Multilayer Complexity Research Articles

Harnessing the power of machine learning for crop improvement and sustainable production.

Crop improvement and production domains encounter large amounts of expanding data with multi-layer complexity that forces researchers to use machine-learning approaches to establish predictive and informative models to understand the sophisticated mechanisms underlying these processes. All machine-learning approaches aim to fit models to target data; nevertheless, it should be noted that a wide range of specialized methods might initially appear confusing. The principal objective of this study is to offer researchers an explicit introduction to some of the essential machine-learning approaches and their applications, comprising the most modern and utilized methods that have gained widespread adoption in crop improvement or similar domains. This article explicitly explains how different machine-learning methods could be applied for given agricultural data, highlights newly emerging techniques for machine-learning users, and lays out technical strategies for agri/crop research practitioners and researchers.

Seal materials in flexible plastic food packaging: A review

AbstractFlexible packaging has many advantages in the food industry, arising from low weight, formability, multilayer complexity and cost. Heat sealing is a very efficient technique to close flexible food packaging. Currently, many thermoplastic materials are used in seal layers. A seal can be formed when these materials are heated and brought into contact; thereafter, polymer chains diffuse across the seal interface and entangle. Hydrogen bonds, polar and ionic interactions are molecular forces that can come into play, depending on the thermoplastic materials that are used in the seal layer. Bonds between identical polymers, referred to as autohesion, are formed in pouch applications (e.g., horizontal and vertical form‐fill‐seal packages). In lidding applications, the flexible film is sealed to a rigid cup, tray or bottle, whereby bonds can be formed between non‐identical polymers because the materials are often provided by different suppliers. All heat seal technologies imply heating of seal layers but differ in the heating principle. In the food industry and in most scientific seal studies, the seals of mono‐ and multilayered packaging are mainly formed by conductive heating. Recently, the use of emerging technologies, such as ultrasonic and laser heating, is increasingly described in recent papers. Applied seals are characterized by strength after a specified cooling time. Immediately after heating, this strength is referred to as hot tack. A good seal performance is crucial to guarantee food safety and quality. Besides strength, tightness is important to prevent food degradation, caused by microorganisms and external gases, and to keep aromatic gases inside the package. This review aims to give a literature overview that can support stakeholders in the food industry to improve and optimize the material selection in flexible packaging, in order to obtain seals with desired tightness and strength. Heat seal studies on materials and seal technology of flexible food packaging, such as pouches and lidding films, are considered. Scientific data are categorized from a materials' perspective, based on chemical structure, which is revealed by chemical and thermal analysis. A majority of the seal studies is categorized in a first section on polyolefins as seal layers. The following sections describe the seal functionality of (i) ethylene copolymers, such as ionomers; and (ii) polyesters, such as poly (ethylene terephthalate), pol (lactic acid) and poly (butylene succinate). The role of plasticizers, fillers and other additives in the seal performance is also described. Finally, material properties, such as chain length and melting temperature (Tm), as underlying causes of seal performance, are summarized.

The impact of whole genome duplications on the human gene regulatory networks.

This work studies the effects of the two rounds of Whole Genome Duplication (WGD) at the origin of the vertebrate lineage on the architecture of the human gene regulatory networks. We integrate information on transcriptional regulation, miRNA regulation, and protein-protein interactions to comparatively analyse the role of WGD and Small Scale Duplications (SSD) in the structural properties of the resulting multilayer network. We show that complex network motifs, such as combinations of feed-forward loops and bifan arrays, deriving from WGD events are specifically enriched in the network. Pairs of WGD-derived proteins display a strong tendency to interact both with each other and with common partners and WGD-derived transcription factors play a prominent role in the retention of a strong regulatory redundancy. Combinatorial regulation and synergy between different regulatory layers are in general enhanced by duplication events, but the two types of duplications contribute in different ways. Overall, our findings suggest that the two WGD events played a substantial role in increasing the multi-layer complexity of the vertebrate regulatory network by enhancing its combinatorial organization, with potential consequences on its overall robustness and ability to perform high-level functions like signal integration and noise control. Lastly, we discuss in detail the RAR/RXR pathway as an illustrative example of the evolutionary impact of WGD duplications in human.

