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Polar van der Waals (vdW) crystals, composed of atomic layers held together by vdW forces, can host phonon polaritons—quasiparticles arising from the interaction between photons in free-space light and lattice vibrations in polar materials. These crystals offer advantages such as easy fabrication, low Ohmic loss, and optical confinement. Recently, hexagonal boron nitride (hBN), known for having hyperbolicity in the mid-infrared range, has been used to explore multiple modes with high optical confinement. This opens possibilities for practical polaritonic nanodevices with subdiffractional resolution. However, polariton waves still face exposure to the surrounding environment, leading to significant energy losses. In this work, we propose a simple approach to inducing a hyperbolic phonon polariton (HPhP) waveguide in hBN by incorporating a low dielectric medium, ZrS2. The low dielectric medium serves a dual purpose—it acts as a pathway for polariton propagation, while inducing high optical confinement. We establish the criteria for the HPhP waveguide in vdW heterostructures with various thicknesses of ZrS2 through scattering-type scanning near-field optical microscopy (s-SNOM) and by conducting numerical electromagnetic simulations. Our work presents a feasible and straightforward method for developing practical nanophotonic devices with low optical loss and high confinement, with potential applications such as energy transfer, nano-optical integrated circuits, light trapping, etc. [ABSTRACT FROM AUTHOR]

Phonons are important lattice vibrations that affect the thermal, electronic, and optical properties of materials. In this work, we studied infrared phonon resonance in a prototype van der Waals (vdW) material—hexagonal boron nitride (hBN)—with the thickness ranging from monolayers to bulk, especially on ultra-thin crystals with atomic layers smaller than 20. Our combined experimental and modeling results show a systematic increase in the intensity of in-plane phonon resonance at the increasing number of layers in hBN, with a sensitivity down to one atomic layer. While the thickness-dependence of the phonon resonance reveals the antenna nature of our nanoscope, the linear thickness-scaling of the phonon polariton wavelength indicates the preservation of electromagnetic hyperbolicity in ultra-thin hBN layers. Our conclusions should be generic for fundamental resonances in vdW materials and heterostructures where the number of constituent layers can be conveniently controlled. The thickness-dependent phonon resonance and phonon polaritons revealed in our work also suggest vdW engineering opportunities for desired thermal and nanophotonic functionalities. [ABSTRACT FROM AUTHOR]

In this work, we report on the growth of hexagonal boron nitride (hBN) crystals from an iron flux at atmospheric pressure and high temperature and demonstrate that (i) the entire sheet of hBN crystals can be detached from the metal in a single step using hydrochloric acid and that (ii) these hBN crystals allow to fabricate high carrier mobility graphene-hBN devices. By combining spatially-resolved confocal Raman spectroscopy and electrical transport measurements, we confirm the excellent quality of these crystals for high-performance hBN-graphene-based van der Waals heterostructures. The full width at half maximum of the graphene Raman 2D peak is as low as 16 cm−1, and the room temperature charge carrier mobilitiy is around 80 000 cm2/(Vs) at a carrier density 1 × 1012 cm−12. This is fully comparable with devices of similar dimensions fabricated using crystalline hBN synthesized by the high pressure and high temperature method. Finally, we show that for exfoliated high-quality hBN flakes with a thickness between 20 and 40 nm the line width of the hBN Raman peak, in contrast to the graphene 2D line width, is not useful for benchmarking hBN in high mobility graphene devices. [ABSTRACT FROM AUTHOR]

The boron-vacancy spin defect ( V B − ) in hexagonal boron nitride (hBN) has a great potential as a quantum sensor in a two-dimensional material that can directly probe various external perturbations in atomic-scale proximity to the quantum sensing layer. Here, we apply first-principles calculations to determine the coupling of the V B − electronic spin to strain and electric fields. Our work unravels the interplay between local piezoelectric and elastic effects contributing to the final response to the electric fields. The theoretical predictions are then used to analyse optically detected magnetic resonance (ODMR) spectra recorded on hBN crystals containing different densities of V B − centres. We prove that the orthorhombic zero-field splitting parameter results from local electric fields produced by surrounding charge defects. This work paves the way towards applications of V B − centres for quantitative electric field imaging and quantum sensing under pressure. [ABSTRACT FROM AUTHOR]

