Cooperative and Distributed Localization for Wireless Sensor Networks in Multipath Environments

We consider the problem of sensor localization in a wireless network in a multipath environment, where time and angle of arrival information are available at each sensor. We propose a distributed algorithm based on belief propagation, which allows sensors to cooperatively self-localize with respect to one single anchor in a multihop network. The algorithm has low overhead and is scalable. Simulations show that although the network is loopy, the proposed algorithm converges, and achieves good localization accuracy

Distributed Local Linear Parameter Estimation using Gaussian SPAWN

We consider the problem of estimating local sensor parameters, where the local parameters and sensor observations are related through linear stochastic models. Sensors exchange messages and cooperate with each other to estimate their own local parameters iteratively. We study the Gaussian Sum-Product Algorithm over a Wireless Network (gSPAWN) procedure, which is based on belief propagation, but uses fixed size broadcast messages at each sensor instead. Compared with the popular diffusion strategies for performing network parameter estimation, whose communication cost at each sensor increases with increasing network density, the gSPAWN algorithm allows sensors to broadcast a message whose size does not depend on the network size or density, making it more suitable for applications in wireless sensor networks. We show that the gSPAWN algorithm converges in mean and has mean-square stability under some technical sufficient conditions, and we describe an application of the gSPAWN algorithm to a network localization problem in non-line-of-sight environments. Numerical results suggest that gSPAWN converges much faster in general than the diffusion method, and has lower communication costs, with comparable root mean square errors

Spatio-temporal mapping of variation potentials in leaves of Helianthus annuus L. seedlings in situ using multi-electrode array.

Damaging thermal stimuli trigger long-lasting variation potentials (VPs) in higher plants. Owing to limitations in conventional plant electrophysiological recording techniques, recorded signals are composed of signals originating from all of the cells that are connected to an electrode. This limitation does not enable detailed spatio-temporal distributions of transmission and electrical activities in plants to be visualised. Multi-electrode array (MEA) enables the recording and imaging of dynamic spatio-temporal electrical activities in higher plants. Here, we used an 8 × 8 MEA with a polar distance of 450 μm to measure electrical activities from numerous cells simultaneously. The mapping of the data that were recorded from the MEA revealed the transfer mode of the thermally induced VPs in the leaves of Helianthus annuus L. seedlings in situ. These results suggest that MEA can enable recordings with high spatio-temporal resolution that facilitate the determination of the bioelectrical response mode of higher plants under stress

Ancient great wall building materials reveal paleoenvironmental Changes in Northwestern China

Plant material used in the construction of segments and beacon towers of the ancient Great Wall in northwestern China contain untapped potential for revealing paleoenvironmental conditions. Here, we characterize the molecular preservation and stable carbon and nitrogen isotope compositions of common reeds (Phragmites) collected from Great Wall fascines dated to the Han Dynasty in today’s Gansu and Xinjiang provinces using a combination of chromatographic techniques and isotope analyses. Our data demonstrates that ancient reeds were harvested from local habitats that were more diverse than exist today. The isotope data also capture differential rates of environmental deterioration along the eastern margin of the Tarim Basin, leading to the intense evaporative stress on modern plants. This study demonstrates the wealth of environmental and climate information obtainable from site-specific organic building material of ancient walls, which have received considerably less attention than the iconic brick and stone masonry walls of the later Ming Dynasty.Introduction Results - Py-GC-MS Analysis - Lipid Concentration and Distribution - Bulk Carbon and Nitrogen Isotope Analysis Discussion - Differential Rates of Environmental Deterioration - Temperature and the Diversity of Ancient Phragmites Populations - Archaeological Significance of the Great Wall in Northwestern China Conclusions Methods - Site Locations and Sampling - Plant Biomolecular Composition -- Molecular Composition -- Plant Wax Lipids - Bulk Carbon and Nitrogen Isotope Analysis -- Carbon -- Nitroge

A current and future perspective on T cell receptor repertoire profiling

T cell receptors (TCR) play a vital role in the immune system’s ability to recognize and respond to foreign antigens, relying on the highly polymorphic rearrangement of TCR genes. The recognition of autologous peptides by adaptive immunity may lead to the development and progression of autoimmune diseases. Understanding the specific TCR involved in this process can provide insights into the autoimmune process. RNA-seq (RNA sequencing) is a valuable tool for studying TCR repertoires by providing a comprehensive and quantitative analysis of the RNA transcripts. With the development of RNA technology, transcriptomic data must provide valuable information to model and predict TCR and antigen interaction and, more importantly, identify or predict neoantigens. This review provides an overview of the application and development of bulk RNA-seq and single-cell (SC) RNA-seq to examine the TCR repertoires. Furthermore, discussed here are bioinformatic tools that can be applied to study the structural biology of peptide/TCR/MHC (major histocompatibility complex) and predict antigenic epitopes using advanced artificial intelligence tools

Differentially expressed genes in the liver of lean and fat chickens

ABSTRACT. This study aimed to investigate gene expression in the chicken liver for lean and fat broiler lines. Birds used in this study were 2 and 4 weeks of age; they were derived from the 14th generation of Northeast Agricultural University broiler lines, which were divergently selected based on abdominal fat content. Chicken Genome Arrays were used to screen differentially expressed genes in the liver tissue from lean and fat birds. At 2 and 4 weeks of age, 770 and 452 genes were differentially expressed between the 2 lines, respectively. The differentially expressed genes were involved in Wnt, insulin signaling, and cell cycle pathways. At 2 and 4 weeks, 42 shared, differentially expressed genes were revealed by the analysis. We speculate that these genes might regulate chicken lipid metabolism

Long-tail hashing

Hashing, which represents data items as compact binary codes, has been becoming a more and more popular technique, e.g., for large-scale image retrieval, owing to its super fast search speed as well as its extremely economical memory consumption. However, existing hashing methods all try to learn binary codes from artificially balanced datasets which are not commonly available in real-world scenarios. In this paper, we propose Long-Tail Hashing Network (LTHNet), a novel two-stage deep hashing approach that addresses the problem of learning to hash for more realistic datasets where the data labels roughly exhibit a long-tail distribution. Specifically, the first stage is to learn relaxed embeddings of the given dataset with its long-tail characteristic taken into account via an end-to-end deep neural network; the second stage is to binarize those obtained embeddings. A critical part of LTHNet is its dynamic meta-embedding module extended with a determinantal point process which can adaptively realize visual knowledge transfer between head and tail classes, and thus enrich image representations for hashing. Our experiments have shown that LTHNet achieves dramatic performance improvements over all state-of-the-art competitors on long-tail datasets, with no or little sacrifice on balanced datasets. Further analyses reveal that while to our surprise directly manipulating class weights in the loss function has little effect, the extended dynamic meta-embedding module, the usage of cross-entropy loss instead of square loss, and the relatively small batch-size for training all contribute to LTHNet's success

Large-Scale Atomistic Simulations of Environmental Effects on the Formation and Properties of Molecular Junctions

Using an updated simulation tool, we examine molecular junctions comprised of benzene-1,4-dithiolate bonded between gold nanotips, focusing on the importance of environmental factors and inter-electrode distance on the formation and structure of bridged molecules. We investigate the complex relationship between monolayer density and tip separation, finding that the formation of multi-molecule junctions is favored at low monolayer density, while single-molecule junctions are favored at high density. We demonstrate that tip geometry and monolayer interactions, two factors that are often neglected in simulation, affect the bonding geometry and tilt angle of bridged molecules. We further show that the structures of bridged molecules at 298 and 77 K are similar.Comment: To appear in ACS Nano, 30 pages, 5 figure