GPI-anchored single chain Fv - an effective way to capture transiently-exposed neutralization epitopes on HIV-1 envelope spike

<p>Abstract</p> <p>Background</p> <p>Identification of broad neutralization epitopes in HIV-1 envelope spikes is paramount for HIV-1 vaccine development. A few broad neutralization epitopes identified so far are present on the surface of native HIV-1 envelope spikes whose recognition by antibodies does not depend on conformational changes of the envelope spikes. However, HIV-1 envelope spikes also contain transiently-exposed neutralization epitopes, which are more difficult to identify.</p> <p>Results</p> <p>In this study, we constructed single chain Fvs (scFvs) derived from seven human monoclonal antibodies and genetically linked them with or without a glycosyl-phosphatidylinositol (GPI) attachment signal. We show that with a GPI attachment signal the scFvs are targeted to lipid rafts of plasma membranes. In addition, we demonstrate that four of the GPI-anchored scFvs, but not their secreted counterparts, neutralize HIV-1 with various degrees of breadth and potency. Among them, GPI-anchored scFv (X5) exhibits extremely potent and broad neutralization activity against multiple clades of HIV-1 strains tested. Moreover, we show that GPI-anchored scFv (4E10) also exhibited more potent neutralization activity than its secretory counterpart. Finally, we demonstrate that expression of GPI-anchored scFv (X5) in the lipid raft of plasma membrane of human CD4⁺T cells confers long-term resistance to HIV-1 infection, HIV-1 envelope-mediated cell-cell fusion, and the infection of HIV-1 captured and transferred by human DCs.</p> <p>Conclusions</p> <p>Thus GPI-anchored scFv could be used as a general and effective way to identify antibodies that react with transiently-exposed neutralization epitopes in envelope proteins of HIV-1 and other enveloped viruses. The GPI-anchored scFv (X5), because of its breadth and potency, should have a great potential to be developed into anti-viral agent for HIV-1 prevention and therapy.</p

Simultaneous Estimation of Sub-canopy Topography and Forest Height with Single-baseline Single-polarization TanDEM-X Interferometric Data Combined with ICESat-2 Data

To address the challenge of retrieving sub-canopy topography using single-baseline single-polarization TanDEM-X InSAR data, we propose a novel InSAR processing framework. Our methodology begins by employing the SINC model to estimate the penetration depth (PD). Subsequently, we establish a linear relationship between PD and phase center height (PCH) to generate a wall-to-wall PCH product. To achieve this, space-borne LiDAR data are employed to capture the elevation bias between actual ground elevation and InSAR-derived elevation. Finally, the sub-canopy topography is derived by subtracting the PCH from the conventional InSAR-based DEM. Moreover, this approach enables the simultaneous estimation of forest height from single-baseline TanDEM-X data by combining the estimated PD and PCH components. The approach has been validated against Airborne Lidar Scanning data over four diverse sites encompassing different forest types, terrain conditions, and climates. The derived sub-canopy topography in the boreal and hemi-boreal forest sites (Krycklan and Remningstorp) demonstrated notable improvement in accuracy. Additionally, the winter acquisitions outperformed the summer ones in terms of inversion accuracy. The achieved RMSEs for the winter scenarios were 2.45 m and 3.83 m, respectively, representing a 50% improvement over the InSAR-based DEMs. And the forest heights are also close to the ALS measurements, with RMSEs of 2.70 m and 3.33 m, respectively. For the Yanguas site in Spain, characterized by rugged terrain, sub-canopy topography in forest areas was estimated with an accuracy of 4.27m, a 35% improvement over the original DEM. For the denser tropical forest site, only an average elevation bias could be corrected.This work is funded by the National Key R&D Program of China (No. 2022YFB3902605), the National Natural Science Foundation of China (Nos. 42227801, 42030112, 42204024, 42104016, 42330717), the Spanish Ministry of Science and Innovation (State Agency of Research, AEI) and the European Funds for Regional Development under Project PID2020-117303GB-C22/AEI/10.13039/501100011033, the Natural Science Foundation for Excellent Young Scholars of Hunan Province (No. 2023JJ20061), and in part by the China Scholarship Council Foundation to the Joint Ph.D. Studies at University of Alicante (No. 202106370125)

PIT: Optimization of Dynamic Sparse Deep Learning Models via Permutation Invariant Transformation

Dynamic sparsity, where the sparsity patterns are unknown until runtime, poses a significant challenge to deep learning. The state-of-the-art sparsity-aware deep learning solutions are restricted to pre-defined, static sparsity patterns due to significant overheads associated with preprocessing. Efficient execution of dynamic sparse computation often faces the misalignment between the GPU-friendly tile configuration for efficient execution and the sparsity-aware tile shape that minimizes coverage wastes (non-zero values in tensor). In this paper, we propose PIT, a deep-learning compiler for dynamic sparsity. PIT proposes a novel tiling mechanism that leverages Permutation Invariant Transformation (PIT), a mathematically proven property, to transform multiple sparsely located micro-tiles into a GPU-efficient dense tile without changing the computation results, thus achieving both high GPU utilization and low coverage waste. Given a model, PIT first finds feasible PIT rules for all its operators and generates efficient GPU kernels accordingly. At runtime, with the novel SRead and SWrite primitives, PIT rules can be executed extremely fast to support dynamic sparsity in an online manner. Extensive evaluation on diverse models shows that PIT can accelerate dynamic sparsity computation by up to 5.9x (average 2.43x) over state-of-the-art compilers

