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With the advancement of the camera-related technology in mobile devices, the vast amount of photos have been taken and shared in our daily life. However, many users still have unsatisfactory experiences with low-visible photos, which are frequently acquired under complicated real-world environments. In this paper, a novel yet simple method for low-light image enhancement has been proposed without any learning procedure. The key idea of the proposed method is to estimate properties of the scene illumination both in global and local manner by exploiting the diffusion pyramid with residuals. Specifically, the residual of each scale level in the diffusion pyramid is combined with the corresponding input. This restored result efficiently highlights local details across different scale spaces, thus it is helpful for preserving the boundary of illuminations. By conducting max-pooling with restored results from different levels of the diffusion pyramid, which are resized to the original resolution, the illumination component is accurately inferred from a given image. Compared to recent learning-based approaches, one important advantage of the proposed method is to effectively avoid the overfitting problem to the specific training dataset. Experimental results on various benchmark datasets demonstrate the efficiency and robustness of the proposed method for low-light image enhancement in real-world scenarios.

PLC 시스템은 자체 모듈에 의한 부분적 확장성을 지원하고 있지만, 확장을 위한 비용이 크고, 센서 및 기타 제어기들과의 언어 호환성 또한 문제가 되고 있다. 본 논문에서는 빠르게 변화하는 기술에 대처하기 위해 텍사스 인스트루먼트 사의 DSP(디지털 시그널 프로세서)를 이용하여 PLC를 대체 및 보조할 수 있는 시스템을 제안한다. 이 시스템은 기존 PLC의 기능을 통합적으로 구현하며, 범용으로 쓰이는 PLC 에 비해 속도가 빠르고, 약 50% 이상의 비용 절감이 가능하다. 또한, 모듈 형태의 24V의 범용디지털 I/O핀을 구성함으로써 선택적으로 입력과 출력의 부분적 확장을 용이하게 하였다. 일반적 산업장비의 제어를 위해 널리 사용되고 있는 모드버스 통신을 RS-485 인터페이스를 기반으로 DSP에서 구현함으로써, 장비 간 다대다 통신을 할 수 있도록 하였다.

Background subtraction is a key prerequisite for intelligent video surveillance, but most of the methods employed are still affected by dynamic changes in the illumination conditions, e.g., shadows cast by passing clouds occur frequently in outdoor scenes. To resolve this problem, a novel approach based on the underlying structure of the difference image is introduced in this study. In particular, local binary patterns (LBPs) are computed based on the frame differencing result, i.e., moving LBP, and then compared with the background model, which is updated according to an online interpolation scheme, in order to determine whether the current pixel belongs to the background. An important advantage of the proposed method is that it efficiently smoothes unexpected noise between frames while also preserving the boundaries of the moving objects by using an edge-aware filtering technique. Experimental results obtained using two benchmark data sets demonstrated that the proposed method is more robust to variable illumination in outdoor scenes compared with previously proposed approaches.

Atomic layer deposition (ALD) is proposed to synthesize ZnS-coated g-C3N4 photocatalysts which form an effective heterojunction for charge separation by reducing carrier recombination. It also, enables decrease in processing time from few days to several hours and circumvents collection process of synthesized powder which leads improvement in the productivity. In ZnS/g-C3N4 heterojunction composite, ZnS quantum-dots are uniformly distributed on g-C3N4 rather than conformal ZnS film due to hydrophobic nature of g-C3N4 surface. Photocatalytic activity of the ZnS/g-C3N4 heterojunction composites is enhanced up to 2.6 times compared to pristine g-C3N4 by tailoring ZnS ALD cycles. A range of ALD cycles from 2 to 50 have applied, out of which 5 cycles are found optimum for best efficiency, above and below 5 cycles it becomes either saturated or less potent, respectively.

There has been much effort exerted to reduce one carbon (C1) gas emission to address climate change. As one promising way to more conveniently utilize C1 gas, several technologies have been developed to convert C1 gas into useful chemicals such as formic acid (FA). In this study, systems metabolic engineering was utilized to engineer Mannheimia succiniciproducens to efficiently utilize FA. 13 C isotope analysis of M. succiniciproducens showed that FA could be utilized through formate dehydrogenase (FDH) reaction and/or the reverse reaction of pyruvate formate lyase (PFL). However, the naturally favored forward reaction of PFL was found to lower the SA yield from FA. In addition, FA assimilation via FDH was found to be more efficient than the reverse reaction of PFL. Thus, the M. succiniciproducens LPK7 strain, which lacks in pfl, ldh, pta, and ack genes, was selected as a base strain. In silico metabolic analysis confirmed that utilization of FA would be beneficial for the enhanced production of SA and suggested FDH as an amplification target. To find a suitable FDH, four different FDHs from M. succiniciproducens, Methylobacterium extorquens, and Candida boidinii were amplified in LPK7 strain to enhance FA assimilation. High-inoculum density cultivation using 13 C labeled sodium formate was performed to evaluate FA assimilation efficiency. Fed-batch fermentations of the LPK7 (pMS3-fdh2 meq) strain was carried out using glucose, sucrose, or glycerol as a primary carbon source and FA as a secondary carbon source. As a result, this strain produced 76.11 g/L SA with the yield and productivity of 1.28 mol/mol and 4.08 g/L/h, respectively, using sucrose and FA as dual carbon sources. The strategy employed here will be similarly applicable in developing microorganisms to utilize FA and to produce valuable chemicals and materials from FA.

