Short Term Probabilistic Load Forecasting at Local Level in Distribution Networks

Along with the growing inclusion of smart technologies into the electrical power grids, benefits, which can be originated form advanced metering infrastructure (AMI), have grabbed noticeable attention from distribution utilities. Since the number of meters are severely ample in practical systems, the utilities now is able to create virtual meter data by aggregating loads for distribution substations, feeders, transformers, or regions with the help of geographic information system. Such an important change brought by smart meter rollout is considered as the main factor which motivates this thesis to delve more into the load pattern modeling and forecasting at local level and find approaches which can yield to the enhanced applications in distribution networks. However, low aggregation level leads to high volatile load characteristic. In this regard, this thesis proposes a comprehensive methodology for uncertainty modeling and short-term probabilistic load forecasting (STPLF) in distribution networks. Existing methods related to uncertainty modeling and forecasting are rarely applied to local level loads and they suffer from over- or under-fitting of data when there is a misfit between the complexity of the model and the amount of data available. These models are limited to specific situations due to the great diversity of loads in distribution networks and need to be tuned every time when the load aggregation level changes. They also need a relatively large data set to support the recovery of the predictive densities. Our proposed method addresses this issue and is based on Bayesian nonparametric model which has unbounded complexity and allow the complexity to automatically grow and be inferred from the observed data. The uncertainty underlying load patterns can be endowed with any type of prior distribution and is given in a nonparametric form, i.e. a mixture model with countably infinite number of mixtures, inferred from the posterior using the Gibbs Sampling, which is a Markov Chain Monte Carlo (MCMC) technique. All effective samples from the sampling procedure along with the exogenous variables are fed to an ensemble learning machine. The final result of the probabilistic load forecasting (PLF) is averaged on the outputs of all learning models, thus reducing the model variance and enhancing the model consistency. The proposed method is tested on both a public data set and a local data set from the Saskatoon Light &Power AMI Meter Replacement Program which offers electricity consumption at a granularity of 30 minutes of more than 65,000 electricity customers including industrial, commercial and residential sectors in the city of Saskatoon, Canada

A Novel Embedded Feature Selection Framework for Probabilistic Load Forecasting With Sparse Data via Bayesian Inference

With the modernization of power industry over recent decades, diverse smart technologies have been introduced to the power systems. Such transition has brought in a significant level of variability and uncertainty to the networks, resulting in less predictable electricity demand. In this regard, load forecasting stands in the breach and is even more challenging. Urgent needs have been raised from different sections, especially for probabilistic analysis for industrial applications. Hence, attentions have been shifted from point load forecasting to probabilistic load forecasting (PLF) in recent years. This research proposes a novel embedded feature selection method for PLF to deal with sparse features and thus to improve PLF performance. Firstly, the proposed method employs quantile regression to connect the predictor variables and each quantile of the distribution of the load. Thereafter, an embedded feature selection structure is incorporated to identify and select subsets of input features by introducing an inclusion indicator variable for each feature. Then, Bayesian inference is applied to the model with a sparseness favoring prior endowed over the inclusion indicator variables. A Markov Chain Monte Carlo (MCMC) approach is adopted to sample the parameters from the posterior. Finally, the samples are used to approximate the posterior distribution, which is achieved by using discrete formulas applied to these samples to approximate the integrals of interest. The proposed approach allows each quantile of the distribution of the dependent load to be affected by different sets of features, and also allows all features to take a chance to show their impact on the load. Consequently, this methodology leads to the improved estimation of more complex predictive densities. The proposed framework has been successfully applied to a linear model, the quantile linear regression, and been extended to improve the performance of a nonlinear model. Three case studies have been designed to validate the effectiveness of the proposed method. The first case study performed on an open dataset validates that the proposed feature selection technique can improve the performance of PLF based on quantile linear regression and outperforms the selected comparable benchmarks. This case study does not consider any recency effect. The second case study further examines the impact of recency effect using another open dataset which contains historical load and weather records of 10 different regions. The third case study explores the potential of extending the application of the proposed framework for nonlinear models. In this case study, the proposed method is used as a wrapper approach and applied to a nonlinear model. The simulation results show that the proposed method has the best overall performance among all the tested methods with and without considering recency effect, and it could slightly improve the performance of other models when applied as a wrapper approach

Investigation of the clinical features and therapeutic methods for the management of inflammatory lacrimal punctum diseases

