Extremum Seeking Control of Hybrid Ground Source Heat Pump System

The ground source heat pump (GSHP) technology is a renewable alternative for space conditioning by rejecting/absorbing heat to/from the ground, which has demonstrated higher energy efficiency for residential and commercial buildings. As the system capacity is limited by the initial cost of construction of ground-loop heat exchanger (GHE), developing the so-called Hybrid GSHP system by utilizing supplemental heat rejecters such as cooling towers has emerged as a cost-effective alternative. In practice, operational efficiency of Hybrid GSHP system mainly depends on 1) the actual characteristics of heat pump, cooling tower, GHE and other equipment; 2) ambient air and ground conditions. In particular, the GHE heat transfer is heavily affected by the ground thermal characteristics which, however, is difficult and expensive in practice to determine due to the complexity of soil type and distribution. In addition, the actual cooling tower characteristics can vary significantly. Such uncertainties bring forth dramatic difficulty for successful application of model based control or optimization methods. In this study, an extremum seeking control (ESC) strategy is proposed for efficient operation of a hybrid GSHP system with cooling tower, which minimizes the total power (i.e. GHE loop water pump, cooling tower fan and pump, and the heat-pump compressor) consumption by tuning the air-flow rate of the cooling tower fan and the GHE loop water flow rate. To evaluate the proposed control method, a Modelica based model of the Hybrid GSHP system is developed by utilizing the Buildings Library developed by the Lawrence Berkeley National Laboratory, which consists of a 20-borehole GHE, a water-to-water heat pump, a counter-flow cooling tower and a plate heat exchanger. The transient conduction model of vertical GHE in the Buildings Library is adopted, which is based on a finite-volume method inside the borehole and cylindrical source model outside the borehole. A variable-flow water pump model is constructed for the GHE water loop, which gives power consumption under different operating scenarios. A cooling tower model in the Buildings Library is adopted, which is a static polynomial model based on a York cooling tower correlation. The relative air flow rate can be regulated to maintain the leaving water temperature at the setpoint, and then the corresponding fan power consumption is obtained. The heat pump model is based on the evaporator temperature, condenser temperature and Carnot efficiency. An inner-loop proportional-integral (PI) controller is implemented to regulate the evaporator leaving water temperature at 7 deg-C. Under the air wet-bulb temperature of 35 deg-C and dry-bulb temperature 23 deg-C, steady-state simulation of the plant model yields the static map of the total power with respect to the cooling tower relative air flow rate and the GHE water flow rate, which indicates about 25% power variation across the adjustable range of inputs. Simulation was conducted in two conditions: change in evaporator inlet water temperature and change in ambient air condition. The simulation study under way is to validate the effectiveness of the proposed ESC strategy, and the potential for energy saving will also be evaluated

Next-generation sequencing and microarray-based interrogation of microRNAs from formalin-fixed, paraffin-embedded tissue: Preliminary assessment of cross-platform concordance

AbstractNext-generation sequencing is increasingly employed in biomedical investigations. Strong concordance between microarray and mRNA-seq levels has been reported in high quality specimens but information is lacking on formalin-fixed, paraffin-embedded (FFPE) tissues, and particularly for microRNA (miRNA) analysis. We conducted a preliminary examination of the concordance between miRNA-seq and cDNA-mediated annealing, selection, extension, and ligation (DASL) miRNA assays. Quantitative agreement between platforms is moderate (Spearman correlation 0.514–0.596) and there is discordance of detection calls on a subset of miRNAs. Quantitative PCR (q-RT-PCR) performed for several discordant miRNAs confirmed the presence of most sequences detected by miRNA-seq but not by DASL but also that miRNA-seq did not detect some sequences, which DASL confidently detected. Our results suggest that miRNA-seq is specific, with few false positive calls, but it may not detect certain abundant miRNAs in FFPE tissue. Further work is necessary to fully address these issues that are pertinent for translational research

