Miracle-dz/UniFAR-FTUs huggingface dataset:
Global analysis of the specificities and targets of endoribonucleases from E. coli toxin-antitoxin systems

Toxin-antitoxin systems are widely distributed genetic modules typically featuring toxins that can inhibit bacterial growth and antitoxins that can reverse inhibition. Although Escherichia coli encodes 11 toxins with known or putative endoribonuclease activity, the target of most of these toxins remain poorly characterized. Using a new RNA-seq pipeline that enables the mapping and quantification of RNA cleavage with single-nucleotide resolution, we characterize the targets and specificities of 9 endoribonuclease toxins from E. coli. We find that these toxins use low-information cleavage motifs to cut a significant proportion of mRNAs in E. coli, but not tRNAs or the rRNAs from mature ribosomes. However, all the toxins, including those that are ribosome-dependent and cleave only translated RNA, inhibit ribosome biogenesis. This inhibition likely results from the cleavage of ribosomal protein transcripts, which disrupts the stoichiometry and biogenesis of new ribosomes and causes the accumulation of aberrant ribosome precursors. Collectively, our results provide a comprehensive, global analysis of endoribonuclease-based toxin-antitoxin systems in E. coli and support the conclusion that, despite their diversity, each disrupts translation and ribosome biogenesis. Importance Toxin-antitoxin systems are widespread genetic modules found in almost all bacteria that can regulate their growth and may play prominent roles in phage defense. Escherichia coli encodes 11 TA systems in which the toxin is a known or predicted endoribonuclease. The targets and cleavage specificities of these endoribonucleases have remained largely uncharacerized, precluding an understanding of how each impacts cell growth and an assessment of whether they have distinct or overlapping targets. Using a new and broadly applicable RNA-seq pipeline, we present a global analysis of 9 endoribonulease toxins from E. coli. We find that each uses a relatively low-information cleavage motif to cut a large proportion of mRNAs in E. coli, but not tRNAs or mature rRNAs. Notably, although the precise set of targets varies, each toxin efficiently disrupts ribosome biogenesis, primarily by cleaving the mRNAs of ribosomal proteins. In sum, the analyses presented provide new, comprehensive insights into the cleavage specificities and targets of almost all endoribonuclease toxins in E. coli. Despite different specificities, our work reveals a striking commonality in function as each toxin disrupts ribosome biogenesis and translation.
Dysregulated myogenesis and autophagy in genetically induced pulmonary emphysema

Patients with chronic obstructive pulmonary disease (COPD)-pulmonary emphysema often develop locomotor muscle dysfunction, which is independently associated with disability and higher mortality in that population. Muscle dysfunction entails reduced muscle mass and force-generation capacity, which are influenced by fibers integrity. Myogenesis, which is muscle turnover driven by progenitor cells such as satellite cells, contributes to the maintenance of muscle integrity in the context of organ development and injury-repair cycles. Injurious events crucially occur in COPD patients’ skeletal muscles in the setting of exacerbations and infections which lead to acute decompensations for limited periods of time after which, patients typically fail to recover the baseline status they had before the acute event. Autophagy, which is dysregulated in muscles from COPD patients, is a key regulator of satellite cells activation and myogenesis, yet very little research has so far investigated the mechanistic role of autophagy dysregulation in COPD muscles. Using a genetically inducible murine model of COPD-driven muscle dysfunction and confirmed with a second genetic animal model, we found a significant myogenic dysfunction associated with a reduced proliferative capacity of freshly isolated satellite cells. Transplantation experiments followed by lineage tracing suggest that an intrinsic defect in satellite cells, and not in the COPD environment, plays a dominant role in the observed myogenic dysfunction. RNA sequencing analysis of freshly isolated satellite cells suggests dysregulation of transcripts associated with control of cell cycle and autophagy, which is confirmed by a direct observation of COPD mice satellite cells fluorescent-tracked autophagosome formation. Moreover, spermidine-induced autophagy stimulation leads to improved satellite cells autophagosome turnover, replication rate and myogenesis. Our data suggests that pulmonary emphysema causes a disrupted myogenesis, which could be improved with stimulation of autophagy and satellite cells activation, leading to an attenuated muscle dysfunction in this context.
Mathematical Modelling of Conveyor-Belt Dryers with Tangential Flow for Food Drying up to Final Moisture Content below the Critical Value

