Translational Connectomics: overview of machine learning in macroscale Connectomics for clinical insights.

Connectomics is a neuroscience paradigm focused on noninvasively mapping highly intricate and organized networks of neurons. The advent of neuroimaging has led to extensive mapping of the brain functional and structural connectome on a macroscale level through modalities such as functional and diffusion MRI. In parallel, the healthcare field has witnessed a surge in the application of machine learning and artificial intelligence for diagnostics, especially in imaging. While reviews covering machine learn ing and macroscale connectomics exist for specific disorders, none provide an overview that captures their evolving role, especially through the lens of clinical application and translation. The applications include understanding disorders, classification, identifying neuroimaging biomarkers, assessing severity, predicting outcomes and intervention response, identifying potential targets for brain stimulation, and evaluating the effects of stimulation intervention on the brain and connectome mapping in patients before neurosurgery. The covered studies span neurodegenerative, neurodevelopmental, neuropsychiatric, and neurological disorders. Along with applications, the review provides a brief of common ML methods to set context. Conjointly, limitations in ML studies within connectomics and strategies to mitigate them have been covered.

Artificial structural proteins: Synthesis, assembly and material applications.

Structural proteins have evolved over billions of years and offer outstanding mechanical properties, such as resilience, toughness and stiffness. Advances in modular protein engineering, polypeptide modification, and synthetic biology have led to the development of novel biomimetic structural proteins to perform in biomedical and military fields. However, the development of customized structural proteins and assemblies with superior performance remains a major challenge, due to the inherent limitations of biosynthesis, difficulty in mimicking the complexed macroscale assembly, etc. This review summarizes the approaches for the design and production of biomimetic structural proteins, and their chemical modifications for multiscale assembly. Furthermore, we discuss the function tailoring and current applications of biomimetic structural protein assemblies. A perspective of future research is to reveal how the mechanical properties are encoded in the sequences and conformations. This review, therefore, provides an important reference for the development of structural proteins-mimetics from replication of nature to even outperforming nature.

Self-Adaptive Macroscale Superlubricity Based on the Tribocatalytic Properties of Partially Oxidized Black Phosphorus.

The high-flash heat generated by direct contact at asperity tips under high contact stress and shear significantly promotes the tribocatalytic reaction between a lubricating medium and a friction interface. Macroscale superlubricity can be achieved by using additives with good lubrication properties to promote the decomposition and transformation of a lubricating medium to form an ultralow shear interface during the friction process. This paper proposed a way to achieve self-adaptive oil-based macroscale superlubricity on different tribopairs, including steel-steel and steel-DLC (diamond-like carbon), which is based on the excellent lubricating performance of black phosphorus with active oxidation and the catalytic cleavage behavior of oil molecules on the surface of oBP. This work potentially expands the industrial application of superlubricity.

Imaging Techniques in Pharmacological Precision Medicine.

Biomedical imaging is a powerful tool for medical diagnostics and personalized medicines. Examples of commonly used imaging modalities include Positron Emission Tomography (PET), Ultrasound (US), Single Photon Emission Computed Tomography (SPECT), and hybrid imaging. By combining these modalities, scientists can gain a comprehensive view and better understand physiology and pathology at the preclinical, clinical, and multiscale levels. This can aid in the accuracy of medical diagnoses and treatment decisions. Moreover, biomedical imaging allows for evaluating the metabolic, functional, and structural details of living tissues. This can be particularly useful for the early diagnosis of diseases such as cancer and for the application of personalized medicines. In the case of hybrid imaging, two or more modalities are combined to produce a high-resolution image with enhanced sensitivity and specificity. This can significantly improve the accuracy of diagnosis and offer more detailed treatment plans. In this book chapter, we showcase how continued advancements in biomedical imaging technology can potentially revolutionize medical diagnostics and personalized medicine.

Model of drug delivery to populations composed of two cell types.

