bluuebunny/crossref_metadata_2025_split huggingface dataset:
Enhanced soil quality after forest conversion to vegetable cropland and tea plantation has contrasting effects on soil microbial structure and functions
Authors: Fan Lichao , Shao Guodong , Pang Yinghua , Dai Hongcui , Zhang Lan , Yan Peng , Zou Zhenhao , Zhang Zheng , Xu Jianchu , Zamanian Kazem , Dorodnikov Maxim , Li Xin , Gui Heng , Han Wenyan 
Published: August 2021

AbstractLand-use changes could potentially exert a strong influence on soil quality and soil microbial communities. Moreover, microbial taxa are also important drivers of soil ecological functions. However, the linkage between soil quality and soil microbial communities is in need of deeper understanding. In this study, we examined the effects of soil quality on microbial community structure and functions after forest conversion to vegetable cropland and tea plantations. Soil quality index was significantly increased after natural forest conversion to vegetable cropland and tea plantations. Soil bacterial beta diversity significantly correlated to soil quality, but the sensitivity of individual microbial groups varied in response to changes in soil quality. Higher soil quality promoted bacterial diversity in vegetable cropland but decreased it in tea plantations, which implied soil quality was a structural factor in bacterial community composition but had contrasting effects for croplands versus plantations. Agricultural management played a negative role in maintaining microbial interactions, as identified by the network analysis, and furthermore the analysis revealed key functions of the microbial communities. After land-use change, the abundance (e.g., level, intensity) of microbial N-cycling function increased in tea plantations but decreased in vegetable cropland. The abundance of C-cycling function featured an opposite trend. Higher level of N-fixation in tea plantations but the higher abundance of N-oxidation in vegetable cropland was demonstrated. Higher abundance of ammonia-oxidizing bacteria and ammonia-oxidizing archaea as identified by qPCR in vegetable cropland corroborated the FAPROTAX function prediction. Therefore, the key taxa of soil microbial communities and microbial functions were largely dependent on changes in soil quality and determined responses to specific agricultural management.
Genomic constraints to drought adaptation
Authors: Ahrens Collin W , Murray Kevin , Mazanec Richard A , Ferguson Scott , Bragg Jason , Jones Ashley , Tissue David T , Byrne Margaret , Borevitz Justin O , Rymer Paul D 
Published: August 2021

AbstractGlobal shifts in climate and precipitation patterns are altering the diversity and structure of forests. The ability for species to adapt is especially difficult for long lived foundation species with unknown genetic and trait diversity. We harnessed genomic, physiological, and climate data to determine adaptation constraints. We used denovo assembly and 6.5 million genomic variants with drought related traits from 432 individuals sourced from across the complete range of the foundation tree speciesCorymbia calophylla. We found genomic variants determining traits predominantly in gene regulatory regions. The ability for populations to adapt was limited by within population genetic diversity associated with traits, and epistatic interactions within traits and pleiotropic interactions among traits. Nevertheless, we could accurately predict adaptive traits using genomic and climate data to guide reforestation. Our study indicated that some populations may contain variation sufficient for the species to adapt to the effects of drought, while other populations will need increased variability from those sources.
Automated tracking reveals the social network of beach mice and their burrows
Authors: Bedford Nicole L. , Gable Jacob T. , Hu Caroline K. , Wooldridge T. Brock , Sokolov Nina A. , Lassance Jean-Marc , Hoekstra Hopi E. 
Published: August 2021

AbstractEvolutionary biologists have long sought to understand the selective pressures driving phenotypic evolution. While most experimental data come from the study of morphological evolution, we know much less about the ultimate drivers of behavioral variation. Among the most striking examples of behavioral evolution are the long, complex burrows constructed by oldfield mice (Peromyscus polionotusssp.). Yet how these mice use burrows in the wild, and whether burrow length may affect fitness, remains unknown. A major barrier to studying behavior in the wild has been the lack of technologies to continuously monitor – in this case, nocturnal and underground – behavior. Here, we designed and implemented a novel radio frequency identification (RFID) system to track patterns of burrow use in a natural population of beach mice. We combine RFID monitoring with burrow measurements, genetic data, and social network analysis to uncover how these monogamous mice use burrows under fully natural ecological and social conditions. We first found that long burrows provide a more stable thermal environment and have higher juvenile activity than short burrows, underscoring the likely importance of long burrows for rearing young. We also find that adult mice consistently use multiple burrows throughout their home range and tend to use the same burrows at the same time as their genetic relatives, suggesting that inclusive fitness benefits may accrue for individuals that construct and maintain multiple burrows. Our study highlights how new automated tracking approaches can provide novel insights into animal behavior in the wild.
Stabilizing proteins through saturation suppressor mutagenesis
Authors: Ahmed Shahbaz , Manjunath Kavyashree , Chattopadhyay Gopinath , Varadarajan Raghavan 
Published: August 2021