This work presents the mathematical modeling of the conveyor-belt dryer with tangential flow operating in co-current, which has the advantage of improving the preservation of the organoleptic and nutritional qualities of the dried food. On the one hand, it is a more cumbersome dryer than the perforated cross flow belt dryer but, on the other hand, it has a low air temperature in the final section where the product has a low moisture content and, therefore, it is more heat sensitive. The results of the mathematical modeling allowed a series of guidelines to be developed for a rational design of the conveyor-belt dryer with tangential flow for the specific case of the moisture content of the final product XF lower than the critical one XC (XF < XC). In fact, this work follows a precedent in which a mathematical model was developed through the differentiation of the drying rate equation along the dryer belt with the hypothesis that the final moisture content XF of the product was higher than the critical one XC. The relationships between the extensive quantities (air flow rate and product flow rate), the intensive quantities (temperatures, moisture content and enthalpies) and the dimensional ones (length and width of the belt) were then obtained. Finally, based on these relationships, the rules for an optimized design for XF < XC were obtained.
Learning by Doing: The Use of Distance, Corners and Length in Rewarded Geometric Tasks by Zebrafish (Danio rerio)

Simple Summary Geometric navigation allows animals to efficiently move towards essential life-spaces by taking advantage of macrostructural information such as distance, angular magnitude, and length, in relation to left-right positional sense. In natural contexts, these cues can be referred to extensive three-dimensional surfaces such as a slope or a riverbed, thus becoming crucial to orient and find useful supplies. In controlled contexts, it is possible to set apart these components by handling the global shape of the experimental space (rectangular or square) as well, with the aim to specially probe the impact of each of them on navigation behavior of animals, including fishes. The present study aimed at investigating whether a well-known vertebrate, the zebrafish, could learn to encode and retain in memory such metric information (in terms of distances, corners, and lengths) in association with left–right directions, to gain rewards. Our results showed that zebrafish learned to use all these geometric attributes when repeatedly exposed to them, over a period of training, thereby giving strength to the ecological relevance of environmental geometry as a source of spatial knowledge. Generally, the engagement of zebrafish may consent to assess computations underlying large-scale-based navigation, also by drawing targeted comparisons, due to its behavioral, cognitive, and even emotional similarities with mammals. Abstract Zebrafish spontaneously use distance and directional relationships among three-dimensional extended surfaces to reorient within a rectangular arena. However, they fail to take advantage of either an array of freestanding corners or an array of unequal-length surfaces to search for a no-longer-present goal under a spontaneous cued memory procedure, being unable to use the information supplied by corners and length without some kind of rewarded training. The present study aimed to tease apart the geometric components characterizing a rectangular enclosure under a procedure recruiting the reference memory, thus training zebrafish in fragmented layouts that provided differences in surface distance, corners, and length. Results showed that fish, besides the distance, easily learned to use both corners and length if subjected to a rewarded exit task over time, suggesting that they can represent all the geometrically informative parts of a rectangular arena when consistently exposed to them. Altogether, these findings highlight crucially important issues apropos the employment of different behavioral protocols (spontaneous choice versus training over time) to assess spatial abilities of zebrafish, further paving the way to deepen the role of visual and nonvisual encodings of isolated geometric components in relation to macrostructural boundaries.
Monitoring of Flotation Systems by Use of Multivariate Froth Image Analysis

Froth image analysis has been considered widely in the identification of operational regimes in flotation circuits, the characterisation of froths in terms of bubble size distributions, froth stability and local froth velocity patterns, or as a basis for the development of inferential online sensors for chemical species in the froth. Relatively few studies have considered flotation froth image analysis in unsupervised process monitoring applications. In this study, it is shown that froth image analysis can be combined with traditional multivariate statistical process monitoring methods for reliable monitoring of industrial platinum metal group flotation plants. This can be accomplished with well-established methods of multivariate image analysis, such as the Haralick feature set derived from grey level co-occurrence matrices and local binary patterns that were considered in this investigation.
Docking Molecular analysis of potential Drug Paritaprevir against Mycobacterium tuberculosis (Mtb)

Background Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis, which kills 1.8 million annually. This is an infectious disease generally affects the lungs, but can also affect other parts of the body. Mtb RNA polymerase (RNAP) is the target of the first-line antituberculosis drug Rifampin (Rif). We report first time a Potential Drug Paritaprevir against with severe infectious disease, by in Silico approach, using Autodock Vina and Autodock 4 (or MGL Tool), estimated with Pyrx and AMDock Software, calculating three different important parameters: Binding Affinity (kcal/mol), estimated Ki (in nM units) and Ligand Efficiency (L.E. in kcal/mol). After a selective analysis of over 1000 drugs, processed with Pyrx (a Virtual Screening software for Computational Drug Discovery) in the Ligand Binding site pocket of the protein (ID PDB 5UHB chain C,DNA-directed RNA polymerase subunit beta), we noticed high values of these 3 parameters mentioned above of Paritaprevir, concluding that it could be an excellent candidate drug for this type of infection. Indeed, from the results of Autodock Vina and Autodock 4 (or Autodock 4.2), implemented with lamarckian genetic algorithm, LGA, trough AMDock Software, This oral drug, approved by FDA in 2014, both by Autodock Vina and Autodock Vina 4 has excellent Binding affinity value, ca. −10.00 kcal/mol, a Ki value 40 nM and Ligand efficiency ca −0.15 kcal/mol. These results are comparable to the drug crystallized in the above-mentioned protein, currently used against TBC.
Approximate Pattern Matching In Sparse Multidimensional Arrays Using Machine Learning Based Methods

