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Abstract Ovarian cancer is a leading cause of death among gynecologic tumors, often detected at advanced stages. Metabolic reprogramming and increased lipid biosynthesis are key factors driving cancer cell growth. Stearoyl-CoA desaturase 1 (SCD1) is a crucial enzyme involved in de novo lipid synthesis, producing mono-unsaturated fatty acids (MUFAs). Here, we aimed to investigate the expression and significance of SCD1 in epithelial ovarian cancer (EOC). Comparative analysis of normal ovarian surface epithelial (NOSE) tissues and cell lines revealed elevated SCD1 expression in EOC tissues and cells. Inhibition of SCD1 significantly reduced the proliferation of EOC cells and patient-derived organoids and induced apoptotic cell death. Interestingly, SCD1 inhibition did not affect the viability of non-cancer cells, indicating selective cytotoxicity against EOC cells. SCD1 inhibition on EOC cells induced endoplasmic reticulum (ER) stress by activating the unfolded protein response (UPR) sensors and resulted in apoptosis. The addition of exogenous oleic acid, a product of SCD1, rescued EOC cells from ER stress-mediated apoptosis induced by SCD1 inhibition, underscoring the importance of lipid desaturation for cancer cell survival. Taken together, our findings suggest that the inhibition of SCD1 is a promising biomarker as well as a novel therapeutic target for ovarian cancer by regulating ER stress and inducing cancer cell apoptosis.

In this paper, we present a distributed robust controller for current sharing and voltage balancing in uncertain DC microgrids. DC microgrids considered in this paper are composed of distributed generation units (DGUs), power lines, and ZI (constant impedance and constant current) loads. It is assumed that the parameters of electric components and those associated to electric and communication network are unknown but belong to known bounded sets. The proposed controller is composed of a robust primary controller and consensus-based secondary controller. The primary controller is a disturbance observer-based voltage stabilizer. It provides robustness against parameter uncertainties and ensures that DGUs under this controller have a desired behavior or dynamics. The secondary controller is a consensus-based controller that generates a reference for the primary controller. It has a distributed structure in the sense that they make decisions by communicating with local neighbors. In addition, it provides a stability condition for the primary control loop to ensure the stability of the whole closed-loop system. A rigorous stability analysis and a constructive design procedure for control parameters are provided. Finally, experiments on a lab-scale microgrid are conducted to validate the proposed controller.

Abstract Enhancing adult neurogenesis in the brain has been suggested as a potential therapeutic strategy for AD. We developed a screening platform, ATRIVIEW®, for molecules that activate neuronal differentiation of adult mouse NSCs. The most potent hit from an FDA-approved drug library was SNR1611 (trametinib), a selective MEK1/2 inhibitor. We found that trametinib increases the levels of P15INK4b and Neurog2, suggesting a mechanism by which MEK1/2 inhibition induces neuronal differentiation. Oral administration of trametinib increased adult neurogenesis in the dentate gyrus and subventricular zone of the 5XFAD AD mouse model. Surprisingly, we also found that trametinib enhanced adult neurogenesis in the cortex. Consequently, trametinib rescued AD pathologies such as neuronal loss and cognitive impairment in 5XFAD mice. Finally, trametinib induced neurogenic differentiation of NSCs derived from AD patient iPSCs, which suggests its potential therapeutic application. Altogether, we suggest that restoration of endogenous adult neurogenesis by trametinib may be a promising therapeutic approach to AD.

