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Abstract Background The role of HER2 amplification level in predicting the effectiveness of HER2-directed therapies has been established. However, its association with survival outcomes in advanced HER2-positive breast cancer treated with dual HER2-blockade remains unexplored. Methods This is a single-center retrospective study of patients with advanced HER2-positive breast cancer treated with first-line pertuzumab, trastuzumab, and docetaxel. The primary objective was to ascertain the relationship between treatment outcomes and the level of HER2 amplification by in situ hybridization (ISH). Results A total of 152 patients were included with a median follow-up duration of 50.0 months. Among the 78 patients who received ISH, a higher HER2/CEP17 ratio correlated significantly with longer PFS (HR 0.50, p = 0.022) and OS (HR 0.28, p = 0.014) when dichotomized by the median. A higher HER2 copy number also correlated significantly with better PFS (HR 0.35, p

Abstract Background Germline mutations of breast cancer susceptibility gene BRCA1 and BRCA2 (gBRCA1/2) are associated with elevated risk of breast cancer in young women in Asia. BRCA1 and BRCA2 proteins contribute to genomic stability through homologous recombination (HR)-mediated double-strand DNA break repair in cooperation with other HR-related proteins. In this study, we analyzed the targeted sequencing data of Korean breast cancer patients with gBRCA1/2 mutations to investigate the alterations in HR-related genes and their clinical implications. Materials and methods Data of the breast cancer patients with pathogenic gBRCA1/2 mutations and qualified targeted next-generation sequencing, SNUH FiRST cancer panel, were analyzed. Single nucleotide polymorphisms, small insertions, and deletions were analyzed with functional annotations using ANNOVAR. HR-related genes were defined as ABL1, ATM, ATR, BARD1, BRCA1, BRCA2, CDKN1A, CDKN2A, CHEK1, CHEK2, FANCA, FANCD2, FANCG, FANCI, FANCL, KDR, MUTYH, PALB2, POLE, POLQ, RAD50, RAD51, RAD51D, RAD54L, and TP53. Mismatch-repair genes were MLH1, MSH2, and MSH6. Clinical data were analyzed with cox proportional hazard models and survival analyses. Results Fifty-five Korean breast cancer patients with known gBRCA1/2 mutations and qualified targeted NGS data were analyzed. Ethnically distinct mutations in gBRCA1/2 genes were noted, with higher frequencies of Val1833Ser (14.8%), Glu1210Arg (11.1%), and Tyr130Ter (11.1%) in gBRCA1 and Arg2494Ter (25.0%) and Lys467Ter (14.3%) in gBRCA2. Considering subtypes, gBRCA1 mutations were associated with triple-negative breast cancers (TNBC), while gBRCA2 mutations were more likely hormone receptor-positive breast cancers. At least one missense mutation of HR-related genes was observed in 44 cases (80.0%). The most frequently co-mutated gene was TP53 (38.1%). In patients with gBRCA1/2 mutations, however, genetic variations of TP53 occurred in locations different from the known hotspots of those with sporadic breast cancers. The patients with both gBRCA1/2 and TP53 mutations were more likely to have TNBC, high Ki-67 values, and increased genetic mutations, especially of HR-related genes. Survival benefit was observed in the TP53 mutants of patients with gBRCA2 mutations, compared to those with TP53 wild types. Conclusion Our study showed genetic heterogeneity of breast cancer patients with gBRCA1 and gBRCA2 mutations in the Korean populations. Further studies on precision medicine are needed for tailored treatments of patients with genetic diversity among different ethnic groups.

