bluuebunny/crossref_metadata_2025 huggingface dataset:
Bagging, optimized dynamic mode decomposition for robust, stable forecasting with spatial and temporal uncertainty quantification
Authors: Sashidhar Diya , Kutz J. Nathan
Published: June 2022

Dynamic mode decomposition (DMD) provides a regression framework for adaptively learning a best-fit linear dynamics model over snapshots of temporal, or spatio-temporal, data. A variety of regression techniques have been developed for producing the linear model approximation whose solutions are exponentials in time. For spatio-temporal data, DMD provides low-rank and interpretable models in the form of dominant modal structures along with their exponential/oscillatory behaviour in time. The majority of DMD algorithms, however, are prone to bias errors from noisy measurements of the dynamics, leading to poor model fits and unstable forecasting capabilities. The optimized DMD algorithm minimizes the model bias with a variable projection optimization, thus leading to stabilized forecasting capabilities. Here, the optimized DMD algorithm is improved by using statistical bagging methods whereby a single set of snapshots is used to produce an
ensemble
of optimized DMD models. The outputs of these models are averaged to produce a bagging, optimized dynamic mode decomposition (BOP-DMD). BOP-DMD improves performance by stabilizing and cross-validating the DMD model by ensembling; it also robustifies the model and provides both spatial and temporal uncertainty quantification (UQ). Thus, unlike currently available DMD algorithms, BOP-DMD provides a stable and robust model for
probabilistic
, or Bayesian, forecasting with comprehensive UQ metrics.

This article is part of the theme issue ‘Data-driven prediction in dynamical systems’.
Gaussian processes meet NeuralODEs: a Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data
Authors: Bhouri Mohamed Aziz , Perdikaris Paris
Published: June 2022

We present a machine learning framework (GP-NODE) for Bayesian model discovery from partial, noisy and irregular observations of nonlinear dynamical systems. The proposed method takes advantage of differentiable programming to propagate gradient information through ordinary differential equation solvers and perform Bayesian inference with respect to unknown model parameters using Hamiltonian Monte Carlo sampling and Gaussian Process priors over the observed system states. This allows us to exploit temporal correlations in the observed data, and efficiently infer posterior distributions over plausible models with quantified uncertainty. The use of the Finnish Horseshoe as a sparsity-promoting prior for free model parameters also enables the discovery of parsimonious representations for the latent dynamics. A series of numerical studies is presented to demonstrate the effectiveness of the proposed GP-NODE method including predator–prey systems, systems biology and a 50-dimensional human motion dynamical system.

This article is part of the theme issue ‘Data-driven prediction in dynamical systems’.
Non-intrusive balancing transformation of highly stiff systems with lightly damped impulse response
Authors: Rezaian Elnaz , Huang Cheng , Duraisamy Karthik
Published: June 2022

Balanced truncation (BT) is a model reduction method that uses a coordinate transformation to retain eigen-directions that are highly observable and reachable. To address realizability and scalability of BT applied to highly stiff and lightly damped systems, a non-intrusive data-driven method is developed for balancing discrete-time linear systems via the eigensystem realization algorithm (ERA). The advantage of ERA for balancing transformation makes full-state outputs tractable. Further, ERA enables balancing despite stiffness, by eliminating computation of balancing modes and adjoint simulations. As a demonstrative example, we create balanced reduced-order models (ROMs) for a one-dimensional reactive flow with pressure forcing, where the stiffness introduced by the chemical source term is extreme (condition number

10

13

), preventing analytical implementation of BT. We investigate the performance of ROMs in prediction of dynamics with unseen forcing inputs and demonstrate stability and accuracy of balanced ROMs in
truly
predictive scenarios, whereas without ERA, proper orthogonal decomposition-Galerkin and least-squares Petrov–Galerkin projections fail to represent the true dynamics. We show that after the initial transients under unit impulse forcing, the system undergoes lightly damped oscillations, which magnifies the influence of sampling properties on predictive performance of the balanced ROMs. We propose an output domain decomposition approach and couple it with tangential interpolation to resolve sharp gradients at reduced computational costs.

This article is part of the theme issue ‘Data-driven prediction in dynamical systems’.
A physics-based digital twin for model predictive control of autonomous unmanned aerial vehicle landing
Authors: McClellan Andrew , Lorenzetti Joseph , Pavone Marco , Farhat Charbel
Published: June 2022

This paper proposes a two-level, data-driven, digital twin concept for the autonomous landing of aircraft, under some assumptions. It features a digital twin instance (DTI) for model predictive control (MPC); and an innovative, real-time, digital twin prototype for fluid–structure interaction and flight dynamics to inform it. The latter digital twin is based on the linearization about a pre-designed glideslope trajectory of a high-fidelity, viscous, nonlinear computational model for flight dynamics; and its projection onto a low-dimensional approximation subspace to achieve real-time performance, while maintaining accuracy. Its main purpose is to predict in real time, during flight, the state of an aircraft and the aerodynamic forces and moments acting on it. Unlike static lookup tables or regression-based surrogate models based on steady-state wind tunnel data, the aforementioned real-time digital twin prototype allows the DTI for MPC to be informed by a truly dynamic flight model, rather than a less accurate set of steady-state aerodynamic force and moment data points. The paper describes in detail the construction of the proposed two-level digital twin concept and its verification by numerical simulation. It also reports on its preliminary flight validation in autonomous mode for an off-the-shelf unmanned aerial vehicle instrumented at Stanford University.

