Search

Abstract Background With over 7000 Mendelian disorders, identifying children with a specific rare genetic disorder diagnosis through structured electronic medical record data is challenging given incompleteness of records, inaccurate medical diagnosis coding, as well as heterogeneity in clinical symptoms and procedures for specific disorders. We sought to develop a digital phenotyping algorithm (PheIndex) using electronic medical records to identify children aged 0–3 diagnosed with genetic disorders or who present with illness with an increased risk for genetic disorders. Results Through expert opinion, we established 13 criteria for the algorithm and derived a score and a classification. The performance of each criterion and the classification were validated by chart review. PheIndex identified 1,088 children out of 93,154 live births who may be at an increased risk for genetic disorders. Chart review demonstrated that the algorithm achieved 90% sensitivity, 97% specificity, and 94% accuracy. Conclusions The PheIndex algorithm can help identify when a rare genetic disorder may be present, alerting providers to consider ordering a diagnostic genetic test and/or referring a patient to a medical geneticist.

Abstract Surface potential in a topological matter could unprecedentedly localize the waves. However, this surface potential is yet to be exploited in topological photonic systems. Here, we demonstrate that photonic surface states can be induced and controlled by the surface potential in a dielectric double gyroid (DG) photonic crystal. The basis translation in a unit cell enables tuning of the surface potential, which in turn regulates the degree of wave localization. The gradual modulation of DG photonic crystals enables the generation of a pseudomagnetic field. Overall, this study shows the interplay between surface potential and pseudomagnetic field regarding the surface states. The physical consequences outlined herein not only widen the scope of surface states in 3D photonic crystals but also highlight the importance of surface treatments in a photonic system.

Abstract The utilization of a zirconium oxide (ZrO2) thin film as the insulator in a metal–insulator–semiconductor (MIS) capacitor to enhance the characteristics of thin‐film transistors is investigated. Although the high crystallinity of ZrO2 is favorable to achieving higher capacitance density in metal–insulator–metal capacitors with ZrO2 thin films, it decreases the capacitance with increasing applied bias in Mo/ZrO2/InGaZnO (IGZO)‐structured MIS capacitors. Through comprehensive physical, chemical, and electrical characterizations, this study investigated the mechanism underlying the decreasing capacitance with increasing the applied bias in the accumulation state of the Mo/ZrO2/IGZO‐structured MIS capacitor depending on the crystallinity of ZrO2. Furthermore, the investigation identifies the optimal crystal structure of ZrO2 thin films for IGZO‐based MIS capacitors, highlighting the importance of forming meso‐crystalline structures in high dielectric constant (k) materials to enhance the k value and mitigating the decrease in capacitance caused by the accumulated carrier loss through grain boundaries of ZrO2.

Abstract Identifying the three-dimensional (3D) crystal plane and strain-field distributions of nanocrystals is essential for optical, catalytic, and electronic applications. However, it remains a challenge to image concave surfaces of nanoparticles. Here, we develop a methodology for visualizing the 3D information of chiral gold nanoparticles ≈ 200 nm in size with concave gap structures by Bragg coherent X-ray diffraction imaging. The distribution of the high-Miller-index planes constituting the concave chiral gap is precisely determined. The highly strained region adjacent to the chiral gaps is resolved, which was correlated to the 432-symmetric morphology of the nanoparticles and its corresponding plasmonic properties are numerically predicted from the atomically defined structures. This approach can serve as a comprehensive characterization platform for visualizing the 3D crystallographic and strain distributions of nanoparticles with a few hundred nanometers, especially for applications where structural complexity and local heterogeneity are major determinants, as exemplified in plasmonics.

