RKHS-based covariate balancing for survival causal effect estimation.

Survival causal effect estimation based on right-censored data is of key interest in both survival analysis and causal inference. Propensity score weighting is one of the most popular methods in the literature. However, since it involves the inverse of propensity score estimates, its practical performance may be very unstable, especially when the covariate overlap is limited between treatment and control groups. To address this problem, a covariate balancing method is developed in this paper to estimate the counterfactual survival function. The proposed method is nonparametric and balances covariates in a reproducing kernel Hilbert space (RKHS) via weights that are counterparts of inverse propensity scores. The uniform rate of convergence for the proposed estimator is shown to be the same as that for the classical Kaplan-Meier estimator. The appealing practical performance of the proposed method is demonstrated by a simulation study as well as two real data applications to study the causal effect of smoking on survival time of stroke patients and that of endotoxin on survival time for female patients with lung cancer respectively.

Del Nido cardioplegia or potassium induces Nrf2 and protects cardiomyocytes against oxidative stress.

Open heart surgery is often an unavoidable procedure for the treatment of coronary artery disease. The procedure-associated reperfusion injury affects postoperative cardiac performance and long-term outcomes. We addressed here whether cardioplegia essential for cardiopulmonary bypass surgery activates Nrf2, a transcription factor regulating the expression of antioxidant and detoxification genes. With commonly used cardioplegic solutions, high K+, low K+, Del Nido (DN), histidine-tryptophan-ketoglutarate (HTK), and Celsior (CS), we found that DN caused a significant increase of Nrf2 protein in AC16 human cardiomyocytes. Tracing the ingredients in DN led to the discovery of KCl at the concentration of 20-60 mM capable of significant Nrf2 protein induction. The antioxidant response element (ARE) luciferase reporter assays confirmed Nrf2 activation by DN or KCl. Transcriptomic profiling using RNA-seq revealed that oxidation-reduction as a main gene ontology group affected by KCl. KCl indeed elevated the expression of classical Nrf2 downstream targets, including TXNRD1, AKR1C, AKR1B1, SRXN1, and G6PD. DN or KCl-induced Nrf2 elevation is Ca2+ concentration dependent. We found that KCl decreased Nrf2 protein ubiquitination and extended the half-life of Nrf2 from 17.8 to 25.1 mins. Knocking out Keap1 blocked Nrf2 induction by K+. Nrf2 induction by DN or KCl correlates with the protection against reactive oxygen species generation or loss of viability by H2O2 treatment. Our data support that high K+ concentration in DN cardioplegic solution can induce Nrf2 protein and protect cardiomyocytes against oxidative damage.NEW & NOTEWORTHY Open heart surgery is often an unavoidable procedure for the treatment of coronary artery disease. The procedure-associated reperfusion injury affects postoperative cardiac performance and long-term outcomes. We report here that Del Nido cardioplegic solution or potassium is an effective inducer of Nrf2 transcription factor, which controls the antioxidant and detoxification response. This indicates that Del Nido solution is not only essential for open heart surgery but also exhibits cardiac protective activity.

Phenomic data-driven biological prediction of maize through field-based high-throughput phenotyping integration with genomic data.

High-throughput phenotyping (HTP) has expanded the dimensionality of data in plant research; however, HTP has resulted in few novel biological discoveries to date. Field-based HTP (FHTP), using small unoccupied aerial vehicles (UAVs) equipped with imaging sensors, can be deployed routinely to monitor segregating plant population interactions with the environment under biologically meaningful conditions. Here, flowering dates and plant height, important phenological fitness traits, were collected on 520 segregating maize recombinant inbred lines (RILs) in both irrigated and drought stress trials in 2018. Using UAV phenomic, single nucleotide polymorphism (SNP) genomic, as well as combined data, flowering times were predicted using several scenarios. Untested genotypes were predicted with 0.58, 0.59, and 0.41 prediction ability for anthesis, silking, and terminal plant height, respectively, using genomic data, but prediction ability increased to 0.77, 0.76, and 0.58 when phenomic and genomic data were used together. Using the phenomic data in a genome-wide association study, a heat-related candidate gene (GRMZM2G083810; hsp18f) was discovered using temporal reflectance phenotypes belonging to flowering times (both irrigated and drought) trials where heat stress also peaked. Thus, a relationship between plants and abiotic stresses belonging to a specific time of growth was revealed only through use of temporal phenomic data. Overall, this study showed that (i) it is possible to predict complex traits using high dimensional phenomic data between different environments, and (ii) temporal phenomic data can reveal a time-dependent association between genotypes and abiotic stresses, which can help understand mechanisms to develop resilient plants.

Using machine learning to predict cardiovascular risk using self-reported questionnaires: Findings from the 45 and Up Study.

