Learning Causes of Functional Dynamic Targets: Screening and Local Methods.

This paper addresses the challenge of identifying causes for functional dynamic targets, which are functions of various variables over time. We develop screening and local learning methods to learn the direct causes of the target, as well as all indirect causes up to a given distance. We first discuss the modeling of the functional dynamic target. Then, we propose a screening method to select the variables that are significantly correlated with the target. On this basis, we introduce an algorithm that combines screening and structural learning techniques to uncover the causal structure among the target and its causes. To tackle the distance effect, where long causal paths weaken correlation, we propose a local method to discover the direct causes of the target in these significant variables and further sequentially find all indirect causes up to a given distance. We show theoretically that our proposed methods can learn the causes correctly under some regular assumptions. Experiments based on synthetic data also show that the proposed methods perform well in learning the causes of the target.

Identification and estimation of causal effects in the presence of confounded principal strata.

Principal stratification has become a popular tool to address a broad class of causal inference questions, particularly in dealing with non-compliance and truncation by death problems. The causal effects within principal strata, which are determined by joint potential values of the intermediate variable, also known as the principal causal effects, are often of interest in these studies. The analysis of principal causal effects from observational studies mostly relies on the ignorability assumption of treatment assignment, which requires practitioners to accurately measure as many covariates as possible so that all potential sources of confounders are captured. However, in practice, collecting all potential confounding factors can be challenging and costly, rendering the ignorability assumption questionable. In this paper, we consider the identification and estimation of causal effects when treatment and principal stratification are confounded by unmeasured confounding. Specifically, we establish the nonparametric identification of principal causal effects using a pair of negative controls to mitigate unmeasured confounding, requiring they have no direct effect on the outcome variable. We also provide an estimation method for principal causal effects. Extensive simulations and a leukemia study are employed for illustration.

Discovery of Novel 1-Phenylpiperidine Urea-Containing Derivatives Inhibiting ß-Catenin/BCL9 Interaction and Exerting Antitumor Efficacy through the Activation of Antigen Presentation of cDC1 Cells.

Aberrant activation of the Wnt/ß-catenin signaling is associated with tumor development, and blocking ß-catenin/BCL9 is a novel strategy for oncogenic Wnt/ß-catenin signaling. Herein, we presented two novel ß-catenin variations and exposed conformational dynamics in several ß-catenin crystal structures at the BCL9 binding site. Furthermore, we identified a class of novel urea-containing compounds targeting ß-catenin/BCL9 interaction. Notably, the binding modalities of inhibitors were greatly affected by the conformational dynamics of ß-catenin. Among them, 28 had a strong affinity for ß-catenin (Kd = 82 nM), the most potent inhibitor reported. In addition, 13 and 35 not only activate T cells but also promote the antigen presentation of cDC1, showing robust antitumor efficacy in the CT26 model. Collectively, our study demonstrated a series of potent small-molecule inhibitors targeting ß-catenin/BCL9, which can enhance antigen presentation and activate cDC1 cells, delivering a potential strategy for boosting innate and adaptive immunity to overcome immunotherapy resistance.

Genome-wide identification of the B3 transcription factor family in pepper (Capsicum annuum) and expression patterns during fruit ripening.

In plants, B3 transcription factors play important roles in a variety of aspects of their growth and development. While the B3 transcription factor has been extensively identified and studied in numerous species, there is limited knowledge regarding its B3 superfamily in pepper. Through the utilization of genome-wide sequence analysis, we identified a total of 106 B3 genes from pepper (Capsicum annuum), they are categorized into four subfamilies: RAV, ARF, LAV, and REM. Chromosome distribution, genetic structure, motif, and cis-acting element of the pepper B3 protein were analyzed. Conserved gene structure and motifs outside the B3 domain provided strong evidence for phylogenetic relationships, allowing potential functions to be deduced by comparison with homologous genes from Arabidopsis. According to the high-throughput transcriptome sequencing analysis, expression patterns differ during different phases of fruit development in the majority of the 106 B3 pepper genes. By using qRT-PCR analysis, similar expression patterns in fruits from various time periods were discovered. In addition, further analysis of the CaRAV4 gene showed that its expression level decreased with fruit ripening and located in the nucleus. B3 transcription factors have been genome-wide characterized in a variety of crops, but the present study is the first genome-wide analysis of the B3 superfamily in pepper. More importantly, although B3 transcription factors play key regulatory roles in fruit development, it is uncertain whether B3 transcription factors are involved in the regulation of the fruit development and ripening process in pepper and their specific regulatory mechanisms because the molecular mechanisms of the process have not been fully explained. The results of the study provide a foundation and new insights into the potential regulatory functions and molecular mechanisms of B3 genes in the development and ripening process of pepper fruits, and provide a solid theoretical foundation for the enhancement of the quality of peppers and their selection and breeding of high-yield varieties.

