Enhancing Grasping Performance of Novel Objects through an Improved Fine-Tuning Process

Grasping algorithms have evolved from planar depth grasping to utilizing point cloud information, allowing for application in a wider range of scenarios. However, data-driven grasps based on models trained on basic open-source datasets may not perform well on novel objects, which are often required in different scenarios, necessitating fine-tuning using new objects. The data driving these algorithms essentially corresponds to the closing region of the hand in 6D pose, and due to the uniqueness of 6D pose, synthetic annotation or real-machine annotation methods are typically employed. Acquiring large amounts of data with real-machine annotation is challenging, making synthetic annotation a common practice. However, obtaining annotated 6D pose data using conventional methods is extremely time-consuming. Therefore, we propose a method to quickly acquire data for novel objects, enabling more efficient fine-tuning. Our method primarily samples grasp orientations to generate and annotate grasps. Experimental results demonstrate that our fine-tuning process for a new object is 400 \% faster than other methods. Furthermore, we propose an optimized grasp annotation framework that accounts for the effects of the gripper closing, making the annotations more reasonable. Upon acceptance of this paper, we will release our algorithm as open-source

Wikiformer: Pre-training with Structured Information of Wikipedia for Ad-hoc Retrieval

With the development of deep learning and natural language processing techniques, pre-trained language models have been widely used to solve information retrieval (IR) problems. Benefiting from the pre-training and fine-tuning paradigm, these models achieve state-of-the-art performance. In previous works, plain texts in Wikipedia have been widely used in the pre-training stage. However, the rich structured information in Wikipedia, such as the titles, abstracts, hierarchical heading (multi-level title) structure, relationship between articles, references, hyperlink structures, and the writing organizations, has not been fully explored. In this paper, we devise four pre-training objectives tailored for IR tasks based on the structured knowledge of Wikipedia. Compared to existing pre-training methods, our approach can better capture the semantic knowledge in the training corpus by leveraging the human-edited structured data from Wikipedia. Experimental results on multiple IR benchmark datasets show the superior performance of our model in both zero-shot and fine-tuning settings compared to existing strong retrieval baselines. Besides, experimental results in biomedical and legal domains demonstrate that our approach achieves better performance in vertical domains compared to previous models, especially in scenarios where long text similarity matching is needed.Comment: Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24

Cold-adaptive alkaline protease from the psychrophilic Planomicrobium sp. 547: enzyme characterization and gene cloning

A psychrophilic bacterium strain 547 producing cold-adaptive alkaline protease was isolated from the deep sea sediment of Prydz Bay, Antarctica. The organism was identified as a Planomicrobium species by 16S rRNA analysis. The optimal and highest growth temperatures for strain 547 were 15℃ and 30℃, respectively. The extracellular protease was purified by ammonium sulfate precipitation and DEAE cellulose-52 chromatography. The optimal temperature and pH for the activity of the purified enzyme were 35℃ and pH 9.0, respectively. The enzyme retained approximately 40% of its activity after 2 h of incubation at 50℃. The enzymatic activity was inhibited by 1 mmol/L phenylmethyl sulfonylfluoride (PMSF) and hydrochloride 4-(2-aminoethyl)-benzenesulfonyl fluoride (AEBSF), indicating that it was a serine protease. The presence of Ca2+ and Mn2+ increased the activity of the enzyme. The protease gene with a size of 1 269 bp was cloned from Planomicrobium sp. 547 using primers designed based on the conserved sequences of proteases in GenBank. The Planomicrobium sp. 547 protease contained a domain belonging to the peptidase S8 family, which has a length of 309 amino acid (AA) residues. The alignment and phylogenetic analysis of the AA sequence indicated that the protease belonged to the subtilisin family

KMT2A promotes melanoma cell growth by targeting hTERT signaling pathway.

