bluuebunny/crossref_metadata_2025 huggingface dataset:
Acceleration of Evolutionary Processes by Learning and Extended Fisher’s Fundamental Theorem
Authors: Nakashima So , Kobayashi Tetsuya J.
Published: June 2021

Natural selection is general and powerful concept not only to explain evolutionary processes of biological organisms but also to design engineering systems such as genetic algorithms and particle filters. There is a surge of interest, both from biology and engineering, in considering natural selection of intellectual agents that can learn individually. Learning by individual agents of better behaviors for survival may accelerate the evolutionary processes by natural selection. We have accumulating pieces of evidence that organisms can transmit its information to the next generation via epigenetic states or memes. Also, such idea is important for engineering applications to improve the genetic algorithms and the particle filter. To accelerate the evolutionary process, an agent should change their strategy so that the population fitness increases the most. Equivalently, an agent should update the strategy towards a gradient (derivative) of the population fitness with respect to the strategy. However, it has not yet been clarified whether and how an agent can estimate the gradient and accelerate the evolutionary process. We also lack methodology to quantify the acceleration to understand and predict the impact of learning. In this paper, we address these problems. We show that an learning agent can accelerate the evolutionary process by proposing ancestral learning, which uses the information transmitted from the ancestor (ancestral information) via epigenetic states or memes. Numerical experiments show that ancestral learning actually accelerates the evolutionary process. We next show that the ancestral information is sufficient to estimate the gradient. In particular, learning can accelerate the evolutionary process without communications between agents. Finally, to quantify the acceleration, we extend the Fisher’s fundamental theorem (FF-thm) for natural selection to ancestral learning. The conventional FF-thm relates the speed of evolution by natural selection to the variety of the individual fitness in the population. Our extended FF-thm relates the acceleration of the evolutionary process to the variety of individual fitness of the agent. By the theorem, we can quantitatively understand when and why learning is beneficial.
A particle size threshold governs diffusion and segregation of PAR-3 during cell polarization
Authors: Chang Yiran , Dickinson Daniel J.
Published: June 2021

Regulation of subcellular components’ localization and motion is a critical theme in cell biology. Cells use the actomyosin cortex to regulate protein distribution on the plasma membrane, but the interplay between membrane binding, cortical movements and protein distribution remains poorly understood. In a polarizing one-cell stage Caenorhabditis elegans embryo, actomyosin flows transport PAR protein complexes into an anterior cortical domain to establish the anterior-posterior axis of the animal. Oligomerization of a key scaffold protein, PAR-3, is required for aPAR cortical localization and segregation. Although PAR-3 oligomerization is essential for polarization, it remains unclear how oligomer size contributes to aPAR segregation because PAR-3 oligomers are a heterogeneous population of many different sizes. To address this question, we engineered PAR-3 to defined sizes. We report that PAR-3 trimers are necessary and sufficient for PAR-3 function during polarization and later embryo development, while larger PAR-3 clusters are dispensable. Quantitative analysis of PAR-3 diffusion showed that PAR-3 clusters with three or more subunits are transported by frictional drag and experience extensive collisions with the actomyosin cortex. Our study provides a quantitative model for size-dependent protein transportation of membrane proteins by cortical flow.
Alpha and beta rhythms differentially support the effect of symbols on visual object recognition
Authors: Morucci Piermatteo , Giannelli Francesco , Richter Craig , Molinaro Nicola
Published: June 2021

Hearing spoken words can enhance visual object recognition, detection and discrimination. Yet, the mechanisms that underpin this facilitation are incompletely understood. On one account, words do not bias early visual processing, but rather affect later semantic or decision-making stages. However, recent proposals suggest that words can alter early visual processes by activating category-specific priors in sensory regions. A prediction of this account is that top-down priors evoke changes in occipital areas in anticipation of visual stimuli. Here, we tested the hypothesis that neural oscillations serve as a mechanism to activate language-generated visual priors. Participants performed a cue-picture matching task where cues were either spoken words, in their native or second language, or natural sounds, while their EEG and reaction times were recorded. Behaviorally, we replicated the previously reported label-advantage effect, with images cued by words being recognized faster than those cued by natural sounds. A time-frequency analysis of cue-target intervals revealed that this behavioral label-advantage was associated with enhanced power in posterior alpha (9-11 Hz) and beta oscillations (17-19 Hz), both of which were larger when the image was preceded by a word compared to a natural sound. Importantly, object recognition performance improved with high alpha power but slowed down with enhancement of beta synchronization. These results suggest that alpha and beta rhythms play distinct functional roles to support language-mediated visual object recognition: alpha might function to amplify sensory priors in posterior regions, while beta may (re)activate the network states elicited by the auditory cue.
Evolutionary velocity with protein language models
Authors: Hie Brian L. , Yang Kevin K. , Kim Peter S.
Published: June 2021

