Deep eutectic solvents based on N-methylacetamide and a lithium salt as suitable electrolytes for lithium-ion batteries

In this work, we present a study on the physical and electrochemical properties of three new Deep Eutectic Solvents (DESs) based on N-methylacetamide (MAc) and a lithium salt (LiX, with X = bis[(trifluoromethyl)sulfonyl]imide, TFSI; hexafluorophosphate, PF; or nitrate, NO). Based on DSC measurements, it appears that these systems are liquid at room temperature for a lithium salt mole fraction ranging from 0.10 to 0.35. The temperature dependences of the ionic conductivity and the viscosity of these DESs are correctly described by using the Vogel-Tammann-Fulcher (VTF) type fitting equation, due to the strong interactions between Li, X and MAc in solution. Furthermore, these electrolytes possess quite large electrochemical stability windows up to 4.7-5 V on Pt, and demonstrate also a passivating behavior toward the aluminum collector at room temperature. Based on these interesting electrochemical properties, these selected DESs can be classified as potential and promising electrolytes for lithium-ion batteries (LIBs). For this purpose, a test cell was then constructed and tested at 25 °C, 60 °C and 80 °C by using each selected DES as an electrolyte and LiFePO (LFP) material as a cathode. The results show a good compatibility between each DES and LFP electrode material. A capacity of up to 160 mA h g with a good efficiency (99%) is observed in the DES based on the LiNO salt at 60 °C despite the presence of residual water in the electrolyte. Finally preliminary tests using a LFP/DES/LTO (lithium titanate) full cell at room temperature clearly show that LiTFSI-based DES can be successfully introduced into LIBs. Considering the beneficial properties, especially, the cost of these electrolytes, such introduction could represent an important contribution for the realization of safer and environmentally friendly LIBs. © 2013 the Owner Societies.

Controlling Subnanometer Gaps in Plasmonic Dimers Using Graphene

Graphene is used as the thinnest possible spacer between gold nanoparticles and a gold substrate. This creates a robust, repeatable, and stable subnanometer gap for massive plasmonic field enhancements. White light spectroscopy of single 80 nm gold nanoparticles reveals plasmonic coupling between the particle and its image within the gold substrate. While for a single graphene layer, spectral doublets from coupled dimer modes are observed shifted into the near-infrared, these disappear for increasing numbers of layers. These doublets arise from charger-transfer-sensitive gap plasmons, allowing optical measurement to access out-of-plane conductivity in such layered systems. Gating the graphene can thus directly produce plasmon tuning.

Use of the γ-H2AX Assay to Investigate DNA Repair Dynamics Following Multiple Radiation Exposures

Radiation therapy is one of the most common and effective strategies used to treat cancer. The irradiation is usually performed with a fractionated scheme, where the dose required to kill tumour cells is given in several sessions, spaced by specific time intervals, to allow healthy tissue recovery. In this work, we examined the DNA repair dynamics of cells exposed to radiation delivered in fractions, by assessing the response of histone-2AX (H2AX) phosphorylation (γ-H2AX), a marker of DNA double strand breaks. γ-H2AX foci induction and disappearance were monitored following split dose irradiation experiments in which time interval between exposure and dose were varied. Experimental data have been coupled to an analytical theoretical model, in order to quantify key parameters involved in the foci induction process. Induction of γ-H2AX foci was found to be affected by the initial radiation exposure with a smaller number of foci induced by subsequent exposures. This was compared to chromatin relaxation and cell survival. The time needed for full recovery of γ-H2AX foci induction was quantified (12 hours) and the 1:1 relationship between radiation induced DNA double strand breaks and foci numbers was critically assessed in the multiple irradiation scenarios.

Identification of a BRCA1-mRNA Splicing Complex Required for Efficient DNA Repair and Maintenance of Genomic Stability

Mutations within BRCA1 predispose carriers to a high risk of breast and ovarian cancers. BRCA1 functions to maintain genomic stability through the assembly of multiple protein complexes involved in DNA repair, cell-cycle arrest, and transcriptional regulation. Here, we report the identification of a DNA damage-induced BRCA1 protein complex containing BCLAF1 and other key components of the mRNA-splicing machinery. In response to DNA damage, this complex regulates pre-mRNA splicing of a number of genes involved in DNA damage signaling and repair, thereby promoting the stability of these transcripts/proteins. Further, we show that abrogation of this complex results in sensitivity to DNA damage, defective DNA repair, and genomic instability. Interestingly, mutations in a number of proteins found within this complex have been identified in numerous cancer types. These data suggest that regulation of splicing by the BRCA1-mRNA splicing complex plays an important role in the cellular response to DNA damage.