This paper proposes a method for forecasting surface solar irradiance (SSI), the most critical factor in solar photovoltaic (PV) power generation. The proposed method uses 16-channel data obtained by the GEO-KOMPSAT-2A (GK2A) satellite of South Korea as the main data for SSI forecasting. To determine feature variables related to SSI from the 16-channel data, the differences and ratios between the channels were utilized. Additionally, to consider the fundamental characteristics of SSI originating from the sun, solar geometry parameters, such as solar declination (SD), solar elevation angle (SEA), and extraterrestrial solar radiation (ESR), were used. Deep learning-based feature selection (Deep-FS) was employed to select appropriate feature variables that affect SSI from various feature variables extracted from the 16-channel data. Lastly, spatio-temporal deep learning models, such as convolutional neural network–long short-term memory (CNN-LSTM) and CNN–gated recurrent unit (CNN-GRU), which can simultaneously reflect temporal and spatial characteristics, were used to forecast SSI. Experiments conducted to verify the proposed method against conventional methods confirmed that the proposed method delivers superior SSI forecasting performance.

In recent years, battery fires have become more common owing to the increased use of lithium-ion batteries. Therefore, monitoring technology is required to detect battery anomalies because battery fires cause significant damage to systems. We used Mahalanobis distance (MD) and independent component analysis (ICA) to detect early battery faults in a real-world energy storage system (ESS). The fault types included historical data of battery overvoltage and humidity anomaly alarms generated by the system management program. These are typical preliminary symptoms of thermal runaway, the leading cause of lithium-ion battery fires. The alarms were generated by the system management program based on thresholds. If a fire occurs in an ESS, the humidity inside the ESS will increase very quickly, which means that threshold-based alarm generation methods can be risky. In addition, industrial datasets contain many outliers for various reasons, including measurement and communication errors in sensors. These outliers can lead to biased training results for models. Therefore, we used MD to remove outliers and performed fault detection based on ICA. The proposed method determines confidence limits based on statistics derived from normal samples with outliers removed, resulting in well-defined thresholds compared to existing fault detection methods. Moreover, it demonstrated the ability to detect faults earlier than the point at which alarms were generated by the system management program: 15 min earlier for battery overvoltage and 26 min earlier for humidity anomalies.

Machine tools are used in a wide range of applications, and they can manufacture workpieces flexibly. Furthermore, they require maintenance; the overall costs include maintenance costs, which constitute a significant portion, and the costs involved in ensuring product quality. Therefore, anomaly detection in tool conditions is required, because these tools are essential industrial elements. However, the data related to tool conditions present some challenges: data imbalances and deficiencies. Data imbalances and deficiencies can affect the performance of anomaly detection models. A model trained using data with imbalances and deficiencies may miscalculate that abnormal data are normal data, leasing to errors. To overcome these problems, the proposed method has been designed using the wavelet transform, color space conversion, color extraction, puzzle-based data augmentation, and double transfer learning. The proposed method generated image data from time-series data, effectively extracted features, and generated new image data using puzzle-based data augmentation. The color information was processed to highlight features, and the proposed puzzle-based data augmentation was applied during processing to increase the amount of data to improve the performance of the anomaly detection model. The experimental results showed that the proposed method can classify normal and abnormal data with greater accuracy. In particular, the accuracy of abnormal data classification increased from 25.00% to 91.67%. This demonstrates that the proposed method is effective and can overcome data imbalances and deficiencies.

IntroductionSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants brought waves of pandemics with breakthrough infections in vaccinated individuals. We analyzed the antibody responses after primary and booster vaccination in healthy controls (HC) and patients with early breast cancer (BC).MethodsIn this prospective longitudinal cohort study, the binding activity of serum antibody level against spike proteins and antigens of SARS-CoV-2 variants was measured within 21 days after each vaccination in the BC group and HC group.ResultsAll participants, 40 in the BC and 20 in the HC group, had increased antibody response after vaccination. BC group, however, had weaker humoral responses than the HC group (IgG: 1.5, 2.3, 2.5-folds in BC vs. 1.9, 3.6, 4.0-folds in HC after each dose; IgA: 2.1, 3.0, 3.6-folds in BC vs. 4.2, 10.4, 5.2-folds in HC after each dose, respectively). Those under concurrent cytotoxic chemotherapy had weaker antibody response than the non-cytotoxic treatment group and HC. Adjunct use of steroids and age were not significant risk factors. The levels of binding antibody against the Delta and the Omicron (BA1) variants were lower than the wild-type, especially in BC.ConclusionIn the waves of new sub-variants, our study suggests that an additional dose of vaccinations should be recommended according to the anti-cancer treatment modality in patients with BC who had received booster vaccination.

