Imaging Strain-Localized Single-Photon Emitters in Layered GaSe below the Diffraction Limit.

Nanoscale strain control of exciton funneling is an increasingly critical tool for the scalable production of single photon emitters (SPEs) in two-dimensional materials. However, conventional far-field optical microscopies remain constrained in spatial resolution by the diffraction limit and thus can provide only a limited description of nanoscale strain localization of SPEs. Here, we quantify the effects of nanoscale heterogeneous strain on the energy and brightness of GaSe SPEs on nanopillars with correlative cathodoluminescence, photoluminescence, and atomic force microscopy, supported by density functional theory simulations. We report the strain-localized SPEs have a broad range of emission wavelengths from 620 to 900 nm. We reveal substantial strain-controlled SPE wavelength tunability over a ∼100 nm spectral range and 2 orders of magnitude enhancement in the SPE brightness at the pillar center due to Type-I exciton funneling. In addition, we show that radiative biexciton cascade processes contribute to observed CL photon superbunching. Also, the GaSe SPEs show excellent stability, where their properties remain unchanged after electron beam exposure. We anticipate that this comprehensive study on the nanoscale strain control of two-dimensional SPEs will provide key insights to guide the development of truly deterministic quantum photonics.

Improving Strain-localized GaSe Single Photon Emitters with Electrical Doping.

Exciton localization through nanoscale strain has been used to create highly efficient single-photon emitters (SPEs) in 2D materials. However, the strong Coulomb interactions between excitons can lead to nonradiative recombination through exciton-exciton annihilation, negatively impacting SPE performance. Here, we investigate the effect of Coulomb interactions on the brightness, single photon purity, and operating temperatures of strain-localized GaSe SPEs by using electrostatic doping. By gating GaSe to the charge neutrality point, the exciton-exciton annihilation nonradiative pathway is suppressed, leading to ∼60% improvement of emission intensity and an enhancement of the single photon purity g(2)(0) from 0.55 to 0.28. The operating temperature also increased from 4.5 K to 85 K consequently. This research provides insight into many-body interactions in excitons confined by nanoscale strain and lays the groundwork for the optimization of SPEs for optoelectronics and quantum photonics.

Identifying the charge density and dielectric environment of graphene using Raman spectroscopy and deep learning.

The impact of the environment on the properties of graphene such as strain, charge density, and dielectric environment can be evaluated by Raman spectroscopy. These environmental interactions are not trivial to determine since they affect the spectra in overlapping ways. Data pre-processing such as background subtraction and peak fitting is typically used. Moreover, collected spectroscopic data vary due to different experimental setups and environments. Such variations, artifacts, and environmental differences pose a challenge for accurate spectral analysis. In this work, we developed a deep learning model to overcome the effects of such variations and classify graphene Raman spectra according to different charge densities and dielectric environments. We consider two approaches: deep learning models and machine learning algorithms to classify spectra with slightly different charge densities or dielectric environments. These two approaches show similar success rates for high signal-to-noise data. However, deep learning models are less sensitive to noise. To improve the accuracy and generalization of all models, we use data augmentation through additive noise and peak shifting. We demonstrated the spectral classification with 99% accuracy using a convolutional neural net (CNN) model. The CNN model can classify Raman spectra of graphene with different charge doping levels and even subtle variations in the spectra of graphene on SiO2 and graphene on silanized SiO2. Our approach has the potential for fast and reliable estimation of graphene doping levels and dielectric environments. The proposed model paves the way for achieving efficient analytical tools to evaluate the properties of graphene.

Broadband micro-transient absorption spectroscopy enabled by improved lock-in amplification.

Recent breakthroughs in material development have increased the demand for characterization methods capable of probing nanoscale features on ultrafast time scales. As the sample reduces to atomically thin levels, an extremely low-level signal limits the feasibility of many experiments. Here, we present an affordable and easy-to-implement solution to expand the maximum sensitivity of lock-in detection systems used in transient absorption spectroscopy by multiple orders of magnitude. By implementation of a tuned RC circuit to the output of an avalanche photodiode, electric pulse shaping allows for vastly improved lock-in detection. Furthermore, a carefully designed "peak detector" circuit provides additional pulse shaping benefits, resulting in even more lock-in detection signal enhancement. We demonstrate the improvement of lock-in detection with each of these schemes by performing benchmark measurements of a white-light continuum signal and micro-transient absorption spectroscopy on a few-layer transition metal dichalcogenide sample. Our results show the practicality of ultrafast pump-probe spectroscopy for many high-sensitivity experimental schemes.

