Diverse Outputs Research Articles

Towards a Material-by-Design Approach to Electrospun Scaffolds for Tissue Engineering Based on Statistical Design of Experiments (DOE)

Electrospinning bears great potential for the manufacturing of scaffolds for tissue engineering, consisting of a porous mesh of ultrafine fibers that effectively mimic the extracellular matrix (ECM) and aid in directing stem cell fate. However, for engineering purposes, there is a need to develop material-by-design approaches based on predictive models. In this methodological study, a rational methodology based on statistical design of experiments (DOE) is discussed in detail, yielding heuristic models that capture the linkage between process parameters (Xs) of the electrospinning and scaffold properties (Ys). Five scaffolds made of polycaprolactone are produced according to a 22-factorial combinatorial scheme where two Xs, i.e., flow rate and applied voltage, are varied between two given levels plus a center point. The scaffolds were characterized to measure a set of properties (Ys), i.e., fiber diameter distribution, porosity, wettability, Young's modulus, and cell adhesion on murine myoblast C1C12 cells. Simple engineering DOE models were obtained for all Ys. Each Y, for example, the biological response, can be used as a driver for the design process, using the process-property model of interest for accurate interpolation within the design domain, enabling a material-by-design strategy and speeding up the product development cycle. The implications are also illustrated in the context of the design of multilayer scaffolds with microstructural gradients and controlled properties of each layer. The possibility of obtaining statistical models correlating between diverse output properties of the scaffolds is highlighted. Noteworthy, the featured DOE approach can be potentially merged with artificial intelligence tools to manage complexity and it is applicable to several fields including 3D printing.

RBUE: a ReLU-based uncertainty estimation method for convolutional neural networks

Convolutional neural networks (CNNs) have successfully demonstrated their powerful predictive performance in a variety of tasks. However, it remains a challenge to estimate the uncertainty of these predictions simply and accurately. Deep Ensemble is widely considered the state-of-the-art method which can estimate the uncertainty accurately, but it is expensive to train and test. MC-Dropout is another popular method that is less costly but lacks the diversity of predictions resulting in less accurate uncertainty estimates. To combine the benefits of both, we introduce a ReLU-Based Uncertainty Estimation (RBUE) method. Instead of using the randomness of the Dropout module during the test phase (MC-Dropout) or using the randomness of the initial weights of CNNs (Deep Ensemble), RBUE uses the randomness of activation function to obtain diverse outputs in the testing phase to estimate uncertainty. Under the method, we propose strategy MC-DropReLU and develop strategy MC-RReLU. The uniform distribution of the activation function’s position in CNNs allows the randomness to be well transferred to the output results and gives a more diverse output, thus improving the accuracy of the uncertainty estimation. Moreover, our method is simple to implement and does not need to modify the existing model. We experimentally validate the RBUE on three widely used datasets, CIFAR10, CIFAR100, and TinyImageNet. The experiments demonstrate that our method has competitive performance but is more favorable in training time.

One-pot generation of lignin microspheres and digestible substrate with a polyol-DES pretreatment in high solid loading

Previous lignin microspheres (LMS) preparation needs multiple steps with very low yield and high cost. Herein, we developed a high-solid deep eutectic solvent (DES) pretreatment for an effective lignin fractionation and enzymatic saccharification of moso bamboo under mild temperature (110 °C) with high-solid loading. Lignin was significantly removed from the plant cell wall, and cellulose properties (e.g., crystallinity and degree of polymerization) were also altered during the pretreatment. As a result, the enzymatic digestibility of the pretreated bamboo was dramatically increased. Uniform micro-spherical lignin was directly produced from the pretreatment system, and its particle size could be regulated by controlling the solid loadings and pretreatment temperatures. The lignin microspheres formation mechanism was investigated by analyzing the lignin's size distribution, molecular weight distribution, chemical structure, and hydrophobicity. The DES showed excellent recyclability, and the recycled DES could still remove 42.78% lignin even after 7th circulation associated with 100% glucan saccharification. The mass balance based on 1000 g biomass showed that 196.28 g LMS was directly recovered, which exhibited a high RhB adsorption. Besides, 404.91 g glucose and 36.67 g xylose were obtained after the enzymatic saccharification process. Specifically, GAPI analysis exhibited a near total green and yellow portions of the pictogram, indicating our DES process was green enough to make this biorefinery sustainable. Overall, the proposed DES generated synergistic productions of digestible solid and LMS which could contribute to establish a green and sustainable biorefinery sequence with diverse outputs in one pot.

