bluuebunny/crossref_metadata_2025 huggingface dataset:
Dense phenotyping from electronic health records enables machine-learning-based prediction of preterm birth
Authors: Abraham Abin , Le Brian , Kosti Idit , Straub Peter , Velez-Edwards Digna R. , Davis Lea K. , Newton J. M. , Muglia Louis J. , Rokas Antonis , Bejan Cosmin A. , Sirota Marina , Capra John A.
Published: July 2020

Identifying pregnancies at risk for preterm birth, one of the leading causes of worldwide infant mortality, has the potential to improve prenatal care. However, we lack broadly applicable methods to accurately predict preterm birth risk. The dense longitudinal information present in electronic health records (EHRs) is enabling scalable and cost-efficient risk modeling of many diseases, but EHR resources have been largely untapped in the study of pregnancy. Here, we apply machine learning to diverse data from EHRs to predict singleton preterm birth. Leveraging a large cohort of 35,282 deliveries, we find that machine learning models based on billing codes alone can predict preterm birth risk at various gestational ages (e.g., ROC-AUC=0.75, PR-AUC=0.40 at 28 weeks of gestation) and outperform comparable models trained using known risk factors (e.g., ROC-AUC=0.65, PR-AUC=0.25 at 28 weeks). Examining the patterns learned by the model reveals it stratifies deliveries into interpretable groups, including high-risk preterm birth sub-types enriched for distinct comorbidities. Our machine learning approach also predicts preterm birth sub-types (spontaneous vs. indicated), mode of delivery, and recurrent preterm birth. Finally, we demonstrate the portability of our approach by showing that the prediction models maintain their accuracy on a large, independent cohort (5,978 deliveries) from a different healthcare system. By leveraging rich phenotypic and genetic features derived from EHRs, we suggest that machine learning algorithms have great potential to improve medical care during pregnancy.
The Effect of Famotidine on SARS-CoV-2 Proteases and Virus Replication
Authors: Loffredo Madeline , Lucero Hector , Chen Da-Yuan , O’Connell Aoife , Bergqvist Simon , Munawar Ahmad , Bandara Asanga , De Graef Steff , Weeks Stephen D. , Douam Florian , Saeed Mohsan , Munawar Ali H.
Published: July 2020

The lack of coronavirus-specific antiviral drugs has instigated multiple drug repurposing studies to redirect previously approved medicines for the treatment of SARS-CoV-2, the coronavirus behind the ongoing COVID-19 pandemic. A recent, large-scale, retrospective clinical study showed that famotidine, when administered at a high dose to hospitalized COVID-19 patients, reduced the rates of intubation and mortality. A separate, patient-reported study associated famotidine use with improvements in mild to moderate symptoms such as cough and shortness of breath. While a prospective, multi-center clinical study is ongoing, two parallelin silicostudies have proposed one of the two SARS-CoV-2 proteases, 3CLproor PLpro, as potential molecular targets of famotidine activity; however, this remains to be experimentally validated. In this report, we systematically analyzed the effect of famotidine on viral proteases and virus replication. Leveraging a series of biophysical and enzymatic assays, we show that famotidine neither binds with nor inhibits the functions of 3CLproand PLpro. Similarly, no direct antiviral activity of famotidine was observed at concentrations of up to 200 μM, when tested against SARS-CoV-2 in two different cell lines, including a human cell line originating from lungs, a primary target of COVID-19. These results rule out famotidine as a direct-acting inhibitor of SARS-CoV-2 replication and warrant further investigation of its molecular mechanism of action in the context of COVID-19.
Lineage recording of zebrafish embryogenesis reveals historical and ongoing lineage commitments
Authors: Chen Zhuoxin , Ye Chang , Liu Zhan , Deng Shanjun , He Xionglei , Xu Jin
Published: July 2020

It has been challenging to characterize the lineage relationships among cells in vertebrates, which comprise a great number of cells. Fortunately, recent progress has been made by combining the CRISPR barcoding system with single-cell sequencing technologies to provide an unprecedented opportunity to track lineage at single-cell resolution. However, due to errors and/or dropouts introduced by amplification and sequencing, reconstruction of accurate lineage relationships in complex organisms remains a challenge. Thus, improvements in both experimental design and computational analysis are necessary for lineage inference. In this study, we employed single-cell Lineage tracing On Endogenous Scarring Sites (scLOESS), a lineage recording strategy based on the CRISPR-Cas9 system, to trace cell fate commitments for zebrafish larvae. With rigorous quality control, we demonstrated that lineage commitments of complex organisms could be inferred from a limited number of barcoding sites. Together with cell-type characterization, our method could homogenously recover lineage information. In combination with the cell-type and lineage information, we depicted the development histories for germ layers as well as cell types. Furthermore, when combined with trajectory analysis, our methods could capture and resolve the ongoing lineage commitment events to gain further biological insights into later development and differentiation in complex organisms.
Metabolic flux fingerprinting differentiates planktonic and biofilm states ofPseudomonas aeruginosaandStaphylococcus aureus
Authors: Lichtenberg Mads , Kragh Kasper Nørskov , Fritz Blaine , Bier-Kirkegaard Julius , Bjarnsholt Thomas
Published: July 2020

The challenges of defining the biofilm phenotype has been clear for decades. Many biomarkers for biofilm are known, but methods for identifying these are often invasive and/or complicated. These methods often rely on disrupting the biofilm matrix or examining virulence factors and compounds, which may only be expressed under certain conditions.

