Rapid Optical Cavity PCR.

Recent outbreaks of deadly infectious diseases, such as Ebola and Middle East respiratory syndrome coronavirus, have motivated the research for accurate, rapid diagnostics that can be administered at the point of care. Nucleic acid biomarkers for these diseases can be amplified and quantified via polymerase chain reaction (PCR). In order to solve the problems of conventional PCR--speed, uniform heating and cooling, and massive metal heating blocks--an innovative optofluidic cavity PCR method using light-emitting diodes (LEDs) is accomplished. Using this device, 30 thermal cycles between 94 °C and 68 °C can be accomplished in 4 min for 1.3 μL (10 min for 10 μL). Simulation results show that temperature differences across the 750 μm thick cavity are less than 2 °C and 0.2 °C, respectively, at 94 °C and 68 °C. Nucleic acid concentrations as low as 10(-8) ng μL(-1) (2 DNA copies per μL) can be amplified with 40 PCR thermal cycles. This simple, ultrafast, precise, robust, and low-cost optofluidic cavity PCR is favorable for advanced molecular diagnostics and precision medicine. It is especially important for the development of lightweight, point-of-care devices for use in both developing and developed countries

A Case of Epidermolysis Bullosa Simplex (Dowling-Meara 1 Type) in Newborn

Epidermolysis bullosa is a rare genetic skin disease in which skin easily peels off and blisters are formed with mild mechanical trauma. It is classified into simple, borderline, dysmorphic, and mixed type. These four subtypes are further classified according to the location of gene mutation and genetic patterns. Epidermolysis bullosa simplex (EBS) is characterized by separation in the epidermal or subepidermal layer. And it is mostly caused by mutation of keratin 5 (KRT5) and KRT14 genes. Recently, genetic test has become increasingly important for diagnosis, confirming subtypes and genetic counseling. And there are moderate correlation exists between the EBS phenotype and genotype. Here, we report a case of 2-day-old boy with EBS Dowling-Meara type (EBS-DM) diagnosed by mutation analysis in KRT14

A Hierachical Evolutionary Algorithm for Multiobjective Optimization in IMRT

Purpose: Current inverse planning methods for IMRT are limited because they are not designed to explore the trade-offs between the competing objectives between the tumor and normal tissues. Our goal was to develop an efficient multiobjective optimization algorithm that was flexible enough to handle any form of objective function and that resulted in a set of Pareto optimal plans. Methods: We developed a hierarchical evolutionary multiobjective algorithm designed to quickly generate a diverse Pareto optimal set of IMRT plans that meet all clinical constraints and reflect the trade-offs in the plans. The top level of the hierarchical algorithm is a multiobjective evolutionary algorithm (MOEA). The genes of the individuals generated in the MOEA are the parameters that define the penalty function minimized during an accelerated deterministic IMRT optimization that represents the bottom level of the hierarchy. The MOEA incorporates clinical criteria to restrict the search space through protocol objectives and then uses Pareto optimality among the fitness objectives to select individuals. Results: Acceleration techniques implemented on both levels of the hierarchical algorithm resulted in short, practical runtimes for optimizations. The MOEA improvements were evaluated for example prostate cases with one target and two OARs. The modified MOEA dominated 11.3% of plans using a standard genetic algorithm package. By implementing domination advantage and protocol objectives, small diverse populations of clinically acceptable plans that were only dominated 0.2% by the Pareto front could be generated in a fraction of an hour. Conclusions: Our MOEA produces a diverse Pareto optimal set of plans that meet all dosimetric protocol criteria in a feasible amount of time. It optimizes not only beamlet intensities but also objective function parameters on a patient-specific basis

RECIPE: How to Integrate ChatGPT into EFL Writing Education

The integration of generative AI in the field of education is actively being explored. In particular, ChatGPT has garnered significant interest, offering an opportunity to examine its effectiveness in English as a foreign language (EFL) education. To address this need, we present a novel learning platform called RECIPE (Revising an Essay with ChatGPT on an Interactive Platform for EFL learners). Our platform features two types of prompts that facilitate conversations between ChatGPT and students: (1) a hidden prompt for ChatGPT to take an EFL teacher role and (2) an open prompt for students to initiate a dialogue with a self-written summary of what they have learned. We deployed this platform for 213 undergraduate and graduate students enrolled in EFL writing courses and seven instructors. For this study, we collect students' interaction data from RECIPE, including students' perceptions and usage of the platform, and user scenarios are examined with the data. We also conduct a focus group interview with six students and an individual interview with one EFL instructor to explore design opportunities for leveraging generative AI models in the field of EFL education

Genome-wide association study of lung adenocarcinoma in East Asia and comparison with a European population.

Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (Pinteraction = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications

Genome-wide association study of lung adenocarcinoma in East Asia and comparison with a European population

Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (P interaction  = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications

Structure-based molecular characterization and regulatory mechanism of the LftR transcription factor from Listeria monocytogenes: Conformational flexibilities and a ligand-induced regulatory mechanism.

Listeria monocytogenes is a foodborne pathogen that causes listeriosis and can lead to serious clinical problems, such as sepsis and meningitis, in immunocompromised patients and neonates. Due to a growing number of antibiotic-resistant L. monocytogenes strains, listeriosis can steadily become refractory to antibiotic treatment. To develop novel therapeutics against listeriosis, the drug resistance mechanism of L. monocytogenes needs to be determined. The transcription factor LftR from L. monocytogenes regulates the expression of a putative multidrug resistance transporter, LieAB, and belongs to the PadR-2 subfamily of the PadR family. Despite the functional significance of LftR, our molecular understanding of the transcriptional regulatory mechanism for LftR and even for the PadR-2 subfamily is highly limited. Here, we report the crystal structure of LftR, which forms a dimer and protrudes two winged helix-turn-helix motifs for DNA recognition. Structure-based mutational and comparative analyses showed that LftR interacts with operator DNA through a LftR-specific mode as well as a common mechanism used by the PadR family. Moreover, the LftR dimer harbors one intersubunit cavity in the center of the dimeric structure as a putative ligand-binding site. Finally, conformational flexibilities in the LftR dimer and in the cavity suggest that a ligand-induced regulatory mechanism would be used by the LftR transcription factor

Intelligent Detection of IoT Botnets Using Machine Learning and Deep Learning

As the number of Internet of Things (IoT) devices connected to the network rapidly increases, network attacks such as flooding and Denial of Service (DoS) are also increasing. These attacks cause network disruption and denial of service to IoT devices. However, a large number of heterogenous devices deployed in the IoT environment make it difficult to detect IoT attacks using traditional rule-based security solutions. It is challenging to develop optimal security models for each type of the device. Machine learning (ML) is an alternative technique that allows one to develop optimal security models based on empirical data from each device. We employ the ML technique for IoT attack detection. We focus on botnet attacks targeting various IoT devices and develop ML-based models for each type of device. We use the N-BaIoT dataset generated by injecting botnet attacks (Bashlite and Mirai) into various types of IoT devices, including a Doorbell, Baby Monitor, Security Camera, and Webcam. We develop a botnet detection model for each device using numerous ML models, including deep learning (DL) models. We then analyze the effective models with a high detection F1-score by carrying out multiclass classification, as well as binary classification, for each model