Improved Synthesis of the Antitubercular Agent SQ109

We presented here an improved procedure for the preparation of the promising anti-tubercular drug SQ109 (10) which is currently in phase Ib/III of clinical trials against Mycobacterium tuberculosis. We investigated and tested the literature synthetic procedure that enables the development of structure-activity relationships and reported the observed inconsistencies as well as presented improvements or novelties for the more efficient preparation of SQ109 (10). Most significantly we applied a novel reduction step of the aminoamide precursor using Me3SiCl/LiAlH4 under mild conditions in the synthesis of SQ109 (10). These findings are important for research groups investigating the efficacy of this drug and analogues in academia and industry.

NOx Reduction Pathways during LNT Operation over Ceria Containing Catalysts: Effect of Copper Presence and Barium Content

Ceria-based catalysts, with Cu in substitution of noble metals, were studied in a vertical microreactor system under isothermal conditions, where NOx was previously stored, followed by the reduction step conducted under H2. The possible remaining ad-NOx species after the reduction stage, were investigated by Temperature Programmed Desorption in He. In situ DRIFTS was used as a complementary technique for the analysis of the surface species formation/transformation on the catalysts’ surface. Catalysts containing both Ba and Cu were found to be selective in the NOx reduction, producing N2 and minor amounts of NH3 during the reduction step, as well as NO. The different ceria-based formulations (containing copper and/or barium) were prepared and tested at two different temperatures in the NOx reduction (NSR) processes. Their catalytic activities were analyzed in terms of their compositions and have been useful in the elucidation of the possible origin and relevant pathways for NOx reduction product formation, which seems to involve the oxygen vacancies of the ceria-based materials (whose generation seems to be promoted by copper) during the rich step. The scope of this work involves an interdisciplinary study of the impact that catalysts’ formulations (noble metal-free) have on their LNT performance under simulated conditions, thus covering aspects of Materials Science and Chemical Engineering in a highly applied context, related to the development of control strategies for hybrid powertrains and/or the reduction of the impact of cold-start emissions.

Microbiome Preprocessing Machine Learning Pipeline

Background16S sequencing results are often used for Machine Learning (ML) tasks. 16S gene sequences are represented as feature counts, which are associated with taxonomic representation. Raw feature counts may not be the optimal representation for ML.MethodsWe checked multiple preprocessing steps and tested the optimal combination for 16S sequencing-based classification tasks. We computed the contribution of each step to the accuracy as measured by the Area Under Curve (AUC) of the classification.ResultsWe show that the log of the feature counts is much more informative than the relative counts. We further show that merging features associated with the same taxonomy at a given level, through a dimension reduction step for each group of bacteria improves the AUC. Finally, we show that z-scoring has a very limited effect on the results.ConclusionsThe prepossessing of microbiome 16S data is crucial for optimal microbiome based Machine Learning. These preprocessing steps are integrated into the MIPMLP - Microbiome Preprocessing Machine Learning Pipeline, which is available as a stand-alone version at: https://github.com/louzounlab/microbiome/tree/master/Preprocess or as a service at http://mip-mlp.math.biu.ac.il/Home Both contain the code, and standard test sets.

A Bifunctional Copper Catalyst Enables Ester Reduction with H2: Expanding the Reactivity Space of Nucleophilic Copper Hydrides

Employing a bifunctional catalyst based on a copper(I)/NHC complex and a guanidine organocatalyst, catalytic ester reductions to alcohols with H₂ as terminal reducing agent are facilitated. The approach taken here enables the simul-taneous activation of esters through hydrogen bonding and formation of nucleophilic copper(I) hydrides from H₂, resulting in a catalytic hydride transfer to esters. The reduction step is further facilitated by a proton shuttle mediated by the guani-dinium subunit. This bifunctional approach to ester reductions for the first time shifts the reactivity of generally considered “soft” copper(I) hydrides to previously unreactive “hard” ester electrophiles and paves the way for a replacement of stoichi-ometric reducing agents by a catalyst and H₂.<br>

A Bifunctional Copper Catalyst Enables Ester Reduction with H2: Expanding the Reactivity Space of Nucleophilic Copper Hydrides

Employing a bifunctional catalyst based on a copper(I)/NHC complex and a guanidine organocatalyst, catalytic ester reductions to alcohols with H₂ as terminal reducing agent are facilitated. The approach taken here enables the simul-taneous activation of esters through hydrogen bonding and formation of nucleophilic copper(I) hydrides from H₂, resulting in a catalytic hydride transfer to esters. The reduction step is further facilitated by a proton shuttle mediated by the guani-dinium subunit. This bifunctional approach to ester reductions for the first time shifts the reactivity of generally considered “soft” copper(I) hydrides to previously unreactive “hard” ester electrophiles and paves the way for a replacement of stoichi-ometric reducing agents by a catalyst and H₂.<br>

Effect of the oxidation state and morphology of SnOx-based electrocatalysts on the CO2 reduction reaction

CO2 electrochemical reduction reaction (CO2RR) is an attractive strategy for closing the anthropogenic carbon cycle and storing intermittent renewable energy. Tin-based electrocatalysts exhibit remarkable properties for reducing CO2 into HCOOH. However, the effects of morphology and oxidation state of tin-based electrocatalysts on the performance of CO2 reduction have not been well-described. We evaluate the oxidation state and particle size of SnOx for CO2 reduction. SnOx was effective for converting CO2 into formic acid, reaching a maximum selectivity of 69%. The SnO exhibited high activity for CO2RR compared to SnO2 electrocatalysts. A pre-reduction step of a SnO2 electrocatalyst increased its CO2 reduction performance, confirming that Sn2+ is more active than Sn4+ sites. The microsized SnO2 is more effective for converting CO2 into formic acid than nanosized SnO2, likely due to the impurities of nanosized SnO2. We illuminated the role played by both SnOx particle size and oxidation state on CO2RR performance. Graphic abstract