The rate of drug delivery to cells and the subsequent rate of drug metabolism are dependent on the cell membrane permeability to the drug. In some cases, tissue may be composed of different types of cells that exhibit order of magnitude differences in their membrane permeabilities. This paper presents a brief review of the components of the tissue scale three-compartment pharmacokinetic model of drug delivery to single-cell-type populations. The existing model is extended to consider tissue composed of two different cell types. A case study is presented of infusion mediated delivery of doxorubicin to a tumor that is composed of a drug reactive cell type and of a drug resistive cell type. The membrane permeabilities of the two cell types differ by an order of magnitude. A parametric investigation of the population composition is conducted and it is shown that the drug metabolism of the low permeability cells are negatively influenced by the fraction of the tissue composed of the permeable drug reactive cells. This is because when the population is composed mostly of drug permeable cells, the extracellular space is rapidly depleted of the drug. This has two compounding effects: (i) locally there is simply less drug available to the neighboring drug resistant cells, and (ii) the depletion of the drug from the extracellular space near the vessel-tissue interface leaves less drug to be transported to both cell types farther away from the vessel.

Size, shape and orientation of macro-sized substrate protrusions affect the toe and foot adhesion of geckos.

Geckos are excellent climbers using compliant, hierarchically arranged adhesive toes to negotiate diverse terrains varying in roughness at multiple size scales. Here, we complement advancements at smaller size scales with measurements at the macro scale. We studied the attachment of a single toe and whole foot of geckos on macroscale rough substrates by pulling them along, across and off smooth rods and spheres mimicking different geometric protrusions of substrates. When we pulled a single toe along rods, the force increased with the rod diameter, whereas the attachment force of dragging toes across rods increased from about 60% on small diameter rods relative to a flat surface to ∼100% on larger diameter rods, but showed no further increase as rod diameter doubled. Toe force also increased as the pulling changed from along-rod loading to across-rod loading. When toes were pulled off spheres, the force increased with increasing sphere diameter as observed for along-rod pulling. For feet with separated toes, attachment on spheres was stronger than that on rods with the same diameter. Attachment force of a foot decreased as rod and sphere size increased but remained sufficient to support the body weight of geckos. These results provide a bridge to the macroscale roughness seen in nature by revealing the importance of the dimension, shape and orientation of macroscale substrate features for compliant toe and foot function of geckos. Our data not only enhance our understanding of geckos' environmental adaptive adhesion but can also provide inspiration for novel robot feet in development.

Derivation of continuum models from discrete models of mechanical forces in cell populations.

In certain discrete models of populations of biological cells, the mechanical forces between the cells are center based or vertex based on the microscopic level where each cell is individually represented. The cells are circular or spherical in a center based model and polygonal or polyhedral in a vertex based model. On a higher, macroscopic level, the time evolution of the density of the cells is described by partial differential equations (PDEs). We derive relations between the modelling on the micro and macro levels in one, two, and three dimensions by regarding the micro model as a discretization of a PDE for conservation of mass on the macro level. The forces in the micro model correspond on the macro level to a gradient of the pressure scaled by quantities depending on the cell geometry. The two levels of modelling are compared in numerical experiments in one and two dimensions.

The thalamic functional gradient and its relationship to structural basis and cognitive relevance.

The human thalamus is an integrative hub richly connected with cortical networks, involving diverse cognitive functions. Emerging evidence suggests that multiscale structural and functional gradients integrate various information across modalities into an abstract representation. However, the presence of functional gradients in the thalamus and its relationship to structural properties and cognitive functions remain unknown. We estimated the functional gradients of the thalamus in two independent normal cohorts using a novel diffusion embedding analysis. We identified two main axes of the functional connectivity patterns, and examined associations with thalamic anatomy, morphology, intrinsic geometry, and specific behavioral relevance. We found that the dominant gradient indicated a lateral/medial axis across the thalamus and captured associations with anatomical nuclei and gray matter volume. The second gradient was an anterior/posterior axis and provided a behavioral characterization from lower level perception to higher level cognition. Furthermore, these two gradients strongly correlated with spatial distance, indicating the prominence of intrinsic geometry in functional hierarchies. These findings were replicated in an independent dataset. Overall, our findings suggested that macroscale gradients showed a coordination of structural and functional interactions, with hierarchical organization contributing to behavior characterization.

