The Development of ELISA and SPR-Based Immunoassays for the Detection of Heat Shock Proteins.

Heat shock proteins (Hsps) are highly conserved molecules in all eukaryotes and prokaryotes. They are named on the basis of their molecular weight and their synthesis is up-regulated by stress conditions such as inflammation, oxidative stress and exposure to high temperature. They function by assisting the folding of newly synthesized proteins and thus prevent aggregation or damage to other cellular components. Beside the role in intracellular protein folding, heat shock proteins can particularly act as intercellular signals with a wide variety of biological effects, such as to stimulate cells to produce proinflammatory cytokines, like TNFα, and other proteins involved in immunity and inflammation, or they are also thought to be involved in the pathogenesis of a wide range of diseases such as diabetes mellitus and cardiovascular disease. Thus, screening for aberrant expression of this protein could be an easy and useful tool to detect at risk individuals for developing and those more susceptible towards developing these diseases. Currently, the Enzyme-Linked ImmunoSorbent Assay (ELISA) is the main immunoassay method used for the measurement of heat shock proteins levels in both clinical and research laboratories. It is relatively more rapid and sensitive than RadioImmunoassay (RIA), and most importantly, it is safer because enzymes are used as labels instead of harmful radioactive substances. However, a newly developed biosensor method, the Surface Plasmon Resonance (SPR), offers a number of advantageous over ELISA. For example, it is much more simple and rapid because a lot of steps can be set up automatically and no labels are required for SPR immunoassays. In this study, a comparison of different types of ELISA assays was made. The results showed that the Sandwich format of ELISA is much more sensitive than Indirect ELISA for detection of the concentrations of Heat Shock Protein 70 (Hsp70), and this sensitivity can be further improved by applying an Avidin-Biotin system together with Sandwich ELISA under certain conditions. To develop the SPR protocol for the detection of heat shock protein concentrations, two Hsp70 binding curves using different assay formats, the Sandwich SPR immunoassay and the Competitive SPR immunoassay, were set up by using CM 5 sensor chip. Another sensor chip, the mixed Self-Assembled Monolayer (mSAM), was also examined using Sandwich SPR format. No subsequent experimental steps were carried on for SPR studies since the regeneration conditions of these tests for both sensor chips need to be well studied. Thus, this study suggests that in order to set up a SPR immunoassay protocol that is a better choice for the detection of heat shock protein levels in complex matrixes than ELISA, experimental conditions, such as the choice of regeneration buffer and the duration of regeneration cycle, need to be well optimized

High-Q exterior whispering gallery modes in a metal-coated microresonator

We propose a kind of plasmonic whispering gallery modes highly localized on the exterior surface of a metal-coated microresonator. This exterior (EX) surface mode possesses high quality factors at room temperature, and can be efficiently excited by a tapered fiber. The EX mode can couple to an interior (IN) mode and this coupling produces a strong anti-crossing behavior, which not only allows conversion of IN to EX modes, but also forms a long-lived anti-symmetric mode. As a potential application, the EX mode could be used for a biosensor with a sensitivity high up to 500 nm per refraction index unit, a large figure of merit, and a wide detection range

Cavity QED treatment of scattering-induced efficient free-space excitation and collection in high-Q whispering-gallery microcavities

Whispering-gallery microcavity laser possesses ultralow threshold, whereas convenient free-space optical excitation and collection suffer from low efficiencies due to its rotational symmetry. Here we analytically study a three-dimensional microsphere coupled to a nano-sized scatterer in the framework of quantum optics. It is found that the scatterer is capable of coupling light in and out of the whispering-gallery modes (WGMs) without seriously degrading their high-Q properties, while the microsphere itself plays the role of a lens to focus the input beam on the scatterer and vice versa. Our analytical results show that (1) the high-Q WGMs can be excited in free space, and (2) over 50% of the microcavity laser emission can be collected within less than ${1}^{\circ}$. This coupling system holds great potential for low threshold microlasers free of external couplers.Comment: 10 pages, 8 figure

Strongly enhanced light-matter interaction in a hybrid photonic-plasmonic resonator