Abstract The vascular complications have been a major cause of morbidity and mortality among all subtypes of BCR-ABL1 negative myeloproliferative neoplasms (MPN), but the ethnicity-specific data was limited. We therefore conducted a multi-center retrospective, longitudinal cohort study to evaluate the incidence, characteristics and risk factors of thromboembolic events of MPN patients. Of 256 patients, 27.3% experienced thromboembolic events, majority of which occurred before or within 12 months of MPN diagnosis. The multivariable Cox proportional analyses identified leukocytosis (HR 2.67, 95% CI 1.36–5.24, q = 0.004) and history of thrombosis (HR 9.68, 95% CI 2.00–46.88, q = 0.005) as the risk factors for thromboembolism. In subgroup analysis of polycythemia vera and hemoglobin concentration (HR 1.97, 95% CI 1.28–3.04, q = 0.002) appeared to be a significant risk factor of thrombosis, along with age and thrombosis history. In essential thrombocythemia, severity of the established IPSET score was closely correlated with the frequency of thromboembolic events. In primary myelofibrosis, history of thrombosis was associated with thrombosis events (HR 13.85, 95% CI 1.2–159.5, q = 0.035). Overall survival was worse in patients who experienced thromboembolic events. Our study highlighted the importance of recognizing high risk patients and implementing personalized intervention.

Particulate matter (PM) in the air can cause various health problems and diseases in humans. In particular, the smaller size of PM2.5 enable them to penetrate deep into the lungs, causing severe health impacts. Exposure to PM2.5 can result in respiratory, cardiovascular, and allergic diseases, and prolonged exposure has also been linked to an increased risk of cancer, including lung cancer. Therefore, forecasting the PM2.5 concentration in the surrounding is crucial for preventing these adverse health effects. This paper proposes a method for forecasting the PM2.5 concentration after 1 h using bidirectional long short-term memory (Bi-LSTM). The proposed method involves selecting input variables based on the feature importance calculated by random forest, classifying the data to assign weight variables to reduce bias, and forecasting the PM2.5 concentration using Bi-LSTM. To compare the performance of the proposed method, two case studies were conducted. First, a comparison of forecasting performance according to preprocessing. Second, forecasting performance between deep learning (long short-term memory, gated recurrent unit, and Bi-LSTM) and conventional machine learning models (multi-layer perceptron, support vector machine, decision tree, and random forest). In case study 1, The proposed method shows that the performance indices (RMSE: 3.98%p, MAE: 5.87%p, RRMSE: 3.96%p, and R2:0.72%p) are improved because weights are given according to the input variables before the forecasting is performed. In case study 2, we show that Bi-LSTM, which considers both directions (forward and backward), can effectively forecast when compared to conventional models (RMSE: 2.70, MAE: 0.84, RRMSE: 1.97, R2: 0.16). Therefore, it is shown that the proposed method can effectively forecast PM2.5 even if the data in the high-concentration section is insufficient.

In modern complex industrial processes, mode changes cause unplanned shutdowns, potentially shortening the lifespan of key equipment and incurring significant maintenance costs. To avoid this problem, a method that can detect the fault of equipment operating in various modes is required. Therefore, we propose a novel fault detection method that uses the k-nearest neighbor normalization-based weight local outlier factor (WLOF). The proposed method performs local normalization using neighbors to consider possible mode changes in the normal data and WLOF is used for fault detection. In contrast to statistical methods, such as principal component analysis (PCA) and independent component analysis (ICA), the local outlier factor (LOF) uses the density of neighbors. However, because LOF is significantly affected by the distance between its neighbors, the weight is multiplied proportionally to the distance between each neighbor to improve the fault detection performance of the LOF. The efficiency of the proposed method was evaluated using a multimode numerical case and a circulating fluidized bed boiler. The experimental results show that the proposed method outperforms conventional PCA, kernel PCA (KPCA), k-nearest neighbor (kNN), and LOF. In particular, the proposed method improved the detection accuracy by 20% compared with conventional methods. Therefore, the proposed method can be applied to a real process operating in multiple modes.