Ecological prediction at macroscales using big data: Does sampling design matter?

Although ecosystems respond to global change at regional to continental scales (i.e., macroscales), model predictions of ecosystem responses often rely on data from targeted monitoring of a small proportion of sampled ecosystems within a particular geographic area. In this study, we examined how the sampling strategy used to collect data for such models influences predictive performance. We subsampled a large and spatially extensive data set to investigate how macroscale sampling strategy affects prediction of ecosystem characteristics in 6,784 lakes across a 1.8-million-km2 area. We estimated model predictive performance for different subsets of the data set to mimic three common sampling strategies for collecting observations of ecosystem characteristics: random sampling design, stratified random sampling design, and targeted sampling. We found that sampling strategy influenced model predictive performance such that (1) stratified random sampling designs did not improve predictive performance compared to simple random sampling designs and (2) although one of the scenarios that mimicked targeted (non-random) sampling had the poorest performing predictive models, the other targeted sampling scenarios resulted in models with similar predictive performance to that of the random sampling scenarios. Our results suggest that although potential biases in data sets from some forms of targeted sampling may limit predictive performance, compiling existing spatially extensive data sets can result in models with good predictive performance that may inform a wide range of science questions and policy goals related to global change.

Visualizing the macroscale spatial distributions of biofilms in complex flow channels using industrial computed tomography.

The presence of biofilms in enclosed pipelines can lead to numerous deleterious issues. To date, it has been difficult to use optical imaging techniques to monitor the macroscale spatial distributions of biofilms. To address this concern, a combination of industrial computed tomography (ICT) and a contrast agent was explored to noninvasively visualize biofilms in three types of drip irrigation emitters. The results showed that ICT successfully observed and quantified the macroscale spatial distributions of biofilms. The complex hydrodynamic characteristics in the emitter channels affected the local distributions of biofilms. Biofilms were mainly attached to the lateral and medial faces and biomass decreased along the flow directions. Based on the distributions of biofilms, some emitter structural design defects were further diagnosed. Applying ICT in combination with the contrast agent could potentially provide a visual and effective way to reveal the formation mechanisms of biofilms and to optimize flow channel structures to avoid biofilm accumulations.

A Comprehensive Review of Integrated Hall Effects in Macro-, Micro-, Nanoscales, and Quantum Devices.

A comprehensive review of the main existing devices, based on the classic and new related Hall Effects is hereby presented. The review is divided into sub-categories presenting existing macro-, micro-, nanoscales, and quantum-based components and circuitry applications. Since Hall Effect-based devices use current and magnetic field as an input and voltage as output. researchers and engineers looked for decades to take advantage and integrate these devices into tiny circuitry, aiming to enable new functions such as high-speed switches, in particular at the nanoscale technology. This review paper presents not only an historical overview of past endeavors, but also the remaining challenges to overcome. As part of these trials, one can mention complex design, fabrication, and characterization of smart nanoscale devices such as sensors and amplifiers, towards the next generations of circuitry and modules in nanotechnology. When compared to previous domain-limited text books, specialized technical manuals and focused scientific reviews, all published several decades ago, this up-to-date review paper presents important advantages and novelties: Large coverage of all domains and applications, clear orientation to the nanoscale dimensions, extended bibliography of almost one hundred fifty recent references, review of selected analytical models, summary tables and phenomena schematics. Moreover, the review includes a lateral examination of the integrated Hall Effect per sub-classification of subjects. Among others, the following sub-reviews are presented: Main existing macro/micro/nanoscale devices, materials and elements used for the fabrication, analytical models, numerical complementary models and tools used for simulations, and technological challenges to overcome in order to implement the effect in nanotechnology. Such an up-to-date review may serve the scientific community as a basis for novel research oriented to new nanoscale devices, modules, and Process Development Kit (PDK) markets.

