Materials and possible mechanisms of extremely large magnetoresistance: A review

Magnetoresistance (MR) is a characteristic that the resistance of a substance changes with the external magnetic field, reflecting various physical origins and microstructures of the substance. A large MR, namely a huge response to a low external field, has always been a useful functional feature in industrial technology and a core goal pursued by physicists and materials scientists. Conventional large MR materials are mainly manganites, whose colossal MR (CMR) can be as high as -90%. The dominant mechanism is attributed to spin configuration aligned by the external field, which reduces magnetic scattering and thus resistance. In recent years, some new systems have shown an extremely large unsaturated MR (XMR). Unlike ordinary metals, the positive MR of these systems can reach 103-108% and is persistent under super high magnetic fields. The XMR materials are mainly metals or semimetals, distributed in high-mobility topological or non-topological systems, and some are magnetic, which suggests a wide range of application scenarios. Various mechanisms have been proposed for the potential physical origin of XMR, including electron-hole compensation, steep band, ultrahigh mobility, high residual resistance ratio, topological fermions, etc. It turns out that some mechanisms play a leading role in certain systems, while more are far from clearly defined. In addition, the researches on XMR are largely overlapped or closely correlated with other recently rising physics and materials researches, such as topological matters and two-dimensional (2D) materials, which makes elucidating the mechanism of XMR even more important. Moreover, the disclosed novel properties will lay a broad and solid foundation for the design and development of functional devices. In this review, we will discuss several aspects in the following order: (I) Introduction, (II) XMR materials and classification, (III) Proposed mechanisms for XMR, (IV) Correlation with other systems (featured), and (V) Conclusions and outlook.

Analysis of Changes in the Tensile Bond Strenght of Soft Relining Material with Acrylic Denture Material

Abrasions and pressure ulcers on the oral mucosa are most often caused by excessive pressure or incorrect fitting of the denture. The use of soft relining materials can eliminate pain sensations and improve patient comfort. The main functional feature of soft elastomeric materials is the ability to discharge loads from the tissues of the mucosa. (1) Background: The aim of the work was a comparative laboratory study of ten materials used for the soft lining of acrylic dentures. (2) Methods: There were materials based on acrylates (Vertex Soft, Villacryl Soft, Flexacryl Soft) and silicones (Sofreliner Tough Medium, Sofreliner Tough Medium, Ufi Gel SC, GC Reline Soft, Elite Soft Relining, Molloplast). Laboratory tests include the analysis of the tensile bond strength between the relining material and the acrylic plate of the prosthesis. The tests were conducted taking into account 90-day term aging in the distilled water environment based on the methodology presented in the European Standard ISO 10139-2. (3) Results: After three months of observation, the highest strength of the joint was characterized by Flexacryl Soft acrylic, for which the average value was 2.5 MPa. The lowest average value of 0.89 MPa was recorded for the GC Reline Soft silicone material. Over time, an increase in the value of the strength of the combination of acrylic materials and a decrease in these values in the case of silicone materials was observed. (4) Conclusion: Each of the tested silicone materials showed all three types of damage, from adhesive to mixed to cohesive. All acrylic-based materials showed an adhesive type of failure. Time did not affect the type of destruction.

Functional Importance of Surface Texture Parameters

Areal 3D analysis of surface texture gives more opportunities than a study of 2D profiles. Surface topography evaluation, considered as 3D dimensional analysis in micro or nanoscales, plays an important role in many fields of science and life. Among many texture parameters, those connected with height are the most often used. However, there are many other parameters and functions that can provide additional important information regarding functional behaviour of surfaces in different applications. The knowledge about the functional importance of various surface properties is low. This review tries to fill this gap. Surface texture parameters are presented in various groups: height, spatial, hybrid, functional, feature, and others. Based on experiences of the present authors and literature review, the relationships among various surface parameters and functional properties are described. A proposal of a selection of parameters on the basis of their functional significations is provided. Considerations for future challenges are addressed.