Objective: Myocardial ischemia/reperfusion (MI/R) injury is a complicated pathophysiological process associated with cardiomyocyte pyroptosis. Methyltransferase-like protein 3 (METTL3) catalyzes the formation of N6-methyl-adenosine (m6A) and participates in various biological processes. This study probed into the mechanism of METTL3 in cardiomyocyte pyroptosis in MI/R injury. Methods: A rat model of MI/R was established. Rat cardiomyocytes were subjected to oxygen–glucose deprivation/reoxygenation (OGD/R) treatment for the establishment of a cell model in vitro. METTL3 expression in myocardial tissues of MI/R rats and OGD/R-treated cardiomyocytes was determined using RT-qPCR and Western blot. The pathological changes of rat myocardial tissues were observed using hematoxylin and eosin staining. The positive expression of NLRP3 in myocardial tissues or cardiomyocytes was observed through immunohistochemistry or immunofluorescence. The activity of caspase-1 was measured using the colorimetric method. The expressions of GSDMD and cleaved caspase-1, as well as the levels of IL-1β and IL-18 in rat myocardial tissues or cardiomyocytes were determined. m6A modification level was quantified. The binding relationship between pri-miR-143-3p and DGCR8 and the enrichment of m6A on pri-miR-143-3p were detected. The binding relationship between miR-143-3p and protein kinase C epsilon (PRKCE) was verified. Results: METTL3 expression was elevated in MI/R rats and OGD/R cardiomyocytes. METTL3 silencing alleviated myocardial injury, reduced the number of NLRP3-positive cardiomyocytes, suppressed caspase-1 activity, decreased the protein levels of GSDMD-N and cleaved caspase-1, and decreased IL-1β and IL-18 levels. METTL3 increased the total m6A level in MI/R rats and injured cardiomyocytes, promoted DGCR8 binding to pri-miR-143-3p, and enhanced miR-143-3p expression. miR-143-3p suppressed PRKCE transcription, and miR-143-3p overexpression reversed the inhibitory effect of METTL3 silencing on cardiomyocyte pyroptosis. Conclusion: METTL3 promoted DGCR8 binding to pri-miR-143-3p through m6A modification, thus enhancing miR-143-3p expression to inhibit PRKCE transcription and further aggravating cardiomyocyte pyroptosis and MI/R injury. [ABSTRACT FROM AUTHOR]

For countries where catalytic cracking gasoline is the primary source, the proposed technology consists in separating a benzene-rich fraction from catalytic cracking gasoline in order to be processed further together with reforming gasoline in a unit dedicated to aromatics extraction. In this way, two benefits are obtained: a benzene-rich fraction as raw material for extraction and the leftover fraction that satisfies the benzene content standards as a qualified product. It is established to use the divided wall distillation model, single-column distillation model, and double-column distillation model. Sensitivity analysis and SQP optimization are used to identify the ideal operating conditions and gasoline yield. Economic research shows that the divided wall and single-column distillation models have more potential for growth. It offers theoretical direction for businesses to design and optimize the pertinent process. [ABSTRACT FROM AUTHOR]

An N‐heterocyclic carbene (NHC) catalyzed atroposelective [3+3] annulation of enals with α‐aminomaleimides is developed. In this strategy, a series of atropisomeric pyrrolo[3,4‐b]pyridine bearing various substituents and substitution patterns are generated in good yields with excellent enantioselectivities. [ABSTRACT FROM AUTHOR]

Phonon polariton (PhP) nanoresonators can dramatically enhance the coupling of molecular vibrations and infrared light, enabling ultrasensitive spectroscopies and strong coupling with minute amounts of matter. So far, this coupling and the resulting localized hybrid polariton modes have been studied only by far-field spectroscopy, preventing access to modal near-field patterns and dark modes, which could further our fundamental understanding of nanoscale vibrational strong coupling (VSC). Here we use infrared near-field spectroscopy to study the coupling between the localized modes of PhP nanoresonators made of h-BN and molecular vibrations. For a most direct probing of the resonator-molecule coupling, we avoid the direct near-field interaction between tip and molecules by probing the molecule-free part of partially molecule-covered nanoresonators, which we refer to as remote near-field probing. We obtain spatially and spectrally resolved maps of the hybrid polariton modes, as well as the corresponding coupling strengths, demonstrating VSC on a single PhP nanoresonator level. Our work paves the way for near-field spectroscopy of VSC phenomena not accessible by conventional techniques. Vibrational strong coupling (VSC) promises ultrasensitive IR spectroscopy and modification of material properties. Here, nanoscale mapping of VSC between organic molecules and individual IR nanoresonators is achieved by remote near-field spectroscopy. [ABSTRACT FROM AUTHOR]

Polaritons enable subwavelength confinement and highly anisotropic flows of light over a wide spectral range, holding the promise for applications in modern nanophotonic and optoelectronic devices. However, to fully realize their practical application potential, facile methods enabling nanoscale active control of polaritons are needed. Here, we introduce a hybrid polaritonic-oxide heterostructure platform consisting of van der Waals crystals, such as hexagonal boron nitride (hBN) or alpha-phase molybdenum trioxide (α-MoO3), transferred on nanoscale oxygen vacancy patterns on the surface of prototypical correlated perovskite oxide, samarium nickel oxide, SmNiO3 (SNO). Using a combination of scanning probe microscopy and infrared nanoimaging techniques, we demonstrate nanoscale reconfigurability of complex hyperbolic phonon polaritons patterned at the nanoscale with high resolution. Hydrogenation and temperature modulation allow spatially localized conductivity modulation of SNO nanoscale patterns, enabling robust real-time modulation and nanoscale reconfiguration of hyperbolic polaritons. Our work paves the way towards nanoscale programmable metasurface engineering for reconfigurable nanophotonic applications. Phonon polaritons in anisotropic van der Waals materials enable subwavelength confinement and controllable flow of light at the nanoscale. Here, the authors exploit correlated perovskite oxide (SmNiO3) substrates with tunable conductivity to obtain real-time modulation and nanoscale reconfiguration of hyperbolic polaritons in hBN and α-MoO3 crystals. [ABSTRACT FROM AUTHOR]

