Building Extraction from LiDAR Point Clouds Based on Revised RandLA-Net

3D building models is crucial for applications in smart cities. Automatic reconstruction of 3D buildings has been investigated based on various data sources. Point clouds from airborne LiDAR scanners can be used to extract buildings data due to its high accuracy and point density. In this paper, we present a methodology to segment buildings and corresponding rooftop structure from point clouds. First, RandLA-Net, which is an efficient and lightweight neural network for semantic segmentation of large-scale point clouds, is revised and adopted for building segmentation. By implementing local feature aggregation of each point, RandLA-Net can effectively preserve geometric details in point clouds. Besides 3D coordinates of point clouds, we incorporated point attributes including pulse intensity and return numbers into the network as additional features. Feature normalizations are applied to the input features. To achieve a better result of the local feature aggregation, hyperparameters of the network are fine-tuned according to the density of points and building size. Based on the classified building point clouds, DBSCAN clustering algorithm is implemented for segmenting individual buildings. Elevation histogram analysis is conducted to determine optimal threshold values for delineating candidate rooftop point clouds of individual buildings. For the buildings with multiple rooftops, multiple elevation threshold values are necessary to extract corresponding rooftops or walls. Then DBSCAN is employed again for segmentation of individual rooftops and denoising of point clouds of each building. Finally, Alpha-shape analysis is applied based on adaptive threshold values to build the envelope of each rooftop. Experiments show that our implementation of building segmentation using RandLA-net achieves higher mean IoU (Intersection over Union) and better classification performance in building segmentation. ISPRS benchmark data was used in our experiment and our methodology produce results with accuracy of 90.79%

STAR-RIS-Assisted Privacy Protection in Semantic Communication System

Semantic communication (SemCom) has emerged as a promising architecture in the realm of intelligent communication paradigms. SemCom involves extracting and compressing the core information at the transmitter while enabling the receiver to interpret it based on established knowledge bases (KBs). This approach enhances communication efficiency greatly. However, the open nature of wireless transmission and the presence of homogeneous KBs among subscribers of identical data type pose a risk of privacy leakage in SemCom. To address this challenge, we propose to leverage the simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) to achieve privacy protection in a SemCom system. In this system, the STAR-RIS is utilized to enhance the signal transmission of the SemCom between a base station and a destination user, as well as to covert the signal to interference specifically for the eavesdropper (Eve). Simulation results demonstrate that our generated task-level disturbance outperforms other benchmarks in protecting SemCom privacy, as evidenced by the significantly lower task success rate achieved by Eve

Cardiospecific Overexpression of ATPGD1 (Carnosine Synthase) Increases Histidine Dipeptide Levels and Prevents Myocardial Ischemia Reperfusion Injury

Background: Myocardial ischemia reperfusion (I/R) injury is associated with complex pathophysiological changes characterized by pH imbalance, the accumulation of lipid peroxidation products acrolein and 4-hydroxy trans-2-nonenal, and the depletion of ATP levels. Cardioprotective interventions, designed to address individual mediators of I/R injury, have shown limited efficacy. The recently identified enzyme ATPGD1 (Carnosine Synthase), which synthesizes histidyl dipeptides such as carnosine, has the potential to counteract multiple effectors of I/R injury by buffering intracellular pH and quenching lipid peroxidation products and may protect against I/R injury. Methods and Results: We report here that β-alanine and carnosine feeding enhanced myocardial carnosine levels and protected the heart against I/R injury. Cardiospecific overexpression of ATPGD1 increased myocardial histidyl dipeptides levels and protected the heart from I/R injury. Isolated cardiac myocytes from ATPGD1-transgenic hearts were protected against hypoxia reoxygenation injury. The overexpression of ATPGD1 prevented the accumulation of acrolein and 4-hydroxy trans-2-nonenal-protein adducts in ischemic hearts and delayed acrolein or 4-hydroxy trans-2-nonenal-induced hypercontracture in isolated cardiac myocytes. Changes in the levels of ATP, high-energy phosphates, intracellular pH, and glycolysis during low-flow ischemia in the wild-type mice hearts were attenuated in the ATPGD1-transgenic hearts. Two natural dipeptide analogs (anserine and balenine) that can either quench aldehydes or buffer intracellular pH, but not both, failed to protect against I/R injury. Conclusions: Either exogenous administration or enhanced endogenous formation of histidyl dipeptides prevents I/R injury by attenuating changes in intracellular pH and preventing the accumulation of lipid peroxidation derived aldehydes

