Search

In this study, type I interferon (IFN-α ve IFN-β) levels, viral load levels, the relationship between viral load and interferons, and the relationship between interferon levels and disease severity and mortality in patients with Crimean-Congo Haemorrhagic Fever (CCHF) were investigated. 100 patients diagnosed with CCHF between April-2010 and September-2011 and 74 healthy individuals were selected as control group. IFN-α ve in patient sera was analysed by IFN-β seviyeleri ELISA method and viral load levels were analysed by Real-Time PCR method. Individuals in the patient group were classified as severe (group1) and mild (group2) patients according to the criteria defined by Swanepoel et al. During the follow-up, 25 of the patients in the severe group died. Patients in the severe group who died were classified as group1a and patients who survived were classified as group1b. INF-α ve β düzeyleri CCHF patients in the control group, group1 patients in group2 and control group was found to be significantly higher than the group (p0.05). Viral load levels in group1a were statistically significant compared to group1b and group2 (p0.05). The cut off value for viral load was 8445500 copies/ml, AUC was 0.870 and statistically significant (p

Background: Adolescence heralds the onset of considerable psychopathology, which may be conceptualized as an emergence of altered covariation between symptoms and brain measures. Multivariate methods can detect such modes of covariation or latent dimensions, but none specifically relating to psychopathology have yet been found using population-level structural brain data. Using voxelwise (instead of parcellated) brain data may strengthen latent dimensions' brain-psychosocial relationships, but this creates computational challenges., Methods: We obtained voxelwise gray matter density and psychosocial variables from the baseline (ages 9-10 years) Adolescent Brain Cognitive Development (ABCD) Study cohort (N = 11,288) and employed a state-of-the-art segmentation method, sparse partial least squares, and a rigorous machine learning framework to prevent overfitting., Results: We found 6 latent dimensions, 4 of which pertain specifically to mental health. The mental health dimensions were related to overeating, anorexia/internalizing, oppositional symptoms (all ps < .002) and attention-deficit/hyperactivity disorder symptoms (p = .03). Attention-deficit/hyperactivity disorder was related to increased and internalizing symptoms related to decreased gray matter density in dopaminergic and serotonergic midbrain areas, whereas oppositional symptoms were related to increased gray matter in a noradrenergic nucleus. Internalizing symptoms were related to increased and oppositional symptoms to reduced gray matter density in the insular, cingulate, and auditory cortices. Striatal regions featured strongly, with reduced caudate nucleus gray matter in attention-deficit/hyperactivity disorder and reduced putamen gray matter in oppositional/conduct problems. Voxelwise gray matter density generated stronger brain-psychosocial correlations than brain parcellations., Conclusions: Voxelwise brain data strengthen latent dimensions of brain-psychosocial covariation, and sparse multivariate methods increase their psychopathological specificity. Internalizing and externalizing symptoms are associated with opposite gray matter changes in similar cortical and subcortical areas., (Copyright © 2024 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Although Li-air rechargeable batteries offer higher energy densities than lithium-ion batteries, the insulating Li 2 O 2 formed during discharge hinders rapid, efficient re-charging. Redox mediators are used to facilitate Li 2 O 2 oxidation; however, fast kinetics at a low charging voltage are necessary for practical applications and are yet to be achieved. We investigate the mechanism of Li 2 O 2 oxidation by redox mediators. The rate-limiting step is the outer-sphere one-electron oxidation of Li 2 O 2 to LiO 2 , which follows Marcus theory. The second step is dominated by LiO 2 disproportionation, forming mostly triplet-state O 2 . The yield of singlet-state O 2 depends on the redox potential of the mediator in a way that does not correlate with electrolyte degradation, in contrast to earlier views. Our mechanistic understanding explains why current low-voltage mediators (<+3.3 V) fail to deliver high rates (the maximum rate is at +3.74 V) and suggests important mediator design strategies to deliver sufficiently high rates for fast charging at potentials closer to the thermodynamic potential of Li 2 O 2 oxidation (+2.96 V)., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Heterogeneous biocatalytic hydrogenation is an attractive strategy for clean, enantioselective C[double bond, length as m-dash]X reduction. This approach relies on enzymes powered by H 2 -driven NADH recycling. Commercially available carbon-supported metal (metal/C) catalysts are investigated here for direct H 2 -driven NAD + reduction. Selected metal/C catalysts are then used for H 2 oxidation with electrons transferred via the conductive carbon support material to an adsorbed enzyme for NAD + reduction. These chemo-bio catalysts show improved activity and selectivity for generating bioactive NADH under ambient reaction conditions compared to metal/C catalysts. The metal/C catalysts and carbon support materials (all activated carbon or carbon black) are characterised to probe which properties potentially influence catalyst activity. The optimised chemo-bio catalysts are then used to supply NADH to an alcohol dehydrogenase for enantioselective (>99% ee) ketone reductions, leading to high cofactor turnover numbers and Pd and NAD + reductase activities of 441 h -1 and 2347 h -1 , respectively. This method demonstrates a new way of combining chemo- and biocatalysis on carbon supports, highlighted here for selective hydrogenation reactions., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