Predicting the order of biological homologs is a fundamental task in evolutionary biology. For protein evolution, this order is often determined by first arranging sequences into a phylogenetic tree, which has limiting assumptions and can suffer from substantial ambiguity. Here, we demonstrate how machine learning algorithms called language models can learn mutational likelihoods that predict the directionality of evolution, thereby enabling phylogenetic analysis that addresses key limitations of existing methods. Our main conceptual advance is to construct a “vector field” of protein evolution through local evolutionary predictions that we refer to as evolutionary velocity (evo-velocity). We show that evo-velocity can successfully predict evolutionary order at vastly different timescales, from viral proteins evolving over years to eukaryotic proteins evolving over geologic eons. Evo-velocity also yields new evolutionary insights, predicting strategies of viral-host immune escape, resolving conflicting theories on the evolution of serpins, and revealing a key role of horizontal gene transfer in the evolution of eukaryotic glycolysis. In doing so, our work suggests that language models can learn sufficient rules of natural protein evolution to enable evolutionary predictability.
Host phenology regulates ecological and eco-evolutionary feedbacks underlying parasite-host demographic cycles
Authors: MacDonald Hannelore , Brisson Dustin
Published: June 2021

Parasite-host interactions can result in periodic population dynamics when parasites over-exploit host populations. The timing of host seasonal activity, or host phenology, determines the frequency and demographic impact of parasite-host interactions which may govern if the parasite can sufficiently over-exploit their hosts to drive population cycles. We describe a mathematical model of a monocyclic, obligate-killer parasite system with seasonal host activity to investigate the consequences of host phenology on host-parasite dynamics. The results suggest that parasites can reach the densities necessary to destabilize host dynamics and drive cycling in only some phenological scenarios, such as environments with short seasons and synchronous host emergence. Further, only parasite lineages that are sufficiently adapted to phenological scenarios with short seasons and synchronous host emergence can achieve the densities necessary to over-exploit hosts and produce population cycles. Host-parasite cycles can also generate an eco-evolutionary feedback that slows parasite adaptation to the phenological environment as rare advantageous phenotypes are driven to extinction when introduced in phases of the cycle where host populations are small and parasite populations are large. The results demonstrate that seasonal environments can drive population cycling in a restricted set of phenological patterns and provides further evidence that the rate of adaptive evolution depends on underlying ecological dynamics.
DNA damage models phenotypes of β cell senescence in Type 1 Diabetes
Authors: Brawerman Gabriel , Pipella Jasmine , Thompson Peter J.
Published: June 2021

Objective

Type 1 Diabetes (T1D) is characterized by progressive loss of insulin-producing pancreatic β cells as a result of autoimmune destruction. In addition to β cell death, recent work has shown that subpopulations of β cells acquire dysfunction during T1D. We previously reported that some β cells adopt a senescent fate involving a DNA damage response (DDR) during the pathogenesis of T1D, however, the question of how senescence develops in β cells has not been investigated.

Methods

Here, we tested the hypothesis that unrepaired DNA damage triggers β cell senescence using culture models including the mouse NIT1 β cell line derived from the T1D-susceptible nonobese diabetic (NOD) strain, human donor islets and EndoC β cells. DNA damage was chemically induced using etoposide or bleomycin and cells or islets were analyzed by a combination of molecular assays for senescence phenotypes including Western blotting, qRT-PCR, Luminex assays, flow cytometry and histochemical staining. RNA-seq was carried out to profile global transcriptomic changes in human islets undergoing DDR and senescence. Insulin ELISAs were used to quantify glucose stimulated insulin secretion from chemically-induced senescent islets and cells in culture.

Results

Sub-lethal DNA damage in NIT1 cells led to several classical hallmarks of senescence including sustained DDR activation, growth arrest, enlarged flattened morphology and a senescence-associated secretory phenotype (SASP) resembling what occurs in primary β cells during T1D in NOD mice. Some of these phenotypes differed between NIT1 cells and the MIN6 β cell line derived from a non-T1D susceptible mouse strain. RNA-seq analysis of human islets undergoing DDR and early senescence revealed a coordinated p53-p21 transcriptional program and upregulation of prosurvival signaling and SASP genes, which were confirmed in independent islet preparations at the protein level. Importantly, chemically induced DNA damage also led to DDR activation and senescence phenotypes in the EndoC-βH5 human β cell line, confirming that this response can occur directly in human β cells. Finally, DNA damage and senescence in both mouse β cell lines and human islets led to decreased insulin content.

Conclusions

Taken together, these findings suggest that some of the phenotypes of β cell senescence during T1D can be modeled by chemically induced DNA damage in mouse β cell lines and human islets and β cells in culture. These culture models will be useful tools to understand some of the mechanisms of β cell senescence in T1D.

