Discovery of large genomic inversions using long range information.

BackgroundAlthough many algorithms are now available that aim to characterize different classes of structural variation, discovery of balanced rearrangements such as inversions remains an open problem. This is mainly due to the fact that breakpoints of such events typically lie within segmental duplications or common repeats, which reduces the mappability of short reads. The algorithms developed within the 1000 Genomes Project to identify inversions are limited to relatively short inversions, and there are currently no available algorithms to discover large inversions using high throughput sequencing technologies.ResultsHere we propose a novel algorithm, VALOR, to discover large inversions using new sequencing methods that provide long range information such as 10X Genomics linked-read sequencing, pooled clone sequencing, or other similar technologies that we commonly refer to as long range sequencing. We demonstrate the utility of VALOR using both pooled clone sequencing and 10X Genomics linked-read sequencing generated from the genome of an individual from the HapMap project (NA12878). We also provide a comprehensive comparison of VALOR against several state-of-the-art structural variation discovery algorithms that use whole genome shotgun sequencing data.ConclusionsIn this paper, we show that VALOR is able to accurately discover all previously identified and experimentally validated large inversions in the same genome with a low false discovery rate. Using VALOR, we also predicted a novel inversion, which we validated using fluorescent in situ hybridization. VALOR is available at https://github.com/BilkentCompGen/VALOR

Analysis of infiltrated immune cells in left atriums from patients with atrial fibrillation and identification of circRNA biomarkers for postoperative atrial fibrillation

Background: Atrial fibrillation (AF) increases the risk of stroke and heart failure. Postoperative AF (POAF) increases the risk of mortality after cardiac surgery. This study aims to explore mechanisms underlying AF, analyze infiltration of immune cells in left atrium (LA) from patients with AF, and identify potential circular RNA (circRNA) biomarkers for POAF.Methods: Raw data of GSE797689, GSE115574, and GSE97455 were downloaded and processed. AF-related gene co-expression network was constructed using weighted gene correlation network analysis and enrichment analysis of genes in relevant module was conducted. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were applied to investigate pathways significantly enriched in AF group. Infiltration of immune cells was analyzed using single-sample GSEA. Differentially expressed genes (DEGs) between patients with or without AF were identified and competing endogenous RNA (ceRNA) networks of DEGs were constructed. To screen biomarkers for POAF, differentially expressed circRNAs (DEcircRNAs) between patients with or without POAF were identified. Intersection between DEcircRNAs and circRNAs in ceRNA networks of DEGs were extracted and circRNAs in the intersection were further screened using support vector machine, random forest, and neural network to identify biomarkers for POAF.Results: Three modules were found to be relevant with AF and enrichment analysis indicated that genes in these modules were enriched in synthesis of extracellular matrix and inflammatory response. The results of GSEA and GSVA suggested that inflammatory response-related pathways were significantly enriched in AF group. Immune cells like macrophages, mast cells, and neutrophils were significantly infiltrated in LA tissues from patients with AF. The expression levels of immune genes such as CHGB, HLA-DRA, LYZ, IGKV1-17 and TYROBP were significantly upregulated in patients with AF, which were correlated with infiltration of immune cells. ceRNA networks of DEGs were constructed and has_circ_0006314 and hsa_circ_0055387 were found to have potential predictive values for POAF.Conclusion: Synthesis of extracellular matrix and inflammatory response were main processes involved in development and progression of AF. Infiltration of immune cells was significantly different between patients with or without AF. Has_circ_0006314 and hsa_circ_0055387 were found to have potential predictive values for POAF

The predictive values of monocyte–lymphocyte ratio in postoperative acute kidney injury and prognosis of patients with Stanford type A aortic dissection

ObjectivesPostoperative acute kidney injury (pAKI) is a serious complication of Stanford type A aortic dissection (TAAD) surgery, which is significantly associated with the inflammatory response. This study aimed to explore the relationship between blood count-derived inflammatory markers (BCDIMs) and pAKI and to construct a predictive model for pAKI.MethodsPatients who underwent TAAD surgery were obtained from our center and the Medical Information Mart for Intensive Care (MIMIC)-IV database. The differences in preoperative BCDIMs and clinical outcomes of patients with and without pAKI were analyzed. Logistic regression was used to construct predictive models based on preoperative BCDIMs or white cell counts (WCCs). The performance of the BCDIMs and WCCs models was evaluated and compared using the receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), Hosmer–Lemeshow test, calibration plot, net reclassification index (NRI), integrated discrimination improvement index (IDI), and decision curve analysis (DCA). The Kaplan–Meier curves were applied to compare the survival rate between different groups.ResultsThe overall incidence of pAKI in patients who underwent TAAD surgery from our center was 48.63% (124/255). The presence of pAKI was associated with longer ventilation time, higher incidence of cerebral complications and postoperative hepatic dysfunction, and higher in-hospital mortality. The results of the logistic regression indicated that the monocyte–lymphocyte ratio (MLR) was an independent risk factor for pAKI. The BCDIMs model had good discriminating ability, predictive ability, and clinical utility. In addition, the performance of the BCDIMs model was significantly better than that of the WCCs model. Analysis of data from the MIMIC-IV database validated that MLR was an independent risk factor for pAKI and had predictive value for pAKI. Finally, data from the MIMIC-IV database demonstrated that patients with a high MLR had a significantly poor 28-day survival rate when compared to patients with a low MLR.ConclusionOur study suggested that the MLR is an independent risk factor for pAKI. A predictive model based on BCDIMs had good discriminating ability, predictive ability, and clinical utility. Moreover, the performance of the BCDIMs model was significantly better than that of the WCCs model. Finally, a high MLR was significantly associated with poor short-term survival of patients who underwent TAAD surgery

