Single-nucleotide variant calling in single-cell sequencing data with Monopogen

Single-cell omics technologies enable molecular characterization of diverse cell types and states, but how the resulting transcriptional and epigenetic profiles depend on the cell\u27s genetic background remains understudied. We describe Monopogen, a computational tool to detect single-nucleotide variants (SNVs) from single-cell sequencing data. Monopogen leverages linkage disequilibrium from external reference panels to identify germline SNVs and detects putative somatic SNVs using allele cosegregating patterns at the cell population level. It can identify 100 K to 3 M germline SNVs achieving a genotyping accuracy of 95%, together with hundreds of putative somatic SNVs. Monopogen-derived genotypes enable global and local ancestry inference and identification of admixed samples. It identifies variants associated with cardiomyocyte metabolic levels and epigenomic programs. It also improves putative somatic SNV detection that enables clonal lineage tracing in primary human clonal hematopoiesis. Monopogen brings together population genetics, cell lineage tracing and single-cell omics to uncover genetic determinants of cellular processes

Apparent negative motion of vortex matter due to inhomogeneous pinning

We investigate the transport of vortices in superconductors with inhomgeneous pinning under a driving force. The inhomogeneity of pinning is simplified as strong-weak pinning regions. It is demonstrated that the interactions between the vortices captured by strong pinning potentials and the vortices in the weak pinning region cause absolute negative motion (ANM) of vortices: The vortices which are climbing toward the high barriers induced by the strong pinning with the help of driving force move toward the opposite direction of the force and back to their equilibrium positions in the weak pinning region as the force decreases or is withdrawn. Our simulations reveal that the hysteresis of ANM is determined by the competition between the speed of the negative motion which depends on the piining inhomogeneity in superconductors and the speed of the driving force. Under the conditions of either larger force scanning rate or higher pinning inhomogeneity, a marked ANM and a larger hysteretic speed-force loop could be observed. This indicates that the time window to observe the ANM should be chosen properly. Moreover, the V-1 characteristics of Ag-sheathed Bi=2223 tapes are measured, and experimental observations are qualitatively in agreement with the simulation

Hydrogen production by sorption-enhanced steam reforming of glycerol

Catalytic steam reforming of glycerol for H(2) production has been evaluated experimentally in a continuous flow fixed-bed reactor. The experiments were carried out under atmospheric pressure within a temperature range of 400-700 degrees C. A commercial Ni-based catalyst and a dolomite sorbent were used for the steam reforming reactions and in situ CO(2) removal. The product gases were measured by on-line gas analysers. The results show that H(2) productivity is greatly increased with increasing temperature and the formation of methane by-product becomes negligible above 500 degrees C. The results suggest an optimal temperature of approximately 500 degrees C for the glycerol steam reforming with in situ CO(2) removal using calcined dolomite as the sorbent, at which the CO(2) breakthrough time is longest and the H(2) purity is highest. The shrinking core model and the 1D-diffusion model describe well the CO(2) removal under the conditions of this work

S4ND: Single-Shot Single-Scale Lung Nodule Detection

The state of the art lung nodule detection studies rely on computationally expensive multi-stage frameworks to detect nodules from CT scans. To address this computational challenge and provide better performance, in this paper we propose S4ND, a new deep learning based method for lung nodule detection. Our approach uses a single feed forward pass of a single network for detection and provides better performance when compared to the current literature. The whole detection pipeline is designed as a single $3D$ Convolutional Neural Network (CNN) with dense connections, trained in an end-to-end manner. S4ND does not require any further post-processing or user guidance to refine detection results. Experimentally, we compared our network with the current state-of-the-art object detection network (SSD) in computer vision as well as the state-of-the-art published method for lung nodule detection (3D DCNN). We used publically available $888$ CT scans from LUNA challenge dataset and showed that the proposed method outperforms the current literature both in terms of efficiency and accuracy by achieving an average FROC-score of $0.897$. We also provide an in-depth analysis of our proposed network to shed light on the unclear paradigms of tiny object detection.Comment: Accepted for publication at MICCAI 2018 (21st International Conference on Medical Image Computing and Computer Assisted Intervention

Novel method for the rapid evaluation of pressure depletion in tight oil reservoirs

Tight oil reservoirs hold immense development potential but are characterized by challenging reservoir properties, severe heterogeneity, and extremely low permeability and porosity. Massive hydraulic fracturing of horizontal wells is applied to achieve sustainable production in these reservoirs. The swift assessment of pressure depletion in tight reservoirs is essential for their successful and cost-effective development. Traditional pressure testing methods necessitate well shutdown, impacting subsequent production, while numerical simulation methods demand significant computational resources and expertise from technical personnel. To identify the sensitivity parameters influencing the reservoir pressure drop, this study uses a Plackett-Burman design and variance analysis. Using numerical simulations, variance analysis and multi-linear regression, we formulate evaluation indices and surrogate models for individual well depletion. The method’s reliability is validated through multiple experiments along with testing data. Our rapid evaluation method accurately assesses pressure depletion in typical well groups, with a fitting rate exceeding 85%. In regions where the pressure maintenance is below 80%, indicating severe reservoir depletion, enhanced oil recovery treatments, e.g., gas or water injection, are applied based on the evaluation results. The proposed method for evaluating individual well pressure depletions provides crucial guidance for realizing the efficient development of tight oil reservoirs.Document Type: Short communicationCited as: Ding, C., Chen, J., Yang, G., Bao, R., Dou, Y., Song, K. Novel method for the rapid evaluation of pressure depletion in tight oil reservoirs. Advances in Geo-Energy Research, 2024, 11(1): 74-80. https://doi.org/10.46690/ager.2024.01.0

First Total Synthesis of a Naturally Occurring Iodinated 5′-Deoxyxylofuranosyl Marine Nucleoside

4-Amino-7-(5′-deoxy-β-D-xylofuranosyl)-5-iodo-pyrrolo[2,3-d]pyrimidine 1, an unusual naturally occurring marine nucleoside isolated from an ascidan, Diplosoma sp., was synthesized from D-xylose in seven steps with 28% overall yield on 10 g scale. The key step was Vorbrüggen glycosylation of 5-iodo-pyrrolo[2,3-d]pyrimidine with 5-deoxy-1,2-O-diacetyl-3-O-benzoyl-D-xylofuranose. Its absolute configuration was confirmed

Sparse Signal Inversion with Impulsive Noise by Dual Spectral Projected Gradient Method

We consider sparse signal inversion with impulsive noise. There are three major ingredients. The first is regularizing properties; we discuss convergence rate of regularized solutions. The second is devoted to the numerical solutions. It is challenging due to the fact that both fidelity and regularization term lack differentiability. Moreover, for ill-conditioned problems, sparsity regularization is often unstable. We propose a novel dual spectral projected gradient (DSPG) method which combines the dual problem of multiparameter regularization with spectral projection gradient method to solve the nonsmooth l1+l1 optimization functional. We show that one can overcome the nondifferentiability and instability by adding a smooth l2 regularization term to the original optimization functional. The advantage of the proposed functional is that its convex duality reduced to a constraint smooth functional. Moreover, it is stable even for ill-conditioned problems. Spectral projected gradient algorithm is used to compute the minimizers and we prove the convergence. The third is numerical simulation. Some experiments are performed, using compressed sensing and image inpainting, to demonstrate the efficiency of the proposed approach