Spin-dependent Andreev reflection tunneling through a quantum dot with intradot spin-flip scattering

We study Andreev reflection (AR) tunneling through a quantum dot (QD) connected to a ferromagnet and a superconductor, in which the intradot spin-flip interaction is included. By using the nonequibrium-Green-function method, the formula of the linear AR conductance is derived at zero temperature. It is found that competition between the intradot spin-flip scattering and the tunneling coupling to the leads dominantes resonant behaviours of the AR conductance versus the gate voltage.A weak spin-flip scattering leads to a single peak resonance.However, with the spin-flip scattering strength increasing, the AR conductance will develop into a double peak resonannce implying a novel structure in the tunneling spectrum of the AR conductance. Besides, the effect of the spin-dependent tunneling couplings, the matching of Fermi velocity, and the spin polarization of the ferromagnet on the AR conductance is eximined in detail.Comment: 14 pages, 4 figure

Ideal Unconventional Weyl Point in a Chiral Photonic Metamaterial

Unconventional Weyl points (WPs), carrying topological charge 2 or higher, possess interesting properties different from ordinary charge-1 WPs, including multiple Fermi arcs that stretch over a large portion of the Brillouin zone. Thus far, such WPs have been observed in chiral materials and acoustic metamaterials, but there has been no clean demonstration in photonics in which the unconventional photonic WPs are separated from trivial bands. We experimentally realize an ideal symmetry-protected photonic charge-2 WP in a three-dimensional topological chiral microwave metamaterial. We use field mapping to directly observe the projected bulk dispersion, as well as the two long surface arcs that form a noncontractible loop wrapping around the surface Brillouin zone. The surface states span a record-wide frequency window of around 22.7% relative bandwidth. We demonstrate that the surface states exhibit a novel topological self-collimation property and are robust against disorder. This work provides an ideal photonic platform for exploring fundamental physics and applications of unconventional WPs.Comment: 6 pages, 4 figure

Narrowband ultraviolet B response in cutaneous T-cell lymphoma is characterized by increased bacterial diversity and reduced Staphylococcus aureus and Staphylococcus lugdunensis

Skin microbiota have been linked to disease activity in cutaneous T-cell lymphoma (CTCL). As the skin microbiome has been shown to change after exposure to narrowband ultraviolet B (nbUVB) phototherapy, a common treatment modality used for CTCL, we performed a longitudinal analysis of the skin microbiome in CTCL patients treated with nbUVB. 16S V4 rRNA gene amplicon sequencing for genus-level taxonomic resolution, tuf2 amplicon next generation sequencing for staphylococcal speciation, and bioinformatics were performed on DNA extracted from skin swabs taken from lesional and non-lesional skin of 25 CTCL patients receiving nbUVB and 15 CTCL patients not receiving nbUVB from the same geographical region. Disease responsiveness to nbUVB was determined using the modified Severity Weighted Assessment Tool: 14 (56%) patients responded to nbUVB while 11 (44%) patients had progressive disease. Microbial α-diversity increased in nbUVB-responders after phototherapy. The relative abundance of Staphylococcus, Corynebacterium, Acinetobacter, Streptococcus, and Anaerococcus differentiated nbUVB responders and non-responders after treatment (q\u3c0.05). Microbial signatures of nbUVB-treated patients demonstrated significant post-exposure depletion of S. aureus (q=0.024) and S. lugdunensis (q=0.004) relative abundances. Before nbUVB, responder lesional skin harboured higher levels of S. capitis (q=0.028) and S. warneri (q=0.026) than non-responder lesional skin. S. capitis relative abundance increased in the lesional skin of responders (q=0.05) after phototherapy; a similar upward trend was observed in non-responders (q=0.09). Post-treatment skin of responders exhibited significantly reduced S. aureus (q=0.008) and significantly increased S. hominis (q=0.006), S. pettenkoferi (q=0.021), and S. warneri (q=0.029) relative abundances compared to that of no-nbUVB patients. Staphylococcus species abundance was more similar between non-responders and no-nbUVB patients than between responders and no-nbUVB patients. In sum, the skin microbiome of CTCL patients who respond to nbUVB is different from that of non-responders and untreated patients, and is characterized by shifts in S. aureus and S. lugdunensis. Non-responsiveness to phototherapy may reflect more aggressive disease at baseline

Effective electro-optical modulation with high extinction ratio by a graphene-silicon microring resonator