Spatial and temporal variation of ecosystem properties at macroscales.

Although spatial and temporal variation in ecological properties has been well-studied, crucial knowledge gaps remain for studies conducted at macroscales and for ecosystem properties related to material and energy. We test four propositions of spatial and temporal variation in ecosystem properties within a macroscale (1000 km's) extent. We fit Bayesian hierarchical models to thousands of observations from over two decades to quantify four components of variation - spatial (local and regional) and temporal (local and coherent); and to model their drivers. We found strong support for three propositions: (1) spatial variation at local and regional scales are large and roughly equal, (2) annual temporal variation is mostly local rather than coherent, and, (3) spatial variation exceeds temporal variation. Our findings imply that predicting ecosystem responses to environmental changes at macroscales requires consideration of the dominant spatial signals at both local and regional scales that may overwhelm temporal signals.

Drivers and spatial structure of abiotic and biotic properties of lakes, wetlands, and streams at the national scale.

Broad-scale studies have improved our ability to make predictions about how freshwater biotic and abiotic properties will respond to changes in climate and land use intensification. Further, fine-scaled studies of lakes, wetlands, or streams have documented the important role of hydrologic connections for understanding many freshwater biotic and abiotic processes. However, lakes, wetlands, and streams are typically studied in isolation of one another at both fine and broad scales. Therefore, it is not known whether these three freshwater types (lakes, wetlands, and streams) respond similarly to ecosystem and watershed drivers nor how they may respond to future global stresses. In this study, we asked, do lake, wetland, and stream biotic and abiotic properties respond to similar ecosystem and watershed drivers and have similar spatial structure at the national scale? We answered this question with three U.S. conterminous data sets of freshwater ecosystems. We used random forest (RF) analysis to quantify the multi-scaled drivers related to variation in nutrients and biota in lakes, wetlands, and streams simultaneously; we used semivariogram analysis to quantify the spatial structure of biotic and abiotic properties and to infer possible mechanisms controlling the ecosystem properties of these freshwater types. We found that abiotic properties responded to similar drivers, had large ranges of spatial autocorrelation, and exhibited multi-scale spatial structure, regardless of freshwater type. However, the dominant drivers of variation in biotic properties depended on freshwater type and had smaller ranges of spatial autocorrelation. Our study is the first to document that drivers and spatial structure differ more between biotic and abiotic variables than across freshwater types, suggesting that some properties of freshwater ecosystems may respond similarly to future global changes.

Portable Wind Energy Harvesters for Low-Power Applications: A Survey.

Energy harvesting has become an increasingly important topic thanks to the advantages in renewability and environmental friendliness. In this paper, a comprehensive study on contemporary portable wind energy harvesters has been conducted. The electrical power generation methods of portable wind energy harvesters are surveyed in three major groups, piezoelectric-, electromagnetic-, and electrostatic-based generators. The paper also takes another view of this area by gauging the required mechanisms for trapping wind flow from ambient environment. In this regard, rotational and aeroelastic mechanisms are analyzed for the portable wind energy harvesting devices. The comparison between both mechanisms shows that the aeroelastic mechanism has promising potential in producing an energy harvester in smaller scale although how to maintain the resonator perpendicular to wind flow for collecting the maximum vibration is still a major challenge to overcome for this mechanism. Furthermore, this paper categorizes the previously published portable wind energy harvesters to macro and micro scales in terms of their physical dimensions. The power management systems are also surveyed to explore the possibility of improving energy conversion efficiency. Finally some insights and research trends are pointed out based on an overall analysis of the previously published works along the historical timeline.

Mechanical properties of a mature biofilm from a wastewater system: from microscale to macroscale level.

