Miracle-dz/UniFAR-FTUs huggingface dataset:
Loss of BCL-3 sensitises colorectal cancer cells to DNA damage, revealing a role for BCL-3 in double strand break repair by homologous recombination

Objective The proto-oncogene BCL-3 is upregulated in a subset of colorectal cancers (CRC) and increased expression of the gene correlates with poor patient prognosis. The aim is to investigate whether inhibiting BCL-3 can increase the response to DNA damage in CRC. Design The function of BCL-3 in DNA damage response was studied in vitro using siRNA and CRISPR-Cas9 genome editing and in vivo using Bcl3-/- mice. DNA damage induced by γ-irradiation and/or cisplatin was quantified using H2AX and RAD51 foci, repair pathways investigated using HR/NHEJ assays and treatment with the PARP inhibitor olaparib. Result Suppression of BCL-3 increases double strand break number and decreases homologous recombination in CRC cells, supported by reduced RAD51 foci number and increased sensitivity to PARP inhibition. Importantly, a similar phenotype is seen in Bcl3-/- mice, where the intestinal crypts of these mice exhibit sensitivity to DNA damage and a greater number of double strand breaks compared to wild type mice. Furthermore Apc.Krasmutant x Bcl3-/- mice exhibit increased DNA damage and reduced RAD51+ cells compared to their wild type counterparts when treated with cisplatin. Conclusion This work identifies BCL-3 as a regulator of the cellular response to DNA damage and suggests that elevated BCL-3 expression could increase resistance of tumour cells to DNA damaging agents including radiotherapy. These findings offer a rationale for targeting BCL-3 in CRC as an adjuvant to conventional therapies and suggest that BCL-3 expression in tumours could be a useful biomarker in stratification of rectal cancer patients for neo-adjuvant chemoradiotherapy.
Auditory cortex activation is modulated nonlinearly by stimulation duration: A functional near-infrared spectroscopy (fNIRS) study

Functional near-infrared spectroscopy (NIRS) is an increasingly popular method in hearing research. However, few studies have considered efficient stimulation parameters for fNIRS auditory experimental design. The objectives of our study are (1) to characterize the auditory hemodynamic responses to trains of white noise with increasing stimulation durations (8s, 10s, 15s, 20s) in terms of amplitude and response linearity; (2) to identify the most-efficient stimulation duration using fNIRS; and (3) to generalize results to more ecological environmental stimuli. We found that cortical activity is augmented following the increments in stimulation durations and reaches a plateau after about 15s of stimulation. The linearity analysis showed that this augmentation due to stimulation duration is not linear in the auditory cortex, the non-linearity being more pronounced for longer durations (15s and 20s). The 15s block duration that we propose as optimal precludes signal saturation, is associated with a high response amplitude and a relatively short total experimental duration. Moreover, the 15s duration remains optimal independently of the nature of presented sounds. The sum of these findings suggests that 15s stimulation duration used in the appropriate experimental setup allows researchers to acquire optimal fNIRS signal quality.
Nanofibrous PEDOT-Carbon Composite on Flexible Probes for Soft Neural Interfacing

In this study, we report a flexible implantable 4-channel microelectrode probe coated with highly porous and robust nanocomposite of poly(3,4-ethylenedioxythiophene) (PEDOT) and carbon nanofiber (CNF) as a solid doping template for high-performance in vivo neuronal recording and stimulation. A simple yet well-controlled deposition strategy was developed via in situ electrochemical polymerization technique to create a porous network of PEDOT and CNFs on a flexible 4-channel gold microelectrode probe. Different morphological and electrochemical characterizations showed that they exhibit remarkable and superior electrochemical properties, yielding microelectrodes combining high surface area, low impedance (16.8 ± 2 MΩ.μm2 at 1 kHz) and elevated charge injection capabilities (7.6 ± 1.3 mC/cm2) that exceed those of pure and composite PEDOT layers. In addition, the PEDOT-CNF composite electrode exhibited extended biphasic charge cycle endurance, resulting in a negligible physical delamination or degradation for long periods of electrical stimulation. In vitro testing on mouse brain slices showed that they can record spontaneous oscillatory field potentials as well as single-unit action potentials and allow to safely deliver electrical stimulation for evoking field potentials. The combined superior electrical properties, durability and 3D microstructure topology of the PEDOT-CNF composite electrodes demonstrate outstanding potential for developing future neural surface interfacing applications.
Involvement of ASIC1a channels in the spinal processing of pain information by deep projection neurons

Dorsal horn of the spinal cord is an important crossroad of pain neuraxis, especially for the neuronal plasticity mechanisms that can lead to chronic pain states. Windup is a well-known spinal pain facilitation process initially described several decades ago, but which exact mechanism is still not fully understood. Here, we combine both ex vivo and in vivo electrophysiological recordings of spinal neurons with computational modelling to demonstrate a role for ASIC1a-containing channels in the windup process. Spinal application of the ASIC1a inhibitory venom peptides mambalgin-1 and psalmotoxin-1 (PcTx1) significantly reduces the ability of deep wide dynamic range (WDR) neurons to develop windup in vivo. All deep WDR-like neurons recorded from spinal slices exhibit an ASIC current with biophysical and pharmacological characteristics consistent with functional expression of ASIC1a/ASIC2 heteromeric channels. A computational model of WDR neuron supplemented with heteromeric ASIC1a/ASIC2 channel parameters accurately reproduces the experimental data, further supporting a positive contribution of these channels to windup. It also predicts a calcium-dependent windup decrease for elevated ASIC conductances, a phenomenon that was experimentally validated using either a combination of calcium-activated potassium channel inhibitory peptides (apamin and iberiotoxin), or the Texas coral snake ASIC-activating toxin (MitTx). This study demonstrates a possible dual contribution to windup of calcium permeable ASIC1a/ASIC2 channels in deep laminae projecting neurons, promoting it upon moderate channel activity, but ultimately leading to calcium-dependent windup inhibition associated to potassium channels when activity increases.
Widely naturalized species are not more promiscuous in the use of different nitrogen forms, but benefit more from inorganic nitrogen

