Miracle-dz/UniFAR-FTUs huggingface dataset:
Assembly processes of bacterial and fungal community differ between desert and oasis habitats in an arid inland river basin, northwest China

Oasis habitat play a critical role in arid areas, yet little is known about microbial community assembly processes and its differences in oasis and desert habitat in arid inland river basin. Herein, using 21 and 18 sample data respectively collected from oasis and desert habitats at the lower reaches of Heihe River, northwest China, we examined the assembly processes of soil bacterial and fungal communities and revealed the driving factors affecting the relative contributions of deterministic and stochastic processes. We found that deterministic processes, especially variable selection, dominated bacterial community assembly in oasis habitat, while stochastic processes were more important in desert habitat. By contrast, stochastic processes dominated fungal community assembly both in oasis and desert habitats, in which dispersal limitation played a more important role. Environmental (e.g. EC) and biotic factors (microbial species associations) significantly mediated the bacterial community assembly. However, both environmental and biotic factors had little/no effects on fungal community assembly. This study highlights the soil microbial community assembly is habitat- and taxon-dependent, and environmental (EC) and biotic factors play an important role in regulating these assembly processes in arid inland river basin.
Longitudinal development of thalamocortical functional connectivity in 22q11.2 deletion syndrome

Background 22q11.2 Deletion Syndrome (22qDel) is a genetic Copy Number Variant (CNV) that strongly increases risk for schizophrenia and other neurodevelopmental disorders. Disrupted functional connectivity between the thalamus and somatomotor/frontoparietal cortex has been implicated in cross-sectional studies of 22qDel, idiopathic schizophrenia, and youth at clinical high risk (CHR) for psychosis. Here, we use a novel functional atlas approach to investigate longitudinal age-related changes in network-specific thalamocortical functional connectivity (TCC) in 22qDel and typically developing (TD) controls. Methods TCC was calculated for nine functional networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) scans collected from n=65 22qDel participants (63.1% female) and n=69 demographically matched TD controls (49.3% female), ages 6 to 23 years. Analyses included 86 longitudinal follow-up scans. Non-linear age trajectories were characterized with general additive mixed models (GAMMs). Results In 22qDel, TCC in the frontoparietal network increases until approximately age 13, while somatomotor and cingulo-opercular TCC decrease from age 6 to 23. In contrast, no significant relationships between TCC and age were found in TD controls. Somatomotor connectivity in 22qDel is significantly higher than TD in childhood, but lower in late adolescence. Frontoparietal TCC shows the opposite pattern. Conclusions 22qDel is associated with aberrant development of functional network connectivity between the thalamus and cortex. Younger individuals with 22qDel have lower frontoparietal connectivity and higher somatomotor connectivity than controls, but this phenotype may normalize or partially reverse by early adulthood. Altered maturation of this circuitry may underlie elevated neuropsychiatric disease risk in this syndrome.
A synthetic biology approach to assemble and reboot clinically relevant Pseudomonas aeruginosa tailed phages

The rise in frequency of antibiotic resistance has made bacterial infections, specifically Pseudomonas aeruginosa, a cause for greater concern. Phage therapy is a promising solution that uses naturally isolated phages to treat bacterial infections. Ecological limitations, which stipulate a discrete host range and the inevitable evolution of resistance, may be overcome through a better understanding of phage biology and the utilization of engineered phages. In this study, we developed a synthetic biology approach to construct tailed phages that naturally target clinically-relevant strains of Pseudomonas aeruginosa. As proof of concept, we successfully cloned and assembled the JG024 and DMS3 phage genomes in yeast using transformation-associated recombination (TAR) cloning and rebooted these two phage genomes in two different strains of P. aeruginosa. We identified factors that affected phage reboot efficiency like the phage species or the presence of antiviral defense systems in the bacterial strain. We have successfully extended this method to two other phage species and observed that the method enables the reboot of phages that are naturally unable to infect the strain used for reboot. This research represents a critical step towards the construction of clinically-relevant, engineered P. aeruginosa phages. Importance Pseudomonas aeruginosa is a bacterium responsible for severe infections and a common major complication in cystic fibrosis. The use of antibiotics to treat bacterial infections has become increasingly difficult as antibiotic resistance has become more prevalent. Phage therapy is an alternative solution that is already being used in some European countries, but its use is limited by narrow host range due to the phage receptor specificity, the presence of antiviral defense systems in the bacterial strain, and the possible emergence of phage resistance. In this study, we demonstrate the use of a synthetic biology approach to construct and reboot clinically-relevant P. aeruginosa tailed phages. This method enables a significant expansion of possibilities through the construction of engineered phages for therapy applications.
Evidence for glutamate excitotoxicity that occurs before the onset of striatal cell loss and motor symptoms in an ovine Huntington’s Disease model