Vibration based seismic damage identification procedure for a steel concentrically braced frame structure

An approach for seismic damage identification of a single-storey steel concentrically braced frame (CBF) structure is presented through filtering and double integration of a recorded acceleration signal. A band-pass filter removes noise from the acceleration signal followed by baseline correction being used to reduce the drift in velocity and displacement during numerical integration. The pre-processing achieves reliable numerical integration that predicts the displacement response accurately when compared to the measured lateral in-plane displacement of the CBF structure. The lateral displacement of the CBF structure is used to infer buckling and yielding of bracing members through seismic tests. The level of interstorey drift of the CBF during a seismic excitation allows the yield and buckling of the bracing members to be identified and indirectly detects damage based on exceedance of calculated displacement limits. The calculated buckling and yielding displacement threshold limits used to identify damage are demonstrated to accurately identify initial buckling and yielding in the bracing members.

Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture

Approaches exploiting trait distribution extremes may be used to identify loci associated with common traits, but it is unknown whether these loci are generalizable to the broader population. In a genome-wide search for loci associated with the upper versus the lower 5th percentiles of body mass index, height and waist-to-hip ratio, as well as clinical classes of obesity, including up to 263,407 individuals of European ancestry, we identified 4 new loci (IGFBP4, H6PD, RSRC1 and PPP2R2A) influencing height detected in the distribution tails and 7 new loci (HNF4G, RPTOR, GNAT2, MRPS33P4, ADCY9, HS6ST3 and ZZZ3) for clinical classes of obesity. Further, we find a large overlap in genetic structure and the distribution of variants between traits based on extremes and the general population and little etiological heterogeneity between obesity subgroups.

The Pan-STARRS Moving Object Processing System

We describe the Pan-STARRS Moving Object Processing System (MOPS), a modern software package that produces automatic asteroid discoveries and identifications from catalogs of transient detections from next-generation astronomical survey telescopes. MOPS achieves >99.5% efficiency in producing orbits from a synthetic but realistic population of asteroids whose measurements were simulated for a Pan-STARRS4-class telescope. Additionally, using a nonphysical grid population, we demonstrate that MOPS can detect populations of currently unknown objects such as interstellar asteroids. MOPS has been adapted successfully to the prototype Pan-STARRS1 telescope despite differences in expected false detection rates, fill-factor loss, and relatively sparse observing cadence compared to a hypothetical Pan-STARRS4 telescope and survey. MOPS remains highly efficient at detecting objects but drops to 80% efficiency at producing orbits. This loss is primarily due to configurable MOPS processing limits that are not yet tuned for the Pan-STARRS1 mission. The core MOPS software package is the product of more than 15 person-years of software development and incorporates countless additional years of effort in third-party software to perform lower-level functions such as spatial searching or orbit determination. We describe the high-level design of MOPS and essential subcomponents, the suitability of MOPS for other survey programs, and suggest a road map for future MOPS development.

Anti-TNF antibody-induced psoriasiform skin lesions in patients with inflammatory bowel disease are characterised by interferon-γ-expressing Th1 cells and IL-17A/IL-22-expressing Th17 cells and respond to anti-IL-12/IL-23 antibody treatment

Background We analysed incidence, predictors, histological features and specific treatment options of anti-tumour necrosis factor alpha (TNF-alpha) antibody-induced psoriasiform skin lesions in patients with inflammatory bowel diseases (IBD).

Design Patients with IBD were prospectively screened for anti-TNF-induced psoriasiform skin lesions. Patients were genotyped for IL23R and IL12B variants. Skin lesions were examined for infiltrating Th1 and Th17 cells. Patients with severe lesions were treated with the anti-interleukin (IL)-12/IL-23 p40 antibody ustekinumab.

Results Among 434 anti-TNF-treated patients with IBD, 21 (4.8%) developed psoriasiform skin lesions. Multiple logistic regression revealed smoking (p=0.007; OR 4.24, 95% CI 1.55 to 13.60) and an increased body mass index (p=0.029; OR 1.12, 95% CI 1.01 to 1.24) as main predictors for these lesions. Nine patients with Crohn's disease and with severe psoriasiform lesions and/or anti-TNF antibody-induced alopecia were successfully treated with the anti-p40-IL-12/IL-23 antibody ustekinumab (response rate 100%). Skin lesions were histologically characterised by infiltrates of IL-17A/IL-22-secreting T helper 17 (Th17) cells and interferon (IFN)-gamma-secreting Th1 cells and IFN-alpha-expressing cells. IL-17A expression was significantly stronger in patients requiring ustekinumab than in patients responding to topical therapy (p=0.001). IL23R genotyping suggests disease-modifying effects of rs11209026 (p.Arg381Gln) and rs7530511 (p.Leu310Pro) in patients requiring ustekinumab.

