Miracle-dz/UniFAR-FTUs huggingface dataset:
AlphaFold2 fails to predict protein fold switching

AlphaFold2 has revolutionized protein structure prediction by leveraging sequence information to rapidly model protein folds with atomic-level accuracy. Nevertheless, previous work has shown that these predictions tend to be inaccurate for structurally heterogeneous proteins. To systematically assess factors that contribute to this inaccuracy, we tested AlphaFold2’s performance on 98 fold-switching proteins, which assume at least two distinct-yet-stable secondary and tertiary structures. Topological similarities were quantified between five predicted and two experimentally determined structures of each fold-switching protein. Overall, 94% of AlphaFold2 predictions captured one experimentally determined conformation but not the other. Despite these biased results, AlphaFold2’s estimated confidences were moderate-to-high for 74% of fold-switching residues, a result that contrasts with overall low confidences for intrinsically disordered proteins, which are also structurally heterogeneous. To investigate factors contributing to this disparity, we quantified sequence variation within the multiple sequence alignments used to generate AlphaFold2’s predictions of fold-switching and intrinsically disordered proteins. Unlike intrinsically disordered regions, whose sequence alignments show low conservation, fold-switching regions had conservation rates statistically similar to canonical single-fold proteins. Furthermore, intrinsically disordered regions had systematically lower prediction confidences than either fold-switching or single-fold proteins, regardless of sequence conservation. These results suggest that AlphaFold2’s high prediction confidences for fold switchers stem from the assumption that proteins assume one dominant fold. Our results emphasize the need to look at protein structure as an ensemble and suggest that systematic examination of fold-switching sequences may reveal propensities for multiple stable secondary and tertiary structures.
Parasite and pathogen prevalence in our closest animal companions is determined by accessibility of sanitation services

Despite the critical role parasites play in ecosystem functioning and their considerable influence on human society, little is known about their variations in abundance on a global scale. This gap in knowledge is amplified by a lack of holistic understanding on how the abundance of parasites of wildlife and humans varies across environmental and socioeconomic gradients, despite a need to integrate study of parasites across social and environmental spheres. Free-roaming companion animals (e.g., domestic cats (Felis catus) and dogs (Canis lupus familiaris)) share pathogens and have frequent contact with humans and wildlife. Thus, they are an effective model to understand how parasite and pathogen prevalence of humans and wildlife varies across environmental and socioeconomic gradients. Through a global systematic review and analysis of socioeconomic and environmental variables, including per capita GDP, income disparity, sanitation, biodiversity, island habitation, and latitude, we find that sanitation and island habitation best explained free-roaming companion animal parasite and pathogen prevalence. Sanitation was significantly associated with parasite and pathogen prevalence in free-roaming companion animals, such that for every 10% increase in the proportion of the human population with improved sanitation access, parasite and pathogen prevalence in free-roaming companion animals decreased by 12% (5-19%, 95% C.L.; p = 0.0023). Since companion animals share many parasites with humans and wildlife, these results suggest that actionable interventions to improve sanitation access could reduce parasite and pathogen exposure risks from companion animals to humans and wildlife. Significance Statement In addition to playing a critical role in ecosystem functioning, parasites also influence human health, behavior, and society. Further, parasites are also impacted by human activities, as much as by ecological phenomena in natural environments. Despite these dualities, little is known about their variations in abundance on a global scale across environmental and socioeconomic gradients. Using free-roaming companion animals (e.g., domestic cats (Felis catus) and dogs (Canis lupus familiaris)) as a model system, we find that access to safely managed sanitation services is strongly associated with parasite and pathogen prevalence. This finding underscores improvements to sanitation as an actionable One Health intervention that could reduce parasite and pathogen exposure risks from companion animals to humans and wildlife.
Ependymal cell maturation is heterogeneous and ongoing in the mouse spinal cord and dynamically regulated in response to injury

The spinal cord neural stem cell potential resides within the ependymal cells lining the central canal. These cells are, however, heterogeneous, and we know little about the biological diversity this represents. Here we use single-cell RNA-sequencing to profile adult mouse spinal cord ependymal cells. We uncover transcriptomes of known subtypes and a new mature ependymal cell state, that becomes more prominent with age. Comparison of ependymal cell transcriptomes from the brain and spinal cord revealed that ongoing cell maturation distinguishes spinal cord ependymal cells from their postmitotic brain counterparts. Using an ex vivo model of spinal cord injury, we show that ependymal cell maturation is reversible but also highly regulated. We revisit ependymal cell identities in adult human spinal cord and uncover evidence for their maturation and surprising ventralisation with age. This first in-depth characterisation of spinal cord ependymal cells paves the way to manipulation of distinct ependymal subtypes, provides insights into ependymal cell maturation dynamics and informs strategies for coaxing ependymal cell-driven spinal cord repair.
Rescue by gene swamping as a gene drive deployment strategy