Polaritons—confined light–matter waves—in van der Waals (vdW) materials are a research frontier in light–matter interactions with demonstrated advances in nanophotonics. Reflection, as a fundamental phenomenon involving waves, is particularly important for vdW polaritons, predominantly because it enables the investigation of polariton standing waves using the scanning probe technique. While previous works demonstrate a rigid phase ≈π/4 for the polariton reflection, herein is reported the altering of the polariton reflection phase by varying the geometry of polaritonic microstructures for the case study of hyperbolic surface polaritons (HSPs) in hexagonal boron nitride (hBN). Specifically, it is demonstrated that the polariton reflection phase can be systematically altered by varying the corner angle of the hBN microstructures, and that it experiences a π jump around a specific angle. This behavior, which is a consequence of the mathematical nature of the reflection coefficient, is therefore expected in other physical phenomena. [ABSTRACT FROM AUTHOR]

The sp2-bonded layered compound boron nitride (BN) exists in more than a handful of different polytypes (i.e., different layer stacking sequences) with similar formation energies, which makes obtaining a pure monotype of single crystals extremely tricky. The co-existence of polytypes in a similar crystal leads to the formation of many interfaces and structural defects having a deleterious influence on the internal quantum efficiency of the light emission and on charge carrier mobility. However, despite this, lasing operation was reported at 215 nm, which has shifted interest in sp2-bonded BN from basic science laboratories to optoelectronic and electrical device applications. Here, we describe some of the known physical properties of a variety of BN polytypes and their performances for deep ultraviolet emission in the specific case of second harmonic generation of light. [ABSTRACT FROM AUTHOR]

Atrial fibrillation (AF) is the most common type of persistent arrhythmia. Although its incidence has been increasing, the pathogenesis of AF in stroke remains unclear. In this study, a total of 30 participants were recruited, including 10 controls, 10 patients with AF and 10 patients with AF and stroke (AF + STROKE). Differentially expressed genes (DEGs) were identified, and functional annotation of DEGs, comparative toxicogenomic database analysis associated with cardiovascular diseases, and predictions of miRNAs of hub genes were performed. Using RT‐qPCR, biological process and support vector machine neural networks, numerous DEGs were found to be related to AF. HBG1, SNCA and GYPB were found to be upregulated in the AF group. Higher expression of hub genes in AF and AF + STROKE groups was detected via RT‐PCR. Upon training the biological process neural network of SNCA and GYPB for HBG1, only small differences were detected. Based on the support vector machine, the predicted value of SNCA and GYPB for HBG1 was 0.9893. Expression of the hub genes of HBG1, SNCA and GYPB might therefore be significantly correlated to AF. These genes are involved in the incidence of AF complicated by stroke, and may serve as targets for early diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Recently space communication technology develops rapidly and space chaotic laser communication serves as an excellent scheme to enhance the security of data transmission. To achieve long-distance transmission, Erbium-doped fiber amplifier (EDFA) is used in a space chaotic laser communication system, but this will inevitably bring amplifier spontaneous emission (ASE) noise. In order to investigate the effect of ASE noise, we give a modified bit error rate (BER) calculation model and numerical simulations are conducted based on it. Quantitative results show that most of ASE noise in the system is caused by the EDFA at the receiver rather than the emitter. Apart from this, deeper researches further indicate that ASE noise will induce mismatch. When ASE noise is small, inherent mismatch noise is the main factor causing mismatch. But as ASE noise power becomes larger, mismatch noise will be dominated by external mismatch noise and internal mismatch noise. Our results have a good reference value for the design of space chaotic laser communication systems. [ABSTRACT FROM AUTHOR]

Aiming at the problem that stereo matching accuracy is easily affected by noise and amplitude distortion, a stereo matching algorithm based on HSV color space and improved census transform is proposed. In the cost calculation stage, the color image is first converted from RGB space to HSV space; moreover, the hue channel is used as the matching primitive to establish the hue absolute difference (HAD) cost calculation function, which reduces the amount of calculation and enhances the robustness of matching. Then, to solve the problem of the traditional census transform overrelying on the central pixel and to improve the noise resistance of the algorithm, an improved census method based on neighborhood weighting is also proposed. Finally, the HAD cost and the improved census cost are nonlinearly fused as the initial cost. In the aggregation stage, an outlier elimination method based on confidence interval is proposed. By calculating the confidence interval of the aggregation window, this paper eliminates the cost value that is not in the confidence interval and subsequently filters as well as aggregates the remaining costs to further reduce the noise interference and improve the matching accuracy. Experiments show that the proposed method can not only effectively suppress the influence of noise, but also achieve a more robust matching effect in scenes with changing exposure and lighting conditions. [ABSTRACT FROM AUTHOR]

Video‐based person re‐identification aims to match pedestrians from video sequences across non‐overlapping cameras. A major challenge of the person re‐identification is the serious intra‐class distance caused by cropped frames variation in video sequences. To address this issue, a novel sequences consistency feature learning (SCFL) framework for video‐based person re‐identification is proposed. Specifically, SCFL utilizes a deep neural network and the proposed sequences consistency loss to learn sequences‐invariant features for each pedestrian, which decreases the intra‐class distance across the partial occlusions, inaccurate detection and viewpoint variation. During the training process, SCFL slices the video‐level features and computes the cosine similarity between disjoint sequences features pairs of the same pedestrian as the sequences consistency loss to minimize the intra‐class distance. The experiments demonstrate that our approach improves the performance of the existing deep networks and achieves competitive performance on the large‐scale benchmark datasets including MARS and DukeMTMC‐VideoReID. [ABSTRACT FROM AUTHOR]

