Miracle-dz/UniFAR-FTUs huggingface dataset:
Excretion from the benthic macrofauna covers little of spring nutrient uptake in a small Danish forest stream

ABSTRACT Freshwater consumers play a role in ecosystem nutrient cycling and dynamics by excreting metabolic waste products containing ammonium (NH4) and phosphate (PO4). In this study we quantified excretion of a selection of macroinvertebrate taxa and measured nutrient uptake metrics in a small Danish forest stream. Macroinvertebrate excretion was measured in situ from February to April 2019, and uptake metrics were measured using short-term nutrient addition experiments on 2 dates in March and June. To calculate area-specific excretion, quantitative benthic samples were taken on the 3 dominating substratum types: sand, gravel-pebbles, and leaf packs. No significant temperature dependency of invertebrate excretion was found over the measured range (0.3–10.3 °C). Average NH4-N excretion rates ranged from 0.027 (Ptychoptera spp.) to 0.097 µg mg−1 h−1 (Paraleptophlebia submarginata) while PO4-P excretion ranged from 0.0077 (Ptychoptera spp. and Nemoura spp.) to 0.0261 µg mg−1 h−1 (P. submarginata). The uptake length increased from 75 to 117 m for NH4 and from 28 to 71 m for PO4 from March to June. Uptake velocity, by contrast, decreased from March (0.63 m h−1 for NH4 and 1.72 m h−1 for PO4) to June (0.13 m h−1 for NH4 and 0.22 m h−1 for PO4). The estimated contribution from excretion from the entire benthic macrofauna to whole-stream net uptake was 3.4% and 8.9% of NH4 and 0.5% and 2.0% of PO4 in March and June, respectively, suggesting that excretion from benthic macroinvertebrates plays a minor role in meeting whole-stream nutrient demand.
Tucker visual search-based hybrid tracking model and Fractional Kohonen Self-Organizing Map for anomaly localization and detection in surveillance videos

ABSTRACT Anomaly detection (AD) in video is a challenging task employed in the intelligent video surveillance applications. This paper presents a technique for localizing and detecting anomalies in surveillance videos by proposing hybrid tracking model and Fractional Kohonen Self-Organizing Map (FKSOM). At first, the objects in the initial frames are detected by extracting the background and comparing with the succeeding frames. Then, a tracking model is developed to track the objects in the frame. Further, the features, such as object shape, speed, energy, correlation, and homogeneity, are extracted in the feature extraction process. Finally, the proposed FKSOM algorithm performs AD by identifying anomalous and normal events in the frame. The performance of the proposed technique is evaluated using the metrics, such as Multiple Object Tracking Precision (MOTP), accuracy, sensitivity, and specificity, where it obtains MOTP of 0.9895 with an average accuracy of 0.9339, the sensitivity of 0.9288 and specificity of 1.
Systemic Considerations in Child Development and the Pursuit of Racial Equality in the United States

In this commentary on Lewis’ (2021) article in Psychological Inquiry, we expand on ways that both systemic and interpersonal contexts contribute to and uphold racial inequalities, with a particular focus on research on child development and socialization. We also discuss the potential roadblocks that may undermine the effectiveness of Lewis’ (2021) recommended strategy of relying on experts as a driving force for change. We conclude by proposing additional strategies for pursuing racial equality that may increase the impact of experts, such as starting anti-racist socialization early in development, family-level interventions, and teaching people about racial injustices and their connections to systemic racism.
Compression and flexural properties of plantation-grown Eucalyptus pellita in Borneo, Malaysia. Potential for structural timber end use

