Machine learning models to evaluate mortality in pediatric patients with pneumonia in the intensive care unit.

OBJECTIVES: This study aimed to predict mortality in children with pneumonia who were admitted to the intensive care unit (ICU) to aid decision-making. STUDY DESIGN: Retrospective cohort study conducted at a single tertiary hospital. PATIENTS: This study included children who were admitted to the pediatric ICU at the National Taiwan University Hospital between 2010 and 2019 due to pneumonia. METHODOLOGY: Two prediction models were developed using tree-structured machine learning algorithms. The primary outcomes were ICU mortality and 24-h ICU mortality. A total of 33 features, including demographics, underlying diseases, vital signs, and laboratory data, were collected from the electronic health records. The machine learning models were constructed using the development data set, and performance matrices were computed using the holdout test data set. RESULTS: A total of 1231 ICU admissions of children with pneumonia were included in the final cohort. The area under the receiver operating characteristic curves (AUROCs) of the ICU mortality model and 24-h ICU mortality models was 0.80 (95% confidence interval [CI], 0.69-0.91) and 0.92 (95% CI, 0.86-0.92), respectively. Based on feature importance, the model developed in this study tended to predict increased mortality for the subsequent 24 h if a reduction in the blood pressure, peripheral capillary oxygen saturation (SpO2), or higher partial pressure of carbon dioxide (PCO2) were observed. CONCLUSIONS: This study demonstrated that the machine learning models for predicting ICU mortality and 24-h ICU mortality in children with pneumonia have the potential to support decision-making, especially in resource-limited settings.

Uniform, Strain-Free, Large-Scale Graphene and h-BN Monolayers Enabled by Hydrogel Substrates.

Translation of the unique properties of 2D monolayers from non-scalable micron-sized samples to macroscopic scale is a longstanding challenge obstructed by the substrate-induced strains, interface nonuniformities, and sample-to-sample variations inherent to the scalable fabrication methods. So far, the most successful strategies to reduce strain in graphene are the reduction of the interface roughness and lattice mismatch by using hexagonal boron nitride (h-BN), with the drawback of limited uniformity and applicability to other 2D monolayers, and liquid water, which is not compatible with electronic devices. This work demonstrates a new class of substrates based on hydrogels that overcome these limitations and excel h-BN and water substrates at strain relaxation enabling superiorly uniform and reproducible centimeter-sized sheets of unstrained monolayers. The ultimate strain relaxation and uniformity are rationalized by the extreme structural adaptability of the hydrogel surface owing to its high liquid content and low Young's modulus, and are universal to all 2D materials irrespective of their crystalline structure. Such platforms can be integrated into field effect transistors and demonstrate enhanced charge carrier mobilities in graphene. These results present a universal strategy for attaining uniform and strain-free sheets of 2D materials and underline the opportunities enabled by interfacing them with soft matter.

Effects of the Degree of Phenol Substitution on Molecular Structures and Properties of Chitosan-Phenol-Based Self-Healing Hydrogels.

Click chemistry is commonly used to prepare hydrogels, and chitosan-phenol prepared by using a Schiff base has been widely employed in the field of biomaterials. Chitosan-phenol is a derivative of chitosan; the phenol groups can disrupt both the inter- and intramolecular hydrogen bonds in chitosan, thereby reducing its crystallinity and improving its water solubility. In addition, chitosan-phenol exhibits various beneficial physiological functions. However, it is still unclear whether the degree of phenol substitution in the chitosan main chain affects the molecular interactions and structural properties of the self-healing hydrogels. To explore this issue, we investigated the molecular structure and network of self-healing hydrogels composed of chitosan-phenol with varying degrees of phenol substitution and dibenzaldehyde poly(ethylene oxide) (DB-PEO) using molecular dynamics simulations. We observed that when the degree of phenol substitution in the self-healing hydrogel was less than 15%, an increase in the degree of phenol substitution led to an increase in the interactions between chitosan-phenol and DB-PEO, and it enhanced the dynamic covalent bond cross-linking generated through the Schiff base reaction. However, when the degree of phenol substitution exceeded 15%, excessive phenol groups caused excessive intramolecular interactions within chitosan-phenol molecules, which reduced the binding between chitosan-phenol and DB-PEO. Our results revealed the influence of the degree of phenol substitution on the molecular structure of the self-healing hydrogels and showed an optimal degree of phenol substitution. These findings provide important insights for the future design of self-healing hydrogels based on chitosan and should help in enhancing the applicability of hydrogels in the field of biomedicine.

