Mapping spatial and temporal distribution information of plantations in Guangxi from 2000 to 2020

Plantations are formed entirely by artificial planting which are different from natural forests. The rapid expansion of plantation forestry has brought about a series of ecological and environmental problems. Timely and accurate information on the distribution of plantation resources and continuous monitoring of the dynamic changes in plantations are of great significance. However, plantations have similar spectral and texture characteristics with natural forests. In addition, cloud and rain greatly affected the image quality of large area mapping. Here, we tested the possibility of applying Continuous Change Detection and Classification to distinguish plantations from natural forests and described the spatiotemporal dynamic changes of plantations. We adopted the Continuous Change Detection and Classification algorithm and used all available Landsat images from 2000 to 2020 to map annual plantation forest distribution in Guangxi Zhuang Autonomous Region, China and analyzed their spatial and temporal dynamic changes. The overall accuracy of the plantation extraction is 88.77%. Plantations in Guangxi increased significantly in the past 20 years, from 2.37 × 106 ha to 5.11 × 106 ha. Guangxi is expanding new plantation land every year, with the largest expansion area in 2009 of about 2.58 × 105 ha. Over the past 20 years, plantations in Guangxi have clearly shown a tendency to expand from the southeast to the northwest, transformed from natural forests and farmland. 30% of plantations have experienced at least one logging-and-replanting rotation event. Logging rotation events more intensively occur in areas with dense plantation forests. Our study proves that using fitting coefficients from Continuous Change Detection and Classification algorithm is effective to extract plantations and mitigating the adverse effects of clouds and rain on optical images in a large scale, which provides a fast and effective method for long-time and large-area plantation identification and spatiotemporal distribution information extraction, and strong data support and decision reference for plantation investigation, monitoring and management

Nociceptin Receptor Is Overexpressed in Non-small Cell Lung Cancer and Predicts Poor Prognosis

Classic opioid receptors, mu (μ), delta (δ), and kappa (κ), have been reported to be expressed in non-small cell lung cancer (NSCLC) cell lines and tumor tissues and to play a role in tumor prognosis. However, the expression and role of the non-classic opioid receptor, nociceptin receptor (NOP) in cancer are unclear. Our hypothesis was that NOP was also highly expressed in NSCLC tumor tissues and this could be correlated with patients' prognostic characters. Expression of NOP was examined in archived cancer tissues from 129 enrolled NSCLC patients by immunohistochemistry and was further analyzed with the patients' outcomes. NOP expression in NSCLC cell lines was also detected. The dataset from Kaplan-Meier Plotter was used to explore the correlation between the levels of NOP mRNA in cancerous tissue and the prognosis of NSCLC patients. Cell functional assays were performed to detect the effect of NOP activation on tumor aggressive furthers. Results showed NOP expression was highly expressed in cancer tissues and human cancer cell lines. NOP expression was not associated with patients' opioid requirement but closely with some clinicopathological indicators which reflected the malignancy. Moreover, NOP staining level was the independent poor prognostic factor for NSCLC patients receiving lobectomy, which was further verified by determining the mRNA expression levels through the online dataset. In vitro experiments revealed that NOP activation promotes the proliferation and invasion of A549 cells via PI3K/Akt signaling pathway. We conclude that NOP is overexpressed in NSCLC and is inversely correlated with patient's postoperative survival

Exploiting dynamic scheduling for VM-based code obfuscation

Code virtualization built upon virtual machine (VM) technologies is emerging as a viable method for implementing code obfuscation to protect programs against unauthorized analysis. State-of-the-art VM-based protection approaches use a fixed scheduling structure where the program follows a single, static execution path for the same input. Such approaches, however, are vulnerable to certain scenarios where the attacker can reuse knowledge extracted from previously seen software to crack applications using similar protection schemes. This paper presents DSVMP, a novel VM-based code obfuscation approach for software protection. DSVMP brings together two techniques to provide stronger code protection than prior VM-based schemes. Firstly, it uses a dynamic instruction scheduler to randomly direct the program to execute different paths without violating the correctness across different runs. By randomly choosing the program execution paths, the application exposes diverse behavior, making it much more difficult for an attacker to reuse the knowledge collected from previous runs or similar applications to perform attacks. Secondly, it employs multiple VMs to further obfuscate the relationship between VM bytecode and their interpreters, making code analysis even harder. We have implemented DSVMP in a prototype system and evaluated it using a set of widely used applications. Experimental results show that DSVMP provides stronger protection with comparable runtime overhead and code size when compared to two commercial VMbased code obfuscation tools

Enhance virtual-machine-based code obfuscation security through dynamic bytecode scheduling