Machine Learning Classifiers for Surface Crack Detection in Fracture Experiments

Correctly determining the onset of fracture is crucial when performing mechanical experiments. Commonly carried out by visual inspection, here an image-based machine learning approach is proposed to classify cracked and un-cracked specimens. It yields the potential to objectify and automate crack detection, thereby removing sources of uncertainty and error from the post-processing of experiments. More than 30’000 speckle-pattern images obtained from 77 experiments on three specimen geometries are evaluated. They comprise uniaxial tension, notched tension as well as axisymmetric V-bending experiments. Statistical texture features are extracted from all images. They include both first-order (variance, skewness, kurtosis) and higher-order statistical texture features, i.e. Haralick features. The discriminatory power of the texture information is evaluated based on the Fisher's Discriminant Ratio and feature correlations are identified and quantified. Image texture feature subsets of high discriminatory power are used to parse neural network architectures of different complexities from simple perceptron to feed-forward and cascade neural networks. It is found that a small subset of the investigated texture features is highly significant for all experiments. Using this feature subset in conjunction with multi-layer, non-linear and low complexity feed-forward network architectures classification accuracies in the order of 99% are obtained. At the same time, it is shown that linear classifiers are not sufficient to robustly distinguish the state of the specimens, even when high discriminatory power features are used.Graphical Abstract: [Display omitted]

Encoding Multilayer Complexity in Anti‐Counterfeiting Heterometallic MOF‐Based Optical Tags

AbstractOptical tags provide a way to quickly and unambiguously identify valuable assets. Current tag fluorophore options lack the tunability to allow combined methods of encoding in a single material. Herein we report a design strategy to encode multilayer complexity in a family of heterometallic rare‐earth metal–organic frameworks based on highly connected nonanuclear clusters. To impart both intricacy and security, a synergistic approach was implemented resulting in both overt (visible) and covert (near‐infrared, NIR) properties, with concomitant multi‐emissive spectra and tunable luminescence lifetimes. Tag authentication is validated with a variety of orthogonal detection methodologies. Importantly, the effect induced by subtle compositional changes on intermetallic energy transfer, and thus on the resulting photophysical properties, is demonstrated. This strategy can be widely implemented to create a large library of highly complex, difficult‐to‐counterfeit optical tags.

Encoding Multilayer Complexity in Anti-Counterfeiting Heterometallic MOF-Based Optical Tags.

Optical tags provide a way to quickly and unambiguously identify valuable assets. Current tag fluorophore options lack the tunability to allow combined methods of encoding in a single material. Herein we report a design strategy to encode multilayer complexity in a family of heterometallic rare-earth metal-organic frameworks based on highly connected nonanuclear clusters. To impart both intricacy and security, a synergistic approach was implemented resulting in both overt (visible) and covert (near-infrared, NIR) properties, with concomitant multi-emissive spectra and tunable luminescence lifetimes. Tag authentication is validated with a variety of orthogonal detection methodologies. Importantly, the effect induced by subtle compositional changes on intermetallic energy transfer, and thus on the resulting photophysical properties, is demonstrated. This strategy can be widely implemented to create a large library of highly complex, difficult-to-counterfeit optical tags.

Pomorskie Towarzystwo Opieki nad Dziećmi jako największe dzieło społeczne ks. Prałata Antoniego Szumana

Servant of God, Mgr. Antoni Henryk Szuman was actively involved in many areas of social work. This article attempts to present his involvement in favour of war orphans after the First World War. His activities in this field are most clearly visible in his foundation and management of The Pomeranian Childcare Society. The paper endeavours to illustrate the multilayer complexity of tasks facing this organisation and to demonstrate its social impact in that historical period.

Organoids by design.

Organoids are multicellular structures that can be derived from adult organs or pluripotent stem cells. Early versions of organoids range from simple epithelial structures to complex, disorganized tissues with large cellular diversity. The current challenge is to engineer cellular complexity into organoids in a controlled manner that results in organized assembly and acquisition of tissue function. These efforts have relied on studies of organ assembly during embryonic development and have resulted in the development of organoids with multilayer tissue complexity and higher-order functions. We discuss how the next generation of organoids can be designed by means of an engineering-based narrative design to control patterning, assembly, morphogenesis, growth, and function.