Water and energy are essential resources for human life, and carbon emissions (CEs) occur in tandem with their use. Thus, water, energy, and carbon are closely inter-related. Approximately 4% of the global energy is used in urban water sectors (UWSs), which encompass various processes such as water intake, treatment, and distribution and wastewater collection and treatment, all of which consume significant energy and emit CO2. Several countries are actively working toward achieving carbon neutrality by 2050–2060. Therefore, increasing energy efficiency and reducing CEs through comprehensive evaluations of UWSs is essential. This study aimed to quantify energy consumption and CEs in UWSs and proposed a methodology for analyzing water–energy–carbon (WEC) links at the city level. By applying it to Incheon Metropolitan City (IMC), we first identified the UWSs and established a WEC database. Based on this database, the WEC consumption and emissions were analyzed by process or administrative district, and visualizations using Sankey diagrams and Geographic Information System Mapping were created to enhance their understandability. In 2021, the UWSs in IMC consumed 308,496,107 kWh of energy, representing 32.7% of the public electricity consumption of IMC, with an average energy intensity of 0.46 and 0.38 kWh/m³ for water supply systems (WSSs) and sewerage systems (SSs), respectively. Their carbon emissions totaled 315,765,358 kg CO2, accounting for 2.7% of IMC’s total carbon emissions, with an average carbon intensity of 0.21 and 0.58 kg CO2/m³ for WSSs and SSs, respectively. The proposed methodology was used to comprehensively evaluate WEC consumption and emissions in IMC. It is expected to enable relevant stakeholders to develop measures, such as water reuse and increasing renewable energy usage in water treatment and wastewater treatment plants, to build sustainable UWSs.

IntroductionTranscranial direct current stimulation (tDCS) has shown positive but inconsistent results in stroke rehabilitation. This could be attributed to inter-individual variations in brain characteristics and stroke lesions, which limit the use of a single tDCS protocol for all post-stroke patients. Optimizing the electrode location in tDCS for each individual using magnetic resonance imaging (MRI) to generate three-dimensional computer models and calculate the electric field (E-field) induced by tDCS at a specific target point in the primary motor cortex may help reduce these inconsistencies. In stroke rehabilitation, locating the optimal position that generates a high E-field in a target area can influence motor recovery. Therefore, this study was designed to determine the effect of personalized tDCS electrode positions on hand-knob activation in post-stroke patients.MethodThis is a crossover study with a sample size of 50 participants, who will be randomly assigned to one of six groups and will receive one session of either optimized-active, conventional-active, or sham tDCS, with 24 h between sessions. The tDCS parameters will be 1 mA (5 × 5 cm electrodes) for 20 min. The motor-evoked potential (MEP) will be recorded before and after each session over the target area (motor cortex hand-knob) and the MEP hotspot. The MEP amplitude at the target location will be the primary outcome.DiscussionWe hypothesize that the optimized-active tDCS session would show a greater increase in MEP amplitude over the target area in patients with subacute and chronic stroke than conventional and sham tDCS sessions.Clinical trial registration:https://cris.nih.go.kr, identifier KCT0007536.

This paper proposes analytical modeling and design of a 27.12 MHz single-switch dc-dc converter with galvanic isolation using a PCB transformer. The ODE-based analytical approach provides a more accurate circuit design by considering the high-order harmonics and non-ideal components such as the diode capacitances. The proposed dimensionless mathematical model simplifies the design of the desired circuit model and requires less tuning procedure. In addition, the effective analytical model offers the design curves to obtain the optimized circuit parameters at any duty ratio. Meanwhile, the multilayer planar PCB transformer is designed to achieve the isolation and high power density for the high-frequency dc-dc converter. Based on Neumann’s formula, the analytical model of the PCB transformer is proposed to calculate the inductances for the spiral structure accurately in the high-frequency conditions. As a result, the computational burden for calculating the coil inductances is alleviated using the proposed method. The simulation and experimental results using a 3W converter prototype verify the effectiveness and feasibility of the analytical modeling and design of the proposed converter.