Highlights

DNA damage induces senescent phenotypes in mouse β cell lines

DNA damage induces a p53-p21 transcriptional program and senescent phenotypes in human islets and EndoC cells

DNA damage and senescence leads to decreased insulin content

DNA damage models some aspects of β cell senescence in Type 1 Diabetes
Metacells untangle large and complex single-cell transcriptome networks
Authors: Bilous Mariia , Tran Loc , Cianciaruso Chiara , Gabriel Aurélie , Michel Hugo , Carmona Santiago J. , Pittet Mikael J. , Gfeller David
Published: June 2021

The exponential scaling of scRNA-seq data represents an important hurdle for downstream analyses. Here we develop a coarse-graining framework where highly similar cells are merged into metacells. We demonstrate that metacells not only preserve but often improve the results of downstream analyses including visualization, clustering, differential expression, cell type annotation, gene correlation, imputation, RNA velocity and data integration. By capitalizing on the redundancy inherent to scRNA-seq data, metacells significantly facilitate and accelerate the construction and interpretation of single-cell atlases, as demonstrated by the integration of 1.46 million cells from COVID-19 patients in less than two hours on a standard desktop.
Analysis-rcs-data: Open-source toolbox for the ingestion, time-alignment, and visualization of sense and stimulation data from the Medtronic Summit RC+S system
Authors: Sellers Kristin K , Gilron Ro’ee , Anso Juan , Louie Kenneth H , Shirvalkar Prasad R , Chang Edward F , Little Simon J , Starr Philip A.
Published: June 2021

Closed-loop neurostimulation is a promising therapy being tested and clinically implemented in a growing number of neurological and psychiatric indications. This therapy is enabled by chronically implanted, bidirectional devices including the Medtronic Summit RC+S system. In order to successfully optimize therapy for patients implanted with these devices, analyses must be conducted offline on the recorded neural data, in order to inform optimal sense and stimulation parameters. The file format, volume, and complexity of raw data from these device necessitate conversion, parsing, and time reconstruction ahead of time-frequency analyses and modeling common to standard neuroscientific analyses. Here, we provide an open-source toolbox written in Matlab which takes raw files from the Summit RC+S and transforms these data into a standardized format amenable to conventional analyses. Furthermore, we provide a plotting tool which can aid in the visualization of multiple data streams and sense, stimulation, and therapy settings. Finally, we describe an analysis module which replicates RC+S on-board power computations, functionality which can accelerate biomarker discovery. This toolbox aims to accelerate the research and clinical advances made possible by longitudinal neural recordings and adaptive neurostimulation in people with neurological and psychiatric illnesses.
Small Molecule Stabilization of PINK-1/PINK1 Improves Neurodegenerative Disease
Authors: Tjahjono Elissa , Pei Jingqi , Revtovich Alexey V. , Liu Terri-Jeanne E. , Swadi Alisha , Kirienko Natalia V.
Published: June 2021

Macroautophagic recycling of dysfunctional mitochondria, known as mitophagy, is essential for mitochondrial homeostasis and cell viability. Accumulation of defective mitochondria and impaired mitophagy have been widely implicated in many neurodegenerative diseases, and loss-of-function mutations of two regulators of mitophagy, PINK1 and Parkin, are amongst the most common causes of recessive Parkinson’s disease. Activation of mitophagy via pharmacological treatments may be a feasible approach for combating neurodegeneration. In this effort, we screened ∼45,000 small molecules for the ability to activate mitophagy. A high-throughput, whole-organism, phenotypic screen was conducted by monitoring stabilization of PINK-1/PINK1, a key event in mitophagy activation, in a Caenorhabditis elegans strain carrying a Ppink-1::PINK-1::GFP reporter. We obtained eight hits that induced mitophagy, as evidenced by increased mitochondrial fragmentation and autophagosome formation. Several of the compounds also reduced ATP production, oxygen consumption, mitochondrial mass, and/or mitochondrial membrane potential. Importantly, we found that treatment with two compounds, which we named PS83 and PS106 (more commonly known as sertraline) reduced neurodegenerative disease phenotypes (including delayed paralysis in a C. elegans Alzheimer’s model) in a PINK-1/PINK1-dependent manner. This report presents a promising step toward the identification of compounds that will stimulate mitochondrial turnover.
The interaction of enteric bacterial effectors with the host engulfment pathway control innate immune responses
Authors: Sayed Ibrahim M , Ibeawuchi Stella-Rita , Lie Dominique , Anandachar Mahitha Shree , Pranadinata Rama , Raffatellu Manuela , Das Soumita
Published: June 2021

Background

Host engulfment protein ELMO1 generates intestinal inflammation following internalization of enteric bacteria. In Shigella, bacterial effector IpgB1 interacts with ELMO1 and promotes bacterial invasion. IpgB1 belongs to the WxxxE effector family, a motif found in several effector of enteric pathogens. Here, we have studied the role of WxxxE effectors, with emphasis on Salmonella SifA and whether it interacts with ELMO1 to regulate inflammation.