Hexagonal boron nitride (h-BN) has been highlighted as a promising low-dimensional material for thermal management of next-generation devices. The theory predicts that the thermal conductivity of h-BN increases above the bulk value as the thickness is reduced, but previous reports on few-layer (5–11 layer) h-BN have shown the opposite trend. We investigated the effect of isotopic engineering on the thermal properties of 11-layer h-BN single-crystal flakes. The thermal conductivities of natural (22% 10B, 78% 11B) and monoisotopic (99% 10B) h-BN were determined by a modified optothermal Raman method in the range 300–400 K. At room temperature, values were as high as (630 + 90/–65) Wm–1 K–1 for monoisotopic h-10BN and (405 + 87/–65) Wm–1 K–1 for natural h-BN, corresponding to an isotopic enhancement of close to 60%. Both measured thermal conductivities either match or exceed previously reported values for bulk crystals, while the isotopic enhancement factor is approximately 35% higher for the isotopically enriched thin crystal compared to the equivalent bulk materials. The work presented here demonstrates isotopic engineering as a viable route to increased thermal conductivity in atomically thin h-BN, making it an outstanding platform material for thermal management in next-generation device applications. [ABSTRACT FROM AUTHOR]

The purpose of this qualitative study is to understand the risk-reducing strategies used by Chinese consumers in a collectivistic culture to balance the psychological dichotomy they face when consuming counterfeit luxury products to construct their social identities. Consumers, on the one hand, are tempted to buy counterfeit luxury products due to their remarkable price advantages, while on the other hand they are reluctant to use these products given the fear of being socially caught out. This dissonance intensifies in a collectivistic culture, where the concept of social face is important. The study involved reviewing a large volume of literature on the research of non-deceptive counterfeit luxury consumption, based on which 26 semi-structured in-depth interviews among Chinese consumers were conducted to explore strategies used by consumers to construct their social identity through using counterfeit luxury products. In addition, the ways they select and use these products to avoid being caught out in order to maintain their constructed social identity were investigated. The data were analyzed by adopting (Mitchell in Manag Decis 30(3):26–31, 1992) risk reduction framework, and the findings illustrate the different strategies taking place through two separate consumption phases: pre-purchase and post-purchase. This research offers new insights into non-deceptive counterfeit consumption by introducing perceived risk and risk-reducing strategies. [ABSTRACT FROM AUTHOR]

Hexagonal boron nitride (hBN) is an important insulator that is incorporated into numerous 2D electronic, optoelectronic, and photonic devices, whereas natural hBN is a mixture of 20% 10B and 80% 11B isotopes, and monoisotopic hBN is a variant with just a single boron isotope, either 10B or 11B. Consequently, monoisotopic hBN has a higher thermal conductivity and a stronger neutron absorption (in the case of h10BN), making it superior for neutron detectors, heat management materials in nano flexible electronic devices, and phonon polariton-based nanophotonics. Here we synthesized approximately monoisotopic hBN using boron powder containing a single boron isotope and nitrogen, and grew single crystals from a Fe–Cr metal flux at atmospheric pressure. Narrow Raman peaks from the shear (≤1.3 cm−1) and intralayer (≤3.3 cm−1) modes demonstrate that the crystals are highly ordered. In the photoluminescence spectra, the presence of phonon-assistant transition peaks is also indicative of the high-quality of the crystals. This growth protocol permits us to get rid of the emission at 4.1 eV. This work provides a novel material for studying the fundamental properties of isotopic effects and the high-performance hBN device. [ABSTRACT FROM AUTHOR]

Space laser optical communication has been widely investigated to achieve high bit rate information transmission. To enhance the information transmission security in space laser optical communication, chaos-based encryption scheme is adopted. It involves many parameters in designing a space chaotic optical communication system, which a proper tuning of them brings good communication performance. To investigate the key design parameters including the masking efficiency, transmitting optical power and mismatch coefficient, we give the calculation model of the bit error ratio (BER) based on traditional space laser optical communication system from the perspective of the authorized receiver and eavesdropper. Numerical results indicate that the BER for the eavesdropper is sensitive to the masking efficiency but not to the transmitting optical power. By contrast, the transmitting optical power plays a crucial role in the computational formula of the BER for the authorized receiver. A saturate optical power is also proposed at last which helps save the energy. These simulation results can be reference for practical system design. [ABSTRACT FROM AUTHOR]

We report the development of a scanning confocal microscope dedicated to photoluminescence in the 200 nm-wavelength range for samples at cryogenic temperatures (5 K–300 K). We demonstrate the performances of our deep ultraviolet cryomicroscope in high-quality hexagonal boron nitride (hBN) crystals, although it can be utilized for biological studies in its range of operating wavelengths. From the mapping of photoluminescence, we bring evidence for the suppression of extrinsic recombination channels in regions free from defects. The observation of emission spectra dominated by intrinsic recombination processes was never reported before in hBN by means of photoluminescence spectroscopy. We show that photoluminescence tomography now competes with cathodoluminescence and that deep ultraviolet cryomicroscopy by photoluminescence is a novel powerful tool in materials science applications, with the great advantage of an efficient non-invasive photo-excitation of carriers. [ABSTRACT FROM AUTHOR]