Purpose: To establish if there are different classes of inflammatory lacrimal punctum diseases (ILPDs) and to examine the various strategies by which they can be managed therapeutically.Methods: Two hundred and fifty nine (259) patients with inflammatory punctum lacrimal disease were identified and used as subjects for this study. Each patient was carefully examined for evidence of morphology of lacrimal punctum which was confirmed mainly by lacrimal duct flushing and probing. Appropriate therapeutic managements were adopted for patients with other inflammatory conditions besides ILPD. The clinical effects of the various therapeutic strategies were documented. .Results: Eighty-seven (87) patients out of the 259 (32.53 %) suffered from acute or chronic conjunctivitis while 66 patients (5.61 %) suffered from inflammatory lacrimal passage diseases. Patients with both conjunctivitis and lacrimal passage inflammation, patients with dry-eye symptoms, patients with just one of the conditions, and patients with mere evidence of superior punctalacrimalis represented 13.15, 14.19, 14.53, and 33.91 %, respectively. Mere evidence of inferior punctalacrimalis, and presence of acute inflammation were seen in 48.76 and 13.49 % of the 259 patients, respectively, while those with chronic inflammation lasting for 2.97 ± 0.13 years, comprised 86.51 %. Antibiotic eye drops were used for acute inflammation, while chronic inflammation was treated with antibiotic eye drops, lacrimal punctum expansion, pus elimination, and punctum-sparing canaliculotomy. Both therapeutic methods produced satisfactory curative effects.Conclusion: The results show that satisfactory therapy of lacrimal punctum inflammation can be achieved if the right therapeutic agents and procedures are adopted based on clinical characteristics of the ILPD manifesting in the patient.Keywords: Lacrimal punctum, Inflammatory disease, Conjunctivitis, Dry-eye symptom

Expression of GCRG213p, LINE-1 endonuclease variant, significantly different in gastric complete and incomplete intestinal metaplasia.

BACKGROUND: Intestinal metaplasia (IM) of the gastric mucosa is classified as complete (Type I) and incomplete IM (Type II and III) subtypes, which showed significantly different risk for developing to gastric adenocarcinoma (GAC). GCRG213, a variant of L1-endonuclease (L1-EN), first identified in our lab, was upregulated in GAC tissue. However, the relationship between GCRG213 and IM subtypes is not clear. Our study explored the association of GCRG213 protein (GCRG213p) with IM subtypes. METHODS: Gastric cancer and/or para-tumor tissue samples were collected from 123 patients who underwent gastrectomy for intestinal type gastric adenocarcinoma. The subtypes of IM were characterized with Alcian blue-periodic acid-Schiff and High Iron Diamine-Alcian blue staining methods. Immunohistochemistry of GCRG213p was performed, and its expression in gastric adenocarcinoma and para-tumor tissue including dysplasia, IM, and normal mucosa were analyzed. RESULTS: GCRG213p was expressed in 48.94% IM, 57.14% dysplasia and 55.32% GAC, respectively. GCRG213p expression was higher in well and moderately differentiated adenocarcinoma (P = 0.037). In IM glands, GCRG213p expressed mainly in the cytoplasm of absorptive enterocytes with defined brush borders, but not in goblet cells. The expression of GCRG213p in type I IM (90.00%) was significantly higher than that in type II (36.36%) and type III (25.00%) (P \u3c 0.001). In normal gastric mucosa, GCRG213p was exclusively positive in the cytoplasm of gastric chief cells. CONCLUSIONS: The expression of GCRG213p in complete IM was significantly higher than in incomplete IM, which implies that GCRG213p may play a role on the developing of IM to adenocarcinoma. GCRG213p was exclusively expressed in chief cells, suggesting that it might be involved in cell differentiation from the chief cells to IM

A β-glucosidase hyper-production Trichoderma reesei mutant reveals a potential role of cel3D in cellulase production