Abstract Background Midlife hypertension has been recognized as a modifiable risk factor for dementia, but association between blood pressure (BP) in late life and dementia has been inconclusive. In addition, few studies have investigated effects of BP control on dementia incidence in the frail elderly. Thus, this study aimed to investigate the association of BP and dementia incidence with concomitant consideration of physical frailty in the young elderly population. Methods Using the Korean National Health Information Database, we identified 804,024 subjects without history of dementia at age 66. Dementia diagnosis was defined with prescription records of anti-dementia drugs and dementia-related diagnostic codes. Physical frailty was measured using the Timed Up and Go test. Association of BP and dementia incidence with concomitant consideration of physical frailty was investigated using Cox hazards analyses. Results The risks of Alzheimer’s and vascular dementia increased from systolic BP ≥ 160 and 130–139 mmHg, respectively; a significant association of dementia incidence with low BP was not observed. In the analyses stratified by the physical frailty status, low BP was not associated with increased risks of dementia within the groups both with and without physical frailty. Conclusions High BP was associated with increased risks of dementia, especially for vascular dementia, while low BP was not associated with increased risks of any type of dementia in young elderly people, even in those with physical frailty. This study suggests the need for tight BP control in young elderly people, irrespective of frailty status, to prevent dementia and supports the current clinical guidelines of hypertension treatment.

Objective In the present study, intracranial pressure (ICP) changes were investigated in out-ofhospital cardiac arrest (OHCA) patients with and without malignant blood-brain barrier (BBB) disruption who underwent target temperature management. Methods This prospective, single-center, observational study was conducted from June 2019 to December 2021. ICP and albumin quotient values were measured on days 1, 2, 3, and 4 of hospitalization. Malignant BBB disruption was defined as the sum of scores for the degree of BBB disruption ≥9 on days 1 to 4. Results ICP in OHCA patients without malignant BBB disruption on days 1, 2, 3, and 4 of hospitalization was 9.58±0.53, 12.32±0.65, 14.39±0.76, and 13.88±0.87 mmHg, respectively, and in OHCA patients with malignant BBB disruption 13.65±0.74, 15.72±0.67, 16.10±0.92, and 15.22±0.87 mmHg, respectively (P

To maximize the multilevel data storage capability for high-density memory applications, precise control of quantized conduction with ultralow transition energy is required. We report the quantized conduction in Ag/Ag2S/vacuum conductive-bridge random access memory under various pulse conditions to regulate atomic motion at room temperature. Using stochastic analysis, we unveil a pulse condition for supplying the optimal energy that allows precise atom detachment and has a high dissolution probability. In addition, we calculate the transition energy required to change each quantized state for an Al2O3 electrolyte and vacuum gap. We determine a large transition energy of Ag in Al2O3 (8–1 mJ), hindering the precise control of quantized conduction, whereas the transition energy of Ag in vacuum is relatively low (397–95 nJ), enabling proper atomic motion.

As people’s demand for eating out is steadily increasing, the number of restaurants is continuously increasing, and catering industry platforms such as Yelp, Open Table, and Zomato provide basic information and evaluation information of restaurants and restaurant recommendation services suitable for users. Existing research on recommending restaurants mainly uses only evaluation information to find neighbors, and the use of user and restaurant information is still in its infancy. In addition, there is little study on how various types of input information affect the performance of the recommender system. This study examines the influence of three component information provided by Yelp.com on the performance of the recommender system using various real restaurants, reviews, and users dataset provided by Yelp.com. For this purpose, Two Phase Experiment was designed, and restaurant data located in Austin, Texas, USA, which has the largest number of review data, was collected. As a result of the experiment, elite status, the cumulative number of reviews, price range and average rating of restaurants could improve the recommendation performance.

Concrete pavement growth can cause blow-ups and other pressure-related issues, such as concrete buckling and crushing at the transverse cracks or joints. In addition, these issues result in damaged to adjoining structures, such as bridge abutments, decks, and access structures in the pavement. However, available theoretical solutions and methods for evaluating pavement growth and predictions of concrete pavement blow-up are considerably limited. In this study, therefore, the pavement growth and blow-up analysis model was developed to predict the blow-up potential due to concrete pavement growth. This model considered factors such as the pavement structure and materials, climatic conditions, configuration of expansion joint, base friction characteristics, geometric imperfection, rotational stiffness of joints, and design reliability. In addition, the pavement growth and blow-up analysis model can be used to predict the service life of expansion joints and the blow-up occurrence time by considering the factors affecting it. Using the developed model, various sensitivity analysis was performed to investigate the factors affecting the potential concrete pavement growth and blow-up occurrence. It was found that the factors of substrate type, coefficient of thermal expansion, precipitation, and alkali–silica reaction had a significant effect on pavement growth and blow-up occurrence times.