Global Epigenetic Regulation of MicroRNAs in Multiple Myeloma

Epigenetic changes frequently occur during tumorigenesis and DNA hypermethylation may account for the inactivation of tumor suppressor genes in cancer cells. Studies in Multiple Myeloma (MM) have shown variable DNA methylation patterns with focal hypermethylation changes in clinically aggressive subtypes. We studied global methylation patterns in patients with relapsed/refractory MM and found that the majority of methylation peaks were located in the intronic and intragenic regions in MM samples. Therefore, we investigated the effect of methylation on miRNA regulation in MM. To date, the mechanism by which global miRNA suppression occurs in MM has not been fully described. In this study, we report hypermethylation of miRNAs in MM and perform confirmation in MM cell lines using bisulfite sequencing and methylation-specific PCR (MSP) in the presence or absence of the DNA demethylating agent 5-aza-2′-deoxycytidine. We further characterized the hypermethylation-dependent inhibition of miR-152, -10b-5p and -34c-3p which was shown to exert a putative tumor suppressive role in MM. These findings were corroborated by the demonstration that the same miRNAs were down-regulated in MM patients compared to healthy individuals, alongside enrichment of miR-152-, -10b-5p, and miR-34c-3p-predicted targets, as shown at the mRNA level in primary MM cells. Demethylation or gain of function studies of these specific miRNAs led to induction of apoptosis and inhibition of proliferation as well as down-regulation of putative oncogene targets of these miRNAs such as DNMT1, E2F3, BTRC and MYCBP. These findings provide the rationale for epigenetic therapeutic approaches in subgroups of MM

Analyses of clinicopathological, molecular, and prognostic associations of KRAS codon 61 and codon 146 mutations in colorectal cancer: cohort study and literature review

Background: KRAS mutations in codons 12 and 13 are established predictive biomarkers for anti-EGFR therapy in colorectal cancer. Previous studies suggest that KRAS codon 61 and 146 mutations may also predict resistance to anti-EGFR therapy in colorectal cancer. However, clinicopathological, molecular, and prognostic features of colorectal carcinoma with KRAS codon 61 or 146 mutation remain unclear. Methods: We utilized a molecular pathological epidemiology database of 1267 colon and rectal cancers in the Nurse’s Health Study and the Health Professionals Follow-up Study. We examined KRAS mutations in codons 12, 13, 61 and 146 (assessed by pyrosequencing), in relation to clinicopathological features, and tumor molecular markers, including BRAF and PIK3CA mutations, CpG island methylator phenotype (CIMP), LINE-1 methylation, and microsatellite instability (MSI). Survival analyses were performed in 1067 BRAF-wild-type cancers to avoid confounding by BRAF mutation. Cox proportional hazards models were used to compute mortality hazard ratio, adjusting for potential confounders, including disease stage, PIK3CA mutation, CIMP, LINE-1 hypomethylation, and MSI. Results: KRAS codon 61 mutations were detected in 19 cases (1.5%), and codon 146 mutations in 40 cases (3.2%). Overall KRAS mutation prevalence in colorectal cancers was 40% (=505/1267). Of interest, compared to KRAS-wild-type, overall, KRAS-mutated cancers more frequently exhibited cecal location (24% vs. 12% in KRAS-wild-type; P < 0.0001), CIMP-low (49% vs. 32% in KRAS-wild-type; P < 0.0001), and PIK3CA mutations (24% vs. 11% in KRAS-wild-type; P < 0.0001). These trends were evident irrespective of mutated codon, though statistical power was limited for codon 61 mutants. Neither KRAS codon 61 nor codon 146 mutation was significantly associated with clinical outcome or prognosis in univariate or multivariate analysis [colorectal cancer-specific mortality hazard ratio (HR) = 0.81, 95% confidence interval (CI) = 0.29-2.26 for codon 61 mutation; colorectal cancer-specific mortality HR = 0.86, 95% CI = 0.42-1.78 for codon 146 mutation]. Conclusions: Tumors with KRAS mutations in codons 61 and 146 account for an appreciable proportion (approximately 5%) of colorectal cancers, and their clinicopathological and molecular features appear generally similar to KRAS codon 12 or 13 mutated cancers. To further assess clinical utility of KRAS codon 61 and 146 testing, large-scale trials are warranted