Outcomes of pediatric community-acquired pneumonia before and after national pneumococcal immunization in Taiwan.

OBJECTIVE: In Taiwan, the incidence of invasive pneumococcal disease (IPD) in children declined after the catch-up primary vaccination programs and the full national immunization program (NIP) with PCV13. The objective of the study was to investigate the clinical outcomes of pediatric community-acquired pneumonia (CAP) before and after the NIP. METHODS: The study included patients aged 3 months to 17 years who were diagnosed with CAP and treated at the National Taiwan University Hospital between 2007 and 2019. Patients were assigned to three birth cohorts according to their birth years and vaccination eligibility: non-NIP, catch-up, and full NIP. We compared the rates of severe outcomes, including case fatality and pathogens. RESULTS: A total of 6557 patients who met the CAP criteria were enrolled during the study period. The case-fatality rate decreased from 3.2% (94/2984) in the non-NIP cohort to 0.3% (7/2176) in the catch-up cohort and 0.8% (11/1397) in the full NIP cohort (p < 0.001). Furthermore, there was a significant decrease in invasive ventilation from the non-NIP (17.9%) to both catch-up (6.8%) and full NIP cohorts (9.1%). The rate of IPD declined from the non-NIP cohort to the catch-up cohort (1.8% vs. 0.6%, p < 0.001) and from the catch-up to the full NIP cohort (0.6% vs. 0.07%, p = 0.014). In contrast, the rates of infections with other pathogens increased after NIP. CONCLUSION: The introduction of PCV13 showed significant reduction in case-fatality and IPD rates. The increasing rates of other pathogens warrant further surveillance for their clinical significance.

Molecular interactions of tannic acid and matrix metalloproteinases 2 and 9.

Tannic acid (TA) has antibacterial, antioxidant, and anti-inflammatory properties and acts as an adhesive, hemostatic, and crosslinking agent in hydrogels. Matrix metalloproteinases (MMPs), a family of endopeptidase enzymes, play important roles in tissue remodeling and wound healing. TA has been reported to inhibit MMP-2/- 9 activities, thereby improving both tissue remodeling and wound healing. However, the mechanism of interaction of TA with MMP-2 and MMP-9 has not been fully elucidated. In this study, the full atomistic modeling approach was applied to explore the mechanisms and structures of TA binding with MMP-2 and MMP-9. Macromolecular models of the TA-MMP-2/- 9 complex were built by docking based on experimentally resolved MMP structures, and further equilibrium processes were examined by molecular dynamics (MD) simulations to investigate the binding mechanism and structural dynamics of the TA-MMP-2/- 9 complexes. The molecular interactions between TA and MMPs, including H-bond formation and hydrophobic and electrostatic interactions, were analyzed and decoupled to elucidate the dominant factors in TA-MMP binding. TA binds to MMPs mainly at two binding regions, residues 163-164 and 220-223 in MMP-2 and residues 179-190 and 228-248 in MMP-9. Two arms of TA participate in binding MMP-2 with 3.61 hydrogen bonds. On the other hand, TA binds MMP-9 with a distinct configuration involving four arms with 4.75 hydrogen bonds, resulting in a tighter binding conformation. Understanding the binding mechanism and structural dynamics of TA with these two MMPs provides crucial and fundamental knowledge regarding the inhibitory and stabilizing effects of TA on MMPs.

Clinical characteristics of hospitalized children with community-acquired pneumonia and respiratory infections: Using machine learning approaches to support pathogen prediction at admission.

