Deep jump learning for off-policy evaluation in continuous treatment settings

We consider off-policy evaluation (OPE) in continuous treatment settings, such as personalized dose-finding. In OPE, one aims to estimate the mean outcome under a new treatment decision rule using historical data generated by a different decision rule. Most existing works on OPE focus on discrete treatment settings. To handle continuous treatments, we develop a novel estimation method for OPE using deep jump learning. The key ingredient of our method lies in adaptively discretizing the treatment space using deep discretization, by leveraging deep learning and multi-scale change point detection. This allows us to apply existing OPE methods in discrete treatments to handle continuous treatments. Our method is further justified by theoretical results, simulations, and a real application to Warfarin Dosing

A real-time interpolator for parametric curves

Driven by the ever increasing need for the high-speed high-accuracy machining of freeform surfaces, the interpolators for parametric curves become highly desirable, as they can eliminate the feedrate and acceleration fluctuation due to the discontinuity in the first derivatives along the linear tool path. The interpolation for parametric curves is essentially an optimization problem, and it is extremely difficult to get the time-optimal solution. This paper presents a novel real-time interpolator for parametric curves (RTIPC), which provides a near time-optimal solution. It limits the machine dynamics (axial velocities, axial accelerations and jerk) and contour error through feedrate lookahead and acceleration lookahead operations, meanwhile, the feedrate is maintained as high as possible with minimum fluctuation. The lookahead length is dynamically adjusted to minimize the computation load. And the numerical integration error is considered during the lookahead calculation. Two typical parametric curves are selected for both numerical simulation and experimental validation, a cubic phase plate freeform surface is also machined. The numerical simulation is performed using the software (open access information is in the Acknowledgment section) that implements the proposed RTIPC, the results demonstrate the effectiveness of the RTIPC. The real-time performance of the RTIPC is tested on the in-house developed controller, which shows satisfactory efficiency. Finally, machining trials are carried out in comparison with the industrial standard linear interpolator and the state-of-the-art Position-Velocity-Time (PVT) interpolator, the results show the significant advantages of the RTIPC in coding, productivity and motion smoothness

Zero-shot Learning of Drug Response Prediction for Preclinical Drug Screening

Conventional deep learning methods typically employ supervised learning for drug response prediction (DRP). This entails dependence on labeled response data from drugs for model training. However, practical applications in the preclinical drug screening phase demand that DRP models predict responses for novel compounds, often with unknown drug responses. This presents a challenge, rendering supervised deep learning methods unsuitable for such scenarios. In this paper, we propose a zero-shot learning solution for the DRP task in preclinical drug screening. Specifically, we propose a Multi-branch Multi-Source Domain Adaptation Test Enhancement Plug-in, called MSDA. MSDA can be seamlessly integrated with conventional DRP methods, learning invariant features from the prior response data of similar drugs to enhance real-time predictions of unlabeled compounds. We conducted experiments using the GDSCv2 and CellMiner datasets. The results demonstrate that MSDA efficiently predicts drug responses for novel compounds, leading to a general performance improvement of 5-10\% in the preclinical drug screening phase. The significance of this solution resides in its potential to accelerate the drug discovery process, improve drug candidate assessment, and facilitate the success of drug discovery.Comment: 16 pages, 3 figures, 3 table

CAPITAL: Optimal Subgroup Identification via Constrained Policy Tree Search

Personalized medicine, a paradigm of medicine tailored to a patient's characteristics, is an increasingly attractive field in health care. An important goal of personalized medicine is to identify a subgroup of patients, based on baseline covariates, that benefits more from the targeted treatment than other comparative treatments. Most of the current subgroup identification methods only focus on obtaining a subgroup with an enhanced treatment effect without paying attention to subgroup size. Yet, a clinically meaningful subgroup learning approach should identify the maximum number of patients who can benefit from the better treatment. In this paper, we present an optimal subgroup selection rule (SSR) that maximizes the number of selected patients, and in the meantime, achieves the pre-specified clinically meaningful mean outcome, such as the average treatment effect. We derive two equivalent theoretical forms of the optimal SSR based on the contrast function that describes the treatment-covariates interaction in the outcome. We further propose a ConstrAined PolIcy Tree seArch aLgorithm (CAPITAL) to find the optimal SSR within the interpretable decision tree class. The proposed method is flexible to handle multiple constraints that penalize the inclusion of patients with negative treatment effects, and to address time to event data using the restricted mean survival time as the clinically interesting mean outcome. Extensive simulations, comparison studies, and real data applications are conducted to demonstrate the validity and utility of our method