A split ribozyme that links detection of a native RNA to orthogonal protein outputs

Individual RNA remains a challenging signal to synthetically transduce into different types of cellular information. Here, we describe Ribozyme-ENabled Detection of RNA (RENDR), a plug-and-play strategy that uses cellular transcripts to template the assembly of split ribozymes, triggering splicing reactions that generate orthogonal protein outputs. To identify split ribozymes that require templating for splicing, we use laboratory evolution to evaluate the activities of different split variants of the Tetrahymena thermophila ribozyme. The best design delivers a 93-fold dynamic range of splicing with RENDR controlling fluorescent protein production in response to an RNA input. We further resolve a thermodynamic model to guide RENDR design, show how input signals can be transduced into diverse outputs, demonstrate portability across different bacteria, and use RENDR to detect antibiotic-resistant bacteria. This work shows how transcriptional signals can be monitored in situ and converted into different types of biochemical information using RNA synthetic biology.

‘I have become all things to all men [and women] that I might by all means save some’ (I Corinthians 9:22, KJV). Theatricality and conversion in the poetry of Pierre Le Moyne

The exceptionally diverse and extensive literary output of the Jesuit poet Pierre Le Moyne is full of apparent paradoxes and ambiguities which have always proved a challenge to commentators, from his own contemporaries to the present day. This article takes as its starting point the recent recovery of a detailed account of Le Moyne’s first known creation, a spectacular dramatic production put on by the Jesuit college in Reims, to consider a pervasive, but relatively unstudied, feature of his works: the poet’s frequent allusions to the theatre, and, beyond this, the relationship between his strikingly original imagination and the creative techniques of dramatists and actors. It is argued that these are crucial to the ways in which he sets out to engage his worldly readers, and to encourage a receptive response to the moral and spiritual ideals which he seeks to transmit.

Behavior Modeling and Digital Predistortion of Mismatched Wireless Transmitters Using Convolutional Neural Networks

In modern wireless compact transmitters, Power Amplifier (PA) behavior is considerably affected by the impedance mismatch between the PA’s output and the antenna’s input. This PA’s output mismatch results in a reflection at the PA–antenna interface. In this brief, reflection-aware PA modeling and digital predistortion (DPD) techniques are proposed to mitigate the negative impact of this mismatch on the forward and reverse models of the PA. An Augmented Convolutional neural network model <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(\boldsymbol{\Gamma }$ </tex-math></inline-formula> ACNN) is proposed to linearize a Doherty PA under different values of the output mismatch using a single set of coefficients. The developed DPD shows robust performance metrics like normalized mean square error (NMSE), and adjacent channel power ratio (ACPR) under diverse complex output mismatch levels.

Emotional generative adversarial network for image emotion transfer

The influence of emotions on viewers is important for that it can judge the value of multimedia materials. However, most of the existing image transfer algorithms do not take the affect information embedded in the image into consideration. An emotion is not only depended on colors or a single image. However, previous works in emotion transfer were limited to colors or a single image and shows no more benefits from more images. Besides, some models proposed for image emotion transfer are deterministic, since they can only generate unimodal output for a given input image once the models are trained. This paper proposes a new emotion-based image transfer algorithm named as Emotional Generative Adversarial Network (EGAN) to deal with these issues. To take benefits from more images, this paper utilizes adversarial training to generalize more emotional features. These generalized features are more objective and comprehensive compared with previous works. To achieve diverse outputs of emotional transfer, a new disentangled strategy is proposed, which assumes that the emotional images from different emotion domains can be embedded into a shared latent neutral high-level concept (NHC) space and a shared latent emotional low-level feature (ELF) space. By combining the NHC representation embedded from the input image with the ELF representation embedded from the target emotional reference image, the model can reconstruct a transferred target-emotion image. Therefore, diverse outputs can be obtained by selecting different reference images. Two quantitative measures are presented to evaluate the effect of image emotion transfer. Experimental results show that the proposed method performs better than other state-of-the-art baselines.

Multicolor emission based on a N, N'-Disubstituted dihydrodibenzo [a, c] phenazine crown ether macrocycle.

Dynamic fluorophore 9,14-diphenyl-9,14-dihydrodibenzo[a,c]phenazine (DPAC) affords a new platform to produce diverse emission outputs. In this paper, a novel DPAC-containing crown ether macrocycle D-6 is synthesized and characterized. Host-guest interactions of D-6 with different ammonium guests produced a variety of fluorescence with hypsochromic shifts up to 130nm, which are found to be affected by choice of solvent or guest and host/guest stoichiometry. Formation of supramolecular complexes were confirmed by UV-vis titration, 1H NMR and HRMS spectroscopy.