Methodology

In-silico-analysis of WxxxE effectors was performed using BLAST search and Clustal W program. The interaction of ELMO1 with SifA was assessed by GST pulldown assay and co-immunoprecipitation. ELMO1 knock-out mice, and ELMO1-depleted murine macrophage J774 cell lines were challenged with WT and SifA mutant Salmonella. Bacterial effectors containing the WxxxE motif were transfected in WT and ELMO1-depleted J774 cells to assess the inflammatory cytokines.

Results

ELMO1 generates differential pro-inflammatory cytokines between pathogenic and non-pathogenic bacteria. WxxxE motif is present in pathogens and in the TIR domain of host proteins. The C-terminal part of ELMO1 interacts with SifA where WxxxE motif is important for interaction. ELMO1-SifA interaction affects the bacterial colonization, dissemination, and inflammatory cytokines in vivo. Moreover, ELMO1-SifA interaction increases TNF-α and IL-6 production from the macrophage cell line and is associated with enhanced Rac1 activity. ELMO1 also interacts with WxxxE effectors IpgB1, IpgB2, and Map, and induces inflammation after challenge with microbe or microbial ligand.

Conclusion

ELMO1 generates a differential response through interaction with the WxxxE motif which is absent in commensals. ELMO1-WxxxE interaction plays a role in bacterial pathogenesis and induction of inflammatory response.

Highlights

ELMO1 generates a differential immune response between enteric pathogens and commensals.

Enteric bacterial effectors containing WxxxE signature motif interact with ELMO1.

The WxxxE effector of Salmonella SifA interacts with the C-terminal part of ELMO1.

ELMO1-SifA interaction increases the inflammatory response in vivo and in vitro.
A dopamine-dependent decrease in dorsomedial striatum direct pathway neuronal activity is required for learned motor coordination
Authors: Cataldi Stefano , Lacefield Clay , Shashaank N , Kumar Gautam , Sulzer David
Published: June 2021

It has been suggested that the dorsomedial striatum (DMS) facilitates the early stages of motor learning for goal-directed actions, whereas at later stages, control is transferred to the dorsolateral striatum (DLS), which enables the motor actions to become a skill or habit. It is unknown whether these striatal regions are simultaneously active while expertise is acquired during skill learning. To address this question, we developed the “treadmill training task” to track changes in mouse locomotor coordination during practice at running that simultaneously provides a means to measure local neuronal activity using photometry. We analyzed body position and paw movement to evaluate changes in motor coordination over practice sessions on the treadmill using DeepLabCut and custom-built code. By correlating improvements in motor coordination during training with simultaneous neuronal calcium activity in the striatum, we found that DMS direct pathway neurons exhibited decreased activity as the mouse gains proficiency at running. In contrast, direct pathway activity in the DLS was similar throughout training and did not correlate with learned skill proficiency. Pharmacological blockade of D1 dopamine receptors in these subregions during task performance confirmed that dopamine neurotransmission in the DMS direct pathway activity is necessary for efficient motor coordination learning, while dopamine signalling in the DLS is important for both coordination learning and maintenance of the acquired skill. These results provide new tools to measure changes in fine motor skills during simultaneous recordings of brain activity, revealing fundamental features of the neuronal substrates of motor learning.
An estimation of the absolute number of axons indicates that human cortical areas are sparsely connected
Authors: Rosen Burke Q. , Halgren Eric
Published: June 2021

The tracts between cortical areas are conceived as playing a central role in cortical information processing, but their actual numbers have never been determined in humans. Here we estimate the absolute number of axons linking cortical areas from a whole-cortex diffusion-MRI (dMRI) connectome, calibrated using the histologically-measured callosal fiber density. Median connectivity is estimated as ~6200 axons between cortical areas within-hemisphere and ~1300 axons inter-hemispherically, with axons connecting functionally-related areas surprisingly sparse. For example, we estimate that <5% of the axons in the trunk of the arcuate and superior longitudinal fasciculi connect Wernicke’s and Broca’s areas. These results suggest that detailed information is transmitted between cortical areas either via linkage of the dense local connections or via rare, extraordinarily privileged long-range connections.
A personalized anti-cancer vaccine for melanoma based on an approved vaccine against measles, mumps, and rubella
Authors: Ylösmäki Erkko , Fusciello Manlio , Uoti Arttu , Feola Sara , Martins Beatriz , Aalto Karri , Hamdan Firas , Ciaro Jacopo , Viitala Tapani , Cerullo Vincenzo
Published: June 2021