The main goal of this work is to propose a solution of approximate pattern matching with the use of machine learning based methods. This is done with the help of Locality Sensitive Hashing and existing algorithms. Idea of LSH is used for searching of positions of potential results and their verification is executed as in existing algorithms. Previous work was focused primarily on low dimensional pattern matching. The outcome of this work is an algorithm together with time measures and comparison with already existing solutions. Some of the comparing algorithms were only theoretically designed and not implemented until now. The solution also uses binary format used in a commercial array database.
A Full Duplex Radio over Fiber Architecture Employing 12 Gbps 16 × 16 Optical MIMO for Next Generation Communication Networks

1Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan 2Department of Physics, COMSATS University Islamabad, Islamabad, Pakistan 3School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad, Pakistan 4School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom
Blood Plasma Self-Separation Technologies during the Self-Driven Flow in Microfluidic Platforms

Blood plasma is the most commonly used biofluid in disease diagnostic and biomedical analysis due to it contains various biomarkers. The majority of the blood plasma separation is still handled with centrifugation, which is off-chip and time-consuming. Therefore, in the Lab-on-a-chip (LOC) field, an effective microfluidic blood plasma separation platform attracts researchers’ attention globally. Blood plasma self-separation technologies are usually divided into two categories: active self-separation and passive self-separation. Passive self-separation technologies, in contrast with active self-separation, only rely on microchannel geometry, microfluidic phenomena and hydrodynamic forces. Passive self-separation devices are driven by the capillary flow, which is generated due to the characteristics of the surface of the channel and its interaction with the fluid. Comparing to the active plasma separation techniques, passive plasma separation methods are more considered in the microfluidic platform, owing to their ease of fabrication, portable, user-friendly features. We propose an extensive review of mechanisms of passive self-separation technologies and enumerate some experimental details and devices to exploit these effects. The performances, limitations and challenges of these technologies and devices are also compared and discussed.
Face Selection for Improving Set-to-Set Face Verification

In this thesis we propose a statistical model which predicts performance of a pre-trained face verification system based on analysing quality of input images. A core part of the proposed model is a convolutional neural network, named CNN-FQ, which marks the input facial image as low-quality or high-quaity one. The concept of quality is not defined explicitly, but instead it is learned from mistakes the verification system makes when ranking triplets of faces. We applied the CNN-FQ in a set-based face verification to down-weight negative impact of low-quality faces when aggregating them to a template descriptor. It is shown on IJB-B 1:1 Face Verification benchmark that using CNN-FQ quality predictor for template aggregation leads to consistently higher recognition accuracy if compared to previously used face quality scores.
Nursing approach to the person in critical situation submitted to extracorporeal membrane oxygenation: Scoping Review

Extracorporeal Membrane Oxygenation (ECMO) corresponds to a form of mechanical cardiopulmonary support used to treat severe respiratory failure, since it allows extracorporeal gas exchange. Additionally, in patients with severe heart failure, or experiencing cardiorespiratory arrest, this technique also provides systemic circulation. Nevertheless, ECMO carries some risks, such as clot formation. It is in this context that the nurse has a key role. Objective: To map the available scientific evidence about the nurses’ approach to the critically ill person subjected to ECMO. Method: We conducted a scoping review, using databases accessed through the EBSCOhost platform and following the methodology proposed by the Joanna Briggs Institute. Through the combination of appropriate MesH descriptors, we selected articles published between 2009 and 2019, written in Portuguese or English. Results: Five studies were included in the review. Their analysis allowed us to verify that the nurse’s approach focuses on the monitoring, surveillance, management and coordination of the care provided to critically ill patients subjected to ECMO. It was also found that the adoption of adequate protocols, the training of the multidisciplinary teams and an efficient communication between team members contributed to an effective, safe and high-quality performance when facing patients subjected to ECMO. Conclusion: An appropriate nursing approach is essential to ensure the provision of care with quality, and safety, to the patient subjected to ECMO.
OccamNet: A Fast Neural Model for Symbolic Regression at Scale