Abstract Background The conversion of cellulose by cellulase to fermentable sugars for biomass-based products such as cellulosic biofuels, biobased fine chemicals and medicines is an environment-friendly and sustainable process, making wastes profitable and bringing economic benefits. Trichoderma reesei is the well-known major workhorse for cellulase production in industry, but the low β-glucosidase activity in T. reesei cellulase leads to inefficiency in biomass degradation and limits its industrial application. Thus, there are ongoing interests in research to develop methods to overcome this insufficiency. Moreover, although β-glucosidases have been demonstrated to influence cellulase production and participate in the regulation of cellulase production, the underlying mechanism remains unclear. Results The T. reesei recombinant strain TRB1 was constructed from T. reesei RUT-C30 by the T-DNA-based mutagenesis. Compared to RUT-C30, TRB1 displays a significant enhancement of extracellular β-glucosidase (BGL1) activity with 17-fold increase, a moderate increase of both the endoglucanase (EG) activity and the exoglucanase (CBH) activity, a minor improvement of the total filter paper activity, and a faster cellulase induction. This superiority of TRB1 over RUT-C30 is independent on carbon sources and improves the saccharification ability of TRB1 cellulase on pretreated corn stover. Furthermore, TRB1 shows better resistance to carbon catabolite repression than RUT-C30. Secretome characterization of TRB1 shows that the amount of CBH, EG and BGL in the supernatant of T. reesei TRB1 was indeed increased along with the enhanced activities of these three enzymes. Surprisingly, qRT-PCR and gene cloning showed that in TRB1 β-glucosidase cel3D was mutated through the random insertion by AMT and was not expressed. Conclusions The T. reesei recombinant strain TRB1 constructed in this study is more desirable for industrial application than the parental strain RUT-C30, showing extracellular β-glucosidase hyper production, high cellulase production within a shorter time and a better resistance to carbon catabolite repression. Disruption of β-glucosidase cel3D in TRB1 was identified, which might contribute to the superiority of TRB1 over RUT-C30 and might play a role in the cellulase production. These results laid a foundation for future investigations to further improve cellulase enzymatic efficiency and reduce cost for T. reesei cellulase production.http://deepblue.lib.umich.edu/bitstream/2027.42/134636/1/12934_2016_Article_550.pd

Application of a biofilm-enhanced A2O system in the treatment of wastewater from mariculture

Development of environment-friendly and efficient aquaculture effluent treatment system is crucial for sustainable intensification of aquaculture, in the face of the rapidly increasing environmental pressure in the mariculture industry. In this study, mariculture wastewater was treated by the anoxic-anaerobic-oxic biochemical treatment system (A2O system) with traditional activated sludge replaced by nitrifying bacteria, denitrification bacteria and phosphorus accumulating bacteria absorbed on PBS carrier biofilms suitable for saline/brackish water. The results showed that biofilm-enhanced A2O system can effectively remove pollutants from aquaculture wastewater. The removal efficiencies of CODMn, NH4+-N, TN and TP in A2O system were approximately 86.3%-90.8%, 97.7%-99.5%, 94.6%-95.2% and 97.0%-98.1%. The results further showed that CODMn, NH4+-N, and TN were mainly removed in anaerobic tank and anoxic tank, while TP was mainly removed in the anoxic tank and oxic tank. The biofilm-enhanced A2O system by adding nitrifying bacteria and phosphorus accumulating bacteria biofilms using PBS as carriers instead of conventional activated sludge could be applied to the treatment of circulating aquaculture wastewater. This study provides a feasible scheme for enhancing the efficiency of A2O system in the treatment of aquaculture tail water, and provides a reference for the immobilization of microorganisms

Ice thickness monitoring for cryo-EM grids by interferometry imaging

While recent technological developments contributed to breakthrough advances in single particle cryo-electron microscopy (cryo-EM), sample preparation remains a significant bottleneck for the structure determination of macromolecular complexes. A critical time factor is sample optimization that requires the use of an electron microscope to screen grids prepared under different conditions to achieve the ideal vitreous ice thickness containing the particles. Evaluating sample quality requires access to cryo-electron microscopes and a strong expertise in EM. To facilitate and accelerate the selection procedure of probes suitable for high-resolution cryo-EM, we devised a method to assess the vitreous ice layer thickness of sample coated grids. The experimental setup comprises an optical interferometric microscope equipped with a cryogenic stage and image analysis software based on artificial neural networks (ANN) for an unbiased sample selection. We present and validate this approach for different protein complexes and grid types, and demonstrate its performance for the assessment of ice quality. This technique is moderate in cost and can be easily performed on a laboratory bench. We expect that its throughput and its versatility will contribute to facilitate the sample optimization process for structural biologists

Half-Sphere Shell Supported Pt Catalyst for Electrochemical Methanol Oxidation

Bi-functional effect, elevated mass transport and increased durability have been combined within one catalyst for electrochemical methanol oxidation reaction. It has niobium (Nb) doped titanium dioxides (TiO2) nanosized half-sphere shell (HSS) as the substrate material deposited with small amount of Pt nanoparticles. These specially designed HSS nanostructure has significantly increased surface areas which are suitable for Pt nanoparticles to be deposited onto them to form the catalyst denoted as Pt/Nb-TiO2 HSS. It exhibits a remarkably high methanol oxidation activity of 0.21 V vs. RHE which is 0.05 V lower than HiSPEC10000 PtRu/C catalyst, due to the substrate's strong metal support interactions effect, bi-functional effect and the special structure. These HSS nanostructures have also increased the methanol diffusion and mass transport within the anode to give a maximum power output of 0.0931 W of cathode polarization in miniature direct methanol fuel cell (DMFC). It also acts as protection shells, which minimises the dissolution of Pt metal nanoparticles to prevent its diffusion through the membrane

