PURPOSE We investigated the effectiveness of the Charlson comorbidity index (CCI) for predicting postoperative 30-day complications and long-term survival outcomes of elderly patients who underwent laparoscopic surgery for colon cancer with radical intent. METHODS We reviewed 178 patients aged ≥70 years who underwent laparoscopic surgery for colon cancer with radical intent between January 2008 and December 2015. Patients were divided into high CCI (CCI ≥ 3; n=71) and low CCI (CCI < 3; n=107) groups for comparative analyses of differences in their short- and long-term outcomes. RESULTS Postoperative 30-day complications were more frequent in the high CCI group than in the low CCI group. Logistic regression analysis revealed that a high CCI was significantly predictive of postoperative 30-day complications. The 5-year overall survival (OS) rates were 65% and 54% for the low and high CCI groups, respectively (p=0.034) and their 5-year disease-free survival (DFS) rates were 60% and 47%, respectively (p=0.030). CONCLUSION CCI predicted the likelihood of postoperative 30-day complications and long-term survival outcomes of elderly patients who underwent laparoscopic surgery for colon cancer with radical intent.

Charlson comorbidity index for evaluatiοn οf the outcomes of elderly patients undergoing laparoscopic surgery for colon cancer.

In recent years, the integration of ‘omics’ technologies, high performance computation, and mathematical modeling of biological processes marks that the systems biology has started to fundamentally impact the way of approaching drug discovery. The LINCS public data warehouse provides detailed information about cell responses with various genetic and environmental stressors. It can be greatly helpful in developing new drugs and therapeutics, as well as improving the situations of lacking effective drugs, drug resistance and relapse in cancer therapies, etc. In this study, we developed a Ternary status based Integer Linear Programming (TILP) method to infer cell-specific signaling pathway network and predict compounds’ treatment efficacy. The novelty of our study is that phosphor-proteomic data and prior knowledge are combined for modeling and optimizing the signaling network. To test the power of our approach, a generic pathway network was constructed for a human breast cancer cell line MCF7; and the TILP model was used to infer MCF7-specific pathways with a set of phosphor-proteomic data collected from ten representative small molecule chemical compounds (most of them were studied in breast cancer treatment). Cross-validation indicated that the MCF7-specific pathway network inferred by TILP were reliable predicting a compound’s efficacy. Finally, we applied TILP to re-optimize the inferred cell-specific pathways and predict the outcomes of five small compounds (carmustine, doxorubicin, GW-8510, daunorubicin, and verapamil), which were rarely used in clinic for breast cancer. In the simulation, the proposed approach facilitates us to identify a compound’s treatment efficacy qualitatively and quantitatively, and the cross validation analysis indicated good accuracy in predicting effects of five compounds. In summary, the TILP model is useful for discovering new drugs for clinic use, and also elucidating the potential mechanisms of a compound to targets.

/pdf/a-linear-programming-computational-framework-integrates-3s4qinzgof.pdf

A linear programming computational framework integrates phosphor-proteomics and prior knowledge to predict drug efficacy

Summary The genetic effect explains the causality from genetic mutations to the development of complex diseases. Existing genome-wide association study (GWAS) approaches are always built under a linear assumption, restricting their generalization in dissecting complicated causality such as the recessive genetic effect. Therefore, a sophisticated and general GWAS model that can work with different types of genetic effects is highly desired. Here, we introduce a deep association kernel learning (DAK) model to enable automatic causal genotype encoding for GWAS at pathway level. DAK can detect both common and rare variants with complicated genetic effects where existing approaches fail. When applied to four real-world GWAS datasets including cancers and schizophrenia, our DAK discovered potential casual pathways, including the association between dilated cardiomyopathy pathway and schizophrenia.

Explaining the Genetic Causality for Complex Phenotype via Deep Association Kernel Learning.

Electric Vehicle (EV) has become one of the promising solutions to the ever-evolving environmental and energy crisis. The key to the wide adoption of EVs is a pervasive charging infrastructure, composed of both private/home chargers and public/commercial charging stations. The security of EV charging, however, has not been thoroughly investigated. This paper investigates the communication mechanisms between the chargers and EVs, and exposes the lack of protection on the authenticity in the SAE J1772 charging control protocol. To showcase our discoveries, we propose a new class of attacks, ChargeX, which aims to manipulate the charging states or charging rates of EV chargers with the goal of disrupting the charging schedules, causing a denial of service (DoS), or degrading the battery performance. ChargeX inserts a hardware attack circuit to strategically modify the charging control signals. We design and implement multiple attack systems, and evaluate the attacks on a public charging station and two home chargers using a simulated vehicle load in the lab environment. Extensive experiments on different types of chargers demonstrate the effectiveness and generalization of ChargeX. Specifically, we demonstrate that ChargeX can force the switching of an EV's charging state from ``stand by"to ``charging", even when the vehicle is not in the charging state. We further validate the attacks on a Tesla Model 3 vehicle to demonstrate the disruptive impacts of ChargeX. If deployed, ChargeX may significantly demolish people's trust in the EV charging infrastructure.

