Miracle-dz/UniFAR-FTUs huggingface dataset:
Bacterial microcompartments linked to the flavin-based extracellular electron transfer drives anaerobic ethanolamine utilization in Listeria monocytogenes

Ethanolamine (EA) is a valuable microbial carbon and nitrogen source derived from phospholipids present in cell membranes. EA catabolism is suggested to occur in so-called bacterial microcompartments (BMCs) and activation of EA utilization (eut) genes is linked to bacterial pathogenesis. Despite reports showing that activation of eut in Listeria monocytogenes is regulated by a vitamin B12-binding riboswitch and that upregulation of eut genes occurs in mice, it remains unknown whether EA catabolism is BMC dependent. Here, we provide evidence for BMC-dependent anaerobic EA utilization via metabolic analysis, proteomics and electron microscopy. First, we show B12-induced activation of the eut operon in L. monocytogenes coupled to uptake and utilization of EA thereby enabling growth. Next, we demonstrate BMC formation in conjunction to EA catabolism with the production of acetate and ethanol in a molar ratio of 2:1. Flux via the ATP generating acetate branch causes an apparent redox imbalance due to reduced regeneration of NAD+ in the ethanol branch resulting in a surplus of NADH. We hypothesize that the redox imbalance is compensated by linking eut BMC to anaerobic flavin-based extracellular electron transfer (EET). Using L. monocytogenes wild type, a BMC mutant and a EET mutant, we demonstrate an interaction between BMC and EET and provide evidence for a role of Fe3+ as an electron acceptor. Taken together, our results suggest an important role of anaerobic BMC-dependent EA catabolism in the physiology of L. monocytogenes, with a crucial role for the flavin-based EET system in redox balancing. IMPORTANCE Listeria monocytogenes is a food-borne pathogen causing severe illness and, as such, it is crucial to understand the molecular mechanisms contributing to pathogenicity. One carbon source that allows L. monocytogenes to grow in humans is ethanolamine (EA), which is derived from phospholipids present in eukaryotic cell membranes. It is hypothesized that EA utilization occurs in bacterial microcompartments (BMCs), self-assembling subcellular proteinaceous structures and analogs of eukaryotic organelles. Here, we demonstrate that BMC-driven utilization of EA in L. monocytogenes results in increased energy production essential for anaerobic growth. However, exploiting BMCs and the encapsulated metabolic pathways also requires balancing of oxidative and reductive pathways. We now provide evidence that L. monocytogenes copes with this by linking BMC activity to flavin-based extracellular electron transfer (EET) using iron as an electron acceptor. Our results shed new light on an important molecular mechanism that enables L. monocytogenes to grow using host-derived phospholipid degradation products.
An adjunctive therapy administered with an antibiotic prevents enrichment of antibiotic-resistant clones of a colonizing opportunistic pathogen

A key challenge in antibiotic stewardship is figuring out how to use antibiotics therapeutically without promoting the evolution of antibiotic resistance. Here, we demonstrate proof of concept for an adjunctive therapy that allows intravenous antibiotic treatment without driving the evolution and onward transmission of resistance. We repurposed the FDA-approved bile sequestrant cholestyramine, which we show binds the antibiotic daptomycin, as an ‘anti-antibiotic’ to disable systemically-administered daptomycin reaching the gut. We hypothesized that adjunctive cholestyramine could enable therapeutic daptomycin treatment in the bloodstream, while preventing transmissible resistance emergence in opportunistic pathogens colonizing the gastrointestinal tract. We tested this idea in a mouse model of Enterococcus faecium gastrointestinal tract colonization. In mice treated with daptomycin, adjunctive cholestyramine therapy reduced the fecal shedding of daptomycin-resistant E. faecium by up to 80-fold. These results provide proof of concept for an approach that could reduce the spread of antibiotic resistance for important hospital pathogens.
Subcortical rather than cortical sources of the frequency-following response (FFR) relate to speech-in-noise perception