This article is part of the theme issue ‘Data-driven prediction in dynamical systems’.
Hybrid quadrature moment method for accurate and stable representation of non-Gaussian processes applied to bubble dynamics
Authors: Charalampopoulos A. , Bryngelson S. H. , Colonius T. , Sapsis T. P.
Published: June 2022

Solving the population balance equation (PBE) for the dynamics of a dispersed phase coupled to a continuous fluid is expensive. Still, one can reduce the cost by representing the evolving particle density function in terms of its moments. In particular, quadrature-based moment methods (QBMMs) invert these moments with a quadrature rule, approximating the required statistics. QBMMs have been shown to accurately model sprays and soot with a relatively compact set of moments. However, significantly non-Gaussian processes such as bubble dynamics lead to numerical instabilities when extending their moment sets accordingly. We solve this problem by training a recurrent neural network (RNN) that adjusts the QBMM quadrature to evaluate unclosed moments with higher accuracy. The proposed method is tested on a simple model of bubbles oscillating in response to a temporally fluctuating pressure field. The approach decreases model-form error by a factor of 10 when compared with traditional QBMMs. It is both numerically stable and computationally efficient since it does not expand the baseline moment set. Additional quadrature points are also assessed, optimally placed and weighted according to an additional RNN. These points further decrease the error at low cost since the moment set is again unchanged.

This article is part of the theme issue ‘Data-driven prediction in dynamical systems’.
Deep learning for centre manifold reduction and stability analysis in nonlinear systems
Authors: Ghadami Amin , Epureanu Bogdan I.
Published: June 2022

Bifurcations cause large qualitative and quantitative changes in the dynamics of nonlinear systems with slowly varying parameters. These changes most often are due to modifications that occur in a low-dimensional subspace of the overall system dynamics. The key challenge is to determine what that low-dimensional subspace is, and construct a low-order model that governs the dynamics in that subspace. Centre manifold theory can provide a theoretical means to construct such low-order models for strongly nonlinear systems that undergo bifurcations. Performing a centre manifold analysis, however, is particularly challenging when the system dimensionality is high or impossible when an accurate model of the system is not available. This paper introduces a data-driven approach for identifying a reduced order model of the system based on centre manifold theory. The approach does not require a model of the full order system. Instead, a deep learning approach capable of identifying the centre manifold and the transformation to the centre space is created using measurements of the system dynamics from random perturbations. This approach unravels the characteristics of the system dynamics in the vicinity of bifurcations, providing critical information regarding the behaviour of the system.

This article is part of the theme issue ‘Data-driven prediction in dynamical systems’.
ICTV Virus Taxonomy Profile: Rhabdoviridae 2022
Authors: Walker Peter J. , Freitas-Astúa Juliana , Bejerman Nicolas , Blasdell Kim R. , Breyta Rachel , Dietzgen Ralf G. , Fooks Anthony R. , Kondo Hideki , Kurath Gael , Kuzmin Ivan V. , Ramos-González Pedro Luis , Shi Mang , Stone David M. , Tesh Robert B. , Tordo Noël , Vasilakis Nikos , Whitfield Anna E. ,   ICTV Report Consortium
Published: June 2022

The family Rhabdoviridae comprises viruses with negative-sense (−) RNA genomes of 10–16 kb. Virions are typically enveloped with bullet-shaped or bacilliform morphology but can also be non-enveloped filaments. Rhabdoviruses infect plants or animals, including mammals, birds, reptiles, amphibians or fish, as well as arthropods, which serve as single hosts or act as biological vectors for transmission to animals or plants. Rhabdoviruses include important pathogens of humans, livestock, fish or agricultural crops. This is a summary of the International Committee on Taxonomy of Viruses (ICTV) Report on the family Rhabdoviridae, which is available at ictv.global/report/rhabdoviridae.
The native cell differentiation program aberrantly recapitulated in yki3S/A-induced intestinal hyperplasia drives invasiveness and cachexia-like wasting phenotypes
Authors: Pranoto Inez K.A. , Lee Jiae , Kwon Young V.
Published: June 2022

Many tumors recapitulate the developmental and differentiation program of their tissue of origin, a basis for tumor cell heterogeneity. Although stem-cell-like tumor cells are well-studied, the roles of tumor cells undergoing differentiation in inducing the phenotypes associated with advanced cancers remains to be elucidated. Here, we employ Drosophila genetics to demonstrate that the native differentiation program of intestinal stem cells plays a key role in determining an intestinal tumor’s capacity to invade and induce various non-tumor-autonomous phenotypes. The differentiation program that generates absorptive cells enterocytes is aberrantly recapitulated in the intestinal tumors generated through activation of the Yap1 ortholog Yorkie. Elimination of tumor cells in the enterocyte lineage allows stem cell-like tumor cells to grow but suppresses invasiveness and reshapes various phenotypes associated with cachexia-like wasting by altering the expression of tumor-derived factors. Our study provides insight into how a native differentiation program determines a tumor’s capacity to induce the phenotypes associated with advanced cancers and suggests that manipulating the differentiation programs co-opted in tumors might be a way to treat some complications of cancer, including cachexia.
Adaptation to climate change through dense planting for sustainable agriculture
Authors: Huang Zhixiong , He Xue , Zhao Xueqiang , Teng Wan , Hu Mengyun , Li Hui , Zhang Yijing , Tong Yiping
Published: June 2022

