Soft-Output Detector Using Multi-Layer Perceptron for Bit-Patterned Media Recording

As conventional data storage systems are faced with critical problems such as the superparamagnetic limit, bit-patterned media recording (BPMR) has received significant attention as a promising next-generation magnetic data storage system. However, the reduced spacing between islands at increased areal density causes severe intersymbol and intertrack interference, which degrade BPMR system performance. In this study, we introduce a soft-output detector using multi-layer perceptron to predict reliable information. A received signal is equalized and detected by the MLP detector. The MLP detector provides a well-estimated value by using the binary-cross entropy function as a loss function and the identity function as an activation function for the output layer of the MLP detector. This study investigates the received probability distributions out of the detectors and compares the performance of various versions against a conventional detector. Compared with the conventional detection, the proposed MLP detectors provide a small variance and better BER performance than the conventional detection. Simulations of MLP designs show an advantage over conventional detection. Moreover, the proposed MLP detectors with the demodulator exhibit better BER performance than the conventional detector with the demodulator.

Detection of Motor-Evoked Potentials Below the Noise Floor: Rethinking the Motor Threshold

Background: Motor-evoked potentials (MEP) are one of the most prominent responses to brain stimulation, such as supra-threshold transcranial magnetic stimulation (TMS) and electrical stimulation. Understanding of the neurophysiology and the determination of the lowest stimulation strength that evokes responses requires the detection of even smaller responses, e.g., from single motor units. However, available detection and quantization methods suffer from a large noise floor. Objective: This paper develops a detection method that extracts MEPs hidden below the noise floor. With this method, we aim to estimate excitatory activations of the corticospinal pathways well below the conventional detection level. Methods: The presented MEP detection method presents a self-learning matched-filter approach for improved robustness against noise. The filter is adaptively generated per subject through iterative learning. For responses that are reliably detected by conventional detection, the new approach is fully compatible with established peak-to-peak readings and provides the same results but extends the dynamic range below the conventional noise floor. Results: In contrast to the conventional peak-to-peak measure, the proposed method increases the signal-to-noise ratio by more than a factor of 5. The first detectable responses appear to be substantially lower than the conventional threshold definition of 50 μV median peak-to-peak amplitude. Conclusion: The proposed method shows that stimuli well below the conventional 50 μV threshold definition can consistently and repeatably evoke muscular responses and thus activate excitable neuron populations in the brain. As a consequence, the IO curve is extended at the lower end, and the noise cut-off is shifted. Importantly, the IO curve extends so far that the 50 μV point turns out to be closer to the center of the logarithmic sigmoid curve rather than close to the first detectable responses. The underlying method is applicable to a wide range of evoked potentials and other biosignals, such as in electroencephalography.

Secure IIoT-Enabled Industry 4.0

The Industrial Internet of things (IIoT) is the main driving force behind smart manufacturing, industrial automation, and industry 4.0. Conversely, industrial IoT as the evolving technological paradigm is also becoming a compelling target for cyber adversaries. Particularly, advanced persistent threats (APT) and especially botnets are the foremost promising and potential attacks that may throw the complete industrial IoT network into chaos. IIoT-enabled botnets are highly scalable, technologically diverse, and highly resilient to classical and conventional detection mechanisms. Subsequently, we propose a deep learning (DL)-enabled novel hybrid architecture that can efficiently and timely tackle distributed, multivariant, lethal botnet attacks in industrial IoT. The proposed approach is thoroughly evaluated on a current state-of-the-art, publicly available dataset using standard performance evaluation metrics. Moreover, our proposed technique has been precisely verified with our constructed hybrid DL-enabled architectures and current benchmark DL algorithms. Our devised mechanism shows promising results in terms of high detection accuracy with a trivial trade-off in speed efficiency, assuring the proposed scheme as an optimal and legitimate cyber defense in prevalent IIoTs. Besides, we have cross-validated our results to show utterly unbiased performance.

Metagenomics Approaches for Improving Food Safety

Advancements in next-generation sequencing technology have dramatically reduced the cost and increased the ease of microbial whole-genome sequencing. This is revolutionizing the identification and analysis of foodborne microbial pathogens, facilitating expedited detection and mitigation of foodborne outbreaks, improving public health outcomes, and limiting costly recalls. However, this approach is still anchored in traditional laboratory practice involving the selection and culture of a single isolate. Metagenomic-based approaches, including metabarcoding, shotgun and long-read metagenomics, comprise the next disruptive revolution in food safety diagnostics and offer the potential to directly identify entire microbial communities in a single food, ingredient, or environmental sample. In this review, metagenomic-based approaches are introduced and placed within the context of conventional detection and diagnostic techniques, and essential considerations for undertaking metagenomic assays and data analysis are described. Recent applications of the use of metagenomics for food safety are discussed, alongside current limitations and knowledge gaps, and new opportunities arising from the use of this technology.

Aptamer-based biosensors for the diagnosis of sepsis

Sepsis, the syndrome of infection complicated by acute organ dysfunction, is a serious and growing global problem, which not only leads to enormous economic losses but also becomes one of the leading causes of mortality in the intensive care unit. The detection of sepsis-related pathogens and biomarkers in the early stage plays a critical role in selecting appropriate antibiotics or other drugs, thereby preventing the emergence of dangerous phases and saving human lives. There are numerous demerits in conventional detection strategies, such as high cost, low efficiency, as well as lacking of sensitivity and selectivity. Recently, the aptamer-based biosensor is an emerging strategy for reasonable sepsis diagnosis because of its accessibility, rapidity, and stability. In this review, we first introduce the screening of suitable aptamer. Further, recent advances of aptamer-based biosensors in the detection of bacteria and biomarkers for the diagnosis of sepsis are summarized. Finally, the review proposes a brief forecast of challenges and future directions with highly promising aptamer-based biosensors.