Analysis of the Hardness of Soft Relining Materials for Removable Dentures

The main functional feature of elastomeric soft linings materials is the ability to discharge loads in the tissues of the mucosa. As a result, there are fewer injuries to the mucosa and chewing ability increases. In addition, these prostheses are more comfortable in the patient’s opinion. To obtain the equal distribution of forces on the muco-bone basis and to reduce the traumatizing effect of the denture plate for patients using full dentures, soft lining materials can be used. Aim of the study: the aim of the work was a comparative laboratory study of ten materials used for soft lining of acrylic complete dentures. Methodology: Materials based on acrylates (Vertex Soft, Villacryl Soft, Flexacryl Soft) and on silicones (Sofreliner Tough Medium, Sofreliner Tough Medium, Ufi Gel SC, GC Reline Soft, Elite Soft Relining, Molloplast) were compared. Laboratory tests include tests of changes in Shore’a A hardness of soft lining material. The tests were conducted taking into account 90 day term aging in the distilled water environment based on the methodology presented in the European Standard ISO 10139-2. Results: For most silicone materials, only small changes in hardness were found in the range of 0.7 (Ufi Gel SC) to 3.3 (Sofreliner Tough Medium) on the Shore A scale. The exception was GC Reline Soft, for which a marked increase in hardness was noted. All materials based on acrylates were characterized by successive increase in hardness over time. However, in the case of the Vertex Soft material, the increase in hardness was relatively small (5.5 ShA).

Modeling and Predicting Performance of Autonomous Rotary Drilling System Using Machine Learning Techniques

This paper presents the development of predictive optimization models for autonomous rotary drilling systems where emphasis is placed on the shift from human (manual) operation as the driving force for drill rate performance to Quantitative Real-time Prediction (QRP) using machine learning. The methodology employed in this work uses real-time offset drilling data with machine learning models to accurately predict Rate of Penetration (ROP) and determine optimum operating parameters for improved drilling performance. Two optimization models (physics-based and energy conservation) were tested using Artificial Neutral Network (ANN) algorithm. Results of analysis using the model performance assessment criteria; correlation coefficient (R2) and Root Mean Square Error (RMSE), show that drill rate is non-linear in nature and the machine learning model (ANN) using energy conservation is most accurate for predicting ROP due to its ability in establishing a functional feature vector based on learning from past events.

Isonicotinamide extends yeast chronological lifespan through a mechanism that diminishes nucleotides

Chronological lifespan (CLS) of budding yeast, Saccharomyces cerevisiae, is a commonly utilized model for cellular aging of non-dividing cells such as neurons. CLS is strongly extended by isonicotinamide (INAM), a non-metabolized isomer of the NAD+ precursor nicotinamide (NAM), but the underlying mechanisms of lifespan extension remain uncharacterized. To identify potential biochemical INAM targets, we performed a chemical genetic screen with the yeast gene knockout (YKO) strain collection for INAM-hypersensitive mutants. Significantly enriched Gene Ontology terms that emerged included SWR1 and other transcription elongation factors, as well as metabolic pathways converging on one-carbon metabolism and contributing to nucleotide biosynthesis, together suggesting that INAM perturbs nucleotide pools. In line with this model, INAM effects on cell growth were synergistic with mycophenolic acid (MPA), which extends lifespan by reducing guanine nucleotide pools. Direct measurements of nucleotides and precursors by mass spectrometry indicated that INAM reduced nucleotides, including cAMP, at 24- and 96-hour time points post-inoculation. Taken together, we conclude that INAM extends CLS by perturbing nucleotide metabolism, which may be a common functional feature of multiple anti-aging interventions.

Fate transitions in Drosophila neural lineages: a single cell road map to mature neurons.

Brain development requires the formation of thousands of neurons and glia and their coordinated maturation to ensure correct circuit formation. Neuronal fate is determined by several layers of gene regulatory networks during neuronal lineage progression. Once specified, neurons must still undergo a critical maturation period, involving the stepwise expression of functional feature genes such as cell surface molecules, ion channels or neurotransmitter receptors. The precise mechanisms that govern neuronal maturation remain however poorly understood. Here, we use single-cell RNA sequencing combined with a conditional genetic strategy to select and analyse neural lineages and their young progeny at a restrictive timepoint to investigate the transcriptional trajectories in young developing secondary neurons in the Drosophila larval brain. Our findings reveal that neuron maturation starts very quickly after neuronal birth, and sub-divide the process of maturation into 3 distinct phases: Phase 1 is composed by immature neurons that have yet to start expressing mRNA of mature neuronal features; Phase 2 includes neurons that start transcribing but not translating maturation markers such as neurotransmitter genes; Phase 3 neurons start translating mature neuronal features, in a coordinated fashion with the animal developmental stage. This dataset represents a complete transcriptomic characterization of the neural lineages generated at this larval stage in the central brain and ventral nerve cord. Its analysis has also allowed for the characterization of a yet undefined transitional state, the immature ganglion mother cells, and has proven useful for the identification of known and novel fate regulators.