Scalp-recorded frequency-following responses (FFRs) reflect a mixture of phase-locked activity across the auditory pathway. FFRs have been widely used as a neural barometer of complex listening skills, especially speech-in noise (SIN) perception. Applying individually optimized source reconstruction to speech-FFRs recorded via EEG (FFREEG), we assessed the relative contributions of subcortical [auditory nerve (AN), brainstem/midbrain (BS)] and cortical [bilateral primary auditory cortex, PAC] source generators with the aim of identifying which source(s) drive the brain-behavior relation between FFRs and SIN listening skills. We found FFR strength declined precipitously from AN to PAC, consistent with diminishing phase-locking along the ascending auditory neuroaxis. FFRs to the speech fundamental (F0) were robust to noise across sources, but were largest in subcortical sources (BS > AN > PAC). PAC FFRs were only weakly observed above the noise floor and only at the low pitch of speech (F0≈100 Hz). Brain-behavior regressions revealed (i) AN and BS FFRs were sufficient to describe listeners’ QuickSIN scores and (ii) contrary to neuromagnetic (MEG) FFRs, neither left nor right PAC FFREEG predicted SIN performance. Our preliminary findings suggest subcortical sources not only dominate the electrical FFR but also the link between speech-FFRs and SIN processing as observed in previous EEG studies.
Three-Fluorophore FRET Enables the Analysis of Ternary Protein Association in Living Plant Cells

Protein-protein interaction studies provide valuable insights into cellular signaling. The well-studied brassinosteroid (BR) signaling is initiated by the hormone-binding receptor Brassinosteroid Insensitive 1 (BRI1) and its co-receptor BRI1 Associated Kinase 1 (BAK1). BRI1 and BAK1 were shown to interact independently with the Receptor-Like Protein 44 (RLP44), which is implicated in BRI1/BAK1-dependent cell wall integrity perception. To demonstrate the proposed complex formation of BRI1, BAK1 and RLP44, we established three-fluorophore intensity-based spectral Förster resonance energy transfer (FRET) and FRET-fluorescence lifetime imaging microscopy (FLIM) for living plant cells. Our evidence indicates that RLP44, BRI1 and BAK1 form a ternary complex in a distinct plasma membrane nanodomain. In contrast, although the immune receptor Flagellin Sensing 2 (FLS2) also forms a heteromer with BAK1, the FLS2/BAK1 complexes are localized in other nanodomains. In general, our three-fluorophore FRET approaches provide a feasible basis for studying the interaction and sub-compartmentalization of proteins in great detail.
Genetic and species-level biodiversity patterns are linked by demography and ecological opportunity

Species richness and genetic diversity are the two most fundamental products of evolution. Both are important conservation targets—species richness contributes to ecosystem functioning and human wellbeing, while genetic diversity allows those species to respond to changes in their environment and persist in the long-term. Biogeographic patterns of species richness are well-described, but we know little about patterns of genome-wide genetic diversity at similar spatial scales. Further, despite considerable attention to latitudinal trends in species richness, we still do not have a solid empirical understanding of the various processes that produce them, how they interact, or how they affect genetic diversity. Here we show that genome-wide genetic diversity and species richness share spatial structure, however, species richness hotspots tend to harbor low levels of within-species genetic variation. A single model encompassing eco-evolutionary processes related to environmental energy availability, niche availability, and proximity to humans explained 75% of variation in gene diversity and 90% of the variation in species richness. Our empirical model of both levels of biodiversity supports theory and demonstrates the importance of carrying capacity and ecological opportunity at individual and species levels for generating continent-wide genetic and species diversity gradients.
Behavioural Intrusion Detection in Water Distribution Systems Using Neural Networks

