Research and Design of Rootkit Detection Method

AbstractRootkit is one of the most important issues of network communication systems, which is related to the security and privacy of Internet users. Because of the existence of the back door of the operating system, a hacker can use rootkit to attack and invade other people's computers and thus he can capture passwords and message traffic to and from these computers easily. With the development of the rootkit technology, its applications are more and more extensive and it becomes increasingly difficult to detect it. In addition, for various reasons such as trade secrets, being difficult to be developed, and so on, the rootkit detection technology information and effective tools are still relatively scarce. In this paper, based on the in-depth analysis of the rootkit detection technology, a new kind of the rootkit detection structure is designed and a new method (software), X-Anti, is proposed. Test results show that software designed based on structure proposed is much more efficient than any other rootkit detection software

Removal of Thin Clouds in Landsat-8 OLI Data with Independent Component Analysis

An approach to remove clouds in Landsat-8 operational land imager (OLI) data was developed with independent component analysis (ICA). Within cloud-covered areas, histograms were derived to quantify changes of the reflectance values before and after the use of the algorithm. Referred to a cloud-free image, changes of histogram curves validated the algorithm. Scatterplots were generated and linear regression performed for the reflectance values of each band before and after the algorithm, and compared to those of the reference image. Band-by-band, results in cloud removal were acceptable. The algorithm had little effect on pixels in cloud-free areas after the analyses of histograms, scatterplots, and linear regression equations. Finally, the algorithm was applied to various land use and land cover types and cloud conditions, and to a full Landsat-8 scene yielding satisfactory results efficiently

Molecular Determinants of Magnolol Targeting Both RXRα and PPARγ

Nuclear receptors retinoic X receptor α (RXRα) and peroxisome proliferator activated receptor γ (PPARγ) function potently in metabolic diseases, and are both important targets for anti-diabetic drugs. Coactivation of RXRα and PPARγ is believed to synergize their effects on glucose and lipid metabolism. Here we identify the natural product magnolol as a dual agonist targeting both RXRα and PPARγ. Magnolol was previously reported to enhance adipocyte differentiation and glucose uptake, ameliorate blood glucose level and prevent development of diabetic nephropathy. Although magnolol can bind and activate both of these two nuclear receptors, the transactivation assays indicate that magnolol exhibits biased agonism on the transcription of PPAR-response element (PPRE) mediated by RXRα:PPARγ heterodimer, instead of RXR-response element (RXRE) mediated by RXRα:RXRα homodimer. To further elucidate the molecular basis for magnolol agonism, we determine both the co-crystal structures of RXRα and PPARγ ligand-binding domains (LBDs) with magnolol. Structural analyses reveal that magnolol adopts its two 5-allyl-2-hydroxyphenyl moieties occupying the acidic and hydrophobic cavities of RXRα L-shaped ligand-binding pocket, respectively. While, two magnolol molecules cooperatively accommodate into PPARγ Y-shaped ligand-binding pocket. Based on these two complex structures, the key interactions for magnolol activating RXRα and PPARγ are determined. As the first report on the dual agonist targeting RXRα and PPARγ with receptor-ligand complex structures, our results are thus expected to help inspect the potential pharmacological mechanism for magnolol functions, and supply useful hits for nuclear receptor multi-target ligand design

Characterization of the neural circuitry of the auditory thalamic reticular nucleus and its potential role in salicylate-induced tinnitus

IntroductionSubjective tinnitus, the perception of sound without an external acoustic source, is often subsequent to noise-induced hearing loss or ototoxic medications. The condition is believed to result from neuroplastic alterations in the auditory centers, characterized by heightened spontaneous neural activities and increased synchrony due to an imbalance between excitation and inhibition. However, the role of the thalamic reticular nucleus (TRN), a structure composed exclusively of GABAergic neurons involved in thalamocortical oscillations, in the pathogenesis of tinnitus remains largely unexplored.MethodsWe induced tinnitus in mice using sodium salicylate and assessed tinnitus-like behaviors using the Gap Pre-Pulse Inhibition of the Acoustic Startle (GPIAS) paradigm. We utilized combined viral tracing techniques to identify the neural circuitry involved and employed immunofluorescence and confocal imaging to determine cell types and activated neurons.ResultsSalicylate-treated mice exhibited tinnitus-like behaviors. Our tracing clearly delineated the inputs and outputs of the auditory-specific TRN. We discovered that chemogenetic activation of the auditory TRN significantly reduced the salicylate-evoked rise in c-Fos expression in the auditory cortex.DiscussionThis finding posits the TRN as a potential modulatory target for tinnitus treatment. Furthermore, the mapped sensory inputs to the auditory TRN suggest possibilities for employing optogenetic or sensory stimulations to manipulate thalamocortical activities. The precise mapping of the auditory TRN-mediated neural pathways offers a promising avenue for designing targeted interventions to alleviate tinnitus symptoms