A fundamental understanding of biofilm mechanical stability is critical in order to describe detachment and develop biofouling control strategies. It is thus important to characterise the elastic deformation and flow behaviour of the biofilm under different modes of applied force. In this study, the mechanical properties of a mature wastewater biofilm were investigated with methods including macroscale compression and microscale indentation using atomic force microscopy (AFM). The mature biofilm was found to be mechanically isotropic at the macroscale level as its mechanical properties did not depend on the scales and modes of loading. However, the biofilm showed a tendency for mechanical inhomogeneity at the microscale level as indentation progressed deeper into the matrix. Moreover, it was observed that the adhesion force had a significant influence on the elastic properties of the biofilm at the surface, subjected to microscale tensile loading. These results are expected to inform a damage-based model for biofilm detachment.

Enhancing higher-order modal response in multifrequency atomic force microscopy with a coupled cantilever system.

Multifrequency atomic force microscopy (AFM) utilizes the multimode operation of cantilevers to achieve rapid high-resolution imaging and extract multiple properties. However, the higher-order modal response of traditional rectangular cantilever is weaker in air, which affects the sensitivity of multifrequency AFM detection. To address this issue, we previously proposed a bridge/cantilever coupled system model to enhance the higher-order modal response of the cantilever. This model is simpler and less costly than other enhancement methods, making it easier to be widely used. However, previous studies were limited to theoretical analysis and preliminary simulations regarding ideal conditions. In this paper, we undertake a more comprehensive investigation of the coupled system, taking into account the influence of probe and excitation surface sizes on the modal response. To facilitate the exploration of the effectiveness and optimal conditions for the coupled system in practical applications, a macroscale experimental platform is established. By conducting finite element analysis and experiments, we compare the performance of the coupled system with that of traditional cantilevers and quantify the enhancement in higher-order modal response. Also, the optimal conditions for the enhancement of macroscale cantilever modal response are explored. Additionally, we also supplement the characteristics of this model, including increasing the modal frequency of the original cantilever and generating additional resonance peaks, demonstrating the significant potential of the coupled system in various fields of AFM.

Superlubricity of Materials: Progress, Potential, and Challenges.

This review paper provides a comprehensive overview of the phenomenon of superlubricity, its associated material characteristics, and its potential applications. Superlubricity, the state of near-zero friction between two surfaces, presents significant potential for enhancing the efficiency of mechanical systems, thus attracting significant attention in both academic and industrial realms. We explore the atomic/molecular structures that enable this characteristic and discuss notable superlubric materials, including graphite, diamond-like carbon, and advanced engineering composites. The review further elaborates on the methods of achieving superlubricity at both nanoscale and macroscale levels, highlighting the influence of environmental conditions. We also discuss superlubricity's applications, ranging from mechanical systems to energy conservation and biomedical applications. Despite the promising potential, the realization of superlubricity is laden with challenges. We address these technical difficulties, specifically those related to achieving and maintaining superlubricity, and the issues encountered in scaling up for industrial applications. The paper also underscores the sustainability concerns associated with superlubricity and proposes potential solutions. We conclude with a discussion of the possible future research directions and the impact of technological innovations in this field. This review thus provides a valuable resource for researchers and industry professionals engaged in the development and application of superlubric materials.

Macroscopic and Microscopic Characteristics of Strength Degradation of Silty Soil Improved by Regenerated Polyester Fibers under Dry-Wet Cycling.

The structural stability of silt foundations, particularly sensitive to moisture content, can be severely compromised by recurring wetting and drying processes. This not only threatens the foundational integrity but also raises grave concerns about the long-term safety of major civil engineering endeavors. Addressing this critical issue, our study delves into the transformative effects of reclaimed polyester fiber on subgrade silt exposed to such environmental stressors. Through rigorous wet-dry cycle tests on this enhanced soil, we evaluate shifts in shear strength across varying confining pressures. We also dissect the interplay between average pore diameter, particle distribution, and morphology in influencing the soil's microstructural responses to these cycles. A detailed analysis traces the structural damage timeline in the treated soil, elucidating the intertwined micro-macro dynamics driving strength reduction. Key discoveries indicate a notably non-linear trajectory of shear strength degradation, marked by distinct phases of rapid, subdued, and stabilized strength attrition. Alterations within the micropores induce a rise in both their count and size, ultimately diminishing the total volume proportion of the reinforced soil. Intriguingly, particle distribution is directly tied to the wet-dry cycle frequency, while the fractal dimension of soil particles consistently wanes. This research identifies cement hydrolysis and pore expansion as the dominant culprits behind the observed macroscopic strength degradation due to incessant wet-dry cycles. These revelations hold profound implications for risk management and infrastructural strategizing in areas dominated by silt foundations.