A Qualitative Examination of a School-Based Implementation of Computer-Assisted Cognitive-Behavioral Therapy for Child Anxiety.

Mental health treatment in schools has the potential to improve youth treatment access. However, school-specific barriers can make implementing evidence-based interventions difficult. Task-shifting (i.e., training lay staff to implement interventions) and computer-assisted interventions may mitigate these barriers. This paper reports on a qualitative examination of facilitators and barriers of a school-based implementation of a computer-assisted intervention for anxious youth (Camp Cope-A-Lot; CCAL). Participants (N = 45) included school staff in first through fourth grades. Providers attended a training in CCAL and received weekly, hour-long group consultation calls for three months. In the second year, the sustainability of CCAL use was assessed. Qualitative interviews were conducted after the first year (initial implementation) and second year (sustainability). Interviews were analyzed using the Consolidated Framework for Implementation Research domains to classify themes. Although participants reported that CCAL included useful skills, they expressed concerns about recommended session length (45 minutes) and frequency (weekly). Time burden of consultation calls was also a barrier. School staff facilitated implementation by enabling flexible scheduling for youth to be able to participate in the CCAL program. However, the sustainability of the program was limited due to competing school/time demands. Results suggest that even with computer assisted programs, there is a need to tailor interventions and implementation efforts to account for the time restrictions experienced by school-based service providers. Optimal fit between the intervention and specific school is important to maintain the potential benefits of computer-assisted treatments delivered by lay service providers in schools.

Mouse mutant phenotyping at scale reveals novel genes controlling bone mineral density.

The genetic landscape of diseases associated with changes in bone mineral density (BMD), such as osteoporosis, is only partially understood. Here, we explored data from 3,823 mutant mouse strains for BMD, a measure that is frequently altered in a range of bone pathologies, including osteoporosis. A total of 200 genes were found to significantly affect BMD. This pool of BMD genes comprised 141 genes with previously unknown functions in bone biology and was complementary to pools derived from recent human studies. Nineteen of the 141 genes also caused skeletal abnormalities. Examination of the BMD genes in osteoclasts and osteoblasts underscored BMD pathways, including vesicle transport, in these cells and together with in silico bone turnover studies resulted in the prioritization of candidate genes for further investigation. Overall, the results add novel pathophysiological and molecular insight into bone health and disease.

Results from the Child/Adolescent Anxiety Multimodal Longitudinal Study (CAMELS): Functional outcomes.

OBJECTIVE: To report functional outcomes from the multisite Child/Adolescent Anxiety Multimodal Extended Long-term Study (CAMELS), which examined the impact of youth anxiety treatment (cognitive-behavioral therapy [CBT], coping cat; Sertraline, SRT; COMB [CBT + SRT]; pill placebo) on (a) global and (b) domain-specific functioning assessed an average of 3.1 times, 3- to 12-years postrandomization (first assessment = mean 6.5 years postrandomization). METHOD: Three-hundred and 19 of 488 families from the Child/Adolescent Anxiety Multimodal Study (CAMS; Walkup et al., 2008) participated. Growth curve modeling examined the impact of treatment condition and acute treatment outcomes (i.e., response, remission) on global functioning, global and domain-specific impairment, and life satisfaction across follow-up visits. Logistic regressions explored the impact of treatment remission and condition on low frequency events (arrests/convictions) and education. RESULTS: Treatment responders and remitters demonstrated better global functioning, decreased overall impairment, and increased life satisfaction at follow-up. Treatment remission, but not response, predicted decreased domain-specific impairment (social relationships, self-care/independence, academic functioning), and maintenance of increased life satisfaction across follow-ups. Participants in the CBT condition, compared with pill placebo, demonstrated improved trajectories pertaining to life satisfaction, overall impairment, and impairment in academic functioning. Randomization to CBT or COMB treatment was associated with increasing employment rates. Trajectories for participants randomized to SRT was not significantly different from placebo. Treatment outcome and condition did not predict legal outcomes, school/work variables, or family life. CONCLUSION: Positive early intervention outcomes are associated with improved overall functioning, life satisfaction, and functioning within specific domains 6.5 years posttreatment. Treatment type differentially predicted trajectories of functioning. Findings support the positive impact of pediatric anxiety treatment into adolescence and early adulthood. (PsycINFO Database Record

Long tailed trions in monolayer MoS2: Temperature dependent asymmetry and resulting red-shift of trion photoluminescence spectra.