A Single-Snapshot Localization for Monostatic FDA-MIMO Radar

We propose a low-complexity method which estimates the angles and the ranges of targets, using single-snapshot of frequency diverse array-multiple input multiple output (FDA-MIMO) radar. Existing single-snapshot-based methods apply one-dimensional discrete Fourier transform (DFT) to a received signal which is made of superposition of Vandermonde structured steering vectors. However, such method is not directly applicable to FDA-MIMO, as joint steering vector of the FDA-MIMO does not obey Vandermonde structure. To tackle this problem, the proposed method alters the signal vector to the form to which two-dimensional DFT (2D-DFT) is applicable for the coarse estimates. Afterwards, precise version is extracted by searching small region around the initial estimates. Numerical results demonstrate superiority of the proposed method over the phased-array-based method in separating targets with close angles. Furthermore, it is proved that the proposed method is more suitable to single-snapshot-based localization than the conventional methods, maintaining low complexity.

Hybrid electrosynthesis as non-genetic approach for regulating microbial metabolism towards waste valorization in circular framework.

Biogenic waste (solid/liquid/gaseous) utilization in biological processes has disruptive potential of inclining towards carbon neutrality, while producing diverse products output. Anaerobic fermentation (methanogenesis and acidogenesis) routes are crucial bioprocesses for production of various renewable chemicals (carboxylate platform/organic acids, short/medium chain alcohols, aldehydes, biopolymers) and fuels (methane, hydrogen, hythane, biodiesel and electricity), while individual operations posing process limitations on their conversion efficiency. Advantageous benefit of using the individual bioprocess technicalities is of utmost importance in the context of sustainability to conceptualize and executeintegrated waste biorefinery. The opinion article intends to document/familiarize the waste-fed biorefinery potential with application of hybrid advancements towards multiple product/energy/renewable chemical spectrum leading to carbon neutrality bioprocesses. Unique and notable challenges with diverse process integrations along with electrochemical/interspecies-redox metabolites-materials synergy/enzymatic interventions are specifically emphasized on application-oriented waste feedstock potential towards achieving sustainability.

Abstract IA017: Impact of oxygen and nutrient availability on sarcoma tumor neighborhood

Abstract Molecular oxygen (O2) sustains intracellular bioenergetics and is consumed by numerous biochemical reactions, making it essential for most species on Earth. Accordingly, decreased oxygen concentration (hypoxia) is a major stressor that generally subverts life of aerobic species and is a prominent feature of pathological states encountered in bacterial infection, inflammation, wounds, cardiovascular defects and cancer. Therefore, key adaptive mechanisms to cope with hypoxia have evolved in mammals. Systemically, these adaptations include increased ventilation, cardiac output, blood vessel growth and circulating red blood cell numbers. On a cellular level, ATP- consuming reactions are suppressed, and metabolism is altered until oxygen homeostasis is restored. A critical question is how mammalian cells sense oxygen levels to coordinate diverse biological outputs during hypoxia. The best- studied mechanism of response to hypoxia involves hypoxia inducible factors (HIFs), which are stabilized by low oxygen availability and control the expression of a multitude of genes, including those involved in cell survival, angiogenesis, glycolysis and invasion/metastasis. Importantly, changes in oxygen can also be sensed via other stress pathways as well as changes in metabolite levels and the generation of reactive oxygen species by mitochondria. Collectively, this leads to cellular adaptations of protein synthesis, energy metabolism, mitochondrial respiration, lipid and carbon metabolism as well as nutrient acquisition. These mechanisms are integral inputs into fine- tuning the responses to hypoxic stress. Citation Format: M. Celeste Simon. Impact of oxygen and nutrient availability on sarcoma tumor neighborhood [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr IA017.

Services and evidence of irremediable market failure in rich countries: Australian experience

