catena-Poly[[tris[silver(I)-μ-4,4′-bi­pyridine-κ2 N:N′]] tris­(perchlorate) di­hydrate]

In the title compound, {[Ag3(C10H8N2)3](ClO4)3·2H2O}n, one of the AgI ions, one of the 4,4′-bipyridine (bipy) ligands and one of the perchlorate anions are each situated on a twofold rotation axis. Each AgI ion is coordinated by two N atoms from two bridging bipy ligands, forming chains along [101]. π–π inter­actions between the pyridine rings [centroid–centroid distances = 3.638 (8) and 3.688 (8) Å] connect the chains. Inter­molecular O—H⋯O hydrogen bonds link the uncoord­inated water mol­ecules and the perchlorate anions

Bis(μ-3-chloro­benzene-1,2-dicarboxyl­ato-κ2 O 2:O 2)bis­[diaqua­(5,5′-dimethyl-2,2′-bipyridine-κ2 N,N′)copper(II)]

In the centrosymmetric binuclear title compound, [Cu2(C8H3ClO4)2(C12H12N2)2(H2O)4], the CuII ion is six-coordinated by two N atoms from a 5,5′-dimethyl-2,2′-bipyridine ligand, two bridging O atoms from two 3-chloro­benzene-1,2-dicarboxyl­ate ligands and two water mol­ecules in a distorted octa­hedral geometry. The binuclear complex mol­ecules are linked together by inter­molecular O—H⋯O hydrogen bonds into a layer parallel to (100). The layers are connected by C—H⋯Cl hydrogen bonds. Intra­molecular O—H⋯O hydrogen bonds and π–π inter­actions [centroid–centroid distance = 3.5958 (16) Å] are also present

Novel N,S-phenacyl protecting group and its application for peptide synthesis

The phenacyl group can be introduced onto amino and thio groups by N,S-alkylation reactions. Conversely, these groups are removed rapidly by employing magnesium in acetic acid. This protecting group was successfully applied to a short peptide synthesis of Boc-L-Cys-Gly-OMe

Benzyl 2,5-dioxopyrrolidin-1-yl carbonate

The asymmetric unit of the title compound, C12H11NO5, contains two independent mol­ecules with similar geometric parameters but different orientations of the phenyl rings. The mol­ecular packing is stabilized by weak nonclassical C—H⋯O hydrogen-bonding inter­actions

Observation of Majorana fermions with spin selective Andreev reflection in the vortex of topological superconductor

Majorana fermion (MF) whose antiparticle is itself has been predicted in condensed matter systems. Signatures of the MFs have been reported as zero energy modes in various systems. More definitive evidences are highly desired to verify the existence of the MF. Very recently, theory has predicted MFs to induce spin selective Andreev reflection (SSAR), a novel magnetic property which can be used to detect the MFs. Here we report the first observation of the SSAR from MFs inside vortices in Bi2Te3/NbSe2 hetero-structure, in which topological superconductivity was previously established. By using spin-polarized scanning tunneling microscopy/spectroscopy (STM/STS), we show that the zero-bias peak of the tunneling differential conductance at the vortex center is substantially higher when the tip polarization and the external magnetic field are parallel than anti-parallel to each other. Such strong spin dependence of the tunneling is absent away from the vortex center, or in a conventional superconductor. The observed spin dependent tunneling effect is a direct evidence for the SSAR from MFs, fully consistent with theoretical analyses. Our work provides definitive evidences of MFs and will stimulate the MFs research on their novel physical properties, hence a step towards their statistics and application in quantum computing.Comment: 4 figures 15 page

Superconductivity Induced by Site-Selective Arsenic Doping in Mo5_5Si3_3

Arsenic doping in silicides has been much less studied compared with phosphorus. In this study, superconductivity is successfully induced by As doping in Mo$_5$Si$_3$. The superconducting transition temperature ($T_c$) reaches 7.7 K, which is higher than those in previously known W$_5$Si$_3$-type superconductors. Mo$_5$Si$_2$As is a type-II BCS superconductor with upper and lower critical fields of 6.65 T and 22.4 mT, respectively. In addition, As atoms are found to selectively take the 8$h$ sites in Mo$_5$Si$_2$As. The emergence of superconductivity is possibly due to the shift of Fermi level as a consequence of As doping, as revealed by the specific heat measurements and first-principles calculations. Our work provides not only another example of As doping, but also a practical strategy to achieve superconductivity in silicides through Fermi level engineering.Comment: Supporting Information available at the corresponding DO