Protein Dynamics and Substrate Protonation State Mediate the Catalytic Action of Trans-4-Hydroxy-L-Proline Dehydratase

The enzyme <i>trans</i>-4-Hydroxy-L-proline (Hyp) dehydratase (HypD) is among the most abundant glycyl radical enzymes (GREs) in the healthy human gut microbiome and is considered a promising antibiotic target for the prominent antibiotic-resistant pathogen <i>C</i><i>lostridium difficile.</i> Although an enzymatic mechanism has been proposed, the role of the greater HypD protein environment in mediating radical reactivity is not well understood. To fill this gap in understanding, we investigate HypD across multiple time- and length- scales using electronic structure modeling and classical molecular dynamics. We observe that the Hyp substrate protonation state significantly alters both its enzyme-free reactivity and its dynamics within the enzyme active site. Accurate coupled cluster modeling suggests the deprotonated form of Hyp to be the most reactive protonation state for C5–H_pro-<i>S</i> activation. In the protein environment, hydrophobic interactions modulate the positioning of Cys434 radical to enhance the reactivity of C5–H_pro-<i>S</i> abstraction. Long-time dynamics reveal that changing Hyp protonation states triggers the switching of a Leu643-gated water tunnel, a functional feature that has not yet been observed for members of the GRE superfamily.

Protein Dynamics and Substrate Protonation State Mediate the Catalytic Action of Trans-4-Hydroxy-L-Proline Dehydratase

The enzyme <i>trans</i>-4-Hydroxy-L-proline (Hyp) dehydratase (HypD) is among the most abundant glycyl radical enzymes (GREs) in the healthy human gut microbiome and is considered a promising antibiotic target for the prominent antibiotic-resistant pathogen <i>C</i><i>lostridium difficile.</i> Although an enzymatic mechanism has been proposed, the role of the greater HypD protein environment in mediating radical reactivity is not well understood. To fill this gap in understanding, we investigate HypD across multiple time- and length- scales using electronic structure modeling and classical molecular dynamics. We observe that the Hyp substrate protonation state significantly alters both its enzyme-free reactivity and its dynamics within the enzyme active site. Accurate coupled cluster modeling suggests the deprotonated form of Hyp to be the most reactive protonation state for C5–H_pro-<i>S</i> activation. In the protein environment, hydrophobic interactions modulate the positioning of Cys434 radical to enhance the reactivity of C5–H_pro-<i>S</i> abstraction. Long-time dynamics reveal that changing Hyp protonation states triggers the switching of a Leu643-gated water tunnel, a functional feature that has not yet been observed for members of the GRE superfamily.

Project Rosetta: a childhood social, emotional, and behavioral developmental feature mapping

Background A wide array of existing instruments are commonly used to assess childhood behavior and development for the evaluation of social, emotional and behavioral disorders such as Autism Spectrum Disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and anxiety. Many of these instruments either focus on one diagnostic category or encompass a broad set of childhood behaviors. We analyze a wide range of standardized behavioral instruments and identify a comprehensive, structured semantic hierarchical grouping of child behavioral observational features. We use the hierarchy to create Rosetta: a new set of behavioral assessment questions, designed to be minimal yet comprehensive in its coverage of clinically relevant behaviors. We maintain a full mapping from every functional feature in every covered instrument to a corresponding question in Rosetta. Results In all, 209 Rosetta questions are shown to cover all the behavioral concepts targeted in the eight existing standardized instruments. Conclusion The resulting hierarchy can be used to create more concise instruments across various ages and conditions, as well as create more robust overlapping datasets for both clinical and research use.