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Person-centered care (PCC) has a wide range of definitions, most based on expert opinion rather than empirical analysis. The current study used an empirical concept mapping approach to identify core components of PCC used in long-term services and supports (LTSS). The aim is to help providers and researchers develop a unified set of domains that can be used to assess and improve the quality of PCC in real-world settings. Results yielded six domains describing essential elements of PCC in LTSS: Enacting Humanistic Values, Direct Care Worker Values, Engagement Facilitators, Living Environment, Communication, and Supportive Systems; and two underlying dimensions: Intrapersonal Activities and Extrapersonal Services and Social and Physical Environment. Nurses can use the results to enhance clinical knowledge and skills around delivery of PCC. Researchers can use the results to build a comprehensive and unified measure to accelerate adoption of PCC practices shown to benefit older adults, families, and the LTSS workforce. [ Journal of Gerontological Nursing, 45 (2), 6–13.]

Based on the laboratory experiments this paper presented that the primary influence factors about the electromagnetic radiation during rock fracture are the rock mechanics characters and mineral components. The brittle samples and samples contained quartz, pyrite, chalopyrite produce electromagnetic radiation easily. There are three fracture-radiation effects. The crystal fracture effect produces the high frequency electromagnetic signals, the piezoelectric effect produces low frequency signals and the natural semiconductor effect produces middle frequency signals possessed distinct wave-shapes.Key words: electromagnetic radiation rock fracture fracture-radiation effect crystal fracture effect piezoelectric effect natural semiconductor effect Experiment method and resultsThe samples are cut into cube approximately, about 30~100 cm s, and are pressed with a single axial force at uniform pressing rate. The eleetromagnetie radiation signals are recorded durhag all process pressing a sample to fracture. The sensors consist of two magnetic core antennas, a ring coil and a microphone, which respectively receive the high, middle, low frequency signals and sound signals. All the signals are amplified and recorded by a high-speed acqusition/analysis * .ACTA SEISMOLOGICA SINICA Vol. 10 system of data (DAS-820). For high, middle frequency signals, the sampling freqency are 10 MHz, the sampling length 100 kB. For low frequency signals and sound signals, the sampling frequency are 100 kHz, sampling length 70 kB. The signals are caught by inter-trigger way and trigger level is set above three times of the worksite noise level. Negative delay time 1~10 kB is set for recording the noise background. After processing the large number of acquired data, the electromagnetic radiation signals and corresponding sound signals are distinguished by the computer.The primary experiment results state that the electromagnetic radiation may be classified the high (1~5 MHz), middle (0. 1~1 MHz) and low (0. 1~100 kHz) frequency signals (For convenience the following is abbreviated to the HEMS, MEMS and LEMS respeetivly). Because it is limit by sampling frequency, the signals above 5 MHz can not be resolved. Not all kinds of rocks can produce signals during fracture. The HEMS, MEMS and LEMS appear not always at the same time. The strong signals appear while the main fracture happen. Before or after it, the weak signals appear. The time domain characters of the three kinds of signals distribution are random and earl be divided into many segments. The wave-shape characters of HEMS are vibratory pulse showers and of the LEMS are similar to non-regular sine attenuation vibration. The samples contained quartz, pyrite or ehalopyrite earl produce MEMS, whose distinct wave-shape include two kinds of haekly pules showers : sine attenuation vibration, and being similar to square waves. A detailed discussion about experiment methods, results and signal characters is published in another paper (Liu, et al. ,1997 ).

Barcode fusion genetics (BFG) utilizes deep sequencing to improve the throughput of protein–protein interaction (PPI) screening in pools. BFG has been implemented in Yeast two-hybrid (Y2H) screens (BFG-Y2H). While Y2H requires test protein pairs to localize in the nucleus for reporter reconstruction, dihydrofolate reductase protein-fragment complementation assay (DHFR-PCA) allows proteins to localize in broader subcellular contexts and proves to be largely orthogonal to Y2H. Here, we implemented BFG to DHFR-PCA (BFG-PCA). This plasmid-based system can leverage ORF collections across model organisms to perform comparative analysis, unlike the original DHFR-PCA that requires yeast genomic integration. The scalability and quality of BFG-PCA were demonstrated by screening human and yeast interactions for >11 000 bait-prey pairs. BFG-PCA showed high-sensitivity and high-specificity for capturing known interactions for both species. BFG-Y2H and BFG-PCA capture distinct sets of PPIs, which can partially be explained based on the domain orientation of the reporter tags. BFG-PCA is a high-throughput protein interaction technology to interrogate binary PPIs that exploits clone collections from any species of interest, expanding the scope of PPI assays.

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