Extended Target Recognition in Cognitive Radar Networks

We address the problem of adaptive waveform design for extended target recognition in cognitive radar networks. A closed-loop active target recognition radar system is extended to the case of a centralized cognitive radar network, in which a generalized likelihood ratio (GLR) based sequential hypothesis testing (SHT) framework is employed. Using Doppler velocities measured by multiple radars, the target aspect angle for each radar is calculated. The joint probability of each target hypothesis is then updated using observations from different radar line of sights (LOS). Based on these probabilities, a minimum correlation algorithm is proposed to adaptively design the transmit waveform for each radar in an amplitude fluctuation situation. Simulation results demonstrate performance improvements due to the cognitive radar network and adaptive waveform design. Our minimum correlation algorithm outperforms the eigen-waveform solution and other non-cognitive waveform design approaches

Detection of neural connections with ex vivo MRI using a ferritin-encoding trans-synaptic virus

The elucidation of neural networks is essential to understanding the mechanisms of brain functions and brain disorders. Neurotropic virus-based trans-synaptic tracing tools have become an effective method for dissecting the structure and analyzing the function of neural-circuitry. However, these tracing systems rely on fluorescent signals, making it hard to visualize the panorama of the labeled networks in mammalian brain in vivo. One MRI method, Diffusion Tensor Imaging (DTI), is capable of imaging the networks of the whole brain in live animals but without information of anatomical connections through synapses. In this report, a chimeric gene coding for ferritin and enhanced green fluorescent protein (EGFP) was integrated into Vesicular stomatitis virus (VSV), a neurotropic virus that is able to spread anterogradely in synaptically connected networks. After the animal was injected with the recombinant VSV (rVSV), rVSV-Ferritin-EGFP, into the somatosensory cortex (SC) for four days, the labeled neural-network was visualized in the postmortem whole brain with a T2-weighted MRI sequence. The modified virus transmitted from SC to synaptically connected downstream regions. The results demonstrate that rVSV-Ferritin-EGFP could be used as a bimodal imaging vector for detecting synaptically connected neural-network with both ex vivo MRI and fluorescent imaging. The strategy in the current study has the potential to longitudinally monitor the global structure of a given neural-network in living animals

The anaphase promoting complex impacts repair choice by protecting ubiquitin signalling at DNA damage sites

Double-strand breaks (DSBs) are repaired through two major pathways, homology-directed recombination (HDR) and non-homologous end joining (NHEJ). While HDR can only occur in S/G2, NHEJ can happen in all cell cycle phases (except mitosis). How then is the repair choice made in S/G2 cells? Here we provide evidence demonstrating that APCCdh1 plays a critical role in choosing the repair pathways in S/G2 cells. Our results suggest that the default for all DSBs is to recruit 53BP1 and RIF1. BRCA1 is blocked from being recruited to broken ends because its recruitment signal, K63-linked poly-ubiquitin chains on histones, is actively destroyed by the deubiquitinating enzyme USP1. We show that the removal of USP1 depends on APCCdh1 and requires Chk1 activation known to be catalysed by ssDNA-RPA-ATR signalling at the ends designated for HDR, linking the status of end processing to RIF1 or BRCA1 recruitment.We thank S.-Y. Lin (MD Anderson Cancer Center) for cell lines; J. Rosen (Baylor College of Medicine) for reagents; H. Masai (Tokyo Metropolitan Institute of Medical Science) for U2OS-Fucci cell line; D. Durocher (University of Toronto) for HeLa-Fucci cell line; E. Citterio (Netherlands Cancer Institute) for GFP-USP3 construct; M.S.Y. Huen (The University of Hong Kong) for RNF168 antibody. This work was performed with facilities and instruments in the Imaging Core of National Center for Protein Science (Beijing), the Cytometry and Cell Sorting Core at Baylor College of Medicine with funding from the NIH (P30 AI036211, P30 CA125123 and S10 RR024574), the Integrated Microscopy Core at Baylor College of Medicine with funding from the NIH (HD007495, DK56338 and CA125123), and the John S. Dunn Gulf Coast Consortium for Chemical Genomics. We also thank other members of the Zhang lab for helpful discussion and support. This work was supported in part by an international collaboration grant (# 2013DFB30210) and a 973 Project grant (# 2013CB910300) from Chinese Minister of Science and Technology, in part by a Chinese National Natural Science Foundation grant (# 81171920), in part by a grant from The Committee of Science and Technology of Beijing Municipality, China (# Z141100000214015), and in part by NIH grants CA116097 and CA122623 to P.Z. J.J. is supported by grants from National Institutes of Health (R01GM102529) and the Welch Foundation (AU-1711). S.H. is supported by grants (# 81272488 and 81472795) from Chinese National Natural Science Foundation. Y.Z. is supported by grants from the National Natural Scientific Foundation of China (No. 81430055), Programs for Changjiang Scholars and Innovative Research Team in University (No. IRT_15R13).S

