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Abstract Background Hyperuricemia could be a risk for various chronic diseases, and it could be largely corrected by diet control. This study was a nationwide cross-sectional study to investigate the association between serum uric acid level and dietary fiber intake. Methods This study analyzed data based on the Korean National Health and Nutrition Examination Survey conducted from 2016 to 2018. Adults over 20 years of age with normal renal function, defined as an estimated glomerular filtration rate (eGFR) over 30mL/min/1.73m2, were included. The criteria for hyperuricemia were ≥ 7 mg/dL in men and ≥ 6 mg/dL in women. Data regarding dietary intake were obtained using the 24-hour recall method. Results A total of 15,278 subjects (6,455 males/8,823 females) were analyzed. The prevalence of hyperuricemia was 19.3% in men and 6.8% in women. There were significant, negative associations between serum uric acid and total fiber intake in both men and women. Consuming more than 27.9 g of dietary fiber in men and 20.7 g in women reduced the risk of hyperuricemia by approximately 30% with odds ratios of 0.72 (0.62–0.83) and 0.71 (0.56–0.88) in men and women, respectively. With regard to the risk reduction by the type of dietary fiber, cereal fiber was significantly identified in both men and women, while fruit fiber was only significant in men. In the subgroup analysis, this association remained significantly in young and metabolically healthy populations with normal weight. Conclusions Dietary fiber intake was inversely associated with serum uric acid levels. This relationship was particularly significant in metabolically healthy young adults.

Abstract Spinal cord injury (SCI) leads to motor and sensory impairment below the site of injury, thereby necessitating rehabilitation. An enriched environment (EE) increases social interaction and locomotor activity in a mouse model, similar to human rehabilitation. However, the impact of EE on presynaptic plasticity in gene expression levels remains unclear. Hence, this study aimed to investigate the therapeutic potential of EE in an SCI mouse model. Mice with spinal cord contusion were divided into two groups: those housed in standard cages (control) and those in EE conditions (EE). Each group was housed separately for either 2- or 8-weeks post-injury, after which RNA sequencing was performed and compared to a sham group (receiving only a dorsal laminectomy). The synaptic vesicle cycle (SVC) pathway and related genes showed significant downregulation after SCI at both time points. Subsequently, we investigated whether exposure to EE for 2- and 8-weeks post-SCI could modulate the SVC pathway and its related genes. Notably, exposure to EE for 8 weeks resulted in a marked reversal effect of SVC-related gene expression, along with stimulation of axon regeneration and mitigation of locomotor activity loss. Thus, prolonged exposure to EE increased presynaptic activity, fostering axon regeneration and functional improvement by modulating the SVC in the SCI mouse model. These findings suggest that EE exposure proves effective in inducing activity-dependent plasticity, offering a promising therapeutic approach akin to rehabilitation training in patients with SCI.

Modular construction is a method of construction that involves prefabricating modular indoor spaces in a factory and then assembling them at the construction site. However, dimensional errors made during production in the modular manufacturing factory may hinder assembly at the construction site and delay the entire construction process. In particular, as the quality of intermodular connections determines the structural performance and serviceability of the entire modular building, meticulous quality control is required for the connections. In this study, a method is developed for inspection of the intermodular connection locations to meet the requirements of being accurate, inexpensive, easy-to-use, rapid, and operable by a single person, and then tested in a modular factory. The proposed inspection method consists of three parts: (a) a laser meter mounted on a gimbal, (b) target reflectors based on the tumbling doll principle, and (c) a mobile device as a remote control and for data storage, data post-processing, and visualization of results. The proposed inspection process was tested on a modular unit that had been manufactured at a modular factory; the proposed method outperformed the conventional method in terms of accuracy, inspection time, and work safety. The proposed inspection method allows the accurate and rapid inspection of locations of intermodular connection holes and, thus, its use in modular factories is likely to improve the economics and efficiency of modular construction.

Abstract The purpose of this study was to demonstrate the performance of a fully automated, deep learning-based brain segmentation (DLS) method in healthy controls and in patients with neurodevelopmental disorders, SCN1A mutation, under eleven. The whole, cortical, and subcortical volumes of previously enrolled 21 participants, under 11 years of age, with a SCN1A mutation, and 42 healthy controls, were obtained using a DLS method, and compared to volumes measured by Freesurfer with manual correction. Additionally, the volumes which were calculated with the DLS method between the patients and the control group. The volumes of total brain gray and white matter using DLS method were consistent with that volume which were measured by Freesurfer with manual correction in healthy controls. Among 68 cortical parcellated volume analysis, the volumes of only 7 areas measured by DLS methods were significantly different from that measured by Freesurfer with manual correction, and the differences decreased with increasing age in the subgroup analysis. The subcortical volume measured by the DLS method was relatively smaller than that of the Freesurfer volume analysis. Further, the DLS method could perfectly detect the reduced volume identified by the Freesurfer software and manual correction in patients with SCN1A mutations, compared with healthy controls. In a pediatric population, this new, fully automated DLS method is compatible with the classic, volumetric analysis with Freesurfer software and manual correction, and it can also well detect brain morphological changes in children with a neurodevelopmental disorder.

Abstract Osteoporosis is a common skeletal disease that results in an increased risk of fractures. However, there is no definitive cure, warranting the development of potential therapeutic agents. 3′-Sialyllactose (3′-SL) in human milk regulates many biological functions. However, its effect on bone metabolism remains unknown. This study aimed to investigate the molecular mechanisms underlying the effect of 3′-SL on bone homeostasis. Treatment of human bone marrow stromal cells (hBMSCs) with 3′-SL enhanced osteogenic differentiation and inhibited adipogenic differentiation of hBMSCs. RNA sequencing showed that 3′-SL enhanced laminin subunit gamma-2 expression and promoted osteogenic differentiation via the phosphatidylinositol 3‑kinase/protein kinase B signaling pathway. Furthermore, 3′-SL inhibited the receptor activator of nuclear factor κB ligand-induced osteoclast differentiation of bone marrow-derived macrophages through the nuclear factor κB and mitogen‑activated protein kinase signaling pathway, ameliorated osteoporosis in ovariectomized mice, and positively regulated bone remodeling. Our findings suggest 3′-SL as a potential drug for osteoporosis.