Overuse of fertilizers increased greenhouse gases emissions, induced global climate changes and extreme weather and made future agriculture unsustainable. Engineering crops to adapt to stressed conditions is crucial. Here, we cloned a transcription factor TabZIP45 (basic region zipper), controlled by a microRNA binding site polymorphism, conferring adaptation to both nitrogen deficiency and dense planting. TabZIP45 interacted with TaFTL43 (Flowering locus T like43) to change gene expression regulation. TabZIP45 coordinated phosphatidylinositol diphosphate (PIP2) metabolism and calcium (Ca2+) signaling to adapt to environmental stresses. Knockout of TabZIP45-4B by genome editing rescued grain yield loss caused by nitrogen deficiency by modulation of TaDwarf4 under dense planting through Ca2+ signaling disruption. Thus, TabZIP45-4B edited wheat warranted a sustainable and environmentally friendly way to enhance grain yield under adverse conditions.

One-Sentence Summary

Calcium and lipids integrated adverse environmental signaling to modulate plant growth
Vehicle pollution is associated with elevated insect damage to street trees
Authors: Meineke Emily K. , Eng David S. , Karban Richard
Published: June 2022

Vehicle pollution is a pervasive aspect of anthropogenic change across rural and urban habitats. The most common emissions are carbon- or nitrogen-based pollutants that may impact diverse interactions between plants and insect herbivores. However, the effects of vehicle pollution on plant-insect interactions are poorly understood.

Here, we combine a city-wide experiment across the Sacramento Metropolitan Area and a laboratory experiment to determine how vehicle emissions affect insect herbivory and leaf nutritional quality.

We demonstrate that leaf damage to a native oak species (Quercus lobata) commonly planted across the western US is substantially elevated on trees exposed to vehicle emissions. In the laboratory, caterpillars preferred leaves from highway-adjacent trees and performed better on leaves from those same trees.

Synthesis and applications. Together, our studies demonstrate that the heterogeneity in vehicle emissions across cities may explain highly variable patterns of insect herbivory on street trees. Our results also indicate that trees next to highways are particularly vulnerable to multiple stressors, including insect damage. To combat these effects, urban foresters may consider installing trees that are less susceptible to insect herbivory along heavily traveled roadways.
‘A Generalized Reinforcement Learning-Based Deep Neural Network (GRL-DNN) Agent Model for Diverse Cognitive Constructs
Authors: Nair Sandeep S. , Muddapu Vignayanandam R. , Vigneswaran C , Balasubramani Pragathi P. , Ramanathan Dhakshin S. , Mishra Jyoti , Chakravarthy V. Srinivasa
Published: June 2022

Human cognition is characterized by a wide range of capabilities including goal-oriented selective attention, distractor suppression, decision making, response inhibition, and working memory. Much research has focused on studying these individual components of cognition in isolation, whereas in several translational applications for cognitive impairment, multiple cognitive functions are altered in a given individual. Hence it is important to study multiple cognitive abilities in the same subject or, in computational terms, model them using a single model. To this end, we propose a unified, reinforcement learning-based agent model comprising of systems for representation, memory, value computation and exploration. We successfully modelled the aforementioned cognitive tasks and show how individual performance can be mapped to model meta-parameters. This model has the potential to serve as a proxy for cognitively impaired conditions, and can be used as a clinical testbench on which therapeutic interventions can be simulated first before delivering to human subjects.
Threshold-awareness in adaptive cancer therapy
Authors: Wang MingYi , Scott Jacob G. , Vladimirsky Alexander
Published: June 2022

Although adaptive cancer therapy shows promise in integrating evolutionary dynamics into treatment scheduling, the stochastic nature of cancer evolution has seldom been taken into account. Various sources of random perturbations can impact the evolution of heterogeneous tumors, making performance metrics of any treatment policy random as well. In this paper, we propose an efficient method for selecting optimal adaptive treatment policies under randomly evolving tumor dynamics. The goal is to improve the cumulative “cost” of treatment, a combination of the total amount of drugs used and the total treatment time. As this cost also becomes random in any stochastic setting, we maximize the probability of reaching the treatment goals (tumor stabilization or eradication) without exceeding a pre-specified threshold (or a “budget”). We use a novel Stochastic Optimal Control formulation and Dynamic Programming to find such “threshold-aware” optimal treatment policies. Our approach enables an efficient algorithm to compute these policies for a range of threshold values simultaneously. Compared to treatment plans shown to be optimal in a deterministic setting, the new “threshold-aware” policies significantly improve the chances of the therapy succeeding under the budget, which is correlated with a lower general drug usage. We illustrate this method using two specific examples, but our approach is far more general and provides a new tool for optimizing adaptive therapies based on a broad range of stochastic cancer models.