Neural networks' expressiveness comes at the cost of complex, black-box models that often extrapolate poorly beyond the domain of the training dataset, conflicting with the goal of finding compact analytic expressions to describe scientific data. We introduce OccamNet, a neural network model that finds interpretable, compact, and sparse symbolic fits to data, \`a la Occam's razor. Our model defines a probability distribution over functions with efficient sampling and function evaluation. We train by sampling functions and biasing the probability mass toward better fitting solutions, backpropagating using cross-entropy matching in a reinforcement-learning loss. OccamNet can identify symbolic fits for a variety of problems, including analytic and non-analytic functions, implicit functions, and simple image classification, and can outperform state-of-the-art symbolic regression methods on real-world regression datasets. Our method requires a minimal memory footprint, fits complicated functions in minutes on a single CPU, and scales on a GPU.
Cold Posteriors Improve Bayesian Medical Image Post-Processing

Cold posteriors have been reported to perform better in practice in the context of Bayesian deep learning (Wenzel et al., 2020). In variational inference, it is common to employ only a partially tempered posterior by scaling the complexity term in the log-evidence lower bound (ELBO). In this work, we optimize the ELBO for a fully tempered posterior in mean-ﬁeld variational inference and use Bayesian optimization to automatically ﬁnd the optimal posterior temperature and prior scale. Choosing an appropriate posterior temperature leads to better predictive performance and improved uncertainty calibration, which we demonstrate for the task of denoising medical X-ray images
Modeling Explicit Concerning States for Reinforcement Learning in Visual Dialogue

To encourage AI agents to conduct meaningful Visual Dialogue (VD), the use of Reinforcement Learning has been proven potential. In Reinforcement Learning, it is crucial to represent states and assign rewards based on the action-caused transitions of states. However, the state representation in previous Visual Dialogue works uses the textual information only and its transitions are implicit. In this paper, we propose Explicit Concerning States (ECS) to represent what visual contents are concerned at each round and what have been concerned throughout the Visual Dialogue. ECS is modeled from multimodal information and is represented explicitly. Based on ECS, we formulate two intuitive and interpretable rewards to encourage the Visual Dialogue agents to converse on diverse and informative visual information. Experimental results on the VisDial v1.0 dataset show our method enables the Visual Dialogue agents to generate more visual coherent, less repetitive and more visual informative dialogues compared with previous methods, according to multiple automatic metrics, human study and qualitative analysis.
A Case for Reversible Coherence Protocol

We propose the ﬁrst Reversible Coherence Protocol (RCP) , a new protocol designed from ground up that enables invisible speculative load. RCP takes a bold approach by including the speculative loads and merge/purge operation in the interface between processor and cache coherence, and allowing them to participate in the coherence protocol. It means, speculative load, ordinary load/store, and merge/purge can all affect the state of a given cache line. RCP is the ﬁrst coherence protocol that enables the commit and squash of the speculative load among distributed cache components in a general memory hierarchy. RCP incurs an average slowdown of (3.0%,8.3%,7.4%) on (SPEC2006,SPEC2017,PARSEC), which is lower compared to (26.5%,12%,18.3%) in InvisiS-pec and (3.2%,9.4%,24.2%) in CleanupSpec. The coherence trafﬁc overhead is on average 46%, compared to 40% and 27% of InvisiSpec and CleanupSpec, respectively. Even with higher trafﬁc overhead ( ∼ 46%), the performance overhead of RCP is lower than InvisiSpec and comparable to Cleanup-Spec. It reveals a key advantage of RCP: the coherence actions triggered by the merge and purge operations are not in the critical path of the execution and can be performed in the cache hierarchy concurrently with processor execution.
Distributed Deep Reinforcement Learning for Intelligent Traffic Monitoring with a Team of Aerial Robots

This paper studies the traffic monitoring problem in a road network using a team of aerial robots. The problem is challenging due to two main reasons. First, the traffic events are stochastic, both temporally and spatially. Second, the problem has a non-homogeneous structure as the traffic events arrive at different locations of the road network at different rates. Accordingly, some locations require more visits by the robots compared to other locations. To address these issues, we define an uncertainty metric for each location of the road network and formulate a path planning problem for the aerial robots to minimize the network's average uncertainty. We express this problem as a partially observable Markov decision process (POMDP) and propose a distributed and scalable algorithm based on deep reinforcement learning to solve it. We consider two different scenarios depending on the communication mode between the agents (aerial robots) and the traffic management center (TMC). The first scenario assumes that the agents continuously communicate with the TMC to send/receive realtime information about the traffic events. Hence, the agents have global and real-time knowledge of the environment. However, in the second scenario, we consider a challenging setting where the observation of the aerial robots is partial and limited to their sensing ranges. Moreover, in contrast to the first scenario, the information exchange between the aerial robots and the TMC is restricted to specific time instances. We evaluate the performance of our proposed algorithm in both scenarios for a real road network topology and demonstrate its functionality in a traffic monitoring system.