The Opioid Crisis: A Contextual Framework and Call for Systems Science Research

Context: The prevalence of opioid use and misuse has provoked a staggering number of deaths over the past two and a half decades. Much attention has focused on individual risks according to various characteristics and experiences. However, broader social and contextual domains are also essential contributors to the opioid crisis, as interpersonal relationships and the conditions of the community and society people live in. Despite efforts to tackle the issue, the rates of opioid misuse, non-fatal overdose, and overdose death remain high. Many call for a broad public health approach, but articulation of what such a strategy could entail has not been fully realized. Broader systems policies, especially those that emphasize prevention, could more effectively intervene in this crisis. Methods: In order to improve the awareness surrounding opioid misuse, we developed a social-ecological framework that helps conceptualize the multivariable risk factors of opioid misuse and facilitates reviewing them in individual, interpersonal, communal, and societal levels. Findings: Our framework illustrates the multi-layer complexity of the opioid crisis that more completely captures the crisis as a multidimensional issue requiring a broader and integrated approach to prevention and treatment. Conclusions: Future research can elucidate the interconnections among the risk factors as well as leverage systems science tools (e.g., simulation modeling) to uncover the complexities of the crisis, and help evaluate, analyze, and forecast the effectiveness of ongoing and new policy interventions. We explain and call for the need for more systems science approaches in opioid research.

Cardiac Hegemony of Senescence

Cardiac senescence and age-related disease development have gained general attention and recognition in the past decades due to increased accessibility and quality of health care. The advancement in global civilization is complementary to concerns regarding population aging and development of chronic degenerative diseases. Cardiac degeneration has been rigorously studied. The molecular mechanisms of cardiac senescence are on multiple cellular levels and hold a multilayer complexity level, thereby hampering development of unambiguous treatment protocols. In particular, the synergistic exchange of the senescence phenotype through a senescence secretome between myocytes and stem cells appears complicated and is of great future therapeutic value. The current review article will highlight hallmarks of senescence, cardiac myocyte and stem cell senescence, and the mutual exchange of senescent secretome. Future cardiac cell therapy approaches require a comprehensive understanding of myocardial senescence to improve therapeutic efficiency as well as efficacy.

Reasoning under Uncertainty for Overlay Fault Diagnosis

The performance and reliability of overlay services rely on the underlying overlay network's ability to effectively diagnose and recover from faults such as link failures and overlay node outages. However, overlay networks bring to fault diagnosis new challenges such as large-scale deployment, inaccessible underlay network information, dynamic symptom-fault causality relationship, and multi-layer complexity. In this paper, we develop an evidential overlay fault diagnosis framework called DigOver to tackle these challenges. Firstly, DigOver identifies a set of potential faulty components based on shared end-user observed negative symptoms. Then, each potential faulty component is evaluated to quantify its fault likelihood and the corresponding evaluation uncertainty. Finally, DigOver dynamically constructs a plausible fault graph to locate the root causes of end-user observed negative symptoms. Both simulation and Internet experiments demonstrate that DigOver can effectively and accurately diagnose overlay faults based on end-user observed negative symptoms.

Spinal Cord Explants Use Carbon Nanotube Interfaces To Enhance Neurite Outgrowth and To Fortify Synaptic Inputs

New developments in nanotechnology are increasingly designed to modulate relevant interactions between nanomaterials and neurons, with the aim of exploiting the physical properties of synthetic materials to tune desired and specific biological processes. Carbon nanotubes have been applied in several areas of nerve tissue engineering to study cell behavior or to instruct the growth and organization of neural networks. Recent reports show that nanotubes can sustain and promote electrical activity in networks of cultured neurons. However, such results are usually limited to carbon nanotube/neuron hybrids formed on a monolayer of dissociated brain cells. In the present work, we used organotypic spinal slices to model multilayer tissue complexity, and we interfaced such spinal segments to carbon nanotube scaffolds for weeks. By immunofluorescence, scanning and transmission electronic microscopy, and atomic force microscopy, we investigated nerve fiber growth when neuronal processes exit the spinal explant and develop in direct contact to the substrate. By single-cell electrophysiology, we investigated the synaptic activity of visually identified ventral interneurons, within the ventral area of the explant, thus synaptically connected, but located remotely, to the substrate/network interface. Here we show that spinal cord explants interfaced for weeks to purified carbon nanotube scaffolds expand more neuronal fibers, characterized by different mechanical properties and displaying higher growth cones activity. On the other hand, exploring spontaneous and evoked synaptic activity unmasks an increase in synaptic efficacy in neurons located at as far as 5 cell layers from the cell-substrate interactions.

Multilayer interconnection problem complexity

An algorithm is given that provides reliable measures of a multilayer interconnection problem complexity. These measures can serve as input data for analysis and other prerouting steps (placement, assignment) of integrated circuit (IC) or printed circuit board (PCB) layout.