional analysis as an interesting computational methodology to detect potential targets for structure-based rational drug design. For example, 2,4-dienoyl-CoA reductase (EC 1.3.1.34) and triacylglycerol lipase (EC 3.1.1.3), classified as analogous proteins in relation to <it>H. sapiens </it>enzymes, were identified as new potential molecular targets.</p

ULEEN: A Novel Architecture for Ultra Low-Energy Edge Neural Networks

The deployment of AI models on low-power, real-time edge devices requires accelerators for which energy, latency, and area are all first-order concerns. There are many approaches to enabling deep neural networks (DNNs) in this domain, including pruning, quantization, compression, and binary neural networks (BNNs), but with the emergence of the "extreme edge", there is now a demand for even more efficient models. In order to meet the constraints of ultra-low-energy devices, we propose ULEEN, a model architecture based on weightless neural networks. Weightless neural networks (WNNs) are a class of neural model which use table lookups, not arithmetic, to perform computation. The elimination of energy-intensive arithmetic operations makes WNNs theoretically well suited for edge inference; however, they have historically suffered from poor accuracy and excessive memory usage. ULEEN incorporates algorithmic improvements and a novel training strategy inspired by BNNs to make significant strides in improving accuracy and reducing model size. We compare FPGA and ASIC implementations of an inference accelerator for ULEEN against edge-optimized DNN and BNN devices. On a Xilinx Zynq Z-7045 FPGA, we demonstrate classification on the MNIST dataset at 14.3 million inferences per second (13 million inferences/Joule) with 0.21 $\mu$s latency and 96.2% accuracy, while Xilinx FINN achieves 12.3 million inferences per second (1.69 million inferences/Joule) with 0.31 $\mu$s latency and 95.83% accuracy. In a 45nm ASIC, we achieve 5.1 million inferences/Joule and 38.5 million inferences/second at 98.46% accuracy, while a quantized Bit Fusion model achieves 9230 inferences/Joule and 19,100 inferences/second at 99.35% accuracy. In our search for ever more efficient edge devices, ULEEN shows that WNNs are deserving of consideration.Comment: 14 pages, 14 figures Portions of this article draw heavily from arXiv:2203.01479, most notably sections 5E and 5F.

Determinants of Depressive Symptoms at 1 Year Following ICU Discharge in Survivors of $ 7 Days of Mechanical Ventilation : Results From the RECOVER Program, a Secondary Analysis of a Prospective Multicenter Cohort Study

Abstract : Background: Moderate to severe depressive symptoms occur in up to one-third of patients at 1 year following ICU discharge, negatively affecting patient outcomes. This study evaluated patient and caregiver factors associated with the development of these symptoms. Methods: This study used the Rehabilitation and Recovery in Patients after Critical Illness and Their Family Caregivers (RECOVER) Program (Phase 1) cohort of 391 patients from 10 medical/surgical university-affiliated ICUs across Canada. We determined the association between patient depressive symptoms (captured by using the Beck Depression Inventory II [BDI-II]), patient characteristics (age, sex, socioeconomic status, Charlson score, and ICU length of stay [LOS]), functional independence measure (FIM) motor subscale score, and caregiver characteristics (Caregiver Assistance Scale and Center for Epidemiologic Studies-Depression Scale) by using linear mixed models at time points 3, 6, and 12 months. Results: BDI-II data were available for 246 patients. Median age at ICU admission was 56 years (interquartile range, 45-65 years), 143 (58%) were male, and median ICU LOS was 19 days (interquartile range, 13-32 days). During the 12-month follow-up, 67 of 246 (27.2%) patients had a BDI-II score ≥ 20, indicating moderate to severe depressive symptoms. Mixed models showed worse depressive symptoms in patients with lower FIM motor subscale scores (1.1 BDI-II points per 10 FIM points), lower income status (by 3.7 BDI-II points; P = .007), and incomplete secondary education (by 3.8 BDI-II points; P = .009); a curvilinear relation with age (P = .001) was also reported, with highest BDI-II at ages 45 to 50 years. No associations were found between patient BDI-II and comorbidities (P = .92), sex (P = .25), ICU LOS (P = .51), or caregiver variables (Caregiver Assistance Scale [P = .28] and Center for Epidemiologic Studies Depression Scale [P = .74]). Conclusions: Increased functional dependence, lower income, and lower education are associated with increased severity of post-ICU depressive symptoms, whereas age has a curvilinear relation with symptom severity. Knowledge of risk factors may inform surveillance and targeted mental health follow-up. Early mobilization and rehabilitation aiming to improve function may serve to modify mood disorders

Estimating the global conservation status of more than 15,000 Amazonian tree species

Estimates of extinction risk for Amazonian plant and animal species are rare and not often incorporated into land-use policy and conservation planning. We overlay spatial distribution models with historical and projected deforestation to show that at least 36% and up to 57% of all Amazonian tree species are likely to qualify as globally threatened under International Union for Conservation of Nature (IUCN) Red List criteria. If confirmed, these results would increase the number of threatened plant species on Earth by 22%. We show that the trends observed in Amazonia apply to trees throughout the tropics, and we predict thatmost of the world’s >40,000 tropical tree species now qualify as globally threatened. A gap analysis suggests that existing Amazonian protected areas and indigenous territories will protect viable populations of most threatened species if these areas suffer no further degradation, highlighting the key roles that protected areas, indigenous peoples, and improved governance can play in preventing large-scale extinctions in the tropics in this century