Enhancing a Sense of Competence at Work by Engaging in Proactive Behavior: The Role of Proactive Personality

To understand how individuals’ senses of competence are cultivated, scholars have primarily focused on situational factors such as job autonomy and supervisor support. Against this backdrop, we propose that individuals can work as active agents and enhance their sense of competence by initiating actions that aim to master the environment. We adopt the behavioral concordance model and propose that people higher in proactive personality are more likely to engage in proactive behavior that elevates their senses of competence over time. We further propose that such behavioral concordance contributes to boosting a sense of competence is more prominent among those with higher proactive personality. Our predictions are supported by data from 172 employees and their direct supervisors in China, after controlling for the effect of job autonomy and supervisor support for autonomy. Specifically, only those higher in proactive personality engaged in more proactive behavior and increased their sense of competence over time. This study highlights both a self-initiated and a behavioral perspective on understanding the development of a sense of competence

Three dimensional spider-web-like superconducting filamentary paths in KxFe2−ySe2K_xFe_{2-y}Se_2 single crystals

Since the discovery of high temperature superconductivity in F-doped LaFeAsO, many new iron based superconductors with different structures have been fabricated2. The observation of superconductivity at about 32 K in KxFe2-ySe2 with the iso-structure of the FeAs-based 122 superconductors was a surprise and immediately stimulated the interests because the band structure calculation8 predicted the absence of the hole pocket which was supposed to be necessary for the theoretical picture of S+- pairing. Soon later, it was found that the material may separate into the insulating antiferromagnetic K2Fe4Se5 phase and the superconducting phase. It remains unresolved that how these two phases coexist and what is the parent phase for superconductivity. In this study we use different quenching processes to produce the target samples with distinct microstructures, and apply multiple measuring techniques to reveal a close relationship between the microstructures and the global appearance of superconductivity. In addition, we clearly illustrate three dimensional spider-web-like superconducting filamentary paths, and for the first time propose that the superconducting phase may originate from a state with one vacancy in every eight Fe-sites with the root8*root10 parallelogram structure.Comment: 22 pages, 7 figure

Melatonin enhances the anti-tumor effect of fisetin by inhibiting COX-2/iNOS and NF-κB/p300 signaling pathways.

Melatonin is a hormone identified in plants and pineal glands of mammals and possesses diverse physiological functions. Fisetin is a bio-flavonoid widely found in plants and exerts antitumor activity in several types of human cancers. However, the combinational effect of melatonin and fisetin on antitumor activity, especially in melanoma treatment, remains unclear. Here, we tested the hypothesis that melatonin could enhance the antitumor activity of fisetin in melanoma cells and identified the underlying molecular mechanisms. The combinational treatment of melanoma cells with fisetin and melatonin significantly enhanced the inhibitions of cell viability, cell migration and clone formation, and the induction of apoptosis when compared with the treatment of fisetin alone. Moreover, such enhancement of antitumor effect by melatonin was found to be mediated through the modulation of the multiply signaling pathways in melanoma cells. The combinational treatment of fisetin with melatonin increased the cleavage of PARP proteins, triggered more release of cytochrome-c from the mitochondrial inter-membrane, enhanced the inhibition of COX-2 and iNOS expression, repressed the nuclear localization of p300 and NF-κB proteins, and abrogated the binding of NF-κB on COX-2 promoter. Thus, these results demonstrated that melatonin potentiated the anti-tumor effect of fisetin in melanoma cells by activating cytochrome-c-dependent apoptotic pathway and inhibiting COX-2/iNOS and NF-κB/p300 signaling pathways, and our study suggests the potential of such a combinational treatment of natural products in melanoma therapy

DiGAN breakthrough: advancing diabetic data analysis with innovative GAN-based imbalance correction techniques

In the rapidly evolving field of medical diagnostics, the challenge of imbalanced datasets, particularly in diabetes classification, calls for innovative solutions. The study introduces DiGAN, a groundbreaking approach that leverages the power of Generative Adversarial Networks (GAN) to revolutionize diabetes data analysis. Marking a significant departure from traditional methods, DiGAN applies GANs, typically seen in image processing, to the realm of diabetes data. This novel application is complemented by integrating the unsupervised Laplacian Score for sophisticated feature selection. The pioneering approach not only surpasses the limitations of existing techniques but also sets a new benchmark in classification accuracy with a 90% weighted F1-score, achieving a remarkable improvement of over 20% compared to conventional methods. Additionally, DiGAN demonstrates superior performance over popular SMOTE-based methods in handling extremely imbalanced datasets. This research, focusing on the integrated use of Laplacian Score, GAN, and Random Forest, stands at the forefront of diabetic classification, offering a uniquely effective and innovative solution to the long-standing data imbalance issue in medical diagnostics

4mCPred-GSIMP: Predicting DNA N4-methylcytosine sites in the mouse genome with multi-Scale adaptive features extraction and fusion

The epigenetic modification of DNA N4-methylcytosine (4mC) is vital for controlling DNA replication and expression. It is crucial to pinpoint 4mC's location to comprehend its role in physiological and pathological processes. However, accurate 4mC detection is difficult to achieve due to technical constraints. In this paper, we propose a deep learning-based approach 4mCPred-GSIMP for predicting 4mC sites in the mouse genome. The approach encodes DNA sequences using four feature encoding methods and combines multi-scale convolution and improved selective kernel convolution to adaptively extract and fuse features from different scales, thereby improving feature representation and optimization effect. In addition, we also use convolutional residual connections, global response normalization and pointwise convolution techniques to optimize the model. On the independent test dataset, 4mCPred-GSIMP shows high sensitivity, specificity, accuracy, Matthews correlation coefficient and area under the curve, which are 0.7812, 0.9312, 0.8562, 0.7207 and 0.9233, respectively. Various experiments demonstrate that 4mCPred-GSIMP outperforms existing prediction tools

Delineating the biosynthesis of gentamicin x2, the common precursor of the gentamicin C antibiotic complex.