BackgroundAutism spectrum disorder (ASD) is characterized by abnormalities in social communication and limited and repetitive behavioral patterns. Children with ASD who lack social communication skills will eventually not interact with others and will lack peer relationships when compared to ordinary people. Thus, it is necessary to develop a program to improve social communication abilities using digital technology in people with ASD. ObjectiveWe intend to develop and apply a metaverse-based child social skills training program aimed at improving the social interaction abilities of children with ASD aged 7-12 years. We plan to compare and analyze the biometric information collected through wearable devices when applying the metaverse-based social skills training program to evaluate emotional changes in children with ASD in stressful situations. MethodsThis parallel randomized controlled study will be conducted on children aged 7-12 years diagnosed with ASD. A metaverse-based social skills training program using digital technology will be administered to children who voluntarily wish to participate in the research with consent from their legal guardians. The treatment group will participate in the metaverse-based social skills training program developed by this research team once a week for 60 minutes per session for 4 weeks. The control group will not intervene during the experiment. The treatment group will use wearable devices during the experiment to collect real-time biometric information. ResultsThe study is expected to recruit and enroll participants in March 2022. After registering the participants, the study will be conducted from March 2022 to May 2022. This research will be jointly conducted by Yonsei University and Dobrain Co Ltd. Children participating in the program will use the internet-based platform. ConclusionsThe metaverse-based Program for the Education and Enrichment of Relational Skills (PEERS) will be effective in improving the social skills of children with ASD, similar to the offline PEERS program. The metaverse-based PEERS program offers excellent accessibility and is inexpensive because it can be administered at home; thus, it is expected to be effective in many children with ASD. If a method can be applied to detect children's emotional changes early using biometric information collected through wearable devices, then emotional changes such as anxiety and anger can be alleviated in advance, thus reducing issues in children with ASD. Trial RegistrationClinical Research Information Service KCT0006859; https://tinyurl.com/4r3k7cmj International Registered Report Identifier (IRRID)PRR1-10.2196/35960

To improve the turn-off speed and uniformity of atomic threshold switching (TS) devices, we propose the use of the AgSe electrode and controlled bipolar pulse forming method. Compared with TS devices with Ag and AgTe electrodes, TS device with the AgSe electrode shows an extremely fast turn-off speed, which can be explained by the limited injection of Ag into the switching layer. By applying positive bias (Icc = 500 nA, 1 ms) followed by negative bias (−0.1 to −0.2 V, $10~\mu \text{s}$ ), the TS device exhibits excellent switching uniformity. It can be explained by the formation of atomic-scale filament under the positive bias at low Icc and drift-back of excessive Ag by the negative bias. By designing the shape of the filament and concentration of the residual Ag, the TS device with the AgSe electrode shows promise for selector applications.

AIM: To introduce a new surgical technique, air-bubble technique for the management of posterior capsule rupture (PCR) and to evaluate the safety and efficacy of the technique. METHODS: A retrospective case series analysis of 24 eyes of 24 patients, in which the air bubble technique was used for the management of PCR, was performed. Once PCR occurred, a dispersive ophthalmic viscosurgical device (OVD) was injected into the tear. And small volumes (0.2-0.3 mL) of air bubbles were injected beneath the OVD. The air bubble served as a physical barrier and supported the posterior capsule. RESULTS: After surgery, none of the patients had serious complications during the follow-up period of 1y. Extension of the PCR size occurred in only 2 cases, and additional OVD injection was required only in 3 cases. Air bubbles imparted great stability to the nuclear pieces and the posterior capsule. CONCLUSION: The air-bubble technique may be considered a safe and effective procedure for managing a PCR. It may be of value to the inexperienced cataract surgeon.

The design and optimisation of 3D DNA-origami can be a barrier to rapid application. Here the authors design barrel structure of stacked 2D double helical rings with complex surface patterns.

