A dataset of recorded electricity outages by United States county 2014-2022.

In this Data Descriptor, we present county-level electricity outage estimates at 15-minute intervals from 2014 to 2022. By 2022 92% of customers in the 50 US States, Washington DC, and Puerto Rico are represented. These data have been produced by the Environment for Analysis of Geo-Located Energy Information (EAGLE-ITM), a geographic information system and data visualization platform created at Oak Ridge National Laboratory to map the population experiencing electricity outages every 15 minutes at the county level. Although these data do not cover every US customer, they represent the most comprehensive outage information ever compiled for the United States. The rate of coverage increases through time between 2014 and 2022. We present a quantitative Data Quality Index for these data for the years 2018-2022 to demonstrate temporal changes in customer coverage rates by FEMA region and indicators of data collection gaps or other errors.

Multi-Wavelength Photobiomodulation Ameliorates Sodium Iodate-Induced Age-Related Macular Degeneration in Rats.

Age-related macular degeneration (AMD) is a global health challenge. AMD causes visual impairment and blindness, particularly in older individuals. This multifaceted disease progresses through various stages, from asymptomatic dry to advanced wet AMD, driven by various factors including inflammation and oxidative stress. Current treatments are effective mainly for wet AMD; the therapeutic options for dry AMD are limited. Photobiomodulation (PBM) using low-energy light in the red-to-near-infrared range is a promising treatment for retinal diseases. This study investigated the effects of multi-wavelength PBM (680, 780, and 830 nm) on sodium iodate-induced oxidatively damaged retinal tissue. In an in vivo rat model of AMD induced by sodium iodate, multi-wavelength PBM effectively protected the retinal layers, reduced retinal apoptosis, and prevented rod bipolar cell depletion. Furthermore, PBM inhibited photoreceptor degeneration and reduced retinal pigment epithelium toxicity. These results suggest that multi-wavelength PBM may be a useful therapeutic strategy for AMD, mitigating oxidative stress, preserving retinal integrity, and preventing apoptosis.

The Anti-Inflammatory Effect of Multi-Wavelength Light-Emitting Diode Irradiation Attenuates Dry Eye Symptoms in a Scopolamine-Induced Mouse Model of Dry Eye.

Dry eye disease is a common condition in patients of all ages, causing discomfort and potential visual problems. Current treatments, including artificial tears and anti-inflammatory drugs, have certain limitations, encouraging research into alternative therapies. We investigated the therapeutic potential of multi-wavelength light-emitting diode (LED) irradiation of mice with dry eye. First, we showed that multi-wavelength LED irradiation was non-toxic to human corneal epithelial cells and improved cell viability. We then used a scopolamine-induced mouse model of dry eye to assess the effects of multi-wavelength LED irradiation on various clinical parameters. This treatment increased the tear volume and reduced corneal irregularity, thus improving dry eye. Histological analysis revealed that multi-wavelength LED irradiation protected against corneal epithelial damage and the associated reduction in epithelial thickness and would thus improve the corneal health of dry eye patients. Multi-wavelength LED irradiation significantly reduced the corneal levels of pro-inflammatory cytokines IL-6, IL-1ß, and TNF-α; the treatment was thus anti-inflammatory. Our results suggest that multi-wavelength LED irradiation may serve as a safe and effective treatment for dry eye, alleviating symptoms, reducing inflammation, and promoting corneal health.

An Accurate Deep Learning-Based System for Automatic Pill Identification: Model Development and Validation.

