The Reactive Sulfur Species Concept: 15 Years On

Fifteen years ago, in 2001, the concept of “Reactive Sulfur Species” or RSS was advocated as a working hypothesis. Since then various organic as well as inorganic RSS have attracted considerable interest and stimulated many new and often unexpected avenues in research and product development. During this time, it has become apparent that molecules with sulfur-containing functional groups are not just the passive “victims” of oxidative stress or simple conveyors of signals in cells, but can also be stressors in their own right, with pivotal roles in cellular function and homeostasis. Many “exotic” sulfur-based compounds, often of natural origin, have entered the fray in the context of nutrition, ageing, chemoprevention and therapy. In parallel, the field of inorganic RSS has come to the forefront of research, with short-lived yet metabolically important intermediates, such as various sulfur-nitrogen species and polysulfides (Sx2−), playing important roles. Between 2003 and 2005 several breath-taking discoveries emerged characterising unusual sulfur redox states in biology, and since then the truly unique role of sulfur-dependent redox systems has become apparent. Following these discoveries, over the last decade a “hunt” and, more recently, mining for such modifications has begun—and still continues—often in conjunction with new, innovative and complex labelling and analytical methods to capture the (entire) sulfur “redoxome”. A key distinction for RSS is that, unlike oxygen or nitrogen, sulfur not only forms a plethora of specific reactive species, but sulfur also targets itself, as sulfur containing molecules, i.e., peptides, proteins and enzymes, preferentially react with RSS. Not surprisingly, today this sulfur-centred redox signalling and control inside the living cell is a burning issue, which has moved on from the predominantly thiol/disulfide biochemistry of the past to a complex labyrinth of interacting signalling and control pathways which involve various sulfur oxidation states, sulfur species and reactions. RSS are omnipresent and, in some instances, are even considered as the true bearers of redox control, perhaps being more important than the Reactive Oxygen Species (ROS) or Reactive Nitrogen Species (RNS) which for decades have dominated the redox field. In other(s) words, in 2017, sulfur redox is “on the rise”, and the idea of RSS resonates throughout the Life Sciences. Still, the RSS story isn’t over yet. Many RSS are at the heart of “mistaken identities” which urgently require clarification and may even provide the foundations for further scientific revolutions in the years to come. In light of these developments, it is therefore the perfect time to revisit the original hypotheses, to select highlights in the field and to question and eventually update our concept of “Reactive Sulfur Species”

Development of CMOS pixel sensors for tracking and vertexing in high energy physics experiments

CMOS pixel sensors (CPS) represent a novel technological approach to building charged particle detectors. CMOS processes allow to integrate a sensing volume and readout electronics in a single silicon die allowing to build sensors with a small pixel pitch ($\sim 20 \mu m$) and low material budget ($\sim 0.2-0.3\% X_0$) per layer. These characteristics make CPS an attractive option for vertexing and tracking systems of high energy physics experiments. Moreover, thanks to the mass production industrial CMOS processes used for the manufacturing of CPS the fabrication construction cost can be significantly reduced in comparison to more standard semiconductor technologies. However, the attainable performance level of the CPS in terms of radiation hardness and readout speed is mostly determined by the fabrication parameters of the CMOS processes available on the market rather than by the CPS intrinsic potential. The permanent evolution of commercial CMOS processes towards smaller feature sizes and high resistivity epitaxial layers leads to the better radiation hardness and allows the implementation of accelerated readout circuits. The TowerJazz $0.18 \mu m$ CMOS process being one of the most relevant examples recently became of interest for several future detector projects. The most imminent of these project is an upgrade of the Inner Tracking System (ITS) of the ALICE detector at LHC. It will be followed by the Micro-Vertex Detector (MVD) of the CBM experiment at FAIR. Other experiments like ILD consider CPS as one of the viable options for flavour tagging and tracking sub-systems

A Deep Learning Approach to Predicting Collateral Flow in Stroke Patients Using Radiomic Features from Perfusion Images

