WITT: A Wireless Image Transmission Transformer for Semantic Communications

In this paper, we aim to redesign the vision Transformer (ViT) as a new backbone to realize semantic image transmission, termed wireless image transmission transformer (WITT). Previous works build upon convolutional neural networks (CNNs), which are inefficient in capturing global dependencies, resulting in degraded end-to-end transmission performance especially for high-resolution images. To tackle this, the proposed WITT employs Swin Transformers as a more capable backbone to extract long-range information. Different from ViTs in image classification tasks, WITT is highly optimized for image transmission while considering the effect of the wireless channel. Specifically, we propose a spatial modulation module to scale the latent representations according to channel state information, which enhances the ability of a single model to deal with various channel conditions. As a result, extensive experiments verify that our WITT attains better performance for different image resolutions, distortion metrics, and channel conditions. The code is available at https://github.com/KeYang8/WITT

Response behavior of antibiotic resistance genes and human pathogens to slope gradient and position: an environmental risk analysis in sloping cultivated land

Soils, especially in farmlands, are key media for the transmission of antibiotic resistance genes (ARGs) and their hosts from the environment to humans. Sloping farmland is an important agricultural resource, but there lack of studies on the fate and risk of ARGs in sloping land. Also, the behavior and drivers of ARGs in response to slope gradient and position are unclear. Here, metagenomics was used to investigate the profiles of ARGs, mobile genetic elements, and microbial communities in soils from lands of five slope gradients (5°, 10°, 15°, 20°, and 25°) with two slope positions (uphill and downhill). Results showed that while the abundance (except 15°) and diversity (except 20°) of ARGs increased as the slope gradient increased, the diversity of ARGs with health risk, especially the high-risk ones, decreased. For slope positions, abundant and diverse ARGs were more likely to accumulate at downhill. Furthermore, 52 bacterial genera and 12 human pathogenic bacteria (HPB) species were identified as the potential hosts for ARGs with high risk, and abundant HPB species were also detected in the soils with low gradients at downhill. Moreover, the structural equation model analysis revealed that the slope gradient and the slope position have both direct and indirect effects on the abundance of ARGs. Further correlation analysis revealed that the slope gradient has a positive effect (p < 0.05) on nitrite nitrogen in the soils. Also, the slope position has a negative effect (p < 0.05) on total phosphorus and microbial nitrogen, while positively affected (p < 0.05) on particulate nitrogen and microbial carbon, which were the key factors driving the behavior of ARGs. Overall, this study provided comprehensive information on ARGs with health risks and their potential pathogenic hosts in sloping farmland. It can be important for controlling antibiotic resistance transmission and be consistent with the One Health framework

Color-NeuS: Reconstructing Neural Implicit Surfaces with Color

The reconstruction of object surfaces from multi-view images or monocular video is a fundamental issue in computer vision. However, much of the recent research concentrates on reconstructing geometry through implicit or explicit methods. In this paper, we shift our focus towards reconstructing mesh in conjunction with color. We remove the view-dependent color from neural volume rendering while retaining volume rendering performance through a relighting network. Mesh is extracted from the signed distance function (SDF) network for the surface, and color for each surface vertex is drawn from the global color network. To evaluate our approach, we conceived a in hand object scanning task featuring numerous occlusions and dramatic shifts in lighting conditions. We've gathered several videos for this task, and the results surpass those of any existing methods capable of reconstructing mesh alongside color. Additionally, our method's performance was assessed using public datasets, including DTU, BlendedMVS, and OmniObject3D. The results indicated that our method performs well across all these datasets. Project page: https://colmar-zlicheng.github.io/color_neus

A Network Pharmacology Approach to Explore the Pharmacological Mechanism of Xiaoyao Powder on Anovulatory Infertility

Aim. To explore the pharmacological mechanism of Xiaoyao powder (XYP) on anovulatory infertility by a network pharmacology approach. Method. Collect XYP’s active compounds by traditional Chinese medicine (TCM) databases, and input them into PharmMapper to get their targets. Then note these targets by Kyoto Encyclopedia of Genes and Genomes (KEGG) and filter out targets that can be noted by human signal pathway. Get the information of modern pharmacology of active compounds and recipe’s traditional effects through databases. Acquire infertility targets by Therapeutic Target Database (TTD). Collect the interactions of all the targets and other human proteins via String and INACT. Put all the targets into the Database for Annotation, Visualization, and Integrated Discovery (DAVID) to do GO enrichment analysis. Finally, draw the network by Cytoscape by the information above. Result. Six network pictures and two GO enrichment analysis pictures are visualized. Conclusion. According to this network pharmacology approach some signal pathways of XYP acting on infertility are found for the first time. Some biological processes can also be identified as XYP’s effects on anovulatory infertility. We believe that evaluating the efficacy of TCM recipes and uncovering the pharmacological mechanism on a systematic level will be a significant method for future studies