Code virtualization built upon virtual machine (VM) technologies is emerging as a viable method for implementing code obfuscation to protect programs against unauthorized analysis. State-of-the-art VM-based protection approaches use a fixed scheduling structure where the program always follows a single, deterministic execution path for the same input. Such approaches, however, are vulnerable in certain scenarios where the attacker can reuse knowledge extracted from previously seen software to crack applications protected with the same obfuscation scheme. This paper presents Dsvmp, a novel VM-based code obfuscation approach for software protection. Dsvmp brings together two techniques to provide stronger code protection than prior VM-based approaches. Firstly, it uses a dynamic instruction scheduler to randomly direct the program to execute different paths without violating the correctness across different runs. By randomly choosing the program execution path, the application exposes diverse behavior, making it much more difficult for an attacker to reuse the knowledge collected from previous runs or similar applications to launch an attack. Secondly, it employs multiple VMs to further obfuscate the mapping from VM opcode to native machine instructions, so that the same opcode could be mapped to different native instructions at runtime, making code analysis even harder. We have implemented Dsvmp in a prototype system and evaluated it using a set of widely used applications. Experimental results show that Dsvmp provides stronger protection with comparable runtime overhead and code size, when it is compared to two commercial VM-based code obfuscation tools

A potential therapeutic drug for osteoporosis: prospect for osteogenic LncRNAs

Long non-coding RNAs (LncRNAs) play essential roles in multiple physiological processes including bone formation. Investigators have revealed that LncRNAs regulated bone formation through various signaling pathways and micro RNAs (miRNAs). However, several problems exist in current research studies on osteogenic LncRNAs, including sophisticated techniques, high cost for in vivo experiment, as well as low homology of LncRNAs between animal model and human, which hindered translational medicine research. Moreover, compared with gene editing, LncRNAs would only lead to inhibition of target genes rather than completely knocking them out. As the studies on osteogenic LncRNA gradually proceed, some of these problems have turned osteogenic LncRNA research studies into slump. This review described some new techniques and innovative ideas to address these problems. Although investigations on osteogenic LncRNAs still have obtacles to overcome, LncRNA will work as a promising therapeutic drug for osteoporosis in the near future

Exploit Dynamic Data Flows to Protect Software Against Semantic Attacks

Unauthorized code modification based on reverse engineering is a serious threat for software industry. Virtual machine based code obfuscation is emerging as a powerful technique for software protection. However, the current Virtual machine code protection are vulnerable under semantic attacks which use dynamic profiling to transform an obfuscated program to construct a simpler program that is functionally equivalent to the obfuscated program but easier to analyze. This paper presents DSA-VMP, a novel VM-based code obfuscation technique, to address the issue of semantic attacks. Our design goal is to exploit dynamic data flows to increase the diversity of the program behaviour. Our approach uses multiple bytecode handlers to interpret a single bytecode and hides the logics that determine the program execution path (it is difficult for the attacker to anticipate the program execution flow). These two techniques greatly increase the diversity of the program execution where the protected code regions exhibit a complex data flow across multiple runs, making it harder and more time consuming to trace the program execution through profiling. Our approach is evaluated using a set of real-world applications. Experimental results show that DSA-VMP can well protect software against semantic attacks at the cost of little extra runtime overhead when compared to two commercial VM-based code obfuscation tools

SEEAD:A Semantic-based Approach for Automatic Binary Code De-obfuscation

Increasingly sophisticated code obfuscation techniques are quickly adopted by malware developers to escape from malware detection and to thwart the reverse engineering effort of security analysts. State-of-the-art de-obfuscation approaches rely on dynamic analysis, but face the challenge of low code coverage as not all software execution paths and behavior will be exposed at specific profiling runs. As a result, these approaches often fail to discover hidden malicious patterns. This paper introduces SEEAD, a novel and generic semantic-based de-obfuscation system. When building SEEAD, we try to rely on as few assumptions about the structure of the obfuscation tool as possible, so that the system can keep pace with the fast evolving code obfuscation techniques. To increase the code coverage, SEEAD dynamically directs the target program to execute different paths across different runs. This dynamic profiling scheme is rife with taint and control dependence analysis to reduce the search overhead, and a carefully designed protection scheme to bring the program to an error free status should any error happens during dynamic profile runs. As a result, the increased code coverage enables us to uncover hidden malicious behaviors that are not detected by traditional dynamic analysis based de-obfuscation approaches. We evaluate SEEAD on a range of benign and malicious obfuscated programs. Our experimental results show that SEEAD is able to successfully recover the original logic from obfuscated binaries

SEEAD:A Semantic-based Approach for Automatic Binary Code De-obfuscation

Increasingly sophisticated code obfuscation techniques are quickly adopted by malware developers to escape from malware detection and to thwart the reverse engineering effort of security analysts. State-of-the-art de-obfuscation approaches rely on dynamic analysis, but face the challenge of low code coverage as not all software execution paths and behavior will be exposed at specific profiling runs. As a result, these approaches often fail to discover hidden malicious patterns. This paper introduces SEEAD, a novel and generic semantic-based de-obfuscation system. When building SEEAD, we try to rely on as few assumptions about the structure of the obfuscation tool as possible, so that the system can keep pace with the fast evolving code obfuscation techniques. To increase the code coverage, SEEAD dynamically directs the target program to execute different paths across different runs. This dynamic profiling scheme is rife with taint and control dependence analysis to reduce the search overhead, and a carefully designed protection scheme to bring the program to an error free status should any error happens during dynamic profile runs. As a result, the increased code coverage enables us to uncover hidden malicious behaviors that are not detected by traditional dynamic analysis based de-obfuscation approaches. We evaluate SEEAD on a range of benign and malicious obfuscated programs. Our experimental results show that SEEAD is able to successfully recover the original logic from obfuscated binaries