ICT and Transport Infrastructure Development

This paper estimates the non-linear impact of ICT network on motorway contribution to total factor productivity. Using dynamic panel data of OECD member countries, the paper finds that there exists a critical mass of broadband penetration rate which has the property that if this threshold level is reached, there will be an accelerating network effects of motorway extension

ICT development and productivity of transport infrastructure

This paper examines the impact of ICT network on productivity contribution of transportation infrastructure. Using dynamic panel data of OECD member countries, the paper finds that there exists significant complementarity between ICT network and transportation infrastructure. The network effect of motorway infrastructure in OECD countries tends to accelerate when the ICT network grows beyond a certain threshold level

Efficiency of transport infrastructure and ICT development

This study examines the impact of ICT growth on the productivity effects of transportation infrastructure. Using dynamic panel data of OECD member countries, the study finds econometrically meaningful results on examining the complementarity between ICT and transportation infrastructures. The network effect of growth of motorway infrastructure in advanced countries tends to accelerate when the ICT network grows beyond a certain threshold level

A Dynamic Resource Manager with Effective Resource Isolation Based on Workload Types in Virtualized Cloud Computing Environments

To use computing resources for processing parallel algorithms on demand, cloud computing has been widely used since it is able to scale in response to load increases and decreases. Typically, cloud computing providers offer virtual machines to cloud users with static configurations, and these configurations are not changed until virtual machines are shutting down. To accelerate parallel processing computations in cloud computing environments, we design and implement a dynamic resource manager by isolating resources based on workload types. To avoid unnecessary context switching and increase CPUs affinity, our dynamic resource manager determines whether vCPU to physical CPU core pinning is required. If so, the VM’s vCPUs are pinned by our dynamic resource manager, which can guarantee the resource and performance isolation. With our proposed resource manager for virtual machines, we can achieve a performance boost and load balancing at the same time. Performance results show that our proposed method outperforms the default scheduler of Xen about 36.2% by reducing the number of context switching for VMs

Decadal variability of the upper ocean heat content in the East/Japan Sea and its possible relationship to northwestern Pacific variability

Author Posting. © American Geophysical Union, 2012. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 117 (2012): C02017, doi:10.1029/2011JC007369.The upper ocean heat content variability in the East/Japan Sea was investigated using a 40 year temperature and salinity data set from 1968 to 2007. Decadal variability was identified as the dominant mode of variability in the upper ocean (0–300 m) aside from the seasonal cycle. The decadal variability is strong to the west of northern Honshu, west of the Tsugaru Strait, and west of southern Hokkaido. Temperature anomalies at 50–125 m exhibit a large contribution to the decadal variability, particularly in the eastern part of the East/Japan Sea. The vertical structure of regressed temperature anomalies and the spatial patterns of regressed 10°C isotherms in the East/Japan Sea suggest that the decadal variability is related to upper ocean circulation in the East/Japan Sea. The decadal variability also exhibits an increasing trend, which indicates that the regions showing large decadal variations experienced warming on decadal time scales. Further analysis shows that the decadal variability in the East/Japan Sea is not locally isolated but is related to variability in the northwestern Pacific.This work was supported by grants from the Ministry of Land, Transport, and Maritime Affairs, Korea (Ocean Climate Variability Program and EAST-I Project).2012-08-0

Nanomechanical In Situ Monitoring of Proteolysis of Peptide by Cathepsin B

Characterization and control of proteolysis of peptides by specific cellular protease is a priori requisite for effective drug discovery. Here, we report the nanomechanical, in situ monitoring of proteolysis of peptide chain attributed to protease (Cathepsin B) by using a resonant nanomechanical microcantilever immersed in a liquid. Specifically, the detection is based on measurement of resonant frequency shift arising from proteolysis of peptides (leading to decrease of cantilever's overall mass, and consequently, increases in the resonance). It is shown that resonant microcantilever enables the quantification of proteolysis efficacy with respect to protease concentration. Remarkably, the nanomechanical, in situ monitoring of proteolysis allows us to gain insight into the kinetics of proteolysis of peptides, which is well depicted by Langmuir kinetic model. This implies that nanomechanical biosensor enables the characterization of specific cellular protease such as its kinetics

Assessment of a novel deep learning-based software developed for automatic feature extraction and grading of radiographic knee osteoarthritis

Background The Kellgren-Lawrence (KL) grading system is the most widely used method to classify the severity of osteoarthritis (OA) of the knee. However, due to ambiguity of terminology, the KL system showed inferior inter- and intra-observer reliability. For a more reliable evaluation, we recently developed novel deep learning (DL) software known as MediAI-OA to extract each radiographic feature of knee OA and to grade OA severity based on the KL system. Methods This research used data from the Osteoarthritis Initiative for training and validation of MediAI-OA. 44,193 radiographs and 810 radiographs were set as the training data and used as validation data, respectively. This AI model was developed to automatically quantify the degree of joint space narrowing (JSN) of medial and lateral tibiofemoral joint, to automatically detect osteophytes in four regions (medial distal femur, lateral distal femur, medial proximal tibia and lateral proximal tibia) of the knee joint, to classify the KL grade, and present the results of these three OA features together. The model was tested by using 400 test datasets, and the results were compared to the ground truth. The accuracy of the JSN quantification and osteophyte detection was evaluated. The KL grade classification performance was evaluated by precision, recall, F1 score, accuracy, and Cohen's kappa coefficient. In addition, we defined KL grade 2 or higher as clinically significant OA, and accuracy of OA diagnosis were obtained. Results The mean squared error of JSN rate quantification was 0.067 and average osteophyte detection accuracy of the MediAI-OA was 0.84. The accuracy of KL grading was 0.83, and the kappa coefficient between the AI model and ground truth was 0.768, which demonstrated substantial consistency. The OA diagnosis accuracy of this software was 0.92. Conclusions The novel DL software known as MediAI-OA demonstrated satisfactory performance comparable to that of experienced orthopedic surgeons and radiologists for analyzing features of knee OA, KL grading and OA diagnosis. Therefore, reliable KL grading can be performed and the burden of the radiologist can be reduced by using MediAI-OA