Summary Poly(lactate-co-glycolate), PLGA, is a representative synthetic biopolymer widely used in medical applications. Recently, we reported one-step direct fermentative production of PLGA and its copolymers by metabolically engineered Escherichia coli from xylose and glucose. In this study, we report development of metabolically engineered E. coli strains for the production of PLGA and poly(d-lactate-co-glycolate-co-d-2-hydroxybutyrate) having various monomer compositions from xylose as a sole carbon source. To achieve this, the metabolic flux towards Dahms pathway was modulated using five different synthetic promoters for the expression of Caulobacter crescentus XylBC. Further metabolic engineering to concentrate the metabolic flux towards d-lactate and glycolate resulted in production of PLGA and poly(d-lactate-co-glycolate-co-d-2-hydroxybutyrate) with various monomer fractions from xylose. The engineered E. coli strains produced polymers containing 8.8–60.9 mol% of glycolate up to 6.93 g l−1 by fed-batch cultivation in a chemically defined medium containing xylose. Finally, the biocompatibility of poly(d-lactate-co-glycolate-co-d-2-hydroxybutyrate) was confirmed by live/dead assay using human mesenchymal stem cells.

Genome-scale metabolic models (GEMs) are valuable in describing an organism's entire metabolism using genomic information and have made huge impact on systems biology and metabolic engineering. Here we review current state of microbial GEMs and their use in various applications. There has recently been much interest in reconstructing GEMs for microbial communities, reflecting the increasing importance of microbiome. Analyses of recent work suggest that the GEMs for the best studied microorganisms, new or less studied microorganisms, and also microbial communities need to be thoroughly validated with experimental data beyond simple gene essentiality and nutrient utilization capabilities. In this paper, computational modeling resources and frameworks developed recently for reconstructing GEMs of microorganisms and microbial communities together with their applications are reviewed.

Visual object tracking is an essential technique for constructing intelligent livestock management systems. Behavior patterns estimated from the trajectories of animals provide substantial useful information related to estrus cycle, disease prognosis and so on. However, similar colors and shapes between animals often lead to the failure of tracking multiple objects, and the background clutter of the breeding space further makes the problem intractable. In this paper, we propose a novel method for tracking animals using a single thermal sensor. The key idea of the proposed method is to represent the foreground (i.e., animals) easily obtained by a simple thresholding in a thermal frame as a topographic surface, which is very helpful for finding the boundary of each object even in cases with overlapping. Based on the segmentation results derived from morphological operations on the topographic surface, the center positions of all the animals are consistently updated with an efficient refinement scheme that is robust to the abrupt motions of animals. Experimental results using various thermal video sequences demonstrate the efficiency and robustness of our method for tracking animals in a breeding space compared to previous approaches proposed in the literature.

Background subtraction is a key prerequisite for a wide range of image processing applications due to its pervasiveness in various contexts. In particular, video surveillance highly requires the reliable background subtraction for further operations, such as object tracking and recognition, and thus, enormous efforts for this task have been devoted in recent decades. However, the path of technological evolution for background subtraction has now faced with an important issue that has started to be resolved: sensitivity to dynamic changes of scene contexts (e.g., illumination variations and moving backgrounds). Such dynamic changes are hardly tolerated by most of traditional background models, since they yield the drastically different statistics of pixel values even onto the relevant position between consecutive frames. To resolve this problem, many researchers in this field have developed robust and efficient methods. The goal of this paper is to provide a comprehensive review with a special attention to schemes related to handling varying illuminations frequently occurring in the outdoor surveillance scenario. This paper covers a systematic taxonomy, methodologies, and performance evaluations on benchmark databases, and also provides constructive discussions for the smart video surveillance under unconstrained outdoor environments.