Considering the urgent demand for reliable and rapid detection of infectious respiratory viruses during unpredictable pandemics, an innovative ultrasensitive colorimetric immunoassay for influenza A (H1N1) virus detection is developed herein. The proposed approach leverages dual amplification by combining layer‐by‐layer interactions with the nanozyme effect of biotinylated gold nanoparticles (BGNPs). BGNPs assemble around the target via repeated incubation cycles under optimized conditions, resulting in a layered structure that increases optical density, producing a more intense signal proportional to the viral titer. Additionally, the nanozyme effect of the layered BGNPs induces oxidation of 3,3',5,5'‐tetramethylbenzidine, which further enhances the visible signal detectable by the naked eye. This synergetic nanoprobe‐based system demonstrates remarkable sensitivity, with a limit of detection of 101.29 EID50 mL−1, which is 2500‐fold higher than that of commercial rapid kits and conventional enzyme‐linked immunosorbent assays, within a rapid 55 min timeframe. Furthermore, the anti‐interference capability and portability of the developed system reinforce its practicality, making it a promising tool for field diagnostic tests that offers advanced, ultrasensitive, and early detection of respiratory viruses.

Background Limited longitudinal evidence exists regarding the potential association between smoking status and hip fracture among individuals with type 2 diabetes. We investigated this association using large-scale, nationwide cohort data for the Korean population. Methods This nationwide cohort study included 1,414,635 adults aged 40 and older who received Korean National Health Insurance Service health examinations between 2009 and 2012. Subjects with type 2 diabetes were categorized according to their smoking status, amount smoked (pack-years), number of cigarettes smoked per day, and duration of smoking. The results are presented as hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations between smoking status parameters and risk of hip fracture in multivariable Cox proportional hazard regression analysis. Results Compared with never-smokers, an increased adjusted HR (aHR) for hip fracture was observed in current smokers (1.681; 95% CI, 1.578 to 1.791), and a comparable aHR for hip fracture was found in former smokers (1.065; 95% CI, 0.999 to 1.136). For former smokers who had smoked 20 pack-years or more, the risk was slightly higher than that for never-smokers (aHR, 1.107; 95% CI, 1.024 to 1.196). The hip fracture risk of female former smokers was similar to that of female current smokers, but the hip fracture risk in male former smokers was similar to that of male never-smokers. Conclusion Smoking is associated with an increased risk of hip fracture in patients with type 2 diabetes. Current smokers with diabetes should be encouraged to quit smoking because the risk of hip fracture is greatly reduced in former smokers.

Abstract Twigs of Morus alba have been used in traditional medicine to treat muscle-related symptoms such as aches, numbness, and stiffness. Despite its clinical use in traditional medicine, its active compounds and mode of action have not yet been investigated. Therefore, we aimed to isolate the compounds from the twigs of M. alba and deduce active compounds, key gene targets, and mechanism of action against sarcopenia using network pharmacology analysis. Using various isolation techniques and spectroscopic methods, 43 phytochemicals, including 3 new flavonoids, were isolated and performed network pharmacology analysis. According to the computational-assistant analysis, 28 compounds, 9 genes, and the PI3K-Akt-mTOR signaling pathway were deduced as expected active compounds (EAC), key targets, and the main signaling pathway. To verify the predicted results, the cell proliferation activities of the EAC were evaluated. Especially, moracin E and M significantly increased by 130% (p

Objective: Real-world-based population data about the optimal low-density lipoprotein cholesterol (LDL-C) level for preventing cardiovascular disease in very high-risk populations is scarce. Methods: From 2009 to 2012, 26,922 people aged ≥ 40 years with type 2 diabetes mellitus (T2DM) who had a history of percutaneous coronary intervention (PCI) were analyzed. Data from the Korean National Health Insurance System were used. They were followed up to the date of a cardiovascular event or the time to death, or until December 31, 2018. Endpoints were recurrent PCI, newly stroke or heart failure, cardiovascular death, and all-cause death. Participants were divided into the following categories according to LDL-C level:

We developed machine learning algorithms for distinguishing scintillation signals from a plastic-liquid coupled detector known as a phoswich. The challenge lies in discriminating signals from organic scintillators with similar shapes and short decay times. Using a single-readout phoswich detector, we successfully identified γ radiation signals from two scintillating components. Our Boosted Decision Tree algorithm demonstrated a maximum discrimination power of 3.02 ± 0.85 standard deviation in the 950 keV region, providing an efficient solution for self-shielding and enhancing radiation detection capabilities.

The currently defined Safe Working Load (SWL) of the U-bolt has been determined excessively conservatively. To address this issue, this study conducted structural tests and numerical analysis on round type U-bolts of various sizes. The structural tests were conducted using a 2.5 MN actuator at the SURF R&D center, and the strain was measured through a uniaxial strain gage. The test showed the failure load greatly exceeded the design load with horizontal force. This necessitates a reevaluation and redefinition of load standards. Nonlinear numerical analysis was carried out, and these results were compared with the structural test results. When subjected to vertical loading, behavior was similar to uniaxial tension. On the other hand, using linear elastic analysis for determining SWL for horizontal loading was found to be irrational. A methodology was proposed for estimating the SWL of the U-bolt.

Background/Aims A re-increasing trend of thyroid cancer since 2015 has been observed despite a similar examination rate, and the incidence of thyroid cancer among young adults continues to rise. Methods This study used data from the Korean National Health Insurance Service. Individuals 20–39 years of age who underwent ≥ 4 health checkups from 2009–2013 were enrolled and followed throughout 2019. To quantify the metabolic burden, groups were divided by the number of diagnoses of metabolic syndrome across four consecutive health examinations. Results Among the study population (n = 1,204,646), 5,929 (0.5%) were diagnosed with thyroid cancer during a follow-up period of 5 years. The hazard ratio (95% confidence interval) values of thyroid cancer occurrence according to the number (1–4) of diagnoses of metabolic syndrome across the four health examinations compared to the group without met-abolic syndrome were significantly greater, as follows: 1.12 (1.02–1.23), 1.25 (1.10–1.42), 1.33 (1.15–1.55), and 1.48 (1.25–1.75) (p for trend < 0.01), respectively. Each component of metabolic syndrome showed a significant increase in hazard ratio according to the number of diagnoses except for impaired fasting glucose criteria. Conclusions Cumulative exposure to metabolic syndrome was associated with thyroid cancer risk in young adults.

The transition to smart grids has served to transform traditional power systems into data-driven power systems. The purpose of this transition is to enable effective energy management and system reliability through an analysis that is centered on energy information. However, energy theft caused by vulnerabilities in the data collected from smart meters is emerging as a primary threat to the stability and profitability of power systems. Therefore, various methodologies have been proposed for energy theft detection (ETD), but many of them are challenging to use effectively due to the limitations of energy theft datasets. This paper provides a comprehensive review of ETD methods, highlighting the limitations of current datasets and technical approaches to improve training datasets and the ETD in smart grids. Furthermore, future research directions and open issues from the perspective of generative AI-based ETD are discussed, and the potential of generative AI in addressing dataset limitations and enhancing ETD robustness is emphasized.