BACKGROUND: Medication errors account for a large proportion of all medical errors. In most homes, patients take a variety of medications for a long period. However, medication errors frequently occur because patients often throw away the containers of their medications. OBJECTIVE: We proposed a deep learning-based system for reducing medication errors by accurately identifying prescription pills. Given the pill images, our system located the pills in the respective pill databases in South Korea and the United States. METHODS: We organized the system into a pill recognition step and pill retrieval step, and we applied deep learning models to train not only images of the pill but also imprinted characters. In the pill recognition step, there are 3 modules that recognize the 3 features of pills and their imprints separately and correct the recognized imprint to fit the actual data. We adopted image classification and text detection models for the feature and imprint recognition modules, respectively. In the imprint correction module, we introduced a language model for the first time in the pill identification system and proposed a novel coordinate encoding technique for effective correction in the language model. We identified pills using similarity scores of pill characteristics with those in the database. RESULTS: We collected the open pill database from South Korea and the United States in May 2022. We used a total of 24,404 pill images in our experiments. The experimental results show that the predicted top-1 candidates achieve accuracy levels of 85.6% (South Korea) and 74.5% (United States) for the types of pills not trained on 2 different databases (South Korea and the United States). Furthermore, the predicted top-1 candidate accuracy of our system was 78% with consumer-granted images, which was achieved by training only 1 image per pill. The results demonstrate that our system could identify and retrieve new pills without additional model updates. Finally, we confirmed through an ablation study that the language model that we emphasized significantly improves the pill identification ability of the system. CONCLUSIONS: Our study proposes the possibility of reducing medical errors by showing that the introduction of artificial intelligence can identify numerous pills with high precision in real time. Our study suggests that the proposed system can reduce patients' misuse of medications and help medical staff focus on higher-level tasks by simplifying time-consuming lower-level tasks such as pill identification.

High resolution dataset from a net-zero home that demonstrates zero-carbon living and transportation capacity.

This dataset includes high resolution, detailed end use data from a net-zero occupied home that demonstrates zero-carbon living and transportation capacity. The house is located in Davis, California, U.S., and the dataset includes full year data from 2020 with 1 minute time resolution. The data has been monitored with more than 230 sensors installed in the house, and there are total 332 channels available. The data includes detailed end use electricity data (e.g., HVAC system, lighting, plug load including major appliances), building's interior thermal conditions (e.g., indoor air temperatures in multiple rooms and relative humidity), HVAC system operation data (e.g., soil temperatures around ground bores and supply water temperatures), on-site power generation system data (e.g., PV power supply and PV surface temperatures) and etc. The original dataset from the house has been curated, and the data has been carefully reviewed for quality check. The data quality check revealed there are 156 minutes of data were missing in the month of April, and around 1,404 minutes of data was missing in August. The data gap was filled with linear interpolation in case the gap is less than continuous 6 hours. Otherwise, the data is filled with -9999. The data curation has been processed using the Tsdat framework (https://github.com/tsdat/tsdat). In addition, a semantic description for the dataset was generated by leveraging the Brick (https://brickschema.org/). The final curated and processed data as well as raw data are currently available through https://bbd.labworks.org/ds/bbd/hshus.

COVID-19 pandemic ramifications on residential Smart homes energy use load profiles.

The COVID-19 pandemic has significantly affected people's behavioral patterns and schedules because of stay-at-home orders and a reduction of social interactions. Therefore, the shape of electrical loads associated with residential buildings has also changed. In this paper, we quantify the changes and perform a detailed analysis on how the load shapes have changed, and we make potential recommendations for utilities to handle peak load and demand response. Our analysis incorporates data from before and after the onset of the COVID-19 pandemic, from an Alabama Power Smart Neighborhood with energy-efficient/smart devices, using around 40 advanced metering infrastructure data points. This paper highlights the energy usage pattern changes between weekdays and weekends pre- and post-COVID-19 pandemic times. The weekend usage patterns look similar pre- and post-COVID-19 pandemic, but weekday patterns show significant changes. We also compare energy use of the Smart Neighborhood with a traditional neighborhood to better understand how energy-efficient/smart devices can provide energy savings, especially because of increased work-from-home situations. HVAC and water heating remain the largest consumers of electricity in residential homes, and our findings indicate an even further increase in energy use by these systems.

Analysis of the Position Recognition of the Bucket Tip According to the Motion Measurement Method of Excavator Boom, Stick and Bucket.

