Why Nursing Home Residents Use Social Network Systems: An Attachment Perspective.

Most long-term care facilities can offer residents' with sufficiently material and physical care, but psychological support may not be always provided due to the tight financial budget or labor resources. Residents' isolation and loneliness then become a big issue, especially for the residents. Social network systems (SNS) have been proved to be a more effective information transmission channel for thoughts, perspectives, and information sharing than traditional channels such as microblogging, e-mails, or telephones. This study conducted a quasi-experiment to identify factors that influence residents' intention of using SNS and the impacts of SNS on them in a long-term care facility. The results showed that residents' attached motivation of personal interacting is a significant factor that influences their intention to use the social network platform. Meanwhile, both the loneliness and depression scales of the participants were decreased significantly.

Biochemical and molecular dynamics studies of archaeal polyisoprenyl pyrophosphate phosphatase from Saccharolobus solfataricus.

The undecaprenyl pyrophosphate phosphatase (UppP) is an integral membrane pyrophosphatase. In bacteria, UppP catalyzes the dephosphorylation of undecaprenyl pyrophosphate (C55-pp) to undecaprenyl phosphate (C55-P) in the periplasmic space, which is an essential step for the isoprenyl lipid carrier to reenter the peptidoglycan synthesis cycle. Besides bacteria, the UppP homologs are widely distributed in archaea genome. However, all archaea lack peptidoglycan structure in their cell wall components, and the major archaeal lipid carriers are dolichol phosphate (Dol-p) and dolichol pyrophosphate (Dol-pp), so the functions of the UppP homolog in archaea remain unclear. Here, we purified a recombinant polyisoprenyl pyrophosphatase of a thermoacidophilic archaeon, Saccharolobus solfataricus (SsUppP), and characterized its enzymatic properties. Two isoprenyl pyrophosphate, farnesyl pyrophosphate (Fpp) and geranylgeranyl pyrophosphate (Ggpp), were used as the surrogate substrates, simulating the bacterial and archaeal lipid carriers. SsUppP dephosphorylated Fpp and Ggpp at 37 °C, but retained the phosphatase activity at high temperatures. The optimal condition for the enzymatic activity was found to be at pH 7 and 70 °C. The thermostability of SsUppP was also supported by molecular dynamics simulation studies. Our results indicated that the archaeal SsUppP can dephosphorylate isoprenyl pyrophosphates at the natural environment of high temperature, and the possibility to catalyze the dephosphorylation of archaeal lipid carriers.

Crystal structure of the blue fluorescent protein with a Leu-Leu-Gly tri-peptide chromophore derived from the purple chromoprotein of Stichodactyla haddoni.

Chromoproteins are a good source of engineered biological tools. We previously reported the development of a blue fluorescent protein, termed shBFP, which was derived from a purple chromoprotein shCP found in the sea anemone Stichodacyla haddoni. shBFP contains a Leu63-Leu64-Gly65 tri-peptide chromophore, and shows maximum excitation and emission wavelengths at 401 nm and 458 nm, along with a high quantum yield. How this chromophore endows shBFP with the unique fluorescence property in the absence of a hydroxyphenyl ring remained unclear. Here, we present the crystal structures of shCP and shBFP at 1.9- and 2.05-Šresolution, respectively. Both proteins crystallized as similar tetramers, but they are more likely to function as dimers in solution. The chromophore in shCP shows a trans-conformation and its non-planarity is similar to most other homologues. The shBFP chromophore also contains an imidazolidone moiety in its structure, but there are a smaller number of conjugated double bonds compared to shCP. Consequently, the chromophore may prefer absorbing shorter wavelength lights in the UV region, followed by the emission of blue fluorescence. These observations provide new insights into the molecular basis that correlates chromophore conformation with light absorption and fluorescence emission for the development of improved biomarkers.

Blue fluorescent protein derived from the mutated purple chromoprotein isolated from the sea anemone Stichodactyla haddoni.

