Use of the Clock Drawing Test and the Rey–Osterrieth Complex Figure Test-copy with convolutional neural networks to predict cognitive impairment

Background The Clock Drawing Test (CDT) and Rey–Osterrieth Complex Figure Test (RCFT) are widely used as a part of neuropsychological test batteries to assess cognitive function. Our objective was to confirm the prediction accuracies of the RCFT-copy and CDT for cognitive impairment (CI) using convolutional neural network algorithms as a screening tool. Methods The CDT and RCFT-copy data were obtained from patients aged 60–80 years who had more than 6 years of education. In total, 747 CDT and 980 RCFT-copy figures were utilized. Convolutional neural network algorithms using TensorFlow (ver. 2.3.0) on the Colab cloud platform ( www.colab.research.google.com ) were used for preprocessing and modeling. We measured the prediction accuracy of each drawing test 10 times using this dataset with the following classes: normal cognition (NC) vs. mildly impaired cognition (MI), NC vs. severely impaired cognition (SI), and NC vs. CI (MI + SI). Results The accuracy of the CDT was better for differentiating MI (CDT, 78.04 ± 2.75; RCFT-copy, not being trained) and SI from NC (CDT, 91.45 ± 0.83; RCFT-copy, 90.27 ± 1.52); however, the RCFT-copy was better at predicting CI (CDT, 77.37 ± 1.77; RCFT, 83.52 ± 1.41). The accuracy for a 3-way classification (NC vs. MI vs. SI) was approximately 71% for both tests; no significant difference was found between them. Conclusions The two drawing tests showed good performance for predicting severe impairment of cognition; however, a drawing test alone is not enough to predict overall CI. There are some limitations to our study: the sample size was small, all the participants did not perform both the CDT and RCFT-copy, and only the copy condition of the RCFT was used. Algorithms involving memory performance and longitudinal changes are worth future exploration. These results may contribute to improved home-based healthcare delivery.The costs for manuscript publication, design of the study, data management, and writing of the manuscript were supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2017S1A6A3A01078538)

Tristetraprolin inhibits the growth of human glioma cells through downregulation of urokinase plasminogen activator/urokinase plasminogen activator receptor mRNAs

Urokinase plasminogen activator (uPA) and urokinase plasminogen activator receptor (uPAR) play a major role in the infiltrative growth of glioblastoma. Downregulatoion of the uPA and uPAR has been reported to inhibit the growth glioblastoma. Here, we demonstrate that tristetraprolin (TTP) inhibits the growth of U87MG human glioma cells through downregulation of uPA and uPAR. Our results show that expression level of TTP is inversely correlated with those of uPA and uPAR in human glioma cells and tissues. TTP binds to the AU-rich elements within the 3' untranslated regions of uPA and uPAR and overexpression of TTP decreased the expression of uPA and uPAR through enhancing the degradation of their mRNAs. In addition, overexpression of TTP inhibited the growth and invasion of U87MG cells. Our findings implicate that TTP can be used as a promising therapeutic target to treat human glioma

The Draft Genome of an Octocoral, Dendronephthya gigantea

Coral reefs composed of stony corals are threatened by global marine environmental changes. However, soft coral communities of octocorallian species, appear more resilient. The genomes of several cnidarians species have been published, including from stony corals, sea anemones, and hydra. To fill the phylogenetic gap for octocoral species of cnidarians, we sequenced the octocoral, Dendronephthya gigantea, a nonsymbiotic soft coral, commonly known as the carnation coral. The D. gigantea genome size is similar to 276 Mb. A high-quality genome assembly was constructed from PacBio long reads (29.85 Gb with 108x coverage) and Illumina short paired-end reads (35.54 Gb with 128x coverage) resulting in the highest N50 value (1.4 Mb) reported thus far among cnidarian genomes. About 12% of the genome is repetitive elements and contained 28,879 predicted protein-coding genes. This gene set is composed of 94% complete BUSCO ortholog benchmark genes, which is the second highest value among the cnidarians, indicating high quality. Based on molecular phylogenetic analysis, octocoral and hexacoral divergence times were estimated at 544 MYA. There is a clear difference in Hox gene composition between these species: unlike hexacorals, the Antp superclass Evx gene was absent in D. gigantea. Here, we present the first genome assembly of a nonsymbiotic octocoral, D. gigantea to aid in the comparative genomic analysis of cnidarians, including stony and soft corals, both symbiotic and nonsymbiotic. The D. gigantea genome may also provide clues to mechanisms of differential coping between the soft and stony corals in response to scenarios of global warming

Prevalence and detection of low-allele-fraction variants in clinical cancer samples

Accurate detection of genomic alterations using high-throughput sequencing is an essential component of precision cancer medicine. We characterize the variant allele fractions (VAFs) of somatic single nucleotide variants and indels across 5095 clinical samples profiled using a custom panel, CancerSCAN. Our results demonstrate that a significant fraction of clinically actionable variants have low VAFs, often due to low tumor purity and treatment-induced mutations. The percentages of mutations under 5% VAF across hotspots in EGFR, KRAS, PIK3CA, and BRAF are 16%, 11%, 12%, and 10%, respectively, with 24% for EGFR T790M and 17% for PIK3CA E545. For clinical relevance, we describe two patients for whom targeted therapy achieved remission despite low VAF mutations. We also characterize the read depths necessary to achieve sensitivity and specificity comparable to current laboratory assays. These results show that capturing low VAF mutations at hotspots by sufficient sequencing coverage and carefully tuned algorithms is imperative for a clinical assay