ABSTRACT Plantation-grown Eucalyptus pellita in Sabah, Malaysia, was analysed for compression and flexural properties to assess the potential for solid-timber and engineered wood product end uses. It is necessary to consider not only the volume of wood produced in a plantation but also the wood quality, particularly those aspects important for end-product performance. Tree volume is readily measured from height and diameter at breast height using appropriate form factors. This paper discusses the compression, strength and stiffness of E. pellita compared with tropical hardwood species, and variation within trees and at different ages (7–23 years). Small clearwood test samples obtained from radial positions within log heights were subjected to analysis of compression parallel to grain and three-point bending according to ISO 13061-4, 2014. Results indicate that plantation-grown E. pellita in the tropics has potential for structural-use applications. The average basic density across the trials was 658 kg m−3, while the bending strength for all trials was in the range of 11.7–15.5 GPa for modulus of elasticity and 96.3−120.1 MPa for modulus of rupture, and the compression strength parallel to grain ranged from 52.3 to 67.8 MPa. Mean mechanical strength increased from pith to bark and from the butt log to the top log. Because the wood-processing sector in Malaysia is transitioning from reliance on a harvest of mixed tropical hardwood towards plantation-grown species, these results indicate that plantation-grown E. pellita meets the structural requirements of strength and stiffness.
Membrane-assisted assembly and selective autophagy of enteroviruses

Enteroviruses are non-enveloped positive-sense RNA viruses that cause diverse diseases in humans. Their rapid multiplication depends on remodeling of cytoplasmic membranes for viral genome replication. It is unknown how virions assemble around these newly synthesized genomes and how they are then loaded into autophagic membranes for release through secretory autophagy. Here, we use cryo-electron tomography of infected cells to show that poliovirus assembles directly on replication membranes. Pharmacological untethering of capsids from membranes abrogates RNA encapsidation. Our data directly visualize a membrane-bound half-capsid as a prominent virion assembly intermediate. Assembly progression past this intermediate depends on the class III phosphatidylinositol 3-kinase VPS34, a key host-cell autophagy factor. On the other hand, the canonical autophagy initiator ULK1 is shown to restrict virion production since its inhibition leads to increased accumulation of virions in vast intracellular arrays, followed by an increased vesicular release at later time points. Finally, we identify multiple layers of selectivity in virus-induced autophagy, with a strong selection for RNA-loaded virions over empty capsids and the segregation of virions from other types of autophagosome contents. These findings provide an integrated structural framework for multiple stages of the poliovirus life cycle.
Impact of Essential Genes on the Success of Genome Editing Experiments Generating 3,313 New Genetically Engineered Mouse Lines

The International Mouse Phenotyping Consortium (IMPC) systematically produces and phenotypes mouse lines with presumptive null mutations to provide insight into gene function. The IMPC now uses the programmable RNA-guided nuclease Cas9 for its increased capacity and flexibility to efficiently generate null alleles in the C57BL/6N strain. In addition to being a valuable novel and accessible research resource, the production of 3,313 knockout mouse lines using comparable protocols provides a rich dataset to analyze experimental and biological variables affecting in vivo gene engineering with Cas9. Mouse line production has two critical steps – generation of founders with the desired allele and germline transmission (GLT) of that allele from founders to offspring. A systematic evaluation of the variables impacting success rates identified gene essentiality as the primary factor influencing successful production of null alleles. Collectively, our findings provide best practice recommendations for using Cas9 to generate alleles in mouse essential genes, many of which are orthologs of genes linked to human disease.
DiviSSR: Simple arithmetic for efficient identification of tandem repeats

Numerical or vector representations of DNA sequences have been applied for identification of specific sequence characteristics and patterns which are not evident in their character (A, C, G, T) representations. These transformations often reveal a mathematical structure to the sequences which can be captured efficiently using established mathematical methods. One such transformation, the 2-bit format, represents each nucleotide using only two bits instead of eight for efficient storage of genomic data. Here we describe a mathematical property that exists in the 2-bit representation of tandemly repeated DNA sequences. Our tool, DiviSSR (pronounced divisor), leverages this property and subsequent arithmetic to achieve ultrafast and accurate identification of tandem repeats. DiviSSR can process the entire human genome in ∼30s, and short sequence reads at a rate of >1 million reads/s on a single CPU thread. Our work also highlights the implications of using simple mathematical properties of DNA sequences for faster algorithms in genomics. Availability DiviSSR can be installed directly using python pip. The source code and documentation of DiviSSR are available at https://github.com/avvaruakshay/divissr.git.
In vivo reprogramming of murine host immune response genes following Leishmania major infection