Author Summary

Tumor heterogeneities provide an opportunity to improve therapies by leveraging complex (often competitive) interactions of different types of cancer cells. These interactions are usually stochastic due to both individual cell differences and random events affecting the patient as a whole. The new generation of cancer models strive to account for this inherent stochasticity, andadaptivetreatment plans need to reflect it as well. In optimizing such treatment, the most common approach is to maximize the probability of eventually stabilizing or eradicating the tumor. In this paper, we consider a more nuanced version of success, maximizing the probability of reaching these therapy goals before the cumulative burden from the disease and treatment exceed a chosen threshold. Importantly, our method allows computing such optimal treatment plans efficiently and for a range of thresholds at once. If used on a high-fidelity personalized model, our general approach could potentially be used by clinicians to choose the most suitable threshold after a detailed discussion of a specific patient’s goals (e.g., to include the trade-offs between toxicity and quality of life).
NRX-101, A RAPID-ACTING ANTI-DEPRESSANT, DOES NOT CAUSE NEUROTOXICITY FOLLOWING KETAMINE ADMINISTRATION IN PRECLINICAL MODELS
Authors: Jordan William , Siegel Richard , Kumar Rajendra , Javitt Jonathan
Published: June 2022

Background

NMDA (N-Methyl-D aspartate) receptor antagonists have gained increasing attention as rapid-acting antidepressants. However, their use has been limited by potential neurotoxicity (Olney Lesions) and recent FDA guidance requires demonstration of safety on histologic parameters prior to initiation of human studies. D-cycloserine is a mixed NMDA agonist/antagonist awarded Breakthrough Therapy Designation and currently in clinical trials for the treatment of bipolar depression with suicidal ideation. The current study was designed to investigate the neurologic safety profile of D-cycloserine by itself and in combination with ketamine and lurasidone.

Methods

Sprague Dawley female rats (n=106) were randomly divided into 7 study groups. Ketamine was administered via tail vein infusion. D-cycloserine and lurasidone were administered via oral gavage in escalating doses to a maximum of 2000 mg/kg DCS. To ascertain toxicity, dose escalation with three different doses of D-cycloserine/lurasidone was given in combination with ketamine. MK-801, a known neurotoxic NMDA antagonist, was administered as a positive control. Brain tissue was sectioned and stained with H&E and Fluorojade stains.

Results

No fatalities were observed in any group. No microscopic abnormalities were found in the brain of animal subjects given ketamine, ketamine followed by DCS/lurasidone, or DCS/lurasidone alone. Neuronal necrosis, as expected, was seen in the MK-801 positive control group.

Conclusion

NRX-101, a fixed-dose combination of D-cycloserine/lurasidone, when administered with or without prior infusion of IV ketamine was tolerated and did not induce neurotoxicity, even at maximum-tolerated doses of D-cycloserine.

HIGHLIGHTS

NRX-101, a fixed dose combination of D-cycloserine and lurasidone does not exhibit histologic neurotoxicity, even at maximum-tolerated doses
Novel Rickettsia spp. in two common overwintering North American passerines
Authors: Becker Daniel J. , Byrd Allison , Smiley Tara M. , Marques Mariana Fernandes , Nunez Julissa Villegas , Talbott Katherine M. , Atwell Jonathan W. , Volokhov Dmitriy V. , Ketterson Ellen D. , Jahn Alex E. , Clark Kerry L.
Published: June 2022

American robins and dark-eyed juncos migrate across North America, but their contributions to arthropod-borne disease remain poorly characterized. We identified novel Rickettsia spp. in one wintering migrant per bird species related to bellii, transitional, and spotted fever group rickettsiae and suggest spring migration could disperse these pathogens hundreds-to-thousands of kilometers.
Conservation management strategy impacts inbreeding and mutation load in scimitar-horned oryx
Authors: Humble Emily , Stoffel Martin A , Dicks Kara , Ball Alex D , Gooley Rebecca M , Chuven Justin , Pusey Ricardo , Remeithi Mohammed Al , Koepfli Klaus-Peter , Pukazhenthi Budhan , Senn Helen , Ogden Rob
Published: June 2022

In an age of habitat loss and overexploitation, small populations, both captive and wild, are increasingly facing the effects of isolation and inbreeding. Genetic management has therefore become a vital tool for ensuring population viability. However, little is known about how the type and intensity of intervention shape the genomic landscape of inbreeding and mutation load. We address this using whole genome sequence data of scimitar-horned oryx (Oryx dammah), an iconic antelope that has been subject to contrasting management strategies since it was declared extinct in the wild. We show that unmanaged populations are enriched for long runs of homozygosity (ROH) and have significantly higher inbreeding coefficients than managed populations. Additionally, despite the total number of deleterious alleles being similar across management strategies, the burden of homozygous deleterious genotypes was consistently higher in unmanaged groups. These findings emphasise the risks associated with deleterious mutations through multiple generations of inbreeding. As wildlife management strategies continue to diversify, our study reinforces the importance of maintaining genome-wide variation in vulnerable populations and has direct implications for one of the largest reintroduction attempts in the world.