Monolayer molybdenum disulfide (MoS2) has emerged as a model system for studying many-body physics because the low dimensionality reduces screening leading to tightly bound states stable at room temperature. Further, the many-body states possess a pseudo-spin degree of freedom that corresponds with the two direct-gap valleys of the band structure, which can be optically manipulated. Here we focus on one bound state, the negatively charged trion. Unlike excitons, trions can radiatively decay with non-zero momentum by kicking out an electron, resulting in an asymmetric trion photoluminescence (PL) peak with a long low-energy tail and peak position that differs from the zero momentum trion energy. The asymmetry of the trion PL peak and resulting peak red-shift depends both on the trion size and a temperature-dependent contribution. Ignoring the trion asymmetry will result in over estimating the trion binding energy by nearly 20 meV at room temperature. We analyze the temperature-dependent PL to reveal the effective trion size, consistent with the literature, and the temperature dependence of the band gap and spin-orbit splitting of the valence band. This is the first time the temperature-dependence of the trion PL has been analyzed with such detail in any system.

2D Raman band splitting in graphene: Charge screening and lifting of the K-point Kohn anomaly.

Pristine graphene encapsulated in hexagonal boron nitride has transport properties rivalling suspended graphene, while being protected from contamination and mechanical damage. For high quality devices, it is important to avoid and monitor accidental doping and charge fluctuations. The 2D Raman double peak in intrinsic graphene can be used to optically determine charge density, with decreasing peak split corresponding to increasing charge density. We find strong correlations between the 2D 1 and 2D 2 split vs 2D line widths, intensities, and peak positions. Charge density fluctuations can be measured with orders of magnitude higher precision than previously accomplished using the G-band shift with charge. The two 2D intrinsic peaks can be associated with the "inner" and "outer" Raman scattering processes, with the counterintuitive assignment of the phonon closer to the K point in the KM direction (outer process) as the higher energy peak. Even low charge screening lifts the phonon Kohn anomaly near the K point for graphene encapsulated in hBN, and shifts the dominant intensity from the lower to the higher energy peak.

A Multi-Informant Examination of Maternal Symptoms and Autonomy Granting in Youth Anxiety.

Evidence suggests the important role of (a) parenting behaviors and (b) parental psychopathology in the development and maintenance of youth anxiety. Using a multi-informant approach, the current study examined the association of maternal autonomy granting and maternal symptoms (i.e., anxiety and depression) with youth anxiety among mothers and 88 youth (ages of 6-17) diagnosed with a principal anxiety disorder. Results from the generalized estimating equations (GEE) analyses indicated that mothers reported higher youth anxiety symptoms compared to youth self-reports. Youth-perceived maternal autonomy granting was inversely associated with youth anxiety, and maternal self-reported anxiety and depressive symptoms significantly moderated this relationship: As mothers reported higher anxiety and depressive symptoms, the inverse association between parental autonomy granting and youth anxiety weakened. The interaction between parenting behavior and parental psychopathology significantly influenced youth anxiety symptoms, which presents important clinical implications to integrate into parenting work in the treatment of youth anxiety disorders.

In Pursuit of Generalization: An Updated Review.

Stokes and Osnes (1989) outlined three principles to facilitate the generalization and maintenance of therapeutic gains. Use of functional contingencies, training diversely, and incorporating functional mediators were recommended. Our review, with most illustrations from studies of youth, updates Stokes and Osnes's original paper with a focus on evidence-based strategies to increase generalization of therapeutic gains across settings, stimuli, and time. Research since 1989 indicates that training for generalization by increasing the frequency of naturally occurring reinforcers for positive behaviors, and altering maladaptive contingencies that inadvertently reinforce problem behaviors, are associated with favorable treatment outcomes. Training diversely by practicing therapy skills across contexts and in response to varying stimuli is also implicated in clinical outcomes for internalizing, externalizing, and neurodevelopmental disorders. Preliminary research recommends the use of internal (e.g., emotion identification) and external (e.g., coping cards) functional mediators to prompt effective coping in session and at home. Strategies for increasing generalization, including the use of technology, are examined and future research directions are identified.

Band Gap Engineering with Ultralarge Biaxial Strains in Suspended Monolayer MoS2.

