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The Network of Cancer Genes (NCG) is a manually curated repository of 2372 genes whose somatic modifications have known or predicted cancer driver roles. These genes were collected from 275 publications, including two sources of known cancer genes and 273 cancer sequencing screens of more than 100 cancer types from 34,905 cancer donors and multiple primary sites. This represents a more than 1.5-fold content increase compared to the previous version. NCG also annotates properties of cancer genes, such as duplicability, evolutionary origin, RNA and protein expression, miRNA and protein interactions, and protein function and essentiality. NCG is accessible at http://ncg.kcl.ac.uk/ .

Background Genetic alterations of somatic cells can drive non-malignant clone formation and promote cancer initiation. However, the link between these processes remains unclear and hampers our understanding of tissue homeostasis and cancer development. Results Here, we collect a literature-based repertoire of 3355 well-known or predicted drivers of cancer and non-cancer somatic evolution in 122 cancer types and 12 non-cancer tissues. Mapping the alterations of these genes in 7953 pan-cancer samples reveals that, despite the large size, the known compendium of drivers is still incomplete and biased towards frequently occurring coding mutations. High overlap exists between drivers of cancer and non-cancer somatic evolution, although significant differences emerge in their recurrence. We confirm and expand the unique properties of drivers and identify a core of evolutionarily conserved and essential genes whose germline variation is strongly counter-selected. Somatic alteration in even one of these genes is sufficient to drive clonal expansion but not malignant transformation. Conclusions Our study offers a comprehensive overview of our current understanding of the genetic events initiating clone expansion and cancer revealing significant gaps and biases that still need to be addressed. The compendium of cancer and non-cancer somatic drivers, their literature support, and properties are accessible in the Network of Cancer Genes and Healthy Drivers resource at http://www.network-cancer-genes.org/ .

Background Identifying the complete repertoire of genes that drive cancer in individual patients is crucial for precision oncology. Most established methods identify driver genes that are recurrently altered across patient cohorts. However, mapping these genes back to patients leaves a sizeable fraction with few or no drivers, hindering our understanding of cancer mechanisms and limiting the choice of therapeutic interventions. Results We present sysSVM2, a machine learning software that integrates cancer genetic alterations with gene systems-level properties to predict drivers in individual patients. Using simulated pan-cancer data, we optimise sysSVM2 for application to any cancer type. We benchmark its performance on real cancer data and validate its applicability to a rare cancer type with few known driver genes. We show that drivers predicted by sysSVM2 have a low false-positive rate, are stable and disrupt well-known cancer-related pathways. Conclusions sysSVM2 can be used to identify driver alterations in patients lacking sufficient canonical drivers or belonging to rare cancer types for which assembling a large enough cohort is challenging, furthering the goals of precision oncology. As resources for the community, we provide the code to implement sysSVM2 and the pre-trained models in all TCGA cancer types ( https://github.com/ciccalab/sysSVM2 ).

A detailed energy flow model consisting of 56 living and 3 non-living compartments was assembled for the intertidal area of the Sylt-Rømø Bight. The model depicts the biomass of each compartment, carbon flow between the components, imports and exports, as well as an energy budget for each. The food web was analysed by means of network analysis which showed that about 17% of the total daily flow through the system is recycled through a complex cycling structure consisting of 1197 cycles. The cycling network indicated that about 99% of the recycling involves 2 to 3 compartments, with sediment bacteria and particulate organic carbon (POC) participating in most instances. Input/output analyses indicated that phytoplankton production in the Bight does not satisfy the demands of filter-feeders on an annual average basis so that about 160 mgC m–2d–1of phytoplankton have to be imported. We compared several dimensionless system level indices, such as internalised and normalised A/DC (ascendancy/development capacity) ratios, calculated for the Bight with those of other marine and estuarine ecosystems on a global basis. These comparisons showed that energy is rather inefficiently transferred within the Bight at a mean trophic efficiency index of 2.61%, and that most of the system level indices are lower than those of other coastal ecosystems. However, higher values were obtained for flow diversity and food web connectance compared to other systems. This study has revealed the Bight to be a highly complex system whose energy pathways appear to be sensitive to external perturbations.