Measuring non-Markovianity of processes with controllable system-environment interaction

Non-Markovian processes have recently become a central topic in the study of open quantum systems. We realize experimentally non-Markovian decoherence processes of single photons by combining time delay and evolution in a polarization-maintaining optical fiber. The experiment allows the identification of the process with strongest memory effects as well as the determination of a recently proposed measure for the degree of quantum non-Markovianity based on the exchange of information between the open system and its environment. Our results show that an experimental quantification of memory in quantum processes is indeed feasible which could be useful in the development of quantum memory and communication devices.Comment: 5 pages, 4 figures. V2: Minor modifications, title change

Cavity Magnonics

Cavity magnonics deals with the interaction of magnons - elementary excitations in magnetic materials - and confined electromagnetic fields. We introduce the basic physics and review the experimental and theoretical progress of this young field that is gearing up for integration in future quantum technologies. Much of its appeal is derived from the strong magnon-photon coupling and the easily-reached nonlinear regime in microwave cavities. The interaction of magnons with light as detected by Brillouin light scattering is enhanced in magnetic optical resonators, which can be employed to manipulate magnon distributions. The cavity photon-mediated coupling of a magnon mode to a superconducting qubit enables measurements in the single magnon limit.Comment: review article, 54 page

Quantitative Proteomic Study of Human Lung Squamous Carcinoma and Normal Bronchial Epithelial Acquired by Laser Capture Microdissection

Objective. To investigate the differential protein profile of human lung squamous carcinoma (HLSC) and normal bronchial epithelium (NBE) and provide preliminary results for further study to explore the carcinogenic mechanism of HLSC. Methods. Laser capture microdissection (LCM) was used to purify the target cells from 10 pairs of HLSC tissues and their matched NHBE, respectively. A stable-isotope labeled strategy using iTRAQ, followed by 2D-LC/Q-STAR mass spectrometry, was performed to separate and identify the differential expression proteins. Results. A total of 96 differential expression proteins in the LCM-purified HLSC and NBE were identified. Compared with NBE, 49 proteins were upregulated and 47 proteins were downregulated in HLSC. Furthermore, the expression levels of the differential proteins including HSPB1, CKB, SCCA1, S100A8, as well as S100A9 were confirmed by western blot and tissue microarray and were consistent with the results of quantitative proteomics. Conclusion. The different expression proteins in HLSC will provide scientific foundation for further study to explore the carcinogenic mechanism of HLSC

Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

The segment anything model (SAM) was released as a foundation model for image segmentation. The promptable segmentation model was trained by over 1 billion masks on 11M licensed and privacy-respecting images. The model supports zero-shot image segmentation with various segmentation prompts (e.g., points, boxes, masks). It makes the SAM attractive for medical image analysis, especially for digital pathology where the training data are rare. In this study, we evaluate the zero-shot segmentation performance of SAM model on representative segmentation tasks on whole slide imaging (WSI), including (1) tumor segmentation, (2) non-tumor tissue segmentation, (3) cell nuclei segmentation. Core Results: The results suggest that the zero-shot SAM model achieves remarkable segmentation performance for large connected objects. However, it does not consistently achieve satisfying performance for dense instance object segmentation, even with 20 prompts (clicks/boxes) on each image. We also summarized the identified limitations for digital pathology: (1) image resolution, (2) multiple scales, (3) prompt selection, and (4) model fine-tuning. In the future, the few-shot fine-tuning with images from downstream pathological segmentation tasks might help the model to achieve better performance in dense object segmentation

A universal programmable Gaussian Boson Sampler for drug discovery

Gaussian Boson Sampling (GBS) exhibits a unique ability to solve graph problems, such as finding cliques in complex graphs. It is noteworthy that many drug discovery tasks can be viewed as the clique-finding process, making them potentially suitable for quantum computation. However, to perform these tasks in their quantum-enhanced form, a large-scale quantum hardware with universal programmability is essential, which is yet to be achieved even with the most advanced GBS devices. Here, we construct a time-bin encoded GBS photonic quantum processor that is universal, programmable, and software-scalable. Our processor features freely adjustable squeezing parameters and can implement arbitrary unitary operations with a programmable interferometer. Using our processor, we have demonstrated the clique-finding task in a 32-node graph, where we found the maximum weighted clique with approximately twice the probability of success compared to classical sampling. Furthermore, a multifunctional quantum pharmaceutical platform is developed. This GBS processor is successfully used to execute two different drug discovery methods, namely molecular docking and RNA folding prediction. Our work achieves the state-of-the-art in GBS circuitry with its distinctive universal and programmable architecture which advances GBS towards real-world applications.Comment: 10 pages, 5 figure