Insulin-like growth factor binding protein 2: a core biomarker of left ventricular dysfunction in dilated cardiomyopathy.

BACKGROUND: RNA modifications, especially N6-methyladenosine, N1-methyladenosine and 5-methylcytosine, play an important role in the progression of cardiovascular disease. However, its regulatory function in dilated cardiomyopathy (DCM) remains to be undefined. METHODS: In the study, key RNA modification regulators (RMRs) were screened by three machine learning models. Subsequently, a risk prediction model for DCM was developed and validated based on these important genes, and the diagnostic efficiency of these genes was assessed. Meanwhile, the relevance of these genes to clinical traits was explored. In both animal models and human subjects, the gene with the strongest connection was confirmed. The expression patterns of important genes were investigated using single-cell analysis. RESULTS: A total of 4 key RMRs were identified. The risk prediction models were constructed basing on these genes which showed a good accuracy and sensitivity in both the training and test set. Correlation analysis showed that insulin-like growth factor binding protein 2 (IGFBP2) had the highest correlation with left ventricular ejection fraction (LVEF) (R = -0.49, P = 0.00039). Further validation expression level of IGFBP2 indicated that this gene was significantly upregulated in DCM animal models and patients, and correlation analysis validation showed a significant negative correlation between IGFBP2 and LVEF (R = -0.87; P = 6*10-5). Single-cell analysis revealed that this gene was mainly expressed in endothelial cells. CONCLUSION: In conclusion, IGFBP2 is an important biomarker of left ventricular dysfunction in DCM. Future clinical applications could possibly use it as a possible therapeutic target.

Random Search in Fluid Flow Aided by Chemotaxis.

In this paper, we consider the dynamics of a 2D target-searching agent performing Brownian motion under the influence of fluid shear flow and chemical attraction. The analysis is motivated by numerous situations in biology where these effects are present, such as broadcast spawning of marine animals and other reproduction processes or workings of the immune systems. We rigorously characterize the limit of the expected hit time in the large flow amplitude limit as corresponding to the effective one-dimensional problem. We also perform numerical computations to characterize the finer properties of the expected duration of the search. The numerical experiments show many interesting features of the process and in particular existence of the optimal value of the shear flow that minimizes the expected target hit time and outperforms the large flow limit.

Landslide hazard cascades can trigger earthquakes.

While earthquakes are well-known to trigger surface hazards and initiate hazard cascades, whether surface hazards can instead trigger earthquakes remains underexplored. In 2018, two landslides on the Tibetan plateau created landslide-dammed lakes which subsequently breached and caused catastrophic outburst floods. Here we build an earthquake catalog using machine-learning and cross-correlation-based methods which shows there was a statistically significant increase in earthquake activity (local magnitude ≤ 2.6) as the landslide-dammed lake approached peak water level which returned to the background level after dam breach. We further find that ~90% of the seismicity occurred where Coulomb stress increased due to the combined effect of direct loading and pore pressure diffusion. The close spatial and temporal correlation between the calculated Coulomb stress increase and earthquake activity suggests that the earthquakes were triggered by these landslide hazard cascades. Finally, our Coulomb stress modeling considering the properties of landslide-dammed lakes and reservoir-induced earthquakes globally suggests that earthquake triggering by landslide-dammed lakes and similar structures may be a ubiquitous phenomenon. Therefore, we propose that earthquake-surface hazard interaction can include bidirectional triggering which should be properly accounted for during geological hazard assessment and management in mountainous regions.

Allometric equations for estimating peak uprooting force of riparian vegetation.

Uprooting caused by flood events is a significant disturbance factor that affects the establishment, growth, and mortality of riparian vegetation. If the hydraulic drag force acting on riparian plants exceeds the peak uprooting force originate from their below-ground portion, it may result in the uprooting of these plants. Despite previous studies have documented and investigated the uprooting processes and factors influencing the peak uprooting force of plants, most of these studies have focused on how the root morphological traits of tree and shrub seedlings affect peak uprooting force or mainly collected data in indoor experiments, which may limit the extrapolation of the results to natural environments. To address these limitations, we assume that the peak uprooting force can be estimated by the morphological traits of the above-ground portion of the vegetation. In this study, we conducted in-situ vertical uprooting tests on three locally dominant species: Conyza canadensis, Daucus carota, and Leonurus sibiricus, in a typical riverine environment. The three species were found to have the highest abundance based on the outcomes of the quadrat method. We measured the peak uprooting force, plant height, stem basal diameter, shoot and root wet biomass, and shoot and root dry biomass of each plant and compared them between species. Furthermore, we quantified the influence of morphology on peak uprooting force. Our results showed significant differences in morphological traits and peak uprooting force among the three species. We found a significant positive correlation between peak uprooting force and the morphological traits of the three species. The peak uprooting force increases with plant size following a power law function which is analogous to allometric equations. The allometric equation provided a convenient and non-destructive method to estimate the peak uprooting force based on the above-ground morphological traits of the plants, which may help to overcome the limitations of measuring root morphological traits.

A robotic system for automated genetic manipulation and analysis of Caenorhabditis elegans.

The nematode Caenorhabditis elegans is one of the most widely studied organisms in biology due to its small size, rapid life cycle, and manipulable genetics. Research with C. elegans depends on labor-intensive and time-consuming manual procedures, imposing a major bottleneck for many studies, especially for those involving large numbers of animals. Here, we describe a general-purpose tool, WormPicker, a robotic system capable of performing complex genetic manipulations and other tasks by imaging, phenotyping, and transferring C. elegans on standard agar media. Our system uses a motorized stage to move an imaging system and a robotic arm over an array of agar plates. Machine vision tools identify animals and assay developmental stage, morphology, sex, expression of fluorescent reporters, and other phenotypes. Based on the results of these assays, the robotic arm selectively transfers individual animals using an electrically self-sterilized wire loop, with the aid of machine vision and electrical capacitance sensing. Automated C. elegans manipulation shows reliability and throughput comparable with standard manual methods. We developed software to enable the system to autonomously carry out complex protocols. To validate the effectiveness and versatility of our methods, we used the system to perform a collection of common C. elegans procedures, including genetic crossing, genetic mapping, and genomic integration of a transgene. Our robotic system will accelerate C. elegans research and open possibilities for performing genetic and pharmacological screens that would be impractical using manual methods.