Abstract Background Paraquat is associated with a high rate of fatalities in acute poisoning. This study aimed to examine the association between the national public health policy that banned the use of paraquat and the incidence of pesticide-associated mortality. Methods All external causes of death from 2009 to 2013 of Korea were analyzed. The intervention was a national public health policy that annulled the authorized use (2011) and banned the purchase of paraquat (2012). Two periods were compared as follows: before (2009-2010) and after (2012-2013) the intervention period. The main outcome was pesticide-associated death coded on the death certificate. Multivariable logistic regression analysis adjustment for gender, age, season and weekday of death, province, education level, marital status, and occupation was performed to calculate adjusted odds ratios (AORs) and 95% confidence intervals (CIs) for pesticide-associated mortality. The effect sizes of the intervention across all intents (Accident, Suicide, Homicide, and Undetermined) were compared by adding an interaction term (intervention*intent group) to the above model. Results A total of 127,866 deaths from for all external causes were analyzed, including 65,538 from 2009 to 2010 and 62,373 from 2012 to 2013. Pesticide-associated mortality decreased from 9.7% (2009-2010) to 6.5% (2012-2013) (p

Artificial intelligence technologies and robot vision systems are core technologies in smart factories. Currently, there is scholarly interest in automatic data feature extraction in smart factories using deep learning networks. However, sufficient training data are required to train these networks. In addition, barely perceptible noise can affect classification accuracy. Therefore, to increase the amount of training data and achieve robustness against noise attacks, a data augmentation method implemented using the adaptive inverse peak signal-to-noise ratio was developed in this study to consider the influence of the color characteristics of the training images. This method was used to automatically determine the optimal perturbation range of the color perturbation method for generating images using weights based on the characteristics of the training images. The experimental results showed that the proposed method could generate new training images from original images, classify noisy images with greater accuracy, and generally improve the classification accuracy. This demonstrates that the proposed method is effective and robust to noise, even when the training data are deficient.

Endotoxin tolerance allows macrophages to produce large quantities of proinflammatory cytokines immediately after their contact with lipopolysaccharides (LPSs), but prevents their further production after repeated exposure to LPSs. While this response is known to prevent overproduction of proinflammatory cytokines, the mechanism through which endotoxin tolerance is established has not been identified. In the current study, we demonstrate that sufficient production of geranylgeraniol (GGOH) in macrophages is required to maintain endotoxin tolerance. We show that increased synthesis of 3-hydroxy-3-methyl-glutaryl-CoA reductase (HMGCR) protein following LPS treatment is required to produce enough GGOH to inhibit expression of Malt1, a protein known to stimulate expression of proinflammatory cytokines, in macrophages repeatedly exposed to LPSs. Depletion of GGOH caused by inhibition of HMGCR led to increased Malt1 expression in macrophages subjected to repeated exposure to LPSs. Consequently, endotoxin tolerance was impaired, and production of interleukin 1-β and other proinflammatory cytokines was markedly elevated in these cells. These results suggest that insufficient production of GGOH in macrophages may cause autoinflammatory diseases.