Background Huntington’s disease (HD) is a neurodegenerative genetic disorder caused by an expansion in the CAG repeat tract of the huntingtin (HTT) gene resulting in a triad of behavioural, cognitive, and motor defects. Current knowledge of disease pathogenesis remains incomplete, and no disease course-modifying interventions are in clinical use. We have previously reported the development and characterisation of the OVT73 transgenic sheep model of HD. OVT73 captures an early prodromal phase of the disease with an absence of motor symptomatology even at 5-years of age and no detectable striatal cell loss. Methods To better understand the disease-initiating events we have undertaken a single nuclei transcriptome study of the striatum of an extensively studied cohort of 5-year-old OVT73 HD sheep and age matched wild-type controls. Results We have identified transcriptional upregulation of genes encoding N-methyl-D-aspartate (NMDA), α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) and kainate receptors in OVT73 medium spiny neurons, the cell type preferentially lost early in HD. This observation supports the glutamate excitotoxicity hypothesis as an early neurodegeneration cascade-initiating process. Moreover, we also observed the downstream consequences of excitotoxic stress, including a downregulation of transcription of components for the oxidative phosphorylation complexes. We also found that pathways whose activity has been proposed to reduce excitotoxicity, including the CREB family of transcription factors (CREB1, ATF2, ATF4 and ATF7) were transcriptionally downregulated. Conclusions To our knowledge, the OVT73 model is the first large mammalian HD model that exhibits transcriptomic signatures of an excitotoxic process in the absence of neuronal loss. Our results suggest that glutamate excitotoxicity is a disease-initiating process. Addressing this biochemical defect early may prevent neuronal loss and avoid the more complex secondary consequences precipitated by cell death.
SR-BI regulates the synergistic mast cell response by modulating the plasma membrane-associated cholesterol pool

The high-affinity IgE receptor FcεRI is the fundamental mast cell (MC) receptor responsible for the involvement of MCs in IgE-associated allergic disorders. Activation of the FcεRI is achieved via crosslinking by multivalent antigen (Ag) recognized by IgE, which results in degranulation and pro-inflammatory cytokine production. In comparison to the T and B cell receptor complexes, for which several co-receptors that orchestrate the initial signaling have been described, information is scarce about FcεRI-associated proteins. Additionally, it is not completely clear how FcεRI signaling synergizes with input from other receptors and how potential regulators affect this synergistic response. We aimed at identifying new regulators of FcεRI and found that the HDL receptor SR-BI (gene name: Scarb1/SCARB1) is expressed in MCs, functionally associates with FcεRI and regulates the local plasma membrane cholesterol content in cholesterol-rich plasma membrane nanodomains as shown by using the cholesterol-sensitive probe GFP-D4. This impacted on the activation of murine MCs upon co-stimulation of the FcεRI with different receptors known to synergize with FcεRI-signaling pathways. Amongst them we investigated the co-activation of the FcεRI with the receptor tyrosine kinase KIT, the IL-33 receptor and GPCRs activated by adenosine or PGE2. Scarb1-deficient bone marrow-derived MCs (BMMCs) showed reduced cytokine secretion in response to these co-stimulation conditions suggesting a role for plasma membrane-associated cholesterol regulating MC-driven inflammation. Mimicking Scarb1 deficiency by membrane cholesterol depletion employing MβCD, we identified PKB and PLCγ1 as cholesterol-sensitive signaling molecules activated downstream of FcεRI in BMMCs. Specifically, when murine MCs were stimulated with SCF and Ag in combination, PLCγ1 activation appeared to be drastically boosted and this could be mitigated by cholesterol depletion. Inhibiting SR-BI in BMMCs phenocopied this effect. Similarly, SR-BI inhibition also attenuated the synergistic response to PGE2 and anti-IgE in the human ROSAKIT WT mast cell line suggesting that SR-BI is a crucial regulator of synergistic MC activation by regulating the local plasma membrane cholesterol concentration.
A Lightweight Approach (BL-DAC) to Secure Storage Sharing in Cloud-IoT Environments