Background: Acellular dermal matrix is a biologic material derived from the skin of human cadaveric donors. It has been used successfully in the past to reduce complications in breast surgery and hernia repair. This investigation was aimed at assessing the feasibility of using acellular dermal matrix to support the anastomosis after gastrectomy with the aim of reducing anastomotic site leakage complications. Methods: Patients were randomly assigned to standard anastomotic reconstruction (control arm) or anastomotic reconstruction with acellular dermal matrix reinforcement (intervention arm). Surgical outcomes related to anastomotic complications were collected. Because actual anastomotic leaks found on imaging studies are infrequent and thus require a very high number of patient recruitment to detect statistically significant difference between the two groups, in this pilot investigation other clinical and laboratory measures that have been shown to correlate to or predict anastomotic leaks were also collected. Each surgical outcome was compared. Results: A total of 94 patients (intervention arm: 50, control arm: 44), were included in the analysis. Two patients in the control arm (4.55%) and one patient in the intervention arm (2.00%) experienced anastomotic leakage (p = 0.598), a difference without statistical significance. However, average postoperative C-reactive protein (CRP) levels and NUn scores, both of which have been shown to reflect likelihood of progressing to anastomotic leakage, were significantly lower for the intervention arm. The control arm showed an average CRP level of 128.77 mg/dL (SD: 97.08) while the intervention arm showed 77.38 mg/dL (SD: 49.08, p = 0.049). Conclusions: Leakage rate reduction with acellular dermal matrix reinforcement of anastomotic site was not detected in this investigation. However, postoperative inflammation levels and numerical predictors of anastomotic leakage development were significantly lower with acellular dermal matrix reinforcement of surgical anastomosis. This finding is worthy of further investigation, as reduction of inflammation with anastomotic site reinforcement is a novel finding, and more in-depth research may lead to discoveries on the physiologic role of the surgical anastomosis in post-gastrectomy patients. In addition, lower CRP and NUn scores for the intervention arm suggest potential for larger studies to detect reduction in clinical leak rates after acellular dermal matrix reinforcement.

Systems metabolic engineering allows efficient development of high performing microbial strains for the sustainable production of chemicals and materials. In recent years, increasing availability of bio big data, for example, omics data, has led to active application of machine learning techniques across various stages of systems metabolic engineering, including host strain selection, metabolic pathway reconstruction, metabolic flux optimization, and fermentation. In this paper, recent contributions of machine learning approaches to each major step of systems metabolic engineering are discussed. As the use of machine learning in systems metabolic engineering will become more widespread in accordance with the ever-increasing volume of bio big data, future prospects are also provided for the successful applications of machine learning.

A heterojunction of TiO

OBJECTIVE. The purposes of this study were to identify the similarities and differences among the Korean Thyroid Imaging Reporting and Data System (TIRADS), American College of Radiology (ACR) TI-R...

Previous reports have shown that PPARβ/δ agonists ameliorate insulin resistance associated with type 2 diabetes mellitus (T2DM). To determine the role of PPARβ/δ in tumor necrosis factor α (TNFα)-mediated insulin resistance, we investigated expression levels of adiponectin and insulin receptor (IR) in response to treatment with the PPARβ/δ agonist GW501516 with or without TNFα, a proinflammatory cytokine, in differentiated 3T3-L1 adipocytes. GW501516 induced adipocyte differentiation and the expression of adiponectin in a dose-dependent manner in differentiated adipocytes. TNFα treatment reduced adiponectin expression at the end of differentiation. This effect was reversed by GW501516 co-treatment with TNFα. TNFα treatment decreased adipogenic marker genes such as PPARγ, aP2, resistin, and GLUT4, and GW501516 reversed the effects of TNFα. GW501516 treatment increased the expression of insulin receptor and inhibited TNFα-mediated repression of insulin receptor. Our results showed that GW501516 abrogated TNFα-induced insulin resistance. In summary, our study demonstrated that the PPARβ/δ agonist, GW501516 reversed TNFα-induced decreases in adipocyte differentiation and adiponectin expression, and improved insulin sensitivity by increasing the expression of insulin receptor. Therefore, PPARδ may be a promising therapeutic target for treatment of insulin resistance in patients with T2DM.

This paper presents a simple, yet powerful local descriptor, so-called the histograms of space---time dominant orientations (HiSTDO). Specifically, our HiSTDO is composed of two main components, i.e., the dominant orientation and its coherence, which represents how intensively gradients in the local region are distributed along the space---time dominant orientation. By incorporating them into the histogram, we define it as our HiSTDO descriptor. In contrast to previous methods vulnerable to the presence of the background clutter and the camera noise, our HiSTDO greatly encodes the space---time shape of underlying structures even under such challenging conditions, and it can thus be efficiently applied to various applications (e.g., object and action detection). Experimental results on diverse datasets demonstrate that the proposed descriptor is effective for human action as well as object detection.