We used microcalorimetric measurements of metabolic energy release to investigate whether unchallenged, planktonicPseudomonas aeruginosadisplayed differences in metabolism compared to surface-bound and non-attached biofilms.

The pattern of energy release observed in the recorded microcalorimetric thermograms clearly depended on growth state, though the total energy expenditure was not different between growth states. To characterize these differences, we developed a classification pipeline utilizing machine learning algorithms to classify growth state, based on the observed patterns of energy release. With this approach, we could with high accuracy detect the growth form of blinded samples. To challenge the algorithm, we attempted to limit the amount of training data. By training the algorithm with only a single data point from each growth form, we obtained a mean accuracy of 90.5% using two principal components. Further validation of the classification pipeline showed that the approach was not limited toP. aeruginosabut could also be used for detection of gram-positiveStaphylococcus aureusbiofilm. We propose that microcalorimetric measurements, in combination with this new quantitative framework, can be used as a non-invasive biomarker to detect the presence of biofilm.

These results could have a significant potential in clinical settings where the detection of biofilms in infections often means a different outcome and treatment regime for the patient.
EM-stellar: benchmarking deep learning for electron microscopy image segmentation
Authors: Khadangi Afshin , Boudier Thomas , Rajagopal Vijay
Published: July 2020

The inherent low contrast of electron microscopy (EM) datasets presents a significant challenge for rapid segmentation of cellular ultrastructures from EM data. This challenge is particularly prominent when working with high resolution big-datasets that are now acquired using electron tomography and serial block-face imaging techniques. Deep learning (DL) methods offer an exciting opportunity to automate the segmentation process by learning from manual annotations of a small sample of EM data. While many DL methods are being rapidly adopted to segment EM data no benchmark analysis has been conducted on these methods to date. We present EM-stellar, a Jupyter Notebook platform that is hosted on google Colab that can be used to benchmark the performance of a range of state-of-the-art DL methods on user-provided datasets. Using EM-Stellar we show that the performance of any DL method is dependent on the properties of the images being segmented. It also follows that no single DL method performs consistently across all performance evaluation metrics.
Transcriptome profiling reveals the mechanism of ripening and epidermal senescence in passion (Passiflora edulia Sims) fruit
Authors: Li Changbao , Xin Ming , Li Li , He Xuemei , Liu Guomin , Li Jiemin , Sheng Jinfeng , Sun Jian
Published: July 2020

Passion fruit (Passiflora edulia Sims), an important tropical and sub-tropical species, is classified as a respiration climacteric fruit, the quality deteriorates rapidly after harvest. To reveal the mechanisms involved in ripening and rapidly fruit senescence, the phytochemical characteristics and RNA sequencing were conducted in the purple passion fruits with different (1-MCP and PF) treatment. Comprehensive functional annotation and KEGG enrichment analysis showed that the starch and sucrose metabolism, plant hormone signal transduction, phenylpropanoid biosynthesis, flavonid biosynthesis, carotenoid biosynthesis were involved in fruit ripening. Applying with PF and 1-MCP significantly affected transcript levels of passion fruit after harvest storage. A large number of differently expressed unigenes (DEGs) were identified significantly enrichen in starch and sucrose metabolism, plant hormone signal transduction and phenylpropanoid biosynthesis at postharvest stage. The preservative film (PF) and 1-Methylcyclopropene (1-MCP) treatments increased superoxide dismutase (SOD), catalase (CAT) and peroxidase (POD) gene expression and enzyme activities, accelerated the lignin accumulation, decline β-galactosidase (β-Gal), polygalacturonase (PG) and cellulose activities and gene expression to delay cell wall degradation during fruit senescence. The RNA sequencing data of cell wall metabolism and hormone signal transduction pathway related unigenes were verified by RT-qPCR. The results indicated that the cell wall metabolism and hormone signal pathways were notably related to passion fruit ripening. PF and 1-MCP treatment might inhibited ethylene signaling and regulated cell wall metabolism pathways to inhibited cell wall degradation. Our results reveal ripening and senescence related networks during passion fruit ripening, which can provide a foundation for understanding the molecular mechanisms underlying PF and 1-MCP treatment on fruit ripening.
Small molecule resensitizesSalmonellato itaconate and decreases bacterial proliferation in macrophages
Authors: Duncan Dustin , Chang Justin H. , Artyomov Maxim N. , Auclair Karine
Published: July 2020

Antimicrobial resistance is a global health crisis offering little reprieve. The situation urgently calls for new drug targets and therapies for infections. We have previously suggested a different approach to treat infections, termed bacterio-modulation, in which a compound modulates the bacterial response to the host immune defense. Herein we show that monocytes infected withSalmonella entericaspp. Typhimurium can be cured using non-antimicrobial compounds that resensitize the bacterium to itaconate, a macrophage-derived antimicrobial metabolite. We propose that this represents a novel strategy to treat infections.
Consequences of a short-term exposure to a sub lethal concentration of CdO nanoparticles on key life history traits in the fruit fly (Drosophila melanogaster)
Authors: El Kholy Samar , Giesy John P. , Al Naggar Yahya
Published: July 2020