On modern construction sites, guidance and automation systems are increasingly applied to excavators. Recently, studies have been actively conducted to compare the estimation results of the bucket tip with the motion measurement method of the boom, stick, and bucket and the sensor selection. This study selected the method of measuring the cylinder length of boom, stick, and bucket, and the method of directly measuring the boom, arm, and bucket, which are commonly used in guidance and automation systems. A low-cost sensor that can be attached and detached to the excavator in modular form was selected to apply the above methods to commercial excavator. After the sensor selection, hardware and excavator simulation models for sensor measurements were constructed. Finally, the trajectory of the bucket tip was compared and analyzed through graphs and simulation results when the boom, stick, and bucket were independently rotated one by one, or together. The results gives a guideline on what kinds of sensors would be better in machine guidance or controlling an excavator according to given external environments.

Disparity-selective stereo matching using correlation confidence measure.

Recently, the cost-volume filtering (CVF) methods for local stereo matching have provided fast and accurate results compared to those of the other method. However, CVF still causes incorrect results in the occlusion and texture-free regions. In particular, cost aggregation by pixel units involves complex computation because of its dependence on the image resolution and search range. This paper presents a robust stereo matching method for occluded regions. First, we generate cost volumes using the CENSUS transform and the scale-invariant feature transform (SIFT). Then, label-based cost volumes are aggregated using adaptive support weight and the simple linear iterative clustering (SLIC) scheme from two generated cost volumes. In order to obtain optimal disparity by two label-based cost volumes, we select the disparity corresponding to high confidence similarity of CENSUS or SIFT with minimum cost point. Experimental results show that our method estimates the optimal disparity in occlusion information, which exists only in the scene of one of the stereo pairs.

Effective Visual Tracking Using Multi-Block and Scale Space Based on Kernelized Correlation Filters.

Accurate scale estimation and occlusion handling is a challenging problem in visual tracking. Recently, correlation filter-based trackers have shown impressive results in terms of accuracy, robustness, and speed. However, the model is not robust to scale variation and occlusion. In this paper, we address the problems associated with scale variation and occlusion by employing a scale space filter and multi-block scheme based on a kernelized correlation filter (KCF) tracker. Furthermore, we develop a more robust algorithm using an appearance update model that approximates the change of state of occlusion and deformation. In particular, an adaptive update scheme is presented to make each process robust. The experimental results demonstrate that the proposed method outperformed 29 state-of-the-art trackers on 100 challenging sequences. Specifically, the results obtained with the proposed scheme were improved by 8% and 18% compared to those of the KCF tracker for 49 occlusion and 64 scale variation sequences, respectively. Therefore, the proposed tracker can be a robust and useful tool for object tracking when occlusion and scale variation are involved.

Petascale supercomputing to accelerate the design of high-temperature alloys.

Recent progress in high-performance computing and data informatics has opened up numerous opportunities to aid the design of advanced materials. Herein, we demonstrate a computational workflow that includes rapid population of high-fidelity materials datasets via petascale computing and subsequent analyses with modern data science techniques. We use a first-principles approach based on density functional theory to derive the segregation energies of 34 microalloying elements at the coherent and semi-coherent interfaces between the aluminium matrix and the θ'-Al2Cu precipitate, which requires several hundred supercell calculations. We also perform extensive correlation analyses to identify materials descriptors that affect the segregation behaviour of solutes at the interfaces. Finally, we show an example of leveraging machine learning techniques to predict segregation energies without performing computationally expensive physics-based simulations. The approach demonstrated in the present work can be applied to any high-temperature alloy system for which key materials data can be obtained using high-performance computing.

Optical range-finding system using a single-image sensor with liquid crystal display aperture.

This Letter presents an optical range-finding camera using a liquid crystal display (LCD) to generate multiple, off-axis color-filtered apertures in a flexible manner. The disparity between the different color channels is measured from a pair of stereo images acquired by two off-axis apertures, and the distance of a scene point from the camera is then estimated from the pre-specified relationship between the color disparity and distance.

Low-complexity topological derivative-based segmentation.