There has been an increasing number of attacks against critical water system infrastructure in recent years. This is largely due to the fact that these systems are heavily dependent on computer networks meaning that an attacker can use conventional techniques to penetrate this network which would give them access to the supervisory control and data acquisition (SCADA) system. The devastating impact of a successful attack in these critical infrastructure applications could be long-lasting with major social and financial implications. Intrusion detection systems are deployed as a secondary defence mechanism in case an attacker is able to bypass the systems preventative security mechanisms. In this thesis, behavioural intrusion detection is addressed in the context of detecting cyber-attacks in water distribution systems. A comparative analysis of various predictive neural network architectures is conducted and from this a novel voting-based ensemble technique is presented. Finally an analysis of how this approach to behavioural intrusion detection can be enhanced by both univariate and multivariate outlier detection techniques It was found that multiple algorithms working together are able to counteract their limitation to produce a more robust algorithm with improved results.
A 0.5-V Supply, 36 nW Bandgap Reference With 42 ppm/°C Average Temperature Coefficient Within −40 °C to 120 °C

This paper presents a switched capacitor network (SCN)-based bandgap voltage reference (BGR) circuit designed and implemented in a 65nm standard CMOS process with a wide temperature range, high precision, low supply voltage and low power consumption for IoT device application. The proposed BGR employs a 2x charge pump with ripple optimization design to supply the <inline-formula> <tex-math notation="LaTeX">${V} _{\mathbf {EB}}$ </tex-math></inline-formula> generator, which can relax <inline-formula> <tex-math notation="LaTeX">${V} _{\mathbf {DD}}$ </tex-math></inline-formula> from 0.9V to 0.5V. A proportional to absolute temperature (PTAT) current source is proposed to bias the PNP BJT in order to reduce the nonlinearity of <inline-formula> <tex-math notation="LaTeX">${V} _{\mathbf {EB}}$ </tex-math></inline-formula>. Moreover, a voltage divider SCN with low leakage consideration to form the complementary to absolute temperature (CTAT) voltage is designed to reduce the nonlinearity of its coefficient, while a series-parallel SCN with adjusted clock swing to form the PTAT voltage is designed to improve the line regulation of the BGR. The measurement result shows that the proposed BGR has a temperature coefficient (TC) of 42 ppm/°C at 0.5V supply within −40 °C to 120 °C. The line regulation is 3.2mV/V or 0.64%/V from 0.5V to 1V. Based on 6-chip test result, it shows a <inline-formula> <tex-math notation="LaTeX">$3\sigma / \mu $ </tex-math></inline-formula> variation of 3.08% before trimming, while 0.36% after trimming.
Image Classification Based on Automatic Neural Architecture Search Using Binary Crow Search Algorithm

Neural architectures have accelerated the advancement in various domains by enabling automatic pattern detection, image classification, audio recognition, and face recognition etc. However, they are computationally expensive to design and expert knowledge in various domains is required. In this paper, a swarm intelligence algorithm is proposed to search for novel architectures without human intervention that can achieve comparable performance to those of human-designed architectures. This work is inspired by current neural architecture search approaches based on reinforcement learning and genetic algorithm. However, not much attention is paid towards swarm intelligence metaheuristics-based neural architecture search. A framework is proposed for automatically designing neural architectures based on a swarm intelligence metaheuristic: Crow Search Algorithm. First, Crow Search Algorithm is integrated with binary network representation. To make it compatible for Neural Architecture Search, the original distance metric is replaced with hamming distance-based similarity measure. Second, the tuning parameters of Crow Search Algorithm are reduced by replacing the static flight length parameter with our dynamic flight length distribution algorithm. Third, the target selection method (random selection) is replaced by tournament select method. The proposed framework is used to search for architectures on MNIST, CIFAR10, and CIFAR100 datasets and achieved 0.18%, 3.48%, and 15.64% test error, respectively. Furthermore, small-scale transfer experiments are conducted to search architectures for Tiny ImageNet and achieved 34.43% test error. Nonparametric statistical analysis is performed to validate the impact of each modification in improving the quality of search space exploration. The proposed framework has achieved comparable performance with the state-of-the-art approaches, with a comparatively simpler approach and minimum human intervention. The proposed framework can be used to develop completely automated systems for designing architectures for various data-based classification applications.
Attention-based Bi-LSTM-CRF Network for Emotion Cause Extraction in Texts