BackgroundA positive relationship was reported between metabolic syndrome and the risk of endometrial cancer. Studies on the relationship between metabolic syndrome and endometrial cancer have been mainly conducted in post-menopausal women. We aimed to investigate the risk of endometrial cancer according to metabolic syndrome and menopausal status using the Korean nationwide population-based cohort.MethodsWe enrolled 2,824,107 adults (endometrial cancer group; N = 5,604 and control group; N= 2,818,503) from the Korean National Health Insurance Service checkup database from January 1 to December 31, 2009. The median follow-up duration was 8.37 years. Metabolic syndrome was diagnosed as having at least three of the following five components: abdominal obesity, hypertriglyceridemia, low levels of high-density lipoprotein cholesterol, raised blood pressure, and hyperglycemia. Multivariate Cox proportional hazard models were used to calculate hazard ratios (HRs) with 95% confidence intervals (CIs) to estimate endometrial cancer risk.ResultsThe endometrial cancer risk was higher in the metabolic syndrome group than that in the non-metabolic syndrome group (HR, 1.362; 95% CI, 1.281–1.449). The association between metabolic syndrome and endometrial cancer risk was significant in the premenopausal subgroup (HR, 1.543; 95% CI, 1.39–1.713) and postmenopausal subgroup (HR, 1.306; 95% CI, 1.213–1.407). The incidence of endometrial cancer was more closely related to metabolic syndrome components in the pre-menopausal subgroup than those in the post-menopausal subgroup (for waist circumference, blood pressure, triglycerides and high-density lipoprotein cholesterol, all p for interaction

The poor prognosis of ovarian cancer patients mainly results from a lack of early diagnosis approaches and a high rate of relapse. Only a very modest improvement has been made in ovarian cancer patient survival with traditional treatments. More targeted therapies precisely matching each patient are strongly needed. The aberrant activation of Wnt/β-catenin signaling pathway plays a fundamental role in cancer development and progression in various types of cancer including ovarian cancer. Recent insight into this pathway has revealed the potential of targeting Wnt/β-catenin in ovarian cancer treatment. This study aims to investigate the effect of CWP232291, a small molecular Wnt/β-catenin inhibitor on ovarian cancer progression. Various in vitro, in vivo and ex vivo models are established for CWP232291 testing. Results show that CWP232291 could significantly attenuate ovarian cancer growth through inhibition of β-catenin. Noticeably, CWP232291 could also s suppress the growth of cisplatin-resistant cell lines and ovarian cancer patient-derived organoids. Overall, this study has firstly demonstrated the anti-tumor effect of CWP232291 in ovarian cancer and proposed Wnt/β-catenin pathway inhibition as a novel therapeutic strategy against ovarian cancer.

In this paper, we propose a new voltage stabilizing controller for a DC microgrid. The microgrid under consideration is composed of Distributed Generation Units (DGUs), power lines, and local loads. The parameters of DGUs and power lines are assumed to be unknown but belong to known bounded sets and the loads, which are modeled by constant current loads, are assumed to be unknown. The proposed controller adopts the disturbance observer based controller which is known to be robust against parameter uncertainties and external disturbances. One benefit of this controller is to make the real uncertain closed-loop system behave like a nominal model, and in this work this nominal model is a desired dynamics relating the voltage reference and output voltages of DGUs. A rigorous stability proof is provided and the results are validated through numerical simulations.

Abstract Exosomes are small extracellular vesicles secreted by almost all the cells. Molecular cargos of exosomes can partially reflect the characteristics of originating cells. Exosome-mediated cell-to-cell interactions in the microenvironment are critical in cancer progression. Hypoxia, a key pro-cancerous feature of the tumor microenvironment, alters the releasing and contents of exosomes. A growing body of evidence shows that hypoxia induces more aggressive phenotypes in cancer. Of note, non-coding RNAs shuttled in hypoxic tumor-derived exosomes have been demonstrated as fundamental molecules in regulating cancer biology and remodeling tumor microenvironment. Furthermore, these hypoxic tumor-derived exosomal non-coding RNAs can be detected in the body fluids, serving as promising diagnostic and prognostic biomarkers. The current review discusses changes in cancer behaviors regulated by exosomes-secreted non-coding RNAs under hypoxic conditions.

