Mid-infrared optical parametric amplifier using silicon nanophotonic waveguides

All-optical signal processing is envisioned as an approach to dramatically decrease power consumption and speed up performance of next-generation optical telecommunications networks. Nonlinear optical effects, such as four-wave mixing (FWM) and parametric gain, have long been explored to realize all-optical functions in glass fibers. An alternative approach is to employ nanoscale engineering of silicon waveguides to enhance the optical nonlinearities by up to five orders of magnitude, enabling integrated chip-scale all-optical signal processing. Previously, strong two-photon absorption (TPA) of the telecom-band pump has been a fundamental and unavoidable obstacle, limiting parametric gain to values on the order of a few dB. Here we demonstrate a silicon nanophotonic optical parametric amplifier exhibiting gain as large as 25.4 dB, by operating the pump in the mid-IR near one-half the band-gap energy (E~0.55eV, lambda~2200nm), at which parasitic TPA-related absorption vanishes. This gain is high enough to compensate all insertion losses, resulting in 13 dB net off-chip amplification. Furthermore, dispersion engineering dramatically increases the gain bandwidth to more than 220 nm, all realized using an ultra-compact 4 mm silicon chip. Beyond its significant relevance to all-optical signal processing, the broadband parametric gain also facilitates the simultaneous generation of multiple on-chip mid-IR sources through cascaded FWM, covering a 500 nm spectral range. Together, these results provide a foundation for the construction of silicon-based room-temperature mid-IR light sources including tunable chip-scale parametric oscillators, optical frequency combs, and supercontinuum generators

Probing EWSB Naturalness in Unified SUSY Models with Dark Matter

We have studied Electroweak Symmetry Breaking (EWSB) fine-tuning in the context of two unified Supersymmetry scenarios: the Constrained Minimal Supersymmetric Model (CMSSM) and models with Non-Universal Higgs Masses (NUHM), in light of current and upcoming direct detection dark matter experiments. We consider both those models that satisfy a one-sided bound on the relic density of neutralinos, $\Omega_{\chi} h^2 < 0.12$, and also the subset that satisfy the two-sided bound in which the relic density is within the 2 sigma best fit of WMAP7 + BAO + H0 data. We find that current direct detection searches for dark matter probe the least fine-tuned regions of parameter-space, or equivalently those of lowest Higgs mass parameter $\mu$, and will tend to probe progressively more and more fine-tuned models, though the trend is more pronounced in the CMSSM than in the NUHM. Additionally, we examine several subsets of model points, categorized by common mass hierarchies; M_{\chi_0} \sim M_{\chi^\pm}, M_{\chi_0} \sim M_{\stau}, M_{\chi_0} \sim M_{\stop_1}, the light and heavy Higgs poles, and any additional models classified as "other"; the relevance of these mass hierarchies is their connection to the preferred neutralino annihilation channel that determines the relic abundance. For each of these subsets of models we investigated the degree of fine-tuning and discoverability in current and next generation direct detection experiments.Comment: 26 pages, 10 figures. v2: references added. v3: matches published versio

Analysis and Computational Dissection of Molecular Signature Multiplicity

Molecular signatures are computational or mathematical models created to diagnose disease and other phenotypes and to predict clinical outcomes and response to treatment. It is widely recognized that molecular signatures constitute one of the most important translational and basic science developments enabled by recent high-throughput molecular assays. A perplexing phenomenon that characterizes high-throughput data analysis is the ubiquitous multiplicity of molecular signatures. Multiplicity is a special form of data analysis instability in which different analysis methods used on the same data, or different samples from the same population lead to different but apparently maximally predictive signatures. This phenomenon has far-reaching implications for biological discovery and development of next generation patient diagnostics and personalized treatments. Currently the causes and interpretation of signature multiplicity are unknown, and several, often contradictory, conjectures have been made to explain it. We present a formal characterization of signature multiplicity and a new efficient algorithm that offers theoretical guarantees for extracting the set of maximally predictive and non-redundant signatures independent of distribution. The new algorithm identifies exactly the set of optimal signatures in controlled experiments and yields signatures with significantly better predictivity and reproducibility than previous algorithms in human microarray gene expression datasets. Our results shed light on the causes of signature multiplicity, provide computational tools for studying it empirically and introduce a framework for in silico bioequivalence of this important new class of diagnostic and personalized medicine modalities

Improving the performance of DomainDiscovery of protein domain boundary assignment using inter-domain linker index

BACKGROUND: Knowledge of protein domain boundaries is critical for the characterisation and understanding of protein function. The ability to identify domains without the knowledge of the structure – by using sequence information only – is an essential step in many types of protein analyses. In this present study, we demonstrate that the performance of DomainDiscovery is improved significantly by including the inter-domain linker index value for domain identification from sequence-based information. Improved DomainDiscovery uses a Support Vector Machine (SVM) approach and a unique training dataset built on the principle of consensus among experts in defining domains in protein structure. The SVM was trained using a PSSM (Position Specific Scoring Matrix), secondary structure, solvent accessibility information and inter-domain linker index to detect possible domain boundaries for a target sequence. RESULTS: Improved DomainDiscovery is compared with other methods by benchmarking against a structurally non-redundant dataset and also CASP5 targets. Improved DomainDiscovery achieves 70% accuracy for domain boundary identification in multi-domains proteins. CONCLUSION: Improved DomainDiscovery compares favourably to the performance of other methods and excels in the identification of domain boundaries for multi-domain proteins as a result of introducing support vector machine with benchmark_2 dataset

“Hot Hand” on Strike: Bowling Data Indicates Correlation to Recent Past Results, Not Causality

