Fragmentation signatures in cancer patients resemble those of patients with vascular or autoimmune diseases.

Multiple case-controlled studies have shown that analyzing fragmentation patterns in plasma cell-free DNA (cfDNA) can distinguish individuals with cancer from healthy controls. However, there have been few studies that investigate various types of cfDNA fragmentomics patterns in individuals with other diseases. We therefore developed a comprehensive statistic, called fragmentation signatures, that integrates the distributions of fragment positioning, fragment length, and fragment end-motifs in cfDNA.

Minimizing and quantifying uncertainty in AI-informed decisions: Applications in medicine.

AI is now a cornerstone of modern dataset analysis. In many real world applications, practitioners are concerned with controlling specific kinds of errors, rather than minimizing the overall number of errors. For example, biomedical screening assays may primarily be concerned with mitigating the number of false positives rather than false negatives.

When no answer is better than a wrong answer: A causal perspective on batch effects.

Batch effects, undesirable sources of variability across multiple experiments, present significant challenges for scientific and clinical discoveries. Batch effects can (i) produce spurious signals and/or (ii) obscure genuine signals, contributing to the ongoing reproducibility crisis. Because batch effects are typically modeled as classical statistical effects, they often cannot differentiate between sources of variability due to confounding biases, which may lead them to erroneously conclude batch effects are present (or not).

Universally Consistent K-Sample Tests via Dependence Measures.

The K-sample testing problem involves determining whether K groups of data points are each drawn from the same distribution. Analysis of variance is arguably the most classical method to test mean differences, along with several recent methods to test distributional differences. In this paper, we demonstrate the existence of a transformation that allows K-sample testing to be carried out using any dependence measure.

FiPhA: an open-source platform for fiber photometry analysis.

Significance: Fiber photometry (FP) is a widely used technique in modern behavioral neuroscience, employing genetically encoded fluorescent sensors to monitor neural activity and neurotransmitter release in awake-behaving animals. However, analyzing photometry data can be both laborious and time-consuming.

Aim: We propose the fiber photometry analysis (FiPhA) app, which is a general-purpose FP analysis application.

Partial or Complete Loss of Norepinephrine Differentially Alters Contextual Fear and Catecholamine Release Dynamics in Hippocampal CA1.

Background: Contextual fear learning is heavily dependent on the hippocampus. Despite evidence that catecholamines contribute to contextual encoding and memory retrieval, the precise temporal dynamics of their release in the hippocampus during behavior is unknown. In addition, new animal models are required to probe the effects of altered catecholamine synthesis on release dynamics and contextual learning.

FiPhA: An Open-Source Platform for Fiber Photometry Analysis.

Significance: Fiber photometry is a widely used technique in modern behavioral neuroscience, employing genetically encoded fluorescent sensors to monitor neural activity and neurotransmitter release in awake-behaving animals, However, analyzing photometry data can be both laborious and time-consuming.

Aim: We propose the FiPhA (Fiber Photometry Analysis) app, which is a general-purpose fiber photometry analysis application. The goal is to develop a pipeline suitable for a wide range of photometry approaches, including spectrally resolved, camera-based, and lock-in demodulation.

The Chi-Square Test of Distance Correlation.

Distance correlation has gained much recent attention in the data science community: the sample statistic is straightforward to compute and asymptotically equals zero if and only if independence, making it an ideal choice to discover any type of dependency structure given sufficient sample size. One major bottleneck is the testing process: because the null distribution of distance correlation depends on the underlying random variables and metric choice, it typically requires a permutation test to estimate the null and compute the p-value, which is very costly for large amount of data. To overcome the difficulty, in this paper we propose a chi-square test for distance correlation.

Selective and Mechanically Robust Sensors for Electrochemical Measurements of Real-Time Hydrogen Peroxide Dynamics in Vivo.

Hydrogen peroxide (HO) is an endogenous molecule that plays several important roles in brain function: it is generated in cellular respiration, serves as a modulator of dopaminergic signaling, and its presence can indicate the upstream production of more aggressive reactive oxygen species (ROS). HO has been implicated in several neurodegenerative diseases, including Parkinson's disease (PD), creating a critical need to identify mechanisms by which HO modulates cellular processes in general and how it affects the dopaminergic nigrostriatal pathway, in particular. Furthermore, there is broad interest in selective electrochemical quantification of HO, because it is often enzymatically generated at biosensors as a reporter for the presence of nonelectroactive target molecules.

Salvage of proximal tibia fracture treated with locked compression plate.

The treatment of displaced proximal tibial fractures is challenging. Recent developments in the techniques of internal fixation, including the development of locking plates and minimally invasive surgical techniques have revolutionaries the treatment of such fractures. We present a case of proximal displaced tibial fracture that was treated with anatomical proximal tibial locking plate using minimally invasive percutaneous plate osteosynthesis.