Observational studies have demonstrated that refractive error (RE) is associated with an increased risk of primary open-angle glaucoma (POAG). However, the underlying mechanism through which RE influences the development of POAG remains unknown. In this study, we analyzed genome-wide association study (GWAS) data for RE (95,505 participants) and POAG (15,229 cases and 177,473 controls) to characterize polygenic architecture and identify genetic loci shared of these conditions. We integrated the conjunctional false discovery rate (FDR) statistical framework with Gene ontology to investigate the potential biological mechanisms underlying shared genetic loci. Our study revealed a substantial and previously underexplored shared genetic basis between these disorders. Genetic correlation analysis not only confirms overall genetic correlation between RE and POAG (r for genetic = -0.15, 95%CI: -0.21 to -0.09, P = 7.70E07) but also uncovers localized genetic associations, pointing to 12 genome regions of interest. MR analysis established a bidirectional causal relationship between RE and POAG. We identified a robust polygenic overlap between RE and POAG beyond what conventional genetic correlation analysis can reveal. Moreover, we found substantial polygenic overlap and identified 16 shared loci of RE and POAG at conjFDR < 0.01, of which four were novel. Functional annotation offered insights into potential biological mechanisms through which these genetic loci exert their influence, particularly within retinal tissues. Together, this study significantly advances our understanding of the genetic underpinnings that link RE and POAG, elucidating the complex genetic landscape that contributes to their co-occurrence.

Abstract Background This study aims to propose a breast cancer prediction model for early diagnosis and prognosis management of breast cancer. Objective In order to explore the pathogenesis of breast cancer and develop accurate breast cancer screening and treatment methods, we have used machine‐learning technologies to conduct an in‐depth study of breast cancer genetic data to obtain new breast cancer signature and prognostic prediction models. Methods We explored an optimal cluster by unsupervised clustering methods with different expression genes (DEGs) between normal (n = 113) and tumour (n = 1,102) samples. Using least absolute shrinkage and selection operator (LASSO) regression, we selected four biomarkers to develop a predictive model by Cox regression method in the training set (n = 1,083) and validated its predictive accuracy and independence in the testing sets (n = 2,480). Then Gene Set Enrichment Analysis (GSEA) revealed enriched biological pathways in clusters. Finally, we constructed a nomogram including this signature and other significant risk factors to predict survival rates in patients. Results Four mRNAs (CD163L1, QPRT, NKAIN1 and TP53AIP1) between two clusters from 4,938 DEGs were identified, and then a four‐gene model (risk scores = 0.454*CD163L1–0.360*NKAIN1 + 0.581*QPRT + 0.788*TP53AIP1) was established to divide patients into high‐ and low‐risk group with significantly different prognosis (p < 0.0001) in the training set. Integrated analysis revealed dysregulated molecular processes including predominantly oncogenic signalling pathway, cell cycle and DNA repair in high‐risk group but enriched metabolism pathway in low‐risk group. In addition, this model had similar predictive value (HR >1.60; p < 0.05) in three independent validation sets, which could predict survival independently with more power compared with single clinical factor. In addition, the nomogram could predict the prognosis of breast cancer patients precisely in the training set and another three testing sets. Conclusion This model could predict prognosis of breast cancer patients precisely and independently, and provide evidence to make treatment decisions and design clinical trials.

Vibration-based monitoring is an efficient approach to assess the reliability and performance of industrial machine applications. To meet the requirements of long range, low cost, low power, and high-accuracy synchronous acquisition in Internet of Things (IoT)-based vibration condition monitoring, a new multichannel micro-electromechanical system (MEMS)-based low-power wide-area network (LPWAN) incorporating LoRa with NB-IoT is proposed in this paper. This approach adopts a multichannel LoRa and NB-IoT gateway and multiple wireless MEMS-based LoRa nodes to construct the LPWAN for machine vibration monitoring in plants. Furthermore, Google cloud server and computing system are also able to facilitate the remote IoT-based communication. Finally, evaluations of proposed approach in machine vibration test and data synchronization test on electrical machinery are validated, showing that the proposed multichannel LPWAN monitoring approach has the abilities of high-precision data synchronous collections, big-data storage, and data stream uploading in a wide monitoring area in comparison with wired system and ordinary wireless sensor network-based system. This paper is conducted to provide new insights into how a LPWAN is designed in vibration monitoring for synchronous acquiring sensing data accurately and carrying out remote network communication efficiently, and further giving advisable reference to the LPWAN in more machine vibration monitoring scenarios.

The recycling of electronic components has become a major concern for the industry and government as it potentially impacts the security and reliability of a wide variety of electronic systems. The sheer number of component types (analog, digital, mixed-signal) and sizes (large or small) makes it extremely challenging to find a one-size-fits-all solution to detect and prevent recycled ICs. In this paper, we propose a suite of solutions for combating die and IC recycling (CDIR). These solutions include light-weight, on-chip structures based on ring oscillators (RO-CDIR), anti-fuses (AF-CDIR) and fuses (F-CDIR). Each structure meets the unique needs and limitations of different part types and sizes providing excellent coverage of recycled parts. HSPICE simulation results using 90nm technology demonstrate the effectiveness of our proposed negative-bias temperature instability (NBTI)-aware RO-CDIR for detecting ICs used for very short period of time. Recycling of large digital ICs can effectively be detected by using AF-CDIR. Small analog and digital recycled components can be identified by testing our F-CDIR with very low cost measurement devices, e.g., a multimeter.