Fabrication of Conducting Polymer-Polyoxometalate Hybrid Photoanodes for Highly Efficient Water Splitting

Tungsten oxide (WO3) is regarded one of the most promising photoanode for solar water splitting due to its intrinsic stability, environmental compatibility, a moderate hole-diffusion length (150-500 nm), and efficient electron transport. However, there are some limitations to be practically applied such as high recombination rate of photogenerated excitons, insufficient quantum efficiency and low absorption coefficient. To address such problems, in this study, we modified the WO3 surface with polyoxometalate (POM) water oxidation catalysts (WOCs) and conducting polymers by a simple electropolymerization method. We have tested three different conducting polymers such as polypyrrole (PPy), polyaniline (PANi) and poly(3,4-ethylenedioxythiophene) (PEDOT). We found that the photoelectrochemical performance of WO3 was significantly improved by the modification, possibly due to the increased visible light absorption and selective and efficient hole transport across the conducting polymer film, and improved charge transfer across the solid-electrolyte interface by the introduction of WOCs

Surface Modification and Work-Functional Engineering of Hematite Photoanodes via Layer-by-Layer Assembly for Solar Water Oxidation

Artificial photosynthesis has drawn great attention for decades as a promising solution to energy and environmental problems. For example, we can produce valuable chemicals (e.g., formate, synthesis gas, and methanol) from abundant carbon dioxide and water through a series of photoelectrochemical processes in a carbon-neutral manner. For the successful development of efficient and stable photosynthetic devices, it is imperative to precisely assemble various functional materials such as semiconducting materials for exciton generation, conducting materials for exciton dissociation and charge transport, and redox catalysts for target-chemical reactions. Here, we report the development of an efficient and stable, hematite-based photoanode for solar water oxidation by layer-by-layer assembly of cationic graphene oxide (GO) nanosheets and anionic molecular metal oxides as a charge transporting/separation material and water oxidation catalyst, respectively. It was found that their sequential deposition significantly improves the photocatalytic performance and stability of the hematite photoanode by facilitating charge transport and transfer across the electrode/electrolyte interface. Unexpectedly, it was also found that deposition of alternating layers of cationic and anionic functional materials allow us to engineer work-function of hematite electrode beneficial for charge transport by forming an interfacial dipole layer. We believe that the present study can provide not only a general and simple method to fabricate an efficient photosynthetic device, but also an insight to scientists and engineers for designing of a novel electrochemical/photoelectrochemical device

Work-Function Engineering by Surface Modification of Hematite Photoelectrode via Layer-by-Layer Assembly for Water Splitting

Artificial photosynthesis has drawn great attention for decades as a promising solution to energy and environmental problems. For example, we can produce valuable chemicals (e.g., formate, synthesis gas, and methanol) from abundant carbon dioxide and water through a series of photoelectrochemical processes in a carbon-neutral manner. For the successful development of efficient and stable photosynthetic devices, it is critical to precisely assemble various functional materials such as semiconductors for exciton generation, conducting materials for exciton dissociation and charge transport, and redox catalysts for target-chemical reactions. Here, we report the improvement of an efficient and stable, hematite-based photoelectrode for solar water splitting by layer-by-layer assembly (LbL) of cationic graphene oxide (GO) nanosheets and anionic molecular metal oxides as a charge transporting/separation material and water oxidation catalyst, respectively. It was found that their serial deposition significantly develops the photocatalytic performance and stability of the hematite photoelectrode by promoting charge transport and transfer across the electrode/electrolyte interface. Unexpectedly, it was also found that deposition of alternating layers of cationic and anionic functional materials allow us to engineer work-function of hematite photoelectrode beneficial for charge transport by forming an interfacial dipole layer at the surface of hematite. We believe that the present study can provide not only a general and simple method to fabricate an efficient photosynthetic device, but also an insight to scientists and engineers for designing of a novel electrochemical/photoelectrochemical device

Learning to Detect Cracks on Damaged Concrete Surfaces Using Two-Branched Convolutional Neural Network

Image sensors are widely used for detecting cracks on concrete surfaces to help proactive and timely management of concrete structures. However, it is a challenging task to reliably detect cracks on damaged surfaces in the real world due to noise and undesired artifacts. In this paper, we propose an autonomous crack detection algorithm based on convolutional neural network (CNN) to solve the problem. To this aim, the proposed algorithm uses a two-branched CNN architecture, consisting of sub-networks named a crack-component-aware (CCA) network and a crack-region-aware (CRA) network. The CCA network is to learn gradient component regarding cracks, and the CRA network is to learn a region-of-interest by distinguishing critical cracks and noise such as scratches. Specifically, the two sub-networks are built on convolution-deconvolution CNN architectures, but also they are comprised of different functional components to achieve their own goals efficiently. The two sub-networks are trained in an end-to-end to jointly optimize parameters and produce the final output of localizing important cracks. Various crack image samples and learning methods are used for efficiently training the proposed network. In the experimental results, the proposed algorithm provides better performance in the crack detection than the conventional algorithms