Impact of Immunization Technology and Assay Application on Antibody Performance – A Systematic Comparative Evaluation

Antibodies are quintessential affinity reagents for the investigation and determination of a protein's expression patterns, localization, quantitation, modifications, purification, and functional understanding. Antibodies are typically used in techniques such as Western blot, immunohistochemistry (IHC), and enzyme-linked immunosorbent assays (ELISA), among others. The methods employed to generate antibodies can have a profound impact on their success in any of these applications. We raised antibodies against 10 serum proteins using 3 immunization methods: peptide antigens (3 per protein), DNA prime/protein fragment-boost (“DNA immunization”; 3 per protein), and full length protein. Antibodies thus generated were systematically evaluated using several different assay technologies (ELISA, IHC, and Western blot). Antibodies raised against peptides worked predominantly in applications where the target protein was denatured (57% success in Western blot, 66% success in immunohistochemistry), although 37% of the antibodies thus generated did not work in any of these applications. In contrast, antibodies produced by DNA immunization performed well against both denatured and native targets with a high level of success: 93% success in Western blots, 100% success in immunohistochemistry, and 79% success in ELISA. Importantly, success in one assay method was not predictive of success in another. Immunization with full length protein consistently yielded the best results; however, this method is not typically available for new targets, due to the difficulty of generating full length protein. We conclude that DNA immunization strategies which are not encumbered by the limitations of efficacy (peptides) or requirements for full length proteins can be quite successful, particularly when multiple constructs for each protein are used

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Knowledge-Graph Augmented Word Representations for Named Entity Recognition

By modeling the context information, ELMo and BERT have successfully improved the state-of-the-art of word representation, and demonstrated their effectiveness on the Named Entity Recognition task. In this paper, in addition to such context modeling, we propose to encode the prior knowledge of entities from an external knowledge base into the representation, and introduce a Knowledge-Graph Augmented Word Representation or KAWR for named entity recognition. Basically, KAWR provides a kind of knowledge-aware representation for words by 1) encoding entity information from a pre-trained KG embedding model with a new recurrent unit (GERU), and 2) strengthening context modeling from knowledge wise by providing a relation attention scheme based on the entity relations defined in KG. We demonstrate that KAWR, as an augmented version of the existing linguistic word representations, promotes F1 scores on 5 datasets in various domains by +0.46∼+2.07. Better generalization is also observed for KAWR on new entities that cannot be found in the training sets

An integrated visualization approach for smart grid attacks

As the world\u27s demand for more and cleaner energy continuously grow, smart electric power grid has become a significant research area for scientists and engineers. In the past few years, smart grid security has become an emerging topic and many new attack and defense strategies have been proposed. However, many of the outcomes of such approaches are presented in complicated ways and lack visualization capability to understand the whole grid behavior under complex attacks. In this paper, we present an integrated visualization approach to understand the smart grid attack. We introduce the ESRI ArcGIS software as our visualization platform and develop an interface so that ArcGIS can effectively exchange information and communicate with MATLAB, where the power grid security attack and defense algorithms are simulated. This integrated approach provides an effective way to understand the power grid behavior with geographical details under complex attacks. Furthermore, it could help to develop new defense strategies to improve security and reliability of smart grid. © 2012 IEEE

Engineering Pyrite-Type Bimetallic Ni-Doped CoS2 Nanoneedle Arrays over a Wide Compositional Range for Enhanced Oxygen and Hydrogen Electrocatalysis with Flexible Property

The development of cheap and efficient catalytic electrodes is of great importance, to promote the sluggish overall water-splitting systems associated with the large-scale application of clean and renewable energy technologies. In this work, we report the controlled synthesis of pyrite-type bimetallic Ni-doped CoS2 nanoneedle (NN) arrays supported on stainless steel (SS) (designated as NixCo1−xS2 NN/SS, 0 ≤ x ≤ 1) and the related compositional influence on electrocatalytic efficiencies for the oxygen and hydrogen evolution reaction (OER/HER). Impressively, the Ni0.33Co0.67S2 NN/SS displays superior activity and faster kinetics for catalyzing OER (low overpotential of 286 mV at 50 mA cm−2; Tafel value of 55 mV dec−1) and HER (low overpotential of 350 mV at 30 mA cm−2; Tafel value of 76 mV dec−1) than those of counterparts with other Ni/Co ratios and also monometallic Ni- or Co-based sulfides, which is attributed to the optimized balance from the improved electron transfer capability, increased exposure of electrocatalytic active sites, and favorable dissipation of gaseous products over the nanoneedle surface. Furthermore, the conductive, flexible SS support and firmly attached in-situ integrated feature, result in the flexibility and remarkable long-term stability of as-prepared binder-free Ni0.33Co0.67S2 NN/SS electrode. These results demonstrate element-doping could be an efficient route at the atomic level to design new materials and further optimize the surface physicochemical properties for enhancing the overall electrochemical water splitting activity