A synthesis of community and ecosystem ecology should yield insights into the role of species in ecosystem function. Concepts from these subdisciplines of ecology, specifically species compensation and ecosystem homeostasis, can be linked by analyzing the effect of changes in the abundance of species on ecosystem processes. Compensatory changes in species populations in response to environmental fluctuations can maintain an approximate steady state between rates of resource supply and resource consumption. We predict that ecosystem-level properties, such as species richness, total population, biomass, and energy use, will exhibit less variability in response to environmental change than will species composition. We tested this prediction using long-term data of a desert rodent community responding to natural environmental fluctuations and of a plant community responding to experimental manipulations. For the rodents, species composition was twice as variable as the ecosystem properties. This result was the same for both the analysis of variability around the 22-yr average and the analysis of variability from one time period to the next. For the plant communities, species composition was more variable among treatments in most years than stem count or species richness. Using the variance ratio proposed by J. L. Klug et al. we detected negative covariances in the rodent community, confirming the presence of compensatory dynamics.

A systematic design approach is proposed for medical splints for individualized treatment of the distal radius fracture. An initial split structural model is first constructed by 3D scanning of an injured limb. Based on the biomechanical theory and clinical experiences, the topology optimization method is applied to design the splint structure. The optimized lightweight splint is realized by additive manufacturing using polylactic acid. Compared to the traditional designs for the distal radius fracture, the optimized design by the proposed approach exhibits a weight reduction of more than 40%. Besides, the mechanical properties of the splint meet the requirements of medical treatment according to the simulation results. Numerical examples are provided to demonstrate the applicability of the approach.

The impact of internet and information systems across various domains have resulted in substantial generation of multidimensional datasets. The use of data mining and knowledge discovery techniques to extract the original information contained in the multidimensional datasets play a significant role in the exploitation of complete benefit provided by them. The presence of large number of features in the high dimensional datasets incurs high computational cost in terms of computing power and time. Hence, feature selection technique has been commonly used to build robust machine learning models to select a subset of relevant features which projects the maximal information content of the original dataset. In this paper, a novel Rough Set based K – Helly feature selection technique (RSKHT) which hybridize Rough Set Theory (RST) and K – Helly property of hypergraph representation had been designed to identify the optimal feature subset or reduct for medical diagnostic applications. Experiments carried out using the medical datasets from the UCI repository proves the dominance of the RSKHT over other feature selection techniques with respect to the reduct size, classification accuracy and time complexity. The performance of the RSKHT had been validated using WEKA tool, which shows that RSKHT had been computationally attractive and flexible over massive datasets.

Cardiac electrical activity is mainly evaluated by monitoring the electrical biosignals. This requires a long-lasting power supply to make implantable devices cost-effective and efficient. Since the current trend is to implant catheter-free stand-alone electrodes (implantable cardiac monitors), the need for smaller devices is at odds with the need for long-life batteries. To avoid these problems, we propose a passive endocardial sensor able to monitor the movement of the considered chamber based on a permanent magnet shaped for implantation in the internal chamber of the heart (i.e. the right atrium) and an external gauss meter unit to measure sensor-induced magnetic field variations. Since the magnet is permanent, no replacement is needed after the first implant, thereby reducing the risks linked to invasive procedures, and the battery in the external device can be substituted more easily. To test our idea we used a permanent magnet mounted on the tip of a commercial catheter for heart mapping together with a dedicated gauss meter built in our laboratory. The device was tested in vitro and the magnetic field variations were acquired and measured in different conditions of movement and distances. The results demonstrate the feasibility of our approach and open an interesting new scenario where permanent magnets can be used to monitor the mechanical behaviour of the heart.

As we face issues of fossil fuel depletion and environmental pollution, it is becoming increasingly important to transition towards clean renewable energies and electric vehicles (EVs). However, designing electric motors with high power density for EVs can be challenging due to space and weight constraints, as well as issues related to power loss and temperature rise. In order to overcome these challenges, a significant amount of research has been conducted on designing high-power-density electric motors with advanced materials, improved physical and mathematical modeling of materials and the motor system, and system-level multidisciplinary optimization of the entire drive system. These technologies aim to achieve high reliability and optimal performance at the system level. This paper provides an overview of the key technologies for designing high-power-density electric motors for EVs with high reliability and system-level optimal performance, with the focus on advanced magnetic materials and the proper modeling of core losses under two-dimensional or three-dimensional vectorial magnetizations. This paper will also discuss the major challenges associated with designing these motors and the possible future research directions in the field.