AbstractWhile there have been recent, transformative advances in the area of protein structure prediction, prediction of point mutations that improve protein stability remains challenging. It is possible to construct and screen large mutant libraries for improved activity or ligand binding, however reliable screens for mutants that improve protein stability do not exist, especially for proteins that are well folded and relatively stable. We demonstrate that incorporation of a single, specific destabilizing, (parent inactive) mutation into each member of a deep mutational scanning library followed by screening for suppressors, allows for robust and accurate identification of stabilizing mutations. When coupled to FACS sorting of a yeast surface display library of the bacterial toxin CcdB, followed by deep sequencing of sorted populations, multiple stabilizing mutations could be identified after a single round of sorting. Multiple libraries with different parent inactive mutations could be pooled and simultaneously screened to further enhance the accuracy of identification of stabilizing mutations. Individual stabilizing mutations could be combined to result in a multimutant with increase in thermal melting temperature of about 20 °C and enhanced tolerance to high temperature exposure. The method employs small library sizes and can be readily extended to other display and screening formats to rapidly isolate stabilized protein mutants.
A divide and conquer metacell algorithm for scalable scRNA-seq analysis
Authors: Ben-Kiki Oren , Bercovich Akhiad , Lifshitz Aviezer , Tanay Amos 
Published: August 2021

ABSTRACTScaling scRNA-seq to profile millions of cells is increasingly feasible. Such data is crucial for the construction of high-resolution maps of transcriptional manifolds. But current analysis strategies, in particular dimensionality reduction and two-phase clustering, offers only limited scaling and sensitivity to define such manifolds. Here we introduce Metacell-2, a recursive divide and conquer algorithm allowing efficient decomposition of scRNA-seq datasets of any size into small and cohesive groups of cells denoted as metacells. We show the algorithm outperforms current solutions in time, memory and quality. Importantly, Metacell-2 also improves outlier cell detection and rare cell type identification, as we exemplify by analysis of human bone marrow cell atlas and mouse embryonic data. Metacell-2 is implemented over the scanpy framework for easy integration in any analysis pipeline.
Topological morphogenesis of neuroepithelial organoids
Authors: Ishihara Keisuke , Mukherjee Arghyadip , Gromberg Elena , Brugués Jan , Tanaka Elly M. , Jülicher Frank 
Published: August 2021

AbstractAnimal organs exhibit complex topologies involving cavities and tubular networks, which underlie their form and function. However, how topology emerges during organ morphogenesis remains elusive. Here, we combine tissue reconstitution and quantitative microscopy to show that trans and cis epithelial fusion govern tissue topology and shape. These two modes of topological transitions can be regulated in neuroepithelial organoids, leading to divergent topologies. The morphological space can be captured by a single control parameter which is analogous to the reduced Gaussian rigidity of an epithelial surface. Finally, we identify a pharmacologically accessible pathway that regulates the frequency of trans and cis fusion, and demonstrate the control of organoid topology and shape. The physical principles uncovered here provide fundamental insights into the self-organization of complex tissues.
ATR facilitates the degradation of Api5 through the ubiquitin-proteasome pathway via FBXW2 to regulate apoptosis upon DNA damage
Authors: Sharma Virender Kumar , Islam Sehbanul , Borkar Janhavi , Mishra Sudiksha , Panda Debiprasad , Santra Manas K , Lahiri Mayurika 
Published: August 2021

SummaryApoptosis inhibitor 5 (Api5) is an inhibitor of apoptosis, which is found to be upregulated in several cancers and promotes invasion as well as metastasis. Over-expression of Api5 is positively co-related with poor survival of cancers and inhibition of DNA damage induced apoptosis in cancerous cells. Acetylation at lysine 251 (K251) on Api5 facilitates the stability of the protein and thus functionally provides resistance to cancer cells against chemotherapeutic or anti-cancerous agents. However, the regulation of Api5 upon DNA damage is not yet known. In this study, we demonstrate that Api5 undergoes degradation following DNA damage via the ubiquitin-proteasome system. Upon DNA damage, ATR was observed to phosphorylate Api5 at serine 138 which led to the cytoplasmic localisation of Api5. The E3-ubiquitin ligase, SCF-FBXW2 ubiquitinates Api5 leading to its proteasomal degradation.
Direct observation of Hsp90-induced compaction in a protein chain
Authors: Mashaghi Alireza , Moayed Fatemeh , Koers Eline J. , Kramer Günter , Mayer Matthias P. , Tans Sander J. 
Published: August 2021