Gene drives are genetic constructs that can spread deleterious alleles with potential application to population suppression of harmful species. Given that a gene drive can potentially spill over to other populations or even other species, control measures and fail-safes strategies must be considered. Gene drives are designed to generate a rapid demographic decline, while at the same time generating a dynamic change in the population’s genetics. Since these evolutionary and demographic processes are linked and are expected to occur at a similar time-scale during gene drive spread, feedback between these processes may significantly affect the outcome of deployment. To study this feedback and to understand how it affects gene drive spillovers, we developed a gene drive model that combines evolutionary and demographic dynamics in a two-population setting. The model demonstrates how feedback between evolutionary and demographic dynamics can generate additional outcomes to those generated by the evolutionary dynamics alone. We identify an outcome of particular interest, where the short-term suppression of the target population is followed by gene swamping and loss of the gene drive. This outcome could be useful for designing gene drive deployments that temporarily suppress the population, but ultimately do not remain in the population. Using our model, we demonstrate the robustness of this outcome to spillover and to the evolution of resistance, and suggest that it could be used as a fail-safe strategy for gene drive deployment.
Insertion of YFP at P5CS1 and AFL1 shows the potential, and potential complications, of gene tagging for functional analyses of stress-related proteins

Crispr/CAS9-enabled homologous recombination to insert a tag in frame with an endogenous gene can circumvent difficulties such as context-dependent promoter activity that complicate analysis of gene expression and protein accumulation patterns. However, there have been few reports examining whether such Gene Targeting/Gene Tagging (GT) can alter expression of the target gene. The enzyme encoded by Δ1-pyrroline-5-carboxylate synthetase 1 (P5CS1) is key for stress-induced proline synthesis and drought resistance, yet its expression pattern and protein localization have been difficult to assay. We used GT to insert YFP in frame with the 5’ or 3’ ends of the endogenous P5CS1 and At14a-Like 1 (AFL1) coding regions. Insertion at the 3’ end of either gene generated homozygous lines with expression of the gene-YFP fusion indistinguishable from the wild type allele. However, for P5CS1 this occurred only after selfing and advancement to the T5 generation allowed initial homozygous lethality of the insertion to be overcome. Once this was done, the GT-generated P5CS1-YFP plants revealed new information about P5CS1 localization and tissue-specific expression. In contrast, insertion of YFP at the 5’ end of either gene blocked expression. The results demonstrate that GT can be useful for functional analyses of genes that are problematic to properly express by other means but also show that, in some cases, GT can disrupt expression of the target gene. Summary statement Gene tagging of Arabidopsis thaliana P5CS1 and AFL1 shows the potential of GT for functional analysis of stress-related genes, but also provides examples of how GT can dramatically disrupt expression of the target gene.
Calcium homeostasis disruption initiates rapid growth after micro‐fragmentation in the scleractinian coral Porites lobata

Coral reefs are ecosystems under increasing threat from global climate change. Coral restoration is a tool for preserving biological and ecological function of coral reefs by mitigating coral loss and maintaining the structural integrity and complexity of reefs. To generate the necessary stock for coral restoration, larger coral colonies are usually fragmented to generate smaller specimens for outplanting, taking advantage of the high regenerative ability of corals. In this study, we utilized RNA-seq technology to understand the physiological responses of Porites lobata colonies to physical fragmentation and outplanting, which have thus far not been characterized. Our results demonstrate that P. lobata fragments undergoing physical injury recover through two distinct phases: rapid wound regeneration of the cut margins, followed by a slower growth phase that cements the colony to the substrate. Our study found rapid physiological responses to acute physical injury and outplanting in the coral host that involved significantly increased energy production, calcium homeostasis disruption, and Endoplasmic Reticulum (ER) stress leading to increased antioxidant expression and rates of protein turnover. Our results suggest that phosphoinositide-mediated acute calcium homeostasis disruption stimulates wound recovery processes in response to physical injury. Symbiont gene expression revealed extremely low gene differences in response to fragmentation, growth, and outplanting. These results provide insight into the physiological mechanisms that allow for rapid wound healing and stabilization in response to physical injury in corals.
Geneticin shows selective antiviral activity against SARS-CoV-2 by interfering with programmed −1 ribosomal frameshifting