Abstract Transient receptor potential vanilloid member 3 (TRPV3) is an ion channel that plays a critical role in temperature sensing in skin. There have been active studies on how TRPV3, which is also known as one of the temperature-sensitive transient receptor potential (thermoTRP) channels, responds to temperature. However, the previous studies were mostly based on TRPV3 originating from mice or rats. Here, we focus on human TRPV3 (hTRPV3) and show that which domain of hTRPV3 undergoes conformational changes as temperature increases by Förster resonance energy transfer (FRET) assay. During the heat-induced activation of hTRPV3, the linker domain close to C-terminus, that is, the C-terminal domain shows a largest structural change whereas there is little change in the ankyrin repeat domain (ARD). Interestingly, the activation of hTRPV3 by an agonist shows structural change patterns that are completely different from those observed during activation by heat; we observe structural changes in ARD and S2–S3 linker after ligand stimulation whereas relatively little change is observed when stimulated by heat. Our results provide insight into the thermal activation of hTRPV3 channel.

Abstract Human sterile α motif and HD domain-containing protein 1 (SAMHD1) has deoxyribonucleoside triphosphohydrolase (dNTPase) activity that allows it to defend against human immunodeficiency virus type I (HIV-1) infections and regulate the cell cycle. Although SAMHD1 mutations have been identified in various cancer types, their role in cancer is unclear. Here, we aimed to investigate the oncogenic role of SAMHD1 in human clear cell renal cell carcinoma (ccRCC), particularly as a core molecule promoting cancer cell migration. We found that SAMHD1 participated in endocytosis and lamellipodia formation. Mechanistically, SAMHD1 contributed to the formation of the endosomal complex by binding to cortactin. Thereafter, SAMHD1-stimulated endosomal focal adhesion kinase (FAK) signaling activated Rac1, which promoted lamellipodia formation on the plasma membrane and enhanced the motility of ccRCC cells. Finally, we observed a strong correlation between SAMHD1 expression and the activation of FAK and cortactin in tumor tissues obtained from patients with ccRCC. In brief, these findings reveal that SAMHD1 is an oncogene that plays a pivotal role in ccRCC cell migration through the endosomal FAK-Rac1 signaling pathway.

Background Persistence with denosumab in male patients has not been adequately investigated, although poor denosumab persistence is associated with a significant risk of rebound vertebral fractures. Methods We retrospectively evaluated 294 Korean male osteoporosis patients treated with denosumab at three medical centers and examined their persistence with four doses of denosumab injection over 24 months of treatment. Persistence was defined as the extent to which a patient adhered to denosumab treatment in terms of the prescribed interval and dose, with a permissible gap of 8 weeks. For patients who missed their scheduled treatment appointment(s) during the follow-up period (i.e., no-shows), Cox proportional regression analysis was conducted to explore the factors associated with poor adherence. Several factors were considered, such as age, prior anti-osteoporotic drug use, the treatment provider’s medical specialty, the proximity to the medical center, and financial burdens of treatment. Results Out of 294 male patients, 77 (26.2%) completed all four sequential rounds of the denosumab treatment. Out of 217 patients who did not complete the denosumab treatment, 138 (63.6%) missed the scheduled treatment(s). Missing treatment was significantly associated with age (odds ratio [OR], 1.03), prior bisphosphonate use (OR, 0.76), and prescription by non-endocrinologists (OR, 2.24). Denosumab was stopped in 44 (20.3%) patients due to medical errors, in 24 (11.1%) patients due to a T-score improvement over –2.5, and in five (2.3%) patients due to expected dental procedures. Conclusion Our study showed that only one-fourth of Korean male osteoporosis patients were fully adherent to 24 months of denosumab treatment.

Abstract The DAMA/LIBRA collaboration has reported the observation of an annual modulation in the event rate that has been attributed to dark matter interactions over the last two decades. However, even though tremendous efforts to detect similar dark matter interactions were pursued, no definitive evidence has been observed to corroborate the DAMA/LIBRA signal. Many studies assuming various dark matter models have attempted to reconcile DAMA/LIBRA’s modulation signals and null results from other experiments, however no clear conclusion can be drawn. Apart from the dark matter hypothesis, several studies have examined the possibility that the modulation is induced by variations in detector’s environment or their specific analysis methods. In particular, a recent study presents a possible cause of the annual modulation from an analysis method adopted by the DAMA/LIBRA experiment in which the observed annual modulation could be reproduced by a slowly varying time-dependent background. Here, we study the COSINE-100 data using an analysis method similar to the one adopted by the DAMA/LIBRA experiment and observe a significant annual modulation, however the modulation phase is almost opposite to that of the DAMA/LIBRA data. Assuming the same background composition for COSINE-100 and DAMA/LIBRA, simulated experiments for the DAMA/LIBRA without dark matter signals also provide significant annual modulation with an amplitude similar to DAMA/LIBRA with opposite phase. Even though this observation does not directly explain the DAMA/LIBRA results directly, this interesting phenomenon motivates more profound studies of the time-dependent DAMA/LIBRA background data.

The oxide passivation layer on the lithium metal surface causes uneven deposition and stripping in lithium metal batteries. Here authors introduce uniform homo-epitaxial lithium deposition on the metal surface to alleviate this issue and improve the cycle stability of the lithium metal batteries.

Corynebacterium glutamicum is a promising organism for the industrial production of amino acids, fuels, and various value-added chemicals. From the whole genome sequence release, C. glutamicum has been valuable in the field of industrial microbiology and biotechnology. Continuous discovery of genetic manipulations and regulation mechanisms has developed C. glutamicum as a synthetic biology platform chassis. This review summarized diverse genomic manipulation technologies and gene expression tools for static, dynamic, and multiplex control at transcription and translation levels. Moreover, we discussed the current challenges and applicable tools to C. glutamicum for future advancements.