Significance statement

Conservation genetic management is becoming increasingly important for safeguarding and restoring wildlife populations. Understanding how the intensity of intervention influences genomic components of fitness is therefore essential for supporting species viability. We investigate the impact of contrasting management strategies on the genomic landscape of inbreeding and mutation load in captive populations of scimitar-horned oryx. We reveal how several decades of management have prevented the formation of long runs of homozygosity and masked the expression of deleterious mutations. Our findings highlight the dynamics between inbreeding, mutation load and population size and have direct implications for future management of threatened species.
multEvol: An interactive pipeline for multitrait evolutionary analysis in a phylogenetic framework
Authors: Basu Dipendra Nath , Bhaumik Vaishali , Kunte Krushnamegh
Published: June 2022

Comparative phylogenetic studies based on 2D image data can reveal macroevolutionary patterns and processes by integrating phylogenetic and ecological analyses. The advent of high-throughput imaging facilities has led to the accumulation of large image datasets; however, large-scale data extraction and analysis using these resources can be labour- and computation-intensive. Moreover, there are few standardized methods to analyse multitrait image-based data in a phylogenetic framework. Several packages in the R statistical environment offer utilities for processing and analysing large-scale image data. Techniques for the comparative phylogenetic analysis of multitrait data have also been introduced in other packages. However, most of these approaches are specialized for discrete purposes, and there is a lack of streamlined and efficient methodological pipelines integrating features across packages. Here, we present multEvol, an interactive pipeline for the extraction and phylogenetic analysis of shape and colour pattern data from 2D images in the R statistical computing environment. Using this pipeline, shape outlines and colour pattern data for multiple colours can be extracted, visualised, and compared in a phylogenetic framework. Using various utilities provided in the phytools, RNiftyReg, Momocs, patternize, convevol, and geiger packages, we introduce an interactive interface that allows user inputs for iterative data extraction. This pipeline provides an efficient method for the bulk processing of images of biological specimens from museum collections. As such, it has potential applications in the large-scale morphometric analysis of museum specimens to reveal macroevolutionary patterns in ecology, evolution and integrative biology.
Pyramidal neuron morphogenesis requires a septin network that stabilizes filopodia and suppresses lamellipodia during neurite initiation
Authors: Radler Megan R. , Liu Xiaonan , Peng Megan , Doyle Brenna , Toyo-Oka Kazuhito , Spiliotis Elias T.
Published: June 2022

Pyramidal neurons are the major cell type of the forebrain, consisting of a pyramidally shaped soma with axonal and apicobasal dendritic processes. It is poorly understood how the neuronal soma morphs from a sphere to pyramid, while generating neurites of the proper shape and orientation. Here, we discovered that the spherical somata of immature neurite-less neurons possess a circumferential wreath-like network of septin filaments, which promotes myosin II localization and suppresses Arp2/3 activity at the base of filopodial actin bundles. The septin network facilitates neurite formation by stabilizing nascent filopodia, which mature to neurites, and concomitantly maintains a consolidated soma by suppressing the extension of lamellipodia. We show that this septin function is critical for the morphogenesis and spatial orientation of pyramidal somata and their neurites in vitro and in vivo. Therefore, the somatic septin cytoskeleton provides a key morphogenetic mechanism for neuritogenesis and the development of pyramidal neurons.

Highlights

- A septin wreath-like network controls the shape of neuronal somata and nascent neurites

- Septins promote and suppress filopodial and lamellipodial protrusions, respectively

- Septins scaffold myosin II and exclude Arp2/3 at the base of filopodial actin

- Development of pyramidally shaped neurons requires septins in vitro and in vivo

eTOC Summary

Radler et al report a new morphogenetic mechanism in the development of pyramidal neurons, which is mediated by a septin wreath-like cytoskeleton in the soma of immature spherical neurons. The septin network stabilizes somatic filopodia and suppresses lamellipodia by differentially controlling the localization of myosin II and Arp2/3.
An insight into cellulolytic capacity of the Trichoderma harzianum P49P11 revealed by omics approaches
Authors: Codima Carla Aloia , Tomazetto Geizecler , Persinoti Gabriela Felix , Riano-Pachon Diego M. , Squina Fábio Marcio , da Cruz Pradella José Geraldo , da Silva Delabona Priscila
Published: June 2022

Cellulases are a group of enzymes with several applications in biofuel production, and the paper, food, pharmaceutical, and chemical industries. Trichoderma harzianum P49P11 secrete all cellulases with high efficiency, representing an alternative to the current filamentous fungi in biotechnological industries. In this study, the cellulolytic mechanisms employed by the strain P49P11 to degrade crystalline cellulose in batch fermentation culture mode were elucidated by combining genome and secretome analysis. The strain P49P11 encodes nineteen cellulase genes from five different CAZyme families (GH5, GH6, GH7, GH12, and GH45), followed by several enzyme families for hemicellulose, pectin, and alpha-and beta-glucans degradation. The diverse CAZymes were also observed in the secretome, including cellulases, hemicellulases, and glucanases. In addition, β-glucosidases and xylanase activities detected during the fermentation process validated our secretome analysis. Taken together, our results revealed all enzymatic machinery used by the T. harzianum P49P11 to degrade cellulose in batch fermentation mode.

