Randomized controlled trials (RCT) are the gold standards for evaluating the efficacy and safety of therapeutic interventions in human subjects. In addition to the pre-specified endpoints, trial participants\' experience reveals the time course of the intervention. Few analytical tools exist to summarize and visualize the individual experience of trial participants. Visual analytics allows integrative examination of temporal event patterns of patient experience, thus generating insights for better care decisions. Towards this end, we introduce TrialView, an information system that combines graph artificial intelligence (AI) and visual analytics to enhance the dissemination of trial data. TrialView offers four distinct yet interconnected views: Individual, Cohort, Progression, and Statistics, enabling an interactive exploration of individual and group-level data. The TrialView system is a general-purpose analytical tool for a broad class of clinical trials. The system is powered by graph AI, knowledge-guided clustering, explanatory modeling, and graph-based agglomeration algorithms. We demonstrate the system\'s effectiveness in analyzing temporal event data through a case study.

In this work, a computational spectroscopy approach was used to provide a complete assignment of the inelastic neutron scattering spectra of three title alkoxysilane derivatives-3-aminopropyltrimethoxysilane (APTS), N-methyl-3-aminopropyltrimethoxysilane (MAPTS), and 3-aminopropyltriethoxysilane (APTES). The simulated spectra obtained from density functional theory (DFT) calculations exhibit a remarkable match with the experimental spectra. The description of the experimental band profiles improves as the number of molecules considered in the theoretical model increases, from monomers to trimers. This highlights the significance of incorporating non-covalent interactions, encompassing classical NH···N, N-H···O, as well as C-H···N and C-H···O hydrogen bond contacts, to achieve a comprehensive understanding of the system. A distinct scenario emerges when considering optical vibrational techniques, infrared and Raman spectroscopy. In these instances, the monomer model provides a reasonable description of the experimental spectra, and no substantial alterations are observed in the simulated spectra when employing dimer and trimer models. This observation underscores the distinctive ability of neutron spectroscopy in combination with DFT calculations in assessing the structure and dynamics of molecular materials.

In this study, the physicochemical properties and molecular interactions between zirconium-based metal-organic framework (UiO-66) and three different ionic liquids based on bis(trifluoromethanesulfonyl)imide anion (EMIM+, BMIM+ and OMIM+) was performed via a combined experimental and computational approach. The ionic liquid loaded UiO-66 or IL@UiO-66 was synthesized and characterized to understand the host-guest interaction. Density functional theory calculation was performed to analyse the electronic structure of IL@UiO-66 to provide molecular insight on the dominant interactions occurred in the hybrid material. Results showed that all ILs were successfully incorporated into the micropores of UiO-66. The 3D framework was retained even after loaded with ILs as analyzed from XRD pattern. FTIR spectrum reveals that interactions of ILs with UiO-66 influenced by the alkyl chain length of the cation. The anion has a profound affinity with the UiO-66 due to the presence of electronegative atoms. Phase transition study from DSC suggested that the incorporation of ILs has stabilized the framework of UiO-66 by shifting the endothermic peak to a higher state. These findings were further elaborated with DFT calculation. Geometrical optimizations confirmed the structural parameter changes of UiO-66 when loaded with ILs. These was mainly contributed by the non-covalent interactions which was confirmed by the reduced density gradient scattered plot. Another important findings are the strength of hydrogen bonding at the host-guest interface was influenced by the alkyl chain length. The molecular orbital analysis also shows that the size of alkyl chain influence the reactivity of the hybrid material. The present study provides fundamental insights on the molecular interaction of UiO-66 and ILs as a hybrid material, which can open new possibilities for advanced material for metal-organic framework applications in energy storage system, catalysis, gas storage and medicinal chemistry.

Location-based services (LBS) are widely used due to the rapid development of mobile devices and location technology. Users usually provide precise location information to LBS to access the corresponding services. However, this convenience comes with the risk of location privacy disclosure, which can infringe upon personal privacy and security. In this paper, a location privacy protection method based on differential privacy is proposed, which efficiently protects users\' locations, without degrading the performance of LBS. First, a location-clustering (L-clustering) algorithm is proposed to divide the continuous locations into different clusters based on the distance and density relationships among multiple groups. Then, a differential privacy-based location privacy protection algorithm (DPLPA) is proposed to protect users\' location privacy, where Laplace noise is added to the resident points and centroids within the cluster. The experimental results show that the DPLPA achieves a high level of data utility, with minimal time consumption, while effectively protecting the privacy of location information.

During channel modeling for high-mobility channels, such as high-speed train (HST) channels, the velocity of the mobile radio station is assumed to be constant. However, this might not be realistic due to the dynamic movement of the train along the track. Therefore, in this paper, an enhanced Gauss-Markov mobility model with a 3D non-stationary geometry based stochastic model (GBSM) for HST in MIMO Wireless Channels is proposed. The non-isotropic scatterers within a cluster are assumed to be around the sphere in which the mobile relay station (MRS) is located. The multi-path components (MPCs) are modeled with varying velocities, whereas the mobility model is a function of time. The MPCs are represented in a death-birth cluster using the Markov process. Furthermore, the channel statistics, i.e., the space-time correlation function, the root-mean-square Doppler shift, and the quasi-stationary interval, are derived from the non-stationary model. The model shows how the quasi-stationary time increases from 0.21 to 0.451 s with a decreasing acceleration of 0.6 to 0.2 m/s2 of the HST. In addition, the impact of the distribution of the angles on the channel statistics is presented. Finally, the simulated results are compared with the measured results. Therefore, there is a close relationship between the proposed model and the measured results, and the model can be used to characterize the channel\'s properties.

