THE OUTPUT FREQUENCY SPECTRUM OF A THYRISTOR PHASE-CONTROLLED CYCLOCONVERTER USING DIGITAL CONTROL TECHNIQUES

The principle of operation dictates that the output of a cycloconverter contains some harmonics. For drive applications, the harmonics at best increase losses in the motor and may well cause instability. Various methods of analysing the output waveform have been considered. A Fortran 77 program employing a modified Fourier series, making use of the fact that the input waveforms are sinusoidal, was used to compute the individual harmonic amplitudes. A six pulse three phase to single phase cycloconverter was built and a Z-80 microprocessor was used for the control of firing angles. Phase locked loops were used for timing, and their effect upon the output with changing input frequency and voltage were established. The experimental waveforms are analysed by a FFT spectrum analyser. The flexibility of the control circuit enables the following investigations not easily carry out using traditional analog control circuit. The phase relationship between the cosine timing and reference wave in the cosinusoidal control method was shown to affect the output waveform and hence the harmonic content. There is no clear optimum value of phase and the T.H.D. up to 500Hz remains virtually constant. However, the changes of individual harmonic amplitudes is quite significant. In practice it may not be possible to keep the value of phase constant but it should be considered when comparing control strategies. Another investigation involves the changing of the last firing angle in a half cycle. It shows that the value of firing angles produced by the cosinusoidal control method is desirable. Operation at theoretical maximum output frequency was also demonstrated.Bristol Universit

Endoscopic submucosal dissection vs laparoscopic colorectal resection for early colorectal epithelial neoplasia

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Automated Long-Term Monitoring of Parallel Microfluidic Operations Applying a Machine Vision-Assisted Positioning Method

As microfluidics has been applied extensively in many cell and biochemical applications, monitoring the related processes is an important requirement. In this work, we design and fabricate a high-throughput microfluidic device which contains 32 microchambers to perform automated parallel microfluidic operations and monitoring on an automated stage of a microscope. Images are captured at multiple spots on the device during the operations for monitoring samples in microchambers in parallel; yet the device positions may vary at different time points throughout operations as the device moves back and forth on a motorized microscopic stage. Here, we report an image-based positioning strategy to realign the chamber position before every recording of microscopic image. We fabricate alignment marks at defined locations next to the chambers in the microfluidic device as reference positions. We also develop image processing algorithms to recognize the chamber positions in real-time, followed by realigning the chambers to their preset positions in the captured images. We perform experiments to validate and characterize the device functionality and the automated realignment operation. Together, this microfluidic realignment strategy can be a platform technology to achieve precise positioning of multiple chambers for general microfluidic applications requiring long-term parallel monitoring of cell and biochemical activities

Recursive SVM Feature Selection and Sample Classification for Mass-Spectrometry and Microarray Data

Background: Like microarray-based investigations, high-throughput proteomics techniques require machine learning algorithms to identify biomarkers that are informative for biological classification problems. Feature selection and classification algorithms need to be robust to noise and outliers in the data. Results: We developed a recursive support vector machine (R-SVM) algorithm to select important genes/biomarkers for the classification of noisy data. We compared its performance to a similar, state-of-the-art method (SVM recursive feature elimination or SVM-RFE), paying special attention to the ability of recovering the true informative genes/biomarkers and the robustness to outliers in the data. Simulation experiments show that a 5 %-~20 % improvement over SVM-RFE can be achieved regard to these properties. The SVM-based methods are also compared with a conventional univariate method and their respective strengths and weaknesses are discussed. R-SVM was applied to two sets of SELDI-TOF-MS proteomics data, one from a human breast cancer study and the other from a study on rat liver cirrhosis. Important biomarkers found by the algorithm were validated by follow-up biological experiments. Conclusion: The proposed R-SVM method is suitable for analyzing noisy high-throughput proteomics and microarray data and it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features. The multivariate SVM-based method outperforms the univariate method in the classification performance, but univariate methods can reveal more of the differentially expressed features especially when there are correlations between the features.Statistic

Calpain activity is negatively regulated by a KCTD7-Cullin-3 complex via non-degradative ubiquitination