Probing of polaritons in 2D materials is facilitated by spectroscopic imaging with nanometer spatial resolution. The combination of atomic force microscopy and infrared laser sources provides access for in situ mappings of phonon polaritons. Here, it is demonstrated that the photothermal‐based peak force infrared microscopy is capable of revealing phonon polaritons with high spatial resolution in isotopically pure hexagonal boron nitride microstructures without damaging the sample. To further improve the sensitivity, peak force infrared microscopy is enhanced with a scheme of multiple laser pulse excitation. The resulting method of multipulse peak force infrared microscopy can detect phonon polaritons with high sensitivity, which is particularly useful for probing polaritons in 2D materials with high damping characteristics. [ABSTRACT FROM AUTHOR]

SEMG signal is a bioelectrical signal produced by the contraction of human surface muscles. Human-computer interaction based on SEMG signal is of great significance in the field of rehabilitation robots. In this study, a feature extraction method of SEMG signal based on activated muscle regionis proposed, which is based on the study of activated muscle regionin human forearm and hand movement. At the same time, the main research object of this study is the multi-object intergroup SEMG signal which is closer to the practical application environment. The new feature extracted is fused with the sample entropy feature and the wavelength feature to obtain better signal features. After combining the fusion feature with KNN algorithm, the hand motion pattern recognition and classification between multi-object groups is carried out. The combination of the fusion feature and KNN classification algorithm can achieve 91.05% in the multi-object intergroup hand motion classification. This method has lower computational cost without expensive hardware support, and improves the robustness of hand motion recognition based on EMG signals. [ABSTRACT FROM AUTHOR]

Based on HSV gamut space, a visualization system of muscle activity is proposed to study the mapping relationship between hand motion and active areas of upper arm muscle. There is a significant threshold change in the starting and ending points of the active segment in the original EMG signal, and the part that exceeds the threshold TH is the active segment date. Set the window width K and fixed increment Kt of time window to remove redundant data. The sEMG intensity information of each sampling electrode is obtained by calculating MAV in each window, and the simulation experiment is conducted in HSV gamut space. Through the human-computer interaction experiment of the visual system, it is proved that this system can visually display the relationship between different channels in the spatial domain, thus intuitively identify the activity intensity of different muscles in hand motion. [ABSTRACT FROM AUTHOR]

Feature extraction is one of most important steps in the control of multifunctional prosthesis based on surface electromyography (sEMG) pattern recognition. In this paper, a new sEMG feature extraction method based on muscle active region is proposed. This paper designs an experiment to classify four hand motions using different features. This experiment is used to prove that new features have better classification performance. The experimental results show that the new feature, active muscle regions (AMR), has better classification performance than other traditional features, mean absolute value (MAV), waveform length (WL), zero crossing (ZC) and slope sign changes (SSC). The average classification errors of AMR, MAV, WL, ZC and SSC are 13%, 19%, 26%, 24% and 22%, respectively. The new EMG features are based on the mapping relationship between hand movements and forearm active muscle regions. This mapping relationship has been confirmed in medicine. We obtain the active muscle regions data from the original EMG signal by the new feature extraction algorithm. The results obtained from this algorithm can well represent hand motions. On the other hand, the new feature vector size is much smaller than other features. The new feature can narrow the computational cost. This proves that the AMR can improve sEMG pattern recognition accuracy rate. [ABSTRACT FROM AUTHOR]

Summary Genome-wide association studies (GWAS), particularly designed with thousands and thousands of single-nucleotide polymorphisms (SNPs) (big p) genotyped on tens of thousands of subjects (small n), are encountered by a major challenge of p  ≪  n. Although the integration of longitudinal information can significantly enhance a GWAS's power to comprehend the genetic architecture of complex traits and diseases, an additional challenge is generated by an autocorrelative process. We have developed several statistical models for addressing these two challenges by implementing dimension reduction methods and longitudinal data analysis. To make these models computationally accessible to applied geneticists, we wrote an R package of computer software, HiG was, designed to analyze longitudinal GWAS datasets. Functions in the package encompass single SNP analyses, significance-level adjustment, preconditioning and model selection for a high-dimensional set of SNPs. HiG was provides the estimates of genetic parameters and the confidence intervals of these estimates. We demonstrate the features of HiG was through real data analysis and vignette document in the package. Availability and implementation https://github.com/wzhy2000/higwas. Contact rwu@phs.psu.edu Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Conventional univariate drought frequency analysis should be replaced by the multivariate framework due to the correlated characteristics, such as duration, peak intensity, and severity. Compared to the bivariate case, trivariate analysis would be more considerable. Furthermore, the absence of historical records has a great impact on the reliability of statistical estimates. Multivariate regional frequency analysis approach would be a better choice for providing reliable information for risks assessment and management purpose. Standardized precipitation evapotranspiration index was used in this study to define and detect drought because of the global warming effect. A method based on the cubic spline interpolation was applied to derive drought samples. Applying the multivariate discordancy and homogeneity test, the Pearl River basin was divided into five homogeneous subregions. Regional distributions including marginal distribution and copula function were chosen based on the goodness-of-fit test. Finally, return periods were computed from a regional perspective to assess the different drought risks. The results indicate that high drought risks might exist in the Pearl River basin and the spatial distinction must be taken seriously. Due to the contradiction of water supply and requirement among different districts or provinces, policies needed to be made seriously and urgently to solve the unbalance between water resource and socioeconomic development. [ABSTRACT FROM AUTHOR]