Conclusions New onset psoriasiform skin lesions develop in nearly 5% of anti-TNF-treated patients with IBD. We identified smoking as a main risk factor for developing these lesions. Anti-TNF-induced psoriasiform skin lesions are characterised by Th17 and Th1 cell infiltrates. The number of IL-17A-expressing T cells correlates with the severity of skin lesions. Anti-IL-12/IL23 antibody therapy is a highly effective therapy for these lesions.

Prediction of integral type failures in semiconductor manufacturing through classification methods

Smart management of maintenances has become fundamental in manufacturing environments in order to decrease downtime and costs associated with failures. Predictive Maintenance (PdM) systems based on Machine Learning (ML) techniques have the possibility with low added costs of drastically decrease failures-related expenses; given the increase of availability of data and capabilities of ML tools, PdM systems are becoming really popular, especially in semiconductor manufacturing. A PdM module based on Classification methods is presented here for the prediction of integral type faults that are related to machine usage and stress of equipment parts. The module has been applied to an important class of semiconductor processes, ion-implantation, for the prediction of ion-source tungsten filament breaks. The PdM has been tested on a real production dataset. © 2013 IEEE.

A predictive maintenance system for integral type faults based on support vector machines:An application to ion implantation

In semiconductor fabrication processes, effective management of maintenance operations is fundamental to decrease costs associated with failures and downtime. Predictive Maintenance (PdM) approaches, based on statistical methods and historical data, are becoming popular for their predictive capabilities and low (potentially zero) added costs. We present here a PdM module based on Support Vector Machines for prediction of integral type faults, that is, the kind of failures that happen due to machine usage and stress of equipment parts. The proposed module may also be employed as a health factor indicator. The module has been applied to a frequent maintenance problem in semiconductor manufacturing industry, namely the breaking of the filament in the ion-source of ion-implantation tools. The PdM has been tested on a real production dataset. © 2013 IEEE.

Learning from time series:Supervised Aggregative Feature Extraction

Many modeling problems require to estimate a scalar output from one or more time series. Such problems are usually tackled by extracting a fixed number of features from the time series (like their statistical moments), with a consequent loss in information that leads to suboptimal predictive models. Moreover, feature extraction techniques usually make assumptions that are not met by real world settings (e.g. uniformly sampled time series of constant length), and fail to deliver a thorough methodology to deal with noisy data. In this paper a methodology based on functional learning is proposed to overcome the aforementioned problems; the proposed Supervised Aggregative Feature Extraction (SAFE) approach allows to derive continuous, smooth estimates of time series data (yielding aggregate local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The SAFE paradigm enjoys several properties like closed form solution, incorporation of first and second order derivative information into the regressor matrix, interpretability of the generated functional predictor and the possibility to exploit Reproducing Kernel Hilbert Spaces setting to yield nonlinear predictive models. Simulation studies are provided to highlight the strengths of the new methodology w.r.t. standard unsupervised feature selection approaches. © 2012 IEEE.

MSC-clustering and forward stepwise regression for virtual metrology in highly correlated input spaces

Increasingly semiconductor manufacturers are exploring opportunities for virtual metrology (VM) enabled process monitoring and control as a means of reducing non-value added metrology and achieving ever more demanding wafer fabrication tolerances. However, developing robust, reliable and interpretable VM models can be very challenging due to the highly correlated input space often associated with the underpinning data sets. A particularly pertinent example is etch rate prediction of plasma etch processes from multichannel optical emission spectroscopy data. This paper proposes a novel input-clustering based forward stepwise regression methodology for VM model building in such highly correlated input spaces. Max Separation Clustering (MSC) is employed as a pre-processing step to identify a reduced srt of well-conditioned, representative variables that can then be used as inputs to state-of-the-art model building techniques such as Forward Selection Regression (FSR), Ridge regression, LASSO and Forward Selection Ridge Regression (FCRR). The methodology is validated on a benchmark semiconductor plasma etch dataset and the results obtained are compared with those achieved when the state-of-art approaches are applied directly to the data without the MSC pre-processing step. Significant performance improvements are observed when MSC is combined with FSR (13%) and FSRR (8.5%), but not with Ridge Regression (-1%) or LASSO (-32%). The optimal VM results are obtained using the MSC-FSR and MSC-FSRR generated models. © 2012 IEEE.