: The growing advent of the Internet of Things (IoT) users is driving the adoption of cloud computing technologies. The integration of IoT in the cloud enables storage and computational capabilities for IoT users. However, security has been one of the main concerns of cloud-integrated IoT. Existing work attempts to address the security concerns of cloud-integrated IoT through authentication, access control, and blockchain-based methods. However, existing frameworks are somewhat limited by scalability, privacy, and centralized structures. To mitigate the existing problems, we propose a blockchain-based distributed access control method for secure storage in the IoT cloud (BL-DAC). Initially, the BL-DAC performs decentralized authentication using the Quantum Neural Network Cryptography (QNNC) algorithm. IoT users and edge nodes are authenticated in the blockchain deployed by distributed Trusted Authorities (TAs) using multiple credentials. The user data is classified into sensitive and non-sensitive categories using the Enhanced Seagull Optimization (ESO) algorithm. Also, the authentication to access this data is performed by a decentralized access control method using smart contract policy. Sensitive user data is encrypted using the QNNC algorithm and stored in the private cloud. In contrast, non-sensitive data is stored in the public cloud, and IPFS is used to store data in a decentralized manner with high reliability. In addition, data security is improved by using a hierarchical blockchain which improves scalability by managing the multiple blockchains hierarchically and is lightweight using Proof of Authentication Consensus (PoAH). The BL-DAC is simulated and validated using the Network Simulator-3.26 simulation tool and validated. This work shows better results than the compared ones in terms of validation metrics such as throughput (26%), encryption time (19%), decryption time (16%), response time (15%), block validation time (31%), attack detection rate (16%), access control precision (13%), and scalability (28%).
Serum apolipoprotein A1 and B are associated with 6-month persistent and incident diabetic macular oedema in type 2 diabetes

Aims To investigate the associations of baseline apolipoprotein A1 (ApoA1) and B (ApoB) levels with persistent and incident diabetic macular oedema (DMO) after 6 months of follow-up. Methods This is a prospective cohort study of patients aged ≥30 years with untreated diabetic retinopathy. Examinations, fundus photography and spectral domain optical coherence tomography (SD-OCT) were assessed at baseline, 1, 3 and 6 months. Serum lipids and apolipoproteins were analysed at a pathology laboratory. DMO was confirmed using SD-OCT, classified as (1) incident DMO, (2) persistent DMO and (3) regressed DMO. Eye-specific data were used, controlling for covariates and cluster effect. Results We recruited 53 patients but only 38 completed the study [(62 eyes), 20 eyes (32.3%) with DMO]. Higher quartile of ApoA1 was associated with lower risk of persistent/incident DMO (p for trend 0.02), while higher ApoB/A1 was associated with higher risk of persistent/incident DMO (p for trend 0.02). Every 10 mg/dL increase in ApoA1 levels was associated with lower risk of persistent/incident DMO (OR 0.69; 95% CI 0.49 to 0.92; p value 0.016), whereas every 0.2 increase in ApoB/A1 was significantly associated with higher risk of persistent/incident DMO (OR 1.4; 95% CI 1.1 to 1.9; p value 0.013) at the end of the study. Conclusion Individuals with diabetes with higher ApoA1 had lower risk of persistent/incident DMO and those with higher ApoB/A1 had higher risk of persistent/incident DMO at the end of 6 months. These suggest that serum ApoA1 and ApoB/A1 levels may be important risk factors for DMO and could be predictive of persistent/incident DMO despite anti-vascular endothelial growth factor treatment.
Lytic/Lysogenic Transition as a Life-History Switch