Collateral circulation results from specialized anastomotic channels which are capable of providing oxygenated blood to regions with compromised blood flow caused by ischemic injuries. The quality of collateral circulation has been established as a key factor in determining the likelihood of a favorable clinical outcome and goes a long way to determine the choice of stroke care model - that is the decision to transport or treat eligible patients immediately. Though there exist several imaging methods and grading criteria for quantifying collateral blood flow, the actual grading is mostly done through manual inspection of the acquired images. This approach is associated with a number of challenges. First, it is time-consuming - the clinician needs to scan through several slices of images to ascertain the region of interest before deciding on what severity grade to assign to a patient. Second, there is a high tendency for bias and inconsistency in the final grade assigned to a patient depending on the experience level of the clinician. We present a deep learning approach to predicting collateral flow grading in stroke patients based on radiomic features extracted from MR perfusion data. First, we formulate a region of interest detection task as a reinforcement learning problem and train a deep learning network to automatically detect the occluded region within the 3D MR perfusion volumes. Second, we extract radiomic features from the obtained region of interest through local image descriptors and denoising auto-encoders. Finally, we apply a convolutional neural network and other machine learning classifiers to the extracted radiomic features to automatically predict the collateral flow grading of the given patient volume as one of three severity classes - no flow (0), moderate flow (1), and good flow (2)..

A deep learning approach to predict collateral flow in stroke patients using radiomic features from perfusion images.

Collateral circulation results from specialized anastomotic channels which are capable of providing oxygenated blood to regions with compromised blood flow caused by arterial obstruction. The quality of collateral circulation has been established as a key factor in determining the likelihood of a favorable clinical outcome and goes a long way to determining the choice of a stroke care model. Though many imaging and grading methods exist for quantifying collateral blood flow, the actual grading is mostly done through manual inspection. This approach is associated with a number of challenges. First, it is time-consuming. Second, there is a high tendency for bias and inconsistency in the final grade assigned to a patient depending on the experience level of the clinician. We present a multi-stage deep learning approach to predict collateral flow grading in stroke patients based on radiomic features extracted from MR perfusion data. First, we formulate a region of interest detection task as a reinforcement learning problem and train a deep learning network to automatically detect the occluded region within the 3D MR perfusion volumes. Second, we extract radiomic features from the obtained region of interest through local image descriptors and denoising auto-encoders. Finally, we apply a convolutional neural network and other machine learning classifiers to the extracted radiomic features to automatically predict the collateral flow grading of the given patient volume as one of three severity classes - no flow (0), moderate flow (1), and good flow (2). Results from our experiments show an overall accuracy of 72% in the three-class prediction task. With an inter-observer agreement of 16% and a maximum intra-observer agreement of 74% in a similar experiment, our automated deep learning approach demonstrates a performance comparable to expert grading, is faster than visual inspection, and eliminates the problem of grading bias

Towards a tensionless string field theory for the N=(2,0) CFT in d=6

We describe progress in using the field theory of tensionless strings to arrive at a Lagrangian for the six-dimensional $\mathcal N=(2,0)$ conformal theory. We construct the free part of the theory and propose an ansatz for the cubic vertex in light-cone superspace. By requiring closure of the $(2,0)$ supersymmetry algebra, we fix the cubic vertex up to two parameters.Comment: 46 pages, 2 figures. V2: references added; minor changes and improvement

Defining myocardial tissue abnormalities in end-stage renal failure with cardiac magnetic resonance imaging using native T1 mapping