A deep learning-based model reduction (DeePMR) method for simplifying chemical kinetics

A deep learning-based model reduction (DeePMR) method for simplifying chemical kinetics is proposed and validated using high-temperature auto-ignitions, perfectly stirred reactors (PSR), and one-dimensional freely propagating flames of n-heptane/air mixtures. The mechanism reduction is modeled as an optimization problem on Boolean space, where a Boolean vector, each entry corresponding to a species, represents a reduced mechanism. The optimization goal is to minimize the reduced mechanism size given the error tolerance of a group of pre-selected benchmark quantities. The key idea of the DeePMR is to employ a deep neural network (DNN) to formulate the objective function in the optimization problem. In order to explore high dimensional Boolean space efficiently, an iterative DNN-assisted data sampling and DNN training procedure are implemented. The results show that DNN-assistance improves sampling efficiency significantly, selecting only $10^5$ samples out of $10^{34}$ possible samples for DNN to achieve sufficient accuracy. The results demonstrate the capability of the DNN to recognize key species and reasonably predict reduced mechanism performance. The well-trained DNN guarantees the optimal reduced mechanism by solving an inverse optimization problem. By comparing ignition delay times, laminar flame speeds, temperatures in PSRs, the resulting skeletal mechanism has fewer species (45 species) but the same level of accuracy as the skeletal mechanism (56 species) obtained by the Path Flux Analysis (PFA) method. In addition, the skeletal mechanism can be further reduced to 28 species if only considering atmospheric, near-stoichiometric conditions (equivalence ratio between 0.6 and 1.2). The DeePMR provides an innovative way to perform model reduction and demonstrates the great potential of data-driven methods in the combustion area

High Throughput Sequencing of Extracellular RNA from Human Plasma

<div><p>The presence and relative stability of extracellular RNAs (exRNAs) in biofluids has led to an emerging recognition of their promise as ‘liquid biopsies’ for diseases. Most prior studies on discovery of exRNAs as disease-specific biomarkers have focused on microRNAs (miRNAs) using technologies such as qRT-PCR and microarrays. The recent application of next-generation sequencing to discovery of exRNA biomarkers has revealed the presence of potential novel miRNAs as well as other RNA species such as tRNAs, snoRNAs, piRNAs and lncRNAs in biofluids. At the same time, the use of RNA sequencing for biofluids poses unique challenges, including low amounts of input RNAs, the presence of exRNAs in different compartments with varying degrees of vulnerability to isolation techniques, and the high abundance of specific RNA species (thereby limiting the sensitivity of detection of less abundant species). Moreover, discovery in human diseases often relies on archival biospecimens of varying age and limiting amounts of samples. In this study, we have tested RNA isolation methods to optimize profiling exRNAs by RNA sequencing in individuals without any known diseases. Our findings are consistent with other recent studies that detect microRNAs and ribosomal RNAs as the major exRNA species in plasma. Similar to other recent studies, we found that the landscape of biofluid microRNA transcriptome is dominated by several abundant microRNAs that appear to comprise conserved extracellular miRNAs. There is reasonable correlation of sets of conserved miRNAs across biological replicates, and even across other data sets obtained at different investigative sites. Conversely, the detection of less abundant miRNAs is far more dependent on the exact methodology of RNA isolation and profiling. This study highlights the challenges in detecting and quantifying less abundant plasma miRNAs in health and disease using RNA sequencing platforms.</p></div

Schematic summarizing plasma sample treatments used in this study.

<p>RNA isolation was performed with or without proteinase K treatment and ribodepletion on fresh or archived samples.</p

Spearman correlation for the expression level of detected microRNAs.

<p>While different numbers of miRNAs were quantified between duplicated samples, the majority of these miRNA were commonly observed. The expression level of these commonly observed miRNA were highly correlated for both duplicated samples (r>0.9) and across samples from different subjects (r>0.74).</p