Nanoparticles of cadmium oxide (CdO NPs) are among the most common industrial metal oxide nanoparticles. Early adulthood (F0) fruit flies (D. melanogaster) were exposed for 7 days to a sub lethal concentration (0.03 mg CdO NPs/ml, which was 20% of the LC50), spiked into food media to test for long term-effects over time and beyond their direct exposure on key life history traits. Effects on survival, developmental time, eclosion rate, fecundity and negative geotaxis performance were assessed. Potential effects on ultrastructure of mid gut cells were also investigated by use of electron microscopy. All studied life history traits, as well as climbing behavior were adversely affected by exposure to CdO NPs. In non-exposed progeny (F1) of adult flies (F0), a blistered wing phenotype was also observed. Lysis of nuclear and rough endoplasmic reticulum (rER) membranes, mitochondrial swelling and lysis were among the most common cellular alterations observed in midgut cells of F0flies exposed to CdO NPs. Genes encoding for metallothionein (MTn A-D) were significantly upregulated in both parent flies (F0) and their progeny (F1) after exposure of F0flies to CdO NPs, compared to unexposed, control flies, a result which indicated potential, long-term effects. Taken together, these results suggest that short-term exposure to a sublethal concentration of CdO NPs is sufficient to cause long-lasting, harmful effects on fruit flies.

Graphical Abstract
A direct RNA-protein interaction atlas of the SARS-CoV-2 RNA in infected human cells
Authors: Schmidt Nora , Lareau Caleb A. , Keshishian Hasmik , Melanson Randy , Zimmer Matthias , Kirschner Luisa , Ade Jens , Werner Simone , Caliskan Neva , Lander Eric S. , Vogel Jörg , Carr Steven A. , Bodem Jochen , Munschauer Mathias
Published: July 2020

SARS-CoV-2 infections pose a global threat to human health and an unprecedented research challenge. Among the most urgent tasks is obtaining a detailed understanding of the molecular interactions that facilitate viral replication or contribute to host defense mechanisms in infected cells. While SARS-CoV-2 co-opts cellular factors for viral translation and genome replication, a comprehensive map of the host cell proteome in direct contact with viral RNA has not been elucidated. Here, we use RNA antisense purification and mass spectrometry (RAP-MS) to obtain an unbiased and quantitative picture of the human proteome that directly binds the SARS-CoV-2 RNA in infected human cells. We discover known host factors required for coronavirus replication, regulators of RNA metabolism and host defense pathways, along with dozens of potential drug targets among direct SARS-CoV-2 binders. We further integrate the SARS-CoV-2 RNA interactome with proteome dynamics induced by viral infection, linking interactome proteins to the emerging biology of SARS-CoV-2 infections. Validating RAP-MS, we show that CNBP, a regulator of proinflammatory cytokines, directly engages the SARS-CoV-2 RNA. Supporting the functional relevance of identified interactors, we show that the interferon-induced protein RYDEN suppresses SARS-CoV-2 ribosomal frameshifting and demonstrate that inhibition of SARS-CoV-2-bound proteins is sufficient to manipulate viral replication. The SARS-CoV-2 RNA interactome provides an unprecedented molecular perspective on SARS-CoV-2 infections and enables the systematic dissection of host dependency factors and host defense strategies, a crucial prerequisite for designing novel therapeutic strategies.
Lithium ameliorates Cornelia de Lange syndrome associated phenotypes in experimental models
Authors: Grazioli Paolo , Parodi Chiara , Mariani Milena , Bottai Daniele , Di Fede Elisabetta , Zulueta Aida , Avagliano Laura , Cereda Anna , Tenconi Romano , Wierzba Jolanta , Adami Raffaella , Iascone Maria , Ajmone Paola Francesca , Vaccari Thomas , Gervasini Cristina , Selicorni Angelo , Massa Valentina
Published: July 2020

Cornelia de Lange Syndrome (CdLS) is a rare developmental disorder affecting a multitude of organs including the central nervous system, inducing a variable neurodevelopmental delay. CdLS malformations derive from deregulation of developmental pathways, inclusive of the canonical WNT pathway.

We have evaluated MRI anomalies and behavioral and neurological clinical manifestations in CdLS patients. Importantly, we observed in our cohort a significant association between behavioral disturbance and structural abnormalities in brain structures of hindbrain embryonic origin.