The counterfeiting and recycling of integrated circuits (ICs) have become major issues in recent years, potentially impacting the security and reliability of electronic systems bound for military, financial, or other critical applications. With identical functionality and packaging, it would be extremely difficult to distinguish recycled ICs from unused ICs. In this paper, two types of on-chip lightweight sensors are proposed to identify recycled ICs by measuring circuit usage time when used in the field. Recycled ICs detection based on aging in ring oscillators (ROs-based) and antifuse (AF-based) are the two techniques presented in this paper. For RO-based sensors, statistical data analysis is used to separate process and temperature variations' effects on the sensor from aging experienced by the sensor in the ICs. For AF-based sensor, counters and embedded one-time programmable memory are used to record the usage time of ICs by counting the cycle of system clock or switching activities of a certain number of nets in the design. Simulation results using 90-nm technology and silicon results from 90-nm test chips show the effectiveness of RO-based sensors for identification of recycled ICs. In addition, the analysis of usage time stored in AF-based sensors shows that recycled ICs, even used for a very short period, can be accurately identified.

As part of the Embedded Systems Challenge, we assess the effectiveness of Trojan detection techniques. The red team inserted different types of Trojans - combinational, sequential, reliability degrading, and performance degrading - into selected variants of a target design; the other variants are Trojan-free. The blue team has to correctly classify the Trojan-free and Trojan-infected variants. Seven different teams from six different universities performed the blue team activity using different types of Trojan-detection techniques, namely activation-based detection, and power- and delay-based side-channels.

Verifying the trustworthiness of Integrated Circuits (ICs) is of utmost importance, as hardware Trojans may destroy ICs bound for critical applications. A novel methodology combining on-chip structure with external current measurements is proposed to verify whether or not an IC is Trojan free. This method considers Trojans' impact on neighboring cells and on the entire IC's power consumption, and effectively localizes the measurement of dynamic power. To achieve this, we develop a new on-chip ring oscillator network structure distributed across the entire chip and place each ring oscillator's components in different rows of a standard-cell design. By developing novel statistical data analysis, the effect of process variations on the ICs' transient power will be separated from the effect of Trojans. Simulation results using 90nm technology and experimental results on Xilinx Spartan-6 FPGAs demonstrate the efficiency of our proposed method.

The counterfeiting and recycling of integrated circuits (ICs) have become major problems in recent years, potentially impacting the security of electronic systems bound for military, financial, or other critical applications. With identical functionality and packaging, it is extremely difficult to distinguish recovered ICs from unused ICs. A technique is proposed to distinguish used ICs from the unused ones using a fingerprint generated by a light-weight on-chip sensor. Using statistical data analysis, process and temperature variations' effects on the sensors can be separated from aging experienced by the sensors in the ICs when used in the field. Simulation results, featuring the sensor using 90nm technology, and silicon results from 90nm test chips demonstrate the effectiveness of this technique for identification of recovered ICs.

The counterfeiting of integrated circuits (ICs) has become a major issue for the electronics industry. Counterfeit ICs that find their way into the supply chains of critical applications can have a major impact on the security and reliability of those systems. This paper presents a new method for uniquely identifying ICs through path delay analysis. There is no overhead in terms of area, timing, or power for this method, since it extracts the intrinsic path delay variation information of the IC. Simulation results from 90nm technology and experimental results from 90nm FPGAs demonstrate the effectiveness of our technique.

The modern integrated circuit (IC) manufacturing process has exposed chip designers to hardware Trojans which threaten circuits bound for critical applications. This paper details the implementation and analysis of a novel ring oscillator network technique for Trojan detection in an application specific integrated circuit (ASIC). The ring oscillator network serves as a power supply monitor by detecting fluctuations in characteristic frequencies due to malicious modifications (i.e. hardware Trojans) in the circuit under authentication. The ring oscillator network was implemented and fabricated in 40 IBM 90nm ASICs with controlled hardware Trojans. This work analyzes the impact of Trojans with varied partial activity, area, and location on the proposed ring oscillator structure and demonstrates that stealthy Trojans can be efficiently detected with this technique even while obfuscated by process variations, background noise, and environment noise.

Integrated circuits (ICs) are becoming increasingly vulnerable to malicious alterations, referred to as hardware Trojans. Detection of these inclusions is of utmost importance, as they may potentially be inserted into ICs bound for military, financial, or other critical applications. A novel on-chip structure including a ring oscillator network (RON), distributed across the entire chip, is proposed to verify whether the chip is Trojan-free. This structure effectively eliminates the issue of measurement noise, localizes the measurement of dynamic power, and additionally compensates for the impact of process variations. Combined with statistical data analysis, the separation of process variations from the Trojan contribution to the circuit's transient power is made possible. Simulation results featuring Trojans inserted into a benchmark circuit using 90nm technology and experimental results on Xilinx Spartan-3E FPGA demonstrate the efficiency and scalability of the RON architecture for Trojan detection.

In the past few years, several Trojan detection approaches have been developed to prevent the damages caused by Trojans, making Trojan insertion more and more difficult. As part of the Embedded Systems Challenge (ESC), we were given two different designs with two different Trojan detection methods, and we tried to design Trojans which could avoid detection. We developed Trojans that remain undetectable by the delay fingerprinting and ring-oscillator monitoring Trojan detection methods embedded into these benchmarks. Experimental results on a Xilinx FPGA demonstrate that most of our hardware Trojans were undetected using the inserted detection mechanisms.