Topological derivative has been employed for image segmentation and restoration. The topological derivative-based segmentation uses two sparse matrices, and the computational complexity of the segmentation grows up dramatically as the image size increases due to the size of the sparse matrix. Therefore, to provide a fast and accurate segmentation with low complexity, an effective scheme is proposed with keeping the same segmentation performance. To further reduce the computational complexity, the parallel processing structure for the proposed scheme is designed and implemented on graphics processing unit (GPU). In particular, to reduce the computational cost of generating and multiplying sparse matrices that are squared symmetric, the 2D filters consisting of the coefficients at nonborder regions of sparse matrices are defined, and the multiplication is converted into a convolution filtering. In addition, to design a parallel processing for the segmentation with the proposed scheme on a GPU, an image is divided into several blocks and they are processed in parallel. Experimental results show that the proposed scheme for topological derivative-based segmentation reduces the computational complexity ~ 908 times, and the complexity of the proposed scheme is reduced ~ 17 times more from the parallel structure. In particular, the higher efficiency can be obtained from large sized images because the complexity of the proposed scheme does not depend on the image size. Moreover, the proposed scheme can provide almost identical segmentation result with the original sparse matrix-based approach. Therefore, we believe that the proposed scheme can be a useful tool for efficient topological derivative-based segmentation.

Numerical twin image suppression by nonlinear segmentation mask in digital holography.

The in-line holography has obvious advantages especially in wider spatial bandwidth over the off-axis holography. However, a direct current(DC)-noise and an unwanted twin image should be separated or eliminated in the in-line holography for a high quality reconstruction. An approach for suppressing the twin image is proposed by separating the real and twin image regions in the digital holography. Specifically, the initial region of real and twin images is obtained by a blind separation matrix, and the segmentation mask to suppress the twin image is calculated by nonlinear quantization from the segmented image. For the performance evaluation, the proposed method is compared with the existing approaches including the overlapping block variance and manual-based schemes. Experimental results showed that the proposed method has a better performance at the overlapped region of the real and twin images. Additionally, the proposed method causes less loss of real image than the overlapping block variance-based scheme. Therefore, we believe that the proposed scheme can be a useful tool for high quality reconstruction in the in-line holography.

A global gene expression analysis of the peripheral blood mononuclear cells reveals the gene expression signature in psoriasis.

BACKGROUND: Psoriasis is a chronic inflammatory skin disease that affects approximately 1~3% of the general population. OBJECTIVE: We performed cDNA microarray analysis with using the dendrimer labelling method to investigate the gene expression profile in the peripheral blood mononuclear cells (PBMCs) of psoriatic patients. METHODS: The peripheral blood mononuclear cells of 5 patients with psoriasis and 8 control subjects were used in the gene expression analyses of psoriasis. RESULTS: We identified 212 differentially expressed genes that showed at least a two-fold induction and/or reduction in psoriatic patients. Among those, 63 genes, including CD44, CD56 and IL7R, were induced, while 139 genes, including the sphingosine kinase 1 and p16-INK genes, were reduced in the psoriatic patients. CONCLUSION: We can speculate that these genes may have a role for the pathogenesis of psoriasis via their affecting different cellular functions. Our results suggest a possible mechanism by which activated immune cells migrate from the blood to the skin in psoriatic patients, and we provide novel putative targets for developing drugs to treat psoriasis.

Expression of neutrophil gelatinase-associated lipocalin in calcium-induced keratinocyte differentiation.

In a previous search for the differentially expressed genes in keratinocyte differentiation, we identified neutrophil gelatinase-associated lipocalin (NGAL) as a calcium-induced gene. In this study, we further verified the expression of NGAL in cultured keratinocytes as well as in several skin diseases. Reverse transcription-polymerase chain reaction (RT-PCR), Western blot, and ELISA clearly showed that NGAL expression was markedly increased in calcium-induced keratinocyte differentiation in vitro. However, in our previous report, NGAL expression was not detected in normal skin tissue except for hair follicle by in situ hybridization and immunohistochemistry, indicating the difference of cell status between in vitro and in vitro conditions. Interestingly, NGAL expression was highly increased in psoriasis-like inflammatory disorders (lichen planus and pityriasis rubura pilaris) and skin cancers (keratoacanthoma and squamous cell carcinoma), implying that NGAL may be related with the epidermal hyperplasia. Collectively, these results reveal the potential importance of NGAL in the maintenance of skin homeostasis.

IFN-gamma regulates the expression of B7-H1 in dermal fibroblast cells.