Considering the cumulative evidence on the cohesin-WNT-hindbrain shaping cascade, we have explored possible ameliorative effects of chemical activation of the canonical WNT pathway with lithium chloride in different models: (I)Drosophila melanogasterCdLS model showing a significant rescue of mushroom bodies morphology in the adult flies; (II) mouse neural stem cells restoring physiological levels in proliferation rate and differentiation capabilities toward the neuronal lineage; (III) lymphoblastoid cell lines from CdLS patients and healthy donors restoring cellular proliferation rate and inducing the expression ofCyclinD1. This work supports a role for WNT-pathway regulation of CdLS brain and behavioral abnormalities and a consistent phenotype rescue by lithium in experimental models.
Inhibition of ITK differentiates GVT and GVHD in allo-HSCT
Authors: Mammadli Mahinbanu , Huang Weishan , Harris Rebecca , Sultana Aisha , Cheng Ying , Tong Wei , Pu Jeffery , Gentile Teresa , Henty-Ridilla Jessica , Dsouza Shanti , Yang Qi , August Avery , Bah Alaji , Karimi Mobin
Published: July 2020

Allogeneic hematopoietic stem cell transplantation is a life-saving treatment for many malignant and nonmalignant diseases. Donor T cells contained within the graft prevent tumor recurrenceviagraft-versus-tumor (GVT) effects, however, also cause graft-versus-host disease (GVHD). Novel treatment strategies are therefore needed to allow maintenance of GVT while suppressing GVHD. Here we show using murine models, that targeting IL-2-inducible T cell kinase (ITK) in donor T cells reduces GVHD while preserving the beneficial GVT effects. Donor T cells fromItk-/-mice exhibit significantly reduced production of inflammatory cytokines and migration to GVHD target organs such as liver and small intestine, while maintaining GVT efficacy against primary B-ALL tumors.Itk-/-T cells exhibited reduced expression of IRF4 and decreased JAK/STAT signaling activity, but preserved cytotoxicity, which was accompanied by upregulation of Eomesodermin (Eomes), which was necessary for GVT function. A novel peptide inhibitor ITK signaling is also able to prevent GVHD. This novel peptide inhibitor also reduced cytokine production in mice and human T cells. Altogether, our data suggest that inhibiting ITK could be a therapeutic strategy to reduce GVHD while preserving the beneficial GVT effects following allo-HSCT treatment.

Key Points

Inhibiting ITK by a novel peptide significantly reduces GVHD but retains GVT.

ITK deficient donor T cells exhibit minimal GVHD, but maintain GVT activity.

ITK deficient donor T cells exhibit significantly reduced production of inflammatory cytokines and migration to GVHD target organs.

Eomes is required for GVT effect.
A deep learning approach for staging embryonic tissue isolates with small data
Authors: Pond Adam , Hwang Seongwon , Verd Berta , Steventon Benjamin
Published: July 2020

Machine learning approaches are becoming increasingly widespread and are now present in most areas of research. Their recent surge can be explained in part due to our ability to generate and store enormous amounts of data with which to train these models. The requirement for large training sets is also responsible for limiting further potential applications of machine learning, particularly in fields where data tend to be scarce such as developmental biology. However, recent research seems to indicate that machine learning and Big Data can sometimes be decoupled to train models with modest amounts of data. In this work we set out to train a CNN-based classifier to stage zebrafish tail buds at four different stages of development using small information-rich data sets. Our results show that two and three dimensional convolutional neural networks can be trained to stage developing zebrafish tail buds based on both morphological and gene expression confocal microscopy images, achieving in each case up to 100% test accuracy scores. Importantly, we show that high accuracy can be achieved with data set sizes of under 100 images, much smaller than the typical training set size for a convolutional neural net. Furthermore, our classifier shows that it is possible to stage isolated embryonic structures without the need to refer to classic developmental landmarks in the whole embryo, which will be particularly useful to stage 3D culture in vitro systems such as organoids. We hope that this work will provide a proof of principle that will help dispel the myth that large data set sizes are always required to train CNNs, and encourage researchers in fields where data are scarce to also apply ML approaches.

Author summary

The application of machine learning approaches currently hinges on the availability of large data sets to train the models with. However, recent research has shown that large data sets might not always be required. In this work we set out to see whether we could use small confocal microscopy image data sets to train a convolutional neural network (CNN) to stage zebrafish tail buds at four different stages in their development. We found that high test accuracies can be achieved with data set sizes of under 100 images, much smaller than the typical training set size for a CNN. This work also shows that we can robustly stage the embryonic development of isolated structures, without the need to refer back to landmarks in the tail bud. This constitutes an important methodological advance for staging organoids and other 3D culture in vitro systems. This work proves that prohibitively large data sets are not always required to train CNNs, and we hope will encourage others to apply the power of machine learning to their areas of study even if data are scarce.
Receptor-like role for PQLC2 amino acid transporter in the lysosomal sensing of cationic amino acids
Authors: Talaia Gabriel , Amick Joseph , Ferguson Shawn M.
Published: July 2020

PQLC2, a lysosomal cationic amino acid transporter, also serves as a sensor that responds to scarcity of its substrates by recruiting a protein complex comprised of C9orf72, SMCR8 and WDR41 to the surface of lysosomes. This protein complex controls multiple aspects of lysosome function. Although it is known that this response to changes in cationic amino acid availability depends on an interaction between PQLC2 and WDR41, the underlying mechanism for the regulated interaction is not known. In this study, we establish that the WDR41-PQLC2 interaction is mediated by a short peptide motif in a flexible loop that extends from the WDR41 β-propeller and inserts into a cavity presented by the inward-facing conformation of PQLC2. This data supports a transceptor model wherein conformational changes in PQLC2 related to substrate transport regulate the availability of the WDR41 binding site on PQLC2 and mediate recruitment of the WDR41-SMCR8-C9orf72 complex to the surface of lysosomes.
IAV-CNN: a 2D convolutional neural network model to predict antigenic variants of influenza A virus
Authors: Yin Rui , Thwin Nyi Nyi , Zhuang Pei , Zhang Yu , Lin Zhuoyi , Kwoh Chee Keong
Published: July 2020