InZnP:Ag nano-rods fabricated by the ion milling method were thermally annealed in the 250~350 °C temperature range and investigated the optimum thermal annealing conditions to further understand the mutual correlation between the optical properties and the microscopic magnetic properties. The formation of InZnP:Ag nano-rods was determined from transmission electron microscopy (TEM), total reflectivity and Raman scattering analyses. The downward shifts of peak position for LO and TO modes in the Raman spectrum are indicative of the production of Ag ion-induced strain during the annealing process of the InZnP:Ag nano-rod samples. The appearance of two emission peaks of both (A0 X) and (e, Ag) in the PL spectrum indicated that acceptor states by Ag diffusion are visible due to the effective incorporation of Ag-creating acceptor states. The binding energy between the acceptor and the exciton measured as a function of temperature was found to be 21.2 meV for the sample annealed at 300 °C. The noticeable MFM image contrast and the clear change in the MFM phase with the scanning distance indicate the formation of the ferromagnetic spin coupling interaction on the surface of InZnP:Ag nano-rods by Ag diffusion. This study suggests that the InZnP:Ag nano-rods should be a potential candidate for the application of spintronic devices.

With many conveniences afforded by advances in smartphone technology, developing advanced data analysis methods for health-related information from smartphone users has become a fast-growing research topic in the healthcare field. Along these lines, this paper addresses smartphone sensor-based characterization of human motions with neural stochastic differential equations (NSDEs) and a Transformer model. NSDEs and modeling via Transformer networks are two of the most prominent deep learning-based modeling approaches, with significant performance yields in many applications. For the problem of modeling dynamical features, stochastic differential equations and deep neural networks are frequently used paradigms in science and engineering, respectively. Combining these two paradigms in one unified framework has drawn significant interest in the deep learning community, and NSDEs are among the leading technologies for combining these efforts. The use of attention has also become a widely adopted strategy in many deep learning applications, and a Transformer is a deep learning model that uses the mechanism of self-attention. This concept of a self-attention based Transformer was originally introduced for tasks of natural language processing (NLP), and due to its excellent performance and versatility, the scope of its applications is rapidly expanding. By utilizing the techniques of neural stochastic differential equations and a Transformer model along with data obtained from smartphone sensors, we present a deep learning method capable of efficiently characterizing human motions. For characterizing human motions, we encode the high-dimensional sequential data from smartphone sensors into latent variables in a low-dimensional latent space. The concept of the latent variable is particularly useful because it can not only carry condensed information concerning motion data, but also learn their low-dimensional representations. More precisely, we use neural stochastic differential equations for modeling transitions of human motion in a latent space, and rely on a Generative Pre-trained Transformer 2 (GPT2)-based Transformer model for approximating the intractable posterior of conditional latent variables. Our experiments show that the proposed method can yield promising results for the problem of characterizing human motion patterns and some related tasks including user identification.

Background: : Studies examining the relationship between obesity and female-specific cancers have been mainly conducted in Western populations. We aimed to investigate the risk of female-specific cancers according to obesity and menopausal status using a nationwide cohort in Korea. Methods: : We identified 2,708,938 women from the National Health Insurance Service cohort, and obtained baseline body mass index (BMI), waist circumference (WC), and other healthcare data, measured and collected during a health examinations and cancer-screening survey. By setting a normal weight/WC group (BMI, 18•5–22•9 kg/m2 or WC, 80•0–84•9 cm) as the reference, we conducted multivariate analyses using the Cox proportional hazard model to estimate adjusted hazard ratios (aHRs) and 95% confidence intervals (95% CIs) for each cancer. Findings: : The total follow-up duration was 22389854•63 person-years. In post-menopausal women, the risk of breast, endometrial, and ovarian cancers significantly increased as the BMI classification level increased from normal to class II obesity (aHRs [95% CIs], 1•49 [1•38–1.61], 2•11 [1•81–2•46], and 1•38 [1•20–1•58], respectively). The risk of breast and endometrial cancers also increased as the WC classification increased from < 75•0 to ≥ 95•0 cm. With a WC of 80•0–84•9 cm as the reference, the lowest risk of breast and endometrial cancers was observed in WC < 75•0 cm (aHRs [95% CIs], 0•85 [0•81–0•89] and 0•75 [0•67–0•84], respectively) while the highest risk was observed in WC ≥ 95•0 cm (aHRs [95% CIs], 1•19 [1•10–1•29] and 1•56 [1•33–1•82], respectively). In pre-menopausal women, the risk of breast cancer significantly decreased in those with class I and II obesity compared to those with normal BMI (aHRs [95% CIs], 0•96 [0•92–0•999] and 0•89 [0•81–0•97], respectively), whereas the trends of endometrial and ovarian cancer incidence in pre-menopausal women were similar to those observed in post-menopausal women. For cervical cancer, only class II obesity was significantly associated with increased risks in both post-menopausal and pre-menopausal women (aHRs [95% CIs], 1•18 [1•01–1•39] and 1•27 [1•02–1•57], respectively). Interpretation: : In this large population-based cohort study in Korean women, we observed that the impact of obesity on the development of female-specific cancers differs according to the malignancy type and menopausal status. Similar trends were observed between Korean and Western women. Funding: : The Korea Health Industry Development Institute (no. HI16C2037).