Highlights

We described a high-quality genome assembly and annotation of the T. harzianum P49P11.

The T. harzianum P49P11 genome possesses a complete set of genes for lignocellulose degradation.

The first report on T. harzianum P49P11 secretome obtained from batch fermentation strategy.

T. harzianum P49P11 produced cellulases, lignocellulases, and auxiliary enzymes produced in response to crystalline cellulose.
Identification of potential natural compound inhibitors and drug like molecules against human METTL3 by docking and molecular dynamics simulation
Authors: Manna Shibambika , Samal Pragati , Basak Rohini , Mitra Anushka , Roy Arijit Kumar , Kundu Raima , Ahir Amrita , Roychowdhury Amlan , Hazra Ditipriya
Published: June 2022

Nucleotide level chemical modification in transcriptome is critical in regulating different cellular processes, including cancer. The most investigated epitranscriptomic modification is methylation at the N6-position of adenosine (m6A). This dynamic modification process is carried out by: writer, reader and eraser proteins. Writers are methyltransferases, METTL3 is the major writer that works in association with METTL14, an accessory protein. Extensive study revealed that cancer progression for acute myeloid leukaemia, gastric cancer, colorectal cancer, hepatocellular carcinoma, and lung cancer is directly contributed by irregular expression of METTL3. Targeting METTL3, has opened a new window in the development of new inhibitors/drugs. In this study, 80 commercially available compounds were found from an unbiased screening by molecular docking, showing better score when compared with the existing substrate/substrate-analogue and the inhibitor bound crystal structures in terms of docking score and binding energy calculation. Among this pool of compounds, the best seven small molecules, AMF, RAD, JNJ, MEH, ECP, MHN, SGI, have been selected and further validated by different computational tools like binding energy calculation, molecular dynamics simulation etc. The novel hits found in this study can function as lead compounds which can be developed into inhibitors as well as drugs, specific against METTL3.
Characterising patterns of COVID-19 and long COVID symptoms: Evidence from nine UK longitudinal studies
Authors: Bowyer Ruth C. E. , Huggins Charlotte , Toms Renin , Shaw Richard J. , Hou Bo , Thompson Ellen J. , Kwong Alex , Williams Dylan , Kibble Milla , Ploubidis George B. , Timpson Nic , Sterne Jonathan , Chaturvedi Nish , Steves Claire J. , Tilling Kate , Silverwood Richard J.
Published: June 2022

Multiple studies across global populations have established the primary symptoms characterising COVID-19 (Coronavirus Disease 2019) and long COVID. However, as symptoms may also occur in the absence of COVID-19, a lack of appropriate controls has often meant that specificity of symptoms to acute COVID-19 or long COVID could not be examined. We aimed to characterise patterns of COVID-19 and long COVID symptoms across nine UK longitudinal studies, totalling over 42,000 participants. Conducting latent class analyses separately in three groups (‘no COVID-19’, ‘COVID-19 in last 12 weeks’, ‘COVID-19 > 12 weeks ago’), the data did not support the presence of more than two distinct symptom patterns, representing high and low symptom burden, in each group. Comparing the high symptom burden classes between the ‘COVID-19 in last 12 weeks’ and ‘no COVID-19’ groups we identified symptoms characteristic of acute COVID-19, including loss of taste and smell, fatigue, cough, shortness of breath and muscle pains or aches. Comparing the high symptom burden classes between the ‘COVID-19 > 12 weeks ago’ and ‘no COVID-19’ groups we identified symptoms characteristic of long COVID, including fatigue, shortness of breath, muscle pain or aches, difficulty concentrating and chest tightness. The identified symptom patterns among individuals with COVID-19 > 12 weeks ago were strongly associated with self-reported length of time unable to function as normal due to COVID-19 symptoms, suggesting that the symptom pattern identified corresponds to long COVID. Building the evidence base regarding typical long COVID symptoms will improve diagnosis of this condition and the ability to elicit underlying biological mechanisms, leading to better patient access to treatment and services.
Complementary coding of behaviors in striatal pathways supports a dual selection-suppression function
Authors: Varin Christophe , Cornil Amandine , Houtteman Delphine , Bonnavion Patricia , de Kerchove d’Exaerde Alban
Published: June 2022

