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All-Solution-Processed InGaO 3

We fabricated the crystallized InGaZnO thin films by sol-gel process and high-temperature annealing at 900°C. Prior to the deposition of the InGaZnO, ZnO buffer layers were also coated by sol-gel process, which was followed by thermal annealing. After the synthesis and annealing of the InGaZnO, the InGaZnO thin film on the ZnO buffer layer with preferred orientation showed periodic diffraction patterns in the X-ray diffraction, resulting in a superlattice structure. This film consisted of nanosized grains with two phases of InGaO3(ZnO)1 and InGaO3(ZnO)2 in InGaZnO polycrystal. On the other hand, the use of no ZnO buffer layer and randomly oriented ZnO buffer induced the absence of the InGaZnO crystal related patterns. This indicated that the ZnO buffer with high c-axis preferred orientation reduced the critical temperature for the crystallization of the layered InGaZnO. The InGaZnO thin films formed with nanosized grains of two-phase InGaO3(ZnO)m superlattice showed considerably low thermal conductivity (1.14 Wm−1 K−1 at 325 K) due to the phonon scattering from grain boundaries as well as interfaces in the superlattice grain

Adaptive laboratory evolution of a genome-reduced Escherichia coli.

Synthetic biology aims to design and construct bacterial genomes harboring the minimum number of genes required for self-replicable life. However, the genome-reduced bacteria often show impaired growth under laboratory conditions that cannot be understood based on the removed genes. The unexpected phenotypes highlight our limited understanding of bacterial genomes. Here, we deploy adaptive laboratory evolution (ALE) to re-optimize growth performance of a genome-reduced strain. The basis for suboptimal growth is the imbalanced metabolism that is rewired during ALE. The metabolic rewiring is globally orchestrated by mutations in rpoD altering promoter binding of RNA polymerase. Lastly, the evolved strain has no translational buffering capacity, enabling effective translation of abundant mRNAs. Multi-omic analysis of the evolved strain reveals transcriptome- and translatome-wide remodeling that orchestrate metabolism and growth. These results reveal that failure of prediction may not be associated with understanding individual genes, but rather from insufficient understanding of the strain's systems biology

The Effect of Neuromuscular Blockade Reversal Agents on Postoperative Pulmonary Complications in Patients undergoing Femur Fracture Repair Surgery: A Retrospective Observational Study

Background Femoral fracture repair surgery under general anesthesia is associated with postoperative pulmonary complications (PPCs). However, information on PPCs caused by residual neuromuscular blockade following perioperative use of neuromuscular blockers is limited. This study aimed to identify the differences in the incidence of PPCs according to the type of neuromuscular blockade reversal agent used in femoral fracture repair surgery, as well as the risk factors for PPCs. Methods We retrospectively analyzed the electronic medical records of 604 patients aged >18 years who underwent general anesthesia for femoral fracture repair surgery at a single university hospital between March 2017 and March 2022. Patients in whom sugammadex or anticholinesterase was used to reverse the neuromuscular block were subjected to propensity score matching. Multivariate logistic regression analysis was performed to identify risk factors for PPCs. Results Among the 604 patients, 108 were matched in each group. The incidence rates of PPCs overall and in the anticholinesterase and sugammadex groups were 7.0%, 8.3%, and 5.6%, respectively, with no significant differences between the groups. Older age, higher ASA (American Society of Anesthesiologists) physical status, and lower preoperative oxygen saturation were risk factors, whereas emergency surgery was a preventive factor. Conclusions Our results demonstrated that the incidence of PPC did not differ significantly between sugammadex and anticholinesterase in patients undergoing femur fracture repair under general anesthesia. Identifying the risk factors and confirming complete recovery from neuromuscular blockade might be more important

Multiple Classifier Fusion using k-Nearest Localized Templates

Abstract. This paper presents a method for combining classifiers that uses knearest localized templates. The localized templates are estimated from a training set using C-means clustering algorithm, and matched to the decision profile of a new incoming sample by a similarity measure. The sample is assigned to the class which is most frequently represented among the k most similar templates. The appropriate value of k is determined according to the characteristics of the given data set. Experimental results on real and artificial data sets show that the proposed method performs better than the conventional fusion methods