mTORC2 Promotes Lipid Storage and Suppresses Thermogenesis in Brown Adipose Tissue in Part Through AKT-Independent Regulation of FoxO1: A Dissertation

Recent studies suggest adipose tissue plays a critical role in regulating whole body energy homeostasis in both animals and humans. In particular, activating brown adipose tissue (BAT) activity is now appreciated as a potential therapeutic strategy against obesity and metabolic disease. However, the signaling circuits that coordinate nutrient uptake and BAT function are poorly understood. Here, I investigated the role of the nutrient-sensing mTOR signaling pathway in BAT by conditionally deleting Rictor, which encodes an essential component of mTOR Complex 2 (mTORC2) either in brown adipocyte precursors or mature brown adipocytes. In general, inhibiting BAT mTORC2 reduces glucose uptake and de novo lipogenesis pathways while increases lipid uptake and oxidation pathways indicating a switch in fuel utilization. Moreover, several key thermogenic factors (Ucp1, Pgc1α, and Irf4) are elevated in Rictor-deficient BAT, resulting in enhanced thermogenesis. Accordingly, mice with mTORC2 loss in BAT are protected from HFD-induced obesity and metabolic disease at thermoneutrality. In vitro culture experiments further suggest that mTORC2 cell-autonomously regulates the BAT thermogenic program, especially Ucp1 expression, which depends on FoxO1 activity. Mechanistically, mTORC2 appears to inhibit FoxO1 by facilitating its lysine-acetylation but not through the canonical AKT-mediated phosphorylation pathway. Finally, I also provide evidence that β-adrenergic signaling which normally triggers thermogenesis also induces FoxO1 deacetylation in BAT. Based on these data, I propose a model in which mTORC2 functions in BAT as a critical signaling hub for coordinating nutrient uptake, fuel utilization, and thermogenic gene expression. These data provide a foundation for future studies into the mTORC2-FoxO1 signaling axis in different metabolic tissues and physiological conditions

A Practical and Secure Stateless Order Preserving Encryption for Outsourced Databases

Order-preserving encryption (OPE) plays an important role in securing outsourced databases. OPE schemes can be either Stateless or Stateful. Stateful schemes can achieve the ideal security of order-preserving encryption, i.e., “reveal no information about the plaintexts besides order.” However, comparing to stateless schemes, stateful schemes require maintaining some state information locally besides encryption keys and the ciphertexts are mutable. On the other hand, stateless schemes only require remembering encryption keys and thus is more efficient. It is a common belief that stateless schemes cannot provide the same level of security as stateful ones because stateless schemes reveal the relative distance among their corresponding plaintext. In real world applications, such security defects may lead to the leakage of statistical and sensitive information, e.g., the data distribution, or even negates the whole encryption. In this paper, we propose a practical and secure stateless order-preserving encryption scheme. With prior knowledge of the data to be encrypted, our scheme can achieve IND-CCPA (INDistinguishability under Committed ordered Chosen Plaintext Attacks) security for static data set. Though the IND-CCPA security can\u27t be met for dynamic data set, our new scheme can still significantly improve the security in real world applications. Along with the encryption scheme, in this paper we also provide methods to eliminate access pattern leakage in communications and thus prevents some common attacks to OPE schemes in practice

On the Improvement of Wiener Attack on RSA with Small Private Exponent

RSA system is based on the hardness of the integer factorization problem (IFP). Given an RSA modulus N=pq, it is difficult to determine the prime factors p and q efficiently. One of the most famous short exponent attacks on RSA is the Wiener attack. In 1997, Verheul and van Tilborg use an exhaustive search to extend the boundary of the Wiener attack. Their result shows that the cost of exhaustive search is 2r+8 bits when extending the Weiner's boundary r bits. In this paper, we first reduce the cost of exhaustive search from 2r+8 bits to 2r+2 bits. Then, we propose a method named EPF. With EPF, the cost of exhaustive search is further reduced to 2r-6 bits when we extend Weiner's boundary r bits. It means that our result is 214 times faster than Verheul and van Tilborg's result. Besides, the security boundary is extended 7 bits