Since the 1990s, applications of holography and Fourier optics have been significantly expanded from the spatial modulation of light propagation to the nanofabrication of superlattices. Holographic mixing of multiple beams is found to be highly compatible with unprecedented engineering of 1D, 2D, and 3D superlattices, particularly at the nanoscales. This is essential for the development of champion photonic crystals working at optical frequencies. However, these extensive efforts toward holographic photonic crystals have declined with the failure to obtain a champion photonic crystal and the rapid rise of other important classes of optical modes in the 2010s such as Weyl points, bound state in continuum (BIC), and exceptional points (EPs). To obtain photonic crystals, the symmetric modulations of the sinusoidally distributed real part of permittivity (Re(ε)), that can be considered as waves of matter from the viewpoint of Fourier optics, are sufficient. However, Weyl points, BIC, and EPs demand more complicated and advanced modulation of such as asymmetrically distributed wave of Re(ε) and the coupled Re(ε) and imaginary ε (Im(ε)). It is, therefore, important to determine whether holographic fabrications can be an efficient solution to Weyl points, BIC, and EPs. In this review, a comprehensive overview of holography, Fourier optical surface/volume gratings, and their implementations for Weyl points, BIC, and EPs is presented.

Purpose In 2015, the South Korean government legislated the Act for the Improvement of Training Conditions and Status of Medical Residents (Medical Resident Act). This study investigated changes in the working and learning environment pre- and post-implementation of the Medical Resident Act in 2017, as well as changes in training conditions by year post-implementation. Methods An annual cross-sectional voluntary survey was conducted by the Korean Intern Resident Association (KIRA) between 2016 and 2019. The learning and working environment, including extended shift length, rest time, learning goals, and job satisfaction, were compared by institution type, training year, and specialty. Results Of the 55,727 enrollees in the KIRA, 15,029 trainees took the survey, and the number of survey participants increased year by year (from 2,984 in 2016 to 4,700 in 2019). Overall working hours tended to decrease; however, interns worked the most (114 hours in 2016, 88 hours in 2019; P

Probabilistic safety assessments (PSA) have been used for several decades to visualize the risk level of commercial nuclear power plants (NPPs). Since the role of a human reliability analysis (HRA) is to provide human error probabilities for safety critical tasks to support PSA, PSA quality is strongly affected by HRA quality. Therefore, it is important to understand the underlying limitations or problems of HRA techniques. For this reason, this study conducted a survey among 14 subject matter experts who represent the HRA community of domestic Korean NPPs. As a result, five significant HRA issues were identified: (1) providing a technical basis for the K-HRA (Korean HRA) method, and developing dedicated HRA methods applicable to (2) diverse external events to support Level 1 PSA, (3) digital environments, (4) mobile equipment, and (5) severe accident management guideline tasks to support Level 2 PSA. In addition, an HRA method to support multi-unit PSA was emphasized because it plays an important role in the evaluation of site risk, which is one of the hottest current issues. It is believed that creating such a catalog of prioritized issues will be a good indication of research direction to improve HRA and therefore PSA quality. Keywords: Human reliability analysis, Nuclear power plant, Remaining and emerging issues, Expert opinion, Importance, Significance, Issue priority

The optimal blood pressure (BP) target in older people according to frailty status remains uncertain. This article investigates how frailty affects the association between BP and cardiovascular diseases or mortality, specifically in young-old adults. A retrospective cohort was created for 708,964 older adults with a uniform age of 66 years. The association between BP and myocardial infarction (MI), stroke, or mortality was analyzed using Cox proportional hazards models. The Timed Up and Go test (TUG) was used as a measure of physical frailty. Mean follow-up was 6.8 years, detecting 38,963 (5.5%) events. There was a linear association between increasing systolic BP (SBP) or diastolic BP (DBP) and increased risk of incident MI and stroke, compared to the reference BP (SBP, 110–119 mmHg or DBP, 80–89 mmHg). The risk patterns with high BP remained similar in each TUG group (

