A speedy cardiovascular diseases classifier using multiple criteria decision analysis

Each year, some 30 percent of global deaths are caused by cardiovascular diseases. This figure is worsening due to both the increasing elderly population and severe shortages of medical personnel. The development of a cardiovascular diseases classifier (CDC) for auto-diagnosis will help address solve the problem. Former CDCs did not achieve quick evaluation of cardiovascular diseases. In this letter, a new CDC to achieve speedy detection is investigated. This investigation incorporates the analytic hierarchy process (AHP)-based multiple criteria decision analysis (MCDA) to develop feature vectors using a Support Vector Machine. The MCDA facilitates the efficient assignment of appropriate weightings to potential patients, thus scaling down the number of features. Since the new CDC will only adopt the most meaningful features for discrimination between healthy persons versus cardiovascular disease patients, a speedy detection of cardiovascular diseases has been successfully implemented

Luminescent platinum(II), palladium(II) and gold(III) complexes containing isocyanide, alkynyl and N-heterocyclic carbene ligands : synthesis, photophysical properties and material applications

Several cyclometalated Pt(II) isocyanide complexes containing C-deprotonated C^N^C ligands (C^N^C = 2,6-diphenylpyridine derivatives) were synthesized. These complexes display orange-red emissions with max at 582–619 nm and quantum yields of up to 26% in CH2Cl2 at room temperature. The incorporation of carbazole/fluorene/thiophene unit(s) to C^N^C ligands leads to minimized structural distortion of complexes in their excited states and thereby suppresses non-radiative decay pathways. The high thermal stability (Td >300 °C) renders these complexes good candidates as phosphorescent dopants in organic light-emitting diodes (OLEDs). Red-emitting OLEDs with CIE coordinates of (0.650.01, 0.350.01) were fabricated by vacuum deposition, showing a maximum external efficiency of 12%. In addition, well-defined nano/microstructures were obtained from self-assembly of these complexes driven by π∙∙∙π, C–H∙∙∙π and C–H∙∙∙H–C interactions as observed in the crystal structures. Two series of organopalladium(II) alkynyl complexes containing a terpy (terpy = 2,2’:6’,2’’-terpyridine) or C^N^C pincer carbene ligand (C^N^C = 2,6-bis(1-butylimidazol-2-ylidenyl)pyridine) were prepared. These complexes are non-emissive in solution at room temperature except that the one containing both C^N^C and pyrenylacetylide ligands shows phosphorescence (Φ = 0.3%) originating from intraligand state of the acetylide ligand. This could be attributed to the strong -donating N-heterocyclic carbene (NHC) in the pincer ligand that strongly destabilizes d-d state, the population of which provides an efficient non-radiative decay channel. To make comparison between Pd(II) and Pt(II) complexes with the two ligand systems, Pt(II) C^N^C alkynyl complexes were also prepared. They are emissive in solution and some display excimer emissions at high concentration (〖10〗^(-4)–〖10〗^(-3) mol 〖10dm〗^(-3)). The X-ray crystal structures of [Pd(L)(CCPh)](〖PF〗_6) (L = terpy and C^N^C) revealed one-dimensional chain stacking of complex cations with alternating Pd(II)∙∙∙Pd(II) contacts of about 3.29–3.35 Å and π-π interactions of about 3.4 Å . Well-defined submicron/nanostructures were obtained from self-assembly of Pd(II) and Pt(II) alkynyl complexes driven by π-π interactions between aromatic moieties and/or metal∙∙∙metal interactions. DFT calculations on the optimized structures of [M(L)(CCPh)]+ (M = Pd(II) and Pt(II)) revealed the existence of metal∙∙∙metal closed-shell interactions. In addition, the complex containing the C^N^C ligand exhibits slightly enhanced metal∙∙∙metal interactions and larger “bonding” energy upon dimerization. Furthermore, spin-orbit coupling between singlet and triplet excited states is more effective which promotes rapid intersystem crossing. A new class of cyclometalated Au(III) complexes containing C-deprotonated C^N ligands (C^N = 2-phenylpyridine and its derivatives) and cis-chelating bis-NHC ligands was synthesized. These are the first examples of Au(III) complexes supported by cis-chelating bis-NHC ligands. They display emissions in solution under degassed condition at room temperature with λmax at 498–633 nm and quantum yields of up to 10.1%. Some exhibit dual emissions which are assigned to prompt fluorescence and phosphorescence. With a sulfonate-functionalized bis-NHC ligand, water-soluble luminescent Au(III) complexes were prepared. They show similar photophysical properties in water when compared with their counterparts. The long phosphorescence lifetime renders a dual emissive complex able to function as ratiometric sensor for oxygen. Moreover, one of the water-soluble complexes displayed a significant inhibitory activity towards deubiquitinase UCHL3 with IC50 value of 0.15 μM.published_or_final_versionChemistryDoctoralDoctor of Philosoph