The rapid evolution of influenza viruses constantly leads to the emergence of novel influenza strains that are capable of escaping from population immunity. The timely determination of antigenic variants is critical to vaccine design. Empirical experimental methods like hemagglutination inhibition (HI) assays are time-consuming and labor-intensive, requiring live viruses. Recently, many computational models have been developed to predict the antigenic variants without considerations of explicitly modeling the interdependencies between the channels of feature maps. Moreover, the influenza sequences consisting of similar distribution of residues will have high degrees of similarity and will affect the prediction outcome. Consequently, it is challenging but vital to determine the importance of different residue sites and enhance the predictive performance of influenza antigenicity. We have proposed a 2D convolutional neural network (CNN) model to infer influenza antigenic variants (IAV-CNN). Specifically, we introduce a new distributed representation of amino acids, named ProtVec that can be applied to a variety of downstream proteomic machine learning tasks. After splittings and embeddings of influenza strains, a 2D squeeze-and-excitation CNN architecture is constructed that enables networks to focus on informative residue features by fusing both spatial and channel-wise information with local receptive fields at each layer. Experimental results on three influenza datasets show IAV-CNN achieves state-of-the-art performance combing the new distributed representation with our proposed architecture. It outperforms both traditional machine algorithms with the same feature representations and the majority of existing models in the independent test data. Therefore we believe that our model can be served as a reliable and robust tool for the prediction of antigenic variants.
TRPM7 is a critical regulator of pancreatic endocrine development and high-fat diet-induced β-cell proliferation
Authors: Altman Molly K. , Schaub Charles M. , Dickerson Matthew T. , Dadi Prasanna K. , Graff Sarah M. , Galletta Thomas J. , Amarnath Gautami , Thorson Ariel S. , Gu Guoqiang , Jacobson David A.
Published: July 2020

The melastatin subfamily of the transient receptor potential channels (TRPM) are regulators of pancreatic β-cell function. TRPM7 is the most abundant islet TRPM channel; however, the role of TRPM7 in β-cell function has not been determined. Here, we utilized various spatiotemporal transgenic mouse models to investigate how TRPM7 knockout influences pancreatic endocrine development, proliferation, and function. Ablation of TRPM7 within pancreatic progenitors reduced pancreatic size, as well as α-cell and β-cell mass. This resulted in impaired glucose tolerance due to decreased serum insulin levels. However, ablation of TRPM7 following endocrine specification or in adult mice did not impact endocrine expansion or glucose tolerance. As TRPM7 regulates cell proliferation, we assessed how TRPM7 influences β-cell hyperplasia under insulin resistant conditions. β-cell proliferation induced by high-fat diet was significantly decreased in TRPM7 deficient β-cells. The endocrine roles of TRPM7 may be influenced by cation flux through the channel, and indeed we find that TRPM7 ablation alters β-cell intracellular Mg2+. Together, these findings reveal that TRPM7 controls pancreatic progenitor expansion and β-cell proliferation, which is likely due to regulation of Mg2+homeostasis.

This manuscript identifies a critical developmental role for TRPM7 channels in pancreatic progenitor cells. The manuscript also determines that TRPM7 plays a key role in β-cell proliferation under insulin-resistant conditions.
Microbial Diversity of Co-occurring Heterotrophs in Cultures of Marine Picocyanobacteria
Authors: Kearney Sean M. , Thomas Elaina , Coe Allison , Chisholm Sallie W.
Published: July 2020

ProchlorococcusandSynechococcusare responsible for around 10% of global net primary productivity, serving as part of the foundation of marine food webs. Heterotrophic bacteria are often co-isolated with these picocyanobacteria in seawater enrichment cultures that contain no added organic carbon; heterotrophs grow on organic carbon supplied by the photolithoautotrophs. We have maintained these cultures ofProchlorococcusandSynechococcusfor 100s to 1000s of generations; they represent ideal microcosms for examining the selective pressures shaping autotroph/heterotroph interactions. Here we examine the diversity of heterotrophs in 74 enrichment cultures of these picocyanobacteria obtained from diverse areas of the global oceans. Heterotroph community composition differed between clades and ecotypes of the autotrophic ‘hosts’ but there was significant overlap in heterotroph community composition. Differences were associated with timing, location, depth, and methods of isolation, suggesting the particular conditions surrounding isolation have a persistent effect on long-term culture composition. The majority of heterotrophs in the cultures are rare in the global ocean; enrichment conditions favor the opportunistic outgrowth of these rare bacteria. We did find a few examples, such as heterotrophs in the family Rhodobacteraceae, that are ubiquitous and abundant in cultures and in the global oceans; their abundance in the wild is also positively correlated with that of picocyanobacteria. Collectively, the cultures converged on similar compositions, likely from bottlenecking and selection that happens during the early stages of enrichment for the picocyanobacteria. We highlight the potential for examining ecologically relevant relationships by identifying patterns of distribution of culture-enriched organisms in the global oceans.