SARS-CoV-2 is currently causing an unprecedented pandemic. While vaccines are massively deployed, we still lack effective large-scale antiviral therapies. In the quest for antivirals targeting conserved structures, we focused on molecules able to bind viral RNA secondary structures. Aminoglycosides are a class of antibiotics known to interact with the ribosomal RNA of both prokaryotes and eukaryotes and have previously been shown to exert antiviral activities by interacting with viral RNA. Here we show that the aminoglycoside geneticin is endowed with antiviral activity against all tested variants of SARS-CoV-2, in different cell lines and in a respiratory tissue model at non-toxic concentrations. The mechanism of action is an early inhibition of RNA replication and protein expression related to a decrease in the efficiency of the -1 programmed ribosomal frameshift (PRF) signal of SARS-CoV-2. Using in silico modelling, we have identified a potential binding site of geneticin in the pseudoknot of frameshift RNA motif. Moreover, we have selected, through virtual screening, additional RNA binding compounds, interacting with the same site with increased potency.
Human repair‐related Schwann cells adopt functions of antigen‐presenting cells in vitro

The plastic potential of Schwann cells (SCs) is increasingly recognized to play a role after nerve injury and in diseases of the peripheral nervous system. In addition, reports on the interaction between SCs and immune cells indicate their involvement in inflammatory processes. However, data about the immunocompetence of human SCs are primarily derived from neuropathies and it is currently unknown whether SCs directly regulate an adaptive immune response after nerve injury. Here, we performed a comprehensive analysis of the immunomodulatory capacities of human repair-related SCs (hrSCs), which recapitulate SC response to nerve injury in vitro. We used our previously established protocol for the culture of primary hrSCs from human peripheral nerves and analyzed the transcriptome, secretome, and cell surface proteins for signatures and markers relevant in innate and adaptive immunity, performed phagocytosis assays, and monitored T-cell subset activation in co-cultures with autologous human T-cells. Our findings show that hrSCs are highly phagocytic, which is in line with high MHCII expression. In addition, hrSCs express co-regulatory molecules, such as CD40, CD80, B7H3, CD58, CD86, HVEM, release a plethora of chemoattractants, matrix remodelling proteins and pro- as well as anti-inflammatory cytokines, and upregulate the T-cell inhibiting PD-L1 molecule upon pro-inflammatory stimulation with IFNγ. Furthermore, hrSC contact reduced the number and activation status of allogenic CD4+ and CD8+ T-cells. This study demonstrates that hrSCs possess features and functions typical for professional antigen presenting cells in vitro, and suggest a new role of these cells as negative regulators of T-cell immunity during nerve regeneration. Main points Human repair-related Schwann cells (hrSC) function as professional antigen presenting cells. HrSCs up-regulate PD-L1 upon pro-inflammatory IFNγ stimulation. HrSCs hamper CD4+ and CD8+ T-cell activation. Graphical abstract
Circuit-Specific Targeting of Astrocytes for Genetic Control

Astrocytes are integral functional components of brain circuits. They ensheath the connections between neurons to form tripartite synapses. They react to local neuronal activities and release signaling molecules to regulate synaptic transmission. Their dysfunctions impair synaptic functions and are implicated in neuropsychiatric disorders. Increasing evidence indicates that astrocytes are diverse and they have distinct features and functions in different circuits. However, selectively targeting and controlling astrocytes in a circuit-specific manner is technically challenging. Recently we constructed a series of anterograde transneuronal viral vectors based on the yellow fever vaccine YFV-17D. These YFV-17D derivatives express fluorescent proteins almost exclusively in neurons. However, we find that YFV-17D carrying DNA recombinase Cre infect astrocytes associated with the traced neuronal pathways and express Cre to turn on reporter genes. The targeting of astrocytes is at a whole-brain level but specific to the neuronal circuits traced. Therefore, YFV-17D vectors carrying DNA recombinases provide tools for selectively and genetically targeting pathway-specific astrocytes. This new technology will also allow us to reveal the roles of astrocytes in specific neuronal circuits in normal brain functions and diseases.
Autoencoder ensembles for network intrusion detection

Machine learning methods have been widely used in the field of intrusion detection. However, most methods require labeled data sets, and the overhead is very high. Network data is often high-dimensional and has the problem of data imbalance, which makes many techniques unable to adapt to the real network environment. In this paper, we propose a network intrusion detection model based on autoencoder ensembles. This model uses a recursive feature addition algorithm to select the optimal subset of features, which can significantly reduce the training time of classifiers, and improve the performance of intrusion detection system. After feature selection, the feature subset is grouped, and then each group is mapped to an autoencoder. Multiple such autoencoders ensembles form the detection model. Only normal samples are used for training. The detection model is unsupervised, which improves the efficiency of detecting known and unknown attacks. The experimental results show that feature selection can effectively reduce training and detection time. Our model has high detection accuracy and strong adaptability.
Exploring Informative Response Features of Two Temperature Modulated Gas Sensors at a Wide Range of Relative Humidity