In this study, a machine learning-based prediction model was developed using the attribute information of underground pipelines and the history information of ground subsidence in order to predict the risk level of ground subsidence in urban areas. The target area was divided into a grid with sizes of 100m $\times 100\text{m}$ , 300m $\times 300\text{m}$ , and 500m $\times 500\text{m}$ , and the attribute information of underground pipelines in the grid and ground subsidence data were utilized to build a dataset. For input data, the pipeline’s diameter, the number of years used, and density were selected based on the pipeline’s length as the basic unit. Additionally, the risk level of ground subsidence was determined as the output data using historical information. A total of 36 datasets were built according to the conditions, and factors with significant correlation were selected through a correlation analysis of the datasets. The developed datasets were divided into training data and evaluation data. The synthetic minority oversampling technique was used to resolve the data imbalance. The model performance evaluation indexes used in this study were F1-score and AUC(Area Under the Curve). The performance of each model was compared, and the comparison results showed that a model that applied a preprocessed dataset with 500m $\times 500\text{m}$ grid size, 10 years in use, 100mm pipeline diameter, and 1–2 ground sinks in Level 1 risk range to the LGBM(Light Gradient Boosting Model) classifier derived the best evaluation indexes(F1-Score:0.750, AUC:0.840). The map was found to be effective for predicting the risk level of ground subsidence in urban areas.

Artificial ion channels are in demand for ionotronic devices. Here, the authors use layered MXene membranes modified with plasmonic gold nanostars and cellulose nanofibers to convert a thermal gradient into an ion current for photosensitive ion channeling.

As large cities are continually being developed around coastal areas, structural damage due to the consolidation settlement of soft ground is becoming more of a problem. Estimating consolidation settlement requires calculating an accurate compressive index through consolidation tests. However, these tests are time-consuming, and there is a risk of the test results becoming compromised while preparing and testing the specimens. Therefore, predicting the compression index based on the results of relatively simple physical property tests enables more reliable and accurate predictions of consolidation settlement by calculating the compression index at multiple points. In this context, this study collected geotechnical data from the soft ground of Korea’s south coast. The collected data were used to construct a dataset for developing a compression index prediction model, and significant influencing factors were identified through Pearson correlation analysis. Simple and multiple linear regression analysis was performed using these factors to derive regression equations, and compression index prediction models were developed by applying machine learning algorithms. The results of deriving the significance of the influencing factors from the developed compression index prediction model showed that natural water content was the most significant factor in predicting the compression index. By collecting a significant amount of high-quality data and using the compression index prediction model and the model construction process proposed in this study, more accurate predictions of the compressive index will be possible in the future.

Energy management systems (EMSs) are regarded as essential components within smart grids. In pursuit of efficiency, reliability, stability, and sustainability, an integrated EMS empowered by machine learning (ML) has been addressed as a promising solution. A comprehensive review of current literature and trends has been conducted with a focus on key areas, such as distributed energy resources, energy management information systems, energy storage systems, energy trading risk management systems, demand-side management systems, grid automation, and self-healing systems. The application of ML in EMS is discussed, highlighting enhancements in data analytics, improvements in system stability, facilitation of efficient energy distribution and optimization of energy flow. Moreover, architectural frameworks, operational constraints, and challenging issues in ML-based EMS are explored by focusing on its effectiveness, efficiency, and suitability. This paper is intended to provide valuable insights into the future of EMS.

Background and purposeTo develop and validate a deep learning-based automatic segmentation model for assessing intracranial volume (ICV) and to compare the accuracy determined by NeuroQuant (NQ), FreeSurfer (FS), and SynthSeg.Materials and methodsThis retrospective study included 60 subjects [30 Alzheimer’s disease (AD), 21 mild cognitive impairment (MCI), 9 cognitively normal (CN)] from a single tertiary hospital for the training and validation group (50:10). The test group included 40 subjects (20 AD, 10 MCI, 10 CN) from the ADNI dataset. We propose a robust ICV segmentation model based on the foundational 2D UNet architecture trained with four types of input images (both single and multimodality using scaled or unscaled T1-weighted and T2-FLAIR MR images). To compare with our model, NQ, FS, and SynthSeg were also utilized in the test group. We evaluated the model performance by measuring the Dice similarity coefficient (DSC) and average volume difference.ResultsThe single-modality model trained with scaled T1-weighted images showed excellent performance with a DSC of 0.989 ± 0.002 and an average volume difference of 0.46% ± 0.38%. Our multimodality model trained with both unscaled T1-weighted and T2-FLAIR images showed similar performance with a DSC of 0.988 ± 0.002 and an average volume difference of 0.47% ± 0.35%. The overall average volume difference with our model showed relatively higher accuracy than NQ (2.15% ± 1.72%), FS (3.69% ± 2.93%), and SynthSeg (1.88% ± 1.18%). Furthermore, our model outperformed the three others in each subgroup of patients with AD, MCI, and CN subjects.ConclusionOur deep learning-based automatic ICV segmentation model showed excellent performance for the automatic evaluation of ICV.

ObjectivesLevothyroxine suppressive therapy following thyroidectomy for thyroid cancer patients is considered as a risk factor for osteoporosis and fragility fractures. We evaluated the association of regular exercise and exercise habit change with fracture risk in adults older than 40 years who underwent thyroidectomy for thyroid cancer.MethodsWe enrolled the patients who underwent thyroidectomy for thyroid cancer older than 40 years between 2010 and 2016 from the Korean National Health Insurance Service data, and they were followed through 2019. Based on the questionnaire of health examination within 2 years before and after surgery, whether regular exercise once a week was evaluated. The reference group for the statistical analysis was the continuing lack of physical activity group that did not exercise before or after surgery. For fractures newly diagnosed during the follow-up period, univariate and multivariate Cox regression analyses were performed for risk evaluation.ResultsWe evaluated 74,774 subjects, of whom 2,924 (3.9%) experienced any fractures during a median follow-up of 4.5 years. Compared with the group consistently lack of physical activity, the group that exercised before and after surgery showed a significant decrease in the risk of any fracture, vertebral fracture, and hip fracture: adjusted hazard ratio 0.848 (95% Confidence Interval 0.771–0.932), 0.703 (0.591–0.836), and 0.405 (0.224–0.732), respectively. For vertebral fracture, a significant reduction in fracture risk was confirmed even in patients who started their regular exercise after surgery: adjusted hazard ratio 0.779 (0.648–0.936). The risk reduction for vertebral fractures upon the initiation of exercise was found to be significant in the high-risk groups of patients: women and total thyroidectomy patients.ConclusionWe suggest that maintaining or starting regular exercise after surgery may help prevent fractures in thyroid cancer patients older than 40 years who have undergone thyroidectomy.