BACKGROUND: Acute respiratory infections (ARIs) are common in children. We developed machine learning models to predict pediatric ARI pathogens at admission. METHODS: We included hospitalized children with respiratory infections between 2010 and 2018. Clinical features were collected within 24 h of admission to construct models. The outcome of interest was the prediction of 6 common respiratory pathogens, including adenovirus, influenza virus types A and B, parainfluenza virus (PIV), respiratory syncytial virus (RSV), and Mycoplasma pneumoniae (MP). Model performance was estimated using area under the receiver operating characteristic curve (AUROC). Feature importance was measured using Shapley Additive exPlanation (SHAP) values. RESULTS: A total of 12,694 admissions were included. Models trained with 9 features (age, event pattern, fever, C-reactive protein, white blood cell count, platelet count, lymphocyte ratio, peak temperature, peak heart rate) achieved the best performance (AUROC: MP 0.87, 95% CI 0.83-0.90; RSV 0.84, 95% CI 0.82-0.86; adenovirus 0.81, 95% CI 0.77-0.84; influenza A 0.77, 95% CI 0.73-0.80; influenza B 0.70, 95% CI 0.65-0.75; PIV 0.73, 95% CI 0.69-0.77). Age was the most important feature to predict MP, RSV and PIV infections. Event patterns were useful for influenza virus prediction, and C-reactive protein had the highest SHAP value for adenovirus infections. CONCLUSION: We demonstrate how artificial intelligence can assist clinicians identify potential pathogens associated with pediatric ARIs upon admission. Our models provide explainable results that could help optimize the use of diagnostic testing. Integrating our models into clinical workflows may lead to improved patient outcomes and reduce unnecessary medical costs.

Unraveling the molecular mechanism of collagen flexibility during physiological warmup using molecular dynamics simulation and machine learning.

Physiological warmup plays an important role in reducing the injury risk in different sports. In response to the associated temperature increase, the muscle and tendon soften and become easily stretched. In this study, we focused on type I collagen, the main component of the Achilles tendon, to unveil the molecular mechanism of collagen flexibility upon slight heating and to develop a model to predict the strain of collagen sequences. We used molecular dynamics approaches to simulate the molecular structures and mechanical behavior of the gap and overlap regions in type I collagen at 307 K, 310 K, and 313 K. The results showed that the molecular model in the overlap region is more sensitive to temperature increases. Upon increasing the temperature by 3 degrees Celsius, the end-to-end distance and Young's modulus of the overlap region decreased by 5% and 29.4%, respectively. The overlap region became more flexible than the gap region at higher temperatures. GAP-GPA and GNK-GSK triplets are critical for providing molecular flexibility upon heating. A machine learning model developed from the molecular dynamics simulation results showed good performance in predicting the strain of collagen sequences at a physiological warmup temperature. The strain-predictive model could be applied to future collagen designs to obtain desirable temperature-dependent mechanical properties.

Tough, Self-Healing, and Conductive Elastomer ─Ionic PEGgel.

Ionically conductive elastomers are necessary for realizing human-machine interfaces, bioelectronic applications, or durable wearable sensors. Current design strategies, however, often suffer from solvent leakage and evaporation, or from poor mechanical properties. Here, we report a strategy to fabricate ionic elastomers (IHPs) demonstrating high conductivity (0.04 S m-1), excellent electrochemical stability (>60,000 cycles), ultra-stretchability (up to 1400%), high toughness (7.16 MJ m-3), and fast self-healing properties, enabling the restoration of ionic conductivity within seconds, as well as no solvent leakage. The ionic elastomer is composed of in situ formed physically cross-linked poly(2-hydroxyethyl methacrylate) networks and poly(ethylene glycol) (PEG). The long molecular chains of PEG serve as a solvent for dissolving electrolytes, improve its long-term stability, reduce solvent leakage, and ensure the outstanding mechanical properties of the IHP. Surprisingly, the incorporation of ions into PEG simultaneously enhances the strength and toughness of the elastomer. The strengthening and toughening mechanisms were further revealed by molecular simulation. We demonstrate an application of the IHPs as (a) flexible sensors for strain or temperature sensing, (b) skin electrodes for recording electrocardiograms, and (c) a tough and sensing material for pneumatic artificial muscles. The proposed strategy is simple and easily scalable and can further inspire the design of novel ionic elastomers for ionotronics applications.