Campylobacter jejuni is one of the leading foodborne pathogens worldwide. C. jejuni is isolated from a wide range of foods, domestic animals, wildlife, and environmental sources. The currently-available culture-based isolation methods are not highly effective for wastewater samples due to the low number of C. jejuni in the midst of competing bacteria. To detect and isolate C. jejuni from wastewater samples, in this study, we evaluated a few different enrichment conditions using five different antibiotics (i.e., cefoperazone, vancomycin, trimethoprim, polymyxin B, and rifampicin), to which C. jejuni is intrinsically resistant. The selectivity of each enrichment condition was measured with Ct value using quantitative real-time PCR (qRT-PCR), and multiplex PCR to determine Campylobacter species. In addition, the efficacy of Campylobacter isolation on different culture media after selective enrichment was examined by growing on Bolton and Preston agar plates. The addition of polymyxin B, rifampicin, or both to the Bolton selective supplements enhanced the selective isolation of C. jejuni. In particular, rifampicin supplementation and an increased culture temperature (i.e., 42°C) had a decisive effect on the selective enrichment of C. jejuni from wastewater. The results of 16S rDNA sequencing also revealed that Enterococcus spp. and Pseudomonas aeruginosa are major competing bacteria in the enrichment conditions. Although it is known to be difficult to isolate Campylobacter from samples with heavy contamination, this study well exhibited that the manipulation of antibiotic selective pressure improves the isolation efficiency of fastidious Campylobacter from wastewater.

Stress Granules (SG) are sites of accumulation of stalled initiation complexes that are induced following a variety of cellular insults. In a genetic screen for factors involved in protecting human myoblasts from acute oxidative stress, we identified a gene encoding a protein we designate SGNP (Stress Granule and Nucleolar Protein).A gene-trap insertional mutagenesis screen produced one insertion that conferred resistance to sodium arsenite. RT-PCR/3' RACE was used to identify the endogenous gene expressed as a GFP-fusion transcript. SGNP is localized in both the cytoplasm and nucleolus and defines a non-nucleolar compartment containing 5.8S rRNA, a component of the 60S ribosomal subunit. Under oxidative stress, SGNP nucleolar localization decreases and it rapidly co-localizes with stress granules. The decrease in nucleolar SGNP following oxidative stress was accompanied by a large increase in nucleolar 5.8S rRNA. Knockdown of SGNP with shRNA increased global mRNA translation but induced growth arrest and cell death.These results suggest that SGNP is an essential gene that may be involved in ribosomal biogenesis and translational control in response to oxidative stress.

Background/Aims: There is a need for a simple and practical tool adapted for the diagnosis of chronic constipation (CC) in the Asian population. This study compared the Asian Neurogastroenterology and Motility Association (ANMA) CC tool and Rome III criteria for the diagnosis of CC in Asian subjects. Methods: This multicenter, cross-sectional study included subjects presenting at outpatient gastrointestinal clinics across Asia. Subjects with CC alert symptoms completed a combination Diagnosis Questionnaire to obtain a diagnosis based on 4 different diagnostic methods: self-defined, investigator's judgment, ANMA CC tool, and Rome III criteria. The primary endpoint was the level of agreement/disagreement between the ANMA CC diagnostic tool and Rome III criteria for the diagnosis of CC. Results: The primary analysis comprised of 449 subjects, 414 of whom had a positive diagnosis according to the ANMA CC tool. Rome III positive/ANMA positive and Rome III negative/ANMA negative diagnoses were reported in 76.8% and 7.8% of subjects, respectively, resulting in an overall percentage agreement of 84.6% between the 2 diagnostic methods. The overall percentage disagreement between these 2 diagnostic methods was 15.4%. A higher level of agreement was seen between the ANMA CC tool and self-defined (374 subjects [90.3%]) or investigator's judgment criteria (388 subjects [93.7%]) compared with the Rome III criteria. Conclusion: This study demonstrates that the ANMA CC tool can be a useful for Asian patients with CC. [ABSTRACT FROM AUTHOR]