SUMMARYThe chaperone Hsp90 is well known to undergo important conformational changes, which depend on nucleotide, co-chaperones, substrate interactions and post-translational modifications. Conversely, how the conformations of its unstable and disordered substrates are affected by Hsp90 is difficult to address experimentally, yet central to its function. Here, using optical tweezers and luciferase and glucocorticoid receptor substrates, we find that Hsp90 promotes local contractions in unfolded chains that drive their global compaction down to dimensions of folded states. This compaction has a gradual nature while showing small steps, is stimulated by ATP, and performs mechanical work against counteracting forces that expand the chain dimensions. The Hsp90 interactions suppress the formation of larger-scale folded, misfolded and aggregated structures. The observations support a model in which Hsp90 alters client conformations directly by promoting local intra-chain interactions while suppressing distant ones. We conjecture that chain compaction may be central to how Hsp90 protects unstable kinases and receptor clients, regulates their activity, and how Hsp90 cooperates with Hsp70.
Autologous human immunocompetent white adipose tissue-on-chip
Authors: Rogal Julia , Xu Raylin , Roosz Julia , Teufel Claudia , Cipriano Madalena , Eisler Wiebke , Weiss Martin , Schenke-Layland Katja , Loskill Peter 
Published: August 2021

AbstractObesity and associated diseases, such as diabetes, have reached epidemic proportions globally. In the era of ‘diabesity’ and due to its central role for metabolic and endocrine processes, adipose tissue (specifically white adipose tissue; WAT) has become a target of high interest for therapeutic strategies. To gain insights in cellular and molecular mechanisms of adipose (patho-)physiology, researchers traditionally relied on animal models sincein vitrostudies on human WAT are challenging due to the large size, buoyancy, and fragility of mature white adipocytes. Leveraging the Organ-on-Chip technology, we introduce a next-generation microphysiologicalin vitromodel of human WAT based on a tailored microfluidic platform featuring vasculature-like perfusion. The platform integrates a 3D tissue comprising all major WAT-associated cellular components in an autologous manner, including not only mature adipocytes but also organotypic endothelial barriers and stromovascular cells featuring tissue-resident innate immune cells, specifically adipose tissue macrophages. This microphysiological tissue model recapitulates pivotal WAT functions, such as energy storage and mobilization as well as endocrine and immunomodulatory activities. The combination of all individual cell types with extra cellular matrix-like hydrogels in a precisely controllable bottom-up approach enables the generation of a multitude of replicates from the same donors circumventing issues of inter-donor variability and paving the way for personalized medicine. Moreover, it allows to adjust the model’s degree of complexity to fit a specific purpose via a flexible mix- and-match approach with different cell component modules. This novel WAT-on-chip system constitutes a human-based, autologous and immunocompetentin vitromodel of adipose tissue that recapitulates almost full tissue heterogeneity. In the future, the new WAT-on-chip model can become a powerful tool for human-relevant research in the field of metabolism and its associated diseases as well as for compound testing and personalized- and precision medicine applications.
Extreme Heterogeneity in Genomic Differentiation between Phenotypically Divergent Songbirds: A Test of Mitonuclear Co-introgression
Authors: Nikelski Ellen , Rubtsov Alexander S. , Irwin Darren 
Published: August 2021

AbstractComparisons of genomic variation among closely related species often show more differentiation in mitochondrial DNA (mtDNA) and sex chromosomes than in autosomes, a pattern expected due to the differing effective population sizes and evolutionary dynamics of these genomic components. Yet, introgression can cause species pairs to deviate dramatically from general differentiation trends. The yellowhammer (Emberiza citrinella) and the pine bunting (E. leucocephalos) are hybridizing avian sister species that differ greatly in appearance and moderately in nuclear DNA, but that show no mtDNA differentiation. This mitonuclear discordance is best explained by adaptive mtDNA introgression—a process that can select for co-introgression at nuclear genes with mitochondrial functions (mitonuclear genes). To better understand the extent of mitonuclear discordance and characterize nuclear differentiation patterns in this system, we investigated genome-wide differentiation between allopatric yellowhammers and pine buntings and compared it to what was seen previously in mtDNA. We found significant nuclear differentiation that was highly heterogeneous across the genome, with a particularly wide differentiation peak on the sex chromosome Z. We further tested for preferential introgression of mitonuclear genes and found statistical support for this process in yellowhammers. A role for mitonuclear coevolution in this system is supported by a stronger signal of co-introgression in genes coding for subunits of the mitoribosome and electron transport chain complexes. Altogether, our study emphasizes the extreme variation seen in differentiation across genomic components and study systems as well as highlights the ramifications of this variation in species evolution.
Today's wastage is tomorrow's shortage: a systematic literature review on food waste from social responsibility perspective
Authors: Chang Yee-Shan , Lim Xin-Jean , Cheah Jun-Hwa 
Published: August 2021