Chromoproteins, especially far-red fluorescent proteins with long stokes shift, are good sources for engineering biological research tools. However, chromoproteins have not been used for developing fluorescent proteins with short emission wavelength. Therefore, we herein report the development of a blue fluorescent protein, termed shBFP, which is derived from a purple chromoprotein isolated from the sea anemone Stichodacyla haddoni (shCP) after shCP was simultaneously mutated on E63L and Y64L. The shBFP chromophore is composed of Leu-Leu-Gly, which introduced a maximum excitation and emission wavelength at 401 nm and 458 nm, respectively, and a quantum yield of 0.79. Interestingly, the N158S and L173I double mutations of shBFP conducted in the chromophore environment further shifted the maximum excitation to 375 nm, and elevated the quantum yield to 0.84. Thus, shBFP, which is based on the Leu-Leu-Gly chromophore composition, results in higher quantum yields and short wavelength emission. Additionally, we found that the cDNA of shBFP is stably expressed in zebrafish embryos with fidelity, indicating the application of shBFP as a biomarker or selective marker.

Zebrafish and Medaka: new model organisms for modern biomedical research.

Although they are primitive vertebrates, zebrafish (Danio rerio) and medaka (Oryzias latipes) have surpassed other animals as the most used model organisms based on their many advantages. Studies on gene expression patterns, regulatory cis-elements identification, and gene functions can be facilitated by using zebrafish embryos via a number of techniques, including transgenesis, in vivo transient assay, overexpression by injection of mRNAs, knockdown by injection of morpholino oligonucleotides, knockout and gene editing by CRISPR/Cas9 system and mutagenesis. In addition, transgenic lines of model fish harboring a tissue-specific reporter have become a powerful tool for the study of biological sciences, since it is possible to visualize the dynamic expression of a specific gene in the transparent embryos. In particular, some transgenic fish lines and mutants display defective phenotypes similar to those of human diseases. Therefore, a wide variety of fish model not only sheds light on the molecular mechanisms underlying disease pathogenesis in vivo but also provides a living platform for high-throughput screening of drug candidates. Interestingly, transgenic model fish lines can also be applied as biosensors to detect environmental pollutants, and even as pet fish to display beautiful fluorescent colors. Therefore, transgenic model fish possess a broad spectrum of applications in modern biomedical research, as exampled in the following review.

Chromophore Deprotonation State Alters the Optical Properties of Blue Chromoprotein.

Chromoproteins (CPs) have unique colors and can be used in biological applications. In this work, a novel blue CP with a maximum absorption peak (λmax) at 608 nm was identified from the carpet anemone Stichodactyla gigantea (sgBP). In vivo expression of sgBP in zebrafish would change the appearance of the fishes to have a blue color, indicating the potential biomarker function. To enhance the color properties, the crystal structure of sgBP at 2.25 Å resolution was determined to allow structure-based protein engineering. Among the mutations conducted in the Gln-Tyr-Gly chromophore and chromophore environment, a S157C mutation shifted the λmax to 604 nm with an extinction coefficient (ε) of 58,029 M-1·cm-1 and darkened the blue color expression. The S157C mutation in the sgBP chromophore environment could affect the color expression by altering the deprotonation state of the phenolic group in the chromophore. Our results provide a structural basis for the blue color enhancement of the biomarker development.

Different visible colors and green fluorescence were obtained from the mutated purple chromoprotein isolated from sea anemone.