Currently, there are many issues related to air pollution worldwide and many countries are tightening their emission regulations on fine dust-causing substances to address these problems. Thus, the removal of these substances is becoming increasingly important. Low-pressure microbubble (LPMB) scrubbers are hybrid scrubbers that combine the advantages of general scrubbers with those of microbubbles. LPMB scrubbers can be used to simultaneously remove particulate matter (PM), SOX, and NOX using microbubbles. Microbubbles are small bubbles with a diameter of 10-50 µm and play a key role in simultaneous removal of PM, SOX, and NOX. The performance of LPMB scrubbers depends on the amount of water inside them. Therefore, the initial water level is an extremely important operating condition in LPMB scrubbers. This study used computational fluid dynamics modelling to determine the optimal initial water level in LPMB scrubbers by conducting an experiment based on the initial water level. The results indicate that, with a pressure difference of 5,000 Pa, the LPMB scrubbers performed best (producing a flow rate of 16.58 m3/min) when the initial water level was the same height as the atomizer.

Hydroxyl radical (•OH) scavenging demand can be an indicator that represents the water quality characteristics of raw water. It is one of the key parameters predicting UV/H2O2 system performance and affects the operating parameters. Based on the •OH scavenging demand, we developed a model predictive control strategy to meet the target compound removal efficiency and energy consumption simultaneously. Selected pharmaceutically active compounds (PhACs) were classified into three groups depending on the UV direct photolysis and susceptibility to •OH. Group 1 for photo-susceptible PhACs (acetaminophen, amoxicillin, diclofenac, iopromide, ketoprofen, and sulfamethoxazole); group 2 for PhACs susceptible to both direct photolysis and •OH oxidation (bisphenol A, carbamazepine, ibuprofen, naproxen, ciprofloxacin, and tetracycline); and group 3 for photo-resistant PhACs (atenolol, atrazine, caffeine, and nitrobenzene). The results of modeling to achieve 90% removal of PhACs at N and B plants were as follows. For group 2, the optimized operating parameter ranges were as follow (N plant: UV 510–702 mJ cm−2, H2O2 2.96–3.80 mg L−1, EED 1088–1302 kWh m−3; B plant: UV dose 1179–1397 mJ cm−2, H2O2 dose 3.56–7.44 mg L−1, EED 1712–2085 kWh m−3). It was confirmed that the optimal operating conditions and EED values changed according to the •OH scavenging demand.