The basal ganglia are known to control actions and modulate movements. Neuronal activity in the two efferent pathways of the dorsal striatum, a major input to the basal ganglia, is critical for appropriate behavioral control. Previous evidence has led to divergent conclusions on the respective engagement of both pathways during actions. We used calcium imaging to evaluate how neurons in the direct and indirect pathways in the dorsal striatum encode behaviors during self-paced spontaneous explorations in an open field. We observed that the two striatal pathways exhibit distinct tuning properties during spontaneous behaviors. We applied supervised learning algorithms and found that direct pathway neurons encode behaviors through their activation, whereas indirect pathway neurons exhibit behavior-specific silencing. These properties remain stable for weeks. Our findings highlight a complementary encoding of behaviors in the two striatal pathways that supports an updated model, reconciling previous conflicting conclusions on motor encoding in the striatum.
Quality standards, crime management and the efficiency of manufacturing firms in middle-income economies
Authors: Boamah Nicholas Addai , Ofori-Yeboah Francis , Asare Nicholas
Published: June 2022

Purpose

This study investigates the ability of crime management expenses, recognised external quality certification and ownership structure to describe the cross-sectional changes in the capital and labour efficiencies of manufacturing firms in middle income economies. It controls for the potential effects of graft incidence and firm age on firm-level efficiency.

Design/methodology/approach

The study adopts a state space model approach within the context of cross-sectional regressions. Data for the study are obtained from the World Bank Enterprise Survey for 2006, 2009, 2013, 2016 and 2019.

Findings

The study provides evidence that crime management expenses impact labour efficiency negatively. Also, its effect on capital efficiency is positive in 2019 and negative in 2013 and 2016 eras. Additionally, external auditor services and internationally recognised quality certification increase labour and capital efficiencies. Graft incidence exerts negative and positive effect on capital efficiency in the recent and earlier periods respectively. In addition, older firms tend to have higher labour efficiency, whilst younger firms have higher capital efficiency. There is evidence of firm size and export orientation effects in the drivers of efficiency.

Originality/value

Policies aimed at creating graft and crime-free business environment will enhance the efficiency and growth of firms' particularly for small firms. Also, the market rewards recognised quality assurance and good reputation.
Resistance to gender education: a case study of a kindergarten in Italy
Authors: De Simone Silvia , Pileri Jessica
Published: June 2022

Purpose

Despite repeated attempts to implement gender education in schools, numerous forms of resistance still persist, maintaining the current gender order, especially in Italy. Thus, in this paper, the authors focus on the practices of resistance opposed to gender education in kindergarten.

Design/methodology/approach

This study takes a qualitative approach, and data collection was conducted using ethnographic observations, a focus group and an in-depth interview. The authors used critical discourse analysis (cf: Fairclough's three-dimensional model).

Findings

As per our findings, teachers' resistance is attributed to “hegemonic masculinity” and “essentialism”. In the case of “hegemonic masculinity”, the discourses emphasise that male feminisation is a threat and female masculinisation is harmless. On the “essentialist” side, teachers' discourses focus on the segregation of genders that justify naturalised gender differences.

Practical implications

This study emphasises the need for specific training for figures as important and authoritative as teachers. In addition to the training of teachers who currently work in kindergarten, it is also necessary to address the issue at the institutional level, adding to the university courses the teaching of specific subjects related to gender.

Social implications

This paper offers causes for reflection on a profession that has profound implications in our society and about the power of resistance to implementing gender education. The implications are discussed.

Originality/value

Different data sources are used simultaneously to disclose discursive practices of resistance to gender education in Italy.
Interdependencies between financial and non-financial performances: a holistic and short-term analytical perspective
Authors: Cupertino Sebastiano , Vitale Gianluca , Taticchi Paolo
Published: June 2022

Purpose

This paper aims to investigate possible interdependencies affecting short-term profitability between internal and process business aspects which can play a critical role in sustainability operationalisation.

Design/methodology/approach

The authors adopted the panel data approach to perform a partial least square structural modelling equation analysis on a sample of 391 Organisation for Economic Co-operation and Development (OECD) non-financial-listed companies, considering a timeframe of five years.

Findings

Corporate sustainability is a result of interplays between managerial commitment, strategy, slack resources’ exploitation, innovation, the sustainable management of internal production and procurement processes that managers can catalyse to foster short-term firms’ profitability.

Research limitations/implications

The study is focused on internal process business determinants of sustainability, and the analysis is limited to a short-term timeframe and on non-financial OECD-listed companies.

Practical implications

Managers searching for trade-offs between financial and non-financial performances should enhance their commitment towards sustainability by defining appropriate strategies suitable to employ mainly slack resources derived from core business activities enabling innovation processes, which, in turn, are able to foster sustainability of internal production and procurement processes.

Originality/value

The execution of sustainability is a complex process that needs to be investigated using a holistic approach net of endogeneity biases to better appreciate those interrelationships within multiple drivers determining the firm sustainable growth.
A digital transformation-enabled framework and strategies for public health risk response and governance: China's experience
Authors: Lee Ching-Hung , Wang Dianni , Lyu Shupeng , Evans Richard David , Li Li
Published: June 2022

Purpose

Under uncertain circumstances, digital technologies are taken as digital transformation enablers and driving forces to integrate with medical, healthcare and emergency management research for effective epidemic prevention and control. This study aims to adapt complex systems in emergency management. Thus, a digital transformation-driven and systematic circulation framework is proposed in this study that can utilize the advantages of digital technologies to generate innovative and systematic governance.