Objectives: The first objective of this study was to evaluate the penetration of particles generated from combustion of plastic through National Institute for Occupational Safety and Health (NIOSH)-certified N95 filtering facepiece respirators (FFRs) using a manikin-based protocol and compare the data to the penetration of NaCl particles. The second objective was to investigate the effect of relative humidity (RH) on the filtration performance of N95 FFRs. Methods: Two NIOSH-certified N95 FFRs (A and B) were fully sealed on a manikin headform and challenged with particles generated by combustion of plastic and NaCl particles. The tests were performed using two cyclic flows [with mean inspiratory flow (MIF) rates = 30 and 85 l min-1, representing human breathing under low and moderate workload conditions] and two RH levels (≈20 and ≈80%, representing dry and moderately humid air). The total and size-specific particle concentrations inside (Cin) and outside (Cout) of the respirators were measured with a condensation particle counter and an aerosol size spectrometer. The penetration values (Cin/Cout) were calculated after each test. Results: The challenge aerosol, RH, MIF rate, and respirator type had significant (P < 0.05) effects on the performance of the manikin-sealed FFR. Its efficiency significantly decreased when the FFR was tested with plastic combustion particles compared to NaCl aerosols. For example, at RH ≈80% and MIF = 85 l min-1, as much as 7.03 and 8.61% of combustion particles penetrated N95 respirators A and B, respectively. The plastic combustion particles and gaseous compounds generated by combustion likely degraded the electric charges on fibers, which increased the particle penetration. Increasing breathing flow rate or humidity increased the penetration (reduced the respirator efficiency) for all tested aerosols. The effect of particle size on the penetration varied depending on the challenge aerosol and respirator type. It was observed that the peak of the size distribution of combustion particles almost coincided with their most penetrating particle size, which was not the case for NaCl particles. This finding was utilized for the data interpretation. Conclusions: N95 FFRs have lower ilter eiciency when challenged with contaminant particles generated by combustion, particularly when used under high humidity conditions compared to NaCl particles. [ABSTRACT FROM AUTHOR]

Campylobacter jejuni is one of the leading bacterial causes of human gastroenteritis. Due to the increasing rates of human campylobacteriosis, C. jejuni is considered as a serious public health concern worldwide. C. jejuni is a microaerophilic, fastidious bacterium. C. jejuni must overcome a wide range of stress conditions during foodborne transmission to humans, such as food preservation and processing conditions, and even in infection of the gastrointestinal tracts of humans. Particularly, this microaerophilic foodborne pathogen must survive in the atmospheric conditions prior to the initiation of infection. C. jejuni possesses unique regulatory mechanisms for oxidative stress resistance. Lacking OxyR and SoxRS that are highly conserved in other Gram-negative foodborne pathogens, C. jejuni modulates the expression of genes involved in oxidative stress resistance mainly via the peroxide resistance regulator and Campylobacter oxidative stress regulator. Based on recent findings of ours and others, in this review, we described how C. jejuni regulates the expression of oxidative stress defense. [ABSTRACT FROM AUTHOR]

Background/Aims: This integrated analysis aimed to identify the factors associated with the most frequently reported treatment-emergent adverse events (TEAEs) in Asian and non-Asian patients with chronic constipation (CC) who receive prucalopride or placebo over 12 weeks. Methods: Pooled data from four randomized, double-blind, placebocontrolled, multicenter, phase III studies (NCT00488137, NCT00483886, NCT00485940, and NCT01116206) on patients treated with prucalopride 2 mg or placebo were analyzed. The associations between predictors and TEAEs were evaluated based on a logistic regression model. Results: Overall, 1,821 patients (Asian, 26.1%; non-Asian, 73.9%) were analyzed. Prucalopride treatment was significantly associated with diarrhea, headache, and nausea (p<0.001), but not with abdominal pain, compared with placebo. Differences in the prevalence of TEAEs between prucalopride and placebo decreased greatly after the first day of treatment. Compared with non-Asians, Asians were more likely to experience diarrhea and less likely to develop abdominal pain, headache, and nausea. Prior laxative use, CC duration, and body weight were not predictive of any of these TEAEs. Conclusions: Prucalopride treatment was positively associated with diarrhea, headache, and nausea. Asian patients tended to have a higher frequency of diarrhea but lower frequencies of headache, abdominal pain, and nausea compared with non-Asians. [ABSTRACT FROM AUTHOR]