Common vaccines for infectious diseases have been repurposed as cancer immunotherapies. Intratumoural administration of these repurposed vaccines can induce immune cell infiltration into the treated tumour. Here, we have used an approved trivalent live attenuated measles, mumps, and rubella (MMR) vaccine in our previously developed PeptiENV anti-cancer vaccine platform. Intratumoural administration of this novel MMR-containing PeptiENV anti-cancer vaccine significantly increased both intratumoural as well as systemic tumour-specific T cell responses. In addition, PeptiENV therapy, in combination with immune checkpoint inhibitor therapy, significantly improved tumour growth control and survival as well as increased the number of mice responsive to immune checkpoint inhibitor therapy.
Differential ampicillin/ceftriaxone susceptibility among diverse Enterococcus faecalis from infective endocarditis
Authors: Westbrook Kevin J. , Chilambi Gayatri Shankar , Nordstrom Hayley R. , Iovleva Alina , Shah Niyati H. , Jones Chelsea E. , Kline Ellen G. , Doi Yohei , Shields Ryan K. , Van Tyne Daria
Published: June 2021

Enterococcus faecalis is a leading cause of infective endocarditis (IE), especially among older patients with comorbidities. Here we investigated the genomic diversity and antimicrobial susceptibility of 33 contemporary E. faecalis isolates from definite or probable IE cases at the University of Pittsburgh Medical Center (UPMC) between 2018 and 2020. Isolates belonging to two multi-locus sequence types (STs), ST6 and ST179, were isolated from nearly 40% of IE patients. Both of these dominant STs carried known beta-lactam resistance-associated mutations affecting the low-affinity penicillin-binding protein 4 (PBP4). We assessed the ability of ampicillin and ceftriaxone (AC) both alone and in combination to inhibit genetically diverse E. faecalis IE isolates in checkerboard synergy assays and an in vitro one-compartment pharmacokinetic-pharmacodynamic (PK-PD) model of AC treatment. ST6 isolates as well as an isolate with a mutation in the PP2C-type protein phosphatase IreP had higher ceftriaxone MICs compared to other isolates, and showed diminished in vitro synergy of AC. Additionally, both ST6 and ST179 isolates exhibited regrowth after 48 hours of humanized exposures to AC. Overall, we found evidence for diminished in vitro AC activity among E. faecalis IE isolates with PBP4 and IreP mutations. This study highlights the need to evaluate alternate antibiotic combinations in clinical practice against diverse contemporary E. faecalis IE isolates.
Aberrant hippocampal transmission and behavior in mice with a stargazin mutation linked to intellectual disability
Authors: Caldeira GL , Inácio AS , Beltrão N , Barreto CAV , Rodrigues MV , Rondão T , Macedo R , Gouveia RP , Edfawy M , Guedes J , Cruz B , Louros SR , Moreira IS , Peça J , Carvalho AL
Published: June 2021

Mutations linked to neurodevelopmental disorders, such as intellectual disability (ID), are frequently found in genes that encode for proteins of the excitatory synapse. Transmembrane AMPA receptor regulatory proteins (TARPs) are AMPA receptor auxiliary proteins that regulate crucial aspects of receptor function. Here, we investigate an ID-associated mutant form of the TARP family member stargazin. Molecular dynamics analyses showed that the stargazin V143L variant weakens the overall interface of the AMPAR:stargazin complex and hinders the stability of the complex. Knock-in mice for the V143L stargazin mutation manifest cognitive and social deficits and hippocampal synaptic transmission defects. In the hippocampus of stargazin V143L mice, CA1 neurons show impaired spine maturation in basal dendrites, and synaptic ultrastructural alterations. These data demonstrate a causal role for mutated stargazin in the pathogenesis of ID and highlight its role in the development and function of hippocampal synapses.
In depth analysis of Cyprus-specific mutations of SARS-CoV-2 strains using computational approaches
Authors: Oulas Anastasis , Richter Jan , Zanti Maria , Tomazou Marios , Michailidou Kyriaki , Christodoulou Kyproula , Christodoulou Christina , Spyrou George M.
Published: June 2021

This study aims to characterize SARS-CoV-2 mutations which are primarily prevalent in the Cypriot population. Moreover, using computational approaches, we assess whether these mutations are associated with changes in viral virulence. We utilize genetic data from 144 sequences of SARS-CoV-2 strains from the Cypriot population obtained between March 2020 and January 2021, as well as all data available from GISAID. We combine this with countries’ regional information, such as deaths and cases per million, as well as COVID-19-related public health austerity measure response times. Initial indications of selective advantage of Cyprus-specific mutations are obtained by mutation tracking analysis. This entails calculating specific mutation frequencies within the Cypriot population and comparing these with their prevalence world-wide throughout the course of the pandemic. We further make use of linear regression models to extrapolate additional information that may be missed through standard statistical analysis. We report a single mutation found in the ORF1ab gene (S6059F) that appears to be significantly enriched within the Cypriot population. We further analyse this mutation using regression models to investigate possible associations with increased deaths and cases per million. Moreover, protein structure prediction tools show that the mutation infers a conformational change to the protein that significantly alters its structure when compared to the reference protein. Investigating Cyprus-specific mutations for SARS-CoV-2 can not only lead to important findings from which to battle the pandemic on a national level, but also provide insights into viral virulence worldwide.
Cognitive functions and underlying parameters of human brain physiology are associated with chronotype
Authors: Salehinejad Mohammad Ali , Wischnewski Miles , Ghanavati Elham , Mosayebi-Samani Mohsen , Kuo Min-Fang , Nitsche Michael A.
Published: June 2021

