A Kinship-Based Modification of the Armitage Trend Test to Address Hidden Population Structure and Small Differential Genotyping Errors

BACKGROUND/AIMS: We propose a modification of the well-known Armitage trend test to address the problems associated with hidden population structure and hidden relatedness in genome-wide case-control association studies. METHODS: The new test adopts beneficial traits from three existing testing strategies: the principal components, mixed model, and genomic control while avoiding some of their disadvantageous characteristics, such as the tendency of the principal components method to over-correct in certain situations or the failure of the genomic control approach to reorder the adjusted tests based on their degree of alignment with the underlying hidden structure. The new procedure is based on Gauss-Markov estimators derived from a straightforward linear model with an imposed variance structure proportional to an empirical relatedness matrix. Lastly, conceptual and analytical similarities to and distinctions from other approaches are emphasized throughout. RESULTS: Our simulations show that the power performance of the proposed test is quite promising compared to the considered competing strategies. The power gains are especially large when small differential differences between cases and controls are present; a likely scenario when public controls are used in multiple studies. CONCLUSION: The proposed modified approach attains high power more consistently than that of the existing commonly implemented tests. Its performance improvement is most apparent when small but detectable systematic differences between cases and controls exist

A Two-Light Version of the Classical Hundred Prisoners and a Light Bulb Problem: Optimizing Experimental Design through Simulations

We propose five original strategies of successively increasing complexity and efficiency that address a novel version of a classical mathematical problem that, in essence, focuses on the determination of an optimal protocol for exchanging limited amounts of information among a group of subjects with various prerogatives. The inherent intricacy of the problem�solving protocols eliminates the possibility to attain an analytical solution. Therefore, we implemented a large-scale simulation study to exhaustively search through an extensive list of competing algorithms associated with the above-mentioned 5 generally defined protocols. Our results show that the consecutive improvements in the average amount of time necessary for the strategy-specific problem-solving completion over the previous simpler and less advantageously structured designs were 18, 30, 12, and 9% respectively. The optimal multi-stage information exchange strategy allows for a successful execution of the task of interest in 1722 days (4.7 years) on average with standard deviation of 385 days. The execution of this protocol took as few as 1004 and as many as 4965 with median of 1616 days

Long Term Ground Based Precipitation Data Analysis: Spatial and Temporal Variability

California is an area of diverse topography and has what many scientists call a Mediterranean climate. Various precipitation patterns exist due to El Niño Southern Oscillation (ENSO) which can cause abnormal precipitation or droughts. As temperature increases mainly due to the increase of CO2 in the atmosphere, it is rapidly changing the climate of not only California but the world. An increase in temperature is leading to droughts in certain areas as other areas are experiencing heavy rainfall/flooding. Droughts in return are providing a foundation for fires harming the ecosystem and nearby population. Various natural hazards can be induced due to the coupling effects from inconsistent precipitation patterns and vice versa. Using wavelets, we were able to identify anomalies of high precipitation and droughts within California\u27s 7 climate divisions using NOAA\u27s hourly precipitation data from rain gauges and compared the results with modeled data, SOI, and PDO. The identification of anomalies can be used to compare and correct remote sensing measurements of precipitation and droughts. Promising results show a possible connection with increasing tropical moisture activity

A Novel Correction for the Adjusted Box-Pierce Test

The classical Box-Pierce and Ljung-Box tests for auto-correlation of residuals possess severe deviations from nominal type I error rates. Previous studies have attempted to address this issue by either revising existing tests or designing new techniques. The Adjusted Box-Pierce achieves the best results with respect to attaining type I error rates closer to nominal values. This research paper proposes a further correction to the adjusted Box-Pierce test that possesses near perfect type I error rates. The approach is based on an inflation of the rejection region for all sample sizes and lags calculated via a linear model applied to simulated data that encompasses a large range of data scenarios. Our results show that the new approach possesses the best type I error rates of all goodness-of-fit time series statistics