BACKGROUND: Programmed cell death ligand 1 (B7-H1) was recently cloned in antigen presenting cells (APCs) and represents a third member of the B7 family. Thus, B7-H1 may be a novel target for clinical intervention in human inflammatory disease. OBJECTIVE: The aim of this study is to investigate the signal transduction mechanism and transcriptional regulation of B7-H1 expression in human dermal fibroblasts. METHODS: We performed reverse transcription PCR (RT-PCR) for the detection of mRNA expression, luciferase reporter assays with B7-H1 promoter constructs, and Western blot analysis. RESULTS: From RT-PCR analysis, IFN-gamma can induce the expression of B7-H1 mRNA in dermal fibroblast. This expression is similar to the results of luciferase reporter assay with B7-H1 promoter. Western blot analysis and EMSA revealed that NF-kappaB transcription factors mediate the induction of B7-H1 expression via the transient phosphorylation of ERK1/2 and PI3K when cells are stimulated by IFN-gamma. Also, Specific destruction of the NF-kappaB binding site abolished the induction of the promoter activity by IFN-gamma. CONCLUSION: Our data not only provides the first evidence to demonstrate that dermal fibroblast express the B7-H1 mRNA in the process of skin inflammation, but also suggests the involvement of NF-kappaB and MAPK and PI3K, that may play some important roles in inflammation process in human skin diseases.

Induction of connective tissue growth factor expression by sphingosylphosphorylcholine in cultured human skin fibroblasts.

Sphingosylphosphorylcholine (SPC) is a bioactive sphingolipid metabolite that can enhance wound healing. In an effort to find downstream effectors of SPC, we performed microarray analysis and found that the expression of the gene for connective tissue growth factor (CTGF) was significantly affected in human skin fibroblasts cultured in vitro. Northern blot analysis showed that SPC markedly induced CTGF mRNA expression in a dose- and time-dependent manner. Consistent with this result, Western blot analysis also showed that SPC significantly induced the CTGF production. Pretreatment with cycloheximide did not prevent the CTGF induction by SPC, indicating that SPC stimulates CTGF mRNA expression without the increased synthesis of a regulatory protein. Inhibition by pretreatment with Y27632, but not by PD98059 (a mitogen-activated protein kinase 1/2 inhibitor) and LY294002 (a phosphatidylinositol 3-kinase inhibitor), indicated that rho-kinase pathway was involved in SPC-induced CTGF expression. Together, these results reveal the potential importance of CTGF induction as a downstream event in SPC-induced cellular responses.

Suppression of the onset and progression of collagen-induced arthritis by chebulagic acid screened from a natural product library.

OBJECTIVE: Chebulagic acid (CHE) from the immature seeds of Terminalia chebula was identified from a natural product library as a potent suppressor of T cell activity. This study examined the effectiveness of CHE against the onset and progression of collagen-induced arthritis (CIA) in mice. METHODS: Arthritis was induced in DBA/1J mice by subcutaneous immunization with bovine type II collagen on days 0 and 21. CHE was administered intraperitoneally for 3 weeks, either as prophylaxis (10 or 20 mg/kg) before disease onset or as therapy (20 mg/kg) after disease onset. Clinical scores, serum antibody levels, and cytokines were measured, and flow cytometric analysis and real-time reverse transcription-polymerase chain reaction were performed to evaluate the knee joints of mice with CIA. RESULTS: In both the prophylactic and therapeutic CHE dosing models, all clinical scores, serum levels of total and anticollagen IgG, and levels of interleukin-10 (IL-10) and IL-6 were reduced, while serum levels of transforming growth factor beta (TGFbeta) were markedly elevated. The number of granulocytes was reduced, but the proportion of CD4+,CD25+ T cells was greater in the knee joints of CHE-treated CIA mice. Expression of Foxp3 and TGFbeta messenger RNA was also augmented significantly in the knee joints of CHE-treated CIA mice in the therapeutic dosing model. CONCLUSION: CHE significantly suppressed the onset and progression of CIA in mice. Immune suppression via the induction of TGFbeta and CD4+,CD25+ T cells may represent a new strategy in the development of therapies for managing rheumatoid arthritis and other inflammatory diseases.