Are missing values important for earnings forecast? a machine learning perspective.

Analysts' forecast is one of the most common and important estimators for firms' future earnings. However, it is challenging to fully utilize because of the missing values. This study applies machine learning techniques to impute missing values in individual analysts' forecasts and subsequently to predict firms' future earnings based on both imputed and observed forecasts. After imputing missing values, the forecast error is reduced by 41% compared to the mean forecast, suggesting that missing values after imputation indeed useful for earnings forecast. We analyze multiple imputation methods and show that the out-performance of matrix factorization (MF) is consistent using different evaluation measures and across firms. Finally, we propose a stochastic gradient descent based coupled matrix factorization (CMF) to augment the imputation quality of missing values with multiple datasets. CMF further reduces the error of earnings forecast by 19% compared to the MF with a single dataset.

Effect of mutations on the hydrophobic interactions of the hierarchical molecular structure and mechanical properties of epithelial keratin 1/10.

Human epithelial keratin is an intermediate filament protein that serves as a backbone to maintain the stability of the cell nucleus and mechanical stability of the whole cells. The present study focused on two point mutations, F231L and S233L, of the 1B domain of keratin K 1/10 related to the rare genetic skin disease palmoplantar keratoderma (PPK). We used molecular dynamics simulation to study the effects of the mutations on various hierarchical structures, including heterodimers, tetramers, and octamers of the K1/10 1B domain at the atomic scale. The initial results demonstrated that the wild type and mutant proteins were highly similar at the dimer level but had different microstructures and mechanics at a higher-level assembly. A decrease in the hydrophobic interactions and hydrogen bonds at the terminus resulted in weakened mechanical properties of the tetramer and octamer of the F231L mutant. The asymmetrical structure of the S233L tetramer with an uneven distribution of the hydrogen bonds decreased its mechanical properties. However, the S233L mutation provided extra hydrophobic interactions between these mutated amino acid residues in the octamer, leading to improved mechanical properties. The results of the present study provided a deeper understanding of how the differences in point mutations induced the changes in the configuration and mechanical properties at the molecular scale. The differences in these properties may influence keratin assembly at the microscopic scale and ultimately cause diseases at the macroscopic scale.

Quantitative Analysis of Dynamic Subacromial Ultrasonography: Reliability and Influencing Factors.

Objective: Current imaging methods used to examine patients with subacromial impingement syndrome (SIS) are limited by their semi-quantitative nature and their capability of capturing dynamic movements. This study aimed to develop a quantitative analytic model to assess subacromial motions using dynamic ultrasound and to examine their reliability and potential influencing factors. Method: We included 48 healthy volunteers and examined their subacromial motions with dynamic ultrasound imaging. The parameters were the minimal vertical acromiohumeral distance, rotation radius, and degrees of the humeral head. The generalized estimating equation (GEE) was used to investigate the impact of different shoulder laterality, postures, and motion phases on the outcome. Result: Using the data of the minimal vertical acromiohumeral distance, the intra-rater and inter-rater reliabilities (intra-class correlation coefficient) were determined as 0.94 and 0.88, respectively. In the GEE analysis, a decrease in the minimal vertical acromiohumeral distance was associated with the abduction phase and full-can posture, with a beta coefficient of -0.02 cm [95% confidence interval (CI), -0.03 to -0.01] and -0.07 cm (95% CI, -0.11 to -0.02), respectively. The abduction phase led to a decrease in the radius of humeral rotation and an increase in the angle of humeral rotation, with a beta coefficient of -1.28 cm (95% CI, -2.16 to -0.40) and 6.60° (95% CI, 3.54-9.67), respectively. A significant negative correlation was observed between the rotation angle and radius of the humeral head and between the rotation angle and the minimal vertical acromiohumeral distance. Conclusion: Quantitative analysis of dynamic ultrasound imaging enables the delineation of subacromial motion with good reliability. The vertical acromiohumeral distance is the lowest in the abduction phase and full-can posture, and the rotation angle of the humeral head has the potential to serve as a new parameter for the evaluation of SIS.

