Weak Lensing Reconstruction by Counting DECaLS Galaxies

Alternative to weak lensing measurements through cosmic shear, we present a weak lensing convergence $\hat{\kappa}$ map reconstructed through cosmic magnification effect in DECaLS galaxies of the DESI imaging surveys DR9. This is achieved by linearly weighing $12$ maps of galaxy number overdensity in different magnitude bins of $grz$ photometry bands. The weight is designed to eliminate the mean galaxy deterministic bias, minimize galaxy shot noise while maintaining the lensing convergence signal. We also perform corrections of imaging systematics in the galaxy number overdensity. The $\hat{\kappa}$ map has $8365$ deg$^2$ sky coverage. Given the low number density of DECaLS galaxies, the $\hat{\kappa}$ map is overwhelmed by shot noise and the map quality is difficult to evaluate using the lensing auto-correlation. Alternatively, we measure its cross-correlation with the cosmic shear catalogs of DECaLS galaxies of DESI imaging surveys DR8, which has $8365$ deg$^2$ overlap in sky coverage with the $\hat{\kappa}$ map. We detect a convergence-shear cross-correlation signal with $S/N\simeq 10$. The analysis also shows that the galaxy intrinsic clustering is suppressed by a factor $\mathcal{O}(10^2)$ and the residual galaxy clustering contamination in the $\hat{\kappa}$ map is consistent with zero. Various tests with different galaxy and shear samples, and the Akaike information criterion analysis all support the lensing detection. So is the imaging systematics corrections, which enhance the lensing signal detection by $\sim 30\%$. We discuss various issues for further improvement of the measurements

A Multi-tasking Model of Speaker-Keyword Classification for Keeping Human in the Loop of Drone-assisted Inspection

Audio commands are a preferred communication medium to keep inspectors in the loop of civil infrastructure inspection performed by a semi-autonomous drone. To understand job-specific commands from a group of heterogeneous and dynamic inspectors, a model must be developed cost-effectively for the group and easily adapted when the group changes. This paper is motivated to build a multi-tasking deep learning model that possesses a Share-Split-Collaborate architecture. This architecture allows the two classification tasks to share the feature extractor and then split subject-specific and keyword-specific features intertwined in the extracted features through feature projection and collaborative training. A base model for a group of five authorized subjects is trained and tested on the inspection keyword dataset collected by this study. The model achieved a 95.3% or higher mean accuracy in classifying the keywords of any authorized inspectors. Its mean accuracy in speaker classification is 99.2%. Due to the richer keyword representations that the model learns from the pooled training data, adapting the base model to a new inspector requires only a little training data from that inspector, like five utterances per keyword. Using the speaker classification scores for inspector verification can achieve a success rate of at least 93.9% in verifying authorized inspectors and 76.1% in detecting unauthorized ones. Further, the paper demonstrates the applicability of the proposed model to larger-size groups on a public dataset. This paper provides a solution to addressing challenges facing AI-assisted human-robot interaction, including worker heterogeneity, worker dynamics, and job heterogeneity.Comment: Accepted by Engineering Applications of Artificial Intelligence journal on Oct 31th. Upload the accepted clean versio

BPTF promotes tumor growth and predicts poor prognosis in lung adenocarcinomas.

BPTF, a subunit of NURF, is well known to be involved in the development of eukaryotic cell, but little is known about its roles in cancers, especially in non-small-cell lung cancer (NSCLC). Here we showed that BPTF was specifically overexpressed in NSCLC cell lines and lung adenocarcinoma tissues. Knockdown of BPTF by siRNA significantly inhibited cell proliferation, induced cell apoptosis and arrested cell cycle progress from G1 to S phase. We also found that BPTF knockdown downregulated the expression of the phosphorylated Erk1/2, PI3K and Akt proteins and induced the cleavage of caspase-8, caspase-7 and PARP proteins, thereby inhibiting the MAPK and PI3K/AKT signaling and activating apoptotic pathway. BPTF knockdown by siRNA also upregulated the cell cycle inhibitors such as p21 and p18 but inhibited the expression of cyclin D, phospho-Rb and phospho-cdc2 in lung cancer cells. Moreover, BPTF knockdown by its specific shRNA inhibited lung cancer growth in vivo in the xenografts of A549 cells accompanied by the suppression of VEGF, p-Erk and p-Akt expression. Immunohistochemical assay for tumor tissue microarrays of lung tumor tissues showed that BPTF overexpression predicted a poor prognosis in the patients with lung adenocarcinomas. Therefore, our data indicate that BPTF plays an essential role in cell growth and survival by targeting multiply signaling pathways in human lung cancers

The pathophysiology of degenerative cervical myelopathy and the physiology of recovery following decompression

Background: Degenerative cervical myelopathy (DCM), also known as cervical spondylotic myelopathy is the leading cause of spinal cord compression in adults. The mainstay of treatment is surgical decompression, which leads to partial recovery of symptoms, however, long term prognosis of the condition remains poor. Despite advances in treatment methods, the underlying pathobiology is not well-known. A better understanding of the disease is therefore required for the development of treatments to improve outcomes following surgery. Objective: To systematically evaluate the pathophysiology of DCM and the mechanism underlying recovery following decompression. Methods: A total of 13,808 published articles were identified in our systematic search of electronic databases (PUBMED, WEB OF SCIENCE). A total of 51 studies investigating the secondary injury mechanisms of DCM or physiology of recovery in animal models of disease underwent comprehensive review. Results: Forty-seven studies addressed the pathophysiology of DCM. Majority of the studies demonstrated evidence of neuronal loss following spinal cord compression. A number of studies provided further details of structural changes in neurons such as myelin damage and axon degeneration. The mechanisms of injury to cells included direct apoptosis and increased inflammation. Only four papers investigated the pathobiological changes that occur in spinal cords following decompression. One study demonstrated evidence of axonal plasticity following decompressive surgery. Another study demonstrated ischaemic-reperfusion injury following decompression, however this phenomenon was worse when decompression was delayed. Conclusions: In preclinical studies, the pathophysiology of DCM has been poorly studied and a number of questions remain unanswered. The physiological changes seen in the decompressed spinal cord has not been widely investigated and it is paramount that researchers investigate the decompressed spinal cord further to enable the development of therapeutic tools, to enhance recovery following surgery

DNAGPT: A Generalized Pre-trained Tool for Versatile DNA Sequence Analysis Tasks

Pre-trained large language models demonstrate potential in extracting information from DNA sequences, yet adapting to a variety of tasks and data modalities remains a challenge. To address this, we propose DNAGPT, a generalized DNA pre-training model trained on over 200 billion base pairs from all mammals. By enhancing the classic GPT model with a binary classification task (DNA sequence order), a numerical regression task (guanine-cytosine content prediction), and a comprehensive token language, DNAGPT can handle versatile DNA analysis tasks while processing both sequence and numerical data. Our evaluation of genomic signal and region recognition, mRNA abundance regression, and artificial genomes generation tasks demonstrates DNAGPT's superior performance compared to existing models designed for specific downstream tasks, benefiting from pre-training using the newly designed model structure