Circadian rhythms have natural relative variations among humans known as chronotype. Chronotype or being a morning or evening person, has a specific physiological, behavioural, and also genetic manifestation. Whether and how chronotype modulates human brain physiology and cognition is, however, not well understood. Here we examine how cortical excitability, neuroplasticity, and cognition are associated with chronotype in early and late chronotype individuals. We monitor motor cortical excitability, brain stimulation-induced neuroplasticity, and examine motor learning and cognitive functions at circadian-preferred and non-preferred times of day in 32 individuals. Motor learning and cognitive performance (working memory, and attention) along with their electrophysiological components are significantly enhanced at the circadian-preferred, compared to the non-preferred time. This outperformance is associated with enhanced cortical excitability (prominent cortical facilitation, diminished cortical inhibition), and long-term potentiation/depression-like plasticity. Our data show convergent findings of chronotype can modulate human brain functions from basic physiological mechanisms to behaviour and higher cognitive functions.
Bacterioplankton Diversity and Distribution in Relation to Phytoplankton Community Structure in the Ross Sea surface waters
Authors: Cordone Angelina , D’Errico Giuseppe , Magliulo Maria , Bolinesi Francesco , Selci Matteo , Basili Marco , de Marco Rocco , Saggiomo Maria , Rivaro Paola , Giovannelli Donato , Mangoni Olga
Published: June 2021

Primary productivity in the Ross Sea region is characterized by intense phytoplankton blooms whose temporal and spatial distribution are driven by changes in environmental conditions as well as interactions with the bacterioplankton community. Exchange of exudates, metabolism by-products and cofactors between the phytoplankton and the bacterioplankton communities drive a series of complex interactions affecting the micronutrient availability and co-limitation, as well as nutrient uptakes in Antarctic waters. Yet, the number of studies reporting the simultaneous diversity of the phytoplankton and bacterioplankton in Antarctic waters are limited. Here we report data on the bacterial diversity in relation to phytoplankton community in the surface waters of the Ross Sea during the austral summer 2017. Our results show partially overlapping bacterioplankton communities between the stations located in the Terra Nova Bay coastal waters and the Ross Sea open waters, suggesting that the two communities are subjected to different drivers. We show that the rate of diversity change between the two locations is influenced by both abiotic (salinity and the nitrogen to phosphorus ratio) and biotic (phytoplankton community structure) factors. Our data provides new insight into the coexistence of the bacterioplankton and phytoplankton in Antarctic waters.
The expression of virulence increases outer-membrane permeability and sensitivity to envelope stress in Salmonella Typhimurium
Authors: Sobota Malgorzata , Ramirez Pilar Natalia Rodilla , Cambré Alexander , Haas Tiphaine , Cornillet Delphine , Rocker Andrea , Aertsen Abram , Diard Médéric
Published: June 2021

Environmental cues modulate the expression of virulence in bacterial pathogens. However, while cues that upregulate virulence are often intuitive and mechanistically well understood, this is less so for cues that downregulate virulence. In this study, we noticed that upregulation of the HilD virulence regulon in Salmonella Typhimurium (S.Tm) sensitized cells to membrane stress mediated by cholate, Tris/EDTA or heat. Further monitoring of membrane status and stress resistance of S.Tm cells in relation to virulence expression, revealed that co-expressed virulence factors embedded in the envelope (including the Type Three Secretion System 1 and the flagella) increased permeability, and stress sensitivity of the membrane. Importantly, pretreating the bacteria by sublethal stress inhibited virulence expression and restored stress resistance. As such, these results demonstrate a trade-off between virulence and stress resistance, which explains the downregulation of virulence expression in response to harsh environments in S.Tm.
Gut bacterial aggregates as living gels
Authors: Schlomann Brandon H. , Parthasarathy Raghuveer
Published: June 2021

The spatial organization of gut microbiota influences both microbial abundances and host-microbe interactions, but the underlying rules relating bacterial dynamics to large-scale structure remain unclear. To this end we studied experimentally and theoretically the formation of three-dimensional bacterial clusters, a key parameter controlling susceptibility to intestinal transport and access to the epithelium. Inspired by models of structure formation in soft materials, we sought to understand how the distribution of gut bacterial cluster sizes emerges from bacterial-scale kinetics. Analyzing imaging-derived data on cluster sizes for eight different bacterial strains in the larval zebrafish gut, we find a common family of size distributions that decay approximately as power laws with exponents close to −2, becoming shallower for large clusters in a strain-dependent manner. We show that this type of distribution arises naturally from a Yule-Simons-type process in which bacteria grow within clusters and can escape from them, coupled to an aggregation process that tends to condense the system toward a single massive cluster, reminiscent of gel formation. Together, these results point to the existence of general, biophysical principles governing the spatial organization of the gut microbiome that may be useful for inferring fast-timescale dynamics that are experimentally inaccessible.
Computation of dimensional variations on the structural analysis of multi-cell aircraft box beams with python scripting
Authors: Yavuz Hande
Published: June 2021

Purpose

Python codes are developed for the versatile structural analysis on a 3 spar multi-cell box beam by means of idealization approach.