PurposeRecognizing food waste as a global issue, it has attracted scholars to conduct numerous relevant studies in the area. Growing concerns about the social and environmental impacts have intensified food waste attention to the practice of socially responsible consumption. The purpose of this study is to undertake a review of existing knowledge to edify and provide a platform for future research.Design/methodology/approachThe present study retrieved and reviewed a total of 76 articles from Web of Science (WoS) database, which were published from 2011 to 2020 in food and nutrition related journals from social responsibility perspective.FindingsIn accordance with the proposed research questions, the findings demonstrate the publication trend, distribution of article sources, research regions, thematic classification, theoretical and methodology framework. The findings also reveal research gaps in the literature and facilitate scholars with extensive gap-specific research directions to explore.Research limitations/implicationsThis review is limited in its consideration of articles from the (WoS) database and focused in food or nutrition related journals.Originality/valueBy mapping what is known in the current state of food waste research, this study identifies existing gaps and opportunities for future research in this area.
Green cost premium for attaining energy-efficiency rating in Nigeria's hot-humid residential buildings
Authors: Ekung Samuel , Odesola Isaac Abiodun , Adewuyi Timothy 
Published: August 2021

PurposeThe dearth of green standards (GS) in sub-Saharan Africa is alarming and the green cost premiums (GCP) in seeking certification in emerging markets are scanty. This paper studied the Building Energy-Efficiency Code of Nigeria (BEEC) and estimated the potential GCPs associated with the various energy-efficiency ratings.Design/methodology/approachThe study retrofitted 150 conventional residential bungalow and maisonette buildings using BEEC's energy-efficiency interventions and performed analytical estimating of the retrofitted designs. The mean cost premium associated with each energy-efficiency intervention is presented as well as their financial benefits and payback periods. The benefits are achievable financial-savings due to a reduction in energy consumption and savings in electricity payment estimated from the average energy demands of each building. An independent t-test was further conducted to determine the cost differential between energy-efficient design (ED) and conventional design over a five-year period.FindingsThe potential GCPs and their payback periods are actually less than feared. The study showed that less than 5% and 21% extra funding would be required to achieve 1 to 4-Star and 5-Star energy-efficiency ratings involving passive design interventions and photovoltaic systems. Passive and active design interventions produced a financial savings of $8.08/m2 in electricity payment and $2.84/m2 per annum in energy consumption reduction. The financial-savings ($10.92/m2) was objective to pay-off the GCPs in less than four years. The independent t-test analysis showed the cost of ED is more economical after four years into the project lifecycle.Originality/valueThe research provides cost benchmarks for navigating cost planning and budgetary decisions during ED implementation and births a departure point for advancing energy-efficient construction in developing markets from the rational economic decision perspective.
CDEC: a constrained deep embedded clustering
Authors: Amirizadeh Elham , Boostani Reza 
Published: August 2021

PurposeThe aim of this study is to propose a deep neural network (DNN) method that uses side information to improve clustering results for big datasets; also, the authors show that applying this information improves the performance of clustering and also increase the speed of the network training convergence.Design/methodology/approachIn data mining, semisupervised learning is an interesting approach because good performance can be achieved with a small subset of labeled data; one reason is that the data labeling is expensive, and semisupervised learning does not need all labels. One type of semisupervised learning is constrained clustering; this type of learning does not use class labels for clustering. Instead, it uses information of some pairs of instances (side information), and these instances maybe are in the same cluster (must-link [ML]) or in different clusters (cannot-link [CL]). Constrained clustering was studied extensively; however, little works have focused on constrained clustering for big datasets. In this paper, the authors have presented a constrained clustering for big datasets, and the method uses a DNN. The authors inject the constraints (ML and CL) to this DNN to promote the clustering performance and call it constrained deep embedded clustering (CDEC). In this manner, an autoencoder was implemented to elicit informative low dimensional features in the latent space and then retrain the encoder network using a proposed Kullback–Leibler divergence objective function, which captures the constraints in order to cluster the projected samples. The proposed CDEC has been compared with the adversarial autoencoder, constrained 1-spectral clustering and autoencoder + k-means was applied to the known MNIST, Reuters-10k and USPS datasets, and their performance were assessed in terms of clustering accuracy. Empirical results confirmed the statistical superiority of CDEC in terms of clustering accuracy to the counterparts.FindingsFirst of all, this is the first DNN-constrained clustering that uses side information to improve the performance of clustering without using labels in big datasets with high dimension. Second, the author defined a formula to inject side information to the DNN. Third, the proposed method improves clustering performance and network convergence speed.Originality/valueLittle works have focused on constrained clustering for big datasets; also, the studies in DNNs for clustering, with specific loss function that simultaneously extract features and clustering the data, are rare. The method improves the performance of big data clustering without using labels, and it is important because the data labeling is expensive and time-consuming, especially for big datasets.