Optimal-kk difference sequence in nonparametric regression

Difference-based methods have been attracting increasing attention in nonparametric regression, in particular for estimating the residual variance.To implement the estimation, one needs to choose an appropriate difference sequence, mainly between {\em the optimal difference sequence} and {\em the ordinary difference sequence}. The difference sequence selection is a fundamental problem in nonparametric regression, and it remains a controversial issue for over three decades. In this paper, we propose to tackle this challenging issue from a very unique perspective, namely by introducing a new difference sequence called {\em the optimal-$k$ difference sequence}. The new difference sequence not only provides a better balance between the bias-variance trade-off, but also dramatically enlarges the existing family of difference sequences that includes the optimal and ordinary difference sequences as two important special cases. We further demonstrate, by both theoretical and numerical studies, that the optimal-$k$ difference sequence has been pushing the boundaries of our knowledge in difference-based methods in nonparametric regression, and it always performs the best in practical situations

Propensity score regression for causal inference with treatment heterogeneity

Understanding how treatment effects vary on individual characteristics is critical in the contexts of personalized medicine, personalized advertising and policy design. When the characteristics are of practical interest are only a subset of full covariate, non-parametric estimation is often desirable; but few methods are available due to the computational difficult. Existing non-parametric methods such as the inverse probability weighting methods have limitations that hinder their use in many practical settings where the values of propensity scores are close to 0 or 1. We propose the propensity score regression (PSR) that allows the non-parametric estimation of the heterogeneous treatment effects in a wide context. PSR includes two non-parametric regressions in turn, where it first regresses on the propensity scores together with the characteristics of interest, to obtain an intermediate estimate; and then, regress the intermediate estimates on the characteristics of interest only. By including propensity scores as regressors in the non-parametric manner, PSR is capable of substantially easing the computational difficulty while remain (locally) insensitive to any value of propensity scores. We present several appealing properties of PSR, including the consistency and asymptotical normality, and in particular the existence of an explicit variance estimator, from which the analytical behaviour of PSR and its precision can be assessed. Simulation studies indicate that PSR outperform existing methods in varying settings with extreme values of propensity scores. We apply our method to the national 2009 flu survey (NHFS) data to investigate the effects of seasonal influenza vaccination and having paid sick leave across different age groups

Globalization? Trade war? A counterbalance perspective

The embrace of globalization and protectionism among economies has ebbed and flowed over the past few decades. These fluctuations call for quantitative analytics to help countries improve their trade policies. Changing attitudes about globalization also imply that the best trade policies may vary over time and be country-specific. We argue that the imports and exports of all economies constitute a counterbalanced network where conflict and cooperation are two sides of the same coin. Quantitative competitiveness is then formulated for each country using a network counterbalance equilibrium. A country could improve its relative strength in the network by embracing globalization, protectionism, trade collaboration, or conflict. This paper presents the necessary conditions for globalization and trade wars, evaluates their side effects, derives national bargaining powers, identifies appropriate targets for conflict or collaboration, and recommends fair resolutions for trade conflicts. Data and events from the past twenty years support these conditions

Research Progress of Machine Learning in Clinical Drug Therapy

With the advancement and development of concepts such as real-world research and precision treatment, the demand of researchers for medical big data processing keeps increasing. Because machine learning technology has unique advantages in processing massive, high-dimensional data and conducting predictive research, it has been deeply applied in the medical field in recent years. In addition to the application in disease diagnosis, image recognition and risk prediction, more and more studies have proved that machine learning can be applied to the decision support related research of clinical drug treatment. This article reviews the research progress of machine learning in clinical drug therapy

Machine Learning Approaches to Predict Risks of Diabetic Complications and Poor Glycemic Control in Nonadherent Type 2 Diabetes