Endoplasmic Reticulum Stress Induces MUC5AC and MUC5B Expression in Human Nasal Airway Epithelial Cells

Objectives Endoplasmic reticulum (ER) stress is known to be associated with inflammatory airway diseases, and three major transmembrane receptors: double-stranded RNA-activated protein kinase-like ER kinase, inositol requiring enzyme 1, and activating transcription factor 6 (ATF6) play important roles in ER stress-related proinflammatory signaling. However, the effects of ER stress and these three major signaling pathways on the regulation of the production of airway mucins in human nasal airway epithelial cells have not been elucidated. Methods In primary human nasal epithelial cells, the effect of tunicamycin (an ER stress inducer) and 4-phenylbutyric acid (4-PBA, ER stress inhibitor) on the expression of MUC5AC and MUC5B was investigated by reverse transcriptasepolymerase chain reaction, real-time polymerase chain reaction, enzyme immunoassay, and immunoblot analysis. Small interfering RNA (siRNA) transfection was used to identify the mechanisms involved. Results Tunicamycin increased the expressions of MUC5AC and MUC5B and the mRNA expressions of ER stress-related signaling molecules, including spliced X-box binding protein 1 (XBP-1), transcription factor CCAAT-enhancer-binding protein homologous protein (CHOP), and ATF6. In addition, 4-PBA attenuated the tunicamycin-induced expressions of MUC5AC and MUC5B and the mRNA expressions of ER stress-related signaling molecules. Furthermore, siRNA knockdowns of XBP-1, CHOP, and ATF6 blocked the tunicamycin-induced mRNA expressions and glycoprotein productions of MUC5AC and MUC5B. Conclusion. These results demonstrate that ER stress plays an important role in the regulation of MUC5AC and MUC5B via the activations of XBP-1, CHOP, and ATF6 in human nasal airway epithelial cells

Two-stage learning-based prediction of bronchopulmonary dysplasia in very low birth weight infants: a nationwide cohort study

IntroductionThe aim of this study is to develop an enhanced machine learning-based prediction models for bronchopulmonary dysplasia (BPD) and its severity through a two-stage approach integrated with the duration of respiratory support (RSd) using prenatal and early postnatal variables from a nationwide very low birth weight (VLBW) infant cohort.MethodsWe included 16,384 VLBW infants admitted to the neonatal intensive care unit (NICU) of the Korean Neonatal Network (KNN), a nationwide VLBW infant registry (2013–2020). Overall, 45 prenatal and early perinatal clinical variables were selected. A multilayer perceptron (MLP)-based network analysis, which was recently introduced to predict diseases in preterm infants, was used for modeling and a stepwise approach. Additionally, we applied a complementary MLP network and established new BPD prediction models (PMbpd). The performances of the models were compared using the area under the receiver operating characteristic curve (AUROC) values. The Shapley method was used to determine the contribution of each variable.ResultsWe included 11,177 VLBW infants (3,724 without BPD (BPD 0), 3,383 with mild BPD (BPD 1), 1,375 with moderate BPD (BPD 2), and 2,695 with severe BPD (BPD 3) cases). Compared to conventional machine learning (ML) models, our PMbpd and two-stage PMbpd with RSd (TS-PMbpd) model outperformed both binary (0 vs. 1,2,3; 0,1 vs. 2,3; 0,1,2 vs. 3) and each severity (0 vs. 1 vs. 2 vs. 3) prediction (AUROC = 0.895 and 0.897, 0.824 and 0.825, 0.828 and 0.823, 0.783, and 0.786, respectively). GA, birth weight, and patent ductus arteriosus (PDA) treatment were significant variables for the occurrence of BPD. Birth weight, low blood pressure, and intraventricular hemorrhage were significant for BPD ≥2, birth weight, low blood pressure, and PDA ligation for BPD ≥3. GA, birth weight, and pulmonary hypertension were the principal variables that predicted BPD severity in VLBW infants.ConclusionsWe developed a new two-stage ML model reflecting crucial BPD indicators (RSd) and found significant clinical variables for the early prediction of BPD and its severity with high predictive accuracy. Our model can be used as an adjunctive predictive model in the practical NICU field

Rapidly Progressive Pericardial Effusion and Cardiac Tamponade in a Term Infant with an Umbilical Venous Catheter: A Case Report

Pericardial effusion (PCE) in neonates has various clinical presentations depending on the amount and speed of fluid accumulation and can cause cardiac tamponade (CT). We report a case of rapidly accumulating PCE and near-fatal CT with an umbilical venous catheter successfully resolved by emergent echo-guided pericardiocentesis in a term infant who had been hospitalized with meconium aspiration syndrome and persistent pulmonary hypertension. This case report suggests that if a patient with an intracardiac umbilical catheter shows sudden cardiopulmonary instability, the possibility of PCE and CT should be considered. Furthermore, if necessary, emergency drainage of the PCE and removal of the umbilical catheter should be immediately performed