Melanoma is an aggressive cutaneous malignancy, illuminating the exact mechanisms and finding novel therapeutic targets are urgently needed. In this study, we identified KMT2A as a potential target, which promoted the growth of human melanoma cells. KMT2A knockdown significantly inhibited cell viability and cell migration and induced apoptosis, whereas KMT2A overexpression effectively promoted cell proliferation in various melanoma cell lines. Further study showed that KMT2A regulated melanoma cell growth by targeting the hTERT-dependent signal pathway. Knockdown of KMT2A markedly inhibited the promoter activity and expression of hTERT, and hTERT overexpression rescued the viability inhibition caused by KMT2A knockdown. Moreover, KMT2A knockdown suppressed tumorsphere formation and the expression of cancer stem cell markers, which was also reversed by hTERT overexpression. In addition, the results from a xenograft mouse model confirmed that KMT2A promoted melanoma growth via hTERT signaling. Finally, analyses of clinical samples demonstrated that the expression of KMT2A and hTERT were positively correlated in melanoma tumor tissues, and KMT2A high expression predicted poor prognosis in melanoma patients. Collectively, our results indicate that KMT2A promotes melanoma growth by activating the hTERT signaling, suggesting that the KMT2A/hTERT signaling pathway may be a potential therapeutic target for melanoma

NMI inhibits cancer stem cell traits by downregulating hTERT in breast cancer.

N-myc and STAT interactor (NMI) has been proved to bind to different transcription factors to regulate a variety of signaling mechanisms including DNA damage, cell cycle and epithelial-mesenchymal transition. However, the role of NMI in the regulation of cancer stem cells (CSCs) remains poorly understood. In this study, we investigated the regulation of NMI on CSCs traits in breast cancer and uncovered the underlying molecular mechanisms. We found that NMI was lowly expressed in breast cancer stem cells (BCSCs)-enriched populations. Knockdown of NMI promoted CSCs traits while its overexpression inhibited CSCs traits, including the expression of CSC-related markers, the number of CD44+CD24- cell populations and the ability of mammospheres formation. We also found that NMI-mediated regulation of BCSCs traits was at least partially realized through the modulation of hTERT signaling. NMI knockdown upregulated hTERT expression while its overexpression downregulated hTERT in breast cancer cells, and the changes in CSCs traits and cell invasion ability mediated by NMI were rescued by hTERT. The in vivo study also validated that NMI knockdown promoted breast cancer growth by upregulating hTERT signaling in a mouse model. Moreover, further analyses for the clinical samples demonstrated that NMI expression was negatively correlated with hTERT expression and the low NMI/high hTERT expression was associated with the worse status of clinical TNM stages in breast cancer patients. Furthermore, we demonstrated that the interaction of YY1 protein with NMI and its involvement in NMI-mediated transcriptional regulation of hTERT in breast cancer cells. Collectively, our results provide new insights into understanding the regulatory mechanism of CSCs and suggest that the NMI-YY1-hTERT signaling axis may be a potential therapeutic target for breast cancers

Frost heave control measures for the frozen double-line tunnel undercrossing an operation station

Aiming at the frost heave characteristics of long-distance and large-section underground freezing projects in cities, the double-line tunnel of Shanghai Metro Line 18 undercrossing an operation station was select as an example,a three-dimensional numerical model was established by using finite element software based on the thermal-mechanical coupling equation. Combined with the physical parameters obtained from laboratory tests, the evolution law of frost heave displacement field of the project was studied, and the influence of measures such as the staggered peak freezing and the adjustment of brine temperature on the frost heave displacement field was explored. Under the experimental conditions, the results show that: ① the deformation of the station floor caused by frost heave which mainly occurs in the active freezing period. When the double-line tunnel is frozen at the same time, the vertical displacement of the station floor at 45days of freezing reaches 77.72% of the displacement at 90 days of freezing. ② When the freezing time reaches 90 days, compared with simultaneous freezing, the displacement of the station floor reduced by 7.7% under the condtion of staggered peak freezing. The superposition effect of frost heave in the same time period is effectively avoided, and the frost heave effect is reduced to a certain extent. ③ When the freezing time reaches 90 days, compared with simultaneous freezing, the displacement of the station floor reduced by 34.2% under the condtion of adjusting the brine temperature, it shown that the expansion rate and frost heave effect of frozen soil are effectively controlled. In engineering practice, measures such as staggered freezing and adjusting brine temperature are used together to control frost heave, and the maximum vertical deformation of the station floor is 25.41 mm. The deformation law of the station floor obtained by numerical simulation is basically consistent with the measured data, which effectively guided the construction of the project

Challenges to recruiting population representative samples of female sex workers in China using Respondent Driven Sampling

