Dendrimers as Carriers for siRNA Delivery and Gene Silencing: A Review

RNA interference (RNAi) was first literaturally reported in 1998 and has become rapidly a promising tool for therapeutic applications in gene therapy. In a typical RNAi process, small interfering RNAs (siRNA) are used to specifically downregulate the expression of the targeted gene, known as the term “gene silencing.” One key point for successful gene silencing is to employ a safe and efficient siRNA delivery system. In this context, dendrimers are emerging as potential nonviral vectors to deliver siRNA for RNAi purpose. Dendrimers have attracted intense interest since their emanating research in the 1980s and are extensively studied as efficient DNA delivery vectors in gene transfer applications, due to their unique features based on the well-defined and multivalent structures. Knowing that DNA and RNA possess a similar structure in terms of nucleic acid framework and the electronegative nature, one can also use the excellent DNA delivery properties of dendrimers to develop effective siRNA delivery systems. In this review, the development of dendrimer-based siRNA delivery vectors is summarized, focusing on the vector features (siRNA delivery efficiency, cytotoxicity, etc.) of different types of dendrimers and the related investigations on structure-activity relationship to promote safe and efficient siRNA delivery system

Alveolar macrophage modulation via the gut–lung axis in lung diseases

Several studies have demonstrated great potential implications for the gut–lung axis in lung disease etiology and treatment. The gut environment can be influenced by diet, metabolites, microbiotal composition, primary diseases, and medical interventions. These changes modulate the functions of alveolar macrophages (AMs) to shape the pulmonary immune response, which greatly impacts lung health. The immune modulation of AMs is implicated in the pathogenesis of various lung diseases. However, the mechanism of the gut–lung axis in lung diseases has not yet been determined. This mini-review aimed to shed light on the critical nature of communication between the gut and AMs during the development of pulmonary infection, injury, allergy, and malignancy. A better understanding of their crosstalk may provide new insights into future therapeutic strategies targeting the gut–AM interaction

On χ−\chi-slice pretzel links

A link is called $\chi-$slice if it bounds a smooth properly embedded surface in the 4-ball with no closed components and Euler characteristic 1. If a link has a single component, then it is $\chi-$slice if and only if it is slice. One motivation for studying such links is that the double cover of the 3-sphere branched along a nonzero determinant $\chi-$slice link is a rational homology 3-sphere that bounds a rational homology 4-ball. This article aims to generalize known results about the sliceness of pretzel knots to the $\chi-$sliceness of pretzel links. In particular, we completely classify positive and negative pretzel links that are $\chi-$slice, and obtain partial classifications of 3-stranded and 4-stranded pretzel links that are $\chi-$slice. As a consequence, we obtain infinite families of Seifert fiber spaces that bound rational homology 4-balls

Bi-level Actor-Critic for Multi-agent Coordination

Coordination is one of the essential problems in multi-agent systems. Typically multi-agent reinforcement learning (MARL) methods treat agents equally and the goal is to solve the Markov game to an arbitrary Nash equilibrium (NE) when multiple equilibra exist, thus lacking a solution for NE selection. In this paper, we treat agents \emph{unequally} and consider Stackelberg equilibrium as a potentially better convergence point than Nash equilibrium in terms of Pareto superiority, especially in cooperative environments. Under Markov games, we formally define the bi-level reinforcement learning problem in finding Stackelberg equilibrium. We propose a novel bi-level actor-critic learning method that allows agents to have different knowledge base (thus intelligent), while their actions still can be executed simultaneously and distributedly. The convergence proof is given, while the resulting learning algorithm is tested against the state of the arts. We found that the proposed bi-level actor-critic algorithm successfully converged to the Stackelberg equilibria in matrix games and find an asymmetric solution in a highway merge environment

Efficiently Measuring the Cognitive Ability of LLMs: An Adaptive Testing Perspective

Large language models (LLMs), like ChatGPT, have shown some human-like cognitive abilities. For comparing these abilities of different models, several benchmarks (i.e. sets of standard test questions) from different fields (e.g., Literature, Biology and Psychology) are often adopted and the test results under traditional metrics such as accuracy, recall and F1, are reported. However, such way for evaluating LLMs can be inefficient and inaccurate from the cognitive science perspective. Inspired by Computerized Adaptive Testing (CAT) used in psychometrics, we propose an adaptive testing framework for LLM evaluation. Rather than using a standard test set and simply reporting accuracy, this approach dynamically adjusts the characteristics of the test questions, such as difficulty, based on the model's performance. This allows for a more accurate estimation of the model's abilities, using fewer questions. More importantly, it allows LLMs to be compared with humans easily, which is essential for NLP models that aim for human-level ability. Our diagnostic reports have found that ChatGPT often behaves like a ``careless student'', prone to slip and occasionally guessing the questions. We conduct a fine-grained diagnosis and rank the latest 6 instruction-tuned LLMs from three aspects of Subject Knowledge, Mathematical Reasoning, and Programming, where GPT4 can outperform other models significantly and reach the cognitive ability of middle-level students. Different tests for different models using efficient adaptive testing -- we believe this has the potential to become a new norm in evaluating large language models