On Low-Rank Directed Acyclic Graphs and Causal Structure Learning.

Despite several advances in recent years, learning causal structures represented by directed acyclic graphs (DAGs) remains a challenging task in high-dimensional settings when the graphs to be learned are not sparse. In this article, we propose to exploit a low-rank assumption regarding the (weighted) adjacency matrix of a DAG causal model to help address this problem. We utilize existing low-rank techniques to adapt causal structure learning methods to take advantage of this assumption and establish several useful results relating interpretable graphical conditions to the low-rank assumption. Specifically, we show that the maximum rank is highly related to hubs, suggesting that scale-free (SF) networks, which are frequently encountered in practice, tend to be low rank. Our experiments demonstrate the utility of the low-rank adaptations for a variety of data models, especially with relatively large and dense graphs. Moreover, with a validation procedure, the adaptations maintain a superior or comparable performance even when graphs are not restricted to be low rank.

Subgroup analysis using Bernoulli-gated hierarchical mixtures of experts models.

When it is suspected that the treatment effect may only be strong for certain subpopulations, identifying the baseline covariate profiles of subgroups who benefit from such a treatment is of key importance. In this paper, we propose an approach for subgroup analysis by firstly introducing Bernoulli-gated hierarchical mixtures of experts (BHME), a binary-tree structured model to explore heterogeneity of the underlying distribution. We show identifiability of the BHME model and develop an EM-based maximum likelihood method for optimization. The algorithm automatically determines a partition structure with optimal prediction but possibly suboptimal in identifying treatment effect heterogeneity. We then suggest a testing-based postscreening step to further capture effect heterogeneity. Simulation results show that our approach outperforms competing methods on discovery of differential treatment effects and other related metrics. We finally apply the proposed approach to a real dataset from the Tennessee's Student/Teacher Achievement Ratio project.

Synthesis and structural characterization of ß -cyclodextrin butenate.

ß-cyclodextrin butenate was synthesized by using N, N'-Carbonyldiimidazole (CDI) activating reagent and 4-Dimethylaminopyridine (DMAP) as catalyst. The best preparation condition of ß-CD butenate was described as below: reaction temperature was 25°C, concentration of 2-butenoic acid was 450 mmol/L, concentration of DMAP was 12.5 mmol/L and reaction time was 20 minutes and at this condition the yield of ß-CD butenate was 0.83 mmol/g. According to the results of FT-IR spectrum, NMR spectroscopy and HPLC-QTof-mass spectrum of ß-CD butenate, there were four types ß-CD butenate synthesized, which were ß-CD-2-butenoic acid monoester, ß-CD-2-butenoic acid diester, ß-CD-2-butenoic acid triester and ß-CD-2-butenoic acid tetraester, respectively.

Quality and stability evaluation of Guizhou spicy chicken treated with gamma irradiation during the storage period.

Irradiation has become a mature processing approach for the quality control of many agricultural products. The effects of gamma irradiation at four different doses (2, 4, 6, and 8 kGy) on microbial quality, Hunter's parameter, lipid oxidation, hydrolyzed amino acids (HAAs), and flavor in spicy chicken were investigated. After treatment, all samples were stored at 4°C. The microbiological results showed that the total viable bacteria (TVB) and total coliform count (TCC) were significantly decreased dose dependently. Values of Δa*, Δb*, and ΔE* of the irradiated specimens were lower compared with the control samples, whereas the ΔL* of the irradiated specimens was higher compared with the controls. The peroxide value (POV) was increased by dose augmentation. Contents of HAAs were gradually decreased in both irradiated and control groups. The odor was affected by both doses of irradiation and storage time. Hence, we conclude that irradiation at a dose of 4.0 kGy barely affected physicochemical properties during storage and extended the shelf life of spicy chicken. This approach could be an alternative to control the quality of spicy chicken during storage.

A Bayesian network model for prediction of low or failed fertilization in assisted reproductive technology based on a large clinical real-world data.