The Redox-Active Tyrosine Is Essential for Proton Pumping in Cytochrome c Oxidase

Cellular respiration involves electron transport via a number of enzyme complexes to the terminal Cytochrome c oxidase (CcO), in which molecular oxygen is reduced to water. The free energy released in the reduction process is used to establish a transmembrane electrochemical gradient, via two processes, both corresponding to charge transport across the membrane in which the enzymes are embedded. First, the reduction chemistry occurring in the active site of CcO is electrogenic, which means that the electrons and protons are delivered from opposite sides of the membrane. Second, the exergonic chemistry is coupled to translocation of protons across the entire membrane, referred to as proton pumping. In the largest subfamily of the CcO enzymes, the A-family, one proton is pumped for every electron needed for the chemistry, making the energy conservation particularly efficient. In the present study, hybrid density functional calculations are performed on a model of the A-family CcOs. The calculations show that the redox-active tyrosine, conserved in all types of CcOs, plays an essential role for the energy conservation. Based on the calculations a reaction mechanism is suggested involving a tyrosyl radical (possibly mixed with tyrosinate character) in all reduction steps. The result is that the free energy released in each reduction step is large enough to allow proton pumping in all reduction steps without prohibitively high barriers when the gradient is present. Furthermore, the unprotonated tyrosine provides a mechanism for coupling the uptake of two protons per electron in every reduction step, i.e. for a secure proton pumping.

A Machine Learning Approach to Evaluate the Performance of Rural Bank

In the current performance evaluation works of commercial banks, most of the researches only focus on the relationship between a single characteristic and performance and lack a comprehensive analysis of characteristics. On the other hand, they mainly focus on causal inference and lack systematic quantitative conclusions from the perspective of prediction. This paper is the first to comprehensively investigate the predictability of multidimensional features on commercial bank performance using boosting regression tree. The dimensionality in the financial-related fields is relatively high. There are not only observable price data, financial fundamentals data, etc., but also many unobservable undisclosed data and undisclosed events; more sources of income cannot be explained by existing models. Aiming at the characteristics of commercial bank data, this paper proposes an adaptively reduced step size gradient boosting regression tree algorithm for bank performance evaluation. In this method, a random subsample sampling is performed before training each regression tree. The adaptive reduction step size is used to replace the reduction step size setting of the original algorithm, which overcomes the shortcomings of low accuracy and poor generalization ability of the existing regression decision tree model. Compared to the BIRCH algorithm for classification of existing data, our proposed gradient boosting regression tree algorithm with adaptively reduced step size obtains better classification results. This paper empirically uses data from rural banks in 30 provinces in China to classify the different characteristics of rural banks’ performance in order to better evaluate their performance.

Task-Driven Learned Hyperspectral Data Reduction Using End-to-End Supervised Deep Learning

An important challenge in hyperspectral imaging tasks is to cope with the large number of spectral bins. Common spectral data reduction methods do not take prior knowledge about the task into account. Consequently, sparsely occurring features that may be essential for the imaging task may not be preserved in the data reduction step. Convolutional neural network (CNN) approaches are capable of learning the specific features relevant to the particular imaging task, but applying them directly to the spectral input data is constrained by the computational efficiency. We propose a novel supervised deep learning approach for combining data reduction and image analysis in an end-to-end architecture. In our approach, the neural network component that performs the reduction is trained such that image features most relevant for the task are preserved in the reduction step. Results for two convolutional neural network architectures and two types of generated datasets show that the proposed Data Reduction CNN (DRCNN) approach can produce more accurate results than existing popular data reduction methods, and can be used in a wide range of problem settings. The integration of knowledge about the task allows for more image compression and higher accuracies compared to standard data reduction methods.

Unravelling the Structural Modification (Meso-Nano-) of Cu/ZnO-Al2O3 Catalysts for Methanol Synthesis by the Residual NaNO3 in Hydroxycarbonate Precursors

The effects of residual NaNO3 on the modification of Cu/ZnO-Al2O3 catalysts have been extensively documented, but the modification mechanism is so far unclear. This work studies in detail the influence of the residual sodium nitrate present in the hydroxycarbonate precursors on their decomposition during calcination and how it affects to the formation and configuration of the final active sites of the Cu/ZnO-Al2O3 catalysts. Different samples with varying sodium content after washing (from 0.01 to 7.3 wt%) were prepared and studied in detail after calcination and reduction steps. The results of this work demonstrated that NaNO3 affects the decomposition mechanism of the hydroxycarbonate precursors during calcination and produces its decarbonation at low temperature. The enhancement of the decarbonation by NaNO3 leads to segregation and crystallization of CuO and ZnO with loss of mesostructure and surface area in the calcined catalysts. The loss of mesostructure in calcined catalysts affects the subsequent reduction step, decreasing the reducibility and damaging the nanostructure of the reduced catalysts forming large Cu particles in poor contact with ZnOx that results in a significant decrease in the intrinsic activity of the copper active sites for methanol synthesis.