ChargeX: Exploring State Switching Attack on Electric Vehicle Charging Systems

This paper studies vulnerabilities at the intersection of wearable devices and automated control systems. Particularly, we focus on exploiting smart glasses as an entry point and unveil the threats of taking over security-critical automated control chains without user verification or interaction. These vulnerabilities can be especially pertinent in scenarios where security mechanisms only depend on entry point security with minimal user verification (relying on complete trust over previous nodes in automated control chains). We have validated the effects of our attacks on real-world systems (e.g., Tesla vehicles) that are controlled by software and automation tools such as Apple Shortcuts or IFTTT. We show how our contactless, speaker-independent, and electromagnetic interference based attacks can control functionalities such as unlocking doors and initiating remote start of Tesla vehicles, even though the victim’s phone is in a lock-screen status. Our findings not only demonstrate the potential for unauthorized control over automated, connected systems but also highlight the urgent need for more robust security measures in the integration of wearable technology with broader automation frameworks.

From Virtual Touch to Tesla Command: Unlocking Unauthenticated Control Chains From Smart Glasses for Vehicle Takeover

—Tesla Model 3 has equipped with Phone Keys and Key Cards in addition to traditional key fobs for better driving experiences. These new features allow a driver to enter and start the vehicle without using a mechanical key through a wireless authentication process between the vehicle and the key. Unlike the requirements of swiping against the car for Key Cards, the Tesla mobile app’s Phone Key feature can unlock a Model 3 while your smartphone is still in a pocket or bag. In this paper, we performed a detailed security analysis aiming at Tesla keys, especially for Key Cards and Phone Keys. Starting with reverse engineering the mobile application and sniffing the communication data, we reestablished pairing and authentication protocols and analyzed their potential issues. Missing the certificate verification allows an unofficial Key Card to work as an official one. Using these third-party products may lead to serious security problems. Also, the weaknesses of the current protocol lead to a man-in-the-middle (MitM) attack through a Bluetooth channel. The MitM attack is an improved relay attack breaking the security of the authentication procedures for Phone Keys. We also developed an App named TESmLA installed on customized Android devices to complete the proof-of-concept. The attackers can break into Tesla Model 3 and drive it away without the awareness of the car owner. Our results bring into question the security of Passive Keyless Entry and Start (PKES) and Bluetooth implementations in security-critical applications. To mitigate the security problems, we discussed the corresponding countermeasures and feasible secure scheme in the future.

Access Your Tesla without Your Awareness: Compromising Keyless Entry System of Model 3

Purpose: The aim of this study was to identify the altered biological pathways associated with ulcerative colitis (UC)-related colorectal carcinoma (CRC) by systematic tracking the dysregulated modules from re-weighted protein-protein interaction (PPI) networks based on the expression profiles from normal, UC and various stages of CRC. Methods: We firstly recruited the UC- and CRC-related microarray data from ArrayExpress database, and obtained 8 expression profiles which contained 5 conditions (normal, UC, early stage CRC, stage II CRC and stage III CRC). Then, the PPI networks of normal and different disease stages were constructed and re-weighted using Pearson correlation coefficient (PCC). Next, the condition-specific modules were extracted from 5 PPI networks via clique-merging algorithm, and altered modules were captured on the basis of module correlation density (MCD). Subsequently, the gene compositions of altered modules and gene differential expressions in different disease stages were identified to screen the dysregulated genes. Finally, pathways enrichment analyses for the genes in altered modules and differentially expressed genes (DEGs) were implemented. Results: The extensive changes of gene correlations existed in 5 condition-specific PPI networks, which made different MCDs among different disease stages. The same number of modules (N=1952) were explored in 5 PPI networks. By comparing with normal condition, there were 463, 791, 1060 and 345 altered modules in UC, early stage CRC, stage II and III CRC, respectively. Overall, 77, 110, 170 and 110 common genes were identified between genes of altered modules and DEGs in UC, early stage CRC, stage II CRC and stage III CRC, respectively. Functional enrichment analyses indicated that cell cycle and oocyte meiosis were the common and most significant pathways in colonic diseases. Conclusions: Tracking the altered modules from PPI networks is useful to uncover disrupted pathways in colonic diseases. Cell cycle and oocyte meiosis might be associated with the pathophysiological background of colonic diseases.

Identification of disrupted pathways in ulcerative colitis-related colorectal carcinoma by systematic tracking the dysregulated modules.

In recent years, with the continuous development of synthetic organic chemistry, a large number of new compounds are synthesized every year. Many of these compounds not only have novel structures, but also have complex structures. The traditional four spectral analysis methods are difficult to understand its spatial structure clearly and comprehensively. At the same time, with the understanding of the spatial structure of related compounds, it is possible to further explore the relationship between the structure and properties of compounds. This paper analyzes different kinds of organic compounds, including nitrogen-containing organic compounds, polycyclic aromatic hydrocarbons and volatile organic compounds. The detection methods of volatile organic compounds and derivatization chromatography were discussed. Through VOC test, the results show that the average concentration of each substance decreases with time.

https://doi.org/10.1088/1742-6596/2152/1/012028

Progress in Structure Determination, Reaction and Classification of Organic Compounds

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Access Your Tesla without Your Awareness: Compromising Keyless Entry System of Model 3

Identification of disrupted pathways in ulcerative colitis-related colorectal carcinoma by systematic tracking the dysregulated modules.

Progress in Structure Determination, Reaction and Classification of Organic Compounds