Calpains are a class of non-lysosomal cysteine proteases that exert their regulatory functions via limited proteolysis of their substrates. Similar to the lysosomal and proteasomal systems, calpain dysregulation is implicated in the pathogenesis of neurodegenerative disease and cancer. Despite intensive efforts placed on the identification of mechanisms that regulate calpains, however, calpain protein modifications that regulate calpain activity are incompletely understood. Here we show that calpains are regulated by KCTD7, a cytosolic protein of previously uncharacterized function whose pathogenic mutations result in epilepsy, progressive ataxia, and severe neurocognitive deterioration. We show that KCTD7 works in complex with Cullin-3 and Rbx1 to execute atypical, non-degradative ubiquitination of calpains at specific sites (K398 of calpain 1, and K280 and K674 of calpain 2). Experiments based on single-lysine mutants of ubiquitin determined that KCTD7 mediates ubiquitination of calpain 1 via K6-, K27-, K29-, and K63-linked chains, whereas it uses K6-mediated ubiquitination to modify calpain 2. Loss of KCTD7-mediated ubiquitination of calpains led to calpain hyperactivation, aberrant cleavage of downstream targets, and caspase-3 activation. CRISPR/Cas9-mediated knockout of Kctd7 in mice phenotypically recapitulated human KCTD7 deficiency and resulted in calpain hyperactivation, behavioral impairments, and neurodegeneration. These phenotypes were largely prevented by pharmacological inhibition of calpains, thus demonstrating a major role of calpain dysregulation in KCTD7-associated disease. Finally, we determined that Cullin-3-KCTD7 mediates ubiquitination of all ubiquitous calpains. These results unveil a novel mechanism and potential target to restrain calpain activity in human disease and shed light on the molecular pathogenesis of KCTD7-associated disease

Nuclear S-nitrosylation impacts tissue regeneration in zebrafish

The role of the post-translational modifications in tissue regeneration is still not clearly understood. Here, the authors show that many nuclear proteins change S-nitrosylation state in the regenerating zebrafish tailfin, highlighting the importance of Kdm1a S-nitrosylation in the repair process

Silencing BMI1 eliminates tumor formation of pediatric glioma CD133+ cells not by affecting known targets but by down-regulating a novel set of core genes

Abstract Clinical outcome of children with malignant glioma remains dismal. Here, we examined the role of over-expressed BMI1, a regulator of stem cell self-renewal, in sustaining tumor formation in pediatric glioma stem cells. Our investigation revealed BMI1 over-expression in 29 of 54 (53.7%) pediatric gliomas, 8 of 8 (100%) patient derived orthotopic xenograft (PDOX) mouse models, and in both CD133+ and CD133− glioma cells. We demonstrated that lentiviral-shRNA mediated silencing of suppressed cell proliferation in vitro in cells derived from 3 independent PDOX models and eliminated tumor-forming capacity of CD133+ and CD133− cells derived from 2 PDOX models in mouse brains. Gene expression profiling showed that most of the molecular targets of BMI1 ablation in CD133+ cells were different from that in CD133- cells. Importantly, we found that silencing BMI1 in CD133+ cells derived from 3 PDOX models did not affect most of the known genes previously associated with the activated BMI1, but modulated a novel set of core genes, including RPS6KA2, ALDH3A2, FMFB, DTL, API5, EIF4G2, KIF5c, LOC650152, C20ORF121, LOC203547, LOC653308, and LOC642489, to mediate the elimination of tumor formation. In summary, we identified the over-expressed BMI1 as a promising therapeutic target for glioma stem cells, and suggest that the signaling pathways associated with activated BMI1 in promoting tumor growth may be different from those induced by silencing BMI1 in blocking tumor formation. These findings highlighted the importance of careful re-analysis of the affected genes following the inhibition of abnormally activated oncogenic pathways to identify determinants that can potentially predict therapeutic efficacy.http://deepblue.lib.umich.edu/bitstream/2027.42/110124/1/40478_2014_Article_160.pd

