Clay fine fissuring monitoring using miniature geo-electrical resistivity arrays

Abstract This article describes a miniaturised electrical imaging (resistivity tomography) technique to map the cracking pattern of a clay model. The clay used was taken from a scaled flood embankment built to study the fine fissuring due to desiccation and breaching process in flooding conditions. The potential of using a miniature array of electrodes to follow the evolution of the vertical cracks and number them during the drying process was explored. The imaging technique generated two-dimensional contoured plots of the resistivity distribution within the model before and at different stages of the desiccation process. The change in resistivity associated with the widening of the cracks were monitored as a function of time. Experiments were also carried out using a selected conductive gel to slow down the transport process into the cracks to improve the scanning capabilities of the equipment. The main vertical clay fissuring network was obtained after inversion of the experimental resistivity measurements and validated by direct observations

Models of Temporal Enhanced Ultrasound Data for Prostate Cancer Diagnosis: The Impact of Time-Series Order

Recent studies have shown the value of Temporal Enhanced Ultrasound (TeUS) imaging for tissue characterization in transrectal ultrasound-guided prostate biopsies. Here, we present results of experiments designed to study the impact of temporal order of the data in TeUS signals. We assess the impact of variations in temporal order on the ability to automatically distinguish benign prostate-tissue from malignant tissue. We have previously used Hidden Markov Models (HMMs) to model TeUS data, as HMMs capture temporal order in time series. In the work presented here, we use HMMs to model malignant and benign tissues; the models are trained and tested on TeUS signals while introducing variation to their temporal order. We first model the signals in their original temporal order, followed by modeling the same signals under various time rearrangements. We compare the performance of these models for tissue characterization. Our results show that models trained over the original order-preserving signals perform statistically significantly better for distinguishing between malignant and benign tissues, than those trained on rearranged signals. The performance degrades as the amount of temporal-variation increases. Specifically, accuracy of tissue characterization decreases from 85% using models trained on original signals to 62% using models trained and tested on signals that are completely temporally-rearranged. These results indicate the importance of order in characterization of tissue malignancy from TeUS data

Feature-by-Feature – Evaluating De Novo Sequence Assembly

The whole-genome sequence assembly (WGSA) problem is among one of the most studied problems in computational biology. Despite the availability of a plethora of tools (i.e., assemblers), all claiming to have solved the WGSA problem, little has been done to systematically compare their accuracy and power. Traditional methods rely on standard metrics and read simulation: while on the one hand, metrics like N50 and number of contigs focus only on size without proportionately emphasizing the information about the correctness of the assembly, comparisons performed on simulated dataset, on the other hand, can be highly biased by the non-realistic assumptions in the underlying read generator. Recently the Feature Response Curve (FRC) method was proposed to assess the overall assembly quality and correctness: FRC transparently captures the trade-offs between contigs' quality against their sizes. Nevertheless, the relationship among the different features and their relative importance remains unknown. In particular, FRC cannot account for the correlation among the different features. We analyzed the correlation among different features in order to better describe their relationships and their importance in gauging assembly quality and correctness. In particular, using multivariate techniques like principal and independent component analysis we were able to estimate the “excess-dimensionality” of the feature space. Moreover, principal component analysis allowed us to show how poorly the acclaimed N50 metric describes the assembly quality. Applying independent component analysis we identified a subset of features that better describe the assemblers performances. We demonstrated that by focusing on a reduced set of highly informative features we can use the FRC curve to better describe and compare the performances of different assemblers. Moreover, as a by-product of our analysis, we discovered how often evaluation based on simulated data, obtained with state of the art simulators, lead to not-so-realistic results

Single-cell sequencing reveals Hippo signaling as a driver of fibrosis in hidradenitis suppurativa

Hidradenitis suppurativa (HS) is a chronic inflammatory disease characterized by abscesses, nodules, dissecting/draining tunnels, and extensive fibrosis. Here, we integrate single-cell RNA sequencing, spatial transcriptomics, and immunostaining to provide an unprecedented view of the pathogenesis of chronic HS, characterizing the main cellular players and defining their interactions. We found a striking layering of the chronic HS infiltrate and identified the contribution of 2 fibroblast subtypes (SFRP4+ and CXCL13+) in orchestrating this compartmentalized immune response. We further demonstrated the central role of the Hippo pathway in promoting extensive fibrosis in HS and provided preclinical evidence that the profibrotic fibroblast response in HS can be modulated through inhibition of this pathway. These data provide insights into key aspects of HS pathogenesis with broad therapeutic implications.</p

Left ventricular function during and after right ventricular pacing

The aim of this research was to evaluate right ventricular pacing effects on left ventricular function. Right ventricular pacing alters the ventricular activation sequence and reduces left ventricular ejection fraction (EF). It is unclear whether the observed reduction in EF can be completely attributed to the alteration in activation sequence. Twelve subjects (eight women), mean age 68 +/- 12 years, with transvenous dual-chamber pacemakers, normal left ventricular function, and intact atrioventricular (AV) conduction were studied with serial-gated blood pool studies. Left ventricular EF was measured at a fixed rate after at least 1 week of atrial pacing only (baseline), during short-term (2 h) and mid-term (1 week) AV sequential pacing with a short AV delay, and after short- and mid-term AV pacing. Baseline EF was 66.5 +/- 4.5%. Short-term AV pacing resulted in a decrease in EF to 60.3 +/- 5.2% (p < 0.0002). After one week of AV pacing, there was a further decline in EF to 52.9 +/- 8.3% (p < 0.0001). After cessation of mid-term pacing, EF was 57.3 +/- 5.9% (p < 0.0001 vs. baseline). A total of 2, 5, 8, and 24 h later, EF remained depressed (59% to 60%, p < 0.007). At 32 h, EF was 62.9 +/- 7.6% (p < 0.11 compared with baseline). The abnormal activation sequence resulting from right ventricular pacing accounts for only part of the reduction in EF as mid-term pacing is associated with a lower EF than short-term pacing, and EF remains depressed after cessation of AV pacing. Changes in ventricular function induced by right ventricular pacing may account for some of its associated adverse effects