STUDY QUESTION: To construct prediction models based on the Bayesian network (BN) learning method for the probability of fertilization failure (including low fertilization rate [LRF] and total fertilization failure [TFF]) in assisted reproductive technology (ART) treatment. A BN model was developed to predict TFF/LFR. The model showed relatively high calibration in external validation, which could facilitate the identification of risk factors for fertilization disorders and improve the efficiency of in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) treatment. WHAT IS KNOWN ALREADY: The prediction of TFF/LFR is very complex. Although some studies attempted to construct prediction models for TFF/LRF, most of the reported models were based on limited variables and traditional regression-based models, which are unsuitable for analyzing real-world clinical data. Therefore, none of the reported models have been widely used in routine clinical practice. To date, BN modeling analysis is a prominent and increasingly popular machine learning method that is powerful in dealing with dynamic and complex real-world data. STUDY DESIGN, SIZE, DURATION: A retrospective study was performed with 106,640 fresh embryo IVF/ICSI cycles from 2009 to 2019 in one of China's largest reproductive health centers. PARTICIPANTS/MATERIALS, SETTING, METHODS: A total of 106, 640 cycles were included in this study, including 97,102 controls, 4,339 LFR cases, and 5,199 TFF cases. Twenty-four predictors were initially included, including 13 female-related variables, five male-related variables, and six variables related to IVF/ICSI treatment. BN modeling analysis with tenfold cross-validation was performed to construct the predictive model for TFF/LFR. The receiver operating characteristic (ROC) curves and the corresponding area under the curves (AUCs) were used to evaluate the performance of the BN model. MAIN RESULTS AND THE ROLE OF CHANCE: All twenty-four predictors were first organized into seven hierarchical layers in a theoretical BN model, according to prior knowledge from previous literature and clinical practice. A machine-learning BN model was generated based on real-world clinical data, containing a total of eighteen predictors, of which the infertility type, ART method, and number of retrieved oocytes directly influence the probabilities of LFR/TFF. The prediction accuracy of the BN model was 91.7%. The AUC of the TFF versus control groups was 0.779 (95% CI: 0.766-0.791), with a sensitivity of 71.2% and specificity of 70.1%; the AUC of of TFF versus LFR groups was 0.807 (95% CI: 0.790-0.824), with a sensitivity of 49.0% and specificity of 99.0%. LIMITATIONS, REASON FOR CAUTION: First, our study was based on clinical data from a single center, and the results of this study should be further verified by external data. In addition, some critical data (e.g., the detailed IVF laboratory parameters of the sperm and oocytes used for insemination) were not available in this study, which should be given full consideration when further improving the performance of the BN model. WIDER IMPLICATIONS OF THE FINDINGS: Based on extensive clinical real-world data, we developed a BN model to predict the probabilities of fertilization failures in ART, which provides new clues for clinical decision-making support for clinicians in formulating personalized treatment plans and further improving ART treatment outcomes. STUDY FUNDING/COMPETING INTEREST(S): Dr. Y. Wang was supported by grants from the Beijing Municipal Science & Technology Commission (Z191100006619086). We declare that there are no conflicts of interest. TRIAL REGISTRATION NUMBER: N/A.

Causal inference with outcomes truncated by death in multiarm studies.

It is challenging to evaluate causal effects when the outcomes of interest suffer from truncation-by-death in many clinical studies; that is, outcomes cannot be observed if patients die before the time of measurement. To address this problem, it is common to consider average treatment effects by principal stratification, for which, the identifiability results and estimation methods with a binary treatment have been established in previous literature. However, in multiarm studies with more than two treatment options, estimation of causal effects becomes more complicated and requires additional techniques. In this article, we consider identification, estimation, and bounds of causal effects with multivalued ordinal treatments and the outcomes subject to truncation-by-death. We define causal parameters of interest in this setting and show that they are identifiable either using some auxiliary variable or based on linear model assumption. We then propose a semiparametric method for estimating the causal parameters and derive their asymptotic results. When the identification conditions are invalid, we derive sharp bounds of the causal effects by use of covariates adjustment. Simulation studies show good performance of the proposed estimator. We use the estimator to analyze the effects of a four-level chronic toxin on fetal developmental outcomes such as birth weight in rats and mice, with data from a developmental toxicity trial conducted by the National Toxicology Program. Data analyses demonstrate that a high dose of the toxin significantly reduces the weights of pups.

Testability of Instrumental Variables in Linear Non-Gaussian Acyclic Causal Models.

This paper investigates the problem of selecting instrumental variables relative to a target causal influence X→Y from observational data generated by linear non-Gaussian acyclic causal models in the presence of unmeasured confounders. We propose a necessary condition for detecting variables that cannot serve as instrumental variables. Unlike many existing conditions for continuous variables, i.e., that at least two or more valid instrumental variables are present in the system, our condition is designed with a single instrumental variable. We then characterize the graphical implications of our condition in linear non-Gaussian acyclic causal models. Given that the existing graphical criteria for the instrument validity are not directly testable given observational data, we further show whether and how such graphical criteria can be checked by exploiting our condition. Finally, we develop a method to select the set of candidate instrumental variables given observational data. Experimental results on both synthetic and real-world data show the effectiveness of the proposed method.