FusionFormer: A Multi-sensory Fusion in Bird's-Eye-View and Temporal Consistent Transformer for 3D Objection

Multi-sensor modal fusion has demonstrated strong advantages in 3D object detection tasks. However, existing methods that fuse multi-modal features through a simple channel concatenation require transformation features into bird's eye view space and may lose the information on Z-axis thus leads to inferior performance. To this end, we propose FusionFormer, an end-to-end multi-modal fusion framework that leverages transformers to fuse multi-modal features and obtain fused BEV features. And based on the flexible adaptability of FusionFormer to the input modality representation, we propose a depth prediction branch that can be added to the framework to improve detection performance in camera-based detection tasks. In addition, we propose a plug-and-play temporal fusion module based on transformers that can fuse historical frame BEV features for more stable and reliable detection results. We evaluate our method on the nuScenes dataset and achieve 72.6% mAP and 75.1% NDS for 3D object detection tasks, outperforming state-of-the-art methods

EARLY BUD-BREAK 1 and EARLY BUD-BREAK 3 control resumption of poplar growth after winter dormancy

Bud-break is an economically and environmentally important process in trees and shrubs from boreal and temperate latitudes, but its molecular mechanisms are poorly understood. Here, we show that two previously reported transcription factors, EARLY BUD BREAK 1 (EBB1) and SHORT VEGETATIVE PHASE-Like (SVL) directly interact to control bud-break. EBB1 is a positive regulator of bud-break, whereas SVL is a negative regulator of bud-break. EBB1 directly and negatively regulates SVL expression. We further report the identification and characterization of the EBB3 gene. EBB3 is a temperature-responsive, epigenetically-regulated, positive regulator of bud-break that provides a direct link to activation of the cell cycle during bud-break. EBB3 is an AP2/ERF transcription factor that positively and directly regulates CYCLIND3.1 gene. Our results reveal the architecture of a putative regulatory module that links temperature-mediated control of bud-break with activation of cell cycle. An AP2/ERF family gene EBB1 and a MADS-box gene SVL encode two regulators of poplar bud break. Here, the authors report another AP2/ERF transcription factor EBB3, which functions together with EBB1, SVL, and cell cycle progression promoter CYCD3.1 to regulate poplar bud break

Comparative Analysis for the Performance of Variant Calling Pipelines on Detecting the de novo Mutations in Humans

Despite of the low occurrence rate in the entire genomes, de novo mutation is proved to be deleterious and will lead to severe genetic diseases via impacting on the gene function. Considering the fact that the traditional family based linkage approaches and the genome-wide association studies are unsuitable for identifying the de novo mutations, in recent years, several pipelines have been proposed to detect them based on the whole-genome or whole-exome sequencing data and were used for calling them in the rare diseases. However, how the performance of these variant calling pipelines on detecting the de novo mutations is still unexplored. For the purpose of facilitating the appropriate choice of the pipelines and reducing the false positive rate, in this study, we thoroughly evaluated the performance of the commonly used trio calling methods on the detection of the de novo single-nucleotide variants (DNSNVs) by conducting a comparative analysis for the calling results. Our results exhibited that different pipelines have a specific tendency to detect the DNSNVs in the genomic regions with different GC contents. Additionally, to refine the calling results for a single pipeline, our proposed filter achieved satisfied results, indicating that the read coverage at the mutation positions can be used as an effective index to identify the high-confidence DNSNVs. Our findings should be good support for the committees to choose an appropriate way to explore the de novo mutations for the rare diseases

Effects of tumor necrosis factor-α polymorphism on the brain structural changes of the patients with major depressive disorder

