Monolithically multi-color lasing from an InGaN microdisk on a Si substrate

An optically pumped multi-color laser has been achieved using an InGaN/GaN based micro-disk with an undercut structure on a silicon substrate. The micro-disk laser has been fabricated by means of a combination of a cost-effective microsphere lithography technique and subsequent dry/wet etching processes. The microdisk laser is approximately 1 μm in diameter. The structure was designed in such a way that the vertical components of the whispering gallery (WG) modes formed can be effectively suppressed. Consequently, three clean lasing peaks at 442 nm, 493 nm and 522 nm have been achieved at room temperature by simply using a continuous-wave diode laser as an optical pumping source. Time–resolved micro photoluminescence (PL) measurements have been performed in order to further confirm the lasing by investigating the excitonic recombination dynamics of these lasing peaks. A three dimensional finite-difference-time-domain (FDTD) simulation has been used for the structure design

Increased DNA methylation variability in type 1 diabetes across three immune effector cell types

The incidence of type 1 diabetes (T1D) has substantially increased over the past decade, suggesting a role for non-genetic factors such as epigenetic mechanisms in disease development. Here we present an epigenome-wide association study across 406,365 CpGs in 52 monozygotic twin pairs discordant for T1D in three immune effector cell types. We observe a substantial enrichment of differentially variable CpG positions (DVPs) in T1D twins when compared with their healthy co-twins and when compared with healthy, unrelated individuals. These T1D-associated DVPs are found to be temporally stable and enriched at gene regulatory elements. Integration with cell type-specific gene regulatory circuits highlight pathways involved in immune cell metabolism and the cell cycle, including mTOR signalling. Evidence from cord blood of newborns who progress to overt T1D suggests that the DVPs likely emerge after birth. Our findings, based on 772 methylomes, implicate epigenetic changes that could contribute to disease pathogenesis in T1D.This work was funded by the EU-FP7 project BLUEPRINT (282510) and the Wellcome Trust (99148). We thank all twins for taking part in this study; Kerra Pearce and Mark Kristiansen (UCL Genomics) for processing the Illumina Infinium HumanMethylation450 BeadChips; Rasmus Bennet for technical assistance; and Laura Phipps for proofreading the manuscript. The BMBF Pediatric Diabetes Biobank recruits patients from the National Diabetes Patient Documentation System (DPV), and is financed by the German Ministry of Education and Research within the German Competence Net Diabetes Mellitus (01GI1106 and 01GI1109B). It was integrated into the German Center for Diabetes Research in January 2015. We thank the Swedish Research Council and SUS Funds for support. We gratefully acknowledge the participation of all NIHR Cambridge BioResource volunteers, and thank the Cambridge BioResource staff for their help with volunteer recruitment. We thank members of the Cambridge BioResource SAB and Management Committee for their support of our study and the NIHR Cambridge Biomedical Research Centre for funding. The Cardiovascular Epidemiology Unit is supported by the UK Medical Research Council (G0800270), BHF (SP/09/002), and NIHR Cambridge Biomedical Research Centre. Research in the Ouwehand laboratory is supported by the NIHR, BHF (PG-0310-1002 and RG/09/12/28096) and NHS Blood and Transplant. K.D. is funded as a HSST trainee by NHS Health Education England. M.F. is supported by the BHF Cambridge Centre of Excellence (RE/13/6/30180). A.D., E.L., L.C. and P.F. receive additional support from the European Molecular Biology Laboratory. A.K.S. is supported by an ADA Career Development Award (1-14-CD-17). B.O.B. and R.D.L. acknowledge support from the Deutsche Forschungsgemeinschaft (DFG) and European Federation for the Study of Diabetes, respectively

Lactate Dehydrogenase-B Is Silenced by Promoter Methylation in a High Frequency of Human Breast Cancers