Probabilistic 3D Multi-Object Cooperative Tracking for Autonomous Driving via Differentiable Multi-Sensor Kalman Filter

Current state-of-the-art autonomous driving vehicles mainly rely on each individual sensor system to perform perception tasks. Such a framework's reliability could be limited by occlusion or sensor failure. To address this issue, more recent research proposes using vehicle-to-vehicle (V2V) communication to share perception information with others. However, most relevant works focus only on cooperative detection and leave cooperative tracking an underexplored research field. A few recent datasets, such as V2V4Real, provide 3D multi-object cooperative tracking benchmarks. However, their proposed methods mainly use cooperative detection results as input to a standard single-sensor Kalman Filter-based tracking algorithm. In their approach, the measurement uncertainty of different sensors from different connected autonomous vehicles (CAVs) may not be properly estimated to utilize the theoretical optimality property of Kalman Filter-based tracking algorithms. In this paper, we propose a novel 3D multi-object cooperative tracking algorithm for autonomous driving via a differentiable multi-sensor Kalman Filter. Our algorithm learns to estimate measurement uncertainty for each detection that can better utilize the theoretical property of Kalman Filter-based tracking methods. The experiment results show that our algorithm improves the tracking accuracy by 17% with only 0.037x communication costs compared with the state-of-the-art method in V2V4Real

Optimal receiver antenna location in indoor environment using dynamic differential evolution and genetic algorithm

Using the impulse responses of these multipath channels, the bit error rate (BER) performance for binary pulse amplitude modulation impulse radio ultra-wideband communication system is calculated. The optimization location of receiving antenna is investigated by dynamic differential evolution (DDE) and genetic algorithm (GA) to minimize the outage probability. Numerical results show that the performance for reducing BER and outage probability by DDE algorithm is better than that by GA

A Cascaded Approach for ultraly High Performance Lesion Detection and False Positive Removal in Liver CT Scans

Liver cancer has high morbidity and mortality rates in the world. Multi-phase CT is a main medical imaging modality for detecting/identifying and diagnosing liver tumors. Automatically detecting and classifying liver lesions in CT images have the potential to improve the clinical workflow. This task remains challenging due to liver lesions' large variations in size, appearance, image contrast, and the complexities of tumor types or subtypes. In this work, we customize a multi-object labeling tool for multi-phase CT images, which is used to curate a large-scale dataset containing 1,631 patients with four-phase CT images, multi-organ masks, and multi-lesion (six major types of liver lesions confirmed by pathology) masks. We develop a two-stage liver lesion detection pipeline, where the high-sensitivity detecting algorithms in the first stage discover as many lesion proposals as possible, and the lesion-reclassification algorithms in the second stage remove as many false alarms as possible. The multi-sensitivity lesion detection algorithm maximizes the information utilization of the individual probability maps of segmentation, and the lesion-shuffle augmentation effectively explores the texture contrast between lesions and the liver. Independently tested on 331 patient cases, the proposed model achieves high sensitivity and specificity for malignancy classification in the multi-phase contrast-enhanced CT (99.2%, 97.1%, diagnosis setting) and in the noncontrast CT (97.3%, 95.7%, screening setting)

Identification of hot regions in protein-protein interactions by sequential pattern mining

