Los chin en el estado de Mizoram, India: una respuesta confesional

La comunidad confesional en el estado de Mizoram en la India ha desempeñado un papel fundamental proporcionando servicios sociales, cambiando las actitudes y percepciones del público respecto a los refugiados y facilitando el acceso y la asistencia, llegando a los más vulnerables allí donde no existe presencia internacional

Crystal structure of NiFe(CO)5[tris(pyridyl-meth-yl)aza-phosphatrane]: a synthetic mimic of the NiFe hydrogenase active site incorporating a pendant pyridine base.

The reaction of Ni(TPAP)(COD) {where TPAP = [(NC5H4)CH2]3P(NC2H4)3N} with Fe(CO)5 resulted in the isolation of the title heterobimetallic NiFe(TPAP)(CO)5 complex di-μ-carbonyl-tricarbon-yl[2,8,9-tris-(pyridin-2-yl-meth-yl)-2,5,8,9-tetra-aza-1-phosphabi-cyclo-[3.3.3]undeca-ne]ironnickel, [FeNi(C24H30N7P)(CO)5]. Characterization of the complex by 1H and 31P NMR as well as IR spectroscopy are presented. The structure of NiFe(TPAP)(CO)5 reveals three terminally bound CO mol-ecules on Fe0, two bridging CO mol-ecules between Ni0 and Fe0, and TPAP coordinated to Ni0. The Ni-Fe bond length is 2.4828 (4) Å, similar to that of the reduced form of the active site of NiFe hydrogenase (∼2.5 Å). Additionally, a proximal pendant base from one of the non-coordinating pyridine groups of TPAP is also present. Although involvement of a pendant base has been cited in the mechanism of NiFe hydrogenase, this moiety has yet to be incorporated in a structurally characterized synthetic mimic with key structural motifs (terminally bound CO or CN ligands on Fe). Thus, the title complex NiFe(TPAP)(CO)5 is an unique synthetic model for NiFe hydrogenase. In the crystal, the complex mol-ecules are linked by C-H⋯O hydrogen bonds, forming undulating layers parallel to (100). Within the layers, there are offset π-π [inter-centroid distance = 3.2739 (5) Å] and C-H⋯π inter-actions present. The layers are linked by further C-H⋯π inter-actions, forming a supra-molecular framework

Installation of internal electric fields by non-redox active cations in transition metal complexes.

Local electric fields contribute to the high selectivity and catalytic activity in enzyme active sites and confined reaction centers in zeolites by modifying the relative energy of transition states, intermediates and/or products. Proximal charged functionalities can generate equivalent internal electric fields in molecular systems but the magnitude of their effect and impact on electronic structure has been minimally explored. To generate quantitative insight into installing internal fields in synthetic systems, we report an experimental and computational study using transition metal (M1) Schiff base complexes functionalized with a crown ether unit containing a mono- or dicationic alkali or alkaline earth metal ion (M2). The synthesis and characterization of the complexes M1 = Ni(ii) and M2 = Na+ or Ba2+ are reported. The electronic absorption spectra and density functional theory (DFT) calculations establish that the cations generate a robust electric field at the metal, which stabilizes the Ni-based molecular orbitals without significantly changing their relative energies. The stabilization is also reflected in the experimental Ni(ii/i) reduction potentials, which are shifted 0.12 V and 0.34 V positive for M2 = Na+ and Ba2+, respectively, compared to a complex lacking a proximal cation. To compare with the cationic Ni complexes, we also synthesized a series of Ni(salen) complexes modified in the 5' position with electron-donating and -withdrawing functionalities (-CF3, -Cl, -H, -tBu, and -OCH3). Data from this series of compounds provides further evidence that the reduction potential shifts observed in the cationic complexes are not due to inductive ligand effects. DFT studies were also performed on the previously reported monocationic and dicatonic Fe(ii)(CH3CN) and Fe(iii)Cl analogues of this system to analyze the impact of an anionic chloride on the electrostatic potential and electronic structure of the Fe site