IMPORTANCE

One of the biggest challenges in marine microbial ecology is to begin to understand the rules that govern the self-assembly of these complex communities. The picocyanobacteriaProchlorococcusandSynechococcuscomprise the most numerous photosynthetic organisms in the sea and supply a significant fraction of the organic carbon that feeds diverse heterotrophic microbes. When initially isolated into cultures,ProchlorococcusandSynechococcuscarry with them select heterotrophic microorganisms that depend on them for organic carbon. The cultures self-assemble into stable communities of diverse microorganisms and are microcosms for understanding microbial interdependencies. Primarily faster-growing, relatively rare, copiotrophic heterotrophic bacteria – as opposed to oligotrophic bacteria that are abundant in picocyanobacterial habitats – are selected for in these cultures, suggesting that these copiotrophs experience these cultures as they would high carbon fluxes associated with particles, phycospheres of larger cells, or actual attachment to picocyanobacteria in the wild.
The S1 protein of SARS-CoV-2 crosses the blood-brain barrier: Kinetics, distribution, mechanisms, and influence of ApoE genotype, sex, and inflammation
Authors: Rhea Elizabeth M. , Logsdon Aric F. , Hansen Kim M. , Williams Lindsey , Reed May , Baumann Kristen , Holden Sarah , Raber Jacob , Banks William A. , Erickson Michelle A.
Published: July 2020

Evidence strongly suggests that SARS-CoV-2, the cause of COVID-19, can enter the brain. SARS-CoV-2 enters cells via the S1 subunit of its spike protein, and S1 can be used as a proxy for the uptake patterns and mechanisms used by the whole virus; unlike studies based on productive infection, viral proteins can be used to precisely determine pharmacokinetics and biodistribution. Here, we found that radioiodinated S1 (I-S1) readily crossed the murine blood-brain barrier (BBB). I-S1 from two commercial sources crossed the BBB with unidirectional influx constants of 0.287 ± 0.024 μL/g-min and 0.294 ± 0.032 μL/g-min and was also taken up by lung, spleen, kidney, and liver. I-S1 was uniformly taken up by all regions of the brain and inflammation induced by lipopolysaccharide reduced uptake in the hippocampus and olfactory bulb. I-S1 crossed the BBB completely to enter the parenchymal brain space, with smaller amounts retained by brain endothelial cells and the luminal surface. Studies on the mechanisms of transport indicated that I-S1 crosses the BBB by the mechanism of adsorptive transcytosis and that the murine ACE2 receptor is involved in brain and lung uptake, but not that by kidney, liver, or spleen. I-S1 entered brain after intranasal administration at about 1/10ththe amount found after intravenous administration and about 0.66% of the intranasal dose entered blood. ApoE isoform or sex did not affect whole brain uptake, but had variable effects on olfactory bulb, liver, spleen, and kidney uptakes. In summary, I-S1 readily crosses the murine BBB, entering all brain regions and the peripheral tissues studied, likely by the mechanism of adsorptive transcytosis.

Graphical Abstract
PhyloBrain atlas: a cortical brain MRI atlas following a phylogenetic approach
Authors: Zhernovaia M , Dadar M , Mahmoud S. , Zeighami Y , Maranzano
Published: July 2020

Cortical atlases constitute a consistent division of the human cortex into areas that have common structural as well as meaningful and distinctive functional characteristics. The most widely used atlases follow the cytoarchitectonic and myeloarchitectonic characteristics of the cortex and have been combined to the standard anatomical nomenclature of gyri and sulci. More recently, common functional features depicted by resting state functional MRI have also guided the division of the cortical brain in functional regions of interest. However, to date, there are no atlases that divide the cortex considering the common evolutionary changes experienced by the mammalian cortex.

Hence, the present study proposes the division of cortical areas into five main regions of interest (ROIs) following a phylogenetic approach: 1- archicortex, 2- paleocortex, 3- peri-archicortex, 4- proisocortex, 5-neocortex, and twelve neocortical sub-ROIs: 5.1.temporopolar, 5.2.post-central, 5.3.pre-central, 5.4.pericalcarine, 5.5.superior temporal, 5.6.middle temporal, 5.7.precuneus, 5.8.insular, 5.9.inferior parietal, 5.10.caudal anterior, 5.11.posterior cingulate, and 5.12.lingual gyrus.

The segmentations were done using the T1-weighted MNI-ICBM152 non-linear 6th generation symmetric average brain MRI model.
The actin nucleation factors JMY and WHAMM enable a rapid p53-dependent pathway of apoptosis
Authors: King Virginia L. , Leclair Nathan K. , Campellone Kenneth G.
Published: July 2020

The actin cytoskeleton is a well-known player in most vital cellular processes, but comparably little is understood about how the actin assembly machinery impacts programmed cell death pathways. In the current study, we explored roles for the humanWiskott-AldrichSyndromeProtein (WASP) family of actin nucleation factors in DNA damage-induced apoptosis. Inactivation of each WASP-family gene revealed that two,JMYandWHAMM, are required for rapid apoptotic responses. JMY and WHAMM enable p53-dependent cell death by enhancing mitochondrial permeabilization, initiator caspase cleavage, and executioner caspase activation. The loss of JMY additionally results in significant changes in gene expression, including upregulation of the small G-protein RhoD. Depletion or deletion ofRHODincreases cell death, suggesting that RhoD normally plays a key role in cell survival. These results give rise to a model in which JMY and WHAMM promote intrinsic cell death responses that can be opposed by RhoD.

