Psychromarinibacter sediminicola sp. nov., a novel moderately halophilic, metabolically diverse bacterium isolated from a solar saltern sediment, and comparison between members of family Roseobacteraceae.

Known for its species abundance and evolutionary status complexity, family Roseobacteraceae is an important subject of many studies on the discovery, identification, taxonomic status, and ecological properties of marine bacteria. This study compared and analyzed the phylogenetic, genomic, biochemical, and chemo taxonomical properties of seven species from three genera (Psychromarinibacter, Lutimaribacter, and Maritimibacter) of the family Roseobacteraceae. Moreover, a novel strain, named C21-152T was isolated from solar saltern sediment in Weihai, China. The values of 16S rRNA gene sequence similarity, the average nucleotide identity (ANI), the average amino acid identity (AAI), and the digital DNA-DNA hybridization (dDDH) between genomes of the novel strain and Psychromarinibacter halotolerans MCCC 1K03203T were 97.19, 78.49, 73.45, and 21.90%, respectively. Genome sequencing of strain C21-152T revealed a complete Sox enzyme system related to thiosulfate oxidization as well as a complete pathway for the final conversion of hydroxyproline to α-ketoglutarate. In addition, strain C21-152T was resistant to many antibiotics and had the ability to survive below 13% salinity. This strain had versatile survival strategies in saline environments including salt-in, compatible solute production and compatible solute transport. Some of its physiological features enriched and complemented the knowledge of the characteristics of the genus Psychromarinibacter. Optimum growth of strain C21-152T occurred at 37 â„ƒ, with 5-6% (w/v) NaCl and at pH 7.5. According to the results of the phenotypic, chemotaxonomic characterization, phylogenetic properties and genome analysis, strain C21-152T should represent a novel specie of the genus Psychromarinibacter, for which the name Psychromarinibacter sediminicola sp. nov. is proposed. The type strain is C21-152T (= MCCC 1H00808T = KCTC 92746T = SDUM1063002T).

Graphene-Doped Piezoelectric Transducers by Kriging Optimal Model for Detecting Various Types of Laryngeal Movements.

This study fabricated piezoelectric fibers of polyvinylidene fluoride (PVDF) with graphene using near-field electrospinning (NFES) technology. A uniform experimental design table U*774 was applied, considering weight percentage (1-13 wt%), the distance between needle and disk collector (2.1-3.9 mm), and applied voltage (14.5-17.5 kV). We optimized the parameters using electrical property measurements and the Kriging response surface method. Adding 13 wt% graphene significantly improved electrical conductivity, increasing from 17.7 µS/cm for pure PVDF to 187.5 µS/cm. The fiber diameter decreased from 21.4 µm in PVDF/1% graphene to 9.1 µm in PVDF/13% graphene. Adding 5 wt% graphene increased the ß-phase content by 6.9%, reaching 65.4% compared to pure PVDF fibers. Material characteristics were investigated using scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction analysis (XRD), contact angle measurements, and tensile testing. Optimal parameters included 3.47 wt% graphene, yielding 15.82 mV voltage at 5 Hz and 5 N force (2.04 times pure PVDF). Force testing showed a sensitivity (S) of 7.67 log(mV/N). Fibers were attached to electrodes for piezoelectric sensor applications. The results affirmed enhanced electrical conductivity, piezoelectric performance, and mechanical strength. The optimized piezoelectric sensor could be applied to measure physiological signals, such as attaching it to the throat under different conditions to measure the output voltage. The force-to-voltage conversion facilitated subsequent analysis.

Streptococcus gordonii empyema: A case report and literature review.

Empyema is often caused by Streptococcus anginous species, followed by Streptococcus pneumoniae. The organism Streptococcus gordonii belongs to the Streptococcus mitis group, which rarely causes empyema. We report the case of a 59-year-old man who presented with exertional dyspnea and chest pain on the right side. The image obtained showed effusion on the right side. Streptococcus gordonii was recovered from purulent pleural effusion culture. The patient underwent video-assisted thoracoscopic surgery with decortication, pneumolysis and received antibiotics for 13 days. A total of seven cases were analyzed after combining six cases in the literature and our presented case. The majority of Streptococcus gordonii empyema patients were male (six patients, 86%) and empyema on the right side (five patients, 71%). Common risk factors included poor dental hygiene or recent dental procedure (three patients, 43%), diabetes mellitus (three patients, 43%), and smoking (three patients, 43%). Only a few cases developed empyema-related complications, including bacteremia (one patient, 14%) and spleen abscesses (one patient, 14%). Most patients underwent chest tube insertion (seven patients, 100%) and survived without recurrent empyema (six patients, 86%).

Development of Deep Learning with RDA U-Net Network for Bladder Cancer Segmentation.

In today's high-order health examination, imaging examination accounts for a large proportion. Computed tomography (CT), which can detect the whole body, uses X-rays to penetrate the human body to obtain images. Its presentation is a high-resolution black-and-white image composed of gray scales. It is expected to assist doctors in making judgments through deep learning based on the image recognition technology of artificial intelligence. It used CT images to identify the bladder and lesions and then segmented them in the images. The images can achieve high accuracy without using a developer. In this study, the U-Net neural network, commonly used in the medical field, was used to extend the encoder position in combination with the ResBlock in ResNet and the Dense Block in DenseNet, so that the training could maintain the training parameters while reducing the overall identification operation time. The decoder could be used in combination with Attention Gates to suppress the irrelevant areas of the image while paying attention to significant features. Combined with the above algorithm, we proposed a Residual-Dense Attention (RDA) U-Net model, which was used to identify organs and lesions from CT images of abdominal scans. The accuracy (ACC) of using this model for the bladder and its lesions was 96% and 93%, respectively. The values of Intersection over Union (IoU) were 0.9505 and 0.8024, respectively. Average Hausdorff distance (AVGDIST) was as low as 0.02 and 0.12, respectively, and the overall training time was reduced by up to 44% compared with other convolution neural networks.