QMR:Q-learning based Multi-objective optimization Routing protocol for Flying Ad Hoc Networks

International audienceA network with reliable and rapid communication is critical for Unmanned Aerial Vehicles (UAVs). Flying Ad Hoc Networks (FANETs) consisting of UAVs is a new paradigm of wireless communication. However, the highly dynamic topology of FANETs and limited energy of UAVs have brought great challenges to the routing design of FANETs. It is difficult for existing routing protocols for Mobile Ad Hoc Networks (MANET-s) and Vehicular Ad Hoc Networks (VANETs) to adapt the high dynamics of FANETs. Moreover, few of existing routing protocols simultaneously meet the requirement of low delay and low energy consumption of FANETs. This paper proposes a novel Q-learning based Multi-objective optimization Routing protocol for FANETs to provide low-delay and low-energy service guarantees. Most of existing Q-learning based protocols use a fixed value for the Q-learning parameters. In contrast, Q-learning parameters can be adaptively adjusted in the proposed protocol to adapt to the high dynamics of FANETs. In addition, a new exploration and exploitation mechanism is also proposed to explore some undiscovered potential optimal routing path while exploiting the acquired knowledge. Instead of using past neighbor relationships, the proposed method re-estimates neighbor relationships in the routing decision process to select the more reliable next hop. Simulation results show that the proposed method can provide higher packet arrival ratio, lower delay and energy consumption than existing good performing Q-learning based routing method

Dissipation Behavior, Residue, and Risk Assessment of Benziothiazolinone in Apples

Benziothiazolinone is the first independently developed fungicide in China. It has been used to effectively control fungal diseases in a variety of fruits, vegetables, and crops. In this study, the degradation behavior and final residue of benziothiazolinone in apples is discussed, and the dietary risk to consumers was evaluated. High-performance liquid chromatography–tandem mass spectrometry (LC-MS/MS) was used to determine benziothiazolinone residues in apple samples from eight different regions of China. The average recovery of benziothiazolinone in apples was 85.5–100.2%, and the relative standard deviation (RSD) was 0.8–14.9%. The limits of the method of quantification of benziothiazolinone in apples was 0.01 mg/kg. Under good agricultural practices (GAP) conditions, the final residues of benziothiazolinone in apples were below 0.01 mg/kg, lower than the maximum residual limit (MRL) of China. Although the degradation half-lives of benziothiazolinone were 23.9 d–33.0 d, the risk quotient (RQ) of benziothiazolinone was 15.5% by calculating the national estimated daily intake and comparing it with the acceptable daily intake. These results suggested that under GAP conditions, the intake of benziothiazolinone from apples exhibits an acceptably low health risk on consumers

1.3 μm p-Modulation Doped InGaAs/GaAs Quantum Dot Lasers with High Speed Direct Modulation Rate and Strong Optical Feedback Resistance

Aiming to realize high-speed optical transmitters for isolator-free telecommunication systems, 1.3 μm p-modulation doped InGaAs/GaAs quantum dot (QD) lasers with a 400 μm long cavity have been reported. Compared with the un-doped QD laser as a reference, the p-doped QD laser emits at ground state, with an ultra-low threshold current and a high maximum output power. The p-doped QD laser also shows enhanced dynamic characteristics, with a 10 Gb/s large-signal direct modulation rate and a 7.8 GHz 3dB-bandwidth. In addition, the p-doped QD laser exhibits a strong coherent optical feedback resistance, which might be beyond −9 dB