This article investigates the liquidity risk of U.S. mortgage-backed securities (MBS) in comparison with government and agency securities from an empirical perspective. An empirical evaluation of liquidity risk is performed by negative tails from the historical changes of daily total transactions. A set of bond market risk factors is applied to control the pure market effects on transaction changes. Once market effects are controlled, negative tails are evaluated from the underlying distributions for residual transaction changes. During the recent five years, liquidity-driven MBS transaction changes show fat-tail, as well as high-sample volatility. This suggests that a sudden, large drop from the normal level of transactions is more likely for MBS than for government and agency bonds, in the case that there is negative liquidity shock. [ABSTRACT FROM AUTHOR]

CCAAT/enhancer-binding protein ∈ (C/EBP∈) is a neutrophil-specific transcription factor whose activity is controlled by juxtaposed activating and regulatory domains. We previously determined that the function of the major regulatory domain (RD1) in C/EBP∈ was dependent on the integrity of a five-amino acid motif that was identical to the recognition site for members of the small ubiquitin-like modifier (SUMO) family of ubiquitin-related proteins. We show here that the SUMO attachment site (the regulatory domain motif) is necessary and sufficient both for the intrinsic inhibitory function of RD1 and for coactivation by PIASxα and PIASxβ, two members of the protein inhibitor of activated STAT (PIAS) family of SUMO E3 ligases. PIASxβ was a more potent coactivator than PIASxα of both full-length C/EBP∈ and fusion proteins containing the N-terminal portion of C/EBP∈, whereas PIASxβ was more active on fusion proteins containing a heterologous activation domain. Two modes of coactivation were observed, one that was dependent on the integrity of the RING finger (RF) domain and was shared by both PIASxα and PIASxβ and a second mode that was independent of the RF and was only observed with PIASxβ. Sumoylation of C/EBP∈ was enhanced by coexpression of PIASxα, suggesting that this modification is associated with the enhanced activity of the target protein. These results suggest that a complex interplay of accessory factors, including SUMO and PIAS proteins, modulates the activity of C/EBP∈. [ABSTRACT FROM AUTHOR]

It is important for an IT service company (IS company) to fully take into account the differences in customer satisfaction across different customer groups. In this article, we consider three layers of customers in the client company. There are project directors who interface with and accept the final product (i.e. IT system) from the IS company, users who actually use the IT system for their daily operations, and finally operators who do maintenance works for the IT system. We propose that each customer group (i.e. project director, user, or operator) evaluates the IT systems success with a different set of criteria. Transaction relationship and partnership turn out to be important determinants for the project directors: task-related and IS-related output performances seem to be less influential. The reverse conclusion can be made for users and operators. One additional insight is that IS company's efforts to understand its customer's tasks and share risks with the customer company might have unexpected effects. Although the project directors seem to like such close involvement, it can be detrimental to the users' satisfaction with the IS outsourcing projects. [ABSTRACT FROM AUTHOR]

This paper presents a sleep monitoring sensor based on a flexible metal strain gauge. As quality of life has improved, interest in sleep quality, and related products, has increased. In this study, unlike a conventional single sensor based on a piezoelectric material, a metal strain gauge-based array sensor based on polyimide and nickel chromium (NiCr) is applied to provide movement direction, respiration, and heartbeat data as well as contact-free use by the user during sleeping. Thin-film-type resistive strain gage sensors are fabricated through the conventional flexible printed circuit board (FPCB) process, which is very useful for commercialization. The measurement of movement direction and respiratory rate during sleep were evaluated, and the heart rate data were compared with concurrent electrocardiogram (ECG) data. An algorithm for analyzing sleep data was developed using MATLAB, and the error rate was 4.2% when compared with ECG for heart rate. [ABSTRACT FROM AUTHOR]