Beckergraversen1247

38% accuracy was obtained for the testing dataset. AptaNet achieved high performance on our constructed aptamer-protein benchmark dataset. The results indicate that AptaNet can help identify novel aptamer-protein interacting pairs and build more-efficient insights into the relationship between aptamers and proteins. Our benchmark dataset and the source codes for AptaNet are available in https//github.com/nedaemami/AptaNet .The objective of this study is to assess prognostic value of surgery for elderly oral tongue squamous cell carcinomas (OTSCC) patients. Patients with OTSCC were extracted from the SEER database between 2010 and 2014. The distributions of categorical demographic and clinicopathological characteristics were determined for different age groups the 75-79, 80-84, and 85-102 years old groups. Univariate and multivariate analyses were performed to determine the effects of each variable on survival. A total of 1064 patients were analyzed. 75-79 years old patients tended to be male and rate of surgery declined with advancing age (P  less then  0.001). 75-79 years old patients more frequently presented with advanced stage compared to their older peers (P = 0.002). Compared to surgery groups, the hazard ratios for no surgery groups were 2.856 (95% CI 2.267-3.599; (P  less then  0.001)) for OS and 3.687 (95% CI 2.561-5.308; (P  less then  0.001)) for CSS in multivariable analysis. In subgroup analysis, the effect of no surgery was significantly associated with a higher risk of poor CSS in patients aged 75-79 years, 80-84 years and 85-102 years (P  less then  0.001, respectively). Our results showed that there were a series of factors contributing to poor outcomes in the elderly OTSCC patients, including clinicopathological characteristics and surgical management. Surgical resection is significantly associated with an improved OS and CSS, but further exploration in larger prospective clinical trials and better prognostic and predictive tools for select old patients for surgery are needed.Zinc (Zn) deficiency hinders growth and development in tomato. This study unveils the responses of how Zn starvation affects physiological and molecular processes in tomato. Zn deficiency negatively affected the biomass, cellular integrity, and chlorophyll synthesis in tomato. Also, Zn deficiency decreased the maximum yield of PSII, photosynthesis performance index and dissipation energy per active reaction center, although the antenna size, trapping energy efficiency and electron transport flux were stable in Zn-starved leaves. Further, Zn shortage caused a substantial reduction in Zn and Fe concentrations in both roots and shoots along with decreased root Fe-reductase activity accompanied by the downregulation of Fe-regulated transporter 1, Zn transporter-like (LOC100037509), and Zn transporter (LOC101255999) genes predicted to be localized in the root plasma membrane. The interactome partners of these Zn transporters are predominantly associated with root-specific metal transporter, ferric-chelate reductase, BHLH transcriptional regulator, and Zn metal ion transporters, suggesting that Zn homeostasis may be tightly linked to the Fe status along with BHLH transcription factor in Zn-deficient tomato. We also noticed elevated O2.- and H2O2 due to Zn deficiency which was consistent with the inefficient antioxidant properties. These findings will be useful in the downstream approach to improve vegetable crops sensitive to Zn-deficiency.We propose and demonstrate a compact and simple vector bending sensor capable of distinguishing any direction and amplitude with high accuracy. The sensor consists of a short segment of asymmetric multicore fiber (MCF) fusion spliced to a standard single mode fiber. The reflection spectrum of such a structure shifts and shrinks in specific manners depending on the direction in which the MCF is bent. By monitoring simultaneously wavelength shift and light power variations, the amplitude and bend direction of the MCF can be unmistakably measured in any orientation, from 0° to 360°. The bending sensor proposed here is highly sensitive even for small bending angles (below 1°).Obesity is the most crucial phenotype in metabolic syndrome (MetS), and waist circumference (WC) and body mass index (BMI) are two common indexes to define obesity. It is an accepted fact that genetic and environmental interaction influence obesity and MetS. Microsatellites are a subcategory of tandem repeats with a length of 1 to 10 nucleotides. Tandem repeats make up repetitive genomic regions. Differences in the number of tandem repeats or their variation (alleles) result in microsatellite polymorphisms. Thus, we attempted to find microsatellite variation associated with WC and BMI in a family-based study. Twelve microsatellite markers were selected to investigate possible genes or chromosomal regions in 91 families with at least one affected MetS. The cut-off values for BMI and WC were considered 25 kg/m2 and 90 cm, respectively. In all members of the families, the strongest association was observed between the marker D11S1304 (allele 1) with both WC and BMI, independently, by the biallelic model in the family-based association test analysis (P  less then  0.05). Besides, when we compared high- and low-level groups in members with MetS, the markers D8S1743 and D11S1304 (allele 1) showed a strong association with WC (P = 0.0080) and BMI (P = 0.0074), respectively. When the simultaneous detection of the high WC and MetS status was used as a trait, the strongest association was observed with the marker D8S1743 (P = 0.0034). Moreover, when BMI with the high MetS status was used as a trait, the strongest association was observed with the marker D8S1743 (allele 4) (P = 0.0034). Tofacitinib in vivo The obtained results showed a relationship between obesity and MetS with markers on the selected regions on chromosomes 8 and 11, and to a lesser degree, on chromosome 12.A common electrophysiology technique used in neuroscience is patch clamp a method in which a glass pipette electrode facilitates single cell electrical recordings from neurons. Typically, patch clamp is done manually in which an electrophysiologist views a brain slice under a microscope, visually selects a neuron to patch, and moves the pipette into close proximity to the cell to break through and seal its membrane. While recent advances in the field of patch clamping have enabled partial automation, the task of detecting a healthy neuronal soma in acute brain tissue slices is still a critical step that is commonly done manually, often presenting challenges for novices in electrophysiology. To overcome this obstacle and progress towards full automation of patch clamp, we combined the differential interference microscopy optical technique with an object detection-based convolutional neural network (CNN) to detect healthy neurons in acute slice. Utilizing the YOLOv3 convolutional neural network architecture, we achieved a 98% reduction in training times to 18 min, compared to previously published attempts.