Antimicrobial Activity of the Quinoline Derivative HT61 against Staphylococcus aureus Biofilms.

Staphylococcus aureus biofilms are a significant problem in health care settings, partly due to the presence of a nondividing, antibiotic-tolerant subpopulation. Here we evaluated treatment of S. aureus UAMS-1 biofilms with HT61, a quinoline derivative shown to be effective against nondividing Staphylococcus spp. HT61 was effective at reducing biofilm viability and was associated with increased expression of cell wall stress and division proteins, confirming its potential as a treatment for S. aureus biofilm infections

A Kind of Affine Weighted Moment Invariants

A new kind of geometric invariants is proposed in this paper, which is called affine weighted moment invariant (AWMI). By combination of local affine differential invariants and a framework of global integral, they can more effectively extract features of images and help to increase the number of low-order invariants and to decrease the calculating cost. The experimental results show that AWMIs have good stability and distinguishability and achieve better results in image retrieval than traditional moment invariants. An extension to 3D is straightforward

A Data-Driven Approach to Measure Web Site Navigability

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A Modified Sequence-to-point HVAC Load Disaggregation Algorithm

This paper presents a modified sequence-to-point (S2P) algorithm for disaggregating the heat, ventilation, and air conditioning (HVAC) load from the total building electricity consumption. The original S2P model is convolutional neural network (CNN) based, which uses load profiles as inputs. We propose three modifications. First, the input convolution layer is changed from 1D to 2D so that normalized temperature profiles are also used as inputs to the S2P model. Second, a drop-out layer is added to improve adaptability and generalizability so that the model trained in one area can be transferred to other geographical areas without labelled HVAC data. Third, a fine-tuning process is proposed for areas with a small amount of labelled HVAC data so that the pre-trained S2P model can be fine-tuned to achieve higher disaggregation accuracy (i.e., better transferability) in other areas. The model is first trained and tested using smart meter and sub-metered HVAC data collected in Austin, Texas. Then, the trained model is tested on two other areas: Boulder, Colorado and San Diego, California. Simulation results show that the proposed modified S2P algorithm outperforms the original S2P model and the support-vector machine based approach in accuracy, adaptability, and transferability

Detection of Lyman-alpha Emitting Galaxies at Redshift z=4.55

Studies of the formation and early history of galaxies have been hampered by the difficulties inherent in detecting faint galaxy populations at high redshift. As a consequence, observations at the highest redshifts (3.5 < z < 5) have been restricted to objects that are intrinsically bright. These include quasars, radio galaxies, and some Ly alpha-emitting objects that are very close to (within ~10 kpc) -- and appear to be physically associated with -- quasars. But the extremely energetic processes which make these objects easy to detect also make them unrepresentative of normal (field) galaxies. Here we report the discovery using Keck spectroscopic observations of two Ly alpha-emitting galaxies at redshift z = 4.55, which are sufficiently far from the nearest quasar (~700 kpc) that radiation from the quasar is unlikely to provide the excitation source of the Ly alpha emission. Instead, these galaxies appear to be undergoing their first burst of star formation, at a time when the Universe was less than one billion years old.Comment: 8 pages, 1 landscape table, and 3 PostScript figures. Uses aaspp4.sty, flushrt.sty, aj_pt4.sty, overcite.sty (style macros available from xxx.lanl.gov) Figure 1 is bitmapped to 100 dpi. The original PostScript version of Fig. 1 is available via anonymous ftp to ftp://hubble.ifa.hawaii.edu/pub/preprints To appear in Natur

An ICA-Based HVAC Load Disaggregation Method Using Smart Meter Data

This paper presents an independent component analysis (ICA) based unsupervised-learning method for heat, ventilation, and air-conditioning (HVAC) load disaggregation using low-resolution (e.g., 15 minutes) smart meter data. We first demonstrate that electricity consumption profiles on mild-temperature days can be used to estimate the non-HVAC base load on hot days. A residual load profile can then be calculated by subtracting the mild-day load profile from the hot-day load profile. The residual load profiles are processed using ICA for HVAC load extraction. An optimization-based algorithm is proposed for post-adjustment of the ICA results, considering two bounding factors for enhancing the robustness of the ICA algorithm. First, we use the hourly HVAC energy bounds computed based on the relationship between HVAC load and temperature to remove unrealistic HVAC load spikes. Second, we exploit the dependency between the daily nocturnal and diurnal loads extracted from historical meter data to smooth the base load profile. Pecan Street data with sub-metered HVAC data were used to test and validate the proposed methods.Simulation results demonstrated that the proposed method is computationally efficient and robust across multiple customers

Increased Epithelial Expression of CTGF and S100A7 with Elevated Subepithelial Expression of IL-1 beta in Trachomatous Trichiasis

Purpose: To characterize the histological appearance and expression of pro-inflammatory mediators, growth factors, matrix metalloproteinases and biomarkers of epithelial-mesenchymal transition (EMT) in healthy control and trachomatous trichiasis (TT) conjunctival tissue. Methods: Conjunctival biopsies were taken from 20 individuals with TT and from 16 individuals with healthy conjunctiva, which served as controls. Study participants were of varying ethnicity and were living in a trachoma-endemic region of northern Tanzania. Formalin-fixed paraffin-embedded tissue sections were stained using hematoxylin and eosin or by immunohistochemistry using antibodies against: IL-1β, IL-6, IL-17A, IL-22, CXCL5, S100A7, cleaved caspase 1 (CC1), PDGF, CTGF, TGFβ2, MMP7, MMP9, E-cadherin, vimentin, and αSMA. Results: Tissue from TT cases had a greater inflammatory cell infiltrate relative to controls and greater disruption of collagen structure. CTGF and S100A7 were more highly expressed in the epithelium and IL-1β was more highly expressed in the substantia propria of TT cases relative to controls. Latent TGFβ2 was slightly more abundant in the substantia propria of control tissue. No differences were detected between TT cases and controls in the degree of epithelial atrophy, the number of myofibroblasts or expression of EMT biomarkers. Conclusions: These data indicate that the innate immune system is active in the immunopathology of trachoma, even in the absence of clinical inflammation. CTGF might provide a direct link between inflammation and fibrosis and could be a suitable target for therapeutic treatment to halt the progression of trachomatous scarring

MultiLoad-GAN: A GAN-Based Synthetic Load Group Generation Method Considering Spatial-Temporal Correlations

This paper presents a deep-learning framework, Multi-load Generative Adversarial Network (MultiLoad-GAN), for generating a group of load profiles in one shot. The main contribution of MultiLoad-GAN is the capture of spatial-temporal correlations among a group of loads to enable the generation of realistic synthetic load profiles in large quantity for meeting the emerging need in distribution system planning. The novelty and uniqueness of the MultiLoad-GAN framework are three-fold. First, it generates a group of load profiles bearing realistic spatial-temporal correlations in one shot. Second, two complementary metrics for evaluating realisticness of generated load profiles are developed: statistics metrics based on domain knowledge and a deep-learning classifier for comparing high-level features. Third, to tackle data scarcity, a novel iterative data augmentation mechanism is developed to generate training samples for enhancing the training of both the classifier and the MultiLoad-GAN model. Simulation results show that MultiLoad-GAN outperforms state-of-the-art approaches in realisticness, computational efficiency, and robustness. With little finetuning, the MultiLoad-GAN approach can be readily extended to generate a group of load or PV profiles for a feeder, a substation, or a service area.Comment: Submitted to IEEE Transactions on Smart Gri