Abstract Atomic force microscopy (AFM) nanomanipulation has been viewed as a deterministic method for the assembly of plasmonic metamolecules because it enables unprecedented engineering of clusters with exquisite control over particle number and geometry. Nevertheless, the dimensionality of plasmonic metamolecules via AFM nanomanipulation is limited to 2D, so as to restrict the design space of available artificial electromagnetisms. Here, we show that “2D” nanomanipulation of the AFM tip can be used to assemble “3D” plasmonic metamolecules in a versatile and deterministic way by dribbling highly spherical and smooth gold nanospheres (NSs) on a nanohole template rather than on a flat surface. Various 3D plasmonic clusters with controlled symmetry were successfully assembled with nanometer precision; the relevant 3D plasmonic modes (i.e., artificial magnetism and magnetic-based Fano resonance) were fully rationalized by both numerical calculation and dark-field spectroscopy. This templating strategy for advancing AFM nanomanipulation can be generalized to exploit the fundamental understanding of various electromagnetic 3D couplings and can serve as the basis for the design of metamolecules, metafluids, and metamaterials.

Detection and classification of unidentified underwater targets maneuvering in complex underwater environments are critical for active sonar systems. In previous studies, many detection methods were applied to separate targets from the clutter using signals that exceed a preset threshold determined by the sonar console operator. This is because the high signal-to-noise ratio target has enough feature vector components to separate. However, in a real environment, the signal-to-noise ratio of the received target does not always exceed the threshold. Therefore, a target detection algorithm for various target signal-to-noise ratio environments is required; strong clutter energy can lead to false detection, while weak target signals reduce the probability of detection. It also uses long pulse repetition intervals for long-range detection and high ambient noise, requiring classification processing for each ping without accumulating pings. In this study, a target classification algorithm is proposed that can be applied to signals in real underwater environments above the noise level without a threshold set by the sonar console operator, and the classification performance of the algorithm is verified. The active sonar for long-range target detection has low-resolution data; thus, feature vector extraction algorithms are required. Feature vectors are extracted from the experimental data using Power-Normalized Cepstral Coefficients for target classification. Feature vectors are also extracted with Mel-Frequency Cepstral Coefficients and compared with the proposed algorithm. A convolutional neural network was employed as the classifier. In addition, the proposed algorithm is to be compared with the result of target classification using a spectrogram and convolutional neural network. Experimental data were obtained using a hull-mounted active sonar system operating on a Korean naval ship in the East Sea of South Korea and a real maneuvering underwater target. From the experimental data with 29 pings, we extracted 361 target and 3351 clutter data. It is difficult to collect real underwater target data from the real sea environment. Therefore, the number of target data was increased using the data augmentation technique. Eighty percent of the data was used for training and the rest was used for testing. Accuracy value curves and classification rate tables are presented for performance analysis and discussion. Results showed that the proposed algorithm has a higher classification rate than Mel-Frequency Cepstral Coefficients without affecting the target classification by the signal level. Additionally, the obtained results showed that target classification is possible within one ping data without any ping accumulation.

This study analyzed the performance evaluation of alkali-activated composites (AAC) with an alkali-sulfate activator and determined the expected effects of applying AACs to actual sites. Results revealed that when the binder weight was increased by 100 kg/m3 at 7 days of age, the homogel strength of ordinary Portland cement (OPC) and AAC increased by 0.9 and 5.0 MPa, respectively. According to the analysis of the matrix microstructures at 7 days of age, calcium silicate hydrates (C–S–H, Ca1.5SiO3.5·H2O) and ettringite (Ca6Al2(SO4)3(OH)12·26H2O) were formed in AAC, which are similar hydration products as found in OPC. Furthermore, the acid resistance analysis showed that the mass change of AAC in HCl and H2SO4 solutions ranged from 36.1% to 88.0%, lower than that of OPC, indicating AAC’s superior acid resistance. Moreover, the OPC and AAC binder weight ranges satisfying the target geltime (20–50 s) were estimated as 180.1–471.1 kg/m3 and 261.2–469.9 kg/m3, respectively, and the global warming potential (GWP) according to binder weight range was 102.3–257.3 kg CO2 eq/m3 and 72.9–126.0 kg CO2 eq/m3. Therefore, by applying AAC to actual sites, GWP is expected to be 29.5 (28.8%)–131.3 (51.0%) kg CO2 eq/m3 less than that of OPC.