Recently, the “hot hand” phenomenon regained interest due to the availability and accessibility of large scale data sets from the world of sports. In support of common wisdom and in contrast to the original conclusions of the seminal paper about this phenomenon by Gilovich, Vallone and Tversky in 1985, solid evidences were supplied in favor of the existence of this phenomenon in different kinds of data. This came after almost three decades of ongoing debates whether the “hot hand” phenomenon in sport is real or just a mis-perception of human subjects of completely random patterns present in reality. However, although this phenomenon was shown to exist in different sports data including basketball free throws and bowling strike rates, a somehow deeper question remained unanswered: are these non random patterns results of causal, short term, feedback mechanisms or simply time fluctuations of athletes performance. In this paper, we analyze large amounts of data from the Professional Bowling Association(PBA). We studied the results of the top 100 players in terms of the number of available records (summed into more than 450,000 frames). By using permutation approach and dividing the analysis into different aggregation levels we were able to supply evidence for the existence of the “hot hand” phenomenon in the data, in agreement with previous studies. Moreover, by using this approach, we were able to demonstrate that there are, indeed, significant fluctuations from game to game for the same player but there is no clustering of successes (strikes) and failures (non strikes) within each game. Thus we were lead to the conclusion that bowling results show correlation to recent past results but they are not influenced by them in a causal manner

The Association Between Pre-pregnancy BMI and Preterm Delivery in a Diverse Southern California Population of Working Women

Whereas preterm birth has consistently been associated with low maternal pre-pregnancy weight, the relationship with high pre-pregnancy weight has been inconsistent. We quantified the pre-pregnancy BMI—preterm delivery (PTD) relationship using traditional BMI categories (underweight, normal weight, overweight and obese) as well as continuous BMI. Eligible women participated in California’s statewide prenatal screening program, worked during pregnancy, and delivered a live singleton birth in Southern California in 2002–2003. The final analytic sample included 354 cases delivering at <37 weeks, as identified by clinical estimate of gestational age from screening records, and 710 term normal-birthweight controls. Multivariable logistic regression models using categorical BMI levels and continuous BMI were compared. In categorical analyses, PTD was significantly associated with pre-pregnancy underweight only. Nonparametric local regression revealed a V-shaped relationship between continuous BMI and PTD, with minimum risk at the high end of normal, around 24 kg/m2. The odds ratio (OR) for PTD associated with low BMI within the normal range (19 kg/m2) was 2.84 (95%CI = 1.61–5.01); ORs for higher BMI in the overweight (29 kg/m2) and obese (34 kg/m2) ranges were 1.42 (95%CI = 1.10–1.84) and 2.01 (95% CI = 1.20–3.39) respectively, relative to 24 kg/m2). BMI categories obscured the preterm delivery risk associated with low-normal, overweight, and obese BMI. We found that higher BMI up to around 24 kg/m2 is increasingly protective of preterm delivery, beyond which a higher body mass index becomes detrimental. Current NHLBI/WHO BMI categories may be inadequate for identifying women at higher risk for PTD

Expanding the Understanding of Biases in Development of Clinical-Grade Molecular Signatures: A Case Study in Acute Respiratory Viral Infections

The promise of modern personalized medicine is to use molecular and clinical information to better diagnose, manage, and treat disease, on an individual patient basis. These functions are predominantly enabled by molecular signatures, which are computational models for predicting phenotypes and other responses of interest from high-throughput assay data. Data-analytics is a central component of molecular signature development and can jeopardize the entire process if conducted incorrectly. While exploratory data analysis may tolerate suboptimal protocols, clinical-grade molecular signatures are subject to vastly stricter requirements. Closing the gap between standards for exploratory versus clinically successful molecular signatures entails a thorough understanding of possible biases in the data analysis phase and developing strategies to avoid them.Using a recently introduced data-analytic protocol as a case study, we provide an in-depth examination of the poorly studied biases of the data-analytic protocols related to signature multiplicity, biomarker redundancy, data preprocessing, and validation of signature reproducibility. The methodology and results presented in this work are aimed at expanding the understanding of these data-analytic biases that affect development of clinically robust molecular signatures.Several recommendations follow from the current study. First, all molecular signatures of a phenotype should be extracted to the extent possible, in order to provide comprehensive and accurate grounds for understanding disease pathogenesis. Second, redundant genes should generally be removed from final signatures to facilitate reproducibility and decrease manufacturing costs. Third, data preprocessing procedures should be designed so as not to bias biomarker selection. Finally, molecular signatures developed and applied on different phenotypes and populations of patients should be treated with great caution

The Human TPR Protein TTC4 Is a Putative Hsp90 Co-Chaperone Which Interacts with CDC6 and Shows Alterations in Transformed Cells

BACKGROUND: The human TTC4 protein is a TPR (tetratricopeptide repeat) motif-containing protein. The gene was originally identified as being localized in a genomic region linked to breast cancer and subsequent studies on melanoma cell lines revealed point mutations in the TTC4 protein that may be associated with the progression of malignant melanoma. METHODOLOGY/PRINCIPLE FINDINGS: Here we show that TTC4 is a nucleoplasmic protein which interacts with HSP90 and HSP70, and also with the replication protein CDC6. It has significant structural and functional similarities with a previously characterised Drosophila protein Dpit47. We show that TTC4 protein levels are raised in malignant melanoma cell lines compared to melanocytes. We also see increased TTC4 expression in a variety of tumour lines derived from other tissues. In addition we show that TTC4 proteins bearing some of the mutations previously identified from patient samples lose their interaction with the CDC6 protein. CONCLUSIONS/SIGNIFICANCE: Based on these results and our previous work with the Drosophila Dpit47 protein we suggest that TTC4 is an HSP90 co-chaperone protein which forms a link between HSP90 chaperone activity and DNA replication. We further suggest that the loss of the interaction with CDC6 or with additional client proteins could provide one route through which TTC4 could influence malignant development of cells