Adaptive Semantic Communications: Overfitting the Source and Channel for Profit

Most semantic communication systems leverage deep learning models to provide end-to-end transmission performance surpassing the established source and channel coding approaches. While, so far, research has mainly focused on architecture and model improvements, but such a model trained over a full dataset and ergodic channel responses is unlikely to be optimal for every test instance. Due to limitations on the model capacity and imperfect optimization and generalization, such learned models will be suboptimal especially when the testing data distribution or channel response is different from that in the training phase, as is likely to be the case in practice. To tackle this, in this paper, we propose a novel semantic communication paradigm by leveraging the deep learning model's overfitting property. Our model can for instance be updated after deployment, which can further lead to substantial gains in terms of the transmission rate-distortion (RD) performance. This new system is named adaptive semantic communication (ASC). In our ASC system, the ingredients of wireless transmitted stream include both the semantic representations of source data and the adapted decoder model parameters. Specifically, we take the overfitting concept to the extreme, proposing a series of ingenious methods to adapt the semantic codec or representations to an individual data or channel state instance. The whole ASC system design is formulated as an optimization problem whose goal is to minimize the loss function that is a tripartite tradeoff among the data rate, model rate, and distortion terms. The experiments (including user study) verify the effectiveness and efficiency of our ASC system. Notably, the substantial gain of our overfitted coding paradigm can catalyze semantic communication upgrading to a new era

CHORD: Category-level Hand-held Object Reconstruction via Shape Deformation

In daily life, humans utilize hands to manipulate objects. Modeling the shape of objects that are manipulated by the hand is essential for AI to comprehend daily tasks and to learn manipulation skills. However, previous approaches have encountered difficulties in reconstructing the precise shapes of hand-held objects, primarily owing to a deficiency in prior shape knowledge and inadequate data for training. As illustrated, given a particular type of tool, such as a mug, despite its infinite variations in shape and appearance, humans have a limited number of 'effective' modes and poses for its manipulation. This can be attributed to the fact that humans have mastered the shape prior of the 'mug' category, and can quickly establish the corresponding relations between different mug instances and the prior, such as where the rim and handle are located. In light of this, we propose a new method, CHORD, for Category-level Hand-held Object Reconstruction via shape Deformation. CHORD deforms a categorical shape prior for reconstructing the intra-class objects. To ensure accurate reconstruction, we empower CHORD with three types of awareness: appearance, shape, and interacting pose. In addition, we have constructed a new dataset, COMIC, of category-level hand-object interaction. COMIC contains a rich array of object instances, materials, hand interactions, and viewing directions. Extensive evaluation shows that CHORD outperforms state-of-the-art approaches in both quantitative and qualitative measures. Code, model, and datasets are available at https://kailinli.github.io/CHORD.Comment: To be presented at ICCV 2023, Pari

GMDD: a database of GMO detection methods

<p>Abstract</p> <p>Background</p> <p>Since more than one hundred events of genetically modified organisms (GMOs) have been developed and approved for commercialization in global area, the GMO analysis methods are essential for the enforcement of GMO labelling regulations. Protein and nucleic acid-based detection techniques have been developed and utilized for GMOs identification and quantification. However, the information for harmonization and standardization of GMO analysis methods at global level is needed.</p> <p>Results</p> <p>GMO Detection method Database (GMDD) has collected almost all the previous developed and reported GMOs detection methods, which have been grouped by different strategies (screen-, gene-, construct-, and event-specific), and also provide a user-friendly search service of the detection methods by GMO event name, exogenous gene, or protein information, etc. In this database, users can obtain the sequences of exogenous integration, which will facilitate PCR primers and probes design. Also the information on endogenous genes, certified reference materials, reference molecules, and the validation status of developed methods is included in this database. Furthermore, registered users can also submit new detection methods and sequences to this database, and the newly submitted information will be released soon after being checked.</p> <p>Conclusion</p> <p>GMDD contains comprehensive information of GMO detection methods. The database will make the GMOs analysis much easier.</p

CE-BLAST makes it possible to compute antigenic similarity for newly emerging pathogens

Major challenges in vaccine development include rapidly selecting or designing immunogens for raising cross-protective immunity against different intra-or inter-subtypic pathogens, especially for the newly emerging varieties. Here we propose a computational method, Conformational Epitope (CE)-BLAST, for calculating the antigenic similarity among different pathogens with stable and high performance, which is independent of the prior binding-assay information, unlike the currently available models that heavily rely on the historical experimental data. Tool validation incorporates influenza-related experimental data sufficient for stability and reliability determination. Application to dengue-related data demonstrates high harmonization between the computed clusters and the experimental serological data, undetectable by classical grouping. CE-BLAST identifies the potential cross-reactive epitope between the recent zika pathogen and the dengue virus, precisely corroborated by experimental data. The high performance of the pathogens without the experimental binding data suggests the potential utility of CE-BLAST to rapidly design cross-protective vaccines or promptly determine the efficacy of the currently marketed vaccine against emerging pathogens, which are the critical factors for containing emerging disease outbreaks.Peer reviewe