The precast concrete (PC) method involves manufacturing reinforced concrete building components in a factory that are then transported to and assembled on a construction site. Compared to conventional methods, PC is widely employed as an advantageous means of creating a sustainable environment and improving construction quality. However, due to time and cost increase, many modern PC factories inspect only randomly selected component samples, for which they write inspection reports using paper-based forms. The storage and management of these documents associated with inspections within factories are essential because any defects that occur during the manufacturing process adversely affect the subsequent delivery and assembly activities. In this study, a mobile application capable of automated documentation and the storage, and input of systematic data was developed to generate a system for comprehensive quality management and assurance within PC factories. The developed system was tested in a PC factory, achieving a 47% time-saving rate compared to the conventional inspection method. Inspection reports of the developed system contain considerably more information than those of the conventional method and fundamentally prevent the risk of document damage and loss as they are automatically archived on a server in digital format.

Background/Aims Despite the prominence of denosumab as the number one prescribed anti-osteoporosis drug in Korea, the effects of denosumab in male osteoporosis patients were not researched sufficiently. Moreover, concerns on rebound vertebral fractures associated with poor denosumab adherence exist. Methods We retrospectively evaluated 147 Korean male osteoporosis patients treated with denosumab. After 12 months of treatment, 60 patients were lost during follow-up, and eight were excluded due to missing data. Out of the initial 147 patients, 79 were considered eligible for the analysis of the efficacy of denosumab. 54 patients were initially drug-naïve, and 25 had previously received bisphosphonate therapy. Results In 54 drug-naïve patients, significant increases in bone mineral density (BMD) were observed in all measurement sites: 5.2% ± 3.7% in the lumbar spine, 2.3% ± 2.8% in the femoral neck, and 1.9% ± 2.8% in the total hip (p < 0.01, respectively). Trabecular bone score showed an increase of 0.5% ± 5.8% in drug-naïve patients. Likewise, in 25 patients with previous bisphosphonate treatment, increase in BMD were observed as well: 4.8% ± 3.5% in the lumbar spine, 1.4% ± 3.6% in the femoral neck, and 0.8% ± 2.1% in the total hip (p < 0.01, p = 0.06, p = 0.06, respectively). Significant declines of −55.1% ± 31.8% in C-terminal telopeptide of type 1 collagen (CTX), and −62.9% ± 21.3% in total procollagen 1 N-terminal propeptide (P1NP), in drug-naïve patients; and −37.7% ± 41.5%, in CTX and −55.4% ± 30.1%, in P1NP in patients with previous bisphosphonate treatment were exhibited after 12 months of treatment. The adherence rates of the second and third dosing schedules were 79.9% and 56.8%, respectively. Conclusions Our study indicates that denosumab is effective in increasing BMD in Korean osteoporosis males regardless of prior bisphosphonate treatment.

The side effects and safety issues tied to calcium supplementation raise questions about its necessity in osteoporosis treatment. We retrospectively evaluated 189 postmenopausal osteoporosis patients treated with denosumab for 12 months. Patients exhibited neither renal dysfunction nor compromised general dietary intake. Patients were divided into three groups as follows: group A, weekly vitamin D 7000 IU; group B, daily vitamin D 1000 IU with elemental calcium 100 mg; and group C, daily vitamin D 1000 IU with elemental calcium 500 mg. All groups showed significant increases in bone density: +6.4 ± 4.7% for the lumbar spine, +2.2 ± 3.5% for the femoral neck, and +2.4 ± 3.8% for the total hip in group A; +7.0 ± 10.9% for the lumbar spine, +2.3 ± 5.2% for the femoral neck, and +2.4 ± 3.8% for the total hip in group B; and + 6.7 ± 8.7% for the lumbar spine, +2.5 ± 8.4% for the femoral neck, and +2.3 ± 4.0% for the total hip in group C. Serum calcium levels increased over time in all three groups with no significant difference. Changes in CTX and P1NP levels did not differ between the groups (all p > 0.05). With regular dietary intake, calcium supplementation levels showed no significant effect on bone density, bone marker changes, or hypocalcemia incidence during denosumab treatment.

Abstract Lifestyle changes after a diagnosis of type 2 diabetes mellitus (DM) can affect vascular health outcomes. The objective of this study was to investigate the effects of changes in smoking and exercise on the risk of cardiovascular disease (CVD) and mortality in patients with newly diagnosed DM. Data were analyzed for 181,591 people with newly diagnosed type 2 DM who underwent 2 serial health examinations within 2 years before and after DM diagnosis. The study population was followed from the baseline to the date of death or cardiovascular events, or until December 31, 2018 and median follow-up was 6.07 years. Based on the change in status from before to after the diagnosis, participants were grouped into smoking groups (continuous smokers, quitters, new smokers, and nonsmokers) and exercise groups (constant exercisers, new exercisers, exercise dropouts, and nonexercisers). Compared with the nonexercisers, those who initiated exercise after their DM diagnosis had a lower risk of myocardial infarction (MI), stroke, and all-cause mortality: the hazard ratio (HR; 95% confidence interval [CI]) was 0.85 (0.76–0.94) for MI, 0.86 (0.78–0.94) for stroke, and 0.84 (0.89–0.90) for all-cause mortality. Quitters had a higher risk of MI, stroke, and all-cause mortality than nonsmokers, but their risk level was much lower than that in continuous smokers. When the group of continuous smokers and nonexercisers was considered as the reference group, participants who quit smoking and remained nonexercisers had a 21% lower risk of CVD (HR 0.79; 95% CI 0.70–0.90). Those who quit smoking and started exercising had a 46% reduced risk of CVD (HR 0.54; 95% CI 0.41–0.71) and a 22% reduced risk in all-cause mortality (HR 0.78; 95% CI 0.63–0.96). Smoking cessation and exercise initiation after a diagnosis of new-onset type 2 DM was associated with a reduced risk of CVD and all-cause mortality.