The transition between lytic and lysogenic life cycles is the most important feature of the life history of temperate viruses. To explain this transition, an optimal life-history model is offered based a discrete-time formulation of phage/bacteria population dynamics that features infection of bacteria by Poisson sampling of virions from the environment. The time step is the viral latency period. In this model density-dependent viral absorption onto the bacterial surface produces virus/bacteria coexistence and density-dependence in bacterial growth is not needed. The formula for the transition between lytic and lysogenic phases is termed the “fitness switch”. According to the model, the virus switches from lytic to lysogenic when its population grows faster as prophage than as virions produced by lysis of the infected cells, and conversely for the switch from lysogenic to lytic. A prophage that benefits the bacterium it infects automatically incurs lower fitness upon exiting the bacterial genome, resulting in its becoming locked into the bacterial genome in what is termed here as a “prophage lock”. The fitness switch qualitatively predicts the ecogeographic rule that environmental enrichment leads to microbialization with a concomitant increase in lysogeny, fluctuating environmental conditions promote virus-mediated horizontal gene transfer, and prophage-containing bacteria can integrate into the microbiome of a eukaryotic host forming a functionally integrated tripartite holobiont. These predictions accord more with the “Piggyback-the-Winner” hypothesis than with the “Kill-the-Winner” hypothesis in virus ecology.
Harish-Chandra’s c-function; 50 years later

— We discuss different aspects of the c-function of HarishChandra with focus on its connection with the horospherical transform. I have known Vadim Schechtman for many years, but it was only a few years ago that I found out that we share two strong interests: surprises which it is possible yet to mine in works of great mathematicians and product-formulas for some special functions on Lie groups. Vadim has collected a very impressive exhibition of such formulas (see unpublished notes on his webpage). It starts with the product-formula for the c-function which Karpelevich and I found more than 50 years ago [19] in the beginning of my mathematical life. Vadim’s anniversary for me is a pleasant reason to talk about this old subject. Starting at least with Gauss (who continuously produced new proofs of the Fundamental Theorem of Algebra), old mathematicians like to return to subjects of their first mathematical love. Perhaps it has a similar nature to criminals, returning to the scene of the crime? What about the c-function, for the past several years, there were quite a few popular generalizations of the product-formula on arbitrary fields, but in these notes we will stay inside old fashioned real or complex considerations. (1) Departm. of Math., Hill Center, Rutgers University, 110 Frelinghysen Road, Piscataway, NJ 08854 gindikinath.rutgers. edu
ORCA: a comprehensive bioinformatics container environment for education and research

Abstract Summary The ORCA bioinformatics environment is a Docker image that contains hundreds of bioinformatics tools and their dependencies. The ORCA image and accompanying server infrastructure provide a comprehensive bioinformatics environment for education and research. The ORCA environment on a server is implemented using Docker containers, but without requiring users to interact directly with Docker, suitable for novices who may not yet have familiarity with managing containers. ORCA has been used successfully to provide a private bioinformatics environment to external collaborators at a large genome institute, for teaching an undergraduate class on bioinformatics targeted at biologists, and to provide a ready-to-go bioinformatics suite for a hackathon. Using ORCA eliminates time that would be spent debugging software installation issues, so that time may be better spent on education and research. Availability and implementation The ORCA Docker image is available at https://hub.docker. com/r/bcgsc/orca/. The source code of ORCA is available at https://github.com/bcgsc/orca under the MIT license.
The Role of Artificial Intelligence for The Architectural Plan Design: Automation in Decision-making