The global adoption of smartphone technology affords many conveniences, and not surprisingly, healthcare applications using wearable sensors like smartphones have received much attention. Among the various potential applications and research related to healthcare, recent studies have been conducted on recognizing human activities and characterizing human motions, often with wearable sensors, and with sensor signals that generally operate in the form of time series. In most studies, these sensor signals are used after pre-processing, e.g., by converting them into an image format rather than directly using the sensor signals themselves. Several methods have been used for converting time series data to image formats, such as spectrograms, raw plots, and recurrence plots. In this paper, we deal with the health care task of predicting human motion signals obtained from sensors attached to persons. We convert the motion signals into image formats with the recurrence plot method, and use it as an input into a deep learning model. For predicting subsequent motion signals, we utilize a recently introduced deep learning model combining neural networks and the Fourier transform, the Fourier neural operator. The model can be viewed as a Fourier-transform-based extension of a convolution neural network, and in these experiments, we compare the results of the model to the convolution neural network (CNN) model. The results of the proposed method in this paper show better performance than the results of the CNN model and, furthermore, we confirm that it can be utilized for detecting potential accidental falls more quickly via predicted motion signals.

The interaction of immune checkpoint molecules in the tumor microenvironment reduces the anti-tumor immune response by suppressing the recognition of T cells to tumor cells. Immune checkpoint inhibitor (ICI) therapy is emerging as a promising therapeutic option for cancer treatment. However, modulating the immune system with ICIs still faces obstacles with severe immunogenic side effects and a lack of response against many cancer types. Plant-derived natural compounds offer regulation on various signaling cascades and have been applied for the treatment of multiple diseases, including cancer. Accumulated evidence provides the possibility of efficacy of phytochemicals in combinational with other therapeutic agents of ICIs, effectively modulating immune checkpoint-related signaling molecules. Recently, several phytochemicals have been reported to show the modulatory effects of immune checkpoints in various cancers in in vivo or in vitro models. This review summarizes druggable immune checkpoints and their regulatory factors. In addition, phytochemicals that are capable of suppressing PD-1/PD-L1 binding, the best-studied target of ICI therapy, were comprehensively summarized and classified according to chemical structure subgroups. It may help extend further research on phytochemicals as candidates of combinational adjuvants. Future clinical trials may validate the synergetic effects of preclinically investigated phytochemicals with ICI therapy.

The problem of finding adequate population models in ecology is important for understanding essential aspects of their dynamic nature. Since analyzing and accurately predicting the intelligent adaptation of multiple species is difficult due to their complex interactions, the study of population dynamics still remains a challenging task in computational biology. In this paper, we use a modern deep reinforcement learning (RL) approach to explore a new avenue for understanding predator-prey ecosystems. Recently, reinforcement learning methods have achieved impressive results in areas, such as games and robotics. RL agents generally focus on building strategies for taking actions in an environment in order to maximize their expected returns. Here we frame the co-evolution of predators and preys in an ecosystem as allowing agents to learn and evolve toward better ones in a manner appropriate for multi-agent reinforcement learning. Recent significant advancements in reinforcement learning allow for new perspectives on these types of ecological issues. Our simulation results show that throughout the scenarios with RL agents, predators can achieve a reasonable level of sustainability, along with their preys.

Memory devices are key building blocks of image sensing circuitry. Here, the authors demonstrate a MoS2monolayer optoelectronic memory device based on charge trapping and subsequent optically-induced charge release, capable of 12-bit operation.