We demonstrate the continuous and reversible tuning of the optical band gap of suspended monolayer MoS2 membranes by as much as 500 meV by applying very large biaxial strains. By using chemical vapor deposition (CVD) to grow crystals that are highly impermeable to gas, we are able to apply a pressure difference across suspended membranes to induce biaxial strains. We observe the effect of strain on the energy and intensity of the peaks in the photoluminescence (PL) spectrum and find a linear tuning rate of the optical band gap of 99 meV/%. This method is then used to study the PL spectra of bilayer and trilayer devices under strain and to find the shift rates and Grüneisen parameters of two Raman modes in monolayer MoS2. Finally, we use this result to show that we can apply biaxial strains as large as 5.6% across micron-sized areas and report evidence for the strain tuning of higher level optical transitions.

Uniaxial Strain Redistribution in Corrugated Graphene: Clamping, Sliding, Friction, and 2D Band Splitting.

Graphene is a promising material for strain engineering based on its excellent flexibility and elastic properties, coupled with very high electrical mobility. In order to implement strain devices, it is important to understand and control the clamping of graphene to its support. Here, we investigate the limits of the strong van der Waals interaction on friction clamping. We find that the friction of graphene on a SiO2 substrate can support a maximum local strain gradient and that higher strain gradients result in sliding and strain redistribution. Furthermore, the friction decreases with increasing strain. The system used is graphene placed over a nanoscale SiO2 grating, causing strain and local strain variations. We use a combination of atomic force microscopy and Raman scattering to determine the friction coefficient, after accounting for compression and accidental charge doping, and model the local strain variation within the laser spot size. By using uniaxial strain aligned to a high crystal symmetry direction, we also determine the 2D Raman Grüneisen parameter and deformation potential in the zigzag direction.

A machine learning heuristic to identify biologically relevant and minimal biomarker panels from omics data.

BACKGROUND: Investigations into novel biomarkers using omics techniques generate large amounts of data. Due to their size and numbers of attributes, these data are suitable for analysis with machine learning methods. A key component of typical machine learning pipelines for omics data is feature selection, which is used to reduce the raw high-dimensional data into a tractable number of features. Feature selection needs to balance the objective of using as few features as possible, while maintaining high predictive power. This balance is crucial when the goal of data analysis is the identification of highly accurate but small panels of biomarkers with potential clinical utility. In this paper we propose a heuristic for the selection of very small feature subsets, via an iterative feature elimination process that is guided by rule-based machine learning, called RGIFE (Rule-guided Iterative Feature Elimination). We use this heuristic to identify putative biomarkers of osteoarthritis (OA), articular cartilage degradation and synovial inflammation, using both proteomic and transcriptomic datasets. RESULTS AND DISCUSSION: Our RGIFE heuristic increased the classification accuracies achieved for all datasets when no feature selection is used, and performed well in a comparison with other feature selection methods. Using this method the datasets were reduced to a smaller number of genes or proteins, including those known to be relevant to OA, cartilage degradation and joint inflammation. The results have shown the RGIFE feature reduction method to be suitable for analysing both proteomic and transcriptomics data. Methods that generate large 'omics' datasets are increasingly being used in the area of rheumatology. CONCLUSIONS: Feature reduction methods are advantageous for the analysis of omics data in the field of rheumatology, as the applications of such techniques are likely to result in improvements in diagnosis, treatment and drug discovery.

Self-trapping of excitons, violation of Condon approximation, and efficient fluorescence in conjugated cycloparaphenylenes.

Cycloparaphenylenes, the simplest structural unit of armchair carbon nanotubes, have unique optoelectronic properties counterintuitive in the class of conjugated organic materials. Our time-dependent density functional theory study and excited state dynamics simulations of cycloparaphenylene chromophores provide a simple and conceptually appealing physical picture explaining experimentally observed trends in optical properties in this family of molecules. Fully delocalized degenerate second and third excitonic states define linear absorption spectra. Self-trapping of the lowest excitonic state due to electron-phonon coupling leads to the formation of spatially localized excitation in large cycloparaphenylenes within 100 fs. This invalidates the commonly used Condon approximation and breaks optical selection rules, making these materials superior fluorophores. This process does not occur in the small molecules, which remain inefficient emitters. A complex interplay of symmetry, π-conjugation, conformational distortion and bending strain controls all photophysics of cycloparaphenylenes.

Charge tuning of nonresonant magnetoexciton phonon interactions in graphene.