Prevalence of group B streptococcus colonization in pregnant women in Jiangsu, East China

Background Group B streptococcus (GBS) is the leading cause of early-onset neonatal sepsis. However, GBS was infrequently reported in the developing world in contrast to western countries. This study assessed the prevalence of GBS colonization among pregnant women in Jiangsu, East China, and revealed the difference of GBS infection between culture and PCR. Methods A total of 16,184 pregnant women at 34 to 37 weeks’ gestation aged 16–47 years were recruited from Nanjing Kingmed Center for Clinical Laboratory. Nine thousand twenty-two pregnant women received GBS screening by PCR detection only. Seven thousand one hundred sixty-two pregnant women received GBS screening by bacterial culture and GBS-positive samples were tested for antibiotic resistance. Results The overall GBS positive rate was 8.7% by PCR and 3.5% by culture. Colonization rate was highest in the “25–29 years” age group. The 249 GBS-positive samples which detected by culture were all sensitive to penicillin. The prevalence of resistance to erythromycin, clindamycin, and levofloxacin was 77.5, 68.3, and 52.2%, respectively. Conclusions This study revealed the data on the prevalence of GBS colonization in pregnant women at 34 to 37 weeks’ gestation in Jiangsu, East China. It compared the difference of the sensitivity to detect GBS between PCR and culture. PCR was expected to become a quick method in pregnancy women conventional detection of GBS infection.

Identifying the Phishing Websites Using the Patterns of TLS Certificates

With the recent rise of HTTPS adoption on the Web, attackers have begun “HTTPSifying” phishing websites. HTTPSifying a phishing website has the advantage of making the website appear legitimate and evading conventional detection methods that leverage URLs or web contents in the network. Further, adopting HTTPS could also contribute to generating intrinsic footprints and provide defenders with a great opportunity to monitor and detect websites, including phishing sites, as they would need to obtain a public-key certificate issued for the preparation of the websites. The potential benefits of certificate-based detection include (1) the comprehensive monitoring of all HTTPSified websites by using certificates immediately after their issuance, even if the attacker utilizes dynamic DNS (DDNS) or hosting services; this could be overlooked with the conventional domain-registration-based approaches; and (2) to detect phishing websites before they are published on the Internet. Accordingly, we address the following research question: How can we make use of the footprints of TLS certificates to defend against phishing attacks? For this, we collected a large set of TLS certificates corresponding to phishing websites from Certificate Transparency (CT) logs and extensively analyzed these TLS certificates. We demonstrated that a template of common names, which are equivalent to the fully qualified domain names, obtained through the clustering analysis of the certificates can be used for the following promising applications: (1) The discovery of previously unknown phishing websites and (2) understanding the infrastructure used to generate the phishing websites. Furthermore, we developed a real-time monitoring system using the analysis techniques. We demonstrate its usefulness for the practical security operation. We use our findings on the abuse of free certificate authorities (CAs) for operating HTTPSified phishing websites to discuss possible solutions against such abuse and provide a recommendation to the CAs.

Rapid and Sensitive Quantification of the Pesticide Lindane by Polymer Modified Electrochemical Sensor

Lindane is documented by the Environmental Protection Agency (EPA) as one of the most toxic registered pesticides. Conventional detection of lindane in the environment requires manual field sampling and complex, time-consuming analytical sample handling relying on skilled labor. In this study, an electrochemical sensing system based on a modified electrode is reported. The system is capable of detecting lindane in aqueous medium in only 20 s. The surface of a conventional carbon electrode is modified with a film of conductive polymer that enables detection of lindane down to 30 nanomolar. The electrode modification procedure is simple and results in a robust sensor that can withstand intensive use. The sensitivity of the sensor is 7.18 µA/µM and the performance was demonstrated in the determination of lindane in spiked ground water. This suggests that the sensor is potentially capable of providing useful readings for decision makers. The rapid and sensitive quantification of lindane in aqueous medium is one step forward to new opportunities for direct, autonomous control of the pesticide level in the environment.

Recent Advances in Aptamer-Based Biosensors for Detection of Pseudomonas aeruginosa

Increasing concerns about nosocomial infection, food and environmental safety have prompted the development of rapid, accurate, specific and ultrasensitive methods for the early detection of critical pathogens. Pseudomonas aeruginosa is one of the most common pathogens that cause infection. It is ubiquitous in nature, being found in water, soil, and food, and poses a great threat to public health. The conventional detection technologies are either time consuming or readily produce false positive/negative results, which makes them unsuitable for early diagnosis and spot detection of P. aeruginosa. To circumvent these drawbacks, many efforts have been made to develop biosensors using aptamers as bio-recognition elements. Various aptamer-based biosensors for clinical diagnostics, food, and environmental monitoring of P. aeruginosa have been developed in recent years. In this review, we focus on the latest advances in aptamer-based biosensors for detection of P. aeruginosa. Representative biosensors are outlined according to their sensing mechanisms, which include optical, electrochemical and other signal transduction methods. Possible future trends in aptamer biosensors for pathogen detection are also outlined.