Purpose: The objective of this study was to evaluate the efficacy of machine learning algorithms in predicting risks of complications and poor glycemic control in nonadherent type 2 diabetes (T2D).Materials and Methods: This study was a real-world study of the complications and blood glucose prognosis of nonadherent T2D patients. Data of inpatients in Sichuan Provincial People’s Hospital from January 2010 to December 2015 were collected. The T2D patients who had neither been monitored for glycosylated hemoglobin A nor had changed their hyperglycemia treatment regimens within the last 12 months were the object of this study. Seven types of machine learning algorithms were used to develop 18 prediction models. The predictive performance was mainly assessed using the area under the curve of the testing set.Results: Of 800 T2D patients, 165 (20.6%) met the inclusion criteria, of which 129 (78.2%) had poor glycemic control (defined as glycosylated hemoglobin A ≥7%). The highest area under the curves of the testing set for diabetic nephropathy, diabetic peripheral neuropathy, diabetic angiopathy, diabetic eye disease, and glycosylated hemoglobin A were 0.902 ± 0.040, 0.859 ± 0.050, 0.889 ± 0.059, 0.832 ± 0.086, and 0.825 ± 0.092, respectively.Conclusion: Both univariate analysis and machine learning methods reached the same conclusion. The duration of T2D and the duration of unadjusted hypoglycemic treatment were the key risk factors of diabetic complications, and the number of hypoglycemic drugs was the key risk factor of glycemic control of nonadherent T2D. This was the first study to use machine learning algorithms to explore the potential adverse outcomes of nonadherent T2D. The performances of the final prediction models we developed were acceptable; our prediction performances outperformed most other previous studies in most evaluation measures. Those models have potential clinical applicability in improving T2D care

LLM-Mini-CEX: Automatic Evaluation of Large Language Model for Diagnostic Conversation

There is an increasing interest in developing LLMs for medical diagnosis to improve diagnosis efficiency. Despite their alluring technological potential, there is no unified and comprehensive evaluation criterion, leading to the inability to evaluate the quality and potential risks of medical LLMs, further hindering the application of LLMs in medical treatment scenarios. Besides, current evaluations heavily rely on labor-intensive interactions with LLMs to obtain diagnostic dialogues and human evaluation on the quality of diagnosis dialogue. To tackle the lack of unified and comprehensive evaluation criterion, we first initially establish an evaluation criterion, termed LLM-specific Mini-CEX to assess the diagnostic capabilities of LLMs effectively, based on original Mini-CEX. To address the labor-intensive interaction problem, we develop a patient simulator to engage in automatic conversations with LLMs, and utilize ChatGPT for evaluating diagnosis dialogues automatically. Experimental results show that the LLM-specific Mini-CEX is adequate and necessary to evaluate medical diagnosis dialogue. Besides, ChatGPT can replace manual evaluation on the metrics of humanistic qualities and provides reproducible and automated comparisons between different LLMs

Efficient estimation of nonparametric genetic risk function with censored data

With an increasing number of causal genes discovered for complex human disorders, it is crucial to assess the genetic risk of disease onset for individuals who are carriers of these causal mutations and compare the distribution of age-at-onset with that in non-carriers. In many genetic epidemiological studies aiming at estimating causal gene effect on disease, the age-at-onset of disease is subject to censoring. In addition, some individuals’ mutation carrier or non-carrier status can be unknown due to the high cost of in-person ascertainment to collect DNA samples or death in older individuals. Instead, the probability of these individuals’ mutation status can be obtained from various sources. When mutation status is missing, the available data take the form of censored mixture data. Recently, various methods have been proposed for risk estimation from such data, but none is efficient for estimating a nonparametric distribution. We propose a fully efficient sieve maximum likelihood estimation method, in which we estimate the logarithm of the hazard ratio between genetic mutation groups using B-splines, while applying nonparametric maximum likelihood estimation for the reference baseline hazard function. Our estimator can be calculated via an expectation-maximization algorithm which is much faster than existing methods. We show that our estimator is consistent and semiparametrically efficient and establish its asymptotic distribution. Simulation studies demonstrate superior performance of the proposed method, which is applied to the estimation of the distribution of the age-at-onset of Parkinson's disease for carriers of mutations in the leucine-rich repeat kinase 2 gene

Tapered Optical Fiber Sensor for Label-Free Detection of Biomolecules

This paper presents a fast, highly sensitive and low-cost tapered optical fiber biosensor that enables the label-free detection of biomolecules. The sensor takes advantage of the interference effect between the fiber’s first two propagation modes along the taper waist region. The biomolecules bonded on the taper surface were determined by demodulating the transmission spectrum phase shift. Because of the sharp spectrum fringe signals, as well as a relatively long biomolecule testing region, the sensor displayed a fast response and was highly sensitive. To better understand the influence of various biomolecules on the sensor, a numerical simulation that varied biolayer parameters such as thickness and refractive index was performed. The results showed that the spectrum fringe shift was obvious to be measured even when the biolayer was only nanometers thick. A microchannel chip was designed and fabricated for the protection of the sensor and biotesting. Microelectromechanical systems (MEMS) fabrication techniques were used to precisely control the profile and depth of the microchannel on the silicon chip with an accuracy of 2 μm. A tapered optical fiber biosensor was fabricated and evaluated with an Immune globulin G (IgG) antibody-antigen pair

