KDM2B/FBXL10 targets c-Fos for ubiquitylation and degradation in response to mitogenic stimulation.

KDM2B (also known as FBXL10) controls stem cell self-renewal, somatic cell reprogramming and senescence, and tumorigenesis. KDM2B contains multiple functional domains, including a JmjC domain that catalyzes H3K36 demethylation and a CxxC zinc-finger that recognizes CpG islands and recruits the polycomb repressive complex 1. Here, we report that KDM2B, via its F-box domain, functions as a subunit of the CUL1-RING ubiquitin ligase (CRL1/SCF(KDM2B)) complex. KDM2B targets c-Fos for polyubiquitylation and regulates c-Fos protein levels. Unlike the phosphorylation of other SCF (SKP1-CUL1-F-box)/CRL1 substrates that promotes substrates binding to F-box, epidermal growth factor (EGF)-induced c-Fos S374 phosphorylation dissociates c-Fos from KDM2B and stabilizes c-Fos protein. Non-phosphorylatable and phosphomimetic mutations at S374 result in c-Fos protein which cannot be induced by EGF or accumulates constitutively and lead to decreased or increased cell proliferation, respectively. Multiple tumor-derived KDM2B mutations impaired the function of KDM2B to target c-Fos degradation and to suppress cell proliferation. These results reveal a novel function of KDM2B in the negative regulation of cell proliferation by assembling an E3 ligase to targeting c-Fos protein degradation that is antagonized by mitogenic stimulations

Atomic configuration controlled photocurrent in van der Waals homostructures

Conventional photocurrents at a p-n junction depend on macroscopic built-in fields and are typically insensitive to the microscopic details of a crystal's atomic configuration. Here we demonstrate how atomic configuration can control photocurrent in van der Waals (vdW) materials. In particular, we find bulk shift photocurrents (SPCs) can display a rich (atomic) configuration dependent phenomenology that range from contrasting SPC currents for different stacking arrangements in a vdW homostructure (e.g. AB vs BA stacking) to a strong light polarization dependence for SPC that align with crystallographic axes. Strikingly, we find that SPC in vdW homostructures can be directed by modest strain, yielding sizeable photocurrent magnitudes under unpolarized light irradiation and manifesting even in the absence of p-n junctions. These demonstrate that SPC are intimately linked to how the Bloch wavefunctions are embedded in real space, and enables a new macroscopic transport probe (photocurrent) of lattice-scale registration in vdW materials

Mystery of Excess Low Energy States in a Disordered Superconductor in a Zeeman Field

Tunneling density of states measurements of disordered superconducting (SC) Al films in high Zeeman fields reveal a significant population of subgap states which cannot be explained by standard BCS theory. We provide a natural explanation of these excess states in terms of a novel disordered Larkin-Ovchinnikov (dLO) phase that occurs near the spin-paramagnetic transition at the Chandrasekhar-Clogston critical field. The dLO superconductor is characterized by a pairing amplitude that changes sign at domain walls. These domain walls carry magnetization and support Andreev bound states, which lead to distinct spectral signatures at low energy.Comment: 5 pages, 4 figures, plus supplementary section describing methods (2 pages

Phase locked-loop with decaying DC transient removal for three-phase grids

Frequency and phase of the power grid, which are critical for reliable control and protection of grid-tied devices, are generally detected by the closed-loop phase locked-loop (PLL). In highly inductive high-voltage transmission systems, decaying DC (DDC) components with large amplitude can be easily introduced by load disturbances and/or grid abnormalities, leading to severe performance degradation of the PLL during the transient. Focusing on this issue, in this paper, modifications to the conventional synchronous reference frame (SRF)-PLL have been made to address the short-term disturbances including the DDC component, and the system operation is divided into the normal state and the DDC-transient state. The SRF-PLL is only adopted for the normal state where the DDC component is negligible. In the presence of a significant DDC component, as well as disturbances including negative-/zero-sequence components and harmonics, the weak effectiveness of the conventional SRF-PLL is proved, and an efficient DDC component extraction method, with a detection time of 0.5 grid cycle, is introduced for the three-phase system. The real-time amplitude and phase of the positive-sequence component can be efficiently extracted via the proposed scheme, by exploiting the transient signal properties in the dq-frame and assuming a constant grid frequency during the short transient. Finally, a proper design of switching logic has been proposed to allow for the fast and precise transition between the normal and the DDC-transient state, thereby ensuring high steady-state accuracy as well as short-term DDC transient immunity. Hardware-in-the-loop based experiments have been used to verify the effectiveness of the proposed PLL technique