In this letter, we propose a novel method for background subtraction in outdoor scenes. Inspired by the observation that the orthogonal decomposition onto a set of pixel intensities efficiently reveals illumination effects, we exploit a simple, yet powerful feature for describing the underlying structure of the local region in a given video, the so-called illumination-invariant structural complexity (IISC). In contrast to previous approaches still suffering from high-level false positives driven by varying illuminations in outdoor environments, our IISC feature has an ability to greatly discriminate structural changes by moving objects from those by illumination effects. We also provide the theoretical analysis to confirm that the proposed IISC feature is useful for modeling the background under diverse lighting conditions. Moreover, our framework does not require any preprocessing task. Experimental results on various datasets demonstrate that the proposed method is effective for video surveillance in a wide range of outdoor environments.

There have recently been many advances in obesity treatment, including lifestyle modifications and pharmacological and surgical treatments. Specifically, pharmacological strategies have improved significantly. However, the history of the development of medications aimed at weight loss is complicated. The Federal Drug Administration (FDA) withdrew anti-obesity drugs such as fenfluramine, dexfenfluramine, and phenylpropylamine due to their unwanted side effects. Moreover, sibutramine was voluntarily withdrawn from the market and a new drug, rimonabant, has been suspended in the middle of a clinical trial due to unacceptable side effects. The FDA has approved four new anti-obesity drugs in recent years. Lorcaserin is a selective 5-hydroxytryptamine receptor 2c (5-HT2c) agonist. The pharmacological mechanism of action of this drug is similar to fenfluramine and dexfenfluramine, but lorcaserin is specific for 5-HT2c, which are located almost exclusively in the central nervous system and are not found in heart valves. Three phase 3 clinical trials for lorcaserin have been published recently; weight reduction was successful and no side effects involving the heart were found. Furthermore, the FDA has also approved phentermine/topiramate controlled-release (PHEN/TPM CR), which is composed of a combination of immediate-release phentermine and controlled-release topiramate. Weight reduction achieved with PHEN/TPM CR was demonstrated to be better than all other anti-obesity drugs. Lastly, the combination therapy bupropion/ naltrexone activates proopiomelanocortin neurons and inhibits opioid-mediated negative feedback by synergism. Similar to liraglutide, a long-acting analogue of the hormone glucagon-like peptide-1, this treatment showed significant weight loss and metabolic improvements. However, in addition to its efficacy, clinicians should consider its side effects before use. (Korean J Med 2016;90:121-126)

Background/Aims: The present study aimed to investigate the associations between a change in body weight from 20 years of age to the present with insulin resistance and several parameters of metabolic syndrome in individuals at an increased risk of diabetes. Methods: From patients with a diagnosis related to an increased risk of diabetes who were examined at our endocrinology clinic from January 2012 to December 2014, the present study recruited 125 individuals between 30 and 64 years of age with hemoglobin A1c levels from 5.7 to 6.4%. The change in body weight of each patient from 20 years of age to the present was retrospectively determined, and the associations of this change with insulin resistance and several parameters of metabolic syndrome were analyzed. Results: The average gain in body weight after 20 years of age was 11.3 kg and there was no significance difference between the male and female patients. The proportion of overweight and obese patients at 20 years of age was 14%, but was 74% at the time of the visits to our endocrinology clinic. A change in body weight was significantly correlated with body mass index, insulin resistance, and waist circumference. Additionally, as the tertile of change in body weight increased, fasting insulin levels, insulin resistance, and the prevalence of abdominal obesity significantly increased. Conclusions: In the present study, an increase in body weight from 20 years of age to the present was correlated with insulin resistance and several parameters of metabolic syndrome in individuals at an increased risk of diabetes. (Korean J Med 2015;89:681-689)

Malonyl-CoA is an important central metabolite for the production of diverse valuable chemicals including natural products, but its intracellular availability is often limited due to the competition with essential cellular metabolism. Several malonyl-CoA biosensors have been developed for high-throughput screening of targets increasing the malonyl-CoA pool. However, they are limited for use only in Escherichia coli and Saccharomyces cerevisiae and require multiple signal transduction steps. Here we report development of a colorimetric malonyl-CoA biosensor applicable in three industrially important bacteria: E. coli , Pseudomonas putida , and Corynebacterium glutamicum . RppA, a type III polyketide synthase producing red-colored flaviolin, was repurposed as a malonyl-CoA biosensor in E. coli . Strains with enhanced malonyl-CoA accumulation were identifiable by the colorimetric screening of cells showing increased red color. Other type III polyketide synthases could also be repurposed as malonyl-CoA biosensors. For target screening, a 1,858 synthetic small regulatory RNA library was constructed and applied to find 14 knockdown gene targets that generally enhanced malonyl-CoA level in E. coli . These knockdown targets were applied to produce two polyketide (6-methylsalicylic acid and aloesone) and two phenylpropanoid (resveratrol and naringenin) compounds. Knocking down these genes alone or in combination, and also in multiple different E. coli strains for two polyketide cases, allowed rapid development of engineered strains capable of enhanced production of 6-methylsalicylic acid, aloesone, resveratrol, and naringenin to 440.3, 30.9, 51.8, and 103.8 mg/L, respectively. The malonyl-CoA biosensor developed here is a simple tool generally applicable to metabolic engineering of microorganisms to achieve enhanced production of malonyl-CoA–derived chemicals.