Protective Factors of Demoralization among Cancer Patients in Taiwan: An Age-matched and Gender-matched Study

Summary: Purpose: This study aimed to explore the protective factors of demoralization in cancer patients via investigation of cancer patients' demographic and disease characteristics. Methods: This was a cross-sectional descriptive study. We used a structured questionnaire, which contained items on demographic and disease characteristics, as well as the Demoralization Scale Mandarin Version (DS-MV), with a cutoff of 30 or more indicating high demoralization. Data were analyzed with age-matched and gender-matched conditional logistic regression analysis. For the study, 428 questionnaires were delivered and 411 were recovered. After being age-matched and gender-matched, 182 participants of high demoralization (DS-MV > 30) and low demoralization (DS-MV ≤ 30) were obtained respectively, for a total of 364 participants. Results: Cancer patients' demoralization was significantly related to family support (p = .019), education (p = .049), and monthly income (p = .001). Family support [odds ratio = 0.38; p = .028; 95% confidence interval (0.16, 0.91)] and monthly income [odds ratio = 0.49; p = .009; 95% confidence interval (0.29, 0.84)] were protective factors of demoralization in cancer patients. Conclusion: Early and appropriate demoralization assessment of cancer patients' demographic and disease characteristics is very important in clinical settings. Healthcare providers might regularly monitor demoralization in cancer patients, and develop related nursing care guidelines or treatment for demoralization in cancer patients. The study results can be a reference for healthcare providers who work with cancer patients. Keywords: cancer, odds ratio, protective factor

Proximity Environmental Feature Based Tree Health Assessment Scheme Using Internet of Things and Machine Learning Algorithm

Improperly grown trees may cause huge hazards to the environment and to humans, through e.g., climate change, soil erosion, etc. A proximity environmental feature-based tree health assessment (PTA) scheme is proposed to prevent these hazards by providing guidance for early warning methods of potential poor tree health. In PTA development, tree health is defined and evaluated based on proximity environmental features (PEFs). The PEF takes into consideration the seven surrounding ambient features that strongly impact tree health. The PEFs were measured by the deployed smart sensors surrounding trees. A database composed of tree health and relative PEFs was established for further analysis. An adaptive data identifying (ADI) algorithm is applied to exclude the influence of interference factors in the database. Finally, the radial basis function (RBF) neural network (NN), a machine leaning algorithm, has been identified as the appropriate tool with which to correlate tree health and PEFs to establish the PTA algorithm. One of the salient features of PTA is that the algorithm can evaluate, and thus monitor, tree health remotely and automatically from smart sensor data by taking advantage of the well-established internet of things (IoT) network and machine learning algorithm

High Accuracy Localization of Long Term Evolution Based on a New Multiple Carrier Noise Model

A high accuracy localization technique using Long Term Evolution (LTE) based on a new and accurate multiple carrier noise model has been developed. In the noise consideration, the LTE multiple carriers phase noise has been incorporated so that a new and accurate noise model is achieved. An experiment was performed to characterize the phase noise of carriers at 2 GHz. The developed noise model was incorporated into LTE localization analysis in a high traffic area in Hong Kong to evaluate the accuracy of localization. The evaluation and analysis reveals that the new localization method achieves an improvement of about 10% accuracy comparing to existing widely adopted schemes