A number of alternative mean-variance portfolio strategies have been recently proposed to improve the empirical performance of the classic Markowitz mean-variance framework. Designed as remedies for parameter uncertainty and estimation errors in portfolio selection problems, these alternative portfolio strategies deliver substantially better out-of-sample performance. In this article, we first show how to solve a general portfolio selection problem in a linear regression framework. Then we propose to reduce the estimation risk of expected returns and the variance-covariance matrix of asset returns by imposing additional constraints on the portfolio weights. With results from linear regression models, we show that portfolio weights derived from new approaches enjoy two favorable properties: sparsity and stability. Moreover, we present insights into these new approaches as well as their connections to alternative strategies in literature. Four empirical studies show that the proposed strategies have better out-of-sample performance and lower turnover than many other strategies, especially when the estimation risk is large. [ABSTRACT FROM PUBLISHER]

Over the past few decades, genome-wide association studies analyzed by efficient statistical procedures have successfully identified single-nucleotide polymorphisms (SNPs) that are associated with complex traits or human diseases. However, due to the overwhelming number of SNPs, most approaches have focused on additive genetic model without genome-wide SNP–SNP interactions. In this study, we propose an efficient statistical procedure in a genetic model-free framework for detecting SNPs exhibiting main genetic effects as well as epistatic interactions. Specifically, the association between phenotype and genotype is characterized by an unknown function to be estimated using nonparametric techniques, and a two-stage non-parametric independence screening procedure is proposed to sequentially identify potentially important main genetic effects and interactions. Finally, the subset of genetic predictors implied by two-stage non-parametric independence screening is analyzed by penalized regressions such as LASSO, and a final model is identified. In this framework, specific genetic model is not assumed and interactions are not only among marginally important SNPs. Therefore, SNPs that are involved in genetic regulatory networks but missed by previous studies are expected to be recognized. In simulation studies, we show that the procedure is computationally efficient and has an outstanding finite sample performance in selecting potential SNPs as well as SNP–SNP interactions. A real data analysis further indicates the importance of epistatic interactions in explaining body mass index. [ABSTRACT FROM PUBLISHER]

Previous genome-wide association study (GWAS) focused on low-order interactions between pairwise single-nucleotide polymorphisms (SNPs) with significant main effects. Little is known how high-order interactions effect, especially one among the SNPs without main effects regulates quantitative traits. Within the frameworks of linear model and generalized linear model, the LASSO with coordinate descent step can be used to simultaneously analyze thousands and thousands of SNPs for normal and discrete traits. With consideration of high-order interactions among SNPs, a huge number of genetic effects make the LASSO failing to work under the presented condition of computation. Forward LASSO analysis is, therefore, proposed to shrink most of genetic effects to be zeros stage by stage. Simulation demonstrates that our proposed method could be used instead of the LASSO method for full model in mapping high-order interactions. Application of forward LASSO method is provided to GWAS for carcass traits and meat quality traits in beef cattle. [ABSTRACT FROM PUBLISHER]

Resveratrol (RSV), a natural polyphenolic antioxidant, has been considered an anticarcinogenic agent as it triggers tumor cell apoptosis through activation of the mitochondrial pathway. In our study, the effects of RSV on mitochondria, especially on the mitochondrial permeability transition (MPT) process, were investigated by multiple methods. We found that RSV induced a collapse of membrane potential and matrix swelling related to MPT. We further demonstrated that Ca was necessary for this RSV-induced MPT opening. In addition, RSV induced the inner membrane permeabilization to H and K, the depression of respiration and changes in membrane fluidity. The results suggested that RSV-induced MPT was accompanied by mitochondrial dysfunction. But the prohibition on lipid peroxidation and different effects of low- and high-dose RSV on membrane fluidity and respiration showed that the interaction of RSV and the mitochondria could not be the result of a single simple mechanism. [ABSTRACT FROM AUTHOR]

Many phenomena of fundamental importance to biology and biomedicine arise as a dynamic curve, such as organ growth and HIV dynamics. The genetic mapping of these traits is challenged by longitudinal variables measured at irregular and possibly subject-specific time points, in which case nonnegative definiteness of the estimated covariance matrix needs to be guaranteed. We present a semiparametric approach for genetic mapping within the mixture-model setting by jointly modeling mean and covariance structures for irregular longitudinal data. Penalized spline is used to model the mean functions of individual quantitative trait locus (QTL) genotypes as latent variables, whereas an extended generalized linear model is used to approximate the covariance matrix. The parameters for modeling the mean-covariances are estimated by MCMC, using the Gibbs sampler and the Metropolis-Hastings algorithm. We derive the full conditional distributions for the mean and covariance parameters and compute Bayes factors to test the hypothesis about the existence of significant QTLs. We used the model to screen the existence of specific QTLs for age-specific change of body mass index with a sparse longitudinal data set. The new model provides powerful means for broadening the application of genetic mapping to reveal the genetic control of dynamic traits. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Despite its central role in the adaptation and microevolution of traits, the genetic architecture of phenotypic plasticity, i.e. multiple phenotypes produced by a single genotype in changing environments, remains elusive. We know little about the genes that underlie the plastic response of traits to the environment, their number, chromosomal locations and genetic interactions as well as environment impact on their effects. Here we review key statistical approaches for analyzing the genetic variation of phenotypic plasticity due to genotype–environment interactions and describe the implementation of a dynamic model to map specific quantitative trait loci (QTLs) that affect the gradient expression of a quantitative trait across a range of environments. This dynamic model is distinct by incorporating mathematical aspects of phenotypic plasticity into a QTL mapping framework, thereby better unraveling the quantitative attribute of trait response to the environment. By testing the curve parameters that specify environment-dependent trajectories of the trait, the model allows a series of fundamental hypotheses to be tested in a quantitative way about the interplay between gene action/interaction and environmental sensitivity. The model can also make the dynamic prediction of genetic control over phenotypic plasticity within the context of changing environments. We demonstrate the usefulness of the model by reanalyzing a QTL data set for rice, gleaning new insights into the genetic basis for phenotypic plasticity in plant height growth. [ABSTRACT FROM PUBLISHER]