Design/methodology/approach

Shear flow distribution, stiffener loads, location of shear center and location of geometric center are computed via numpy module. Data visualization is performed by using Matplotlib module.

Findings

Python scripts are developed for the structural analysis of multi-cell box beams in lieu of long hand solutions. In-house developed python codes are made available to be used with finite element analysis for verification purposes.

Originality/value

The use of python scripts for the structural analysis provides prompt visualization, especially once dimensional variations are concerned in the frame of aircraft structural design. The developed python scripts would serve as a practical tool that is widely applicable to various multi-cell wing boxes for stiffness purposes. This would be further extended to the structural integrity problems to cover the effect of gaps and/or cut-outs in shear flow distribution in box-beams.
Ontology modelling of intelligent HCI in aircraft cockpit
Authors: Zhang Xia , Sun Youchao , Zhang Yanjun
Published: June 2021

Purpose

Semantic modelling is an essential prerequisite for designing the intelligent human–computer interaction in future aircraft cockpit. The purpose of this paper is to outline an ontology-based solution to this issue.

Design/methodology/approach

The scenario elements are defined considering the cognitive behaviours, system functions, interaction behaviours and interaction situation. The knowledge model consists of a five-tuple array including concepts, relations, functions, axioms and instances. Using the theory of belief-desire-intention, the meta-model of cognitive behaviours is established. The meta-model of system functions is formed under the architecture of sub-functions. Supported by information flows, the meta-model of interaction behaviours is presented. Based on the socio-technical characteristics, the meta-model of interaction situation is proposed. The knowledge representation and reasoning process is visualized with the semantic web rule language (SWRL) on the Protégé platform. Finally, verification and evaluation are carried out to assess the rationality and quality of the ontology model. Application scenarios of the proposed modelling method are also illustrated.

Findings

Verification results show that the knowledge reasoning based on SWRL rules can further enrich the knowledge base in terms of instance attributes and thereby improve the adaptability and learning ability of the ontology model in different simulations. Evaluation results show that the ontology model has a good quality with high cohesion and low coupling.

Practical implications

The approach presented in this paper can be applied to model complex human–machine–environment systems, from a semantics-driven perspective, especially for designing future cockpits.

Originality/value

Different from the traditional approaches, the method proposed in this paper tries to deal with the socio-technical modelling issues concerning multidimensional information semantics. Meanwhile, the constructed model has the ability of autonomous reasoning to adapt to complex situations.
Optimization of a two-echelon supply chain with random demand and random defect rate under strict carbon cap policy
Authors: Ghosh Arindam
Published: June 2021

Purpose

The yield of defective items and emissions of greenhouse gases in supply chains are areas of concern. Organizations try to reduce the yield defective items and emissions. In this paper, a constrained optimization model is developed with consideration of the yield of defective items and strict carbon cap policy simultaneously and then optimized. Further, sensitivity analyses have been carried out to draw different managerial insights. Precisely, we have tried to address the following research questions: (1) how to optimize the cost for a two-echelon supply chain considering yield of defective items and strict carbon cap policy, (2) how the total expected cost and total expected emissions act with changing parameters.

Design/methodology/approach

The mathematical modeling approach has been adopted to develop a model and further optimized it with optimization software. Costs and emissions from different areas of a supply chain have been derived and then the total cost and total emissions have been formulated mathematically. One constrained mixed-integer nonlinear programming (MINLP) problem has been formulated and solved considering emissions-related, velocity and production related-constraints. Further, different sensitivity analyses have been derived to draw some managerial insights.

Findings

In this paper, many decision variables have been calculated with a set of basic values of other parameters. It has been found that both cost and emissions can be controlled by controlling different parameters. It has been also found that some parameters have very little or no influence either on cost or emissions. In most cases, originations may exhaust the given limit of carbon cap to optimize their costs.