Single Nucleotide Polymorphic (SNP) variations of proinflammatory cytokines such as Tumor Necrosis Factor-α (TNF-α) have been reported to be closely associated with the major depressive disorder (MDD). However, it is unclear if proinflammatory genetic burden adversely affects the regional gray matter volume in patients with MDD. The aim of this study was to test whether rs1799724, an SNP of TNF-α, contributes to the neuroanatomical changes in MDD. In this cross-sectional study, a total of 144 MDD patients and 111 healthy controls (HC) well matched for age, sex and education were recruited from Shanghai Mental Health Center. Voxel-based morphometry (VBM) followed by graph theory based structural covariance analysis was applied to locate diagnosis x genotype interactions. Irrespective of diagnosis, individuals with the high-risk genotype (T-carriers) had reduced volume in left angular gyrus (main effect of genotype). Diagnosis x genotype interaction was exclusively localized to the visual cortex (right superior occipital gyrus). The same region also showed reduced volume in patients with MDD than HC (main effect of diagnosis), with this effect being most pronounced in patients carrying the high-risk genotype. However, neither global nor regional network of structural covariance was found to have group difference. In conclusion, a genetic variation which can increase TNF-α expression selectively affects the anatomy of the visual cortex among the depressed subjects, with no effect on the topographical organization of multiple cortical regions. This supports the notion that anatomical changes in depression are in part influenced by the genetic determinants of inflammatory activity

Identification and quantification of viable Lacticaseibacillus rhamnosus in probiotics using validated PMA-qPCR method

The identification and quantification of viable bacteria at the species/strain level in compound probiotic products is challenging now. Molecular biology methods, e.g., propidium monoazide (PMA) combination with qPCR, have gained prominence for targeted viable cell counts. This study endeavors to establish a robust PMA-qPCR method for viable Lacticaseibacillus rhamnosus detection and systematically validated key metrics encompassing relative trueness, accuracy, limit of quantification, linear, and range. The inclusivity and exclusivity notably underscored high specificity of the primers for L. rhamnosus, which allowed accurate identification of the target bacteria. Furthermore, the conditions employed for PMA treatment were fully verified by 24 different L. rhamnosus including type strain, commercial strains, etc., confirming its effective discrimination between live and dead bacteria. A standard curve constructed by type strain could apply to commercial strains to convert qPCR Cq values to viable cell numbers. The established PMA-qPCR method was applied to 46 samples including pure cultures, probiotics as food ingredients, and compound probiotic products. Noteworthy is the congruity observed between measured and theoretical values within a 95% confidence interval of the upper and lower limits of agreement, demonstrating the relative trueness of this method. Moreover, accurate results were obtained when viable L. rhamnosus ranging from 103 to 108 CFU/mL. The comprehensive appraisal of PMA-qPCR performances provides potential industrial applications of this new technology in quality control and supervision of probiotic products

Free Fatty Acids Rewire Cancer Metabolism in Obesity-Associated Breast Cancer via Estrogen Receptor and mTOR Signaling

Obesity is a risk factor for postmenopausal estrogen receptor alpha (ERα)-positive (ER+) breast cancer. Molecular mechanisms underlying factors from plasma that contribute to this risk and how these mechanisms affect ERα signaling have yet to be elucidated. To identify such mechanisms, we performed whole metabolite and protein profiling in plasma samples from women at high risk for breast cancer, which led us to focus on factors that were differentially present in plasma of obese versus nonobese postmenopausal women. These studies, combined with in vitro assays, identified free fatty acids (FFA) as circulating plasma factors that correlated with increased proliferation and aggressiveness in ER+ breast cancer cells. FFAs activated both the ERα and mTOR pathways and rewired metabolism in breast cancer cells. Pathway preferential estrogen-1 (PaPE-1), which targets ERα and mTOR signaling, was able to block changes induced by FFA and was more effective in the presence of FFA. Collectively, these data suggest a role for obesity-associated gene and metabolic rewiring in providing new targetable vulnerabilities for ER+ breast cancer in postmenopausal women. Furthermore, they provide a basis for preclinical and clinical trials where the impact of agents that target ERα and mTOR signaling cross-talk would be tested to prevent ER+ breast cancers in obese postmenopausal women