Evaluation of the Need for Intensive Care in Children With Pneumonia: Machine Learning Approach.

BACKGROUND: Timely decision-making regarding intensive care unit (ICU) admission for children with pneumonia is crucial for a better prognosis. Despite attempts to establish a guideline or triage system for evaluating ICU care needs, no clinically applicable paradigm is available. OBJECTIVE: The aim of this study was to develop machine learning (ML) algorithms to predict ICU care needs for pediatric pneumonia patients within 24 hours of admission, evaluate their performance, and identify clinical indices for making decisions for pediatric pneumonia patients. METHODS: Pneumonia patients admitted to National Taiwan University Hospital from January 2010 to December 2019 aged under 18 years were enrolled. Their underlying diseases, clinical manifestations, and laboratory data at admission were collected. The outcome of interest was ICU transfer within 24 hours of hospitalization. We compared clinically relevant features between early ICU transfer patients and patients without ICU care. ML algorithms were developed to predict ICU admission. The performance of the algorithms was evaluated using sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and average precision. The relative feature importance of the best-performing algorithm was compared with physician-rated feature importance for explainability. RESULTS: A total of 8464 pediatric hospitalizations due to pneumonia were recorded, and 1166 (1166/8464, 13.8%) hospitalized patients were transferred to the ICU within 24 hours. Early ICU transfer patients were younger (P<.001), had higher rates of underlying diseases (eg, cardiovascular, neuropsychological, and congenital anomaly/genetic disorders; P<.001), had abnormal laboratory data, had higher pulse rates (P<.001), had higher breath rates (P<.001), had lower oxygen saturation (P<.001), and had lower peak body temperature (P<.001) at admission than patients without ICU transfer. The random forest (RF) algorithm achieved the best performance (sensitivity 0.94, 95% CI 0.92-0.95; specificity 0.94, 95% CI 0.92-0.95; AUC 0.99, 95% CI 0.98-0.99; and average precision 0.93, 95% CI 0.90-0.96). The lowest systolic blood pressure and presence of cardiovascular and neuropsychological diseases ranked in the top 10 in both RF relative feature importance and clinician judgment. CONCLUSIONS: The ML approach could provide a clinically applicable triage algorithm and identify important clinical indices, such as age, underlying diseases, abnormal vital signs, and laboratory data for evaluating the need for intensive care in children with pneumonia.

Molecular origin of the effects of mutation on the structure and mechanical properties of human epithelial keratin K5/K14.

Epithelial keratin, a type of intermediate filament (IF) protein, is one of the key components in maintaining the stability of the cell nucleus in the epidermis of the skin, the largest organ in the human body. It absorbs water and withstands external pressure, affecting the structural stability and mechanical properties of the skin. Epidermolysis bullosa simplex (EBS) is a rare genetic skin disease related to genetic mutations in epithelial keratin K5/K14. The resulting structural defects can cause keratinocytes in the basal layer to become fragile and rupture when subjected to mechanical stress. Its pathological feature is that the skin and mucous membranes are extremely fragile, and wounds and blisters occur under even slight external force. In this study, we focused on the amino acid sequence of the wild-type human keratin K5/K14 and sequences with point mutations, beginning with a full atomistic model of the K5/K14 heterodimer and proceeding to the higher hierarchical structure of the tetramer model. For the heterodimer, the structures of the wild type and the mutants share a high degree of similarity, and the helical structure is preserved. Then, based on the heterodimer model, we considered the keratin tetramer model with the ID1 contact from previous experimental observations. Our results suggested that in the wild-type tetramer, the hydrogen bonds formed in the middle and contact regions provide extra stability to tetramer 2B-2B interactions during IF assembly. The probabilities of hydrogen bond formation are lower in the mutant tetramers than in the wild-type tetramer in the contact region; the point mutations do not necessarily affect the structure for dimer formation, but changes in the interactions of amino acids may affect the higher-order assembly of IFs. We observed that the structures of the tetramers with point mutations were loosely stacked, and the mechanical properties were weaker than those of the wild-type tetramer. We further compared our results with the latest experimental measurements and discussed the relationship between the genotype of EBS disease and the atomic-level mutated structures. The atomistic model allowed us to study point mutations at the molecular level. The results can be further applied to reveal the effect of point mutations on EBS disease.