The Trickle-down Impact of Reward (In-)consistency on RLHF

Standard practice within Reinforcement Learning from Human Feedback (RLHF) involves optimizing against a Reward Model (RM), which itself is trained to reflect human preferences for desirable generations. A notable subject that is understudied is the (in-)consistency of RMs -- whether they can recognize the semantic changes to different prompts and appropriately adapt their reward assignments -- and their impact on the downstream RLHF model. In this paper, we visit a series of research questions relevant to RM inconsistency: (1) How can we measure the consistency of reward models? (2) How consistent are the existing RMs and how can we improve them? (3) In what ways does reward inconsistency influence the chatbots resulting from the RLHF model training? We propose Contrast Instructions -- a benchmarking strategy for the consistency of RM. Each example in Contrast Instructions features a pair of lexically similar instructions with different ground truth responses. A consistent RM is expected to rank the corresponding instruction and response higher than other combinations. We observe that current RMs trained with the standard ranking objective fail miserably on Contrast Instructions compared to average humans. To show that RM consistency can be improved efficiently without using extra training budget, we propose two techniques ConvexDA and RewardFusion, which enhance reward consistency through extrapolation during the RM training and inference stage, respectively. We show that RLHF models trained with a more consistent RM yield more useful responses, suggesting that reward inconsistency exhibits a trickle-down effect on the downstream RLHF process

Emodin targets the β-hydroxyacyl-acyl carrier protein dehydratase from Helicobacter pylori: enzymatic inhibition assay with crystal structural and thermodynamic characterization

<p>Abstract</p> <p>Background</p> <p>The natural product Emodin demonstrates a wide range of pharmacological properties including anticancer, anti-inflammatory, antiproliferation, vasorelaxant and anti-<it>H. pylori </it>activities. Although its <it>H. pylori </it>inhibition was discovered, no acting target information against Emodin has been revealed to date.</p> <p>Results</p> <p>Here we reported that Emodin functioned as a competitive inhibitor against the recombinant β-hydroxyacyl-ACP dehydratase from <it>Helicobacter pylori </it>(HpFabZ), and strongly inhibited the growth of <it>H. pylori </it>strains SS1 and ATCC 43504. Surface plasmon resonance (SPR) and isothermal titration calorimetry (ITC) based assays have suggested the kinetic and thermodynamic features of Emodin/HpFabZ interaction. Additionally, to inspect the binding characters of Emodin against HpFabZ at atomic level, the crystal structure of HpFabZ-Emodin complex was also examined. The results showed that Emodin inhibition against HpFabZ could be implemented either through its occupying the entrance of the tunnel or embedding into the tunnel to prevent the substrate from accessing the active site.</p> <p>Conclusion</p> <p>Our work is expected to provide useful information for illumination of Emodin inhibition mechanism against HpFabZ, while Emodin itself could be used as a potential lead compound for further anti-bacterial drug discovery.</p

Microorganisms in coastal wetland sediments: a review on microbial community structure, functional gene, and environmental potential

Coastal wetlands (CW) are the junction of the terrestrial and marine ecosystems and have special ecological compositions and functions, which are important for maintaining biogeochemical cycles. Microorganisms inhabiting in sediments play key roles in the material cycle of CW. Due to the variable environment of CW and the fact that most CW are affected by human activities and climate change, CW are severely degraded. In-depth understanding of the community structure, function, and environmental potential of microorganisms in CW sediments is essential for wetland restoration and function enhancement. Therefore, this paper summarizes microbial community structure and its influencing factors, discusses the change patterns of microbial functional genes, reveals the potential environmental functions of microorganisms, and further proposes future prospects about CW studies. These results provide some important references for promoting the application of microorganisms in material cycling and pollution remediation of CW

A Low-Complexity Optimal Switching Time-Modulated Model-Predictive Control for PMSM With Three-Level NPC Converter

Conventional finite control set model-predictive control (FCS-MPC) presents a high computational burden, especially in three-level neutral-point-clamped (NPC) converters. This article proposes a low-complexity optimal switching time-modulated model-predictive control (OST-M2PC) method for a three-level NPC converter. In the proposed OST-M2PC method, the optimal switching time is calculated using a cost function. Compared with the conventional FCS-MPC, the proposed OST-M2PC method has a fixed switching frequency as well as better power quality. The proposed OST-M2PC can operate at a 20-kHz sampling frequency, reducing the computational burden of the processor. Simulation and experimental results validate the operation of the proposed method