BACKGROUND: Machine learning has been shown to outperform traditional statistical methods for risk prediction model development. We aimed to develop machine learning-based risk prediction models for cardiovascular mortality and hospitalisation for ischemic heart disease (IHD) using self-reported questionnaire data. METHODS: The 45 and Up Study was a retrospective population-based study in New South Wales, Australia (2005-2009). Self-reported healthcare survey data on 187,268 participants without a history of cardiovascular disease was linked to hospitalisation and mortality data. We compared different machine learning algorithms, including traditional classification methods (support vector machine (SVM), neural network, random forest and logistic regression) and survival methods (fast survival SVM, Cox regression and random survival forest). RESULTS: A total of 3687 participants experienced cardiovascular mortality and 12,841 participants had IHD-related hospitalisation over a median follow-up of 10.4 years and 11.6 years respectively. The best model for cardiovascular mortality was a Cox survival regression with L1 penalty at a re-sampled case/non-case ratio of 0.3 achieved by under-sampling of the non-cases. This model had the Uno's and Harrel's concordance indexes of 0.898 and 0.900 respectively. The best model for IHD hospitalisation was a Cox survival regression with L1 penalty at a re-sampled case/non-case ratio of 1.0 with Uno's and Harrel's concordance indexes of 0.711 and 0.718 respectively. CONCLUSION: Machine learning-based risk prediction models developed using self-reported questionnaire data had good prediction performance. These models may have the potential to be used in initial screening tests to identify high-risk individuals before undergoing costly investigation.

Delay-related activity in marmoset prefrontal cortex.

Persistent delay-period activity in prefrontal cortex (PFC) has long been regarded as a neural signature of working memory (WM). Electrophysiological investigations in macaque PFC have provided much insight into WM mechanisms; however, a barrier to understanding is the fact that a portion of PFC lies buried within the principal sulcus in this species and is inaccessible for laminar electrophysiology or optical imaging. The relatively lissencephalic cortex of the New World common marmoset (Callithrix jacchus) circumvents such limitations. It remains unknown, however, whether marmoset PFC neurons exhibit persistent activity. Here, we addressed this gap by conducting wireless electrophysiological recordings in PFC of marmosets performing a delayed-match-to-location task on a home cage-based touchscreen system. As in macaques, marmoset PFC neurons exhibited sample-, delay-, and response-related activity that was directionally tuned and linked to correct task performance. Models constructed from population activity consistently and accurately predicted stimulus location throughout the delay period, supporting a framework of delay activity in which mnemonic representations are relatively stable in time. Taken together, our findings support the existence of common neural mechanisms underlying WM performance in PFC of macaques and marmosets and thus validate the marmoset as a suitable model animal for investigating the microcircuitry underlying WM.

Ketamine disrupts gaze patterns during face viewing in the common marmoset.

Faces are stimuli of critical importance for primates. The common marmoset (Callithrix jacchus) is a promising model for investigations of face processing, as this species possesses oculomotor and face-processing networks resembling those of macaques and humans. Face processing is often disrupted in neuropsychiatric conditions such as schizophrenia (SZ), and thus, it is important to recapitulate underlying circuitry dysfunction preclinically. The N-methyl-d-aspartate (NMDA) noncompetitive antagonist ketamine has been used extensively to model the cognitive symptoms of SZ. Here, we investigated the effects of a subanesthetic dose of ketamine on oculomotor behavior in marmosets during face viewing. Four marmosets received systemic ketamine or saline injections while viewing phase-scrambled or intact videos of conspecifics' faces. To evaluate effects of ketamine on scan paths during face viewing, we identified regions of interest in each face video and classified locations of saccade onsets and landing positions within these areas. A preference for the snout over eye regions was observed following ketamine administration. In addition, regions in which saccades landed could be significantly predicted by saccade onset region in the saline but not the ketamine condition. Effects on saccade control were limited to an increase in saccade peak velocity in all conditions and a reduction in saccade amplitudes during viewing of scrambled videos. Thus, ketamine induced a significant disruption of scan paths during viewing of conspecific faces but limited effects on saccade motor control. These findings support the use of ketamine in marmosets for investigating changes in neural circuits underlying social cognition in neuropsychiatric disorders.NEW & NOTEWORTHY Face processing, an important social cognitive ability, is impaired in neuropsychiatric conditions such as schizophrenia. The highly social common marmoset model presents an opportunity to investigate these impairments. We administered subanesthetic doses of ketamine to marmosets to model the cognitive symptoms of schizophrenia. We observed a disruption of scan paths during viewing of conspecifics' faces. These findings support the use of ketamine in marmosets as a model for investigating social cognition in neuropsychiatric disorders.

Statistical and Machine Learning Methods Applied to the Prediction of Different Tropical Rainfall Types.

Predicting rain from large-scale environmental variables remains a challenging problem for climate models and it is unclear how well numerical methods can predict the true characteristics of rainfall without smaller (storm) scale information. This study explores the ability of three statistical and machine learning methods to predict 3-hourly rain occurrence and intensity at 0.5° resolution over the tropical Pacific Ocean using rain observations the Global Precipitation Measurement (GPM) satellite radar and large-scale environmental profiles of temperature and moisture from the MERRA-2 reanalysis. We also separated the rain into different types (deep convective, stratiform, and shallow convective) because of their varying kinematic and thermodynamic structures that might respond to the large-scale environment in different ways. Our expectation was that the popular machine learning methods (i.e., the neural network and random forest) would outperform a standard statistical method (a generalized linear model) because of their more flexible structures, especially in predicting the highly skewed distribution of rain rates for each rain type. However, none of the methods obviously distinguish themselves from one another and each method still has issues with predicting rain too often and not fully capturing the high end of the rain rate distributions, both of which are common problems in climate models. One implication of this study is that machine learning tools must be carefully assessed and are not necessarily applicable to solving all big data problems. Another implication is that traditional climate model approaches are not sufficient to predict extreme rain events and that other avenues need to be pursued.