Emotion cause extraction is to identify the causes behind an emotion expressed in a document, a more challenging task for the fine-grained emotion analysis in natural language processing. Most existing methods regard the task as an independent clause classification problem, ignoring the relationships among multiple clauses in the same document. Moreover, the relative position of the candidate clause and emotion clause provide critical emotion cause clue. In the paper, an attention-based Bi-LSTM-CRF network is proposed to integrate the above information. In this network, a bi-directional long short-term memory is first used to capture both the contextual information and the latent semantic relations of emotion expression and candidate clause. Then, two attention mechanisms are designed to encode the mutual influence of the emotion expression and candidate clause, the relative position and candidate clause. Better-distributed representations are created with the former design. Finally, these representations are into the Condition Random Fields for labeling. The results experimented on a benchmark Chinese emotion cause dataset proved the effectiveness of our method by achieving the F score of 88.40 %.
A Predictive Algorithm for Crosstalk Peaks of SiC MOSFET by Considering the Nonlinearity of Gate-Drain Capacitance

A predictive algorithm for accurately determining the crosstalk peaks caused by SiC MOSFETs is proposed in this article, which is very important to better design the drive and protect circuits of SiC MOSFETs. Compared to the conventional algorithm of crosstalk peaks, the nonlinearity of gate-drain capacitance (Cgd) is first considered in the proposed algorithm. In detail, the differential expression of the crosstalk peaks is deduced, in which, the main factors affecting crosstalk peaks are all included. Following, the execution flow of the predictive algorithm is provided according to the differential expression. To verify the accuracy of the proposed algorithm with considering the nonlinearity of Cgd, the simulation and experiment platforms are established and verified by using Kelvin packaged SiC MOSFET IMZ120R045M1 (1200 V/52 A). The calculation results of the proposed algorithm are compared with the conventional algorithm without considering the nonlinearity of Cgd under different working conditions of SiC MOSFET. Finally, the calculation results, the simulation, and experiment results show that the proposed algorithm is effective and has better accuracy. Therefore, the predictive algorithm proposed in this article provides a theoretical reference to better design the drive and protect circuits of SiC MOSFETs.
Interaction-Aware Motion Prediction for Autonomous Driving: A Multiple Model Kalman Filtering Scheme

We consider the problem of predicting the motion of vehicles in the surrounding of an autonomous car, for improved motion planning in lane-based driving scenarios without inter-vehicle communication. First, we address the problem of single-vehicle estimation by designing a filtering scheme based on an Interacting Multiple Model Kalman Filter equipped with novel intention-based models. Second, we augment the proposed scheme with an optimization-based projection that enables the generation of non-colliding predictions. We then extend the approach to the problem of simultaneously estimating multiple vehicles by using a hierarchical approach based on a priority list. The priority list is dynamically adapted in real-time according to a proposed sorting algorithm. Finally, we evaluate the proposed scheme in simulations using real-life vehicle data from the Next Generation Simulation (NGSIM) dataset.
Analysis of 5G Outdoor and Indoor Coexistence Scenarios for Spectrum Sharing with Active Antenna System

In this work spectrum sharing under novel authorisation regimes is investigated for 5G innovations in views of spectrum usage also for vertical applications. We mainly focus on the 26 GHz band recently harmonised for IMT at WRC-19 and on the 6 GHz band which has been identified among future candidate bands for IMT at next WRC23. Isolation between outdoor and indoor environments is considered as an enabler to share the spectrum and provide more bandwidth per operator in the same band. Co-channel coexistence analyses were performed based on the widely adopted Minimum Coupling Loss (MCL) methodology. The impact of active antenna systems (AAS) as a means to improve spectrum sharing is also investigated. Results show that coexistence is possible in real scenarios provided that adequate distance and angular separation is guaranteed between 5G networks and 5G and incumbent services.
User Grouping and Hybrid RF/Baseband Precoding for Multi-User Massive MIMO Systems