The response signals of temperature modulated gas sensors contain essential information about measured target gas that must be separated from other correlated, redundant, or noisy data. This issue becomes more critical when variations in environmental factors such as relative humidity of target gas or background odors affect the sensor response. Conductance values of two electronic noses based on a single TGS-2602 and a single FIS SP-53B sensors to four gases and clean air at a wide range of relative humidity levels were measured for analyzing the response features. The role of each feature and increasing the number of features in the accuracy of an SVM classifier are investigated. A method is proposed based on removing non-informative features and compared to four conventional feature selection techniques. It is shown that our proposed scheme with a simple SVM classifier results in 96.7% detection accuracy for TGS-2602 and 98.8% for FIS SP-53B, which is up to the accuracy value of common or advanced methods of selecting features. It is concluded that employing feature selection techniques is more beneficial for the TGS-2602 dataset, which had more destructive features than FIS SP-53B. In conclusion, when working with an E-Nose dataset, it is first necessary to explore the important features to find out whether feature selection is required or not, and if needed, which feature selection method provides the best accuracy.
Autonomous oil spill and pollution detection for large-scale conservation in marine eco-cyber-physical systems

In recent years, advancement of the industry and increased human activities created significant pollution in the marine environment and coastal regions of the Persian Gulf. These pollutions cause various diseases and serious damages to the human health and animal species. Early identification of various pollutions helps the coastal management to organize their resources and rapidly respond to the problems. Due to the large scale of the coastal regions, manual investigation of the pollutions is a very time-consuming task. Unmanned robots can be used as autonomous agents for rapid large-scale detection and classification of pollutions in the coastal regions. In this paper, an artificial intelligence-based vision system for autonomous marine pollution detection is proposed. A combination of computer vision and machine learning methods are used for autonomous detection of various pollutions in the coastal and marine environment. In this study, 3000 images of Persian Gulf coastal pollutions is collected and used for training an artificial intelligence system for coastal conservation. The experimental results shows that the proposed framework has a 98% accuracy for identifying and classifying coastal and marine pollutions. The proposed system can be used as the vision system of an autonomous coastal conservation robot and increase the speed of coastal conservation and management significantly.
A Recommender System for the Upselling of Telecommunications Products

Telecommunication providers are always seeking ways to upsell products to corporate customers. Traditionally, the telecommunication provider’s Account Managers build a business relationship with the customers and try to persuade them to upsell. However, only some instances result in a successful upsell while others are unsuccessful. First, we focus on a binary classification framework for predicting the successful upsell of products and services, using data from one such telecommunications provider. Through this prediction model, we illustrate a recommender system for voice products/services to corporate customers of the telecommunications company. We use a logistic regression classifier to automate the selection of customers that are most likely to upsell. We also acknowledge that there may be monetary costs associated with misclassification errors. Note that minimizing losses (or maximizing revenue) may conflict with the objective of minimizing errors and so we address this trade-off. We apply our predictive model to recommend a set of target customers to approach for upsell, illustrating the different accuracy results for different cost weightings. We also show that the success rate of upselling products to the selected customers is dramatically improved when compared to the traditional approach.
0.5-4 GHz 7-bit GaAs MMIC Digital Attenuator with High Accuracy and Low Insertion Phase

This paper introduces a 0.5 to 4 GHz 7-Bit MMIC Digital attenuator for phased array system applications. Digital Attenuator designed and fabricated using 0.25-um pHEMT GaAs process. Optimum topologies have been selected for designing the individual bits to achieve minimum phase error, attenuation error and good returnloss. Maximum Attenuation range of proposed attenuator is 31.75dB with 0.25dB resolution. Simulation has been carried using AWR Design Environment tool. The die level response of 128 states shows a linear phase variation of 7 degree, returnloss better than 15dB, RMS attenuation error better than 0.35 dB with a input third order intercept point greater than 45dBm over 128 states. Die dimension of attenuator is 2.6 mm × 2.5 mm.
Automatic Adaptation of Reliability and Performance Trade-Offs in Service- and Cloud-Based Dynamic Routing Architectures

Many different dynamic routing architectures are available, including sidecar-based routing, routing through a central entity such as an event store or gateway, or architectures with multiple routers. These architectures are currently based on vastly different implementation concepts, such as API Gateways, Message Brokers, or Service Proxies. We propose a new approach that abstracts all these architecture patterns using one Adaptive Dynamic Routers architecture. We hypothesize that a dynamic self-adaptation of the routing architecture is beneficial over any fixed architecture selections for reliability and performance trade-offs. That is, if encountered with traffic and load changes, our approach dynamically self-adapts between more central or distributed routing to optimize system reliability and performance. We evaluate our approach by analyzing our previously-measured data during an experiment of 1200 hours of runtime. Our extensive systematic evaluation with 1089 cases confirms that our hypothesis holds and our approach is beneficial in terms of reliability and performance. Moreover, we empirically validate our results on Google Cloud Platform infrastructure.