Gentamicin C complex is a mixture of aminoglycoside antibiotics used worldwide to treat severe Gram-negative bacterial infections. Despite its clinical importance, the enzymology of its biosynthetic pathway has remained obscure. We report here insights into the four enzyme-catalyzed steps that lead from the first-formed pseudotrisaccharide gentamicin A2 to gentamicin X2, the last common intermediate for all components of the C complex. We have used both targeted mutations of individual genes and reconstitution of portions of the pathway in vitro to show that the secondary alcohol function at C-3″ of A2 is first converted to an amine, catalyzed by the tandem operation of oxidoreductase GenD2 and transaminase GenS2. The amine is then specifically methylated by the S-adenosyl-l-methionine (SAM)-dependent N-methyltransferase GenN to form gentamicin A. Finally, C-methylation at C-4″ to form gentamicin X2 is catalyzed by the radical SAM-dependent and cobalamin-dependent enzyme GenD1.This work was supported by a project grant from the Medical Research Council, UK (G1001687) to P.F.L.; and by the 973 and 863 programs from the Ministry of Science and Technology of China, National Science Foundation of China, and the Translational Medical Research Fund of Wuhan University School of Medicine to Y.S.; E.M. thanks the Gates Cambridge Trust for a scholarship. We also gratefully acknowledge Dr. Xinzhou Yang, SouthCentral University for Nationalities, for his assistance in separation of gentamicin A2. We thank Dr. Andrew Truman (John Innes Institute) for helpful discussions.This is the final published version. It was originally published in Chemistry and Biology, Volume 22, Issue 2, 19 February 2015, Pages 251–261, doi:10.1016/j.chembiol.2014.12.01

Methyltransferases of gentamicin biosynthesis

Gentamicin C complex from Micromonospora echinospora remains a globally important antibiotic, and there is revived interest in the semisynthesis of analogs that might show improved therapeutic properties. The complex consists of five components differing in their methylation pattern at one or more sites in the molecule. We show here, using specific gene deletion and chemical complementation, that the gentamicin pathway up to the branch point is defined by the selectivity of the methyltransferases GenN, GenD1, and GenK. Unexpectedly, they comprise a methylation network in which early intermediates are ectopically modified. Using whole-genome sequence, we have also discovered the terminal 6'-N-methyltransfer required to produce gentamicin C2b from C1a or gentamicin C1 from C2, an example of an essential biosynthetic enzyme being located not in the biosynthetic gene cluster but far removed on the chromosome. These findings fully account for the methylation pattern in gentamicins and open the way to production of individual gentamicins by fermentation, as starting materials for semisynthesis.This work was supported by National Natural Science Foundation of China Grant 31470186; by the 973 Program Grant 2012CB721005 from the Ministry of Science and Technology of China; by Open Project Grant MMLKF15-12 from the State Key Laboratory of Microbial Metabolism, Shanghai Jiao Tong University (to Y.S.); by Medical Research Council (MRC) Grants G1001687 and MR/M019020/1 (to P.F.L.); and by an MRC postgraduate studentship (1343325) (to A.R.)

Memory Deduplication: An Effective Approach to Improve the Memory System

Programs now have more aggressive demands of memory to hold their data than before. This paper analyzes the characteristics of memory data by using seven real memory traces. It observes that there are a large volume of memory pages with identical contents contained in the traces. Furthermore, the unique memory content accessed are much less than the unique memory address accessed. This is incurred by the traditional address-based cache replacement algorithms that replace memory pages by checking the addresses rather than the contents of those pages, thus resulting in many identical memory contents with different addresses stored in the memory. For example, in the same file system, opening two identical files stored in different directories, or opening two similar files that share a certain amount of contents in the same directory, will result in identical data blocks stored in the cache due to the traditional address-based cache replacement algorithms. Based on the observations, this paper evaluates memory compression and memory deduplication. As expected, memory deduplication greatly outperforms memory compression. For example, the best deduplication ratio is 4.6 times higher than the best compression ratio. The deduplication time and restore time are 121 times and 427 times faster than the compression time and decompression time, respectively. The experimental results in this paper should be able to offer useful insights for designing systems that require abundant memory to improve the system performance

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

3D snapshot: Invertible embedding of 3D neural representations in a single image

3D neural rendering enables photo-realistic reconstruction of a specific scene by encoding discontinuous inputs into a neural representation. Despite the remarkable rendering results, the storage of network parameters is not transmission-friendly and not extendable to metaverse applications. In this paper, we propose an invertible neural rendering approach that enables generating an interactive 3D model from a single image (i.e., 3D Snapshot). Our idea is to distill a pre-trained neural rendering model (e.g., NeRF) into a visualizable image form that can then be easily inverted back to a neural network. To this end, we first present a neural image distillation method to optimize three neural planes for representing the original neural rendering model. However, this representation is noisy and visually meaningless. We thus propose a dynamic invertible neural network to embed this noisy representation into a plausible image representation of the scene. We demonstrate promising reconstruction quality quantitatively and qualitatively, by comparing to the original neural rendering model, as well as video-based invertible methods. On the other hand, our method can store dozens of NeRFs with a compact restoration network (5MB), and embedding each 3D scene takes up only 160KB of storage. More importantly, our approach is the first solution that allows embedding a neural rendering model into image representations, which enables applications like creating an interactive 3D model from a printed image in the metaverse