Graphene opens up for novel optoelectronic applications thanks to its high carrier mobility, ultra-large absorption bandwidth, and extremely fast material response. In particular, the opportunity to control optoelectronic properties through tuning of Fermi level enables electro-optical modulation, optical-optical switching, and other optoelectronics applications. However, achieving a high modulation depth remains a challenge because of the modest graphene-light interaction in the graphene-silicon devices, typically, utilizing only a monolayer or few layers of graphene. Here, we comprehensively study the interaction between graphene and a microring resonator, and its influence on the optical modulation depth. We demonstrate graphene-silicon microring devices showing a high modulation depth of 12.5 dB with a relatively low bias voltage of 8.8 V. On-off electro-optical switching with an extinction ratio of 3.8 dB is successfully demonstrated by applying a square-waveform with a 4 V peak-to-peak voltage.Comment: 12 pages, including 7 figure

Plasmon-phonon coupling in large-area graphene dot and antidot arrays

Nanostructured graphene on SiO2 substrates pave the way for enhanced light-matter interactions and explorations of strong plasmon-phonon hybridization in the mid-infrared regime. Unprecedented large-area graphene nanodot and antidot optical arrays are fabricated by nanosphere lithography, with structural control down to the sub-100 nanometer regime. The interaction between graphene plasmon modes and the substrate phonons is experimentally demonstrated and structural control is used to map out the hybridization of plasmons and phonons, showing coupling energies of the order 20 meV. Our findings are further supported by theoretical calculations and numerical simulations.Comment: 7 pages including 6 figures. Supporting information is available upon request to author

Whole-exome sequencing identifies genes associated with Tourette’s disorder in multiplex families

Tourette’s Disorder (TD) is a neurodevelopmental disorder (NDD) that affects about 0.7% of the population and is one of the most heritable NDDs. Nevertheless, because of its polygenic nature and genetic heterogeneity, the genetic etiology of TD is not well understood. In this study, we combined the segregation information in 13 TD multiplex families with high-throughput sequencing and genotyping to identify genes associated with TD. Using whole-exome sequencing and genotyping array data, we identified both small and large genetic variants within the individuals. We then combined multiple types of evidence to prioritize candidate genes for TD, including variant segregation pattern, variant function prediction, candidate gene expression, protein–protein interaction network, candidate genes from previous studies, etc. From the 13 families, 71 strong candidate genes were identified, including both known genes for NDDs and novel genes, such as HtrA Serine Peptidase 3 (HTRA3), Cadherin-Related Family Member 1 (CDHR1), and Zinc Finger DHHC-Type Palmitoyltransferase 17 (ZDHHC17). The candidate genes are enriched in several Gene Ontology categories, such as dynein complex and synaptic membrane. Candidate genes and pathways identified in this study provide biological insight into TD etiology and potential targets for future studies.This study was supported by a grant from the National Institute of Mental Health (R01MH092293 to GAH and JAT) and by a grant from the New Jersey Center for Tourette Syndrome (to GAH and JAT). This study was also supported by grants from the National Institute of Mental Health (K08MH099424 to TVF) and the National Institute for Environmental Health Science (R01 ES021462 for YSK and BLL). PM has received grants from the Instituto de Salud Carlos III (PI10/01674, PI13/01461), the Consejería de Economía, Innovación, Ciencia y Empresa de la Junta de Andalucía (CVI-02526, CTS-7685), the Consejería de Salud y Bienestar Social de la Junta de Andalucía (PI-0741/2010, PI-0437-2012, PI-0471-2013), the Sociedad Andaluza de Neurología, the Fundación Alicia Koplowitz, the Fundación Mutua Madrileña, and the Jaques and Gloria Gossweiler Foundation. AM has received grants from the Fundacion Alicia Koplowitz and belongs to the research group of the Comissionat per Universitats i Recerca del Departmanent d’Innovacio (DIUE) 2009SGR1119. AM has received grants from the Deutsche Forschungsgemeinschaft (DFG: MU 1692/3-1, MU 1692/4-1, and FOR 2698). AJW received a Young Investigator Award from Tourette Association of America. IH declares that all research at Great Ormond Street Hospital NHS Foundation Trust and UCL Great Ormond Street Institute of Child Health is made possible by the NIHR Great Ormond Street Hospital Biomedical Research Centre

Space resource utilization of dominant species integrates abundance- and functional-based processes for better predictions of plant diversity dynamics