Objective: Under normoxia, non-malignant cells rely on oxidative phosphorylation for their ATP production, whereas cancer cells rely on Glycolysis; a phenomenon known as the Warburg effect. We aimed to elucidate the mechanisms contributing to the Warburg effect in human breast cancer. Experimental design: Lactate Dehydrogenase (LDH) isoenzymes were profiled using zymography. LDH-B subunit expression was assessed by reverse transcription PCR in cells, and by Immunohistochemistry in breast tissues. LDH-B promoter methylation was assessed by sequencing bisulfite modified DNA. Results: Absent or decreased expression of LDH isoenzymes 1-4, were seen in T-47D and MCF7 cells. Absence of LDH-B mRNA was seen in T-47D cells, and its expression was restored following treatment with the demethylating agent 5'Azacytadine. LDH-B promoter methylation was identified in T-47D and MCF7 cells, and in 25/ 25 cases of breast cancer tissues, but not in 5/ 5 cases of normal breast tissues. Absent immuno-expression of LDH-B protein (<10% cells stained), was seen in 23/ 26 (88%) breast cancer cases, and in 4/8 cases of adjacent ductal carcinoma in situ lesions. Exposure of breast cancer cells to hypoxia (1% O2), for 48 hours resulted in significant increases in lactate levels in both MCF7 (14.0 fold, p = 0.002), and T-47D cells (2.9 fold, p = 0.009), but not in MDA-MB-436 (-0.9 fold, p = 0.229), or MCF10AT (1.2 fold, p = 0.09) cells. Conclusions: Loss of LDH-B expression is an early and frequent event in human breast cancer occurring due to promoter methylation, and is likely to contribute to an enhanced glycolysis of cancer cells under hypoxia

Can Interactions between Timing of Vaccine-Altered Influenza Pandemic Waves and Seasonality in Influenza Complications Lead to More Severe Outcomes?

Vaccination can delay the peak of a pandemic influenza wave by reducing the number of individuals initially susceptible to influenza infection. Emerging evidence indicates that susceptibility to severe secondary bacterial infections following a primary influenza infection may vary seasonally, with peak susceptibility occurring in winter. Taken together, these two observations suggest that vaccinating to prevent a fall pandemic wave might delay it long enough to inadvertently increase influenza infections in winter, when primary influenza infection is more likely to cause severe outcomes. This could potentially cause a net increase in severe outcomes. Most pandemic models implicitly assume that the probability of severe outcomes does not vary seasonally and hence cannot capture this effect. Here we show that the probability of intensive care unit (ICU) admission per influenza infection in the 2009 H1N1 pandemic followed a seasonal pattern. We combine this with an influenza transmission model to investigate conditions under which a vaccination program could inadvertently shift influenza susceptibility to months where the risk of ICU admission due to influenza is higher. We find that vaccination in advance of a fall pandemic wave can actually increase the number of ICU admissions in situations where antigenic drift is sufficiently rapid or where importation of a cross-reactive strain is possible. Moreover, this effect is stronger for vaccination programs that prevent more primary influenza infections. Sensitivity analysis indicates several mechanisms that may cause this effect. We also find that the predicted number of ICU admissions changes dramatically depending on whether the probability of ICU admission varies seasonally, or whether it is held constant. These results suggest that pandemic planning should explore the potential interactions between seasonally varying susceptibility to severe influenza outcomes and the timing of vaccine-altered pandemic influenza waves

National Outbreak of Salmonella Serotype Saintpaul Infections: Importance of Texas Restaurant Investigations in Implicating Jalapeño Peppers

