Initial severity-dependent longitudinal model with application to a randomized controlled trial of women with depression

The assessment of initial severity of a disease is arguably one of the most important factors in identifying appropriate therapies. In this paper, we propose an initial severity-dependent longitudinal model to account for the influence of the initial severity of a disease on the posttreatment severity and the efficacy of medical treatments. The proposed model has the flexibility of nonparametric modeling, as it allows coefficients to vary with the initial severity of the disease. It also provides attractive and practical patient-specific interpretation of initial severity-dependent coefficients. As a result, the proposed model enables patient-specific modeling and treatment recommendations consistent with the assessment of the patient's initial severity, and thus, it can be used as a decision support tool for clinicians. A new empirical likelihood approach is employed for efficient estimation and statistical inference about the initial severity-dependent coefficients. In contrast to the literature on marginal regression models, the proposed estimation procedure allows nuisance parameters associated with the working correlation matrix and the error variances to vary smoothly with the initial severity. The effectiveness of the proposed procedure is demonstrated via simulation studies. We further apply the proposed method by analyzing a data set arising from a randomized controlled trial of women with depression and discover an interesting phenomenon; antidepressant medication intervention is effective for patients with moderate or severe depression, whereas psychotherapy intervention using manual-guided cognitive behavior therapy is effective for patients with a severe case of depression.     

Errors-in-variables jump regression using local clustering

Errors-in-variables (EIV) regression is widely used in econometric models. The statistical analysis becomes challenging when the regression function is discontinuous and the distribution of measurement error is unknown. In the literature, most existing jump regression methods either assume that there is no measurement error involved or require that jumps are explicitly detected before the regression function can be estimated. In some applications, however, the ultimate goal is to estimate the regression function and to preserve the jumps in the process of estimation. In this paper, we are concerned with reconstructing jump regression curve from data that involve measurement error. We propose a direct jump-preserving method that does not explicitly detect jumps. The challenge of restoring jump structure masked by measurement error is handled by local clustering. Theoretical analysis shows that the proposed curve estimator is statistically consistent. A numerical comparison with an existing jump regression method highlights its jump-preserving property. Finally, we demonstrate our method by an application to a health tax policy study in Australia.     

Acceptability of Reduced-Fat and Fried-Food-Free Menu in Self-Service Restaurant

The main purpose of this study was to determine how the reduction of fat content in meals affects food acceptability in a self-service restaurant. Technical Preparation Files and combined ovens were used to modify the menu. The 14-month trial was divided in two phases, in which 75 clients evaluated the menu, without knowing the changes. Flavor, texture, appearance and global quality were measured using a 7-point hedonic scale. Principal and Hierarchical Factor Analysis as well as Fischer-test (? = .05) were used to test differences in acceptance proportion. The reduction of added fat (up to 2%) and the removal of fried food resulted in an equivalent/higher acceptance (from 71 ± 8 to 86 ± 7), whereas the rejection proportion decreased. The mean acceptance score, in both phases, was satisfactory for all dishes. Hence, it was concluded that the reduction of added fat and the use of combined ovens resulted in a better acceptance of the menu.     

白果仁化学成分研究

目的研究白果仁的化学成分。方法应用溶剂法进行提取,采用硅胶柱色谱分离和纯化化合物,通过理化性质和波谱数据鉴定化合物结构。结果从体积分数95%乙醇提取物中分离并鉴定了19个化合物,分别为二十六烷酸(Ⅰ)、棕榈酸(Ⅱ)、白果醇(Ⅲ)、β-谷甾醇(Ⅳ)、正十六烷酸-1-甘油酯(Ⅴ)、熊果酸(Ⅵ)、金松双黄酮(Ⅶ)、银杏黄素(Ⅷ)、异银杏黄素(Ⅸ)、胡萝卜苷(Ⅹ)、银杏内酯A(Ⅺ)、银杏内酯B(Ⅻ)、银杏内酯C(ⅩⅢ)、尿嘧啶(ⅩⅣ)、松柏苷(ⅩⅤ)、甘草苷(ⅩⅥ)、腺苷(ⅩⅦ)、D-葡萄糖(ⅩⅧ)和蔗糖(ⅪⅩ)。结论化合物Ⅵ、ⅩⅤ～ⅩⅦ为首次从该植物中分离得到,化合物Ⅰ～Ⅴ、Ⅶ～Ⅹ、ⅩⅣ为首次从白果仁中分离得到。     

Surgical gesture classification from video and kinematic data

Much of the existing work on automatic classification of gestures and skill in robotic surgery is based on dynamic cues (e.g., time to completion, speed, forces, torque) or kinematic data (e.g., robot trajectories and velocities). While videos could be equally or more discriminative (e.g., videos contain semantic information not present in kinematic data), they are typically not used because of the difficulties associated with automatic video interpretation. In this paper, we propose several methods for automatic surgical gesture classification from video data. We assume that the video of a surgical task (e.g., suturing) has been segmented into video clips corresponding to a single gesture (e.g., grabbing the needle, passing the needle) and propose three methods to classify the gesture of each video clip. In the first one, we model each video clip as the output of a linear dynamical system (LDS) and use metrics in the space of LDSs to classify new video clips. In the second one, we use spatio-temporal features extracted from each video clip to learn a dictionary of spatio-temporal words, and use a bag-of-features (BoF) approach to classify new video clips. In the third one, we use multiple kernel learning (MKL) to combine the LDS and BoF approaches. Since the LDS approach is also applicable to kinematic data, we also use MKL to combine both types of data in order to exploit their complementarity. Our experiments on a typical surgical training setup show that methods based on video data perform equally well, if not better, than state-of-the-art approaches based on kinematic data. In turn, the combination of both kinematic and video data outperforms any other algorithm based on one type of data alone.     

