Competitive intra- and extracellular nutrient sensing by the transporter homologue Ssy1p

Recent studies of Saccharomyces cerevisiae revealed sensors that detect extracellular amino acids (Ssy1p) or glucose (Snf3p and Rgt2p) and are evolutionarily related to the transporters of these nutrients. An intriguing question is whether the evolutionary transformation of transporters into nontransporting sensors reflects a homeostatic capability of transporter-like sensors that could not be easily attained by other types of sensors. We previously found SSY1 mutants with an increased basal level of signaling and increased apparent affinity to sensed extracellular amino acids. On this basis, we propose and test a general model for transporter- like sensors in which occupation of a single, central ligand binding site increases the activation energy needed for the conformational shift between an outward-facing, signaling conformation and an inward-facing, nonsignaling conformation. As predicted, intracellular leucine accumulation competitively inhibits sensing of extracellular amino acids. Thus, a single sensor allows the cell to respond to changes in nutrient availability through detection of the relative concentrations of intra- and extracellular ligand

E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation

Deep neural networks have evolved as the leading approach in 3D medical image segmentation due to their outstanding performance. However, the ever-increasing model size and computation cost of deep neural networks have become the primary barrier to deploying them on real-world resource-limited hardware. In pursuit of improving performance and efficiency, we propose a 3D medical image segmentation model, named Efficient to Efficient Network (E2ENet), incorporating two parametrically and computationally efficient designs. i. Dynamic sparse feature fusion (DSFF) mechanism: it adaptively learns to fuse informative multi-scale features while reducing redundancy. ii. Restricted depth-shift in 3D convolution: it leverages the 3D spatial information while keeping the model and computational complexity as 2D-based methods. We conduct extensive experiments on BTCV, AMOS-CT and Brain Tumor Segmentation Challenge, demonstrating that E2ENet consistently achieves a superior trade-off between accuracy and efficiency than prior arts across various resource constraints. E2ENet achieves comparable accuracy on the large-scale challenge AMOS-CT, while saving over 68\% parameter count and 29\% FLOPs in the inference phase, compared with the previous best-performing method. Our code has been made available at: https://github.com/boqian333/E2ENet-Medical

Effects of a Pseudomonas H6 surfactant on rainbow trout and Ichthyophthirius multifiliis: In vivo exposure

The Pseudomonas H6 lipopeptide is a surfactant, which is able to eliminate various parasitic pathogens including the ciliate Ichthyophthirius multifiliis in vitro. This suggests an application for aquaculture purposes. However, further information on efficacy of the compound and possible immune modulation of surfactant-exposed fish should be gathered before usage at farm level is considered. We performed an in vivo infection experiment using rainbow trout fry (mean weight 4.6 g, mean length 7.6 cm) as hosts and I. multifiliis theronts as the parasitic pathogen. We compared infection level, immune gene regulation and immune cell density in gills of 1) no exposed control fish, 2) parasite exposed but untreated fish, 3) surfactant treated fish without parasite exposure, and 4) fish exposed both to parasites and surfactant. The surfactant concentration was 10 mg/L, the infection dosage 1000 theronts/fish and the exposure period 6 h. The parasite infection was recorded and samples were taken from rainbow trout gills at day 0 and 10 post-exposure. We performed an immunohistochemical investigation (detecting cells positive for MHC II, SAA, CD8, IgM, IgT and IgD) and measured the expression of genes encoding cathelidin-1, CD8, hepcidin, IFN γ, IgDs, IL-1β, IL-6 and SAA. Theront exposed fish (without surfactant treatment) became heavily infected whereas concomitant surfactant treatment (10 mg/l), along with parasite exposure, could prevent infection. A significant inflammation (upregulation of il-1β, il6, ifn γ, cathelicidin, hepcidin) was elicited in non-treated and parasite exposed fish but it was prevented by the surfactant treatment. When investigated 10 days after treatment no immune gene regulation was seen in fish exposed to surfactant only. The therapeutic effect may be due to a direct parasitical action of the surfactant, but it cannot be excluded that a modulation of the host immune reaction may influence the infection success

E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation

Deep neural networks have evolved as the leading approach in 3D medical image segmentation due to their outstanding performance. However, the ever-increasing model size and computation cost of deep neural networks have become the primary barrier to deploying them on real-world resource-limited hardware. In pursuit of improving performance and efficiency, we propose a 3D medical image segmentation model, named Efficient to Efficient Network (E2ENet), incorporating two parametrically and computationally efficient designs. i. Dynamic sparse feature fusion (DSFF) mechanism: it adaptively learns to fuse informative multi-scale features while reducing redundancy. ii. Restricted depth-shift in 3D convolution: it leverages the 3D spatial information while keeping the model and computational complexity as 2D-based methods. We conduct extensive experiments on BTCV, AMOS-CT and Brain Tumor Segmentation Challenge, demonstrating that E2ENet consistently achieves a superior trade-off between accuracy and efficiency than prior arts across various resource constraints. E2ENet achieves comparable accuracy on the large-scale challenge AMOS-CT, while saving over 68\% parameter count and 29\% FLOPs in the inference phase, compared with the previous best-performing method. Our code has been made available at: https://github.com/boqian333/E2ENet-Medical.3. Good health and well-bein

Evaluating the effect of SARS-CoV-2 spike mutations with a linear doubly robust learner

Driven by various mutations on the viral Spike protein, diverse variants of SARS-CoV-2 have emerged and prevailed repeatedly, significantly prolonging the pandemic. This phenomenon necessitates the identification of key Spike mutations for fitness enhancement. To address the need, this manuscript formulates a well-defined framework of causal inference methods for evaluating and identifying key Spike mutations to the viral fitness of SARS-CoV-2. In the context of large-scale genomes of SARS-CoV-2, it estimates the statistical contribution of mutations to viral fitness across lineages and therefore identifies important mutations. Further, identified key mutations are validated by computational methods to possess functional effects, including Spike stability, receptor-binding affinity, and potential for immune escape. Based on the effect score of each mutation, individual key fitness-enhancing mutations such as D614G and T478K are identified and studied. From individual mutations to protein domains, this paper recognizes key protein regions on the Spike protein, including the receptor-binding domain and the N-terminal domain. This research even makes further efforts to investigate viral fitness via mutational effect scores, allowing us to compute the fitness score of different SARS-CoV-2 strains and predict their transmission capacity based solely on their viral sequence. This prediction of viral fitness has been validated using BA.2.12.1, which is not used for regression training but well fits the prediction. To the best of our knowledge, this is the first research to apply causal inference models to mutational analysis on large-scale genomes of SARS-CoV-2. Our findings produce innovative and systematic insights into SARS-CoV-2 and promotes functional studies of its key mutations, serving as reliable guidance about mutations of interest