Synthesis and reactivity of a nickel(ii) thioperoxide complex: demonstration of sulfide-mediated N2O reduction.

The thiohyponitrite ([SNNO]2-) complex, [K(18-crown-6)][L tBuNiII(κ2-SNNO)] (L tBu = {(2,6-iPr2C6H3)NC( t Bu)}2CH), extrudes N2 under mild heating to yield [K(18-crown-6)][L tBuNiII(η2-SO)] (1), along with minor products [K(18-crown-6)][L tBuNiII(η2-OSSO)] (2) and [K(18-crown-6)][L tBuNiII(η2-S2)] (3). Subsequent reaction of 1 with carbon monoxide (CO) results in the formation of [K(18-crown-6)][L tBuNiII(η2-SCO)] (4), [K(18-crown-6)][L tBuNiII(S,O:κ2-SCO2)] (5), [K(18-crown-6)][L tBuNiII(κ2-CO3)] (6), carbonyl sulfide (COS) (7), and [K(18-crown-6)][L tBuNiII(S2CO)] (8). To rationalize the formation of these products we propose that 1 first reacts with CO to form [K(18-crown-6)][L tBuNiII(S)] (I) and CO2, via O-atom abstraction. Subsequently, complex I reacts with CO or CO2 to form 4 and 5, respectively. Similarly, the formation of complex 6 and COS can be rationalized by the reaction of 1 with CO2 to form a putative Ni(ii) monothiopercarbonate, [K(18-crown-6)][L tBuNiII(κ2-SOCO2)] (11). The Ni(ii) monothiopercarbonate subsequently transfers a S-atom to CO to form COS and [K(18-crown-6)][L tBuNiII(κ2-CO3)] (6). Finally, the formation of 8 can be rationalized by the reaction of COS with I. Critically, the observation of complexes 4 and 5 in the reaction mixture reveals the stepwise conversion of [K(18-crown-6)][L tBuNiII(κ2-SNNO)] to 1 and then I, which represents the formal reduction of N2O by CO

Synthesis of a terminal Ce(iv) oxo complex by photolysis of a Ce(iii) nitrate complex.

Reaction of [Ce(NR2)3] (R = SiMe3) with LiNO3 in THF, in the presence of 2,2,2-cryptand, results in the formation of the Ce(iii) "ate" complex, [Li(2,2,2-cryptand)][Ce(κ2-O2NO)(NR2)3] (1) in 38% yield. Photolysis of 1 at 380 nm affords [Li(2,2,2-cryptand)][Ce(O)(NR2)3] (2), in 33% isolated yield after reaction work-up. Complex 2 is the first reported example of a Ce(iv) oxo complex where the oxo ligand is not supported by hydrogen bonding or alkali metal coordination. Also formed during photolysis are [Li(2,2,2-cryptand)]2[(μ3-O){Ce(μ-O)(NR2)2}3] (3) and [Li(2,2,2-cryptand)][Ce(OSiMe3)(NR2)3] (4). Their identities were confirmed by X-ray crystallography. Complex 4 can also be prepared via reaction of [Ce(NR2)3] with LiOSiMe3 in THF, in the presence of 2,2,2-cryptand. When synthesized in this fashion, 4 can be isolated in 47% yield. To rationalize the presence of 2, 3, and 4 in the reaction mixture, we propose that photolysis of 1 first generates 2 and NO2, via homolytic cleavage of the N-O bond in its nitrate co-ligand. Complex 2 then undergoes decomposition via two separate routes: (1) ligand scrambling and oligomerization to form 3; and, (2) abstraction of a trimethylsilyl cation to form a transient Ce(iv) silyloxide, [CeIV(OSiMe3)(NR2)3], followed by 1e- reduction to form 4. Alternatively, complex 4 could form directly via ·SiMe3 abstraction by 2

Use of 15N NMR spectroscopy to probe covalency in a thorium nitride.

Reaction of the thorium metallacycle, [Th{N(R)(SiMe2)CH2}(NR2)2] (R = SiMe3) with 1 equiv. of NaNH2 in THF, in the presence of 18-crown-6, results in formation of the bridged thorium nitride complex, [Na(18-crown-6)(Et2O)][(R2N)3Th(μ-N)(Th(NR2)3] ([Na][1]), which can be isolated in 66% yield after work-up. Complex [Na][1] is the first isolable molecular thorium nitride complex. Mechanistic studies suggest that the first step of the reaction is deprotonation of [Th{N(R)(SiMe2)CH2}(NR2)2] by NaNH2, which results in formation of the thorium bis(metallacycle) complex, [Na(THF) x ][Th{N(R)(SiMe2CH2)}2(NR2)], and NH3. NH3 then reacts with unreacted [Th{N(R)(SiMe2)CH2}(NR2)2], forming [Th(NR2)3(NH2)] (2), which protonates [Na(THF) x ][Th{N(R)(SiMe2CH2)}2(NR2)] to give [Na][1]. Consistent with hypothesis, addition of excess NH3 to a THF solution of [Th{N(R)(SiMe2)CH2}(NR2)2] results in formation of [Th(NR2)3(NH2)] (2), which can be isolated in 51% yield after work-up. Furthermore, reaction of [K(DME)][Th{N(R)(SiMe2CH2)}2(NR2)] with 2, in THF-d 8, results in clean formation of [K][1], according to 1H NMR spectroscopy. The electronic structures of [1]- and 2 were investigated by 15N NMR spectroscopy and DFT calculations. This analysis reveals that the Th-Nnitride bond in [1]- features more covalency and a greater degree of bond multiplicity than the Th-NH2 bond in 2. Similarly, our analysis indicates a greater degree of covalency in [1]- vs. comparable thorium imido and oxo complexes