In the present study, we set out to examine an energy-reduction method whereby the reflux ratio of the iso-/normal-butane gas-splitting process is optimised. The splitting process used in this study was designed to be capable of handling 4,000 kg/h of mixed butane as a feedstock, to produce 2,600 kg/h of n-butane as the major product. Conventionally, three different feedstock compositions are used in the process. The n-butane concentration of the feedstock can vary from 60 to 98%, depending on the supplier. If a feedstock with a high n-butane concentration is fed into the splitter, the top product would have a high n-butane content, such that it cannot be sold as i-butane. To prevent this, in conventional processing, the operator must manually operate a valve to recirculate the overhead flow to the splitter. As a result, the reflux ratio of the splitter exceeds 90. Although a feedstock with a high n-butane content is fed into the splitter for only 2 h/day, the heat duty of the reboiler and condenser in the splitter increases due to the increased reflux rate. In addition, the amount of gas in the splitter increases due to the unequal mass balance, which imposes an extra load on the splitter. If the process could be modified to satisfy product specifications by minimising the reflux rate and thus reducing the unnecessary heat duty, energy optimisation would be possible. Therefore, to reduce the heat duty, in this study, we considered the use of an additional overhead buffer tank to store the top product, instead of recirculating the product to the splitter. In addition, we defined the following three constraint functions to identify the objective function, thus minimising the heat duty: 1) The concentration of n-butane in the major product is always > 99%; 2) When a feedstock with a high n-butane concentration is used, the n-butane concentration in the overhead flow is > 98.5%; 3) When a feedstock with a low concentration of n-butane is used, the concentration of the i-butane overhead flow is > 75%. As a result, when a feedstock with a high concentration of n-butane was used, the composition of n-butane in the overhead flow was 98.683% and the heat duty reduced to 7,242.017 kW. For a feedstock with a low n-butane concentration, the i-butane concentration of the overhead flow was 88.448% and the heat duty was 1116.469 kW. We assumed that the process would run for 8,000 h/year and that feedstocks with low and high n-butane contents would be fed to the splitter for 22 and 2 h/day, respectively. The reduction in the energy consumption as a result of this optimisation was determined to be 13.015 GW/year, corresponding to a financial saving of $1.124 million/year.

Most research on the effects of disclosure on close relationships have been done using offline disclosure. However, disclosure done online has disparate features and thus its effects on relationships may also differ. In five studies and using primes emulating Facebook timelines and messages, we compared the effects of disclosure depth on intimacy and satisfaction in online vs. offline contexts, in romantic vs. friend relationships, and with differing content (self- vs. partner-focused). After demonstrating consistent differences, we examined one mechanism that accounted for the differential effects of online vs. offline disclosure in romantic relationships: perceived inclusivity of the recipients. Results revealed that greater disclosure was associated with higher relational intimacy and satisfaction when done offline (Studies 1 and 4), and lower intimacy and satisfaction when done online (Studies 1-4), in both the discloser (Study 1) and his or her partner (Studies 2-4). The negative association between online disclosure and intimacy was present in romantic relationships, but not in friendships (Study 1). Importantly, this effect only appeared when perceived inclusivity of recipients was high (Study 4). Focusing the online disclosure content on the partner/relationship dissipated its negative effects (Study 5). Together, these studies extend further knowledge on how the effects of disclosure are contextualized, and suggest that disclosure done publicly online may be detrimental to romantic relationships.

Respiratory involvement in Crohn disease (CD) is rare condition with only about a dozen reported cases. We report the first case of CD with tracheal involvement in Korea. An 18-year-old woman with CD was hospitalized because of coughing, dyspnea, and fever sustained for 3 weeks. Because she had stridor in her neck, we performed computed tomography of the neck, which showed circumferential wall thickening of the larynx and hypopharynx. Bronchoscopy revealed mucosal irregularity, ulceration, and exudates debris in the proximal trachea, and bronchial biopsy revealed chronic inflammation with granulation tissue. Based on these findings, we suspected CD with tracheal involvement and began administering intravenous methylprednisolone at 1 mg/kg per day, after which her symptoms and bronchoscopic findings improved.

For this study, polarity-controlled ZnO films were grown on lithium niobate (LiNbO3) substrates without buffer layers using the pulsed-laser deposition technique. The interfacial structure between the ZnO films and the LiNbO3 was inspected using high-resolution transmission electron microscopy (HR-TEM) measurements, and X-ray diffraction (XRD) measurements were performed to support these HR-TEM results. The polarity determination of the ZnO films was investigated using piezoresponse force microscopy (PFM) and a chemical-etching analysis. It was verified from the PFM and chemical-etching analyses that the ZnO film grown on the (+z) LiNbO3 was Zn-polar ZnO, while the O-polar ZnO occurred on the (-z) LiNbO3. Further, a possible mechanism of the interfacial atomic configuration between the ZnO on the (+z) LiNbO3 and that on the (-z) LiNbO3 was suggested. It appears that the electrostatic stability at the substrate surface determines the initial nucleation of the ZnO films, leading to the different polarities in the ZnO systems.