Abstract Background The co-occurrence of diabetes and osteoporosis is common in postmenopausal women. For the treatment of postmenopausal osteoporosis, current guidelines recommend initial treatment with bisphosphonates, but it is unclear whether bisphosphonates provide a similar degree of therapeutic efficacy in patients with diabetes. This study sought to compare the efficacy of monthly oral ibandronate for retaining bone mineral density (BMD) in diabetic and non-diabetic postmenopausal women with osteoporosis. Methods Postmenopausal osteoporotic women with or without diabetes were enrolled in this study from three hospitals in an open-label approach from 2018 to 2020. Each group of patients received oral ibandronate 150 mg once monthly for 1 year. BMD, trabecular bone score (TBS), serum C-terminal telopeptide of type I collagen (CTx) and procollagen type 1 N-terminal propeptide (P1NP) were evaluated prospectively. Treatment-emergent adverse events and changes in glucose metabolism during drug use were also monitored. Results Among the 120 study participants, 104 (86.7%) completed the study. Following 1 year of treatment, BMD increased by 3.41% vs. 3.71% in the lumbar spine, 1.30% vs. 1.18% in the femur neck, and 1.51% vs. 1.58% in the total hip in the non-diabetes and diabetes groups, respectively. There were no significant differences in BMD changes between the groups, and the differences in CTx or P1NP changes between groups were not significant. We did not observe any significant differences in baseline TBS values or the degree of change between before and after 1 year of ibandronate treatment in either group in this study. A total of 11 adverse events (9.2%) that recovered without sequelae occurred among the 120 included patients, and there was no significant difference in the frequency of adverse events between the groups (p = 0.862). The changes in fasting glucose and glycated hemoglobin levels between before and after treatment were not significant in the diabetic group. Conclusions Bisphosphonate therapy showed similar increases in BMD and decreases in CTx and P1NP of postmenopausal women with and without diabetes. Monthly oral ibandronate can be a safe and effective therapeutic option in postmenopausal osteoporosis patients with type 2 diabetes. Trial registration NCT number: NCT05266261, Date of registration: 04 March 2022.

Background High-density lipoprotein cholesterol (HDL-C) plays an important role in the reverse cholesterol transport pathway and prevents atherosclerosis-mediated disease. It has also been suggested that HDL-C may be a protective factor against cancer. However, an inverse correlation between HDL-C and cancer has not been established, and few studies have explored thyroid cancer. Methods The study participants received health checkups provided by the Korean National Health Insurance Service from 2009 to 2013 and were followed until 2019. Considering the variability of serum HDL-C level, low HDL-C level was analyzed by grouping based on four consecutive health checkups. The data analysis was performed using univariate and multivariate Cox proportional hazard regression models. Results A total of 3,134,278 total study participants, thyroid cancer occurred in 16,129. In the crude model, the hazard ratios for the association between repeatedly measured low HDL-C levels and thyroid cancer were 1.243, 1.404, 1.486, and 1.680 (P for trend

In road construction, the compaction process using vibratory rollers is essential to increase the ground stiffness in earthworks. There need to be clear standards regarding the operation of compaction rollers during compaction work. Various simple quality inspection techniques have been developed to check the stiffness of the ground, but plate load test and field density test are the most commonly used test methods to evaluate the degree of compaction during road construction. However, both inspection methods could be more efficient, as they cannot be performed in all sections due to time and cost. Additionally, in compaction work today, the worker will individually judge the number of compactions, thickness, speed, vibration etc. based on his or her own experience. This means that the quality of compaction varies depending on the worker. In this study, quality inspection results for all sections were obtained using an intelligent quality control system that employs an IC roller, a technology that is now commercially available. The effect of the operating conditions of the vibrating roller (roller compaction direction, compaction roller speed, and compaction roller vibration method) on the compaction quality was analyzed using the intelligent quality control value. Through our highway construction site tests, it was found that the speed of the compaction roller and the vibration method of the compaction roller affected the degree of compaction, but the direction of compaction did not. Therefore, if the compaction work is performed by adjusting the driving speed and vibration method of the vibrating roller according to the ground conditions, repetitive compaction work can be reduced, thereby reducing construction costs and lowering work time, which will achieve an improved work efficiency.

A durable normoglycemic state was observed in several studies that treated type 2 diabetes mellitus (T2DM) patients through metabolic surgery, intensive therapeutic intervention, or significant lifestyle modification, and it was confirmed that the functional β-cell mass was also restored to a normal level. Therefore, expert consensus introduced the concept of remission as a common term to express this phenomenon in 2009. Throughout this article, we introduce the recently updated consensus statement on the remission of T2DM in 2021 and share our perspective on the remission of diabetes. There is a need for more research on remission in Korea as well as in Western countries. Remission appears to be prompted by proactive treatment for hyperglycemia and significant weight loss prior to irreversible β-cell changes. T2DM is not a diagnosis for vulnerable individuals to helplessly accept. We attempt to explain how remission of T2DM can be achieved through a personalized approach. It may be necessary to change the concept of T2DM towards that of an urgent condition that requires rapid intervention rather than a chronic, progressive disease. We must grasp this paradigm shift in our understanding of T2DM for the benefit of our patients as endocrine experts.

In recent years, pure iron (Fe) has attracted significant attention as a promising biodegradable orthopedic implant material due to its excellent mechanical and biological properties. However, in physiological conditions, Fe has an extremely slow degradation rate with localized and irregular degradation, which is problematic for practical applications. In this study, we developed a novel combination of a nanostructured surface topography and galvanic reaction to achieve uniform and accelerated degradation of an Fe implant. The target-ion induced plasma sputtering (TIPS) technique was applied on the Fe implant to introduce biologically compatible and electrochemically noble tantalum (Ta) onto its surface and develop surface nano-galvanic couples. Electrochemical tests revealed that the uniformly distributed nano-galvanic corrosion cells of the TIPS-treated sample (nano Ta–Fe) led to relatively uniform and accelerated surface degradation compared to that of bare Fe. Furthermore, the mechanical properties of nano Ta–Fe remained almost constant during a long-term in vitro immersion test (~40 weeks). Biocompatibility was also assessed on surfaces of bare Fe and nano Ta–Fe using in vitro osteoblast responses through direct and indirect contact assays and an in vivo rabbit femur medullary cavity implantation model. The results revealed that nano Ta–Fe not only enhanced cell adhesion and spreading on its surface, but also exhibited no signs of cellular or tissue toxicity. These results demonstrate the immense potential of Ta-implanted surface nanostructures as an effective solution for the practical application of Fe-based orthopedic implants, ensuring long-term biosafety and clinical efficacy.

Autophagy is important for pancreatic β-cell function, however, the role of mitophagy and mechanism for mitophagy in β-cells are unclear. Here the authors report that in stressed β-cells, lysosomal Ca2+ release promotes mitophagy via activation of the transcription factor EB (TFEB) and loss of β-cell TFEB aggravates glucose intolerance during high-fat diet.