Focal amplification of chromosome 1q23.3 in patients with advanced primary or relapsed urothelial carcinomas is associated with poor survival. We interrogated chromosome 1q23.3 and the nearby focal amplicon 1q21.3, as both are associated with increased lymph node disease in patients with urothelial carcinoma. Specifically, we assessed whether the oncogene MCL1 that resides in 1q21.3 and the genes that reside in the 1q23.3 amplicon were required for the proliferation or survival of urothelial carcinoma. We observed that suppressing MCL1 or the death effector domain–containing protein (DEDD) in the cells that harbor amplifications of 1q21.3 or 1q23.3, respectively, inhibited cell proliferation. We also found that overexpression of MCL1 or DEDD increased anchorage independence growth in vitro and increased experimental metastasis in vivo in the nonamplified urothelial carcinoma cell line, RT112. The expression of MCL1 confers resistance to a range of apoptosis inducers, while the expression of DEDD led to resistance to TNFα-induced apoptosis. These observations identify MCL1 and DEDD as genes that contribute to aggressive urothelial carcinoma. Implications: These studies identify MCL1 and DEDD as genes that contribute to aggressive urothelial carcinomas.

Multiple object tracking is still a challenging problem in computer vision even though there have been several attempts lately to resolve the tracking problem in the framework of deep neural networks. In this paper, a novel method for multiple object tracking in soccer videos, which often contain complicated interactions between players with severe occlusions, is introduced. To do this, we propose to interpret the extracted foreground regions in a given frame as the topographic surface. This gives a great help to reliably chase target players by accurately providing the boundary lines of each object even with occlusions. Color similarity and spatial proximity are subsequently employed to refine the estimated position of target players for continuous tracking over whole video sequences. Experimental results on various soccer videos, which are taken of the actual games with the wide-angle camera, demonstrate that the proposed method is effective for tracking multiple players in the dynamic scene of the soccer video.

Astaxanthin is a reddish keto-carotenoid classified as a xanthophyll found in various microbes and marine organisms. As a powerful antioxidant having up to 100 times more potency than other carotenoids such as β-carotene, lutein, and lycopene, astaxanthin is a versatile compound utilized in animal feed, food pigment, health promotion and cosmetic industry. Here, we report development of metabolically engineered Escherichia coli capable of producing astaxanthin to a high concentration with high productivity. First, the heterologous crt genes (crtE, crtY, crtI, crtB, and crtZ) from Pantoea ananatis and the truncated BKT gene (trCrBKT) from Chlamydomonas reinhardtii were introduced to construct the astaxanthin biosynthetic pathway. Then, eight different fusion tags were examined by attaching them to the N- or C-terminus of the trCrBKT membrane protein to allow stable expression and to efficiently guide trCrBKT to the E. coli membrane. When the signal peptide of OmpF and TrxA were tagged to the N-terminus and C-terminus of trCrBKT, respectively, astaxanthin production reached 12.90 mg/L (equivalent to 3.84 mg/gDCW), which was 2.08-fold higher than that obtained without tagging. Upon optimization of culture conditions, this engineered strain WLGB-RPP harboring pAX15 produced 332.23 mg/L (5.38 mg/gDCW) of astaxanthin with the productivity of 3.79 mg/L/h by fed-batch fermentation. In order to further increase astaxanthin production, in silico flux variability scanning based on enforced objective flux (FVSEOF) was performed to identify gene overexpression targets. The engineered strain WLGB-RPP (pAX15, pTrc-ispDF) which simultaneously overexpressing the ispD and ispF genes identified by FVSEOF produced astaxanthin to a higher concentration of 377.10 mg/L (6.26 mg/gDCW) with a productivity of 9.20 mg/L/h upon induction with 1 mM IPTG. When cells were induced with 0.5 mM IPTG to reduce the metabolic burden, astaxanthin concentration further increased to 432.82 mg/L (7.12 mg/gDCW) with a productivity of 9.62 mg/L/h. To more stably maintain plasmid during the fed-batch fermentation of WLGB-RPP (pAX15, pTrc-ispDF), the post-segregational killing hok/sok system was introduced. This strain produced 385.04 mg/L (6.98 mg/gDCW) of astaxanthin with a productivity of 7.86 mg/L/h upon induction with 0.5 mM IPTG. The strategies reported here will be useful for the enhanced production of astaxanthin and related carotenoid products by engineered E. coli strains.