Bismuth doped ZnO (ZnBi0.03O0.97) thin films are grown using pulsed laser deposition. The existence of positively charged Bi, absence of metallic zinc and the Zn-O bond formation in Bi doped ZnO are confirmed using X-ray Photoelectron Spectroscopy (XPS). Temperature dependent resistivity and UV-visible absorption spectra show lowest resistivity with 8.44 × 10-4 Ω cm at 300 K and average transmittance of 93 % in the visible region respectively. The robust ferromagnetic signature is observed at 350 K (7.156 × 10-4 emu/g). This study suggests that Bi doped ZnO films should be a potential candidate for spin based optoelectronic applications.

Recently, data from built-in sensors in smartphones have been readily available, and analyzing data for various types of health information from smartphone users has become a popular health care application area. Among relevant issues in the area, one of the most prominent topics is analyzing the characteristics of human movements. In this paper, we focus on characterizing the human movements of walking and running based on a novel machine learning approach. Since walking and running are human fundamental activities, analyzing their characteristics promptly and automatically during daily smartphone use is particularly valuable. In this paper, we propose a machine learning approach, referred to as ’two-stage latent dynamics modeling and filtering’ (TS-LDMF) method, where we combine a latent space modeling stage with a nonlinear filtering stage, for characterizing individual dynamic walking and running patterns by analyzing smartphone sensor data. For the task of characterizing movements, the proposed method makes use of encoding the high-dimensional sequential data from movements into random variables in a low-dimensional latent space. The use of random variables in the latent space, often called latent variables, is particularly useful, because it is capable of conveying compressed information concerning movements and efficiently handling the uncertainty originating from high-dimensional sequential observation. Our experimental results show that the proposed use of two-stage latent dynamics modeling and filtering yields promising results for characterizing individual dynamic walking and running patterns.

Recently, wearable devices have become a prominent health care application domain by incorporating a growing number of sensors and adopting smart machine learning technologies. One closely related topic is the strategy of combining the wearable device technology with skill assessment, which can be used in wearable device apps for coaching and/or personal training. Particularly pertinent to skill assessment based on high-dimensional time series data from wearable sensors is classifying whether a player is an expert or a beginner, which skills the player is exercising, and extracting some low-dimensional representations useful for coaching. In this paper, we present a deep learning-based coaching assistant method, which can provide useful information in supporting table tennis practice. Our method uses a combination of LSTM (Long short-term memory) with a deep state space model and probabilistic inference. More precisely, we use the expressive power of LSTM when handling high-dimensional time series data, and state space model and probabilistic inference to extract low-dimensional latent representations useful for coaching. Experimental results show that our method can yield promising results for characterizing high-dimensional time series patterns and for providing useful information when working with wearable IMU (Inertial measurement unit) sensors for table tennis coaching.

Spatial and temporal variations in planetary boundary layer height (PBLH) over the Korean Peninsula and its surrounding oceans are investigated using a regional grid model operated at the Korea Meteorological Administration (KMA). Special attention is placed on daily maximum mixing height for evaluation against two radiosonde observation datasets. In order to construct a new high-resolution PBLH database with 3-hour time and 10 km spatial resolution, short-term integrations with the regional model are carried out for a one-year period from June 2010 to May 2011. The resulting dataset is then utilized to explore the seasonal patterns of horizontal PBLH distribution over the peninsula for one year. Frequency distributions as well as monthly and diurnal variations of PBLH at two selected locations are examined. This study reveals specific spatiotemporal structure of boundary layer depth over the Korean Peninsula for the first time at a relatively high-resolution scale. The results are expected to provide insights into the direction for operational tuning and future development in the model boundary layer schemes at KMA.