We explore the network coverage of a sample of female sex workers (FSWs) in China recruited through Respondent Drive Sampling (RDS) as part of an effort to evaluate the claim of RDS of population representation with empirical data. We take advantage of unique information on the social networks of FSWs obtained from two overlapping studies --RDS and a venue-based sampling approach (PLACE) -- and use an exponential random graph modeling (ERGM) framework from local networks to construct a likely network from which our observed RDS sample is drawn. We then run recruitment chains over this simulated network to assess the assumption that the RDS chain referral process samples participants in proportion to their degree and the extent to which RDS satisfactorily covers certain parts of the network. We find evidence that, contrary to assumptions, RDS oversamples low degree nodes and geographically central areas of the network. Unlike previous evaluations of RDS which have explored the performance of RDS sampling chains on a non-hidden population, or the performance of simulated chains over previously mapped realistic social networks, our study provides a robust, empirically grounded evaluation of the performance of RDS chains on a real-world hidden population

Ku80 cooperates with CBP to promote COX-2 expression and tumor growth.

Cyclooxygenase-2 (COX-2) plays an important role in lung cancer development and progression. Using streptavidin-agarose pulldown and proteomics assay, we identified and validated Ku80, a dimer of Ku participating in the repair of broken DNA double strands, as a new binding protein of the COX-2 gene promoter. Overexpression of Ku80 up-regulated COX-2 promoter activation and COX-2 expression in lung cancer cells. Silencing of Ku80 by siRNA down-regulated COX-2 expression and inhibited tumor cell growth in vitro and in a xenograft mouse model. Ku80 knockdown suppressed phosphorylation of ERK, resulting in an inactivation of the MAPK pathway. Moreover, CBP, a transcription co-activator, interacted with and acetylated Ku80 to co-regulate the activation of COX-2 promoter. Overexpression of CBP increased Ku80 acetylation, thereby promoting COX-2 expression and cell growth. Suppression of CBP by a CBP-specific inhibitor or siRNA inhibited COX-2 expression as well as tumor cell growth. Tissue microarray immunohistochemical analysis of lung adenocarcinomas revealed a strong positive correlation between levels of Ku80 and COX-2 and clinicopathologic variables. Overexpression of Ku80 was associated with poor prognosis in patients with lung cancers. We conclude that Ku80 promotes COX-2 expression and tumor growth and is a potential therapeutic target in lung cancer

miR-16-2* Interferes with WNT5A to Regulate Osteogenesis of Mesenchymal Stem Cells

Background/Aims: Osteoporosis is a bone metabolic disease characterized by a systemic impairment of bone mass, which results in increased propensity of fragility fractures. A reduction in the differentiation of MSCs into osteoblasts contributes to the impaired bone formation observed in osteoporosis. Mesenchymal stem cells (MSCs) are induced to differentiate into preosteoblasts, which are regulated by the signaling cascades initiated by the various signals, including miRNAs. miR-16-2* is a newly discovered miRNA that participates in diagnosis and prognosis of hepatocellular carcinoma, cervical cancer and chronic lymphocytic leukemia. However, the effect of miR-16-2* on the regulation of osteoblast differentiation and the mechanism responsible are still unclear. Here we discuss the contribution of miR-16-2* to osteoporosis, osteoblast differentiation and mineralization. Methods: The expression pattern of miR-16-2* during osteogenesis or in osteoporosis bone samples was validated by quantitative real-time PCR (qRT-PCR). The human bone marrow mesenchymal stem cells (hBMSCs) were induced to differentiate into osteoblasts by osteogenic induced medium containing dexamethasone, ascorbate-2-phosphat, beta-glycerophosphate and vitamin-D3. The target genes of miR-16-2* were predicted by TargetScan and PicTar. The mRNA and protein levels of osteogenic key markers were detected using qRT-PCR or western blot respectively. The WNT signal activity was analyzed by TOP/FOP reporter assay. Results: The expression of miR-16-2* in patient bone tissue with osteoporosis was negatively correlated with bone formation related genes. During osteoblast differentiation process, the expression of miR-16-2* was significantly decreased. Upregulation of miR-16-2* in hBMSCs impaired the osteogenic differentiation while the downregulation of miR-16-2* increased this process. Upregulation the expression of miR-16-2* could also block the WNT signal pathway by directly target WNT5A. Furthermore, knockdown of miR-16-2* could promote the activation of RUNX2, possibly by lifting the inhibitory effect of miR-16-2* on WNT pathway. Conclusion: Taken together, we report a novel biological role of miR-16-2* in osteogenesis through regulating WNT5A response for the first time. Our data support the potential utilization of miRNA-based therapies in regenerative medicine