Sustainable ecosystem management relies on our ability to predict changes in plant diversity and to understand the underlying mechanisms. Empirical evidence demonstrates that abundance- and functional-based processes simultaneously explain the loss of plant diversity in response to human activities. Recently, a novel indicator based on percent cover (CoverD) and maximum height (HeightD) of the dominant plant species – space resource utilization (SRUD) – has proven to give robust and better predictions of plant diversity dynamics than community biomass. Whether the superior predictive ability of SRUD is due to its capacity to simultaneously capture abundance- and functional-based processes remains unknown. Here, we tested this hypothesis by quantifying mechanistic links between changes in SRUD and biodiversity in response to nutrients and herbivores. Furthermore, we assessed the relative contribution of dominant, intermediate and rare species to reduced density of individuals by combining null model analysis with field experiments. We found that SRUD successfully captured changes in ground-level light availability and changes in the number of individuals to predict plant diversity dynamics, and each of CoverD and HeightD partly and independently contributed to both processes. Comparative results from null model analysis and field experiments confirmed that individual losses of dominant, intermediate and rare species followed non-random processes. Specifically, compared with random loss process, rare species lost proportionally more individuals and thus disproportionately contributed to species loss, while dominant and intermediate species lost less. Our results demonstrate that SRUD captures both abundance- and functional-based processes thus explaining why SRUD provides more accurate predictions of changes in species diversity. Given that rare species can play an important role in shaping community structure, resisting against invasion, impacting higher trophic levels and providing multiple ecosystem functions, reducing the SRU of dominant species could alleviate the risk of exclusion of rare species by mitigating abundance- and functional-based competition processes

Spondin-2 (SPON2), a More Prostate-Cancer-Specific Diagnostic Biomarker

BACKGROUND: Prostate-specific antigen (PSA) screening, although common, has recently been called into question. To find prostate cancer (PCa) diagnostic biomarkers that can make up for the defects of PSA, we compared the secretomes of several benign and PCa cell lines, selected candidate molecules, and then confirmed their clinical value. METHODOLOGY/PRINCIPAL FINDINGS: We first identified extracellular proteins by two-dimensional gel electrophoresis (2-DE) coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS) identification. We then validated the secreted proteins on a cellular level, and finally determined whether they could be used as PCa diagnostic biomarkers using prostate tissue and serum specimens of Chinese volunteers by immunohistostaining and sandwich ELISA. We obtained credible extracellular protein 2-DE graphs of prostate cell lines. The 5 spots that showed superior repeatability were selected for LC-MS/MS analysis, which identified seven candidate molecules. One of the candidate molecules, spondin-2 (SPON2), was only expressed in the conditioned media (CM) of androgen receptor (AR) positive PCa cell lines. Using tissue microarray by immunohistostaining, we found SPON2 to be over-expressed in PCa. SPON2 staining was more intense in Gleason score sum 7-8 and in PCa patients with metastasis. By receiver operator characteristic (ROC) curve analysis, we found that the serum SPON2 level was elevated in PCa patients, showing sensitivity and specificity suitable for diagnostic use. We also found that SPON2 could be used to identify PCa patients with serum PSA levels no higher than 10 ng/ml from healthy elderly men. CONCLUSION/SIGNIFICANCE: SPON2 is a new serum and histological diagnostic biomarker for PCa. It can avoid some of the problems of PSA testing and was here found to offer relatively high sensitivity and specificity relative to PSA

Prediction of protein binding sites in protein structures using hidden Markov support vector machine

<p>Abstract</p> <p>Background</p> <p>Predicting the binding sites between two interacting proteins provides important clues to the function of a protein. Recent research on protein binding site prediction has been mainly based on widely known machine learning techniques, such as artificial neural networks, support vector machines, conditional random field, etc. However, the prediction performance is still too low to be used in practice. It is necessary to explore new algorithms, theories and features to further improve the performance.</p> <p>Results</p> <p>In this study, we introduce a novel machine learning model hidden Markov support vector machine for protein binding site prediction. The model treats the protein binding site prediction as a sequential labelling task based on the maximum margin criterion. Common features derived from protein sequences and structures, including protein sequence profile and residue accessible surface area, are used to train hidden Markov support vector machine. When tested on six data sets, the method based on hidden Markov support vector machine shows better performance than some state-of-the-art methods, including artificial neural networks, support vector machines and conditional random field. Furthermore, its running time is several orders of magnitude shorter than that of the compared methods.</p> <p>Conclusion</p> <p>The improved prediction performance and computational efficiency of the method based on hidden Markov support vector machine can be attributed to the following three factors. Firstly, the relation between labels of neighbouring residues is useful for protein binding site prediction. Secondly, the kernel trick is very advantageous to this field. Thirdly, the complexity of the training step for hidden Markov support vector machine is linear with the number of training samples by using the cutting-plane algorithm.</p