BACKGROUND: In May 2008, PulseNet detected a multistate outbreak of Salmonella enterica serotype Saintpaul infections. Initial investigations identified an epidemiologic association between illness and consumption of raw tomatoes, yet cases continued. In mid-June, we investigated two clusters of outbreak strain infections in Texas among patrons of Restaurant A and two establishments of Restaurant Chain B to determine the outbreak's source. METHODOLOGY/PRINCIPAL FINDINGS: We conducted independent case-control studies of Restaurant A and B patrons. Patients were matched to well controls by meal date. We conducted restaurant environmental investigations and traced the origin of implicated products. Forty-seven case-patients and 40 controls were enrolled in the Restaurant A study. Thirty case-patients and 31 controls were enrolled in the Restaurant Chain B study. In both studies, illness was independently associated with only one menu item, fresh salsa (Restaurant A: matched odds ratio [mOR], 37; 95% confidence interval [CI], 7.2-386; Restaurant B: mOR, 13; 95% CI 1.3-infinity). The only ingredient in common between the two salsas was raw jalapeño peppers. Cultures of jalapeño peppers collected from an importer that supplied Restaurant Chain B and serrano peppers and irrigation water from a Mexican farm that supplied that importer with jalapeño and serrano peppers grew the outbreak strain. CONCLUSIONS/SIGNIFICANCE: Jalapeño peppers, contaminated before arrival at the restaurants and served in uncooked fresh salsas, were the source of these infections. Our investigations, critical in understanding the broader multistate outbreak, exemplify an effective approach to investigating large foodborne outbreaks. Additional measures are needed to reduce produce contamination

Social Isolation-Induced Aggression Potentiates Anxiety and Depressive-Like Behavior in Male Mice Subjected to Unpredictable Chronic Mild Stress

Accumulating epidemiological evidence shows that life event stressors are major vulnerability factors for psychiatric diseases such as major depression. It is also well known that social isolation in male mice results in aggressive behavior. However, it is not known how social isolation-induced aggression affects anxiety and depressive-like behavior in isolated male mice subjected to unpredictable chronic mild stress (CMS), an animal model of depression.C57/B6 male mice were divided into 3 groups; non-stressed controls, in Group I; isolated mice subjected to the CMS protocol in Group II and aggression by physical contact in socially isolated mice subjected to the CMS protocol in Group III. In the sucrose intake test, ingestion of a 1% sucrose solution by mice in Groups II and III was significantly lower than in Group I. Furthermore, intake of this solution in Group III mice was significantly lower than in Group II mice. In the open field test, mice in Group III, showed reduced locomotor activity and reduced entry and retention time in the central zone, compared to Groups I and II mice. Moreover, the distances moved in 1 hour by Group III mice did not differ between night and morning. In the light/black box test, Groups II and III animals spent significantly less time in the light box compared to Group I animals. In the tail suspension test (TST) and forced swimming test (FST), the immobility times of Group II and Group III mice were significantly longer than in Group I mice. In addition, immobility times in the FST were significantly longer in Group III than in Group II mice.These findings show that social isolation-induced aggression could potentiate anxiety and depressive-like behaviors in isolated male mice subjected to CMS

Structure-Based Predictive Models for Allosteric Hot Spots

In allostery, a binding event at one site in a protein modulates the behavior of a distant site. Identifying residues that relay the signal between sites remains a challenge. We have developed predictive models using support-vector machines, a widely used machine-learning method. The training data set consisted of residues classified as either hotspots or non-hotspots based on experimental characterization of point mutations from a diverse set of allosteric proteins. Each residue had an associated set of calculated features. Two sets of features were used, one consisting of dynamical, structural, network, and informatic measures, and another of structural measures defined by Daily and Gray [1]. The resulting models performed well on an independent data set consisting of hotspots and non-hotspots from five allosteric proteins. For the independent data set, our top 10 models using Feature Set 1 recalled 68–81% of known hotspots, and among total hotspot predictions, 58–67% were actual hotspots. Hence, these models have precision P = 58–67% and recall R = 68–81%. The corresponding models for Feature Set 2 had P = 55–59% and R = 81–92%. We combined the features from each set that produced models with optimal predictive performance. The top 10 models using this hybrid feature set had R = 73–81% and P = 64–71%, the best overall performance of any of the sets of models. Our methods identified hotspots in structural regions of known allosteric significance. Moreover, our predicted hotspots form a network of contiguous residues in the interior of the structures, in agreement with previous work. In conclusion, we have developed models that discriminate between known allosteric hotspots and non-hotspots with high accuracy and sensitivity. Moreover, the pattern of predicted hotspots corresponds to known functional motifs implicated in allostery, and is consistent with previous work describing sparse networks of allosterically important residues