Miracle-dz/UniFAR-FTUs huggingface dataset:
Protein interaction network analysis reveals growth conditions-specific crosstalk between chromosomal DNA replication and other cellular processes in E. coli

E. coli and many other bacterial species can alter their cell cycle according to nutrient availability. Under optimal conditions bacteria grow and divide very fast but they slow down the cell cycle when conditions deteriorate. This adaptability is underlined by mechanisms coordinating cell growth with duplication of genetic material and cell division. Several mechanisms regulating DNA replication process in E. coli have been described with biochemical details so far. Nevertheless we still don’t fully understand the source of remarkable precision that allows bacterial cells to coordinate their growth and chromosome replication. To shed light on regulation of E. coli DNA replication at systemic level, we used affinity purification coupled with mass spectrometry (AP-MS) to characterize protein-protein interactions (PPIs) formed by key E. coli replication proteins, under disparate bacterial growth conditions and phases. We present the resulting dynamic replication protein interaction network (PIN) and highlight links between DNA replication and several cellular processes, like outer membrane synthesis, RNA degradation and modification or starvation response. Importance DNA replication is a vital process, ensuring propagation of genetic material to progeny cells. Despite decades of studies we still don’t fully understand how bacteria coordinate chromosomal DNA duplication with cell growth and cell division under optimal and stressful conditions. At molecular level, regulation of processes, including DNA replication, is often executed through direct protein-protein interactions (PPIs). In this work we present PPIs formed by the key E. coli replication proteins under three different bacterial growth conditions. We show novel PPIs with confirmed impact on chromosomal DNA replication. Our results provide also alternative explanations of genetic interactions uncovered before by others for E.coli replication machinery.
Evolution of plasmid mobility: origin and fate of non-conjugative plasmids

Conjugation drives horizontal gene transfer of many adaptive traits across prokaryotes. Yet, only a fourth of the plasmids encode the functions necessary to conjugate autonomously, others being non-mobile or mobilizable by other elements. How these different plasmids evolve is poorly understood. Here, we studied plasmid evolution in terms of their gene repertoires and relaxases. We observed that gene content in plasmid varies rapidly in relation to the rate of evolution of relaxases, such that plasmids with 95% identical relaxases have on average fewer than 50% of homologs. The identification of 249 recent transitions in terms of mobility types revealed that they are associated with even greater changes in gene repertoires, possibly mediated by transposable elements that are more abundant in such plasmids. These changes include pseudogenization of the conjugation locus, exchange of replication initiators, and extensive gene loss. In some instances, the transition between mobility types also leads to the genesis of novel plasmid taxonomic units. Most of these transitions are short-lived, suggesting a source-sink dynamic, where conjugative plasmids constantly generate mobilizable and putatively non-mobilizable plasmids by gene deletion. Yet, in few cases such transitions resulted in the emergence of large clades of relaxases present only in mobilizable plasmids, suggesting successful specialization of these families in the hijacking of diverse conjugative systems. Our results shed further light on the huge plasticity of plasmids, suggest that many non-conjugative plasmids emerged recently from conjugative elements and allowed to quantify how changes in plasmid mobility shape the variation of their gene repertoires.
Rule-based and stimulus-based cues bias auditory decisions via different computational and physiological mechanisms

Expectations, such as those arising from either learned rules or recent stimulus regularities, can bias subsequent auditory perception in diverse ways. However, it is not well understood if and how these diverse effects depend on the source of the expectations. Further, it is unknown whether different sources of bias use the same or different computational and physiological mechanisms. We examined how rule-based and stimulus-based expectations influenced behavior and pupil- linked arousal, a marker of certain forms of expectation-based processing, of human subjects performing an auditory frequency-discrimination task. Rule-based cues consistently biased choices and response times (RTs) toward the more-probable stimulus. In contrast, stimulus-based cues had a complex combination of effects, including choice and RT biases toward and away from the frequency of recently presented stimuli. These different behavioral patterns also had: 1) distinct computational signatures, including different modulations of key components of a novel form of a drift-diffusion decision model and 2) distinct physiological signatures, including substantial bias- dependent modulations of pupil size in response to rule-based but not stimulus-based cues. These results imply that different sources of expectations can modulate auditory processing via distinct mechanisms: one that uses arousal-linked, rule-based information and another that uses arousal- independent, stimulus-based information to bias the speed and accuracy of auditory perceptual decisions. Author summary Prior information about upcoming stimuli can bias our perception of those stimuli. Whether different sources of prior information bias perception in similar or distinct ways is not well understood. We compared the influence of two kinds of prior information on tone-frequency discrimination: rule-based cues, in the form of explicit information about the most-likely identity of the upcoming tone; and stimulus-based cues, in the form of sequences of tones presented before the to-be-discriminated tone. Although both types of prior information biased auditory decision- making, they demonstrated distinct behavioral, computational, and physiological signatures. Our results suggest that the brain processes prior information in a form-specific manner rather than utilizing a general-purpose prior. Such form-specific processing has implications for understanding decision biases real-world contexts, in which prior information comes from many different sources and modalities.
Boron Supply Restores Aluminum-Blocked Auxin Transport by Modulation PIN2 Trafficking in the Arabidopsis Roots