cDNA cloning and heterologous expression of the major allergens from peach and apple belonging to the lipid‐transfer protein family

BACKGROUND: Lipid-transfer proteins (LTPs) have been identified as major allergens of Rosaceae fruits in populations living in areas virtually free of Fagales trees, such as several Mediterranean communities. Pru p 3 and Mal d 3, the allergens from peach and apple, respectively, have a main clinical relevance in these areas. OBJECTIVE: To isolate and characterize cDNAs for Pru p 3 and Mal d 3, and to produce recombinant Pru p 3 in the yeast Pichiapastoris. METHODS: cDNAs for both allergens were isolated by polymerase chain reaction using nondegenerated primers. Expression of Pru p 3 was performed in P. pastoris using the pPIC 9 vector. The recombinant product was purified by gel-filtration chromatography followed by RP-HPLC. Immunodetection and immunoblot inhibition assays were carried out with sera from peach-allergic patients. RESULTS: The cDNAs for both Pru p 3 and Mal d 3 showed a 273 open reading frame coding for the 91 amino acid mature polypeptides. The deduced amino acid sequences exhibited N-terminal regions fully identical to those previously determined for the natural peach and apple allergens. Pru p 3 was expressed in P. pastoris at 20 mg/L of culture medium. The recombinant allergen showed the same N-terminal sequence (plus a glutamic acid added for proper extracellular expression) and apparent molecular size as natural Pru p 3. Both the recombinant and natural forms of Pru p 3 displayed similar immunoglobulin (Ig)E-binding capacity in immunodetection and immunoblot inhibition assays. CONCLUSIONS: Comparison of the complete primary structures of mature Pru p 3 and Mal d 3 deduced from their corresponding cDNA clones supports the close relationship between both allergens. Recombinant Pru p 3 binds IgE in vitro like its natural counterpart. Therefore, it can be a useful tool for specific diagnosis and structural studies.     

Semiparametric estimation and testing of the trend of temperature series

Summary The application of a partially linear model to global and hemispheric temperature series is proposed. Partially linear modelling allows the trend to take a very general form rather than imposing the restriction of linearity seen in existing studies. The removal of the linearity restriction is based on the fact that it is well accepted that a significant trend is present in global temperature series. The model will allow for the data to ‘speak for themselves’ with regard to the form of the trend. The results initially reveal that a linear trend does not approximate well the behaviour of global or hemispheric temperature series. This is further confirmed through a formal testing procedure. The results suggest that little faith should be instilled in long‐term forecasts of temperatures in which the trend of global and hemispheric series is assumed to be linear. All the current evidence suggest that temperatures will continue to rise in an unknown and probably nonlinear fashion.     

Examining sex differences in pleiotropic effects for depression and smoking using polygenic and gene‐region aggregation techniques

Sex differences in rates of depression are thought to contribute to sex differences in smoking initiation (SI) and number of cigarettes smoked per day (CPD). One hypothesis is that women smoke as a strategy to cope with anxiety and depression, and have difficulty quitting because of concomitant changes in hypothalamic–pituitary–adrenocortical (HPA) axis function during nicotine withdrawal states. Despite evidence of biological ties, research has not examined whether genetic factors that contribute to depression‐smoking comorbidity differ by sex. We utilized two statistical aggregation techniques—polygenic scores (PGSs) and sequence kernel association testing—to assess the degree of pleiotropy between these behaviors and moderation by sex in the Health and Retirement Study (N = 8,086). At the genome‐wide level, we observed associations between PGSs for depressive symptoms and SI, and measured SI and depressive symptoms (all p < .01). At the gene level, we found evidence of pleiotropy in FKBP5 for SI (p = .028), and sex‐specific pleiotropy in females in NR3C2 (p = .030) and CHRNA5 (p = .025) for SI and CPD, respectively. Results suggest bidirectional associations between depression and smoking may be partially accounted for by shared genetic factors, and genetic variation in genes related to HPA‐axis functioning and nicotine dependence may contribute to sex differences in SI and CPD.     

牡丹皮免煎颗粒与饮片有效成分含量的差异研究

目的:比较中药牡丹皮免煎颗粒与其饮片中有效成分含量差异。方法:采用高效液相色谱法(HPLC),伊利特C18柱,以甲醇-水(50:50)为流动相,流速1.0 mL/min,检测波长274 nm。结果:牡丹皮免煎颗粒中丹皮酚含量为4.65 mg/g,牡丹皮中药饮片中丹皮酚含量为24.23 mg/g;丹皮酚在55～275μg范围内呈良好的线性关系,平均回收率为97.10%,RSD为0.7%(n=6)。结论:中药牡丹皮免煎颗粒与其饮片所含丹皮酚含量差异较大。