Author Summary

The actin cytoskeleton is a collection of protein polymers that assemble and disassemble within cells at specific times and locations. Cytoskeletal regulators called nucleation-promoting factors ensure that actin polymerizes when and where it is needed, and many of these factors are members of theWiskott-AldrichSyndromeProtein (WASP) family. Humans express 8 WASP-family proteins, but whether the different factors function in programmed cell death pathways is not well understood. In this study, we explored roles for each WASP-family member in apoptosis and found that a subfamily consisting of JMY and WHAMM are critical for a rapid pathway of cell death. Furthermore, the loss of JMY results in changes in gene expression, including a dramatic upregulation of the small G-protein RhoD, which appears to be crucial for cell survival. Collectively, our results point to the importance of JMY and WHAMM in driving intrinsic cell death responses plus a distinct function for RhoD in maintaining cell viability.
Are the upper bounds for new SARS-CoV-2 infections in Germany useful?
Authors: Bock Wolfgang , Götz Thomas , Jayathunga Yashika , Rockenfeller Robert
Published: July 2020

At the end of 2019, an outbreak of a new coronavirus, called SARS–CoV–2, was reported in China and later in other parts of the world. First infections were reported in Germany by the end of January and on March 16th the federal government announced a partial lockdown in order to mitigate the spread. Since the dynamics of new infections started to slow down, German states started to relax the confinement measures as to the beginning of May. As a fall back option, a limit of 50 new infections per 100,000 inhabitants within seven days was introduced for each city or district in Germany. If a district exceeds this limit, measures to control the spread of the virus should be taken. Based on a multi– patch SEAIRD–type model, we will simulate the effect of choosing a specific upper limit for new infections. We investigate, whether the politically motivated bound is low enough to detect new outbreaks at an early stage. Subsequently, we introduce an optimal control problem to tackle the multi–criteria problem of finding a bound for new infections that is low enough to avoid new outbreaks, which might lead to an overload of the health care system, but is large enough to curb the expected economic losses.
Neural production of kynurenic acid in Caenorhabditis elegans requires the AAT-1 transporter
Authors: Lin Lin , Lemieux George A. , Enogieru Osatohanmwen Jessica , Giacomini Kathleen M. , Ashrafi Kaveh
Published: July 2020

Kynurenic acid (KynA) levels link peripheral metabolic status to neural functions including learning and memory. Since neural KynA levels dampen learning capacity, KynA reduction has been proposed as a therapeutic strategy for conditions of cognitive deficit such as neurodegeneration. While KynA is generated locally within the nervous system, its precursor, kynurenine (Kyn), is largely derived from peripheral resources. The mechanisms that import Kyn into the nervous system are poorly understood. Here, we provide genetic, anatomical, biochemical, and behavioral evidence showing that in C. elegans an ortholog of the human LAT1 transporter, AAT-1, imports Kyn into sites of KynA production.
From atoms to bonds, angles and torsions: molecular metrics from crystal space, and two Excel implementations
Authors: Glasser Leslie
Published: July 2020

Values of molecular bond lengths, bond angles and (less frequently) bond torsion angles are readily available from databases, from crystallographic software, and/or from interactive molecular and crystal visualization programs such as Jmol. However, the methods used to calculate these values are less well known. In this paper, the computational methods are described in detail, and live Excel implementations, which permit readers to readily perform the calculations for their own molecular systems, are provided. The methods described apply to both fractional coordinates in crystal space and Cartesian coordinates in Euclidean space (space in which the geometric postulates of Euclid are valid) and are vector/matrix based. In their simplest computational form, they are applied as algebraic expansions which are summed. They are also available in matrix formulations, which are readily manipulated and calculated using the matrix functions of Excel. In particular, their general formulation as metric matrices is introduced. The methods in use are illustrated by a detailed example of the calculations. This contribution provides a significant practical application which can also act as motivation for the study of matrix mathematics with respect to its many uses in chemistry.
What prevent you from stepping into the entrepreneurship? Evidence from Chinese makers
Authors: Liu Xinmin , Zhang Yanan , Fan Liu
Published: July 2020

Purpose

This study aims to investigate the influence of three key categories of perceived entrepreneurial obstacles (perceived loss of financial resources, perceived loss of customer demand and perceived loss of social support) on entrepreneurial behavior tendency through fear of failure and negative emotion to shed light on why Chinese entrepreneurship has fallen into a decline from the individual level.

Design/methodology/approach

A sample of 256 Chinese makers who were achieving their innovative ideas in makerspaces was used to test the research model. Then, the structural equation modeling was adopted for data analysis.