A new gene for regulation of epidermal cell production in Arabidopsis cotyledons

Plant Biology, Ecology and Evolutio

A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges

The software engineering community recently has witnessed widespread deployment of AI programming assistants, such as GitHub Copilot. However, in practice, developers do not accept AI programming assistants' initial suggestions at a high frequency. This leaves a number of open questions related to the usability of these tools. To understand developers' practices while using these tools and the important usability challenges they face, we administered a survey to a large population of developers and received responses from a diverse set of 410 developers. Through a mix of qualitative and quantitative analyses, we found that developers are most motivated to use AI programming assistants because they help developers reduce key-strokes, finish programming tasks quickly, and recall syntax, but resonate less with using them to help brainstorm potential solutions. We also found the most important reasons why developers do not use these tools are because these tools do not output code that addresses certain functional or non-functional requirements and because developers have trouble controlling the tool to generate the desired output. Our findings have implications for both creators and users of AI programming assistants, such as designing minimal cognitive effort interactions with these tools to reduce distractions for users while they are programming.Comment: Accepted to ICSE'2

Kinesin-II is required for axonal transport of choline acetyltransferase in Drosophila

KLP64D and KLP68D are members of the kinesin-II family of proteins in Drosophila. Immunostaining for KLP68D and ribonucleic acid in situ hybridization for KLP64D demonstrated their preferential expression in cholinergic neurons. KLP68D was also found to accumulate in cholinergic neurons in axonal obstructions caused by the loss of kinesin light chain. Mutations in the KLP64D gene cause uncoordinated sluggish movement and death, and reduce transport of choline acetyltransferase from cell bodies to the synapse. The inviability of KLP64D mutations can be rescued by expression of mammalian KIF3A. Together, these data suggest that kinesin-II is required for the axonal transport of a soluble enzyme, choline acetyltransferase. in a specific subset of neurons in Drosophila. Furthermore, the data lead to the conclusion that the cargo transport requirements of different classes of neurons may lead to upregulation of specific pathways of axonal transport

Cats Teach Stats: An Interactive Module to Help Reduce Anxiety When Learning Statistics in Biology

Tools that teach quantitative skills and foster positive student attitudes toward these skills are important in biology curricula. Math and statistics anxiety is common and can interfere with student learning in biology courses. We describe a new framework for alleviating this anxiety. In our module, students watch a cute internet cat video, which inspires them to ask scientific questions about animal behavior and collect, analyze, and interpret data. We developed two freely available interactive tools to implement our module. We successfully implemented these tools with undergraduate students at two institutions. Based on this experience, we provide ideas for extension along with assessment

GRAPHICAL MODELS FOR HIGH DIMENSIONAL DATA WITH GENOMIC APPLICATIONS

Many previous studies have demonstrated that gene expression or other types of -omic features collected from patients can help disease diagnosis or treatment selection. For example, a few recent studies demonstrated that gene expression data collected from cancer cell lines are highly informative to predict cancer drug sensitivity (Garnett et al., 2012; Barretina et al., 2012; Chen et al., 2016b). This is partly because many cancer drugs are targeted drugs that perturb a particular mutated gene or protein, and thus having that mutation, or observing the consequence of such mutation in gene expression data, is highly informative for drug sensitivity prediction. Such systematic studies of drug sensitivities require giving different drugs in a series of doses to the same cell line, which is obviously not possible for the human studies. More sophisticated methods are needed to estimate potential effects of cancer drugs based on observational data. Since the effect of a targeted cancer drug can be considered as an intervention to the molecular system of cancer cells, a directed graphical model for gene-gene associations is a natural choice to model the molecular system and to study the consequence of such interventions. In this dissertation, we develop new statistical methods to estimate DAGs using high dimensional -omic data under two scenarios: i) with a model-free approach and ii) single cell RNA-seq data (scRNAseq). In the 1st chapter, we will give a brief introduction to graphical models, the various statistical characterizations of graphical models and the most current approaches to estimate graph structures. Then, we will review the scRNAseq data and current approaches to analyze scRNAseq data. Next, in Chapter 2, we propose a model-free method to estimate graphical models in two steps. The first step uses a model-free variable selection method based on the principles of sufficient dimension reduction. Then, the second step uses a non-parametric conditional independence testing method which utilizes embeddings of the conditional spaces into reproducing kernel Hilbert spaces. We will review some theoretical background in order to establish the asymptotic graphical model estimation consistency of this two-step approach. We examine its performance in simulations and TCGA breast cancer data, where we find significant improvements from current methods that require strong model assumptions. In Chapter 3, we propose a graphical model algorithm to analyze scRNAseq data. Similar to the previous algorithm, we create a two-step estimation method which utilizes a joint penalized zero-inflation model. We assess its performance and drawbacks in simulations. Then, we examined its utility when applied after clustering to a sample of 68k peripheral blood mononuclear cells with multiple subpopulations.Doctor of Philosoph