Prenatal Polycyclic Aromatic Hydrocarbon (PAH) Exposure and Child Behavior at Age 6–7 Years

Background: Airborne polycyclic aromatic hydrocarbons (PAH) are widespread urban air pollutants from fossil fuel burning and other combustion sources. We previously reported that a broad spectrum of combustion-related DNA adducts in cord blood was associated with attention problems at 6–7 years of age in the Columbia Center for Children’s Environmental Health (CCCEH) longitudinal cohort study

Deep functional analysis of synII, a 770-kilobase synthetic yeast chromosome

INTRODUCTION Although much effort has been devoted to studying yeast in the past few decades, our understanding of this model organism is still limited. Rapidly developing DNA synthesis techniques have made a “build-to-understand” approach feasible to reengineer on the genome scale. Here, we report on the completion of a 770-kilobase synthetic yeast chromosome II (synII). SynII was characterized using extensive Trans-Omics tests. Despite considerable sequence alterations, synII is virtually indistinguishable from wild type. However, an up-regulation of translational machinery was observed and can be reversed by restoring the transfer RNA (tRNA) gene copy number. RATIONALE Following the “design-build-test-debug” working loop, synII was successfully designed and constructed in vivo. Extensive Trans-Omics tests were conducted, including phenomics, transcriptomics, proteomics, metabolomics, chromosome segregation, and replication analyses. By both complementation assays and SCRaMbLE (synthetic chromosome rearrangement and modification by loxP -mediated evolution), we targeted and debugged the origin of a growth defect at 37°C in glycerol medium. RESULTS To efficiently construct megabase-long chromosomes, we developed an I- Sce I–mediated strategy, which enables parallel integration of synthetic chromosome arms and reduced the overall integration time by 50% for synII. An I- Sce I site is introduced for generating a double-strand break to promote targeted homologous recombination during mitotic growth. Despite hundreds of modifications introduced, there are still regions sharing substantial sequence similarity that might lead to undesirable meiotic recombinations when intercrossing the two semisynthetic chromosome arm strains. Induction of the I- Sce I–mediated double-strand break is otherwise lethal and thus introduced a strong selective pressure for targeted homologous recombination. Since our strategy is designed to generate a markerless synII and leave the URA3 marker on the wild-type chromosome, we observed a tenfold increase in URA3 -deficient colonies upon I- Sce I induction, meaning that our strategy can greatly bias the crossover events toward the designated regions. By incorporating comprehensive phenotyping approaches at multiple levels, we demonstrated that synII was capable of powering the growth of yeast indistinguishably from wild-type cells (see the figure), showing highly consistent biological processes comparable to the native strain. Meanwhile, we also noticed modest but potentially significant up-regulation of the translational machinery. The main alteration underlying this change in expression is the deletion of 13 tRNA genes. A growth defect was observed in one very specific condition—high temperature (37°C) in medium with glycerol as a carbon source—where colony size was reduced significantly. We targeted and debugged this defect by two distinct approaches. The first approach involved phenotype screening of all intermediate strains followed by a complementation assay with wild-type sequences in the synthetic strain. By doing so, we identified a modification resulting from PCRTag recoding in TSC10 , which is involved in regulation of the yeast high-osmolarity glycerol (HOG) response pathway. After replacement with wild-type TSC10 , the defect was greatly mitigated. The other approach, debugging by SCRaMbLE, showed rearrangements in regions containing HOG regulation genes. Both approaches indicated that the defect is related to HOG response dysregulation. Thus, the phenotypic defect can be pinpointed and debugged through multiple alternative routes in the complex cellular interactome network. CONCLUSION We have demonstrated that synII segregates, replicates, and functions in a highly similar fashion compared with its wild-type counterpart. Furthermore, we believe that the iterative “design-build-test-debug” cycle methodology, established here, will facilitate progression of the Sc2.0 project in the face of the increasing synthetic genome complexity. SynII characterization. ( A ) Cell cycle comparison between synII and BY4741 revealed by the percentage of cells with separated CEN2-GFP dots, metaphase spindles, and anaphase spindles. ( B ) Replication profiling of synII (red) and BY4741 (black) expressed as relative copy number by deep sequencing. ( C ) RNA sequencing analysis revealed that the significant up-regulation of translational machinery in synII is induced by the deletion of tRNA genes in synII. </jats:sec