AbstractA large and increasing share of economic activity in modern rich economies can be shown to lie outside what mainstream neoclassical economics itself says it can be applied to: according to neoclassical economics, such activities are unsuited to markets. Since World War II rich economies have de-industrialised, shifted to services. Since the 1970s neoclassical economics has come to dominate policy advice. This worldview justifies state interventions when there is remediable market failure, but it ignores irremediable market failure, where its theory says that markets cannot work efficiently (such as where there is joint production or own consumption). Servicisation, this article argues, has increased and is increasing the extent to which neoclassical economics is, in its own terms, ‘knowably ignorant’. This is because, where service provision is subject to irremediable market failure, neoclassical economics, in its own terms, is inapplicable. The GDP [Gross Domestic Product] share of sub-sectors serving diverse human needs, such as aged care and education, is large and rising. Such sub-sectors are dominated by joint production, manifest as diverse outputs (complex and inter-related changes in the welfare of their diverse clients). Setting aside questions of the wider ‘scientific’ validity of neoclassical theory, the article focuses on its assertion that markets cannot generate economically efficient outcomes in such activities. Arguing that the constrained optimisation of its conceptual world cannot happen in the case of joint production and own consumption of service outputs, neoclassical theory reports itself as unable to determine optimal resource allocation, and so by implication cannot fulfil its role as the foundational basis of state economic policy. Yet the literature shows that the coverage of this theoretical result is not publicised – it is ‘knowable’ but not widely known. The article concludes that, as neoclassical economics is foundational to contemporary state policy, both GDP data and neoclassical theory show that it, and markets, are knowably and increasingly inapplicable. Therefore, mainstream policy thinking continues to face an increasing ‘state of ignorance’, and the cognitive foundations of state policy are correspondingly weak.

The structural effect between the output module and chromophore-binding domain is a two-way street via the hairpin extension

Signal transduction typically starts with either ligand binding or cofactor activation, eventually affecting biological activities in the cell. In red light-sensing phytochromes, isomerization of the bilin chromophore results in regulation of the activity of diverse output modules. During this process, several structural elements and chemical events influence signal propagation. In our study, we have studied the full-length bacteriophytochrome from Deinococcus radiodurans as well as a previously generated optogenetic tool where the native histidine kinase output module has been replaced with an adenylate cyclase. We show that the composition of the output module influences the stability of the hairpin extension. The hairpin, often referred as the PHY tongue, is one of the central structural elements for signal transduction. It extends from a distinct domain establishing close contacts with the chromophore binding site. If the coupling between these interactions is disrupted, the dynamic range of the enzymatic regulation is reduced. Our study highlights the complex conformational properties of the hairpin extension as a bidirectional link between the chromophore-binding site and the output module, as well as functional properties of diverse output modules.

Fast 2-step regularization on style optimization for real face morphing

StyleGAN is now capable of achieving excellent results, especially high-quality face synthesis. Recently, some studies have tried to invert real face images into style latent space via StyleGAN. However, morphing real faces via latent representation is still in its infancy. Training costs are high and/or require huge samples with labels. By adding regularization to style optimization, we propose a novel method to morph real faces based on StyleGAN. To do the supervised task, we label latent vectors via synthesized faces and release the label set; then we utilize logistic regression to fast discover interpretable directions in latent space. Appropriate regularization helps us to optimize both latent vectors (faces and directions). Moreover, we use learned directions under different layer representations to handle real face morphing. Compared to the existing methods, our method faster yields a larger diverse and realistic output. Code and cases are available at https://github.com/disanda/RFM.

The microscopic principle and traits on pulsed discharge of negative corona

A systematic investigation of the dynamical behaviors and traits of negative corona discharge in pure oxygen is performed and the microscopic physical mechanism of the discharge is studied. In the meantime, a new dynamical model is presented, which can predict different discharge phases including steady-state discharge and periodic discharge of saw-tooth pulses. The change in the discharge outputs with time is analyzed comprehensively using the nonlinear dynamics method, and the Trichel pulsed oscillation and the dynamical characteristics are described. In addition, the positive ions impacting the cathode and the negative ions can be treated as feedback signals for maintaining pulsed discharge at the constant external excitation. It is found that the gas pressure levels significantly affect the discharge behaviors and the pulsed characteristics of charged particles and glow radiation. In particular, the microscopic mechanism of gas pressure level influencing the negative corona discharge is discovered. Meanwhile, the pulsed characteristics of the discharge outputs are studied, and an explanation of the microscopic formation mechanism of the symmetrical and asymmetrical jagged pulses of diverse outputs is given. It is revealed that the mass, migration speed, and migration time of various particles have a great influence on discharge behaviors.