This study tested a hypothesis that boron (B) supply alleviates aluminum (Al) toxicity by modifying auxin distribution in functionally different root zones. Auxin distribution and transport at various Al and B ratios were analyzed using the range of molecular and imaging techniques. Al stress resulted in increased auxin accumulation in root apical meristem (MZ) and transition zones (TZ) while reducing its content in elongation zone (EZ). This phenomenon was explained by reduction in basipetal auxin transport caused by Al blockage of PIN2 endocytosis, regulated at posttranscriptional level. This inhibition of PIN2 endocytosis was dependent on actin filaments and microtubules. B supply facilitated the endocytosis and exocytosis of PIN2 carriers via recycling endosomes conjugated with IAA to modify Al-induced auxin depletion in the EZ. However, disruption of auxin signaling with auxinole did not alleviate Al-induced inhibition of root growth. B supply alleviates Al-induced inhibition of root growth via restoring the endocytic recycling of PIN2 proteins involved in the basipetal (shootward) auxin transport, restoring Al-induced auxin depletion in the elongation zone. Short summary Aluminum-intensified PIN2 abundance, nontranscriptional, via repressing PIN2 endocytosis to block polar auxin transport, and this adverse effect could be alleviated by boron supply.
Salmonella Typhimurium induces NAIP/NLRC4- and NLRP3/ASC-independent, caspase-1/4-dependent inflammasome activation in human intestinal epithelial cells

Salmonella enterica serovar Typhimurium is a gram-negative pathogen that causes diseases ranging from gastroenteritis to systemic infection and sepsis. Salmonella uses type III secretion systems (T3SSs) to inject effectors into host cells. While these effectors are necessary for bacterial invasion and intracellular survival, intracellular delivery of T3SS products also enables detection of Salmonella by cytosolic immune sensors. Upon detecting translocated Salmonella ligands, these sensors form multimeric complexes called inflammasomes, which activate caspases that lead to proinflammatory cytokine release and pyroptosis. In particular, the Salmonella T3SS needle, inner rod, and flagellin proteins activate the NAIP/NLRC4 inflammasome in murine intestinal epithelial cells (IECs), which leads to restriction of bacterial replication and extrusion of infected IECs into the intestinal lumen, thereby preventing systemic dissemination of Salmonella. While these processes are studied quite well in mice, the role of the NAIP/NLRC4 inflammasome in human IECs remains unknown. Unexpectedly, we found the NAIP/NLRC4 inflammasome is dispensable for early inflammasome responses to Salmonella in both human intestinal epithelial cell lines and organoids. Additionally, the NLRP3 inflammasome and the adaptor protein ASC are not required for inflammasome activation in Caco-2 cells. Instead, we observed a partial requirement for caspase-1, and a necessity for caspase-4 and GSDMD pore-forming activity in mediating inflammasome responses to Salmonella in Caco-2 cells. These findings suggest that unlike murine IECs, human IECs do not rely on NAIP/NLRC4, and also do not use NLRP3/ASC. Instead, they primarily use caspases-1 and −4 to mediate early inflammasome responses to SPI-1-expressing Salmonella.
Modeling Asphalt Concrete Mix Design Using Least Square Optimization

This paper presents a computerized method for asphalt concrete job-mix formula design; the preparation of overall gradation of aggregates for mix design is considered as an essential step in the design of asphalt concrete mixture. The target gradation should be dense, uniform, and should not go for one extreme boundary to another or move along one of the boundaries. On the other hand, the maximum size of aggregates (start point of the gradation curve) and the percent filler (end of the gradation curve) are fixed for each type of asphalt concrete mix. The parabola fit using least square method was adopted in this paper; it cares for job mix formula mathematical equation smoothing. The developed program uses parabola fit method to find all possible equations that combines the various material gradations as per the specification requirements, the optimization process will select and print the best six formulas (eq. 1 to eq.6) ascended according to the sum of errors squares. Decision can be made by the user, and given to the computer to choose one of the six equations after considering the economic issue. For several months with every day execution of the program, it proved that it is efficient and quick enough to be used in such mix design.
Energy-efficient network activity from disparate circuit parameters