Noninvasive quantification of myocardial fibrosis in end-stage renal disease is challenging. Gadolinium contrast agents previously used for cardiac magnetic resonance imaging (MRI) are contraindicated because of an association with nephrogenic systemic fibrosis. In other populations, increased myocardial native T1 times on cardiac MRI have been shown to be a surrogate marker of myocardial fibrosis. We applied this method to 33 incident hemodialysis patients and 28 age- and sex-matched healthy volunteers who underwent MRI at 3.0T. Native T1 relaxation times and feature tracking–derived global longitudinal strain as potential markers of fibrosis were compared and associated with cardiac biomarkers. Left ventricular mass indices were higher in the hemodialysis than the control group. Global, Septal and midseptal T1 times were all significantly higher in the hemodialysis group (global T1 hemodialysis 1171 ± 27 ms vs. 1154 ± 32 ms; septal T1 hemodialysis 1184 ± 29 ms vs. 1163 ± 30 ms; and midseptal T1 hemodialysis 1184 ± 34 ms vs. 1161 ± 29 ms). In the hemodialysis group, T1 times correlated with left ventricular mass indices. Septal T1 times correlated with troponin and electrocardiogram-corrected QT interval. The peak global longitudinal strain was significantly reduced in the hemodialysis group (hemodialysis -17.7±5.3% vs. -21.8±6.2%). For hemodialysis patients, the peak global longitudinal strain significantly correlated with left ventricular mass indices (R = 0.426), and a trend was seen for correlation with galectin-3, a biomarker of cardiac fibrosis. Thus, cardiac tissue properties of hemodialysis patients consistent with myocardial fibrosis can be determined noninvasively and associated with multiple structural and functional abnormalities

BRCA2 polymorphic stop codon K3326X and the risk of breast, prostate, and ovarian cancers

Background: The K3326X variant in BRCA2 (BRCA2*c.9976A>T; p.Lys3326*; rs11571833) has been found to be associated with small increased risks of breast cancer. However, it is not clear to what extent linkage disequilibrium with fully pathogenic mutations might account for this association. There is scant information about the effect of K3326X in other hormone-related cancers. Methods: Using weighted logistic regression, we analyzed data from the large iCOGS study including 76 637 cancer case patients and 83 796 control patients to estimate odds ratios (ORw) and 95% confidence intervals (CIs) for K3326X variant carriers in relation to breast, ovarian, and prostate cancer risks, with weights defined as probability of not having a pathogenic BRCA2 variant. Using Cox proportional hazards modeling, we also examined the associations of K3326X with breast and ovarian cancer risks among 7183 BRCA1 variant carriers. All statistical tests were two-sided. Results: The K3326X variant was associated with breast (ORw = 1.28, 95% CI = 1.17 to 1.40, P = 5.9x10- 6) and invasive ovarian cancer (ORw = 1.26, 95% CI = 1.10 to 1.43, P = 3.8x10-3). These associations were stronger for serous ovarian cancer and for estrogen receptor–negative breast cancer (ORw = 1.46, 95% CI = 1.2 to 1.70, P = 3.4x10-5 and ORw = 1.50, 95% CI = 1.28 to 1.76, P = 4.1x10-5, respectively). For BRCA1 mutation carriers, there was a statistically significant inverse association of the K3326X variant with risk of ovarian cancer (HR = 0.43, 95% CI = 0.22 to 0.84, P = .013) but no association with breast cancer. No association with prostate cancer was observed. Conclusions: Our study provides evidence that the K3326X variant is associated with risk of developing breast and ovarian cancers independent of other pathogenic variants in BRCA2. Further studies are needed to determine the biological mechanism of action responsible for these associations

BRCA1 mutations and other sequence variants in a population-based sample of Australian women with breast cancer

The frequency, in women with breast cancer, of mutations and other variants in the susceptibility gene, BRCA1, was investigated using a population-based case–control-family study. Cases were women living in Melbourne or Sydney, Australia, with histologically confirmed, first primary, invasive breast cancer, diagnosed before the age of 40 years, recorded on the state Cancer Registries. Controls were women without breast cancer, frequency-matched for age, randomly selected from electoral rolls. Full manual sequencing of the coding region of BRCA1 was conducted in a randomly stratified sample of 91 cases; 47 with, and 44 without, a family history of breast cancer in a first- or second-degree relative. All detected variants were tested in a random sample of 67 controls. Three cases with a (protein-truncating) mutation were detected. Only one case had a family history; her mother had breast cancer, but did not carry the mutation. The proportion of Australian women with breast cancer before age 40 who carry a germline mutation in BRCA1 was estimated to be 3.8% (95% Cl 0.3–12.6%). Seven rare variants were also detected, but for none was there evidence of a strong effect on breast cancer susceptibility. Therefore, on a population basis, rare variants are likely to contribute little to breast cancer incidence. © 1999 Cancer Research Campaig