Genomic imprinting, an epigenetic phenomenon of parent-of-origin-specific gene expression, has been widely observed in plants, animals, and humans. To detect imprinting genes influencing quantitative traits, the least squares and maximum likelihood approaches for fitting a single quantitative trait locus (QTL) and Bayesian methods for simultaneously modeling multiple QTL have been adopted, respectively, in various studies. However, most of these studies have only estimated imprinting main effects and thus ignored imprinting epistatic effects. In the presence of extremely complex genomic imprinting architectures, we introduce a Bayesian model selection method to analyze the multiple interacting imprinted QTL (iQTL) model. This approach will greatly enhance the computational efficiency through setting the upper bound of the number of QTLs and performing selective sampling for QTL parameters. The imprinting types of detected main-effect QTLs can be estimated from the Bayes factor statistic formulated by the posterior probabilities for the genetic effects being compared. The performance of the proposed method is demonstrated by several simulation experiments. Moreover, this method is applied to dissect the imprinting genetic architecture for body weight in mouse and fruit weight in tomato. Matlab code for implementing this approach will be available from the authors upon request. [ABSTRACT FROM AUTHOR]

In this paper, we propose a revenue optimization framework integrating demand learning and dynamic pricing for firms in monopoly or oligopoly markets. We introduce a state-space model for this revenue management problem, which incorporates game-theoretic demand dynamics and nonparametric techniques for estimating the evolution of underlying state variables. Under this framework, stringent model assumptions are removed. We develop a new demand learning algorithm using Markov chain Monte Carlo methods to estimate model parameters, unobserved state variables, and functional coefficients in the nonparametric part. Based on these estimates, future price sensitivities can be predicted, and the optimal pricing policy for the next planning period is obtained. To test the performance of demand learning strategies, we solve a monopoly firm's revenue maximizing problem in simulation studies. We then extend this paradigm to dynamic competition, where the problem is formulated as a differential variational inequality. Numerical examples show that our demand learning algorithm is efficient and robust. [ABSTRACT FROM PUBLISHER]

Owing to their ability and flexibility to describe individual gene expression at different time points, random regression (RR) analyses have become a popular procedure for the genetic analysis of dynamic traits whose phenotypes are collected over time. Specifically, when modelling the dynamic patterns of gene expressions in the RR framework, B-splines have been proved successful as an alternative to orthogonal polynomials. In the so-called Bayesian B-spline quantitative trait locus (QTL) mapping, B-splines are used to characterize the patterns of QTL effects and individual-specific time-dependent environmental errors over time, and the Bayesian shrinkage estimation method is employed to estimate model parameters. Extensive simulations demonstrate that (1) in terms of statistical power, Bayesian B-spline mapping outperforms the interval mapping based on the maximum likelihood; (2) for the simulated dataset with complicated growth curve simulated by B-splines, Legendre polynomial-based Bayesian mapping is not capable of identifying the designed QTLs accurately, even when higher-order Legendre polynomials are considered and (3) for the simulated dataset using Legendre polynomials, the Bayesian B-spline mapping can find the same QTLs as those identified by Legendre polynomial analysis. All simulation results support the necessity and flexibility of B-spline in Bayesian mapping of dynamic traits. The proposed method is also applied to a real dataset, where QTLs controlling the growth trajectory of stem diameters in Populus are located. [ABSTRACT FROM AUTHOR]

Genomic imprinting, a genetic phenomenon of non-equivalent allele expression that depends on parental origins, has been ubiquitously observed in nature. It does not only control the traits of growth and development but also may be responsible for survival traits. Based on the accelerated failure time model, we construct a general parametric model for mapping the imprinted QTL (iQTL). Within the framework of interval mapping, maximum likelihood estimation of iQTL parameters is implemented via EM algorithm. The imprinting patterns of the detected iQTL are statistically tested according to a series of null hypotheses. BIC model selection criterion is employed to choose an optimal baseline hazard function with maximum likelihood and parsimonious parameters. Simulations are used to validate the proposed mapping procedure. A published dataset from a mouse model system was used to illustrate the proposed framework. Results show that among the five commonly used survival distributions, Log-logistic distribution is the optimal baseline hazard function for mapping QTL of hyperoxic acute lung injury (HALI) survival; under the log-logistic distribution, four QTLs were identified, in which only one QTL was inherited in Mendelian fashion, whereas others were imprinted in different imprinting patterns. [ABSTRACT FROM AUTHOR]