Positive regulatory effects of perioperative probiotic treatment on postoperative liver complications after colorectal liver metastases surgery: a double-center and double-blind randomized clinical trial

BACKGROUND: Colorectal liver metastases (CLM) occur frequently and postoperative intestinal infection is a common complication. Our previous study showed that probiotics could decrease the rate of infectious complications after colectomy for colorectal cancer. To determine the effects of the perioperative administration of probiotics on serum zonulin levels which is a marker of intestinal permeability and the subsequent impact on postoperative infectious complications in patients with CLM. METHODS: 150 patients with CLM were randomly divided into control group (n = 68) and probiotics group (n = 66). Probiotics and placebo were given orally for 6 days preoperatively and 10 days postoperatively to control group and probiotics group respectively. We used the local resection for metastatic tumor ,while for large tumor, the segmental hepatectomy. Postoperative outcome were recorded. Furthermore, complications in patients with normal intestinal barrier function and the relation with serum zonulin were analyzed to evaluate the impact on the liver barrier dysfunction. RESULTS: The incidence of infectious complications in the probiotics group was lower than control group. Analysis of CLM patients with normal postoperative intestinal barrier function paralleled with the serum zonulin level. And probiotics could also reduce the concentration of serum zonulin (P = 0.004) and plasma endotoxin (P < 0.001). CONCLUSION: Perioperative probiotics treatment could reduce the serum zonulin level, the rate of postoperative septicemia and maintain the liver barrier in patients undergoing CLM surgery. we propose a new model about the regulation of probiotics to liver barrier via clinical regulatory pathway. We recommend the preoperative oral intake of probiotics combined with postoperative continued probiotics treatment in patients who undergo CLM surgery. TRIAL REGISTRATION: ChiCTR-TRC-12002841. 2012/12/21 ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12876-015-0260-z) contains supplementary material, which is available to authorized users

Phenolic acid-induced phase separation and translation inhibition mediate plant interspecific competition

Phenolic acids (PAs) secreted by donor plants suppress the growth of their susceptible plant neighbours. However, how structurally diverse ensembles of PAs are perceived by plants to mediate interspecific competition remains a mystery. Here we show that a plant stress granule (SG) marker, RNA-BINDING PROTEIN 47B (RBP47B), is a sensor of PAs in Arabidopsis. PAs, including salicylic acid, 4-hydroxybenzoic acid, protocatechuic acid and so on, directly bind RBP47B, promote its phase separation and trigger SG formation accompanied by global translation inhibition. Salicylic acid-induced global translation inhibition depends on RBP47 family members. RBP47s regulate the proteome rather than the absolute quantity of SG. The rbp47 quadruple mutant shows a reduced sensitivity to the inhibitory effect of the PA mixture as well as to that of PA-rich rice when tested in a co-culturing ecosystem. In this Article, we identified the long sought-after PA sensor as RBP47B and illustrated that PA-induced SG-mediated translational inhibition was one of the PA perception mechanisms.This work was supported by funds from the National Natural Science Foundation of China (31970641); the State Key Laboratory for Protein and Plant Gene Research, School of Life Sciences, Peking University, Center for Life Sciences; the USDA National Institute of Food and Agriculture, Hatch project 3808 to W.W.; the National Natural Science Foundation of China (31970283); Beijing Nova Program of Science and Technology (Z191100001119027); Capital Normal University and State Key Laboratory for Protein and Plant Gene Research, School of Life Sciences, Peking University, to M.Z.; the European Commission Marie Curie-IEF reSGulating-702473 to E.G.B.; Natural Science Foundation of Fujian Province (2020J01546) to J.L.; Knut and Alice Wallenberg Foundation and Swedish Research Council VR to P.V.B.; International Postdoctoral Exchange Fellowship Program and Postdoctoral Fellowship of Center for Life Sciences, and National Natural Science Foundation of China (3220050423) to Z.X.; and the Postdoctoral Fellowship of Center for Life Sciences to S.Z., Y.L. and C.C.Peer reviewe