Leishmania parasites cause cutaneous leishmaniasis (CL), a pathologic disease characterized by disfiguring, ulcerative skin lesions. Both parasite and host gene expression following infection with various Leishmania species has been investigated in vitro, but global transcriptional analysis following L. major infection in vivo is lacking. Thus, we conducted a comprehensive transcriptomic profiling study combining bulk RNA sequencing (RNA-Seq) and single-cell RNA sequencing (scRNA-Seq) to identify global changes in gene expression in vivo following L. major infection. Bulk RNA-Seq analysis revealed that host immune response pathways like the antigen processing and presentation pathway were significantly enriched amongst differentially expressed genes (DEGs) upon infection, while ribosomal pathways were significantly downregulated in infected mice compared to naive controls. scRNA-Seq analyses revealed cellular heterogeneity including distinct resident and recruited cell types in the skin following murine L. major infection. Within the individual immune cell types, several DEGs indicative of many interferon induced GTPases and antigen presentation molecules were significantly enhanced in the infected ears including macrophages (Gbp2, H2-K1, H2-Aa, H2-Ab1), resident macrophages (H2-K1, H2-D1, Gbp4, Gbp8, Gbp2), and inflammatory monocytes (Gbp2, Gbp5, Gbp7, Gbp3). Ingenuity Pathway Analysis of scRNA-Seq data indicated the antigen presentation pathway was increased with infection, while EIF2 signaling is the top downregulated pathway followed by eIF4/p70S6k and mTOR signaling in multiple cell types including macrophages, BECs, and LECs. Altogether, this transcriptomic profile highlights known recruitment of myeloid cells to lesions and recognizes a previously undefined role for EIF2 signaling in murine L. major infection in vivo. Significance statement Leishmania major cause cutaneous leishmaniasis, which is characterized by disfiguring, ulcerative skin lesions. Here, we show murine L. major-directed reprogramming of the host transcriptome in vivo. Our bulk RNA-Seq analyses revealed upregulation of antigen processing and presentation pathway, while the host ribosomal pathway was downregulated following L. major infection. Similarly, scRNA-Seq analyses revealed the upregulation of transcripts responsible for antigen presentation and host defense proteins like guanylate binding proteins (GBPs) alongside the downregulation of EIF2 signalling at the site of L. major infection. Overall, our transcriptomic dataset not only provides the comprehensive list of gene expression at the single-cell resolution, and highlights a previously undefined role for EIF2 signalling during murine L. major infection in vivo.
Inter-species microbiota transplantation recapitulates microbial acquisition and persistence in mosquitoes

Background Mosquitoes harbor microbial communities that play important roles in their growth, survival, reproduction, and ability to transmit human pathogens. Microbiome transplantation approaches are often used to study host-microbe interactions and identify microbial taxa and assemblages associated with health or disease. However, no such approaches have been developed to manipulate the microbiota of mosquitoes. Results Here, we developed an approach to transfer entire microbial communities between mosquito cohorts. We undertook transfers between (Culex quinquefasciatus to Aedes aegypti) and within (Ae. aegypti to Ae. aegypti) species to validate the approach and determine the number of mosquitoes required to prepare donor microbiota. After the transfer, we monitored mosquito development and microbiota dynamics throughout the life cycle. Typical holometabolous lifestyle-related microbiota structures were observed, with higher dynamics of microbial structures in larval stages, including the larval water, and less diversity in adults. Microbiota diversity in recipient adults was also more similar to the microbiota diversity in donor adults. Conclusions This study provides the first evidence for successful microbiome transplantation in mosquitoes. Our results highlight the value of such methods for studying mosquito-microbe interactions and lay the foundation for future studies to elucidate the factors underlying microbiota acquisition, assembly, and function in mosquitoes under controlled conditions.
Ataxia-linked SLC1A3 mutations alter EAAT1 chloride channel activity and glial regulation of CNS function

