Optimization of loading protocols for tissue engineering experiments

Tissue engineering (TE) combines cells and biomaterials to treat orthopedic pathologies. Maturation of de novo tissue is highly dependent on local mechanical environments. Mechanical stimulation influences stem cell differentiation, however, the role of various mechanical loads remains unclear. While bioreactors simplify the complexity of the human body, the potential combination of mechanical loads that can be applied make it difficult to assess how different factors interact. Human bone marrow-derived mesenchymal stromal cells were seeded within a fibrin-polyurethane scaffold and exposed to joint-mimicking motion. We applied a full factorial design of experiment to investigate the effect that the interaction between different mechanical loading parameters has on biological markers. Additionally, we employed planned contrasts to analyze differences between loading protocols and a linear mixed model with donor as random effect. Our approach enables screening of multiple mechanical loading combinations and identification of significant interactions that could not have been studied using classical mechanobiology studies. This is useful to screen the effect of various loading protocols and could also be used for TE experiments with small sample sizes and further combinatorial medication studies

Clinical characteristics and outcome of patients with autoimmune hemolytic anemia (AIHA) uniformly defined as primary by a diagnostic work-up

Primary autoimmune hemolytic anemia (P-AIHA) is a relatively uncommon and hetereogeneous disease characterized by the destruction of red blood cells due to anti-erythrocyte autoantibodies (AeAbs) in the absence of an associated disease [1–3]. Secondary AHIA is frequently associated with lymphoproliferative diseases (LD) in particular, chronic lymphocytic leukemia, aggressive or indolent lymphomas, autoimmune disorders, malignancies other than lymphoid, and infections [1,2,4]. On the hypothetical assumption that in a significant proportion of cases defined as P-AIHA the clinical heterogeneity could be due to an ignored associated disease, we retrospectively analyzed the clinical characteristics and outcome of patients with a diagnosis of P-AIHA based on a diagnostic work-up aimed at excluding or identifying an associated disease. ..

A Stimuli-Responsive Nanocomposite for 3D Anisotropic Cell-Guidance and Magnetic Soft Robotics

Stimuli-responsive materials have the potential to enable the generation of new bioinspired devices with unique physicochemical properties and cell-instructive ability. Enhancing biocompatibility while simplifying the production methodologies, as well as enabling the creation of complex constructs, i.e., via 3D (bio)printing technologies, remains key challenge in the field. Here, a novel method is presented to biofabricate cellularized anisotropic hybrid hydrogel through a mild and biocompatible process driven by multiple external stimuli: magnetic field, temperature, and light. A low-intensity magnetic field is used to align mosaic iron oxide nanoparticles (IOPs) into filaments with tunable size within a gelatin methacryloyl matrix. Cells seeded on top or embedded within the hydrogel align to the same axes of the IOPs filaments. Furthermore, in 3D, C2C12 skeletal myoblasts differentiate toward myotubes even in the absence of differentiation media. 3D printing of the nanocomposite hydrogel is achieved and creation of complex heterogeneous structures that respond to magnetic field is demonstrated. By combining the advanced, stimuli-responsive hydrogel with the architectural control provided by bioprinting technologies, 3D constructs can also be created that, although inspired by nature, express functionalities beyond those of native tissue, which have important application in soft robotics, bioactuators, and bionic devices

Real Time RF Simulator (RTS) and control

The multi-cavity RTS allows LLRF algorithm development and lab testing prior to commissioning with real cavities and cryomodules. The RTS is a valuable tool since it models the functions, errors and disturbances of real RF systems. The advantage of a RTS over an off-line simulator is that it can be implemented on the actual LLRF hardware, on the same FPGA and processor, and run at the same speed of the LLRF control loop. Additionally the RTS can be shared by collaborators who do not have access to RF systems or when the systems are not available to LLRF engineers. The RTS simulator incorporates hardware, firmware and software errors and limitations of a real implementation, which would be hard to identify and time consuming to model in off-line simulations

High-throughput screening of perovskite alloys for piezoelectric performance and thermodynamic stability

We screen a large chemical space of perovskite alloys for systems with optimal properties to accommodate a morphotropic phase boundary (MPB) in their composition-temperature phase diagram, a crucial feature for high piezoelectric performance. We start from alloy end points previously identified in a high-throughput computational search. An interpolation scheme is used to estimate the relative energies between different perovskite distortions for alloy compositions with a minimum of computational effort. Suggested alloys are further screened for thermodynamic stability. The screening identifies alloy systems already known to host an MPB and suggests a few others that may be promising candidates for future experiments. Our method of investigation may be extended to other perovskite systems, e.g., (oxy-)nitrides, and provides a useful methodology for any application of high-throughput screening of isovalent alloy systems

High-throughput screening of perovskite alloys for piezoelectric performance and thermodynamic stability

We screen a large chemical space of perovskite alloys for systems with optimal properties to accommodate a morphotropic phase boundary (MPB) in their composition-temperature phase diagram, a crucial feature for high piezoelectric performance. We start from alloy end points previously identified in a high-throughput computational search. An interpolation scheme is used to estimate the relative energies between different perovskite distortions for alloy compositions with a minimum of computational effort. Suggested alloys are further screened for thermodynamic stability. The screening identifies alloy systems already known to host an MPB and suggests a few others that may be promising candidates for future experiments. Our method of investigation may be extended to other perovskite systems, e.g., (oxy-)nitrides, and provides a useful methodology for any application of high-throughput screening of isovalent alloy systems

Database-driven High-Throughput Calculations and Machine Learning Models for Materials Design

This paper reviews past and ongoing efforts in using high-throughput ab-inito calculations in combination with machine learning models for materials design. The primary focus is on bulk materials, i.e., materials with fixed, ordered, crystal structures, although the methods naturally extend into more complicated configurations. Efficient and robust computational methods, computational power, and reliable methods for automated database-driven high-throughput computation are combined to produce high-quality data sets. This data can be used to train machine learning models for predicting the stability of bulk materials and their properties. The underlying computational methods and the tools for automated calculations are discussed in some detail. Various machine learning models and, in particular, descriptors for general use in materials design are also covered.Comment: 19 pages, 2 figure

Electrify Italy

This study explores a possible pathway to implement a new energy paradigm in Italy based on electrification. The objectives are: • To build a forward-looking vision of possible scenarios at 2022, 2030 and 2050 by integrating a multi-focus perspective on the penetration of renewables and the electrification potential of the residential, industrial and transport sectors. • To estimate the potential benefits of further electrification through the calculation of Key Performance Indicators in four different areas: energy, economy, environment and society. The study shows how the electricity triangle, a paradigm based on clean generation by renewable sources, electrification of final uses, and electricity exchange through efficient smart grids, closes the loop of clean energy and efficient consumption. This leads to improvements in energy, environment, economy and social performances, and boosts the share of renewables in final consumption