Effect of obesity and body mass index on coronavirus disease 2019 severity: A systematic review and meta-analysis.

We conducted a systematic review of observational studies to examine the effects of body mass index (BMI) and obesity (BMI ≥ 30 kg/m2 ) on coronavirus disease 2019 (COVID-19). Medline, Embase, and the Cochrane Library were searched. Sixteen articles were finally included in the meta-analysis, and a random effects model was used. BMI was found to be higher in patients with severe disease than in those with mild or moderate disease (MD 1.6, 95% CI, 0.8-2.4; p = .0002) in China; however, the heterogeneity was high (I2 = 75%). Elevated BMI was associated with invasive mechanical ventilation (IMV) use (MD 4.1, 95% CI, 2.1-6.1; p < .0001) in Western countries, and this result was consistent across studies (I2 = 0%). Additionally, there were increased odds ratios of IMV use (OR 2.0, 95% CI, 1.4-2.9; p < .0001) and hospitalization (OR 1.4, 95% CI, 1.3-1.60; p < .00001) in patients with obesity. There was no substantial heterogeneity (I2 = 0%). In conclusion, obesity or high BMI increased the risk of hospitalization, severe disease and invasive mechanical ventilation in COVID-19. Physicians must be alert to these early indicators to identify critical patients.

Ion Effect and Metal-Coordinated Cross-Linking for Multiscale Design of Nereis Jaw Inspired Mechanomutable Materials.

The Nvjp-1 protein is a key component in the jaws of Nereis virens, a species of marine worm. It contains over 25 mol % of histidine, which is believed to play a key role in the metal-coordinated cross-linking responsible for the structural stability and exceptional mechanical performance of the worm jaw. Understanding the nanoscale mechanism behind this cross-linking and its pathway in affecting the macroscopic mechanical behavior of the material is crucial to develop bioinspired mechanomutable materials based on Nvjp-1. Here, we use a combination of multiscale modeling and experimental synthesis to understand the behavior of this heterologous-expressed protein from the nano- to the macroscale. We have built a bottom-up molecular-based model, which includes electronic-based density functional theory calculations, atomistic simulation of the nanoscale properties with replica exchange molecular dynamics, and an elastic network model for describing the macroscale behavior at different pHs. This multiscale modeling supports the experimental synthesis of a photo-cross-linked Nvjp-1 hydrogel by proving both the nanoscale mechanisms and mechanical behavior predictions. Our theoretical results agree well with the experimental observations, showing that Nvjp-1 forms a more compact structure in the presence of Zn2+ ions with a suitable pH environment, leading to the formation of more stable intramolecular metal-coordinated cross-links. These metal-coordinated cross-links induce nanoscale aggregation of Nvjp-1, which is responsible for the hydrogel contraction observed in experiments and predicted by the model.

Determinants of Sir2-Mediated, Silent Chromatin Cohesion.

Cohesin associates with distinct sites on chromosomes to mediate sister chromatid cohesion. Single cohesin complexes are thought to bind by encircling both sister chromatids in a topological embrace. Transcriptionally repressed chromosomal domains in the yeast Saccharomyces cerevisiae represent specialized sites of cohesion where cohesin binds silent chromatin in a Sir2-dependent fashion. In this study, we investigated the molecular basis for Sir2-mediated cohesion. We identified a cluster of charged surface residues of Sir2, collectively termed the EKDK motif, that are required for cohesin function. In addition, we demonstrated that Esc8, a Sir2-interacting factor, is also required for silent chromatin cohesion. Esc8 was previously shown to associate with Isw1, the enzymatic core of ISW1 chromatin remodelers, to form a variant of the ISW1a chromatin remodeling complex. When ESC8 was deleted or the EKDK motif was mutated, cohesin binding at silenced chromatin domains persisted but cohesion of the domains was abolished. The data are not consistent with cohesin embracing both sister chromatids within silent chromatin domains. Transcriptional silencing remains largely intact in strains lacking ESC8 or bearing EKDK mutations, indicating that silencing and cohesion are separable functions of Sir2 and silent chromatin.