Higher IL-6 and IL6:IGF Ratio in Patients with Barth Syndrome

Background: Barth Syndrome (BTHS) is a serious X-linked genetic disorder associated with mutations in the tafazzin gene (TAZ, also called G4.5). The multi-system disorder is primarily characterized by the following pathologies: cardiac and skeletal myopathies, neutropenia, growth delay, and exercise intolerance. Although growth anomalies have been widely reported in BTHS, there is a paucity of research on the role of inflammation and the potential link to alterations in growth factors levels in BTHS patients. Methods: Plasma from 36 subjects, 22 patients with Barth Syndrome (0.5 - 24 yrs) and 14 healthy control males (8 - 21 yrs) was analyzed for two growth factors: IGF-1 (bound and free) and Growth Hormone (GH); and two inflammatory cytokines IL-6 and TNF-α using high-sensitivity enzyme-linked immunosorbent assays. Results: The average IL-6 and IL6:IGF ratio levels were significantly higher in the BTHS (p = 0.046 and 0.02 respectively). As for GH, there was a significant group by age interaction (p = 0.01), such that GH was lower for BTHS patients under the age of 14.4 years and higher than controls after age 14.4 years. TNF-α levels were not significantly different, however, the TNF-α:GH was lower in BTHS patients than controls (p = 0.01). Conclusions: Comparison of two anabolic growth mediators, IGF and GH, and two catabolic cytokines, IL-6 and TNF-α, in BTHS patients and healthy age-matched controls demonstrated a potential imbalance in inflammatory cytokines and anabolic growth factors. Higher rates of IL-6 (all ages) and lower GH levels were observed in BTHS patients (under age 14.5) compared to controls. These findings may implicate inflammatory processes in the catabolic nature of Barth Syndrome pathology as well as provide a link to mitochondrial function. Furthermore, interactions between growth factors, testosterone and inflammatory mediators may explain some of the variability in cardiac and skeletal myopathies seen in Barth Syndrome

De Novo Drug Design Using Transformer-Based Machine Translation and Reinforcement Learning of an Adaptive Monte Carlo Tree Search

The discovery of novel therapeutic compounds through de novo drug design represents a critical challenge in the field of pharmaceutical research. Traditional drug discovery approaches are often resource intensive and time consuming, leading researchers to explore innovative methods that harness the power of deep learning and reinforcement learning techniques. Here, we introduce a novel drug design approach called drugAI that leverages the Encoder–Decoder Transformer architecture in tandem with Reinforcement Learning via a Monte Carlo Tree Search (RL-MCTS) to expedite the process of drug discovery while ensuring the production of valid small molecules with drug-like characteristics and strong binding affinities towards their targets. We successfully integrated the Encoder–Decoder Transformer architecture, which generates molecular structures (drugs) from scratch with the RL-MCTS, serving as a reinforcement learning framework. The RL-MCTS combines the exploitation and exploration capabilities of a Monte Carlo Tree Search with the machine translation of a transformer-based Encoder–Decoder model. This dynamic approach allows the model to iteratively refine its drug candidate generation process, ensuring that the generated molecules adhere to essential physicochemical and biological constraints and effectively bind to their targets. The results from drugAI showcase the effectiveness of the proposed approach across various benchmark datasets, demonstrating a significant improvement in both the validity and drug-likeness of the generated compounds, compared to two existing benchmark methods. Moreover, drugAI ensures that the generated molecules exhibit strong binding affinities to their respective targets. In summary, this research highlights the real-world applications of drugAI in drug discovery pipelines, potentially accelerating the identification of promising drug candidates for a wide range of diseases

Empirical Type I error rates for all tests: with hidden stratification.

a<p>nominal α = 0.05.</p>b<p>nominal α = 0.0001.</p

Power comparison among all tests: with hidden stratification.

a<p>α = 0.05.10⁻⁵.</p>b<p>α = 0.0001.</p

Observed type 1 error rates: with both hidden stratification and hidden relatedness between subjects.

<p>Observed type 1 error rates: with both hidden stratification and hidden relatedness between subjects.</p