This paper presents a novel method for detecting shadow regions from a single image. The key idea of the proposed method is to filter out dark objects, which still give a great difficulty in shadow detection, by using the very-high order residual model. To reduce the mismatching problem at the boundary of shadow regions, we also propose to apply the edge-aware matting technique to our detection results. Experimental results show that the proposed method is effective for shadow detection under various environments.

This paper presents a novel method for increasing the reliability of feature matching based on visual saliency. The key idea of the proposed method is to suppress outliers frequently occurring in the background clutter by adopting visually attractable (i.e., salient) regions as a filtering weight, which are highly relevant to underlying structures (e.g., signs, buildings, etc.) in a given scene. This significantly improves the accuracy of feature matching while reducing the memory usage. Experimental results on the KlTTI dataset demonstrate that the proposed method is effective for increasing the accuracy of feature matching under diverse outdoor environments.

The purpose of this study is to improve microbial air quality by improving water quality, particularly concerning microbiological aspects, by applying an ultraviolet water purifier system to a direct evaporative cooling (DEC) system. A direct evaporative cooler is an air cooling technique that uses the evaporation of water. Most DECs recirculate water to reduce water use. Evaporative cooling pads and water are biologically contaminated by recirculating water. This contamination can develop into air contamination and cause respiratory illnesses in occupants. It is necessary to use sterilized water in a DEC to prevent respiratory diseases and maintain air quality. In this study, we examine whether improvements in water quality in a DEC affect air quality by dividing experiments into a control group (Control) and a treated group (UV-treated). In the control group, the degree of contamination was measured when a DEC operated for four weeks without ultraviolet water treatment. In UV-treated, the degree of contamination was measured when UV water treatment was applied to a DEC for four weeks. In both Control and UV-treated, microbes were sampled from the water, the evaporative cooling pad surface, and the DEC inlet and outlet air samples in order to compare the levels of contamination. The surface was measured once at four points, and the air was measured four times at two points. A comparison of the two experiments indicated that the degree of microbial contamination of water and air was significantly reduced in the UV-treated group when compared to that in the control group. When the pollution degree of the evaporative cooling pad was compared to the degree of air pollution, it was difficult to obtain a correlation between the two factors, although the results confirmed that the contamination of the evaporative cooling pad caused water pollution. Therefore, it is necessary to operate a water treatment system to maintain the clean air in DECs.

Due to the development of machine learning, interest in automatic recognition of objects in videos is increasing. In the sports field, there is a growing demand for a technology for automatically recognizing and tracking various objects in a sports game video. However, it is very difficult to recognize and track an object without proper preprocessing due to its small size and large sports field. In this paper, we compare background substraction and edge detection, which are typical preprocessing methods for automatic recognition and tracking of objects in sports images, and analyze advantages and disadvantages of each method.

An illumination-invariant method for background subtraction is presented. Inspired by the observation that the degree of dispersion in the colour space, which is computed from the local region of each frame, is not severely changed even with varying illuminations, To exploit the colour coherence as the features for formulating the background model is proposed. Experimental results on various benchmark databases show the efficiency and robustness of the proposed method compared to the previous approaches introduced in the literature.

Article history: In this study, a half-duplex mutual communication device via human skin was developed. Frequency-shift keying (FSK) digital modulation and demodulation control were used to transmit and receive data. Data communication through the human body is an effective communication technique and has high-grade security characteristics. In addition, making contact is a natural instinct of humans. Transmitting data through the human body is currently a highly conspicuous technology, because recently a lot of commercial sensors for humans have been developed. A body area network (BAN) can be easily constructed by this communication method on human skin. In this study, a half-duplex FSK mutual communication device was developed using a commercial FSK modulator and demodulator. A special control switching circuit and communication sequence were developed for mutual communication through human skin.

A novel image descriptor for fingerprint liveness detection is presented. Inspired by the observation that textural distortions due to the fabrication process are well revealed by edge-aware filtering, it is proposed to exploit the local patterns on the accumulated smoothing space for determining whether a given fingerprint is fake or not. Experimental results on LivDet 2009 and 2011 datasets show the efficiency and robustness of the proposed method compared with previous local image descriptors.

