Local linear m-estimators in null recurrent time series

In this paper, we study a nonlinear cointegration type model Yκ = m(Xκ) + wκ, where {Yκ} and {Xκ} are observed nonstationary processes and {Wκ} is an unobserved stationary process. The process {Xκ} is assumed to be a null-recurrent Markov chain. We apply a robust version of local linear regression smoothers to estimate m(-). Under mild conditions, the uniform weak consistency and asymptotic normality of the local linear M-estimators are established. Furthermore, a one-step iterated procedure is introduced to obtain the local linear M-estimator and the optimal bandwidth selection is discussed. Meanwhile, some numerical examples are given to show that the proposed theory and methods perform well in practice

Transcriptome analysis of the adenoma–carcinoma sequences identifies novel biomarkers associated with development of canine colorectal cancer

The concept of adenoma-to-cancer transformation in human colorectal cancer (CRC) is widely accepted. However, the relationship between transcriptome features and adenoma to carcinoma transformation in canines is not clear. We collected transcriptome data from 8 normal colon tissues, 4 adenoma tissues, and 15 cancer tissues. Differential analysis was unable to determine the dynamic changes of genes but revealed that PFKFB3 may play a key role in this process. Enrichment analysis explained metabolic dysregulation, immunosuppression, and typical cancer pathways in canine colorectal tumors. MFuzz generated specific dynamic expression patterns of five differentially expressed genes (DEGs). Weighted correlation network analysis showed that DEGs in cluster 3 were associated with malignant tissues, revealing the key role of inflammatory and immune pathways in canine CRC, and the S100A protein family was also found to be involved in the malignant transformation of canine colorectal tumors. By comparing strategies between humans and dogs, we found five novel markers that may be drivers of CRC. Among them, GTBP4 showed excellent diagnostic and prognostic ability. This study was the first systematic exploration of transformation in canine CRC, complemented the molecular characteristics of the development and progression of canine CRC, and provided new potential biomarkers and comparative oncologic evidence for biomarker studies in human colorectal cancer

Benzyl isothiocyanate induces apoptosis and inhibits tumor growth in canine mammary carcinoma via down-regulation of the cyclin B1/Cdk1 pathway

Background: Canine mammary carcinoma is common in female dogs, and its poor prognosis remains a serious clinical challenge, especially in developing countries. Benzyl isothiocyanate (BITC) has attracted great interest because of its inhibitory effect against tumor activity. However, its effect and the underlying mechanisms of action in canine mammary cancer are not well-understood. Here, we show that BITC suppresses mammary tumor growth, both in vivo and in vitro, and reveal some of the potential mechanisms involved. Methods: The effect of BITC on canine mammary cancer was evaluated on CIPp and CMT-7364, canine mammary carcinoma lines. The cell lines were treated with BITC and then subjected to wound healing and invasion assays. Cell cycles and apoptosis were measured using flow cytometry; TUNEL assay; immunohistochemistry (IHC) for caspase 3, caspase 9, and cyclin D1; hematoxylin and eosin (H&E) staining; and/or quantitative polymerase chain reaction (qPCR). Results: BITC showed a strong suppressive effect in both CIPp and CMT-7364 cells by inhibiting cell growth in vitro; these effects were both dose- and time-dependent. BITC also inhibited migration and invasion of CIPp and CMT-7364 cells. BITC induced G2 arrest and apoptosis, decreasing tumor growth in nude mice by downregulation of cyclin B1 and Cdk1 expression. Conclusion: BITC suppressed both invasion and migration of CIPp and CMT-7364 cells and induced apoptosis. BITC inhibited canine mammary tumor growth by suppressing cyclinB1 and Cdk1 expression in nude mice

A Versatile Method of Engineering the Electron Wavefunction of Hybrid Quantum Devices

With the development of quantum technology, hybrid devices that combine superconductors (S) and semiconductors (Sm) have attracted great attention due to the possibility of engineering structures that benefit from the integration of the properties of both materials. However, until now, none of the experiments have reported good control of band alignment at the interface, which determines the strength of S-Sm coupling and the proximitized superconducting gap. Here, we fabricate hybrid devices in a generic way with argon milling to modify the interface while maintaining its high quality. First, after the milling the atomically connected S-Sm interfaces appear, resulting in a large induced gap, as well as the ballistic transport revealed by the multiple Andreev reflections and quantized above-gap conductance plateaus. Second, by comparing transport measurement with Schr\"odinger-Poisson (SP) calculations, we demonstrate that argon milling is capable of varying the band bending strength in the semiconducting wire as the electrons tend to accumulate on the etched surface for longer milling time. Finally, we perform nonlocal measurements on advanced devices to demonstrate the coexistence and tunability of crossed Andreev reflection (CAR) and elastic co-tunneling (ECT) -- key ingredients for building the prototype setup for realization of Kitaev chain and quantum entanglement probing. Such a versatile method, compatible with the standard fabrication process and accompanied by the well-controlled modification of the interface, will definitely boost the creation of more sophisticated hybrid devices for exploring physics in solid-state systems.Comment: 18 pages, 9 figure

Local Composite Quantile Regression Smoothing for Harris Recurrent Markov Processes

In this paper, we study the local polynomial composite quantile regression (CQR) smoothing method for the nonlinear and nonparametric models under the Harris recurrent Markov chain framework. The local polynomial CQR regression method is a robust alternative to the widely-used local polynomial method, and has been well studied in stationary time series. In this paper, we relax the stationarity restriction on the model, and allow that the regressors are generated by a general Harris recurrent Markov process which includes both the stationary (positive recurrent) and nonstationary (null recurrent) cases. Under some mild conditions, we establish the asymptotic theory for the proposed local polynomial CQR estimator of the mean regression function, and show that the convergence rate for the estimator in nonstationary case is slower than that in stationary case. Furthermore, a weighted type local polynomial CQR estimator is provided to improve the estimation efficiency, and a data-driven bandwidth selection is introduced to choose the optimal bandwidth involved in the nonparametric estimators. Finally, we give some numerical studies to examine the finite sample performance of the developed methodology and theory

Asymptotic normality for L1-norm kernel estimator of conditional median under association dependence

Let be a set of observations from a stationary jointly associated process and [theta](x) be the conditional median, that is, . We consider the problem of estimating [theta](x) based on the L1-norm kernel and establish asymptotic normality of the resulting estimator [theta]n(x).Associated processes Asymptotic normality Conditional median L1-norm kernel