Learning feature embeddings for pattern recognition is a relevant task for many applications. Deep learning methods such as convolutional neural networks can be employed for this assignment with different training strategies: leveraging pre-trained models as baselines; training from scratch with the target dataset; or fine-tuning from the pre-trained model. Although there are separate systems used for learning features from labelled and unlabelled data, there are few models combining all available information. Therefore, in this paper, we present a novel semi-supervised deep network training strategy that comprises a convolutional network and an autoencoder using a joint classification and reconstruction loss function. We show our network improves the learned feature embedding when including the unlabelled data in the training process. The results using the feature embedding obtained by our network achieve better classification accuracy when compared with competing methods, as well as offering good generalisation in the context of transfer learning. Furthermore, the proposed network ensemble and loss function is highly extensible and applicable in many recognition tasks., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

We describe the use of carbon as a versatile support for H 2 -driven redox biocatalysis for NADH-dependent CX bond reductions in batch and flow reactions. In each case, carbon is providing an electronic link between enzymes for H 2 oxidation and reduction of the biological cofactor NAD + , as well as a support for a multi-enzyme biocatalysis system. Carbon nanopowders offer high surface areas for enzyme immobilization and good dispersion in aqueous solution for heterogeneous batch reactions. Difficulties in handling multi-wall carbon nanotubes in aqueous solution are overcome by growing them on quartz tubes to form carbon nanotube column reactors, and we show that these facilitate simple translation of H 2 -driven biocatalysis into flow processes. Using this flow reactor design, high conversions (90%) and total enzyme turnover numbers up to 54,000 could be achieved. Use of an entirely heterogeneous biocatalysis system simplifies recovery and re-use of the enzymes; combined with highly atom-efficient cofactor recycling, this means that high product purity can be achieved. We demonstrate these methods as platform approaches for overcoming challenges with NADH-dependent biocatalysis., (© 2020 Elsevier Inc. All rights reserved.)

In pattern recognition, disagreement between two classifiers regarding the predicted class membership of an observation can be indicative of an anomaly and its nuance. Since, in general, classifiers base their decisions on class a posteriori probabilities, the most natural approach to detecting classifier incongruence is to use divergence. However, existing divergences are not particularly suitable to gauge classifier incongruence. In this paper, we postulate the properties that a divergence measure should satisfy and propose a novel divergence measure, referred to as delta divergence. In contrast to existing measures, it focuses on the dominant (most probable) hypotheses and, thus, reduces the effect of the probability mass distributed over the non dominant hypotheses (clutter). The proposed measure satisfies other important properties, such as symmetry, and independence of classifier confidence. The relationship of the proposed divergence to some baseline measures, and its superiority, is shown experimentally.

LiCoBO 3 could be a promising cathode material given the electronic and ionic conductivity problems are addressed. Here, Mg substitution in LiCoBO 3 is employed to stabilise the structure and improve the electrochemical performance. LiMg 0.1 Co 0.9 BO 3 is synthesised for the first time via sol-gel method and Mg substitution in the structure is verified by X-ray powder diffraction and energy dispersive X-ray analyses. The electrochemical properties are investigated by galvanostatic cycling and cyclic voltammetry tests. The composite electrode with conductive carbon (reduced graphite oxide and carbon black) delivers a first discharge capacity of 32 mA h g -1 within a 4.7-1.7 voltage window at a rate of 10 mA g -1 . The cycling is relatively stable compared to the unsubstituted LiCoBO 3 . Mg substitution may enhance the electrochemical performance of borate-based electrode materials when combined with suitable electrode design techniques., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

We describe the implementation of a system of immobilised enzymes for H 2 -driven NADH recycling coupled to a selective biotransformation to enable H 2 -driven biocatalysis in flow. This approach represents a platform that can be optimised for a wide range of hydrogenation steps and is shown here for enantioselective ketone reduction and reductive amination.

Two-class supervised learning in the context of a classifier ensemble may be formulated as learning an incompletely specified Boolean function, and the associated Walsh coefficients can be estimated without the knowledge of the unspecified patterns. Using an extended version of the Tumer-Ghosh model, the relationship between added classification error and second-order Walsh coefficients is established. In this brief, the ensemble is composed of multilayer perceptron base classifiers, with the number of hidden nodes and epochs systematically varied. Experiments demonstrate that the mean second-order coefficients peak at the same number of training epochs as ensemble test error reaches a minimum.