Primary gastric non-Hodgkin's lymphoma in Chinese patients: clinical characteristics and prognostic factors

<p>Abstract</p> <p>Background</p> <p>Optimal management and outcome of primary gastric lymphoma (PGL) have not been well defined in the rituximab era. This study aimed to analyze the clinical characteristics, prognostic factors, and roles of different treatment modalities in Chinese patients with PGL.</p> <p>Methods</p> <p>The clinicopathological features of 83 Chinese patients with PGL were retrospectively reviewed. Staging was performed according to the Lugano staging system for gastrointestinal non-Hodgkin's lymphoma.</p> <p>Results</p> <p>The predominant pathologic subtype among Chinese patients with PGL in our study was diffuse large B cell lymphoma (DLBCL), followed by mucosa-associated lymphoid tissue (MALT) lymphoma. Among the 57 patients with gastric DLBCL, 20 patients (35.1%) were classified as the germinal center B cell-like (GCB) subtype and 37 patients (64.9%) as the non-GCB subtype. The 83 patients had a five-year overall survival (OS) and event-free survival (EFS) of 52% and 59%, respectively. Cox regression analysis showed that stage-modified international prognostic index (IPI) and performance status (PS) were independent predictors of survival. In the 67 B-cell lymphoma patients who received chemotherapy, 36 patients treated with rituximab (at least 3 cycles) had a mean OS of 72 months (95% CI 62-81) versus 62 months (95% CI 47-76) for patients without rituximab treatment (P = 0.021).</p> <p>Conclusion</p> <p>The proportion of Chinese gastric DLBCL cases with non-GCB subtype was higher than the GCB subtype. Stage-modified IPI and PS were effective prognostic factors in Chinese patients with PGL. Our data suggested that primary gastric B-cell lymphoma might have an improved outcome with rituximab in addition to chemotherapy. More studies are necessary, preferentially large prospective randomized clinical trials to obtain more information on the impact of the rituximab in the primary gastric B-cell lymphoma.</p

Primate-specific endogenous retrovirus-driven transcription defines naive-like stem cells

Naive embryonic stem cells hold great promise for research and therapeutics as they have broad and robust developmental potential. While such cells are readily derived from mouse blastocysts it has not been possible to isolate human equivalents easily, although human naive-like cells have been artificially generated (rather than extracted) by coercion of human primed embryonic stem cells by modifying culture conditions or through transgenic modification. Here we show that a sub-population within cultures of human embryonic stem cells (hESCs) and induced pluripotent stem cells (hiPSCs) manifests key properties of naive state cells. These naive-like cells can be genetically tagged, and are associated with elevated transcription of HERVH, a primate-specific endogenous retrovirus. HERVH elements provide functional binding sites for a combination of naive pluripotency transcription factors, including LBP9, recently recognized as relevant to naivety in mice. LBP9-HERVH drives hESC-specific alternative and chimaeric transcripts, including pluripotency-modulating long non-coding RNAs. Disruption of LBP9, HERVH and HERVH-derived transcripts compromises self-renewal. These observations define HERVH expression as a hallmark of naive-like hESCs, and establish novel primate-specific transcriptional circuitry regulating pluripotency

Gradient descent with adaptive momentum for active contour models

In active contour models (snakes), various vector force fields replacing the gradient of the original external energy in the equations of motion are a popular way to extract the object boundary. Gradient descent method is usually used to obtain the equations of motion by minimising the energy functional. However, it always suffers from local minimum in extracting complex geometries because of non‐convex functional. Gradient descent method with adaptive momentum term is proposed in this study. First, an acceleration function of evolution is defined. Then, the adaptive momentum term is obtained by calculating the product between the edge stopping function and the defined acceleration function. Finally, adaptive momentum is compatible with the snakes. The edge stopping function is used to decide the influence region of the momentum, whereas the defined acceleration function determines the magnitude of the momentum. It is used to extract the complex geometries (such as deep concavity) when adding the adaptive momentum into some snakes, such as gradient vector field or vector field convolution snakes. On the other hand, the proposed method also accelerates the rate of convergence. It can be applied to extract a single object in real images. The experimental results show that the proposed method is effective and efficient

Boosting Video Popularity through Recommendation Systems

While search engines are the major sources of content discovery on online content providers and e-commerce sites, their capability is limited since textual descriptions cannot fully describe the semantic of content such as videos. Recommendation systems are now widely used in online content providers and e-commerce sites and play an important role in discovering content. In this paper, we describe how one can boost the popularity of a video through the recommendation system in YouTube. We present a model that captures the view propagation between videos through the recommendation linkage and quantifies the influence that a video has on the popularity of another video. Furthermore, we identify that the similarity in titles and tags is an important factor in forming the recommendation linkage between videos. This suggests that one can manipulate the metadata of a video to boost its popularity