<p>Abstract</p> <p>Background</p> <p>Identification of protein interacting sites is an important task in computational molecular biology. As more and more protein sequences are deposited without available structural information, it is strongly desirable to predict protein binding regions by their sequences alone. This paper presents a pattern mining approach to tackle this problem. It is observed that a functional region of protein structures usually consists of several peptide segments linked with large wildcard regions. Thus, the proposed mining technology considers large irregular gaps when growing patterns, in order to find the residues that are simultaneously conserved but largely separated on the sequences. A derived pattern is called a cluster-like pattern since the discovered conserved residues are always grouped into several blocks, which each corresponds to a local conserved region on the protein sequence.</p> <p>Results</p> <p>The experiments conducted in this work demonstrate that the derived long patterns automatically discover the important residues that form one or several hot regions of protein-protein interactions. The methodology is evaluated by conducting experiments on the web server MAGIIC-PRO based on a well known benchmark containing 220 protein chains from 72 distinct complexes. Among the tested 218 proteins, there are 900 sequential blocks discovered, 4.25 blocks per protein chain on average. About 92% of the derived blocks are observed to be clustered in space with at least one of the other blocks, and about 66% of the blocks are found to be near the interface of protein-protein interactions. It is summarized that for about 83% of the tested proteins, at least two interacting blocks can be discovered by this approach.</p> <p>Conclusion</p> <p>This work aims to demonstrate that the important residues associated with the interface of protein-protein interactions may be automatically discovered by sequential pattern mining. The detected regions possess high conservation and thus are considered as the computational hot regions. This information would be useful to characterizing protein sequences, predicting protein function, finding potential partners, and facilitating protein docking for drug discovery.</p

A Secure RFID Authentication Protocol Adopting Error Correction Code

RFID technology has become popular in many applications; however, most of the RFID products lack security related functionality due to the hardware limitation of the low-cost RFID tags. In this paper, we propose a lightweight mutual authentication protocol adopting error correction code for RFID. Besides, we also propose an advanced version of our protocol to provide key updating. Based on the secrecy of shared keys, the reader and the tag can establish a mutual authenticity relationship. Further analysis of the protocol showed that it also satisfies integrity, forward secrecy, anonymity, and untraceability. Compared with other lightweight protocols, the proposed protocol provides stronger resistance to tracing attacks, compromising attacks and replay attacks. We also compare our protocol with previous works in terms of performance

Risk of pneumocystosis after early discontinuation of prophylaxis among HIV-infected patients receiving highly active antiretroviral therapy

<p>Abstract</p> <p>Background</p> <p>Risk of pneumocystosis after discontinuation of primary or secondary prophylaxis among HIV-infected patients before CD4 counts increase to ≧200 cells/μL (early discontinuation) after receiving highly active antiretroviral therapy (HAART) is rarely investigated.</p> <p>Methods</p> <p>Medical records of 660 HIV-infected patients with baseline CD4 counts <200 cells/μL who sought HIV care and received HAART at a university hospital in Taiwan between 1 April, 1997 and 30 September, 2007 were reviewed to assess the incidence rate of pneumocystosis after discontinuation of prophylaxis for pneumocystosis.</p> <p>Results</p> <p>The incidence rate of pneumocystosis after HAART was 2.81 per 100 person-years among 521 patients who did not initiate prophylaxis or had early discontinuation of prophylaxis, which was significantly higher than the incidence rate of 0.45 per 100 person-years among 139 patients who continued prophylaxis until CD4 counts increased to ≧200 cells/μL (adjusted risk ratio, 5.32; 95% confidence interval, 1.18, 23.94). Among the 215 patients who had early discontinuation of prophylaxis after achievement of undetectable plasma HIV RNA load, the incidence rate of pneumocystosis was reduced to 0.31 per 100 person-years, which was similar to that of the patients who continued prophylaxis until CD4 counts increased to ≧200 cells/μL (adjusted risk ratio, 0.63; 95% confidence interval, 0.03, 14.89).</p> <p>Conclusions</p> <p>Compared with the risk of pneumocystosis among patients who continued prophylaxis until CD4 counts increased to ≧200 cells/μL after HAART, the risk was significantly higher among patients who discontinued prophylaxis when CD4 counts remained <200 cells/μL, while the risk could be reduced among patients who achieved undetectable plasma HIV RNA load after HAART.</p