Glutamate is the predominant excitatory neurotransmitter in the mammalian central nervous system (CNS). Excitatory Amino Acid Transporters (EAATs) regulate extracellular glutamate by transporting it into cells, mostly glia, to terminate neurotransmission and to avoid neurotoxicity. EAATs are also chloride (Cl−) channels, but the physiological role of Cl− conductance through EAATs is poorly understood. Mutations of human EAAT1 (hEAAT1) have been identified in patients with episodic ataxia type 6 (EA6). One mutation showed increased Cl− channel activity and decreased glutamate transport, but the relative contributions of each function of hEAAT1 to mechanisms underlying the pathology of EA6 remain unclear. Here we investigated the effects of five additional EA6-related mutations on hEAAT1 function in Xenopus laevis oocytes, and on CNS function in a Drosophila melanogaster model of locomotor behavior. Our results indicate that mutations with decreased hEAAT1 Cl− channel activity and functional glutamate transport can also contribute to the pathology of EA6, highlighting the importance of Cl− homeostasis in glial cells for proper CNS function. We also identified a novel mechanism involving an ectopic sodium (Na+) leak conductance in glial cells. Together, these results strongly support the idea that EA6 is primarily an ion channelopathy of CNS glia.
Anomalous Frictional Behaviors of Ir and Au Tips Sliding on Graphene/Ni(111) Substrate: Density Functional Theory Calculations

The atomic force microscope (AFM) provides a facilitating tool to investigate the atomic-scale friction properties of surfaces through the sliding of the scanning tip; therefore, the interaction between the tip and the surface should play an important role to determine the frictional behaviors. In this study, density functional theory (DFT) calculations have been carried out to perform the pushing down processes of a tip (10 atom Ir or Au tip) on the top, hollow, and bridge sites of the graphene/Ni(111) substrate. The calculation results indicate that the interactions between the tips and the graphene/Ni(111) substrate influence the adsorption energy remarkably, leading to the sequence of bridge < top < hollow for Ir and Au tips, which is totally different from the adsorption energy of an inert Ar atom, following the sequence of hollow < bridge < top. The strong interactions between the (Ir or Au) tip and the graphene/Ni(111) substrate will introduce novel frictional properties into the system, and an ano.
On Blind Frame Synchronization of LDPC Codes

We consider in this letter the problem of blind frame synchronization of Low Density Parity-Check (LDPC) codes. We present and compare new methods of blind frame synchronization based on the calculation of Likelihood functions of the syndrome. Simulation results show that the four proposed synchronization techniques are effective once applied to LDPC codes. The method that involves the maximization of the Likelihood Difference (LD) function of the parity-checks, presents the best results in terms of probability of false synchronization.
Prediction of Bearings Remaining Useful Life Across Working Conditions based on Transfer Learning and Time Series Clustering

Recently, data-driven remaining useful life (RUL) prediction has become a promising tool in prognostics and health management for rolling bearings. In many actual applications, however, it is not easy to collect whole-life degradation data of bearings, while training with an insufficient amount of data would result in a biased RUL prediction model. If the monitoring data historically accumulated under different working conditions are introduced to facilitate model training, it probably tends to get inadequate prediction performance due to the violation of the precondition of independent and identical distribution (i.i.d.). To solve this problem, a new bearing RUL prediction approach is proposed by utilizing the transfer learning strategy. First, a new time series clustering algorithm is proposed to exploit clusters of different degradation series. By integrating phase space warping and dynamic time warping, this algorithm is able to measure the similarity by using global degradation information and then get better clustering performance. Second, a new temporal domain adaptation method is proposed to obtain the pivot feature set of different bearings based on meta-degradation information which is represented by the principal curve of each cluster. Finally, support vector machine is run to establish the prediction model by means of these features, and RUL of the target bearing can be predicted via the same feature adaptation. A theoretical analysis is also provided to prove that the proposed approach has an upper bound of information loss in the transfer process. Experimental results on the IEEE PHM challenge 2012 dataset demonstrate the effectiveness of the proposed approach.
PM2.5 prevention and control strategy and analysis of clean energy utilization

