Species-specific FMRP regulation of RACK1 is critical for prenatal cortical development.

Fragile X messenger ribonucleoprotein 1 protein (FMRP) deficiency leads to fragile X syndrome (FXS), an autism spectrum disorder. The role of FMRP in prenatal human brain development remains unclear. Here, we show that FMRP is important for human and macaque prenatal brain development. Both FMRP-deficient neurons in human fetal cortical slices and FXS patient stem cell-derived neurons exhibit mitochondrial dysfunctions and hyperexcitability. Using multiomics analyses, we have identified both FMRP-bound mRNAs and FMRP-interacting proteins in human neurons and unveiled a previously unknown role of FMRP in regulating essential genes during human prenatal development. We demonstrate that FMRP interaction with CNOT1 maintains the levels of receptor for activated C kinase 1 (RACK1), a species-specific FMRP target. Genetic reduction of RACK1 leads to both mitochondrial dysfunctions and hyperexcitability, resembling FXS neurons. Finally, enhancing mitochondrial functions rescues deficits of FMRP-deficient cortical neurons during prenatal development, demonstrating targeting mitochondrial dysfunction as a potential treatment.

Graph-based machine learning improves just-in-time defect prediction.

The increasing complexity of today's software requires the contribution of thousands of developers. This complex collaboration structure makes developers more likely to introduce defect-prone changes that lead to software faults. Determining when these defect-prone changes are introduced has proven challenging, and using traditional machine learning (ML) methods to make these determinations seems to have reached a plateau. In this work, we build contribution graphs consisting of developers and source files to capture the nuanced complexity of changes required to build software. By leveraging these contribution graphs, our research shows the potential of using graph-based ML to improve Just-In-Time (JIT) defect prediction. We hypothesize that features extracted from the contribution graphs may be better predictors of defect-prone changes than intrinsic features derived from software characteristics. We corroborate our hypothesis using graph-based ML for classifying edges that represent defect-prone changes. This new framing of the JIT defect prediction problem leads to remarkably better results. We test our approach on 14 open-source projects and show that our best model can predict whether or not a code change will lead to a defect with an F1 score as high as 77.55% and a Matthews correlation coefficient (MCC) as high as 53.16%. This represents a 152% higher F1 score and a 3% higher MCC over the state-of-the-art JIT defect prediction. We describe limitations, open challenges, and how this method can be used for operational JIT defect prediction.

Application of XGBoost model for in-situ water saturation determination in Canadian oil-sands by LF-NMR and density data.

Water saturation determination is among the most challenging tasks in petrophysical well-logging, which directly impacts the decision-making process in hydrocarbon exploration and production. Low-field nuclear magnetic resonance (LF-NMR) measurements can provide reliable evaluation. However, quantification of oil and water volumes is problematic when their NMR signals are not distinct. To overcome this, we developed two machine learning frameworks for predicting relative water content in oil-sand samples using LF-NMR spin-spin (T2) relaxation and bulk density data to derive a model based on Extreme Gradient Boosting. The first one facilitates feature engineering based on empirical knowledge from the T2 relaxation distribution analysis domain and mutual information feature extraction technique, while the second model considers whole samples' NMR T2-relaxation distribution. The NMR T2 distributions were obtained for 82 Canadian oil-sands samples at ambient and reservoir temperatures (164 data points). The true water content was determined by Dean-Stark extraction. The statistical scores confirm the strong generalization ability of the feature engineering LF-NMR model in predicting relative water content by Dean-Stark-root-mean-square error of 0.67% and mean-absolute error of 0.53% (R2 = 0.90). Results indicate that this approach can be extended for the improved in-situ water saturation evaluation by LF-NMR and bulk density measurements.

Care Alert Program in Chronic Recurrent ED Utilizing Patients.

Description The Care Alert program is designed to help navigate encounters with patient populations that are high utilizers of emergency department (ED) resources. These populations often have chronic medical conditions, have a poor understanding of their conditions, are unfamiliar with the EDs' role in the management of these conditions, and commonly lack outpatient resources. The Care Alert program intends to address the needs of this challenging patient population by designing individualized care plans that are approved through a multidisciplinary committee. Data from this study showed a 37% decrease in ED visits and a 47% decrease in hospitalizations during the initial 8 months of implementation.

Emulating Control Arms for Cancer Clinical Trials Using External Cohorts Created From Electronic Health Record-Derived Real-World Data.

Electronic health record (EHR)-derived real-world data (RWD) can be sourced to create external comparator cohorts to oncology clinical trials. This exploratory study assessed whether EHR-derived patient cohorts could emulate select clinical trial control arms across multiple tumor types. The impact of analytic decisions on emulation results was also evaluated. By digitizing Kaplan-Meier curves, we reconstructed published control arm results from 15 trials that supported drug approvals from January 1, 2016, to April 30, 2018. RWD cohorts were constructed using a nationwide EHR-derived de-identified database by aligning eligibility criteria and weighting to trial baseline characteristics. Trial data and RWD cohorts were compared using Kaplan-Meier and Cox proportional hazards regression models for progression-free survival (PFS) and overall survival (OS; individual cohorts) and multitumor random effects models of hazard ratios (HRs) for median endpoint correlations (across cohorts). Post hoc, the impact of specific analytic decisions on endpoints was assessed using a case study. Comparing trial data and weighted RWD cohorts, PFS results were more similar (HR range = 0.63-1.18, pooled HR = 0.84, correlation of median = 0.91) compared to OS (HR range = 0.36-1.09, pooled HR = 0.76, correlation of median = 0.85). OS HRs were more variable and trended toward worse for RWD cohorts. The post hoc case study had OS HR ranging from 0.67 (95% confidence interval (CI): 0.56-0.79) to 0.92 (95% CI: 0.78-1.09) depending on specific analytic decisions. EHR-derived RWD can emulate oncology clinical trial control arm results, although with variability. Visibility into clinical trial cohort characteristics may shape and refine analytic approaches.