Strong-Coupling Superconductivity with TcT_c ∼\sim 10.8 K Induced by P Doping in the Topological Semimetal Mo5_5Si3_3

By performing P doping on the Si sites in the topological semimetal Mo$_5$Si$_3$, we discover strong-coupling superconductivity in Mo$_5$Si$_{3-x}$P$_x$ (0.5 $\le$ $x$ $\le$ 2.0). Mo$_5$Si$_3$ crystallizes in the W$_5$Si$_3$-type structure with space group of $I4/mcm$ (No. 140), and is not a superconductor itself. Upon P doping, the lattice parameter $a$ decreases while $c$ increases monotonously. Bulk superconductivity is revealed in Mo$_5$Si$_{3-x}$P$_x$ (0.5 $\le$ $x$ $\le$ 2.0) from resistivity, magnetization, and heat capacity measurements. $T_c$ in Mo$_5$Si$_{1.5}$P$_{1.5}$ reaches as high as 10.8 K, setting a new record among the W$_5$Si$_3$-type superconductors. The upper and lower critical fields for Mo$_5$Si$_{1.5}$P$_{1.5}$ are 14.56 T and 105 mT, respectively. Moreover, Mo$_5$Si$_{1.5}$P$_{1.5}$ is found to be a fully gapped superconductor with strong electron-phonon coupling. First-principles calculations suggest that the enhancement of electron-phonon coupling is possibly due to the shift of the Fermi level, which is induced by electron doping. The calculations also reveal the nontrivial band topology in Mo$_5$Si$_3$. The $T_c$ and upper critical field in Mo$_5$Si$_{3-x}$P$_x$ are fairly high among pseudobinary compounds. Both of them are higher than those in NbTi, making future applications promising. Our results suggest that the W$_5$Si$_3$-type compounds are ideal platforms to search for new superconductors. By examinations of their band topologies, more candidates for topological superconductors can be expected in this structural family.Comment: 15 pages, 5 figures. Supplementary Information availabe at the corresponding DO

Targeting Gpr52 lowers mutant HTT levels and rescues Huntington's disease-associated phenotypes.

See Huang and Gitler (doi:10.1093/brain/awy112) for a scientific commentary on this article.Lowering the levels of disease-causing proteins is an attractive treatment strategy for neurodegenerative disorders, among which Huntington's disease is an appealing disease for testing this strategy because of its monogenetic nature. Huntington's disease is mainly caused by cytotoxicity of the mutant HTT protein with an expanded polyglutamine repeat tract. Lowering the soluble mutant HTT may reduce its downstream toxicity and provide potential treatment for Huntington's disease. This is hard to achieve by small-molecule compound drugs because of a lack of effective targets. Here we demonstrate Gpr52, an orphan G protein-coupled receptor, as a potential Huntington's disease drug target. Knocking-out Gpr52 significantly reduces mutant HTT levels in the striatum and rescues Huntington's disease-associated behavioural phenotypes in a knock-in Huntington's disease mouse model expressing endogenous mutant Htt. Importantly, a novel Gpr52 antagonist E7 reduces mutant HTT levels and rescues Huntington's disease-associated phenotypes in cellular and mouse models. Our study provides an entry point for Huntington's disease drug discovery by targeting Gpr52

Applications of machine learning in familial hypercholesterolemia

Familial hypercholesterolemia (FH) is a common hereditary cholesterol metabolic disease that usually leads to an increase in the level of low-density lipoprotein cholesterol in plasma and an increase in the risk of cardiovascular disease. The lack of disease screening and diagnosis often results in FH patients being unable to receive early intervention and treatment, which may mean early occurrence of cardiovascular disease. Thus, more requirements for FH identification and management have been proposed. Recently, machine learning (ML) has made great progress in the field of medicine, including many innovative applications in cardiovascular medicine. In this review, we discussed how ML can be used for FH screening, diagnosis and risk assessment based on different data sources, such as electronic health records, plasma lipid profiles and corneal radian images. In the future, research aimed at developing ML models with better performance and accuracy will continue to overcome the limitations of ML, provide better prediction, diagnosis and management tools for FH, and ultimately achieve the goal of early diagnosis and treatment of FH