Soil organic matter and clay zeta potential influence aggregation of a clayey red soil (Ultisol) under long-term fertilization.

The effect of soil organic matter (SOM) on aggregation of variably-charged red soils (Ultisol) through clay zeta potential is not fully understood. Therefore, the objectives of this study were to investigate the SOM effect on the clay zeta potential and soil aggregation after fertilization. Soils under 17 years of fertilization (manure, NPK + straw, NPK, and control (CK) were adjusted by KCl solution to reach varying soil pH and concentration in order to determine clay zeta potential, cations, and aggregate size distribution. The SOM content and C-functional groups by 13C-NMR analysis were also determined. Results showed that the negative zeta potential displayed a bell-shaped pattern with increasing concentration of KCl, but displayed different amplitude of variation among treatments. Manure had the highest zeta potential value and its degree of variation in relative to the value at KCl concentration of 0.1 mol L-1 (19%), NPK + straw and NPK treatments were similar, and CK was the least. Greater negative zeta potential for manure treatment was attributed to higher SOM content, aromatic-C functional groups, and their greater concentrations of Ca2+ and Mg2+ than did the CK. As a result, higher SOM and clay zeta potential yielded in less release of amount of soil particles (< 10 µm) (r = - 0.46*) and enhanced water stable macroaggregates for manure instead of NPK + straw. Long-term manure fertilization would be suggested as a conservation practice for red soil due to its increase in soil aggregate stability and negative zeta potential in subtropical climate.

Thermally induced isomerization of linoleic acid and α-linolenic acid in Rosa roxburghii Tratt seed oil.

Rosa roxburghii seed oil is obtained from the seeds left following pressing of the juice from R. roxburghii fruit. The total oil content of R. roxburghii seed was around 9.30%. The fatty acid profile of the oil was determined by gas chromatography (GC). Among the 11 fatty acids identified in the oil, seven were unsaturated fatty acids (UFAs) (92.6%); four were saturated fatty acids (SFAs) (7.17%). Then, the kinetics of formation of trans-fatty acids was studied by GC. Heat treatment of R. roxburghii seed oil showed an increase in the relative percentage of linoleic acid and α-linolenic acid isomers with increasing temperature and time. The formation of linoleic acid and α-linolenic acid isomers followed a zero-order reaction. The presence of O2 enhanced the isomerization of these UFAs. In addition, the rate constants and activation energies for the geometrical isomerization of UFAs in R. roxburghii seed oil were presented. Overall, R. roxburghii seed oil contains high UFA contents. Heating temperature and duration and the presence of O2 should be considered to reduce the formation of trans-fatty acids during thermal treatment of R. roxburghii seed oil.

Molecular analysis of early rice stamen development using organ-specific gene expression profiling.

Elucidating the regulatory mechanisms of plant organ formation is an important component of plant developmental biology and will be useful for crop improvement applications. Plant organ formation, or organogenesis, occurs when a group of primordial cells differentiates into an organ, through a well-orchestrated series of events, with a given shape, structure and function. Research over the past two decades has elucidated the molecular mechanisms of organ identity and dorsalventral axis determinations. However, little is known about the molecular mechanisms underlying the successive processes. To develop an effective approach for studying organ formation at the molecular level, we generated organ-specific gene expression profiles (GEPs) reflecting early development in rice stamen. In this study, we demonstrated that the GEPs are highly correlated with early stamen development, suggesting that this analysis is useful for dissecting stamen development regulation. Based on the molecular and morphological correlation, we found that over 26 genes, that were preferentially up-regulated during early stamen development, may participate in stamen development regulation. In addition, we found that differentially expressed genes during early stamen development are clustered into two clades, suggesting that stamen development may comprise of two distinct phases of pattern formation and cellular differentiation. Moreover, the organ-specific quantitative changes in gene expression levels may play a critical role for regulating plant organ formation.

Complete synchronization of the noise-perturbed Chua's circuits.

In this paper, complete synchronization between unidirectionally coupled Chua's circuits within stochastic perturbation is investigated. Sufficient conditions of complete synchronization between these noise-perturbed circuits are established by means of the so-called LaSalle-type invariance principle for stochastic differential equations. Specific examples and their numerical simulations are also provided to demonstrate the feasibility of these conditions. Furthermore, the results obtained for the coupled Chua's circuits are further generalized to the wide class of coupled systems within stochastic perturbation.