Macroscale Fabrication of Lightweight and Strong Porous Carbon Foams through Template-Coating Pair Design.

Manufacturing of low-density-high-strength carbon foams can benefit the construction, transportation, and packaging industries. One successful route to lightweight and mechanically strong carbon foams involves pyrolysis of polymeric architectures, which is inevitably accompanied by drastic volumetric shrinkage (usually >98%). As such, a challenge of these materials lies in maintaining bulk dimensions of building struts that span orders of magnitude difference in length scale from centimeters to nanometers. This work demonstrates fabrication of macroscale low-density-high-strength carbon foams that feature exceptional dimensional stability through pyrolysis of robust template-coating pairs. The template serves as the architectural blueprint and contains strength-imparting properties (e.g., high node density and small strut dimensions); it is composed of a low char-yielding porous polystyrene backbone with a high carbonization-onset temperature. The coating serves to imprint and transcribe the template architecture into pyrolytic carbon; it is composed of a high char-yielding conjugated polymer with a relatively low carbonization-onset temperature. The designed carbonization mismatch enables structural inheritance, while the decomposition mismatch affords hollow struts, minimizing density. The carbons synthesized through this new framework exhibit remarkable dimensional stability (≈80% dimension retention; ≈50% volume retention) and some of the highest specific strengths (≈0.13 GPa g-1 cm3 ) among reported carbon foams derived from porous polymer templates.

Development of Thiol-Maleimide hydrogels incorporating graphene-based nanomaterials for cancer chemo-photothermal therapy.

Nano-sized materials have been widely explored in the biomedicine field, especially due to their ability to encapsulate drugs intended to be delivered to cancer cells. However, systemically administered nanomaterials face several barriers that can hinder their tumor-homing capacity. In this way, researchers are now focusing their efforts in developing technologies that can deliver the nanoparticles directly into the tumor tissue. Particularly, hydrogels assembled using Thiol-Maleimide Michael type additions are emerging for this purpose due to their capacity to incorporate high nanoparticles' doses in a compact 3D structure as well as good chemical selectivity, biocompatibility, and straightforward preparation. Nevertheless, such hydrogels have been mostly prepared using synthetic polymers, which is not ideal due to their poor biodegradability. In this work, a novel natural polymer-based Thiol-Maleimide hydrogel was produced for application in breast cancer chemo-photothermal therapy. To obtain natural polymers compatible with this crosslinking chemistry, Hyaluronic acid was endowed with Thiol groups and deacetylated Chitosan was grafted with Maleimide groups. Parallelly, Doxorubicin loaded Dopamine-reduced graphene oxide (DOX/DOPA-rGO) was prepared for attaining Near Infrared (NIR) light responsive chemo-photothermal nanoagents. By simply mixing Hyaluronic Acid-Thiol, deacetylated Chitosan-Maleimide and DOX/DOPA-rGO, Thiol-Maleimide crosslinked hydrogels incorporating this nanomaterial could be assembled (DOX/DOPA-rGO@TMgel). When breast cancer cells were incubated with DOPA-rGO@TMgel and exposed to NIR light (photothermal therapy), their viability was reduced to about 59 %. On the other hand, DOX/DOPA-rGO@TMgel (chemotherapy) reduced cancer cells' viability to 50 %. In stark contrast, the combined action of DOX/DOPA-rGO@TMgel and NIR light decreased breast cancer cells' viability to just 21 %, highlighting its chemo-photothermal potential.