Genomic value prediction for quantitative traits under the epistatic model

Abstract Background Most quantitative traits are controlled by multiple quantitative trait loci (QTL). The contribution of each locus may be negligible but the collective contribution of all loci is usually significant. Genome selection that uses markers of the entire genome to predict the genomic values of individual plants or animals can be more efficient than selection on phenotypic values and pedigree information alone for genetic improvement. When a quantitative trait is contributed by epistatic effects, using all markers (main effects) and marker pairs (epistatic effects) to predict the genomic values of plants can achieve the maximum efficiency for genetic improvement. Results In this study, we created 126 recombinant inbred lines of soybean and genotyped 80 makers across the genome. We applied the genome selection technique to predict the genomic value of somatic embryo number (a quantitative trait) for each line. Cross validation analysis showed that the squared correlation coefficient between the observed and predicted embryo numbers was 0.33 when only main (additive) effects were used for prediction. When the interaction (epistatic) effects were also included in the model, the squared correlation coefficient reached 0.78. Conclusions This study provided an excellent example for the application of genome selection to plant breeding

Non‑linear association of anthropometric measurements and pulmonary function

This study examined the association of anthropometric measurements [body mass index (BMI), waist circumference (WC), percentage body fat (PBF), body roundness index (BRI) and A Body Shape Index (ABSI)] with pulmonary function using a United States national cohort. This cross-sectional study included 7346 participants. The association between anthropometric measurements and pulmonary function was assessed by multivariable linear regression. Where there was evidence of non-linearity, we applied a restricted cubic spline to explore the non-linear association. All analyses were weighted to represent the U.S. population and to account for the intricate survey design. After adjusting for age, race, education, smoking, and physical activity, both underweight and obesity were associated with reduced forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC). Furthermore, the associations between BMI and FEV1, as well as FVC, were reversed U-shape in both males and females. Similar non-linear association shape occurred in WC, PBF, BRI and ABSI. Conclusion: BMI, WC, PBF, BRI, ABSI are non-linearly associated with pulmonary function. Reduced pulmonary function is a risk factor for future all-cause mortality and cardiovascular events; thus, this nonlinearity may explain the U-shape or J-shape association of BMI with overall mortality and cardiovascular events.Tis work was supported by (i) the National Natural Science Foundation of China (No. 82070851, 81870556, 81670738, 81930019), Beijing Municipal Administration of Hospital`s Youth Program (QML20170204), Excellent Talents in Dongcheng District of Beijing and (ii) the Instituto de Salud Carlos III (Fondo de Investigación Sanitaria, PI 15/00260 and PI18/00640), European Union, European Regional Development Fund (Fondo Europeo de Desarrollo Regional, FEDER, “Una manera de hacer Europa”). CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM) are initiatives of the Instituto Carlos III. Te European Foundation for the Study of Diabetes (EFSD/Lilly-Mental Health and Diabetes Programme) and Laboratorios Menarini. Te funders played no role in the design or conduct of the study; in the collection, management, analysis, or interpretation of the data; or in the preparation, review, or approval of the manuscript

Epithelial Heat Shock Proteins Mediate the Protective Effects of Limosilactobacillus reuteri in Dextran Sulfate Sodium-Induced Colitis

Defects in gut barrier function are implicated in gastrointestinal (GI) disorders like inflammatory bowel disease (IBD), as well as in systemic inflammation. With the increasing incidence of IBD worldwide, more attention should be paid to dietary interventions and therapeutics with the potential to boost the natural defense mechanisms of gut epithelial cells. The current study aimed to investigate the protective effects of Limosilactobacillus reuteri ATCC PTA 4659 in a colitis mouse model and delineate the mechanisms behind it. Wild-type mice were allocated to the control group; or given 3% dextran sulfate sodium (DSS) in drinking water for 7 days to induce colitis; or administered L. reuteri for 7 days as pretreatment; or for 14 days starting 7 days before subjecting to the DSS. Peroral treatment with L. reuteri improved colitis severity clinically and morphologically and reduced the colonic levels of Tumor necrosis factor-alpha (TNF-alpha) (Tnf), Interleukin 1-beta (Il1 beta), and nterferon-gamma (Ifng), the crucial pro-inflammatory cytokines in colitis onset. It also prevented the CD11b(+)Ly6G(+) neutrophil recruitment and the skewed immune responses in mesenteric lymph nodes (MLNs) of CD11b(+)CD11c(+) dendritic cell (DC) expansion and Foxp3(+)CD4(+) T-cell reduction. Using 16S rRNA gene amplicon sequencing and RT-qPCR, we demonstrated a colitis-driven bacterial translocation to MLNs and gut microbiota dysbiosis that were in part counterbalanced by L. reuteri treatment. Moreover, the expression of barrier-preserving tight junction (TJ) proteins and cytoprotective heat shock protein (HSP) 70 and HSP25 was reduced by colitis but boosted by L. reuteri treatment. A shift in expression pattern was also observed with HSP70 in response to the pretreatment and with HSP25 in response to L. reuteri-DSS. In addition, the changes of HSPs were found to be correlated to bacterial load and epithelial cell proliferation. In conclusion, our results demonstrate that the human-derived L. reuteri strain 4659 confers protection in experimental colitis in young mice, while intestinal HSPs may mediate the probiotic effects by providing a supportive protein-protein network for the epithelium in health and colitis