Named Entity Recognition (NER) plays a vital role in natural language processing (NLP). Currently, deep neural network models have achieved significant success in NER. Recent advances in NER systems have introduced various feature selections to identify appropriate representations and handle Out-Of-the-Vocabulary (OOV) words. After selecting the features, they are all concatenated at the embedding layer before being fed into a model to label the input sequences. However, when concatenating the features, information collisions may occur and this would cause the limitation or degradation of the performance. To overcome the information collisions, some works tried to directly connect some features to latter layers, which we call the delayed combination and show its effectiveness by comparing it to the early combination. As feature encodings for input, we selected the character-level Convolutional Neural Network (CNN) or Long Short-Term Memory (LSTM) word encoding, the pre-trained word embedding, and the contextual word embedding and additionally designed CNN-based sentence encoding using a dictionary. These feature encodings are combined at early or delayed position of the bidirectional LSTM Conditional Random Field (CRF) model according to each feature’s characteristics. We evaluated the performance of this model on the CoNLL 2003 and OntoNotes 5.0 datasets using the F1 score and compared the delayed combination model with our own implementation of the early combination as well as the previous works. This comparison convinces us that our delayed combination is more effective than the early one and also highly competitive.

Background In‐hospital cardiac arrest is a major burden to public health, which affects patient safety. Although traditional track‐and‐trigger systems are used to predict cardiac arrest early, they have limitations, with low sensitivity and high false‐alarm rates. We propose a deep learning–based early warning system that shows higher performance than the existing track‐and‐trigger systems. Methods and Results This retrospective cohort study reviewed patients who were admitted to 2 hospitals from June 2010 to July 2017. A total of 52 131 patients were included. Specifically, a recurrent neural network was trained using data from June 2010 to January 2017. The result was tested using the data from February to July 2017. The primary outcome was cardiac arrest, and the secondary outcome was death without attempted resuscitation. As comparative measures, we used the area under the receiver operating characteristic curve (AUROC), the area under the precision–recall curve (AUPRC), and the net reclassification index. Furthermore, we evaluated sensitivity while varying the number of alarms. The deep learning–based early warning system (AUROC: 0.850; AUPRC: 0.044) significantly outperformed a modified early warning score (AUROC: 0.603; AUPRC: 0.003), a random forest algorithm (AUROC: 0.780; AUPRC: 0.014), and logistic regression (AUROC: 0.613; AUPRC: 0.007). Furthermore, the deep learning–based early warning system reduced the number of alarms by 82.2%, 13.5%, and 42.1% compared with the modified early warning system, random forest, and logistic regression, respectively, at the same sensitivity. Conclusions An algorithm based on deep learning had high sensitivity and a low false‐alarm rate for detection of patients with cardiac arrest in the multicenter study.

AIM:Triage is important in identifying high-risk patients amongst many less urgent patients as emergency department (ED) overcrowding has become a national crisis recently. This study aims to validate that a Deep-learning-based Triage and Acuity Score (DTAS) identifies high-risk patients more accurately than existing triage and acuity scores using a large national dataset. METHODS:We conducted a retrospective observational cohort study using data from the Korean National Emergency Department Information System (NEDIS), which collected data on visits in real time from 151 EDs. The NEDIS data was split into derivation data (January 2014-June 2016) and validation data (July-December 2016). We also used data from the Sejong General Hospital (SGH) for external validation (January-December 2017). We predicted in-hospital mortality, critical care, and hospitalization using initial information of ED patients (age, sex, chief complaint, time from symptom onset to ED visit, arrival mode, trauma, initial vital signs and mental status as predictor variables). RESULTS:A total of 11,656,559 patients were included in this study. The primary outcome was in-hospital mortality. The Area Under the Receiver Operating Characteristic curve (AUROC) and Area Under the Precision and Recall Curve (AUPRC) of DTAS were 0.935 and 0.264. It significantly outperformed Korean triage and acuity score (AUROC:0.785, AUPRC:0.192), modified early warning score (AUROC:0.810, AUPRC:0.116), logistic regression (AUROC:0.903, AUPRC:0.209), and random forest (AUROC:0.910, AUPRC:0.179). CONCLUSION:Deep-learning-based Triage and Acuity Score predicted in-hospital mortality, critical care, and hospitalization more accurately than existing triages and acuity, and it was validated using a large, multicenter dataset.

