Structures and Spin States of Crystalline [Fe(NCS)2L2] and [FeL3]2+ Complexes (L = an Annelated 1,10-Phenanthroline Derivative)

The phase behaviour and spin states of [Fe(NCS)2(dpq)2] (1; dpq = dipyrido[3,2-f:2′,3′-h]quinoxaline), [Fe(NCS)2(dppz)2] (2; dppz = dipyrido[3,2-a:2′3′-c]phenazine) and [Fe(NCS)2(dppn)2] (3; dppn = dipyrido[3,2-a:2′3′-c]benzophenazine) have been investigated. Solvent-free 1 and 2 are isostructural and low-spin in the crystalline state, in contrast to previously published 2·py (py = pyridine) which exhibits a hysteretic spin-crossover (SCO) transition near 140 K. The inactivity of 1 and 2 towards SCO may relate to their more crowded intermolecular lattice environment, particularly two very short intermolecular anion⋯π contacts involving the NCS− ligands. Two solvate phases of 1 are also described, including 1·2py which undergoes gradual SCO with T½ca. 188 K. Bulk samples of 2 and 3 are predominantly low-spin and isostructural with the crystals of 2 by powder diffraction, but bulk samples of 1 contain an extra phase that exhibits hysteretic SCO, but was not crystallographically characterised. Crystal structures of low-spin [Fe(dppz)3][ClO4]2 (4) and a solvate of [Fe(dppn)3][BF4]2 (5) are also described, which are the first homoleptic complexes of these ligands to be crystallographically characterised

SalaciaML-2-Arctic — a deep learning quality control algorithm for Arctic Ocean temperature and salinity data

We have extended a classical quality control (QC) algorithm by integrating a deep learning neural network, resulting in SalaciaML-2-Arctic, a tool for automated QC of Arctic Ocean temperature and salinity profile data. The neural network component was trained on the Unified Database for Arctic and Subarctic Hydrography (UDASH), which has been quality-controlled and labeled by expert oceanographers. SalaciaML-2-Arctic successfully reproduces human expertise by correcting misclassifications made by the classical algorithm, reducing False Negatives (samples incorrectly classified as “bad”) by 96% for temperature and 99% for salinity. When used in combination with a visual post-QC by human experts, it achieves a workload reduction of approximately 60% for temperature and 85% for salinity. All code and data required to reproduce the analysis or apply the method to other datasets are openly available via PANGAEA and GitHub. Moreover, SalaciaML-2-Arctic is accessible as a browser-based application at https://mvre.autoqc.cloud.awi.de, enabling its use without software installation or programming knowledge

An investigation on 3-acetyl-7-methoxy-coumarin Schiff bases and their Ru(II) metallates with potent antiproliferative activity and enhanced LDH and NO release†

New cyclometallated ruthenium(ii) complexes of 3-acetyl-7-methoxycoumarin-4N-substituted thiosemicarbazones were synthesized and characterized by analytical and spectral techniques. The crystal structures of the ligands H2L1-3 and complexes (1, 2 and 4) were confirmed by X-ray crystallography. The analysis showed that the ligands have undergone C-H activation at the C(4) carbon of the pyrone ring and acted in a tridentate fashion by binding through C, N and S atoms. CT-DNA and protein (BSA/HSA) binding studies were carried out to analyze their interaction with biomolecules. Good binding affinity with DNA was observed with intercalative binding mode, which was further confirmed by EB displacement and viscosity measurement studies. The quenching mechanism with BSA/HSA was found to be static. Three dimensional (3D) fluorescence measurements were carried out to validate the micro environmental changes in the serum albumins. Their antioxidant propensity and antimicrobial study insisted that the compounds displayed good spectrum of activity. Evaluation of their anticancer potential against MCF-7 (human breast cancer) and A549 (human lung carcinoma) cell lines revealed that the complexes exhibited better activity than the ligands and cisplatin. Further, the results of LDH and NO release assays supported the cytotoxic nature of the compounds. The non-toxic nature of the compounds was established by testing against the non-cancerous cell line HaCaT (human normal keratinocyte). © 2018 The Royal Society of Chemistry

CSU-Net: semantická segmentace kontur pro vylepšení metod detekce lidských hlav

The computer vision community has made tremendous progress in solving a variety of semantic image understanding tasks, such as classification and segmentation. With the advancement of imaging technology and hardware, image semantic segmentation, through the use of deep learning, is among the most common topics which have been worked on in the last decade. However, image semantic segmentation suffers from several drawbacks such as insufficient detection of object boundaries. In this study, we present a new convolutional neural network architecture called CSU-Net that aims to self-enhance the results of semantic segmentation. The proposed model consists of two strongly concatenated encoder-decoder blocks. With this design, we reduced requirements on computing power and memory size to decrease costs and increase the training/prediction speed. This study also demonstrates the advantage of the proposed system for small training data sets. The proposed approach has been implemented on our private dataset, as well as on a publicly available dataset. A comparative analysis was carried out with four popular segmentation models and three other recently introduced architectures to show the efficiency of the proposed system. CSU-Net outperformed the other competing neural networks that we considered for the comparative study. As an example, it succeeded in improving the traditional U-Net result by approximately 50% in mean Intersection over Union (mIoU) for both tested datasets. Based on our experience, the CSU-Net can improve results of semantic segmentation in many applications.Komunita zabývající se počítačovým viděním dosáhla obrovského pokroku při řešení různých sémantických úloh porozumění obrazu, jako je klasifikace a segmentace. S rozvojem zobrazovacích technologií a hardwaru patří sémantická segmentace obrazu pomocí hlubokého učení k nejčastějším tématům, na kterých se v posledním desetiletí pracuje. Sémantická segmentace obrazu však trpí několika nedostatky, jako je nedostatečná detekce hranic objektů. V této studii představujeme novou architekturu konvoluční neuronové sítě nazvanou CSU-Net, jejímž cílem je samoúčelné zlepšení výsledků sémantické segmentace. Navrhovaný model se skládá ze dvou silně provázaných bloků kodér-dekodér. Díky této konstrukci jsme snížili požadavky na výpočetní výkon a velikost paměti, abychom snížili náklady a zvýšili rychlost trénování/předpovídání. Tato studie také ukazuje výhodu navrhovaného systému pro malé soubory trénovacích dat. Navrhovaný přístup byl implementován na naší soukromé datové sadě i na veřejně dostupné datové sadě. Byla provedena srovnávací analýza se čtyřmi populárními segmentačními modely a třemi dalšími nedávno představenými architekturami, aby se ukázala účinnost navrhovaného systému. Síť CSU-Net překonala ostatní konkurenční neuronové sítě, které jsme pro srovnávací studii zvažovali. Jako příklad lze uvést, že se jí podařilo zlepšit výsledek tradiční sítě U-Net přibližně o 50 % v průměrné hodnotě Intersection over Union (mIoU) pro obě testované datové sady. Na základě našich zkušeností může CSU-Net zlepšit výsledky sémantické segmentace v mnoha aplikacích