Design/methodology/approach

Aiming at adapting complex systems in emergency management, a systematic circulation framework based on the interpretive research is proposed in this study that can utilize the advantages of digital technologies to generate innovative and systematic governance. The framework consists of four phases: (1) analysis of emergency management stages, (2) risk identification in the emergency management stages, (3) digital-enabled response model design for emergency management, and (4) strategy generation for digital emergency governance. A case study in China was illustrated in this study.

Findings

This paper examines the role those digital technologies can play in responding to pandemics and outlines a framework based on four phases of digital technologies for pandemic responses. After the phase-by-phase analysis, a digital technology-enabled emergency management framework, titled “Expected digital-enabled emergency management framework (EDEM framework)” was adapted and proposed. Moreover, the social risks of emergency management phases are identified. Then, three strategies for emergency governance and digital governance from the three perspectives, namely “Strengthening weaknesses for emergency response,” “Enhancing integration for collaborative governance,” and “Engaging foundations for emergency management” that the government can adopt them in the future, fight for public health emergency events.

Originality/value

The novel digital transformation-driven systematic circulation framework for public health risk response and governance was proposed. Meanwhile, an “Expected digital-enabled emergency management framework (EDEM model)” was also proposed to achieve a more effective empirical response for public health risk response and governance and contribute to studies about the government facing the COVID-19 pandemic effectively.
Modeling self-identification, memorable tourism experience, overall satisfaction and destination loyalty: empirical evidence from small island destinations
Authors: Kahraman Onur Cuneyt , Cifci Ibrahim
Published: June 2022

Purpose

Although many studies addressed destination marketing concepts, the relationship between self-identification, memorable tourism experiences, overall satisfaction, and destination loyalty in small island destinations remains unknown. To address this issue, the authors established a model based on social identity theory to investigate the effects of self-identification on overall satisfaction and loyalty, taking into account the mediating role of memorable tourism experiences.

Design/methodology/approach

Drawing on 335 useable questionnaires from the visitors of Princes' Islands in Turkey, a quantitative study approach was administrated to empirically analyze a partial least squares path model in PLS-SEM.

Findings

The findings revealed that self-identification positively affects memorable tourism experiences, overall satisfaction, and destination loyalty. The findings also confirm the indirect effects of self-identification on overall satisfaction and destination loyalty through mediating the role of memorable tourism experiences.

Research limitations/implications

The findings of the study will be useful both theoretically and practically. Theoretically, this research will be important to determine tourist behavior through the viewpoint of self-identification in the context of a small island destination. Practically, the findings of this study will assist small island destinations' policymakers and practitioners to develop strategies and make effective future actions.

Originality/value

This is the first study that uses a complete structural model linking self-identification, memorable tourism experiences, overall satisfaction, and destination loyalty.
Libraries, decolonization and the data deluge
Authors: Frederick Donna Ellen
Published: June 2022

Purpose

The purpose of this study is to bring attention to the topics of colonization and decolonization in libraries by helping librarians to see how many library practices and policies are embedded in and arise from the dominant culture and can unwittingly function to subjugate the culture, language and worldview of indigenous peoples.

Design/methodology/approach

This column defines colonization and decolonization, demonstrates how and why libraries are agents of colonization and examines examples of practices and policies in need of decolonization.

Findings

While it may be challenging for librarians to detect colonizing elements in libraries because most of them are embedded in the dominant culture, it is possible to learn how to find them by listening to indigenous communities and gaining a better understanding of their worldview. Even so, decolonization is not an easy task because so much is already invested in the status quo and libraries often function with minimal or diminishing resources.

Originality/value

While there have been studies writing about specific aspects of bias and colonization in libraries, there is not much attention paid to the bigger picture of this topic. For example, the idea that the book-based culture of libraries conflicts with some facets of indigenous oral cultures is not widely discussed elsewhere, but it is addressed in this column.
A programme curricula evaluation framework as a pillar of internal quality assurance
Authors: Paşaoğluları Şahin Nil , Olgaç Türker Özlem
Published: June 2022

Purpose

This study aims to explore ways of developing, implementing and validating a new framework and criteria for self-evaluation of programme curricula, with specific reference to the quality assurance certification process in a particular case.

Design/methodology/approach

The framework is developed using a case study research methodology and implemented based on criteria extraction through the triangulation of indicators achieved from internal goals and external directives.

Findings

The findings reveal that this is an improvement-led framework that can be adapted to other contexts during the quality assurance processes to facilitate periodical programme evaluations with a focus on the curriculum.

Research limitations/implications

The proposed practical tool is developed for the programme evaluation process with a curricular focus during the quality assurance certification process of an interior architectural programme while enlightening the processes for the periodical self-evaluations of other institutions. The framework depends on both institution-specific internal and external directives and fulfilling curriculum-related criteria of the European Standards and Guidelines for Quality Assurance.

Originality/value

Within quality assurance processes, despite external quality assurance mechanisms, there is a shortage of self-evaluation tools for internal quality assurance procedures, which allow the dissociation of programme-specific qualities. The proposed framework is developed as an example of a self-tailored internal quality assurance tool and process for educational institutions to reveal their unique qualities.