This letter describes a fluidic microstrip bandstop filter with transmission properties that change in discrete states. The filter consists of a liquid metal alloy - eutectic gallium indium (EGaIn) - as the conductive component in microfluidic channels. The fluidity of EGaIn allows the open stub resonator of the filter to change its length by flowing in response to an applied pressure. A series of posts in the channel defines the length of the stub filled by the metal and dictates the pressure needed for the liquid metal to flow and thereby extend the stub length. The frequency response of the filter changes in response to the changes in the length of the resonator stub. This approach is a simple method for creating tunable filters and impedance matching sections using soft materials that change dimensions in response to pressure.

Background: Breast cancer is intrinsically heterogeneous and is commonly classified into four main subtypes associated with distinct biological features and clinical outcomes. However, currently available data resources and methods are limited in identifying molecular subtyping on protein-coding genes, and little is known about the roles of long non-coding RNAs (lncRNAs), which occupies 98% of the whole genome. lncRNAs may also play important roles in subgrouping cancer patients and are associated with clinical phenotypes. Methods: The purpose of this project was to identify lncRNA gene signatures that are associated with breast cancer subtypes and clinical outcomes. We identified lncRNA gene signatures from The Cancer Genome Atlas (TCGA )RNAseq data that are associated with breast cancer subtypes by an optimized 1-Norm SVM feature selection algorithm. We evaluated the prognostic performance of these gene signatures with a semi-supervised principal component (superPC) method. Results: Although lncRNAs can independently predict breast cancer subtypes with satisfactory accuracy, a combined gene signature including both coding and non-coding genes will give the best clinically relevant prediction performance. We highlighted eight potential biomarkers (three from coding genes and five from non-coding genes) that are significantly associated with survival outcomes. Conclusion: Our proposed methods are a novel means of identifying subtype-specific coding and non-coding potential biomarkers that are both clinically relevant and biologically significant.

Identifying statistical dependence between the features and the label is a fundamental problem in supervised learning. This paper presents a framework for estimating dependence between numerical features and a categorical label using generalized Gini distance, an energy distance in reproducing kernel Hilbert spaces (RKHS). Two Gini distance based dependence measures are explored: Gini distance covariance and Gini distance correlation. Unlike Pearson covariance and correlation, which do not characterize independence, the above Gini distance based measures define dependence as well as independence of random variables. The test statistics are simple to calculate and do not require probability density estimation. Uniform convergence bounds and asymptotic bounds are derived for the test statistics. Comparisons with distance covariance statistics are provided. It is shown that Gini distance statistics converge faster than distance covariance statistics in the uniform convergence bounds, hence tighter upper bounds on both Type I and Type II errors. Moreover, the probability of Gini distance covariance statistic under-performing the distance covariance statistic in Type II error decreases to 0 exponentially with the increase of the sample size. Extensive experimental results are presented to demonstrate the performance of the proposed method.

Cerebellar mutism syndrome is a disorder of speech, movement and affect that can occur after tumour removal from the posterior fossa. Projections from the fastigial nuclei to the periaqueductal grey area were recently implicated in its pathogenesis, but the functional consequences of damaging these projections remain poorly understood. Here, we examine functional MRI data from patients treated for medulloblastoma to identify functional changes in key brain areas that comprise the motor system for speech, which occur along the timeline of acute speech impairment in cerebellar mutism syndrome. One hundred and twenty-four participants, all with medulloblastoma, contributed to the study: 45 with cerebellar mutism syndrome, 11 patients with severe postoperative deficits other than mutism, and 68 without either (asymptomatic). We first performed a data-driven parcellation to spatially define functional nodes relevant to the cohort that align with brain regions critical for the motor control of speech. We then estimated functional connectivity between these nodes during the initial postoperative imaging sessions to identify functional deficits associated with the acute phase of the disorder. We further analysed how functional connectivity changed over time within a subset of participants that had suitable imaging acquired over the course of recovery. Signal dispersion was also measured in the periaqueductal grey area and red nuclei to estimate activity in midbrain regions considered key targets of the cerebellum with suspected involvement in cerebellar mutism pathogenesis. We found evidence of periaqueductal grey dysfunction in the acute phase of the disorder, with abnormal volatility and desynchronization with neocortical language nodes. Functional connectivity with periaqueductal grey was restored in imaging sessions that occurred after speech recovery and was further shown to be increased with left dorsolateral prefrontal cortex. The amygdalae were also broadly hyperconnected with neocortical nodes in the acute phase. Stable connectivity differences between groups were broadly present throughout the cerebrum, and one of the most substantial differences—between Broca’s area and the supplementary motor area—was found to be inversely related to cerebellar outflow pathway damage in the mutism group. These results reveal systemic changes in the speech motor system of patients with mutism, centred on limbic areas tasked with the control of phonation. These findings provide further support for the hypothesis that periaqueductal grey dysfunction (following cerebellar surgical injury) contributes to the transient postoperative non-verbal episode commonly observed in cerebellar mutism syndrome but highlights a potential role of intact cerebellocortical projections in chronic features of the disorder.

Molecularly targeted therapies significantly contribute to the efforts of personalized approaches for cancer diagnosis and chemotherapeutic treatment. One of a critical step to identify target molecules is to determine the most representative features for different patient's sub-groups. Breast cancer, one of the most heterogeneous cancer has five main subtypes, so accurately identify gene signatures associated with intrinsic subtypes based on currently available data resource will benefit for precision medicine. Traditional ways to identify subtype-specific targeted molecular biomarkers are based on the platforms such as microarray, or immunohistochemistry (IHC) markers. Very few studies using RNAseq data to predict cancer subtype, due to the limited data resource. Gene expression in RNAseq platform is highly correlated with the microarray. However, it shows superior benefits than microarray in many ways, such as detection of low abundance transcripts, the broader dynamic range of gene expression in both coding and non-coding genes. Therefore, RNAseq platform will become the predominant tool for transcriptome analysis in the long term. To benefit the identification of molecule targets for transcriptomic data including RNAseq and microarray expression, we developed a new feature selection method. Our results of classification accuracy for intrinsic subtypes and prognostic evaluation of selected genes outperform than the traditional PAM50 gene signature, which is derived from microarray and widely used in clinical practice.