This paper aimed to evaluate the applicability of adiabatic humidification in the heating, ventilation, and air conditioning (HVAC) systems of semiconductor cleanrooms. Accurate temperature and humidity control are essential in semiconductor cleanrooms and high energy consumption steam humidification is commonly used. Therefore, we propose an adiabatic humidification system employing a pressurized water atomizer to reduce the energy consumption. The annual energy consumption of three different HVAC systems were analyzed to evaluate the applicability of adiabatic humidification. The studied cases were as follows: (1) CASE 1: a make-up air unit (MAU) with a steam humidifier, a dry cooling coil (DCC), and a fan filter unit (FFU); (2) CASE 2: a MAU with the pressurized water atomizer, a DCC, and a FFU; and (3) CASE 3: a MAU, a DCC, and a FFU, and the pressurized water atomizer installed in the return duct. The energy saving potential of adiabatic humidification over steam humidification has been proved, with savings of 8% and 23% in CASE 2 and CASE 3 compared to CASE 1, respectively. Furthermore, the pressurized water atomizer installed in the return duct exhibits greater energy saving effect than when installed in the MAU.

Summary Escherichia coli gene expression knockdown using synthetic small RNA (sRNA) can be fine-tuned by altering sRNA sequences to modulate target mRNA-binding ability, but this requires thorough checking for off-target effects. Here, we present an sRNA gene expression knockdown system fine-tuned by using different promoters to modulate synthetic sRNA abundance. Our approach entails selecting knockdown target genes resulting from in silico flux response analysis and those related to product biosynthesis then screening strains transformed with a library of synthetic sRNA-promoter combinations for enhanced production. We engineered two E. coli strains, both utilizing fine-tuned repression of argF and glnA through our approach; one produced putrescine (42.3 ± 1.0 g/L) and the other L-proline (33.8 ± 1.6 g/L) by fed-batch culture. Fine-tuned gene knockdown by controlling sRNA abundance will be useful for rapid design of microbial strains through simultaneously optimizing expression of multiple genes at a systems level, as it overcomes the difficulties of constructing and testing many different sRNAs and checking their cross-reactivity.

Due to the rapid growth of machine learning technology, there is a need for research to automatically recognize objects and analyze their behavior in various fields, as is the case with sports. Currently, a system for detecting and tracking multiple objects in a sporting event is not accurate enough. Since most of the services depend on the manual operation of an experienced operator, it is necessary to develop a real time tracking technique for detecting the position of an object. In this paper, we propose an algorithm for multi-object tracking in a sporting event by presenting the results of comparing the performance of existing algorithms for multi-object tracking.

In this letter, we propose a novel method to extract illumination-invariant features for face recognition and verification under varying illuminations. Inspired by the fact that normalized coefficients of the singular value decomposition (SVD) are insensitive to different illumination conditions, we exploit a simple, yet powerful scheme for describing underlying structures of faces, so-called SVD face. In contrast to previous approaches still suffering from the loss of details, our SVD face greatly preserves textures of the original image based on the relaxation of SVD coefficients. Theoretical analysis shows that our SVD face is an illumination-invariant measure and has an ability to discover meaningful components (e.g., eyes, mouth, etc.) of face images while suppressing the effect of various illuminations. Experimental results on both Yale B and our illuminated face (IF) datasets demonstrate that the SVD face is effective for face recognition and verification compared to previous approaches proposed in literature.

Saliency detection in video sequences has attracted great attention in recent years due to its promising contributions for various computer vision applications. However, most existing methods often fail to correctly find salient regions in complex scenes due to ambiguities between salient motions and irrelevant ones generated from the background. In this letter, we present a simple and powerful framework for video saliency detection based on the contrast of the spatiotemporal directional coherence. Our approach is designed in a general way so that it can be applied to videos taken under various environments including dynamic illuminations and textures in the background, which still bother previous saliency detection methods. Based on various challenging datasets, we compare ours with several competitive approaches proposed in literature and results demonstrate that the proposed method is effective and robust for detecting salient regions in diverse video sequences.

Finding regions of interest (ROIs) is a fundamentally important problem in the area of computer vision and image processing. Previous studies addressing this issue have mainly focused on investigating chromatic cues to characterize visually salient image regions, while less attention has been devoted to monochromatic cues. The purpose of this paper is the study of monochromatic cues, which have the potential to complement chromatic cues, for the detection of ROIs in an image. This paper first presents a taxonomy of existing ROI detection approaches using monochromatic cues, ranging from well-known algorithms to the most recently published techniques. We then propose a novel monochromatic cue for ROI detection. Finally, a comparative evaluation has been conducted on large scale challenging test sets of real-world natural scenes. Experimental results demonstrate that the use of our proposed monochromatic cue yields a more accurate identification of ROIs. This paper serves as a benchmark for future research on this particular topic and a steppingstone for developers and practitioners interested in adopting monochromatic cues to ROI detection systems and methodologies.