Despite the efforts for precise alignment of imaging sensors and attitude sensors before launch, the accuracy of pre-launch alignment is limited. The misalignment between attitude frame and camera frame is especially important as it is related to the localization error of the spacecraft, which is one of the essential factors of satellite image quality. In this paper, a framework for camera misalignment estimation is presented with its application to a high-resolution earth-observation satellite—Deimos-2. The framework intends to provide a solution for estimation and correction of the camera misalignment of a spacecraft, covering image acquisition planning to mathematical solution of camera misalignment. Considerations for effective image acquisition planning to obtain reliable results are discussed, followed by a detailed description on a practical method for extracting many GCPs automatically using reference ortho-photos. Patterns of localization errors that commonly occur due to the camera misalignment are also investigated. A mathematical model for camera misalignment estimation is described comprehensively. The results of simulation experiments showing the validity and accuracy of the misalignment estimation model are provided. The proposed framework was applied to Deimos-2. The real-world data and results from Deimos-2 are presented.

Myotis rufoniger is a vesper bat in the genus Myotis. Here we report the whole genome sequence and analyses of the M. rufoniger. We generated 124 Gb of short-read DNA sequences with an estimated genome size of 1.88 Gb at a sequencing depth of 66× fold. The sequences were aligned to M. brandtii bat reference genome at a mapping rate of 96.50% covering 95.71% coding sequence region at 10× coverage. The divergence time of Myotis bat family is estimated to be 11.5 million years, and the divergence time between M. rufoniger and its closest species M. davidii is estimated to be 10.4 million years. We found 1,239 function-altering M. rufoniger specific amino acid sequences from 929 genes compared to other Myotis bat and mammalian genomes. The functional enrichment test of the 929 genes detected amino acid changes in melanin associated DCT, SLC45A2, TYRP1, and OCA2 genes possibly responsible for the M. rufoniger's red fur color and a general coloration in Myotis. N6AMT1 gene, associated with arsenic resistance, showed a high degree of function alteration in M. rufoniger. We further confirmed that the M. rufoniger also has bat-specific sequences within FSHB, GHR, IGF1R, TP53, MDM2, SLC45A2, RGS7BP, RHO, OPN1SW, and CNGB3 genes that have already been published to be related to bat's reproduction, lifespan, flight, low vision, and echolocation. Additionally, our demographic history analysis found that the effective population size of Myotis clade has been consistently decreasing since ~30k years ago. M. rufoniger's effective population size was the lowest in Myotis bats, confirming its relatively low genetic diversity.

MicroRNAs (miRNAs) in blood plasma are stable under high levels of ribonuclease activity and could function in tissue-to-tissue communication, suggesting that they may have distinctive structural characteristics compared with non-circulating miRNAs. In this study, the expression of miRNAs in horse plasma and their characteristic nucleotide composition were examined and compared with non-plasma miRNAs. Highly expressed plasma miRNA species were not part of the abundant group of miRNAs in non-plasma tissues, except for the eca-let-7 family. eca-miR-486-5p, -92a, and -21 were among the most abundant plasma miRNAs, and their human orthologs also belong to the most abundant group of miRNAs in human plasma. Uracil and guanine were the most common nucleotides of both plasma and non-plasma miRNAs. Cytosine was the least common in plasma and non-plasma miRNAs, although levels were higher in plasma miRNAs. Plasma miRNAs also showed higher expression levels of miRNAs containing adenine and cytosine repeats, compared with non-plasma miRNAs. These observations indicate that miRNAs in the plasma have a unique nucleotide composition.