A series of conditional shuttle vectors for targeted genomic integration in budding yeast.

The capacity of Saccharomyces cerevisiae to repair exposed DNA ends by homologous recombination has long been used by experimentalists to assemble plasmids from DNA fragments in vivo. While this approach works well for engineering extrachromosomal vectors, it is not well suited to the generation, recovery and reuse of integrative vectors. Here, we describe the creation of a series of conditional centromeric shuttle vectors, termed pXR vectors, that can be used for both plasmid assembly in vivo and targeted genomic integration. The defining feature of pXR vectors is that the DNA segment bearing the centromere and origin of replication, termed CEN/ARS, is flanked by a pair of loxP sites. Passaging the vectors through bacteria that express Cre recombinase reduces the loxP-CEN/ARS-loxP module to a single loxP site, thereby eliminating the ability to replicate autonomously in yeast. Each vector also contains a selectable marker gene, as well as a fragment of the HO locus, which permits targeted integration at a neutral genomic site. The pXR vectors provide a convenient and robust method to assemble DNAs for targeted genomic modifications.

Real-time obstructive sleep apnea detection from frequency analysis of EDR and HRV using Lomb Periodogram.

In this study, an effective real-time obstructive sleep apnea (OSA) detection method from frequency analysis of ECG-derived respiratory (EDR) and heart rate variability (HRV) is proposed. Compared to traditional Polysomnography (PSG) which needs several physiological signals measured from patients, the proposed OSA detection method just only use ECG signals to determine the time interval of OSA. In order to be feasible to be implemented in hardware to achieve the real-time detection and portable application, the simplified Lomb Periodogram is utilized to perform the frequency analysis of EDR and HRV in this study. The experimental results of this work indicate that the overall accuracy can be effectively increased with values of Specificity (Sp) of 91%, Sensitivity (Se) of 95.7%, and Accuracy of 93.2% by integrating the EDR and HRV indexes.

An efficient ASIC implementation of 16-channel on-line recursive ICA processor for real-time EEG system.

This is a proposal for an efficient very-large-scale integration (VLSI) design, 16-channel on-line recursive independent component analysis (ORICA) processor ASIC for real-time EEG system, implemented with TSMC 40 nm CMOS technology. ORICA is appropriate to be used in real-time EEG system to separate artifacts because of its highly efficient and real-time process features. The proposed ORICA processor is composed of an ORICA processing unit and a singular value decomposition (SVD) processing unit. Compared with previous work [1], this proposed ORICA processor has enhanced effectiveness and reduced hardware complexity by utilizing a deeper pipeline architecture, shared arithmetic processing unit, and shared registers. The 16-channel random signals which contain 8-channel super-Gaussian and 8-channel sub-Gaussian components are used to analyze the dependence of the source components, and the average correlation coefficient is 0.95452 between the original source signals and extracted ORICA signals. Finally, the proposed ORICA processor ASIC is implemented with TSMC 40 nm CMOS technology, and it consumes 15.72 mW at 100 MHz operating frequency.

Structure and mechanical properties of human trichocyte keratin intermediate filament protein.

Keratin is a protein in the intermediate filament family and the key component of hair, nail, and skin. Here we report a bottom-up atomistic model of the keratin dimer, using the complete human keratin type k35 and k85 amino acid sequence. A detailed analysis of geometric and mechanical properties through full-atomistic simulation with validation against experimental results is presented. We introduce disulfide cross-links in a keratin tetramer and compare the mechanical behavior of the disulfide bonded systems with a system without disulfide bonds. Disulfide bond results in a higher strength (20% increase) and toughness (49% increase), but the system loses α-helical structures under loading, suggesting that disulfide bonds play a significant role in achieving the characteristic mechanical properties of trichocyte α-keratin. Our study provides general insight into the effect of disulfide cross-link on mechanical properties. Moreover, the availability of an atomistic model of this protein opens the possibility to study the mechanical properties of hair fibrils and other fibers from a bottom-up perspective.