Far from resonance, the coupling of the G-band phonon to magnetoexcitons in single layer graphene displays kinks and splittings versus filling factor that are well described by Pauli blocking and unblocking of inter- and intra-Landau level transitions. We explore the nonresonant electron-phonon coupling by high-magnetic field Raman scattering while electrostatic tuning of the carrier density controls the filling factor. We show qualitative and quantitative agreement between spectra and a linearized model of electron-phonon interactions in magnetic fields. The splitting is caused by dichroism of left- and right-handed circular polarized light due to lifting of the G-band phonon degeneracy, and the piecewise linear slopes are caused by the linear occupancy of sequential Landau levels versus ν.

A probabilistic and individualized approach for predicting treatment gains: an extension and application to anxiety disordered youth.

The objective of this study was to extend the probability of treatment benefit method by adding treatment condition as a stratifying variable, and illustrate this extension of the methodology using the Child and Adolescent Anxiety Multimodal Study data. The probability of treatment benefit method produces a simple and practical way to predict individualized treatment benefit based on pretreatment patient characteristics. Two pretreatment patient characteristics were selected in the production of the probability of treatment benefit charts: baseline anxiety severity, measured by the Pediatric Anxiety Rating Scale, and treatment condition (cognitive-behavioral therapy, sertraline, their combination, and placebo). We produced two charts as exemplars which provide individualized and probabilistic information for treatment response and outcome to treatments for child anxiety. We discuss the implications of the use of the probability of treatment benefit method, particularly with regard to patient-centered outcomes and individualized decision-making in psychology and psychiatry.

Analysis of mass spectrometry data from the secretome of an explant model of articular cartilage exposed to pro-inflammatory and anti-inflammatory stimuli using machine learning.

BACKGROUND: Osteoarthritis (OA) is an inflammatory disease of synovial joints involving the loss and degeneration of articular cartilage. The gold standard for evaluating cartilage loss in OA is the measurement of joint space width on standard radiographs. However, in most cases the diagnosis is made well after the onset of the disease, when the symptoms are well established. Identification of early biomarkers of OA can facilitate earlier diagnosis, improve disease monitoring and predict responses to therapeutic interventions. METHODS: This study describes the bioinformatic analysis of data generated from high throughput proteomics for identification of potential biomarkers of OA. The mass spectrometry data was generated using a canine explant model of articular cartilage treated with the pro-inflammatory cytokine interleukin 1 ß (IL-1ß). The bioinformatics analysis involved the application of machine learning and network analysis to the proteomic mass spectrometry data. A rule based machine learning technique, BioHEL, was used to create a model that classified the samples into their relevant treatment groups by identifying those proteins that separated samples into their respective groups. The proteins identified were considered to be potential biomarkers. Protein networks were also generated; from these networks, proteins pivotal to the classification were identified. RESULTS: BioHEL correctly classified eighteen out of twenty-three samples, giving a classification accuracy of 78.3% for the dataset. The dataset included the four classes of control, IL-1ß, carprofen, and IL-1ß and carprofen together. This exceeded the other machine learners that were used for a comparison, on the same dataset, with the exception of another rule-based method, JRip, which performed equally well. The proteins that were most frequently used in rules generated by BioHEL were found to include a number of relevant proteins including matrix metalloproteinase 3, interleukin 8 and matrix gla protein. CONCLUSIONS: Using this protocol, combining an in vitro model of OA with bioinformatics analysis, a number of relevant extracellular matrix proteins were identified, thereby supporting the application of these bioinformatics tools for analysis of proteomic data from in vitro models of cartilage degradation.

Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology.

Mass spectrometry is an analytical technique for the characterization of biological samples and is increasingly used in omics studies because of its targeted, nontargeted, and high throughput abilities. However, due to the large datasets generated, it requires informatics approaches such as machine learning techniques to analyze and interpret relevant data. Machine learning can be applied to MS-derived proteomics data in two ways. First, directly to mass spectral peaks and second, to proteins identified by sequence database searching, although relative protein quantification is required for the latter. Machine learning has been applied to mass spectrometry data from different biological disciplines, particularly for various cancers. The aims of such investigations have been to identify biomarkers and to aid in diagnosis, prognosis, and treatment of specific diseases. This review describes how machine learning has been applied to proteomics tandem mass spectrometry data. This includes how it can be used to identify proteins suitable for use as biomarkers of disease and for classification of samples into disease or treatment groups, which may be applicable for diagnostics. It also includes the challenges faced by such investigations, such as prediction of proteins present, protein quantification, planning for the use of machine learning, and small sample sizes.