Neural circuits can produce similar activity patterns from vastly different combinations of channel and synaptic conductances. These conductances are tuned for specific activity patterns but might also reflect additional constraints, such as metabolic cost or robustness to perturbations. How do such constraints influence the range of permissible conductances? Here, we investigate how metabolic cost affects the parameters of neural circuits with similar activity in a model of the pyloric network of the crab Cancer borealis. We use a novel machine learning method to identify a range of network models that can generate activity patterns matching experimental data, and find that neural circuits can consume largely different amounts of energy despite similar circuit activity. Furthermore, a reduced but still significant range of circuit parameters gives rise to energy-efficient circuits. We then examine the space of parameters of energy-efficient circuits and identify potential tuning strategies for low metabolic cost. Finally, we investigate the interaction between metabolic cost and temperature robustness. We show that metabolic cost can vary across temperatures, but that robustness to temperature changes does not necessarily incur an increased metabolic cost. Our analyses show that, despite metabolic efficiency and temperature robustness constraining circuit parameters, neural systems can generate functional, efficient, and robust network activity with widely disparate sets of conductances.
Intelligent On-demand Routing Protocol for Ad Hoc Network

Ad hoc networks play an important role in mobile communications, and the performance of nodes has a significant impact on the choice of communication links. To ensure efficient and secure data forwarding and delivery, an intelligent routing protocol (IAODV) based on learning method is constructed. Five attributes of node energy, rate, credit value, computing power and transmission distance are taken as the basis of segmentation. By learning the selected samples and calculating the information gain of each attribute, the decision tree of routing node is constructed, and the rules of routing node selection are determined. IAODV algorithm realizes the adaptive evaluation and classification of network nodes, so as to determine the optimal transmission path from the source node to the destination node. The simulation results verify the feasibility, effectiveness and security of IAODV.
Implementation of an Autostereoscopic Virtual 3DButton in Non-contact Manner Using SimpleDeep Learning Network

This research presented an implementation of autostereoscopic virtual three-dimensional (3D) button device as non-contact style. The proposed device has several characteristics about visible feature, non-contact use and artificial intelligence (AI) engine. The device was designed to be contactless to prevent virus contamination and consists of 3D buttons in a virtual stereoscopic view. To specify the button pressed virtually by fingertip pointing, a simple deep learning network having two stages without convolution filters was designed. As confirmed in the experiment, if the input data composition is clearly designed, the deep learning network does not need to be configured so complexly. As the results of testing and evaluation by the certification institute, the proposed button device shows high reliability and stability.
Toward coordinated decision between human-and machine-autonomies through their proactive agency of cognition

This paper proposes a framework for a machine agent to constantly adapt its behavior toward the naturalistic collaboration with a human operator, that is inspired by the proactive aspect of human cognition. We introduce the proactive agency into the adaptation process of the machine agent to get more information about the partner's internal state that is difficult to understand under the impoverished conditions where only passive information is available, and to establish their mutual understandings. As an experimental testbed for our proposing model, some experiments using a simulated robot teleoperation environment were prepared to consider the collaboration between a human operator and an autonomous tele-operated robot.
Design and fabrication of a photonic crystal directional coupler for use as an optical switch

We have designed and fabricated closely spaced photonic crystal waveguides in the silicon-on-insulator material system for use as an optical switch. By providing a slow-light region, the switch can have a total length of just 5Pm. The design incorporates a silica overlayer, thus providing a robust solution suitable for integration.
Gait Recognition Based on GF-CNN and Metric Learning

Gait recognition, as a promising biometric, can be used in video-based surveillance and other security systems. However, due to the complexity of leg movement and the difference of external sampling conditions, gait recognition still faces many problems to be addressed. In this paper, an improved convolutional neural network (CNN) based on Gabor filter is therefore proposed to achieve gait recognition. Firstly, a gait feature extraction layer based on Gabor filter is inserted into the traditional CNNs, which is used to extract gait features from gait silhouette images. Then, in the process of gait classification, using the output of CNN as input, we utilize metric learning techniques to calculate distance between two gaits and achieve gait classification by k-nearest neighbors classifiers. Finally, several experiments are conducted on two open-accessed gait datasets and demonstrate that our method reaches state-of-the-art performances in terms of correct recognition rate on the OULP and CASIA-B datasets.
Compact Rotman Lens with Size-Reduced Transition Structures between the Lens Body and the Connecting Lines