Assessing Key performance indicators in Blockchain-Based Supply Chain Financing: Case Study of Chain Stores

In recent years, due to the growth and development of financing tools and methods, financial institutions are always looking for new, efficient, and low-cost methods. In carrying out their daily production operations, companies are faced with different and diverse financial input and output flows from purchasing orders and inventory to receiving the price of sold goods, which are not the same in terms of time. Therefore, they will inevitably look for the financing of these processes, which is referred to as working capital financing. However, in the conditions of recession and lack of bank loans and considering the problems caused by the unilateral management of this issue, there is a need for innovative financial tools such as supply chain financing (SCF). In recent years, with the introduction and evolution of blockchain technology, this opportunity has also arisen in financial fields to make the most of this new technology. Considering the growth of this technology and the importance of supply chain financing, in this research, it was first tried to fully investigate the dimensions and key components of blockchain-based supply chain financing. chain stores were considered as a case study and the importance of supply chain financing was analyzed in them and the SCF framework was presented in these applied enterprises. In addition, the most important key performance indicators in a blockchain-based supply chain financing system are identified and evaluated with an emphasis on chain store processes. To evaluate the data, a non-linear hierarchical analysis method was used. The results show that transferring and reducing risks in different types is one of the most important performance indicators of the blockchain-based supply chain financing system.

Increasing the Diversity of Deep Generative Models

Generative models are used in a variety of applications that require diverse output. Yet, models are primarily optimised for sample fidelity and mode coverage. My work aims to increase the output diversity of generative models for multi-solution tasks. Previously, we analysed the use of generative models in artistic settings and how its objective diverges from distribution fitting. For specific use cases, we quantified the limitations of generative models. Future work will focus on adapting generative modelling for downstream tasks that require a diverse set of high-quality artefacts.

Social isolation modulates appetite and avoidance behavior via a common oxytocinergic circuit in larval zebrafish

Animal brains have evolved to encode social stimuli and transform these representations into advantageous behavioral responses. The commonalities and differences of these representations across species are not well-understood. Here, we show that social isolation activates an oxytocinergic (OXT), nociceptive circuit in the larval zebrafish hypothalamus and that chemical cues released from conspecific animals are potent modulators of this circuit’s activity. We delineate an olfactory to subpallial pathway that transmits chemical social cues to OXT circuitry, where they are transformed into diverse outputs simultaneously regulating avoidance and feeding behaviors. Our data allow us to propose a model through which social stimuli are integrated within a fundamental neural circuit to mediate diverse adaptive behaviours.

Tectonophysics Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science

AbstractThis article is composed of two independent opinion pieces about the state of integrated, coordinated, open, and networked (ICON) principles (Goldman et al., 2021, https://doi.org/10.1029/2021EO153180; Goldman et al., 2022, https://doi.org/10.1029/2021ea002099) in Tectonophysics and discussion on the opportunities and challenges of adopting them. Each opinion piece focuses on a different topic: (a) global collaboration, technology transfer and application, reproducibility, and data sharing and infrastructure; and (b) field, experimental, remote sensing, and real‐time data research and application. Within tectonophysics science, ICON‐FAIR principles are starting to be adopted and implemented, however they have not become frequent and there are still plenty of opportunities for further development. During the last decade, standardization reduced fragmentation, facilitated openly available databases, and enabled different modeling methods to be combined. On the other hand, integration and coordination remained insufficient as exemplified by numerous geophysical interpretation programs running on different platforms, lacking the proper documentation and with diverse output formats. We agree that adapting the principles of ICON‐FAIR brings high efforts and risks, but in the end, it has great benefits and potential in the tectonophysics community.

Model-based characterization of permeability damage control through inhibitor injection under parametric uncertainty

Damage in subsurface formations caused by mineral precipitation decreases the porosity and permeability, eventually reducing the production rate of wells in plants producing oil, gas or geothermal fluids. A possible solution to this problem consists in stopping the production followed by the injection of inhibiting species that slow down the precipitation process. In this work we model inhibitor injection and quantify the impact of a set of model parameters on the outputs of the system. The parameters investigated concern three key factors contributing to the success of the treatment: i) the inhibitor affinity, described by an adsorption Langmuir isotherm, ii) the concentration and time related to the injection and iii) the efficiency of the inhibitor in preventing mineral precipitation. Our simulations are set in a stochastic framework where these inputs are characterized in probabilistic terms. Forward simulations rely on a purpose-built code based on finite differences approximation of the reactive transport setup in radial coordinates. We explore the sensitivity diverse outputs, encompassing the well bottom pressure and space-time scales characterizing the transport of the inhibitor. We find that practically relevant output variables, such as inhibitor lifetime and well bottom pressure, display a diverse response to input uncertainties and display poor mutual dependence. Our results quantify the probability of treatment failure for diverse scenarios of inhibitor-rock affinity. We find that treatment optimization based on single outputs may lead to high failure probability when evaluated in a multi-objective framework. For instance, employing an inhibitor displaying an appropriate lifetime may fail in satisfying criteria set in terms of well-bottom pressure history or injected inhibitor mass.