Laccases are regarded as versatile green biocatalysts, and recent scientific research has focused on improving their redox potential for broader industrial and environmental applications. The density functional theory (DFT) quantum mechanics approach, sufficiently rigorous and efficient for the calculation of electronic structures, is conducted to better comprehend the connection between the redox potential and the atomic structural feature of laccases. According to the crystal structure of wild type laccase CueO and its variant, a truncated miniature cluster model method was established in this research. On the basic of thermodynamic cycle, the overall Gibbs free energy variations before and after the one-electron reduction were calculated. It turned out that the trends of redox potentials to increase after variant predicted by the theoretical calculations correlated well with those obtained by experiments, thereby validating the feasibility of this cluster model method for simulating the redox potentials of laccases.

The study substantiates the possibility of the cluster approach for the development of the healthcare system in Russia. The analysis of foreign experience in the functioning of medical clusters made it possible to identify factors that contribute to their successful development. Based on the assessment of the domestic practice of creating clusters in the healthcare sector, their typification was carried out with the identification of groups of high-tech clusters of biomedical technologies and innovations, medical clusters of drug provision and specialized equipment, clusters of medical services, internships and transfer of medical innovations. The grouping of clusters in the healthcare sector operating in the Russian Federation according to the proposed types showed that there are practically no clusters of medical services that are able to bring the quality of medical care to the population to a higher level in the country. The article identifies the problems that prevent the formation of clusters of medical services on the territory of the regions. To solve these problems, an approach was proposed to create a medical technology park on the basis of public-private partnership, which will serve as the basis for a cluster of medical services and built its structural and functional model.

Starch is a water-insoluble polymer of glucose synthesized as discrete granules inside the stroma of plastids in plant cells. Starch reserves provide a source of carbohydrate for immediate growth and development, and act as long term carbon stores in endosperms and seed tissues for growth of the next generation, making starch of huge agricultural importance. The starch granule has a highly complex hierarchical structure arising from the combined actions of a large array of enzymes as well as physicochemical self-assembly mechanisms. Understanding the precise nature of granule architecture, and how both biological and abiotic factors determine this structure is of both fundamental and practical importance. This review outlines current knowledge of granule architecture and the starch biosynthesis pathway in relation to the building block-backbone model of starch structure. We highlight the gaps in our knowledge in relation to our understanding of the structure and synthesis of starch, and argue that the building block-backbone model takes accurate account of both structural and biochemical data.

We present the study of the phosphorus local environment by using 31P MAS NMR in a series of seven double monophosphates MIIMIV(PO4)2 (MII and MIV being divalent and tetravalent cations, respectively) of yavapaiite and low-yavapaiite type crystal structures. Solid-state and cluster DFT calculations were found to be efficient for predicting the 31P isotropic chemical shift and chemical shift anisotropy. To achieve this performance, however, a proper computational optimisation of the experimental structural data was required. From the three optimisation methods tested, the full optimisation provided the best reference structure for the calculation of the NMR parameters of the studied phosphates. Also, a better prediction of the chemical shifts was possible by using a correction to the GIPAW calculated shielding.

To explore the parotid normal tissue complication probability (NTCP) modeling with percolation-based dose clusters for head-and-neck patients receiving concomitant chemotherapy and radiation therapy. Cluster models incorporating the spatial dose distribution in the parotid gland were developed to evaluate the radiation induced complication. Cluster metrics including the mean cluster size (NMCS) and the largest cluster size both normalized by the gland volume (NSLC) were evaluated and scrutinized against the benchmark NTCP. Two fitting strategies to the Lyman-Kutcher-Burman (LKB) model using the maximum likelihood method were devised: the volume parameter n fixed at 1.0 (mean dose model) and unrestricted (full LKB model). The fitted parameters TD50 and m were assessed with the LKB NTCP models with the available xerostomia data. NSLC was a better metric than NMCS with reference to the LKB model and strong correlation (r ~ 0.95) was observed between NTCP and NSLC. The mean dose model returned the parameter TD50 (39.9 Gy) and m (0.4) from the NSLC of threshold dose at around 40 Gy. Drastically different TD50 and m values were obtained from the fittings via the full LKB model, where the threshold dose would be near 27 Gy. Bootstrapping analyses further confirmed the fitting outcomes. Strong correlation with the traditional NTCP models revealed that the cluster model could achieve what NTCP models attain and may offer additional information. Parameterization of the model indicated that the model could have different predictions from current clinical recommendations. Further investigation using toxicity data is under way to validate the cluster model.