Objective: Longitudinal measurements with bivariate response have been analyzed by several authors using two separate models for each response. However, for most of the biological or medical experiments, the two responses are highly correlated and hence a separate model for each response might not be a desirable way to analyze such data. A single model considering a bivariate response provides a more powerful inference as the correlation between the responses is modeled appropriately. In this article, we propose a dynamic statistical model to detect the genes controlling human blood pressure (systolic and diastolic). Methods: By modeling the mean function with orthogonal Legendre polynomials and the covariance matrix with a stationary parametric structure, we incorporate the statistical ideas in functional genome-wide association studies to detect SNPs which have significant control on human blood pressure. The traditional false discovery rate is used for multiple comparisons. Results: We analyze the data from the Framingham Heart Study to detect such SNPs by appropriately considering gender-gene interaction. We detect 8 SNPs for males and 7 for females which are most significant in controlling blood pressure. The genotype-specific mean curves and additive and dominant effects over time are shown for each significant SNP for both genders. Simulation studies are performed to examine the statistical properties of our model. The current model will be extremely useful in detecting genes controlling different traits and diseases for humans or non-human subjects. Copyright © 2011 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

Without consideration of other linked QTLs responsible for dynamic trait, original functional mapping based on a single QTL model is not optimal for analyzing multiple dynamic trait loci. Despite that composite functional mapping incorporates the effects of genetic background outside the tested QTL in mapping model, the arbitrary choice of background markers also impact on the power of QTL detection. In this study, we proposed Bayesian functional mapping strategy that can simultaneously identify multiple QTL controlling developmental patterns of dynamic traits over the genome. Our proposed method fits the change of each QTL effect with the time by Legendre polynomial and takes the residual covariance structure into account using the first autoregressive equation. Also, Bayesian shrinkage estimation was employed to estimate the model parameters. Especially, we specify the gamma distribution as the prior for the first-order auto-regressive coefficient, which will guarantee the convergence of Bayesian sampling. Simulations showed that the proposed method could accurately estimate the QTL parameters and had a greater statistical power of QTL detection than the composite functional mapping. A real data analysis of leaf age growth in rice is used for the demonstration of our method. It shows that our Bayesian functional mapping can detect more QTLs as compared to composite functional mapping. [ABSTRACT FROM AUTHOR]

lthough genome-wide association studies (GWAS) are widely used to identify the genetic and environmental etiology of a trait, several key issues related to their statistical power and biological relevance have remained unexplored. Here, we describe a novel statistical approach, called functional GWAS or fGWAS, to analyze the genetic control of traits by integrating biological principles of trait formation into the GWAS framework through mathematical and statistical bridges. fGWAS can address many fundamental questions, such as the patterns of genetic control over development, the duration of genetic effects, as well as what causes developmental trajectories to change or stop changing. In statistics, fGWAS displays increased power for gene detection by capitalizing on cumulative phenotypic variation in a longitudinal trait over time and increased robustness for manipulating sparse longitudinal data. [ABSTRACT FROM AUTHOR]

Motivation: Despite their success in identifying genes that affect complex disease or traits, current genome-wide association studies (GWASs) based on a single SNP analysis are too simple to elucidate a comprehensive picture of the genetic architecture of phenotypes. A simultaneous analysis of a large number of SNPs, although statistically challenging, especially with a small number of samples, is crucial for genetic modeling.Method: We propose a two-stage procedure for multi-SNP modeling and analysis in GWASs, by first producing a ‘preconditioned’ response variable using a supervised principle component analysis and then formulating Bayesian lasso to select a subset of significant SNPs. The Bayesian lasso is implemented with a hierarchical model, in which scale mixtures of normal are used as prior distributions for the genetic effects and exponential priors are considered for their variances, and then solved by using the Markov chain Monte Carlo (MCMC) algorithm. Our approach obviates the choice of the lasso parameter by imposing a diffuse hyperprior on it and estimating it along with other parameters and is particularly powerful for selecting the most relevant SNPs for GWASs, where the number of predictors exceeds the number of observations.Results: The new approach was examined through a simulation study. By using the approach to analyze a real dataset from the Framingham Heart Study, we detected several significant genes that are associated with body mass index (BMI). Our findings support the previous results about BMI-related SNPs and, meanwhile, gain new insights into the genetic control of this trait.Availability: The computer code for the approach developed is available at Penn State Center for Statistical Genetics web site, http://statgen.psu.edu.Contact: rwu@hes.hmc.psu.eduSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Functional mapping is a statistical method for mapping quantitative trait loci (QTLs) that regulate the dynamic pattern of a biological trait. This method integrates mathematical aspects of biological complexity into a mixture model for genetic mapping and tests the genetic effects of QTLs by comparing genotype-specific curve parameters. As a way of quantitatively specifying the dynamic behaviour of a system, differential equations have proved to be powerful for modelling and unravelling the biochemical, molecular, and cellular mechanisms of a biological process, such as biological rhythms. The equipment of functional mapping with biologically meaningful differential equations provides new insights into the genetic control of any dynamic processes. We formulate a new functional mapping framework for a dynamic biological rhythm by incorporating a group of ordinary differential equations (ODE). The Runge-Kutta fourth-order algorithm was implemented to estimate the parameters that define the system of ODE. The new model will find its implications for understanding the interplay between gene interactions and developmental pathways in complex biological rhythms. [ABSTRACT FROM AUTHOR]