NAND flash memory is becoming smaller and denser to have a larger storage capacity as technologies related to fine processes are developed. As a side effect of high-density integration, the memory can be vulnerable to circuit-level noise such as random telegraph noise, decreasing the reliability of the memory. Therefore, low-density parity-check code that provides multiple decoding modes is adopted in the NAND flash memory systems to have a strong error correcting capability. Conventional static error recovery flow (ERF) applies decoding modes sequentially, and read latency can increase when preceding decoding modes fail. In this paper, we consider a dynamic ERF using machine learning (ML) that predicts an optimal decoding mode guaranteeing successful decoding and minimum read latency and applies it directly to reduce read latency. Due to process variation incurred in the manufacturing of memory, memory characteristics are different by chips and it becomes difficult to apply a trained prediction model to different chips. Training the customized prediction model at each memory chip is impractical because the computational burden of training is heavy, and a large number of training data is required. Therefore, we consider ERF prediction based on reusable ML to deal with varying input and output relationships by chips due to process variation. Reusable ML methods reuse pre-trained model architecture or knowledge learned from source tasks to adapt the model to perform its task without any loss of performance in different chips. We adopt two reusable ML approaches for ERF prediction based on transfer learning and meta learning. Transfer learning method reuses the pre-trained model by reducing domain shift between a source chip and a target chip using a domain adaptation algorithm. On the other hand, meta learning method learns shared features from multiple source chips during the meta training procedure. Next, the meta-trained model reuses previously learned knowledge to fastly adapt to the different chips. Numerical results validate the advantages of the proposed methods with high prediction accuracy in multiple chips. In addition, the proposed ERF prediction based on transfer and meta learning can yield a noticeable reduction in average read latency as compared to conventional schemes.

To quantitatively classify the results provided by engineers, we built a sound quality (SQ) classification model using neural networks. The data used in this study were recorded wav files obtained from various vehicle specifications while driving under wide open throttle conditions. However, the data lengths were not constant. The upsampling and interpolation scheme (USIS) was used to achieve constant data lengths. After the USIS was applied to the data, dynamic time warping was used to verify that there was no change in the data characteristics. The verified dataset was transformed into Mel-spectrogram to confirm the characteristics, and dimensionality reduction was applied by using a high-pass filter. Clarifying the differences between clusters improves the model performance. The classification models of 1D convolutional neural network and long short-term memory exhibited training accuracies of about 94.9% (64 or 65 out of 68 classified) and test accuracies of about 87.5% (7 out of 8 classified). For additional undefined label classification, the quantitative evaluation and statistical classification of undefined sound quality labels are successfully identified in the present study. Both neural networks produced effective results that can be used by sound design engineers to quantitatively examine the SQ of vehicle interior noise.

Identifying early special traffic events is crucial for efficient traffic control management. If there are a sufficient number of vehicles equipped with automatic event detection and report gadgets, this enables a more rapid response to special events, including road debris, unexpected pedestrians, accidents, and malfunctioning vehicles. To address the needs of such a system and service, we propose a framework for an in-vehicle module-based special traffic event and emergency detection and safe driving monitoring service, which utilizes the modified ResNet classification algorithm to improve the efficiency of traffic management on highways. Due to the fact that this type of classification problem has scarcely been proposed, we have adapted various classification algorithms and corresponding datasets specifically designed for detecting special traffic events. By utilizing datasets containing data on road debris and malfunctioning or crashed vehicles obtained from Korean highways, we demonstrate the feasibility of our algorithms. Our main contributions encompass a thorough adaptation of various deep-learning algorithms and class definitions aimed at detecting actual emergencies on highways. We have also developed a dataset and detection algorithm specifically tailored for this task. Furthermore, our final end-to-end algorithm showcases a notable 9.2% improvement in performance compared to the object accident detection-based algorithm.

In recent years, we have witnessed an increase in road subsidence accidents in urban areas, threatening the safety of citizens. Various road facilities, such as water and sewage pipes, and telecommunication facilities are buried under roads, and the aging of these facilities is one of the factors causing road subsidence. In particular, old sewer pipes are a primary cause of road subsidence. However, most maintenance work on such facilities is carried out based on how long ago they were buried underground, without considering the risk of road subsidence caused by them. Therefore, this study aims to present a reliable method to assess road subsidence risk that considers various sewer pipe specifications and the environment surrounding them. To derive the factors influencing subsidence, sewer pipes near the target region, where road subsidence occurs the most, were extracted to analyze the correlation between road subsidence, pipe integrity, and the surrounding environment. An effective analysis method was selected by comparing logistic regression analysis and AHP (Analytic Hierarchy Process) analysis, and a weighted road subsidence risk assessment method was proposed by evaluating the importance of factors affecting ground subsidence. Its applicability was examined by comparing actual road subsidence data and analyzing risk in a pilot study area to validate the reliability of the proposed methodology. The results showed that it was possible to make reliable predictions of road subsidence risk areas.

The main cause of ground subsidence accidents in urban areas is cavities formed by damage to underground utilities. For this reason, the attribute information of underground utilities should be used to prepare against ground subsidence accidents. In this study, attribute information (pipe age, diameter, burial depth, and density) of six types of underground utilities (water, sewer, gas, power, heating, and communication) and history information of ground subsidence were collected. A correlation analysis was conducted using the collected data, and a prediction model of vulnerability to ground subsidence was developed through the ordinary least squares (OLS) method and spatial regression analysis (spatial lag model (SLM) and spatial error model (SEM)). To do this, the target area was divided into a grid of 100 m × 100 m. Datasets were constructed using the attribute information of underground utilities included in the divided grid and the number of ground subsidence occurrences. To analyze the OLS of the constructed data, the variance inflation factor (VIF) of the attribute information of underground utilities was studied. An OLS analysis was conducted using the appropriate factors, and the results show that the spatial data were autocorrelated. Subsequently, SEM and SLM analyses, which were spatial regression analyses, were conducted. As a result, the model using SLM was selected as suitable for analyzing the vulnerability of ground subsidence, and the density of six types of underground utilities was found to be the highest influencing factor. In addition, a vulnerability map of ground subsidence in the target area was prepared using the model. The vulnerability map demonstrates that regions with frequent ground subsidence can be predicted to be highly vulnerable.

Extremely fast charging such as charging 80% of capacity within 15 min is a pressing requirement for current lithium-ion battery technology. Here the authors achieve this by incorporating an artificial solid-electrolyte interphase rich in inorganic components on the graphite electrode.