Cynomolgus Macaque as an Animal Model for Severe Acute Respiratory Syndrome

BACKGROUND: The emergence of severe acute respiratory syndrome (SARS) in 2002 and 2003 affected global health and caused major economic disruption. Adequate animal models are required to study the underlying pathogenesis of SARS-associated coronavirus (SARS-CoV) infection and to develop effective vaccines and therapeutics. We report the first findings of measurable clinical disease in nonhuman primates (NHPs) infected with SARS-CoV. METHODS AND FINDINGS: In order to characterize clinically relevant parameters of SARS-CoV infection in NHPs, we infected cynomolgus macaques with SARS-CoV in three groups: Group I was infected in the nares and bronchus, group II in the nares and conjunctiva, and group III intravenously. Nonhuman primates in groups I and II developed mild to moderate symptomatic illness. All NHPs demonstrated evidence of viral replication and developed neutralizing antibodies. Chest radiographs from several animals in groups I and II revealed unifocal or multifocal pneumonia that peaked between days 8 and 10 postinfection. Clinical laboratory tests were not significantly changed. Overall, inoculation by a mucosal route produced more prominent disease than did intravenous inoculation. Half of the group I animals were infected with a recombinant infectious clone SARS-CoV derived from the SARS-CoV Urbani strain. This infectious clone produced disease indistinguishable from wild-type Urbani strain. CONCLUSIONS: SARS-CoV infection of cynomolgus macaques did not reproduce the severe illness seen in the majority of adult human cases of SARS; however, our results suggest similarities to the milder syndrome of SARS-CoV infection characteristically seen in young children

Lupus nephritis in Chinese children--a territory-wide cohort study in Hong Kong

We report a multicenter study of Chinese children in Hong Kong with systemic lupus erythematosus (SLE) nephritis. Children were included if: they fulfilled the ACR criteria, had significant proteinuria or casturia, were Chinese and younger than 19 years and had been diagnosed with SLE between January 1990 and December 2003. Investigators in each center retrieved data on clinical features, biopsy reports, treatment and outcome of these patients. There were 128 patients (eight boys, 120 girls; mean age: 11.9+/-2.8 years). About 50% presented with multisystem illness and 40% with nephritic/nephrotic symptoms. Negative anti-dsDNA antibodies were found in 6% of the patients. Renal biopsy revealed WHO Class II, III, IV and V nephritis in 13 (10%), 22 (17%), 69 (54%) and 13 (10%) patients, respectively. The clinical severity of the nephritis did not accurately predict renal biopsy findings. The follow-up period ranged from 1 to 16.5 years (mean+/-SD: 5.76+/-3.61 years). During the study five patients died (two from lupus flare, one from cardiomyopathy, two from infections). Four patients had endstage renal failure (ESRF) (one died during a lupus flare). All deaths and end-stage renal failure occurred in the Class IV nephritis group. Chronic organ damage was infrequent in the survivors. The actuarial patient survival rates at 5, 10 and 15 years of age were 95.3, 91.8, and 91.8%, respectively. For Class IV nephritis patients, the survival rates without ESRF at 5, 10, and 15 years were 91.5, 82.3 and 76%, respectively. The survival and chronic morbidity rates of the Chinese SLE children in the present study are comparable to those of other published studies.postprin

Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data

BACKGROUND: Like microarray-based investigations, high-throughput proteomics techniques require machine learning algorithms to identify biomarkers that are informative for biological classification problems. Feature selection and classification algorithms need to be robust to noise and outliers in the data. RESULTS: We developed a recursive support vector machine (R-SVM) algorithm to select important genes/biomarkers for the classification of noisy data. We compared its performance to a similar, state-of-the-art method (SVM recursive feature elimination or SVM-RFE), paying special attention to the ability of recovering the true informative genes/biomarkers and the robustness to outliers in the data. Simulation experiments show that a 5 %-~20 % improvement over SVM-RFE can be achieved regard to these properties. The SVM-based methods are also compared with a conventional univariate method and their respective strengths and weaknesses are discussed. R-SVM was applied to two sets of SELDI-TOF-MS proteomics data, one from a human breast cancer study and the other from a study on rat liver cirrhosis. Important biomarkers found by the algorithm were validated by follow-up biological experiments. CONCLUSION: The proposed R-SVM method is suitable for analyzing noisy high-throughput proteomics and microarray data and it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features. The multivariate SVM-based method outperforms the univariate method in the classification performance, but univariate methods can reveal more of the differentially expressed features especially when there are correlations between the features