Originality/value

In spite of my sincere efforts, no paper has been found that has considered the yield of defective items and strict carbon cap policy simultaneously. In this paper, it is assumed that both demand and defect rates are random in nature. The model, presented in this paper may give insights to develop different supply chain models with consideration of both defective items and strict carbon cap policy. Sensitivity analyses, drawn in this paper may give deep insights to managers and carbon regulatory bodies.
Design of an enhanced fractional order PID controller for a class of second-order system
Authors: Kanagaraj N. , Jha Vishwa Nath
Published: June 2021

Purpose

This paper aims to design a modified fractional order proportional integral derivative (PID) (FO[PI]λDµ) controller based on the principle of fractional calculus and investigate its performance for a class of a second-order plant model under different operating conditions. The effectiveness of the proposed controller is compared with the classical controllers.

Design/methodology/approach

The fractional factor related to the integral term of the standard FO[PI]λDµ controller is applied as a common fractional factor term for the proportional plus integral coefficients in the proposed controller structure. The controller design is developed using the regular closed-loop system design specifications such as gain crossover frequency, phase margin, robustness to gain change and two more specifications, namely, noise reduction and disturbance elimination functions.

Findings

The study results of the designed controller using matrix laboratory software are analyzed and compared with an integer order PID and a classical FOPIλDµ controller, the proposed FO[PI]λDµ controller exhibit a high degree of performance in terms of settling time, fast response and no overshoot.

Originality/value

This paper proposes a methodology for the FO[PI]λDµ controller design for a second-order plant model using the closed-loop system design specifications. The effectiveness of the proposed control scheme is demonstrated under different operating conditions such as external load disturbances and input parameter change.
Do better agricultural extension and climate information sources enhance adaptive capacity? A micro-level assessment of farm households in rural India
Authors: Jha Chandan Kumar , Gupta Vijaya
Published: June 2021

Purpose

The farmers used several information sources to gather information about the climatic variability and modern agricultural practices to cope with climate change. The choice of adaptation strategies and the successful implication of adaptation strategies depend on accurate, timely information on the climate variability and precise technical details of adaptation strategies. By keeping the importance of climate information and agricultural extension information in the center, this study aims to conduct a micro-level evaluation of farmers’ choice of climate information, agriculture extension services and agricultural credit sources. This study’s primary objective is to understand how the different sources of climate information and agricultural extension influence farm household adaptation decisions.

Design/methodology/approach

This study has been conducted in three subs agro-climatic zone of the Middle Gangetic Plain region, which falls in India’s Bihar state. This paper has randomly selected seven districts from these three subs agro-climatic zone to collect the data. The analysis of this study is based on survey data collected from 700 farm households. This study has used descriptive statistics and a logistic regression model to assess the sources of climate information, agricultural extension and credit sources and how these sources influence farm households’ adaptation decisions.

Findings

The result of this study shows farmers are using different traditional (sharing experience, newspaper and radio), information and communication technology (mobile and TV) and institutional arrangements (agricultural officer and meteorological department) in the study area. The study’s finding identifies different farm households’ different sources and how these options farming farmers’ adaptation decisions. The study further revealed that institutional factors such as extension services and access to information on climate change increase the probability of adopting knowledge-intensive adaptation strategies such as soil conservation, water conservation, crop insurance and planting horticulture and vegetables.

Research limitations/implications

The study has conducted a micro-level assessment of adaptation behavior at the local level to understand the factor influencing the adaptation decision. This study’s finding is useful in designing the appropriate policy framework for the farm household’s capacity building to enhance their technical skills and awareness toward the institutional arrangements.

Originality/value

This paper’s finding pointed out institutional arrangements’ requirement to improve adaptive capacity to make long-term strategic decisions to cope with climate change.
A comparative study of modified SIR and logistic predictors using local level database of COVID-19 in India
Authors: Bajaj Naman S. , Pardeshi Sujit S. , Patange Abhishek D. , Khade Hrushikesh S. , Mate Kavidas
Published: June 2021

Purpose

Several national- and state-level studies have been predicting the course of the COVID-19 pandemic using supervised machine learning algorithms. However, the comparison of such models has not been discussed before. This is the first-ever research wherein the two leading models, susceptible-infected-recovered (SIR) and logistic are compared. The purpose of this study is to observe their utility, at both the National and Municipal Corporation level in India.

Design/methodology/approach

The modified SIR and the logistic were deployed for analysis of the COVID-19 patients’ database of India and three Municipal Corporations, namely, Akola, Kalyan-Dombivli and Mira-Bhayander. The data for the study was collected from the official notifications for COVID-19 released by respective government websites.

Findings

This study provides evidence to show the superiority of the modified SIR over the logistic model. The models give accurate predictions for a period up to 14 days. The prediction accuracy of the models is limited due to change in government policies. This can be observed by the drastic increase in the COVID-19 cases after Unlock 1.0 in India. The models have proven that they can effectively predict for both National and Municipal Corporation level database.

Practical implications

The modified SIR model can be used to signify the practicality and effectiveness of the decisions taken by the authorities to contain the spread of coronavirus.

Originality/value

This study modifies the SIR model and also identifies and fulfills the need to find a more accurate prediction algorithm to help curb the pandemic.