Accelerating AI Adoption with Responsible AI Signals and Employee Engagement Mechanisms in Health Care

Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s10796-021-10154-4.Artificial Intelligence (AI) technology is transforming the healthcare sector. However, despite this, the associated ethical implications remain open to debate. This research investigates how signals of AI responsibility impact healthcare practitioners’ attitudes toward AI, satisfaction with AI, AI usage intentions, including the underlying mechanisms. Our research outlines autonomy, beneficence, explainability, justice, and non-maleficence as the five key signals of AI responsibility for healthcare practitioners. The findings reveal that these five signals significantly increase healthcare practitioners’ engagement, which subsequently leads to more favourable attitudes, greater satisfaction, and higher usage intentions with AI technology. Moreover, ‘techno-overload’ as a primary ‘techno-stressor’ moderates the mediating effect of engagement on the relationship between AI justice and behavioural and attitudinal outcomes. When healthcare practitioners perceive AI technology as adding extra workload, such techno-overload will undermine the importance of the justice signal and subsequently affect their attitudes, satisfaction, and usage intentions with AI technology

Tuning and Locking the Localized Surface Plasmon Resonances of CuS (Covellite) Nanocrystals by an Amorphous CuPdxS Shell

[Image: see text] We demonstrate the stabilization of the localized surface plasmon resonance (LSPR) in a semiconductor-based core–shell heterostructure made of a plasmonic CuS core embedded in an amorphous-like alloyed CuPd(x)S shell. This heterostructure is prepared by reacting the as-synthesized CuS nanocrystals (NCs) with Pd(2+) cations at room temperature in the presence of an electron donor (ascorbic acid). The reaction starts from the surface of the CuS NCs and proceeds toward the center, causing reorganization of the initial lattice and amorphization of the covellite structure. According to density functional calculations, Pd atoms are preferentially accommodated between the bilayer formed by the S–S covalent bonds, which are therefore broken, and this can be understood as the first step leading to amorphization of the particles upon insertion of the Pd(2+) ions. The position and intensity in near-infrared LSPRs can be tuned by altering the thickness of the shell and are in agreement with the theoretical optical simulation based on the Mie–Gans theory and Drude model. Compared to the starting CuS NCs, the amorphous CuPd(x)S shell in the core–shell nanoparticles makes their plasmonic response less sensitive to a harsh oxidation environment (generated, for example, by the presence of I(2))

Heterogeneous Metric Learning with Content-Based Regularization for Software Artifact Retrieval

The problem of software artifact retrieval has the goal to effectively locate software artifacts, such as a piece of source code, in a large code repository. This problem has been traditionally addressed through the textual query. In other words, information retrieval techniques will be exploited based on the textual similarity between queries and textual representation of software artifacts, which is generated by collecting words from comments, identifiers, and descriptions of programs. However, in addition to these semantic information, there are rich information embedded in source codes themselves. These source codes, if analyzed properly, can be a rich source for enhancing the efforts of software artifact retrieval. To this end, in this paper, we develop a feature extraction method on source codes. Specifically, this method can capture both the inherent information in the source codes and the semantic information hidden in the comments, descriptions, and identifiers of the source codes. Moreover, we design a heterogeneous metric learning approach, which allows to integrate code features and text features into the same latent semantic space. This, in turn, can help to measure the artifact similarity by exploiting the joint power of both code and text features. Finally, extensive experiments on real-world data show that the proposed method can help to improve the performances of software artifact retrieval with a significant margin