Green fluorescent protein (GFP)-like proteins have been studied with the aim of developing fluorescent proteins. Since the property of color variation is understudied, we isolated a novel GFP-like chromoprotein from the carpet anemone Stichodactyla haddoni, termed shCP. Its maximum absorption wavelength peak (λ(max)) is located at 574 nm, resulting in a purple color. The shCP protein consists of 227 amino acids (aa), sharing 96 % identity with the GFP-like chromoprotein of Heteractis crispa. We mutated aa residues to examine any alteration in color. When E63, the first aa of the chromophore, was replaced by serine (E63S), the λ(max) of the mutated protein shCP-E63S was shifted to 560 nm and exhibited a pink color. When Q39, T194, and I196, which reside in the surrounding 5 Å of the chromophore's microenvironment, were mutated, we found that (1) the λ(max) of the mutated protein shCP-Q39S was shifted to 518 nm and exhibited a red color, (2) shCP-T194I exhibited a purple-blue color, and (3) an additional mutation at I196H of the mutated protein shCP-E63L exhibited green fluorescence. In contrast, when the aa located neither at the chromophore nor within its microenvironment were mutated, the resultant proteins shCP-L122H, -E138G, -S137D, -T95I, -D129N, -T194V, -E138Q, -G75E, -I183V, and -I70V never altered their purple color, suggesting that mutations at the shCP chromophore and the surrounding 5 Å microenvironment mostly control changes in color expression or cause fluorescence to develop. Additionally, we found that the cDNAs of shCP and its mutated varieties are faithfully and stably expressed both in Escherichia coli and zebrafish embryos.

Channel selection for epilepsy seizure prediction method based on machine learning.

The studies on seizure prediction problem have shown great improvement these years. Machine learning based seizure prediction method shows great performance by doing pattern recognition on high-dimensional bivariate synchronization features. However, the computation loading of the machine learning based method may be too high to meet wearable or implantable devices with the power and area constraints. In this work, channel selection is proposed to reduce the channel number from 22 to less than 6 channels and therefore more than 93.73% of the computation loading is saved through the method. The best result shows successful rate of 60.6% in 3-channel cases of ECoG database and successful rate of 70% in 3-channel cases of EEG database.

Cubic spline interpolation with overlapped window and data reuse for on-line Hilbert Huang transform biomedical microprocessor.

On-chip implementation of Hilbert-Huang transform (HHT) has great impact to analyze the non-linear and non-stationary biomedical signals on wearable or implantable sensors for the real-time applications. Cubic spline interpolation (CSI) consumes the most computation in HHT, and is the key component for the HHT processor. In tradition, CSI in HHT is usually performed after the collection of a large window of signals, and the long latency violates the realtime requirement of the applications. In this work, we propose to keep processing the incoming signals on-line with small and overlapped data windows without sacrificing the interpolation accuracy. 58% multiplication and 73% division of CSI are saved after the data reuse between the data windows.

Seizure prediction based on classification of EEG synchronization patterns with on-line retraining and post-processing scheme.

Epilepsy is one of the most common brain disorders in the world. The spontaneous seizure onset influences the daily life of epilepsy patients. The studies on feature extraction and feature classification from Electroencephalography(EEG) signal in seizure prediction methods have shown great improvement these years. However, the variation issue of EEG signal (being awake, being asleep, severity of epilepsy, etc.) poses a fundamental difficulty in seizure prediction problem. The traditional off-line training method trains the model using a fixed training set, and expects the performance of the model to remain stable even after a long period of time, and thus suffers from variation issue. In this paper, we propose an on-line retraining method to leverage the recent input data by gradually enlarging the training set and retraining the model. Also, a simple post-processing scheme is incorporated to reduce false alarms. We develop our method based on the state of the art machine learning based classification of bivariate patterns method. The performance of the method is evaluated on Electrocorticogram(ECoG) recording from Freiburg database as well as long-term scalp EEG recording from CHB-MIT EEG Database and National Taiwan University Hospital. The proposed method achieves 74.2% sensitivity on ECoG database and 52.2% sensitivity on scalp EEG database, while improving the sensitivity of off-line training method by 29.0% and 17.4% in ECoG database and EEG database respectively. The experimental result suggests that on-line retraining can greatly improve the reliability and is promising for future seizure prediction method development.