This paper introduces a new design for a Rotman lens that has been proposed to minimize its size and provide a suitable design for a compact beamforming system. The size reduction is realized by minimizing the length of the transition structures, which are positioned between the lens body and the connecting lines. The proposed structure is much shorter than a conventional transition structure, which is a tapered line in general. As a result, a 45% size reduction can be achieved by using the proposed transition structure, compared to a typical Rotman lens with linearly tapered lines. Therefore, the proposed Rotman lens will be suitable for compact beamforming systems.
Classroom Roll-Call System Based on ResNet Networks

A convolution neural networks (CNNs) has demonstrated outstanding performance compared to other algorithms in the field of face recognition. Regarding the over-fitting problem of CNN, researchers have proposed a residual network to ease the training for recognition accuracy improvement. In this study, a novel face recognition model based on game theory for call-over in the classroom was proposed. In the proposed scheme, an image with multiple faces was used as input, and the residual network identified each face with a confidence score to form a list of student identities. Face tracking of the same identity or low confidence were determined to be the optimisation objective, with the game participants set formed from the student identity list. Game theory optimises the authentication strategy according to the confidence value and identity set to improve recognition accuracy. We observed that there exists an optimal mapping relation between face and identity to avoid multiple faces associated with one identity in the proposed scheme and that the proposed game-based scheme can reduce the error rate, as compared to the existing schemes with deeper neural network. identification accuracy of the residual network. The improved method includes two steps: one is to improve the structure of the residual network, and the other is to investigate other CNNs.
Smart Contracts CategorizationWith Topic Modeling Techniques

One of the main advantages of the Ethereum blockchain is the possibility of developing smart contracts in a Turing complete environment. These general-purpose programs provide a higher level of security than traditional contracts and reduce other transaction costs associated with the bargaining practice. Developers use smart contracts to build their tokens and set up gambling games, crowdsales, ICO, and many others. Since the number of smart contracts inside the Ethereum blockchain is several million, it is unthinkable to check every program manually to understand its functionality. At the same time, it would be of primary importance to group sets of Smart Contracts according to their purposes and functionalities. One way to group Ethereum’s smart contracts is to use topic modeling techniques, taking advantage of the fact that many programs representing a speciﬁc topic are similar in the program structure. Starting from a dataset of 130k smart contracts, we built a Latent Dirichlet Allocation (LDA) model to spot the number of topics within our sample. Computing the coherence values for a different number of topics, we found out that the optimal number was 15. As we expected, most programs are tokens, games, crowdfunding platforms, and ICO.
High Rate Denial-of-Service Attack Detection System for Cloud Environment Using Flume and Spark

Nowadays, cloud computing is being adopted for more organizations. However, since cloud computing has a virtualized, volatile, scalable and multi-tenancy distributed nature, it is challenging task to perform attack detection in the cloud following conventional processes. This work proposes a solution which aims to collect web server logs by using Flume and filter them through Spark Streaming in order to only consider suspicious data or data related to denial-of-service attacks and reduce the data that will be stored in Hadoop Distributed File System for posterior analysis with the frequent pattern (FP)-Growth algorithm. With the proposed system, we can address some of the difficulties in security for cloud environment, facilitating the data collection, reducing detection time and consequently enabling an almost real-time attack detection.
Generalization Across Acetone Peroxide Homemade Explosives by Detection Dogs

Effective explosives detection requires dogs to generalize their response to untrained variations of targets that are related to those with which they were trained. Previous research suggests that dogs tend to be highly specific to their trained odors, and are sensitive to alterations in odor profiles. Triacetone triperoxide (TATP) is an increasingly popular homemade explosive due to the widespread accessibility of starting materials. The large variety of reagent sources and production approaches yields high variability in deployed formulations. Whether dogs trained with pure forms of TATP generalize to other variations is unknown, representing a potentially significant security gap. In the current study, we tested dogs (n = 11) previously trained to detect pure TATP with four variants: diacetone diperoxide (DADP), a homologue often created as a TATP byproduct, and three different clandestine TATP formulations designed to emulate those used by terrorists or insurgents. On average, dogs detected each untrained variant at rates equivalent to the trained TATP (ps > 0.07), with individual variability in first-trial alerts for some of the variants. Chemical analyses paralleled the canine results, showing distinct similarities and differences. For the TATP samples, the laboratory-grade was the purest sample tested and did not contain DADP or the TATP homologue that the three clandestine versions showed in their respective headspace profiles. The headspace results showed that each sample could be clearly identified as TATP, yet they showed recognizable differences due to their individual syntheses. These findings suggest that training on pure TATP may be effective for generalization to untrained variants. Further research is necessary to identify factors that influence individual variation in generalization between dogs, as well as other explosives.