Método híbrido para categorización de texto basado en aprendizaje y reglas

En este artículo se presenta un nuevo método híbrido de categorización automática de texto, que combina un algoritmo de aprendizaje computacional, que permite construir un modelo base de clasificación sin mucho esfuerzo a partir de un corpus etiquetado, con un sistema basado en reglas en cascada que se emplea para filtrar y reordenar los resultados de dicho modelo base. El modelo puede afinarse añadiendo reglas específicas para aquellas categorías difíciles que no se han entrenado de forma satisfactoria. Se describe una implementación realizada mediante el algoritmo kNN y un lenguaje básico de reglas basado en listas de términos que aparecen en el texto a clasificar. El sistema se ha evaluado en diferentes escenarios incluyendo el corpus de noticias Reuters-21578 para comparación con otros enfoques, y los modelos IPTC y EUROVOC. Los resultados demuestran que el sistema obtiene una precisión y cobertura comparables con las de los mejores métodos del estado del arte

Valorisation of Biomass Waste for Sustainable Bioenergy and Biofuel Production

Although the rapid development of industrialisation has brought great benefits to our societies, waste accumulation and energy depletion have inevitably grown to be critical issues in recent decades. Biomass wastes from a wide range of sources, including household leftovers, agricultural, forestry, and industries [1], are among the scientific community’s gravest concerns today. These wastes can be disposed of by either landfill or traditional combustion. However, disposal uses a large amount of land and results in greenhouse gas emissions. Hence, it is critical to investigate the correct ways of managing or re-utilizing the biowaste and ultimately pursuing a green economy. For example, biomass wastes could be used to serve as alternative feedstocks, providing green and sustainable biofuels such as bioethanol, biogas, and biodiesel [2]. Cellulose-rich crop residues like wheat straw and corn stover are common agricultural wastes that possess potential uses as feedstock for bio-refinery. Digestate wastewater, poultry litter, and animal manure are also versatile sources of bioenergy and nutrients for microbial growth due to their abundance levels of nitrogen and trace elements. Microorganisms including yeasts, algae and bacteria generally participate in the utilisation of biomass wastes. With the rapid development of synthetic biology-guided bioeconomy, superior microbial cell factories will be constructed to degrade the biowaste and produce value-added bioproducts [3]. Thus, the utilisation of biomass wastes is important in efforts to alleviate the waste burden and promises to reduce human reliance on non-renewable petrol energy resources.This editorial paper summarises papers published recently in Bioengineering (Basel) on the study of bioenergy and biofuels production using biomass waste as feedstocks

BRCA1 and Breast Cancer: Molecular Mechanisms and Therapeutic Strategies

Breast cancer susceptibility gene 1 () is a tumor suppressor gene, which is mainly involved in the repair of DNA damage, cell cycle regulation, maintenance of genome stability, and other important physiological processes. Mutations or defects in the gene significantly increase the risk of breast, ovarian, prostate, and other cancers in carriers. In this review, we summarized the molecular functions and regulation of and discussed recent insights into the detection and treatment of mutated breast cancer

Trapping Methylglyoxal by Genistein and Its Metabolites in Mice

Increasing evidence supports dicarbonyl stress such as methylglyoxal (MGO) as one of the major pathogenic links between hyperglycemia and diabetic complications. <i>In vitro</i> studies have shown that dietary flavonoids can inhibit the formation of advanced glycation end products (AGEs) by trapping MGO. However, whether flavonoids can trap MGO <i>in vivo</i> and whether biotransformation limits the trapping capacity of flavonoids remain virtually unknown. In this study, we investigated whether genistein (GEN), the major soy isoflavone, could trap MGO in mice by promoting the formation of MGO adducts of GEN and its metabolites. Two different mouse studies were conducted. In the acute study, a single dose of MGO and GEN were administered to mice via oral gavage. In the chronic study, MGO was given to mice in drinking water for 1 month and then GEN was given to mice for 4 consecutive days via oral gavage. Two mono-MGO adducts of GEN and six mono-MGO adducts of GEN phase I and microbial metabolites were identified in mouse urine samples from these studies using liquid chromatography/electrospray ionization tandem mass spectrometry. The structures of these MGO adducts were confirmed by analyzing their MS^<i>n</i> (<i>n</i> = 1–4) spectra as well as by comparing them with the tandem mass spectra of authentic standards. All of the MGO adducts presented in their phase II conjugated forms in mouse urine samples in the acute and chronic studies. To our knowledge, this is the first <i>in vivo</i> evidence to demonstrate the trapping efficacy of GEN in mice and to show that the metabolites of GEN remain bioactive