3-Amino-4,6-dimethyl­thieno[2,3-b]pyridine-2-carbonitrile

The mol­ecule of the title compound, C10H9N3S, is almost planar, with a dihedral angle of 1.38 (4)° between the thio­phene and pyridine rings. In the crystal packing, mol­ecules are linked into layers parallel to the ab plane by inter­molecular N—H⋯N hydrogen bonds and by π⋯π stacking inter­actions involving adjacent pyridine and thio­phene rings with a centroid–centroid distance of 3.537 (3) Å

Preparation and Enhancement of Thermal Conductivity of Heat Transfer Oil-Based MoS 2

The lipophilic MoS2 nanoparticles are synthesized by surface modification with stearic acid (SA). The heat transfer oil-based nanofluids, with the mass fraction of lipophilic nanoparticles varying from 0.25% up to 1.0%, are prepared and their thermal conductivity is determined at temperatures ranging from 40 to 200°C using an apparatus based on the laser flash method. It has been found that the nanofluids have higher thermal conductivity and the thermal conductivity enhancement increased not only with increasing mass fraction of nanoparticles, but also with increasing temperature in the range 40–180°C The results show a 38.7% enhancement of the thermal conductivity of MoS2 nanofluid with only 1.0% mass fraction at 180°C

Towards Better Dermoscopic Image Feature Representation Learning for Melanoma Classification

Deep learning-based melanoma classification with dermoscopic images has recently shown great potential in automatic early-stage melanoma diagnosis. However, limited by the significant data imbalance and obvious extraneous artifacts, i.e., the hair and ruler markings, discriminative feature extraction from dermoscopic images is very challenging. In this study, we seek to resolve these problems respectively towards better representation learning for lesion features. Specifically, a GAN-based data augmentation (GDA) strategy is adapted to generate synthetic melanoma-positive images, in conjunction with the proposed implicit hair denoising (IHD) strategy. Wherein the hair-related representations are implicitly disentangled via an auxiliary classifier network and reversely sent to the melanoma-feature extraction backbone for better melanoma-specific representation learning. Furthermore, to train the IHD module, the hair noises are additionally labeled on the ISIC2020 dataset, making it the first large-scale dermoscopic dataset with annotation of hair-like artifacts. Extensive experiments demonstrate the superiority of the proposed framework as well as the effectiveness of each component. The improved dataset publicly avaliable at https://github.com/kirtsy/DermoscopicDataset.Comment: ICONIP 2021 conferenc

Optimal 1,3-propanediol production: Exploring the trade-off between process yield and feeding rate variation

This paper proposes a new optimal control model for the production of 1,3-propanediol (1,3-PD) viamicrobial fed-batch fermentation. The proposed model is governed by a nonlinear multi stage dynamic system with two modes: feeding mode, in which glycerol and alkali substrates are added continuously to the fermentor; and batch mode, in which no substrates are added to the fermentor. The non-standard objective function incorporates both the final 1,3-PD yield and the cost of changing the input feeding rate, which is the control variable for the fed-batch fermentation process. Continuous state inequality constraints are imposed to ensure that the concentrations of biomass, glycerol, and reaction products lie within specified limits. Using the constraint transcription method, we approximate the continuous state inequality constraints by a conventional inequality constraint to yield an approximate parameter optimization problem. We then develop a combined particle swarm and gradient-based optimization algorithm to solve this approximate problem. The paper concludes with simulation results

Parameter Identification in a Probabilistic Setting

Parameter identification problems are formulated in a probabilistic language, where the randomness reflects the uncertainty about the knowledge of the true values. This setting allows conceptually easily to incorporate new information, e.g. through a measurement, by connecting it to Bayes's theorem. The unknown quantity is modelled as a (may be high-dimensional) random variable. Such a description has two constituents, the measurable function and the measure. One group of methods is identified as updating the measure, the other group changes the measurable function. We connect both groups with the relatively recent methods of functional approximation of stochastic problems, and introduce especially in combination with the second group of methods a new procedure which does not need any sampling, hence works completely deterministically. It also seems to be the fastest and more reliable when compared with other methods. We show by example that it also works for highly nonlinear non-smooth problems with non-Gaussian measures.Comment: 29 pages, 16 figure

Comparison of diffusion-weighted MRI acquisition techniques for normal pancreas at 3.0 Tesla

PURPOSEWe aimed to optimize diffusion-weighted imaging (DWI) acquisitions for normal pancreas at 3.0 Tesla.MATERIALS AND METHODSThirty healthy volunteers were examined using four DWI acquisition techniques with b values of 0 and 600 s/mm2 at 3.0 Tesla, including breath-hold DWI, respiratory-triggered DWI, respiratory-triggered DWI with inversion recovery (IR), and free-breathing DWI with IR. Artifacts, signal-to-noise ratio (SNR) and apparent diffusion coefficient (ADC) of normal pancreas were statistically evaluated among different DWI acquisitions.RESULTSStatistical differences were noticed in artifacts, SNR, and ADC values of normal pancreas among different DWI acquisitions by ANOVA (P < 0.001). Normal pancreas imaging had the lowest artifact in respiratory-triggered DWI with IR, the highest SNR in respiratory-triggered DWI, and the highest ADC value in free-breathing DWI with IR. The head, body, and tail of normal pancreas had statistically different ADC values on each DWI acquisition by ANOVA (P < 0.05).CONCLUSIONThe highest image quality for normal pancreas was obtained using respiratory-triggered DWI with IR. Normal pancreas displayed inhomogeneous ADC values along the head, body, and tail structures

Fabrication of Chitosan/Silk Fibroin Composite Nanofibers for Wound-dressing Applications

Chitosan, a naturally occurring polysaccharide with abundant resources, has been extensively exploited for various biomedical applications, typically as wound dressings owing to its unique biocompatibility, good biodegradability and excellent antibacterial properties. In this work, composite nanofibrous membranes of chitosan (CS) and silk fibroin (SF) were successfully fabricated by electrospinning. The morphology of electrospun blend nanofibers was observed by scanning electron microscopy (SEM) and the fiber diameters decreased with the increasing percentage of chitosan. Further, the mechanical test illustrated that the addition of silk fibroin enhanced the mechanical properties of CS/SF nanofibers. The antibacterial activities against Escherichia coli (Gram negative) and Staphylococcus aureus (Gram positive) were evaluated by the turbidity measurement method; and results suggest that the antibacterial effect of composite nanofibers varied on the type of bacteria. Furthermore, the biocompatibility of murine fibroblast on as-prepared nanofibrous membranes was investigated by hematoxylin and eosin (H&E) staining and MTT assays in vitro, and the membranes were found to promote the cell attachment and proliferation. These results suggest that as-prepared chitosan/silk fibroin (CS/SF) composite nanofibrous membranes could be a promising candidate for wound healing applications