Modularity in FOLIO: principles, techniques and tools

From its earliest inception, FOLIO was conceived not as an ILS (Integrated Library System), but as a true Services Platform, composed of many independent but interdependent modules, and forming a foundation on which an ILS or other library software could be built out of relevant modules. This vision of modularity is crucial to FOLIO’s appeal to the library community, because it lowers the bar to participation: individual libraries may create modules that meet their needs, or hire developers to do so, or contribute to funding modules that will be of use to a broader community — all without needing “permission” from a central authority. The technical design of FOLIO is deeply influenced by the requirements of modularity, with the establishment of standard specifications and an emphasis on machine-readable API descriptions. While FOLIO’s modular design has proved advantageous, it also introduces difficulties, including cross-module searching and data consistency. Some conventions have been established to address these difficulties, and others are in the process of crystallizing. As the ILS built on FOLIO’s platform grows and matures, and as other application suites are built on it, it remains crucial to resist the shortcuts that monolithic systems can benefit from, and retain the vision of modularity that has so successfully brought FOLIO this far.

Ring Cross-Over Based Ga For Dfmb Chip Design And Medical Image Compression

: Currently, the clinical data stored in the cloud is easily accessible, and the patient’s data can be shared among treatment centers. In such a case, to handle additional data, the cloud data must be of a lesser scale. A process of compression was introduced to minimize the data with no losing data in order to achieve this size reduction. This paper conducts the experiment in two approaches: fast routing operations and compression from the chip in the DMFB approach. To apply this process of compression, the collected data from the chip was transformed into an image, and then compression of the image was performed utilizing a genetic algorithm (GA) based on a ring crossover. Consequently, the biochip of the 8x8 array is integrated into the power and area with the ring cross-module for an effective energy consumption operation. The technique of the process is utilized by the Microfluidic (MF) feature to handle and maintain the droplets. Also, the optimization process is performed by combining related pin actuation segments in parallel and the control pin to prevent pin-actuation conflicts. Through the optimization process, it synchronizes the length. This proposed approach decreases the consumption of the power and area. The outcome of the simulation indicates an increase in dynamic power, static power, and delay. Image compression is performed with the aid of this algorithm. In addition, for better outcomes, this GA compression application was contrasted with wavelet compressions.

Integrative framework of cross-module deep biomarker for the prognosis of clear cell renal cell carcinoma

PurposeWe aimed to integrate cross-module data for predicting the prognosis of clear cell renal cell carcinoma (ccRCC) based on deep learning and to explore the relationship between deep features from images and eigengenes form gene data.Experimental designA total of 209 patients with ccRCC with computed tomography (CT), histopathological images and RNA sequences were enrolled. A deep biomarker-based integrative framework was proposed to construct a prognostic model. Deep features extracted from CT and histopathological images by using deep learning combined with eigengenes generated from functional genomic data were used to predict ccRCC prognosis. Furthermore, the relationship between deep features and eigengenes was explored, and two survival subgroups identified by integrative cross-module biomarkers were subjected to functional analysis.ResultsThe model based on the integrative framework stratified two subgroups of patients with a significant prognostic difference (P = 6.51e-6, concordance index [C-index] = 0.808, 95% confidence interval [CI] = 0.728-0.888) and outperformed the prediction based on their individual biomarkers in the independent validation cohort (n = 70, gene data: C-index = 0.452, CI = 0.336-0.567; histopathological images: C-index = 0.677, CI = 0.577-0.776; CT images: C-index = 0.774, CI = 0.670-0.879). On the basis of statistical relationship, deep features correlated or complemented with eigengenes both enhanced the predictive performance of eigengenes (P = 0.439, correlated: C-index = 0.785, CI = 0.685-0.886; complemented: C-index = 0.778, CI = 0.683-0.872). The functional analysis of subgroups also exhibited reasonable results.ConclusionThe model based on the integrative framework of cross-module deep biomarkers can efficiently predict ccRCC prognosis, and the framework with a code is shared to act as a reliable and powerful tool for further studies.