Biodegradable stents made of magnesium (Mg) and its alloys have been developed to minimize persistent inflammation or in-stent restenosis, which are the main problems for permanent stents. However, their rapid corrosion behavior under physiological conditions leads to poor vascular compatibility and premature structural failure, which remains an important unsolved clinical problem. Herein, we demonstrate a new strategy for solving this problem by combining poly (ether imide) (PEI) coating and subsequent tantalum (Ta) ion implantation. The PEI coating covers the whole surface of the Mg stent uniformly via a spray coating technique and provides Mg with superior corrosion resistance and stable sirolimus-carrying ability. Ta ion implantation is conducted by a sputtering-based plasma immersion ion implantation technique only onto the luminal surface of the PEI-coated Mg stent. Its extremely short processing time (

Sleep architecture and microstructures alter with aging and sleep disorder-led accelerated aging. We proposed a sleep EEG based brain age prediction model using convolutional neural networks. We then associated the estimated brain age index with brain structural aging features, sleep disorders and various sleep parameters. Our model also showed a higher BAI (predicted brain age minus chronological age) is associated with cortical thinning in various functional areas. We found a higher BAI for sleep disorder groups compared to healthy sleepers, as well as significant differences in the spectral pattern of EEG among different sleep disorders (lower power in slow and ϑ waves for sleep apnea vs. higher power in β and σ for insomnia), suggesting sleep disorder-dependent pathomechanisms of aging. Our results demonstrate that the new EEG-BAI can be a biomarker reflecting brain health in normal and various sleep disorder subjects, and may be used to assess treatment efficacy.

Abstract Liquefied natural gas (LNG) gasification coupled with adsorbed natural gas (ANG) charging (LNG–ANG coupling) is an emerging strategy for efficient delivery of natural gas. However, the potential of LNG–ANG to attain the advanced research projects agency‐energy (ARPA‐E) target for onboard methane storage has not been fully investigated. In this work, large‐scale computational screening is performed for 5446 metal–organic frameworks (MOFs), and over 193 MOFs whose methane working capacities exceed the target (315 cm3(STP) cm−3) are identified. Furthermore, structure–performance relationships are realized under the LNG–ANG condition using a machine learning method. Additional molecular dynamics simulations are conducted to investigate the effects of the structural changes during temperature and pressure swings, further narrowing down the materials, and two synthetic targets are identified. The synthesized DUT‐23(Cu) and DUT‐23(Co) show higher working capacities (≈373 cm3(STP) cm−3) than that of any other porous material under ANG or LNG–ANG conditions, and excellent stability during cyclic LNG–ANG operation.

As ground subsidence accidents in urban areas that occur due to damage to underground utilities can cause great damage, it is necessary to predict and prepare for such accidents in order to minimize such damage. It has been reported that the main cause of ground subsidence in urban areas is cavities in the ground formed by damage to underground utilities. Thus, in this study, attribute information and historical ground subsidence information of six types of underground utility lines (water supply, sewage, power, gas, heating, and communication) were collected to develop a ground subsidence risk prediction model based on machine learning. To predict the risk of ground subsidence in the target area, it was divided into a grid with a square size of 500 m × 500 m, and attribute information of underground utility lines and historical information of ground subsidence included in the grid were extracted. Six types of underground utility lines were merged into single-type attribute information, and the risk of ground subsidence was categorized into three levels using the number of ground subsidence occurrences to develop a dataset. In addition, 12 datasets, which were developed based on the conditions of certain divided ranges of attribute information and risk levels, and 12 additional datasets, which were developed using the Synthetic Minority Oversampling Technique to resolve the imbalance of data, were built. Then, factors that represented significant correlations between input and output data were singled out and were then applied to the RandomForest, XGBoost, and LightGBM algorithms to select a model that produced the best performance. By classifying the ground subsidence risk levels through the selected model, it was found that density was the most important influencing factor used in the model. A risk map of ground subsidence in the target area was made through the model; the map showed the trend of well-predicted risk levels in the area where ground subsidence was concentrated.

The identification and validation of biomarkers of food intake (BFIs) is a promising approach to develop more objective and complementary tools to the traditional dietary assessment methods. Concerning dairy, their evaluation in terms of intake is not simple, given the variety of existing foods, making it difficult to establish the association between specific dairy products consumption and the effects on human health, which is also dependent on the study population. Here, we aimed at identifying BFI of both milk (M) and yogurt (Y) in 14 healthy young (20–35 years) and 14 older (65–80 years). After a 3-week run-in period of dairy exclusion from the diet, the subjects acutely consumed 600 ml of M or Y. Metabolomics analyses were conducted on serum samples during the following 6 h (LC-MS and GC-MS). Several metabolites showing increased iAUC after milk or yogurt intake were considered as potential BFI, including lactose (M > Y, 2-fold), galactitol (M > Y, 1.5-fold), galactonate (M > Y, 1.2-fold), sphingosine-1-phosphate (M > Y from 2.1-fold), as well as an annotated disaccharide (Y > M, 3.6-fold). Delayed serum kinetics were also observed after Y compared to M intake lysine (+22 min), phenylalanine (+45 min), tyrosine (+30min), threonine (+38 min) 3-phenyllactic acid (+30 min), lactose (+30 min), galactitol (+45min) and galactonate (+30 min). The statistical significance of certain discriminant metabolites, such as sphingosine-1-phosphate and several free fatty acids, was not maintained in the older group. This could be related to the physiological modifications induced by aging, like dysregulated lipid metabolism, including delayed appearance of dodecanoic acid (+60 min) or altered postprandial appearance of myristic acid (+70% Cmax), 3-dehydroxycarnitine (−26% Cmin), decanoylcarnitine (−51% Cmin) and dodecanoylcarnitine (−40% Cmin). In conclusion, candidate BFI of milk or yogurt could be identified based on the modified postprandial response resulting from the fermentation of milk to yogurt. Moreover, population specificities (e.g., aging) should also be considered in future studies to obtain more accurate and specific BFI.

Abstract Studies on continuous and selective chlorination by using ilmenite have been actively conducted because the efficient removal of FeO from ilmenite(FeTiO3) ore using selective chlorination not only improves the reaction purity of TiCl4 but it also leads to price competitiveness compared to TiCl4 synthesized from natural rutile. The chlorination of synthetic rutile with FeO removed was compared with that of natural rutile to examine the reaction efficiency. The selective chlorination efficiency depends on the input amounts of coke and Cl2, as shown by thermodynamic calculation, when FeO is selectively removed. It was found that manufacturing of TiCl4 was easier by using the synthetic rutile, because it had greater porosity than natural rutile. Relatively greater pore volumes were found in the synthetic rutile than in natural rutile. It was confirmed that the reaction efficiency of chlorination for TiCl4 production was directly related to the difference in the porosity distribution between the titanium ores, as verified by a kinetic comparison of synthetic and natural rutiles.