With the economy developing, the environment problems, especially about air quality, have been a hit problem. In the PM2.5 source analysis, coal combustion is one of the important factors in the formation of PM2.5 local source. In this paper, we build network model with PM2.5 data of 366 cities in China from January 2015 to June 2017. Specifically, we construct a symmetric correlation network by partial correlation coefficient and an asymmetric information transportation network by transfer entropy. Then the spatial and temporal characteristics of PM2.5 are studied through time-variant complex networks. Structural control theory of complex networks helps to find the control node sets, which provide suggestions to prevent PM2.5 pollution. Further, we analyze the national power data and the correlation analysis shows the clean energy ratio and PM2.5 concentration are strongly correlated. Finally, control strategy of clean energy for PM2.5 is provided.
Research on Fault Identification of the Transmission Line Based on Improved Convolutional Neural Network

In view of the high false alarm rate of the power grid transmission line warning system and the reliance on the post-analysis of operation and maintenance personnel, a fault identification model of the power grid transmission line based on an improved convolutional neural network (CNN) is proposed. Firstly, preprocess the current time series data of power grid transmission lines. Secondly, improve the convolutional neural network through dual-channel fusion, multi-layer convolution and pooling, and combine the batch normalization method in the convolutional layer to separately detect short circuit faults in the line feature extraction is performed on the false alarm data, and then classified and identified through the soft-max classifier to construct an intelligent and efficient fault identification model, which effectively reduces the false alarm rate. Finally, the actual data of the State Grid Dispatching Center is used to verify the effectiveness of the proposed method.
Resilience evaluation of distribution network considering fault repair

Resilience is used to assess the ability to maintain and recover system performance in an extreme weather, which is one of the most important performance indicators of smart grid. A model for evaluating the hurricane resilience of distribution network considering fault repair is proposed. First, the difference between resilience and reliability is described. Second, to perfect the resilience evaluation and analysis method of distribution systems, a component failure probability model under hurricane condition is built, the latin hypercube sampling is used to reduce the failure scenes and a distribution network restoration model considering fault repair is established. Finally, a simulation verifies the validity and accuracy of the proposed method in this paper.
DeepHerb: A Vision Based System for Medicinal Plants using Xception features

The conservation of biodiversity is crucial as many plant species are critically under extinction. The traditional medicinal system, an alternative to synthetic drugs, promote healthy living and mainly depends on the wide repository of plants. A vision-based automatic medicinal plant identification system is proposed using different neural network techniques in computer vision and deep learning. The challenge lies in the unavailability of the medicinal herb dataset. The paper showcases a novel medicinal leaf dataset entitled DeepHerb dataset comprising of 2515 leaf images from 40 varied species of Indian herbs. The efficacy of the dataset is revealed by comparing pre-trained deep convolution neural network architectures such as VGG16, VGG19, InceptionV3 and Xception. The work concentrates on adopting the transfer learning technique on the pre-trained models to extract features and classify using Artificial Neural Network (ANN) and Support Vector Machine (SVM). The SVM hyperparameters are tuned further by Bayesian optimization to achieve a better performance model. The proposed DeepHerb model learned from Xception and ANN outperformed by 97.5% accuracy. A cross-platform mobile application entitled HerbSnap developed integrating the DeepHerb model identifies the herb image with a prediction time of 1 second per image and reveals the pertinent details of herbs from the database. This research will further focus on expanding the dataset to benefit stakeholders and thus, enriches society with the knowledge of herbs and their medicinal properties.