Neural Network Model Extraction Attacks in Edge Devices by Hearing Architectural Hints

As neural networks continue their reach into nearly every aspect of software operations, the details of those networks become an increasingly sensitive subject. Even those that deploy neural networks embedded in physical devices may wish to keep the inner working of their designs hidden -- either to protect their intellectual property or as a form of protection from adversarial inputs. The specific problem we address is how, through heavy system stack, given noisy and imperfect memory traces, one might reconstruct the neural network architecture including the set of layers employed, their connectivity, and their respective dimension sizes. Considering both the intra-layer architecture features and the inter-layer temporal association information introduced by the DNN design empirical experience, we draw upon ideas from speech recognition to solve this problem. We show that off-chip memory address traces and PCIe events provide ample information to reconstruct such neural network architectures accurately. We are the first to propose such accurate model extraction techniques and demonstrate an end-to-end attack experimentally in the context of an off-the-shelf Nvidia GPU platform with full system stack. Results show that the proposed techniques achieve a high reverse engineering accuracy and improve the one's ability to conduct targeted adversarial attack with success rate from 14.6\%$\sim$25.5\% (without network architecture knowledge) to 75.9\% (with extracted network architecture)

Understand how machine learning impact lung cancer research from 2010 to 2021: A bibliometric analysis

Advances in lung cancer research applying machine learning (ML) technology have generated many relevant literature. However, there is absence of bibliometric analysis review that aids a comprehensive understanding of this field and its progress. Present article for the first time performed a bibliometric analysis to clarify research status and focus from 2010 to 2021. In the analysis, a total of 2,312 relevant literature were searched and retrieved from the Web of Science Core Collection database. We conducted a bibliometric analysis and further visualization. During that time, exponentially growing annual publication and our model have shown a flourishing research prospect. Annual citation reached the peak in 2017. Researchers from United States and China have produced most of the relevant literature and strongest partnership between them. Medical image analysis and Nature appeared to bring more attention to the public. The computer-aided diagnosis, precision medicine, and survival prediction were the focus of research, reflecting the development trend at that period. ML did make a big difference in lung cancer research in the past decade

Application of Double Width Seismic Data to Channel Sand Body Prediction of X Gas Field in the East China Sea

Due to that the main reservoirs of the X gas field in the East China Sea were deeply buried and have large lateral changes, the conventional seismic data resulted in poor quality and low resolution, which couldn’t meet the increasingly refined geological requirements in exploration and development. Seismic data with wideband and wide azimuth was obtained by using the acquisition method of three ships and four sources with oblique cables, which held the characteristics of high resolution, high signal-to-noise ratio and high fidelity. By taking advantage of the superior information of wideband and wide azimuth seismic data which was high-resolutional and anisotropic, combined with the simultaneous prestack inversion, the inversion body of sensitive elastic parameters of channel sand bodies in different azimuths can be obtained, and superimpose multiple azimuth inversion bodies perpendicular to the direction of the river channel to carry out fine predictions of channel sand bodies. Compared the conventional seismic data, reservoir inversion based on wideband and wide azimuth seismic data improved the prediction accuracy of channel sand bodies, laying a foundation for the progressive exploration and development of the X gas field in the East China Sea

NRT-YOLO: Improved YOLOv5 Based on Nested Residual Transformer for Tiny Remote Sensing Object Detection

To address the problems of tiny objects and high resolution of object detection in remote sensing imagery, the methods with coarse-grained image cropping have been widely studied. However, these methods are always inefficient and complex due to the two-stage architecture and the huge computation for split images. For these reasons, this article employs YOLO and presents an improved architecture, NRT-YOLO. Specifically, the improvements can be summarized as: extra prediction head and related feature fusion layers; novel nested residual Transformer module, C3NRT; nested residual attention module, C3NRA; and multi-scale testing. The C3NRT module presented in this paper could boost accuracy and reduce complexity of the network at the same time. Moreover, the effectiveness of the proposed method is demonstrated by three kinds of experiments. NRT-YOLO achieves 56.9% mAP0.5 with only 38.1 M parameters in the DOTA dataset, exceeding YOLOv5l by 4.5%. Also, the results of different classifications show its excellent ability to detect small sample objects. As for the C3NRT module, the ablation study and comparison experiment verified that it has the largest contribution to accuracy increment (2.7% in mAP0.5) among the improvements. In conclusion, NRT-YOLO has excellent performance in accuracy improvement and parameter reduction, which is suitable for tiny remote sensing object detection