One of the key issues for providing users user-customized or context-aware services is to automatically detect latent topics, users’ interests, and their changing patterns from large-scale social network information. Most of the current methods are devoted either to discovering static latent topics and users’ interests or to analyzing topic evolution only from intrafeatures of documents, namely, text content, without considering directly extrafeatures of documents such as authors. Moreover, they are applicable only to the case of single processor. To resolve these problems, we propose a dynamic users’ interest discovery model with distributed inference algorithm, named as Distributed Author-Topic over Time (D-AToT) model. The collapsed Gibbs sampling method following the main idea of MapReduce is also utilized for inferring model parameters. The proposed model can discover latent topics and users’ interests, and mine their changing patterns over time. Extensive experimental results on NIPS (Neural Information Processing Systems) dataset show that our D-AToT model is feasible and efficient.

As the sensor networks are broadly used in diverse range of applications, Semantic Web technologies have been adopted as a means to manage the huge amount of heterogeneous sensor nodes and their observation data. Large amount of sensor data are annotated with spatial, temporal, and thematic semantic metadata. As a consequence, efficient query processing over large RDF graph is becoming more important in retrieving contextual information from semantic sensor data. In this paper we propose a novel path querying scheme which uses RDF schema information. By utilizing the class path expressions precalculated from RDF schema, the graph search space is significantly reduced. Compared with the conventional BFS algorithm, the proposed algorithm (bidirectional BFS combined with class path lookup approach) achieves performance improvement by 3 orders of magnitude. Additionally, we show that the proposed algorithm is efficiently parallelizable, and thus, the proposed algorithm returns graph search results within a reasonable response time on even much larger RDF graph.

Sensor data is structured and generally lacks of meaning by itself, but life-logging data (time, location, etc.) out of sensor data can be utilized to create lots of meaningful information combined with social data from social networks like Facebook and Twitter. There have been many platforms to produce meaningful information and support human behavior and context-awareness through integrating diverse mobile, social, and sensing input streams. The problem is that these platforms do not guarantee the performance in terms of the processing time and even let the accuracy of output data be addressed by new studies in each area where the platform is applied. Thus, this study proposes an improved platform which builds a knowledge base for context awareness by applying distributed and parallel computing approach considering the characteristics of sensor data that is collected and processed in real-time, and compares the proposed platform with existing platforms in terms of performance. The experiment shows the proposed platform is an advanced platform in terms of processing time. We reduce the processing time by 40% compared with existing platform. The proposed platform also guarantees the accuracy compared with existing platform.

Recently, due to the popularization of the smartphone and social network service (SNS), many SNS users write their opinions for social events. According to these social phenomena, social sensor network which analyzes social events by utilizing those users' text data is proposed. Parsing is essential module to analyze user's text contents because it gives the understanding of semantics by extracting the words and their classes from texts. However, parsing requires much time because it needs to analyze all context information from the users' text. In addition, as users' text data are generated and transferred in streaming, the required parsing time increases too. This situation occurs that it is hard to parse the text on the single machine. Therefore, to drastically enhance the parsing speed, we propose distributed and parallel parsing system on the MapReduce. It applies the legacy parser to the MapReduce through loose coupling. Also, to reduce communication overheads, the statistical model used by the parser is resided on local cache in each mapper. The experimental result shows that the speed of proposed system is 2–19 times better than that of the legacy parser. As a result, we prove that the proposed system is useful for parsing text data in social sensor network.

This study examined whether the perceptions of the theory of planned behavior (TPB) and individual and environmental factors related to healthy eating changed after the COVID-19 pandemic among adults living in Daqing, China. Data were collected through two rounds of online surveys conducted from April to May 2021 and from March to April 2023, using a questionnaire previously validated for use in Daqing. Using the snowball sampling method, 571 adult participants were recruited, most of whom were Daqing oilfield workers or members of their families. Multiple linear regression analyses were used to determine if the differences in the perceptions of the TPB and dietary environments exist during and after the pandemic after controlling for potential confounders. Scores of several subcomponents of TPB and mean scores of longterm intentions increased but scores of subcomponents and overall mean of motivation decreased after the outbreak. Multiple linear regression showed that only motivation for healthy eating decreased after the pandemic. Influenced by the pandemic, people increased their healthy eating behaviors. Nevertheless, as the pandemic subsided and the pace of life accelerated, people tended to choose convenient foods over healthy options. Consequently, the motivation for healthy eating has declined post-pandemic compared to during the pandemic. [ABSTRACT FROM AUTHOR]