We propose a hybrid photonic-plasmonic resonant structure which consists of a metal nanoparticle (MNP) and a whispering gallery mode (WGM) microcavity. It is found that the hybrid mode enables a strong interaction between the light and matter, and the single-atom cooperativity is enhanced by more than two orders of magnitude compared to that in a bare WGM microcavity. This remarkable improvement originates from two aspects: (1) the MNP offers a highly enhanced local field in the vicinity of an emitter, and (2), surprisingly, the high-\textit{Q} property of WGMs can be maintained in the presence of the MNP. Thus the present system has great advantages over a single microcavity or a single MNP, and holds great potential in quantum optics, nonlinear optics and highly sensitive biosening.Comment: 5 pages, 4 figure

Private-Library-Oriented Code Generation with Large Language Models

Large language models (LLMs), such as Codex and GPT-4, have recently showcased their remarkable code generation abilities, facilitating a significant boost in coding efficiency. This paper will delve into utilizing LLMs for code generation in private libraries, as they are widely employed in everyday programming. Despite their remarkable capabilities, generating such private APIs poses a formidable conundrum for LLMs, as they inherently lack exposure to these private libraries during pre-training. To address this challenge, we propose a novel framework that emulates the process of programmers writing private code. This framework comprises two modules: APIFinder first retrieves potentially useful APIs from API documentation; and APICoder then leverages these retrieved APIs to generate private code. Specifically, APIFinder employs vector retrieval techniques and allows user involvement in the retrieval process. For APICoder, it can directly utilize off-the-shelf code generation models. To further cultivate explicit proficiency in invoking APIs from prompts, we continuously pre-train a reinforced version of APICoder, named CodeGenAPI. Our goal is to train the above two modules on vast public libraries, enabling generalization to private ones. Meanwhile, we create four private library benchmarks, including TorchDataEval, TorchDataComplexEval, MonkeyEval, and BeatNumEval, and meticulously handcraft test cases for each benchmark to support comprehensive evaluations. Numerous experiments on the four benchmarks consistently affirm the effectiveness of our approach. Furthermore, deeper analysis is also conducted to glean additional insights

On chip, high-sensitivity thermal sensor based on high-Q polydimethylsiloxane-coated microresonator

A high-sensitivity thermal sensing is demonstrated by coating a layer of polydimethylsiloxane (PDMS) on the surface of a silica toroidal microresonator on a silicon wafer. Possessing high-Q whispering gallery modes (WGMs), the PDMS-coated microresonator is highly sensitive to the temperature change of the surroundings. We find that, when the PDMS layer becomes thicker, the WGM experiences a transition from red- to blue-shift with temperature increasing due to the negative thermal-optic coefficient of PDMS. The measured sensitivity (0.151 nm/K) is one order of magnitude higher than pure silica microcavity sensors. The ultra-high resolution of the thermal sensor is also analyzed to reach 10-4 K

MICRAT: A Novel Algorithm for Inferring Gene Regulatory Networks Using Time Series Gene Expression Data