In this paper, we investigate hybrid precoders for massive MIMO systems with the objective to reduce the number of radio-frequency (RF) chains. The users are first partitioned into groups that share similar angles of departure (AoDs) to design an angular-based RF-beamforming stage. Then a multi-user-Multiple Input Multiple Output (MIMO) baseband precoding stage is developed with reduced channel state information (CSI) dimension in massive MIMO systems. We develop a systematic approach to simultaneously determine the number of groups and to cluster the users accordingly. The partition is done by minimizing the inter-group interference using a similarity graph. We also incorporate a simple transfer block between the digital precoding and RF-beamforming stages to reduce the number of RF chains in massive MIMO systems. For a sufficiently large number of antennas and over a wide range of signal-to-noise ratio (SNR), the proposed hybrid RF/baseband precoding schemes can offer a sum-rate performance approaching that of the single-stage full digital optimal precoder, while keeping the number of RF chains equal to the number of users. Next, we propose a simple way to design the RF and baseband digital precoding stages in the case of multiple scatterings to fully exploit the multiple reflections that occur in dense urban environments. The proposed hybrid 2-stage RF/baseband schemes significantly outperform other hybrid RF/baseband precoding schemes using orthogonal multiplexing.
A Wideband Equivalent Circuit Electric Dipoles

The problem of obtaining a wideband equivalent circuit for dipole antennas is addressed backward; the four-element circuit topology is taken as given; its three-element part was suggested by Chu and its extension to four elements by Stuart. The goals here are to obtain the element values with the least effort and to explore the available bandwidth. Based on the impedance spectrum, rather than on radiation or current eigenmodes, this approach identifies elements of the circuit from their behavior with frequency over a wideband impedance spectrum that includes both first and second resonances. Element values are derived from their impedance behaviors at three frequencies: zero (dc), first (series or short-circuit), and second (parallel or open-circuit) resonance. An Argand diagram is introduced to determine <inline-formula> <tex-math notation="LaTeX">${R}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${L}$ </tex-math></inline-formula> at the first resonance; the two capacitances, <inline-formula> <tex-math notation="LaTeX">${C}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${C} _{t}$ </tex-math></inline-formula>, at first and second resonances, respectively, complete the circuit. Optimization follows. Several examples are given and limitations discussed.
A Discrete-Time Non-Adaptive SOGI-Based Frequency-Locked Loop

Second-order generalized integrator (SOGI)-based phase-locked loops (PLLs)/frequency-locked loops (FLLs) are the most commonly used methods for grid synchronization. While the SOGI in these standard SOGI-PLLs/FLLs is adaptive to the estimated frequency, which results in the coupling between the SOGI and PLL/FLL, and this coupling brings about difficulties in stability analysis and parameter tuning. In order to reduce this coupling between the SOGI and PLL/FLL, this letter proposes a novel discrete-time non-adaptive SOGI-FLL based on gradient descent algorithm, of which the relationship of the orthogonal signals generated by discrete-time non-adaptive SOGI is used to construct the linear model of frequency parameter. The global convergence analysis and parameter tuning are given. The proposed FLL features no discretization errors, simple convergence analysis and parameter tuning. Experimental results show that the proposed FLL has smoother dynamic performance and can be designed to have higher disturbance rejection ability or faster response speed than the commonly used methods.
Enhanced Fault-Tolerant Model Predictive Current Control for a Five-Phase PM Motor With Continued Modulation

To deal with the single-phase open-circuited fault, an enhanced fault-tolerant model predictive current control with continued modulation is proposed in this article. This method includes reconstructing the postfault vector distribution and adopting a multistep vector selection method. In the reconstruction procedure, the transition vectors are generating to realize standard pulsewidth modulation waveforms. Then, the unreachable area can be filled by creating new synthetic vectors. Finally, the vectors with oversized amplitudes are optimized by using null vectors. In order to cover the full modulation area, a multistep vector selection method is used to determine the phase angle and amplitude of the optimal vector. The phase angle can be determined by defining two cost functions and adopting duty cycle modulation technology. The amplitude of the optimal vector can be optimized by utilizing the duty cycle modulation technology again. The oscillation, due to the back electromotive force, is compensated in each step. A thorough experimental evaluation has been conducted to confirm the effectiveness and superiority of the proposed fault-tolerant method.