The growing use of artificial intelligence in the complexity of the world we live in continues to inflame the debates it has created in the fields of science, art and design. In the architectural sector, it is possible to observe the first tangible results of the implementation of new increasingly autonomous digital processes that support the design and approval of a project, although it is still at an embryonic stage. The new limits of the expansion of AI systems enabled by digital transformation require careful consideration of the impact of new technologies in redefining the role of the designer in decision making. Even if a full design action cannot be mentioned yet, it is predicted that artificial intelligence can develop into an algorithm that can learn the design action with new algorithms and new hardware in the future. In parallel with the development of artificial intelligence and technologies, the discipline of architecture will constantly transform and develop.
Action Classification in Human Robot Interaction Cells in Manufacturing: Moving Towards Mutual Performance Monitoring Capacity

Action recognition has become a prerequisite approach to fluent Human-Robot Interaction (HRI) due to a high degree of movement flexibility. With the improvements in machine learning algorithms, robots are gradually transitioning into more human-populated areas. However, HRI systems demand the need for robots to possess enough cognition. The action recognition algorithms require massive training datasets, structural information of objects in the environment, and less expensive models in terms of computational complexity. In addition, many such algorithms are trained on datasets derived from daily activities. The algorithms trained on non-industrial datasets may have an unfavorable impact on implementing models and validating actions in an industrial context. This study proposed a lightweight deep learning model for classifying low-level actions in an assembly setting. The model is based on optical flow feature elicitation and mobilenetV2-SSD action classification and is trained and assessed on an actual industrial activities’ dataset. The experimental outcomes show that the presented method is futuristic and does not require extensive preprocessing; therefore, it can be promising in terms of the feasibility of action recognition for mutual performance monitoring in real-world HRI applications. The test result shows 80% accuracy for low-level RGB action classes. The study's primary objective is to generate experimental results that may be used as a reference for future HRI algorithms based on the InHard dataset.
Contextualised Modelling for Effective Citation Function Classification

Citation function classification is an important task in scientific text mining. The past two decades have witnessed many computerised algorithms working on various citation function datasets tailored to various annotation schemes. Recently, deep learning has pushed the state of the art by a large margin. Several pitfalls exist. Due to annotation difficulty, data sizes, especially the minority classes, are often not big enough for training effective deep learning models. Being less discussed, most state-of-the-art deep learning solutions in fact generate a feature representation for the citation sentence or context, instead of modelling individual in-text citations. This is conceptually flawed as it is common to see multiple in-text citations with different functions in the same citation sentence. In addition, existing deep learning studies have only explored a rather limited design space of encoding citation and its surrounding context. This paper explored a wide range of modelling options based on SciBERT, the popular cross-disciplinary pre-trained scientific language model, and their performances on citation function classification, for the purpose of determining the most effective way of modeling citation and its context. To deal with the data size issue, we created a large-scale citation function dataset by mapping, merging and re-annotating six publicly available datasets from the computational linguistics domain by adapting Teufel et al.’s 12-class scheme. The best F1 scores we achieved were around 66.16%, 71.39% and 73.56% on a 11-class annotation scheme slightly adapted from Teufel et al.’s 12-class scheme, a reduced 7-class scheme by merging comparison functions, and Jurgens et al.’s 6-class scheme respectively. A useful observation is that there is no single best model that is superior for all functions, therefore the trained model variants allow for applications which emphasise on a specific type of or a specific group of citation functions.
Extracting Source Information From News Articles: Information Extraction

One of the factors influencing the credibility of news is source attribution. Ideally, news would be based on a balanced variety of sources. In this work we use spaCy1 and Python2 to identify sources of information cited in news articles and assign the sources to categories, as a first step in building software that assesses the balance and breadth of the sourcing in news articles. The preliminary testing of the software indicates that identification of the sources has a recall of 73% and accuracy of 95%, and the sources are categorized with overall accuracy of 78%.