Adiponectin and tumor necrosis factor‑α (TNF‑α) exert opposite effects on obesity‑associated inflammation and insulin signaling. The purpose of the present study was to investigate the effects of chronic TNF‑α on adiponectin levels in 3T3‑L1 adipocytes, as well as the potential reversal mechanisms. Differentiated 3T3‑L1 adipocytes were exposed to TNF‑α for three different incubation times and then to various wash‑off periods with or without mitogen‑activated protein kinase (MAPK) inhibitors. TNF‑α significantly reduced adiponectin gene expression in a dose‑ and time‑dependent manner and activated c‑Jun N‑terminal kinases (JNK), extracellular signal‑regulated kinases (ERK) and p38 MAPK. A 16 h restoration period fully reversed the decrease in adiponectin levels following 16 h treatment with TNF‑α; however, 16 h withdrawal of TNF‑α following 32 or 48 h treatment did not completely reverse the TNF‑α‑induced decrease in adiponectin levels. In 3T3‑L1 adipocytes, 32 or 48 h wash‑off periods were required following 32 or 48 h TNF‑α treatments, respectively. The pattern of ERK activation following TNF‑α exposure and removal was similar to the pattern of adiponectin expression. Furthermore, ERK1/2 inhibition accelerated the recovery of adiponectin levels compared with the levels in the untreated control adipocytes. Therefore, the inhibitory effects of TNF‑α on adiponectin levels in differentiated 3T3‑L1 cells were fully reversed following a wash‑out period equivalent to the TNF‑α treatment time, potentially through the ERK 1/2 pathway.

Saliency detection has been extensively studied due to its promising contributions for various computer vision applications. However, most existing methods are easily biased toward edges or corners, which are statistically significant, but not necessarily relevant. Moreover, they often fail to find salient regions in complex scenes due to ambiguities between salient regions and highly textured backgrounds. In this paper, we present a novel unified framework for spatiotemporal saliency detection based on textural contrast. Our method is simple and robust, yet biologically plausible; thus, it can be easily extended to various applications, such as image retargeting, object segmentation, and video surveillance. Based on various datasets, we conduct comparative evaluations of 12 representative saliency detection models presented in the literature, and the results show that the proposed scheme outperforms other previously developed methods in detecting salient regions of the static and dynamic scenes.

The present study investigated the presence and mechanism of esculin-mediated renoprotection to assess its therapeutic potential. Esculin was orally administered at 20 mg/kg/day for 2 weeks to streptozotocin-induced diabetic mice, and its effects were compared with those of the vehicle in normal and diabetic mice. After oral administration of esculin to mice, the concentrations of esculin and esculetin in blood were 159.5 ± 29.8 and 9.7 ± 4.9 ng/mL at 30 min, respectively. Food and water intake were significantly increased in the diabetic mice compared to normal mice but attenuated in mice receiving esculin. The elevated blood glucose level and hepatic glucose-6-phosphatase expression were significantly reduced in esculin-treated diabetic mice, supporting the antidiabetic effect of esculin. Esculin also increased the uptake of glucose and induced the insulin-evoked phosphorylation of insulin receptor, Akt, and glycogen synthase kinase 3β in C2C12 myotubes, indicating a potential for improvement of insulin sensitivity. In addition, esculin lessened the elevated blood creatinine levels in diabetic mice and ameliorated diabetes-induced renal dysfunction by reducing caspase-3 activation in the kidney. Data support the beneficial effect of esculin against diabetes and oxidative stress-related inflammatory processes in the kidney.

This paper presents an overview of liveness detection particularly based on fingerprint images. Since fingerprints start to be widely employed in the mobile devices such as smartphones and tablets for payment as well as security, high-performed algorithms have been explored in literature. At the same time, such mobile systems are highly required to detect spoofing attacks by fabricated fingerprints with malicious intends. To this end, many researchers have developed liveness detection methods based on local image descriptors. In this paper, we briefly review those descriptors used for fingerprint liveness detection and compare the detection performance based on representative datasets, i.e., ATVS, LivDet2009, and LivDet2011. From experimental results, we can conclude that textural patterns of a local region in the fingerprint image have a good ability to discriminate fake fingerprints from live ones.

Obesity increases the risks of diabetes, hypertension, and cardiovascular diseases, ultimately contributing to mortality. Korean Society for the Study of Obesity (KSSO) was established to improve the management of obesity through research and education; to that end, the Committee of Clinical Practice Guidelines of KSSO reviews systemic evidence using expert panels to develop clinical guidelines. The clinical practice guidelines for obesity were revised in 2018 using National Health Insurance Service Health checkup data from 2006 to 2015. Following these guidelines, we added a category, class III obesity, which includes individuals with body mass index (BMI) ≥35 kg/m