Findings

The results indicate that fear of failure is the strongest psychological barrier to entrepreneurial behavior tendency and the strongest trigger for negative emotion; both negative emotion and fear of failure are affected by entrepreneurial obstacles of perceived loss of financial resources and perceived loss of social support. However, perceived loss of customer demand is not the inducement for both fear of failure and negative emotion.

Originality/value

This study adopts stressor-strain-outcome framework in studying entrepreneurship to help understand what prevent potential entrepreneurs from stepping into entrepreneurship. In addition, this study offers a new insight into entrepreneurship by emphasizing the decisive impact of inhibitive factors on personal entrepreneurship.
Developments in the UK road transport from a smart cities perspective
Authors: Suresh Subashini , Renukappa Suresh , Abdul-Aziz Abdul-Rashid , Paloo Yogeswary , Jallow Haddy
Published: July 2020

Purpose

A smart city is a city that functions in a sustainable and intelligent way, by integrating all of its infrastructures and services in a cohesive way using intelligent devices for monitoring and control, to ensure efficiency and better quality of life for its citizens. As other countries globally, the UK is keen on economic development and investment in smart-city missions to create interest in monetary environment and inward investment. This paper aims to explore the driving forces of smart road transport transformation and implementation in the UK.

Design/methodology/approach

The study involved interviews with 16 professionals from the UK road transport sector. A semi-structured interview technique was used to collect experts' perception, which was then examined using content analysis.

Findings

The results of the study revealed that the technological advancement is a key driver. The main challenges faced during the implementation of smart-city elements in the UK road network are lack of investment, maintenance, state of readiness and the awareness of the smart road transport concept. The study concludes that an understanding of the concept of smart cities from a road transport perspective is very important to create awareness of the benefits and the way it works. A wider collaboration between every sector is crucial to create a successful smart city.

Originality/value

The study contributes to the field of digitalisation of road transport sector. This paper reveals the key driving forces of smart road transport transformation, the current status of smart road transport implementation in the UK and challenges of the smart road transport development in the UK.
When classroom interactions have to go online: the move to specifications grading in a project-based design course
Authors: Quintana Rebecca , Quintana Chris
Published: July 2020

Purpose

The events surrounding the COVID-19 crisis had a profound effect on higher education, forcing students and instructors to face a sudden transition to wholly online learning contexts. This paper aims to examine how the design of a residential course was adapted to an online context and how this adaptation may prove beneficial to future iterations of the course.

Design/methodology/approach

This analysis centers on a master’s-level course in which students design software to support learning. One of the major changes to the course revolves around the transition from a traditional rubric-based grading scheme to a specifications grading system. This latter approach provides a series of binary (pass/fail) requirements (specifications) that students must meet to pass. Various forms of interactions were also altered during the transition; the authors investigate these in the paper.

Findings

This study found that the move to specifications grading helped students and the instructor to focus on the important work of meeting course learning goals. The approach also aligned well with authentic scenarios in which software projects are tested against certain specifications. Finally, this study concludes that thinking about specifications grading in the future can help us to develop more resilient pedagogical design approaches that respond to various forms of disruptions and changes.

Originality/value

The course design insights described in this paper illustrate alternative ways of instruction that can be especially useful during times of emergency, but which may also provide an added level of authenticity and learner motivation during times of stability.
Factors influencing the borrower loan size in microfinance group lending: a survey from Indian microfinance institutions
Authors: Sangwan Sunil , Nayak Narayan Chandra
Published: July 2020

Purpose

The purpose of this paper is to analyze the impact of the cost of microfinance intermediation on borrowers’ loan size. The identified transaction cost and credit risk factors tell about what a lender takes into accounts while screening and allocating loan amounts to the borrowers, where the lender has limited information about the client’s ability to repay.

Design/methodology/approach

The analysis is based on the primary data collected from a sample of 498 microfinance institutions (MFI) linked group clients covering two microfinance leading states of India.

Findings

Empirical findings suggest that the cost of microfinance intermediation has an impact on borrowers’ loan size. To reduce the cost, the MFIs lend big loans to clients having a high income, assets, land size, lower informal borrowings and having longer loan experiences. In MFI lending, the younger and less educated people are the ones who demand bigger loan amounts. The geographical distance of borrowers’ location from MFI offices, group size and interest rate are the other factors that influence the loan size.

Originality/value

The past empirical works seem to have not focused on how the cost of microfinance intermediation creates loan size variation among the borrowers in joint liability group lending. The endogeneity problem has not been resolved. The present article thus identifies the factors that influence the individual member loan size by using two-stage least squared regression to tackle the issue of endogeneity.
Evolving from a rum state: Australia's alcohol consumption
Authors: Anderson Kym
Published: July 2020

Europeans settlers in the Australian colonies had a reputation of being heavy drinkers. Rum dominated during the first few decades, followed by beer. It took until the 1970s before Australia's annual per capita consumption of wine exceeded 10 L, and even then, wine represented only one‐fifth of national alcohol consumption. But over the next two decades, per capita wine consumption nearly trebled and beer consumption shrunk – the opposite of what happened to global alcohol consumption shares. This paper draws on newly compiled data sets to (i) reveal that Australia was not much more alcoholic than Britain or southern Europe during the nineteenth century and (ii) help explain why it took so long for a consumer interest in wine to emerge in Australia.