Energy Consumption and Economic Growth: A Trivariate Framework of South Africa

The study investigates the causal relationship between energy consumption and economic growth in South Africa, covering the period of 1980-2014. In a trivariate framework which includes electricity and inflation as additional variables by applying the Autoregressive Distributed Lag (ARDL) integration method. First unit root test was employed; results indicated that all variables were non-stationary at the level and stationary at their first differences, using the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP). The results show a long-run relationship among the variables using the ARDL integration approach. The Granger causality test indicates a unidirectional running from inflation to economic growth, which supports the growth hypothesis as documented in the literature and there was no causality between electricity consumption and economic growth, supporting the neutrality hypothesis. Any policies concerning energy consumption should be re-evaluated to confirm that it will not disturb economic growth. Keywords: ARDL, Economic Growth, Energy Consumption, Granger Causality, and South Africa. DOI: 10.7176/JESD/10-8-12 Publication date: April 30th 201

Enhancing clinical concept extraction with distributional semantics

AbstractExtracting concepts (such as drugs, symptoms, and diagnoses) from clinical narratives constitutes a basic enabling technology to unlock the knowledge within and support more advanced reasoning applications such as diagnosis explanation, disease progression modeling, and intelligent analysis of the effectiveness of treatment. The recent release of annotated training sets of de-identified clinical narratives has contributed to the development and refinement of concept extraction methods. However, as the annotation process is labor-intensive, training data are necessarily limited in the concepts and concept patterns covered, which impacts the performance of supervised machine learning applications trained with these data. This paper proposes an approach to minimize this limitation by combining supervised machine learning with empirical learning of semantic relatedness from the distribution of the relevant words in additional unannotated text.The approach uses a sequential discriminative classifier (Conditional Random Fields) to extract the mentions of medical problems, treatments and tests from clinical narratives. It takes advantage of all Medline abstracts indexed as being of the publication type “clinical trials” to estimate the relatedness between words in the i2b2/VA training and testing corpora. In addition to the traditional features such as dictionary matching, pattern matching and part-of-speech tags, we also used as a feature words that appear in similar contexts to the word in question (that is, words that have a similar vector representation measured with the commonly used cosine metric, where vector representations are derived using methods of distributional semantics). To the best of our knowledge, this is the first effort exploring the use of distributional semantics, the semantics derived empirically from unannotated text often using vector space models, for a sequence classification task such as concept extraction. Therefore, we first experimented with different sliding window models and found the model with parameters that led to best performance in a preliminary sequence labeling task.The evaluation of this approach, performed against the i2b2/VA concept extraction corpus, showed that incorporating features based on the distribution of words across a large unannotated corpus significantly aids concept extraction. Compared to a supervised-only approach as a baseline, the micro-averaged F-score for exact match increased from 80.3% to 82.3% and the micro-averaged F-score based on inexact match increased from 89.7% to 91.3%. These improvements are highly significant according to the bootstrap resampling method and also considering the performance of other systems. Thus, distributional semantic features significantly improve the performance of concept extraction from clinical narratives by taking advantage of word distribution information obtained from unannotated data

Guest editors' introduction to the special section on learning with Shared information for computer vision and multimedia analysis

The twelve papers in this special section focus on learning systems with shared information for computer vision and multimedia communication analysis. In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes containing a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with shared information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different levels of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems

Synthesis, structure and bonding of hexaphenyl thorium(IV): observation of a non-octahedral structure

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.We report herein the synthesis of the first structurally characterized homoleptic actinide aryl complexes, [Li(DME)(3)](2)[Th(C6H5)(6)] (1) and [Li(THF)(12-crown-4)](2)[Th(C6H5)(6)] (2), which feature an anion possessing a regular octahedral (1) or a severely distorted octahedral (2) geometry. The solid-state structure of 2 suggests the presence of pseudo-agostic ortho C-H center dot center dot center dot Th interactions, which arise from sigma(C-H) -> Th(5f) donation. The non-octahedral structure is also favoured in solution at low temperatures.DFG, EXC 314, Unifying Concepts in Catalysi