Background: Reconstruction of gene regulatory networks (GRNs), also known as reverse engineering of GRNs, aims to infer the potential regulation relationships between genes. With the development of biotechnology, such as gene chip microarray and RNA-sequencing, the high-throughput data generated provide us with more opportunities to infer the gene-gene interaction relationships using gene expression data and hence understand the underlying mechanism of biological processes. Gene regulatory networks are known to exhibit a multiplicity of interaction mechanisms which include functional and non-functional, and linear and non-linear relationships. Meanwhile, the regulatory interactions between genes and gene products are not spontaneous since various processes involved in producing fully functional and measurable concentrations of transcriptional factors/proteins lead to a delay in gene regulation. Many different approaches for reconstructing GRNs have been proposed, but the existing GRN inference approaches such as probabilistic Boolean networks and dynamic Bayesian networks have various limitations and relatively low accuracy. Inferring GRNs from time series microarray data or RNA-sequencing data remains a very challenging inverse problem due to its nonlinearity, high dimensionality, sparse and noisy data, and significant computational cost, which motivates us to develop more effective inference methods. Results: We developed a novel algorithm, MICRAT (Maximal Information coefficient with Conditional Relative Average entropy and Time-series mutual information), for inferring GRNs from time series gene expression data. Maximal information coefficient (MIC) is an effective measure of dependence for two-variable relationships. It captures a wide range of associations, both functional and non-functional, and thus has good performance on measuring the dependence between two genes. Our approach mainly includes two procedures. Firstly, it employs maximal information coefficient for constructing an undirected graph to represent the underlying relationships between genes. Secondly, it directs the edges in the undirected graph for inferring regulators and their targets. In this procedure, the conditional relative average entropies of each pair of nodes (or genes) are employed to indicate the directions of edges. Since the time delay might exist in the expression of regulators and target genes, time series mutual information is combined to cooperatively direct the edges for inferring the potential regulators and their targets. We evaluated the performance of MICRAT by applying it to synthetic datasets as well as real gene expression data and compare with other GRN inference methods. We inferred five 10-gene and five 100-gene networks from the DREAM4 challenge that were generated using the gene expression simulator GeneNetWeaver (GNW). MICRAT was also used to reconstruct GRNs on real gene expression data including part of the DNA-damaged response pathway (SOS DNA repair network) and experimental dataset in E. Coli. The results showed that MICRAT significantly improved the inference accuracy, compared to other inference methods, such as TDBN, etc. Conclusion: In this work, a novel algorithm, MICRAT, for inferring GRNs from time series gene expression data was proposed by taking into account dependence and time delay of expressions of a regulator and its target genes. This approach employed maximal information coefficients for reconstructing an undirected graph to represent the underlying relationships between genes. The edges were directed by combining conditional relative average entropy with time course mutual information of pairs of genes. The proposed algorithm was evaluated on the benchmark GRNs provided by the DREAM4 challenge and part of the real SOS DNA repair network in E. Coli. The experimental study showed that our approach was comparable to other methods on 10-gene datasets and outperformed other methods on 100-gene datasets in GRN inference from time series datasets

MalFox: Camouflaged Adversarial Malware Example Generation Based on Conv-GANs Against Black-Box Detectors

Deep learning is a thriving field currently stuffed with many practical applications and active research topics. It allows computers to learn from experience and to understand the world in terms of a hierarchy of concepts, with each being defined through its relations to simpler concepts. Relying on the strong capabilities of deep learning, we propose a convolutional generative adversarial network-based (Conv-GAN) framework titled MalFox, targeting adversarial malware example generation against third-party black-box malware detectors. Motivated by the rival game between malware authors and malware detectors, MalFox adopts a confrontational approach to produce perturbation paths, with each formed by up to three methods (namely Obfusmal, Stealmal, and Hollowmal) to generate adversarial malware examples. To demonstrate the effectiveness of MalFox, we collect a large dataset consisting of both malware and benignware programs, and investigate the performance of MalFox in terms of accuracy, detection rate, and evasive rate of the generated adversarial malware examples. Our evaluation indicates that the accuracy can be as high as 99.0% which significantly outperforms the other 12 well-known learning models. Furthermore, the detection rate is dramatically decreased by 56.8% on average, and the average evasive rate is noticeably improved by up to 56.2%

LCDMA: Lightweight Cross-domain Mutual Identity Authentication scheme for Internet of Things

With the widespread popularity of mobile terminals in the Internet of things (IoT), the demand for cross-domain access of mobile terminals between different regions has also increased significantly. The nature of wireless communication media makes mobile terminals vulnerable to security threats in cross-domain access. Identity authentication is a prerequisite for secure data transmission in cross-domain, and it is also the first step to guarantee the credibility of data sources. Most existing authentication schemes are based on bilinear pairing or public key encryption and decryption with high computation overhead, which are not suitable for the resource-limited mobile IoT terminals. Moreover, these schemes have some security drawbacks and cannot meet the security requirements of cross-domain access. In this paper, we propose a lightweight cross-domain mutual identity authentication (LCDMA) for mobile IoT environments. LCDMA uses symmetric polynomials instead of high-complexity bilinear pairing in the traditional schemes. We theoretically analyze the security performance under the random oracle model. Our results show that LCDMA not only resists common attacks but also preserves secure traceability while guaranteeing anonymity. Performance evaluation further demonstrates that our scheme has better performance in terms of computation and communication overhead, compared with other existing representative schemes