Nitrogen (N) is an essential mineral nutrient necessary for plant growth and has been considered a crucial factor influencing invasion success. Former studies mainly focused on responses of alien plants to different levels of N availability. However, in natural soils, N comes in different forms. Few experimental studies have explored responses of naturalized alien species to different N forms, and whether such responses are related to their naturalization success globally. We selected 22 common herbaceous species native to Germany that have all become naturalized and thus been introduced elsewhere in the world. We grew these species under six different N conditions that differed in the amount or form of N, and assessed their growth performance in a greenhouse experiment. We found that plants produced more biomass when grown under high N levels than under low N levels, and when N was provided in inorganic form than when provided in organic form. Neither biomass production nor promiscuity to different N forms was related to naturalization success of the species. However, the biomass response to inorganic N, relative to organic N, was stronger for the widely naturalized species than for the less widely naturalized ones. Our study shows that although the widely naturalized alien species were not more promiscuous than the less widely naturalized species, they took more advantage of the inorganic-N forms. This indicates that naturalization success might be partly driven by a species’ ability to take advantage of increased inorganic N levels.
Running title: Oleic and palmitic acid effects on hypothalamic neurons Concentration-dependent change in hypothalamic neuronal transcriptome by the dietary fatty acids: oleic and palmitic acids

Prenatal high-fat diet exposure increases hypothalamic neurogenesis events in embryos and programs offspring to be obesity-prone. The molecular mechanism involved in these dietary effects of neurogenesis are unknown. This study investigated the effects of oleic and palmitic acids, which are abundant in a high-fat diet, on the hypothalamic neuronal transcriptome and how these changes impact neurogenesis events. The results show differential effects of low and high concentrations of oleic or palmitic acid treatment on differential gene transcription. Gene ontology analysis uncovered significant gene enrichment in several cellular pathways involved in gene regulation and protein production, particularly with proliferation, migration, and cell survival. The enriched signaling pathways include Wnt, integrin, PDGF, and apoptosis, in addition endocrine function signaling pathways CCKR and GnRH. Further examination of proliferation and migration show low concentrations of oleic acid to stimulate proliferation and high concentrations of both oleic and palmitic acid to stimulate apoptosis. Oleic acid also reduced hypothalamic neuronal migration, with little effects by palmitic acid. The results show direct impact of the two most abundant fatty acids in a high fat diet to directly impact hypothalamic neuronal proliferation and migration. The results also uncovered signaling pathways affected by oleic and palmitic acid and suggest a mechanism of prenatal high-fat diet induced neurogenesis events is through these two abundant fatty acids.
Predictive framework of macrophage activation

Macrophages populate every organ during homeostasis and disease, displaying features of tissue imprinting and heterogeneous activation. The disjointed picture of macrophage biology that emerged from these observations are a barrier for integration across models or with in vitro macrophage activation paradigms. We set out to contextualize macrophage heterogeneity across mouse tissues and inflammatory conditions, specifically aiming to define a common framework of macrophage activation. We built a predictive model with which we mapped the activation of macrophages across 12 tissues and 25 biological conditions, finding a striking commonality and finite number of transcriptional profiles, which we modelled as defined stages along four conserved activation paths. We verified this model with adoptive cell transfer experiments and identified transient RELMɑ expression as a feature of macrophage tissue engraftment. We propose that this integrative approach of macrophage classification allows the establishment of a common predictive framework of macrophage activation in inflammation and homeostasis. One Sentence Summary We propose an integrative approach of macrophage classification that allows the establishment of a common framework of macrophage activation in inflammation and homeostasis.
UWB-Assisted Fast mmWave Beam Alignment

Due to their large bandwidth and impressive data speed, millimeter-wave (mmWave) radios are expected to play a key role in the 5G and beyond (e.g., 6G) communication networks. Yet, to release mmWave’s true power, the highly directional mmWave beams need to be aligned perfectly. Most existing beam alignment methods adopt an exhaustive or semi-exhaustive space scanning, which introduces up to seconds of delays.To eliminate the need of a complex space scanning, this paper presents an Ultra-wideband (UWB)-assisted mmWave communication framework, which leverages the co-located UWB antennas to estimate the best angles for mmWave beam alignment. One major challenge to apply this idea in real-world is the barrier of limited antenna numbers. Commercial-Off-The-Shelf (COTS) devices are usually equipped with only a few number of UWB antennas, which are not enough for the existing algorithms to provide an accurate angle estimation. To solve this challenge, we design a novel Multi-Frequency MUSIC (MF-MUSIC) algorithm, which extends the classic MUSIC algorithm to the frequency domain and overcomes the antenna limitation barrier in the spatial domain. By doing this, our framework uses only 3 antennas to achieve an accurate angle estimation, which is merely 0.15° different from the state-of-the-art 16-antenna method.
Probability-Guaranteed Distributed Secure Estimation for Nonlinear Systems Over Sensor Networks Under Deception Attacks on Innovations

This paper studies the distributed secure state estimation problem for a class of general nonlinear systems over sensor networks under unknown deception attacks on innovations. At each sensing node, an estimator is designed to generate the state estimate by making use of the local measurements in combination with the neighbours’ information shared via the communication network. During the transmission of innovations among nodes, the data are maliciously falsified by adversaries in a random way. A neural-network-based mechanism is put forward to approximate the unknown falsified innovations with the aim to mitigate the effects on the estimation performance. The objective of the addressed problem is to develop a distributed estimation approach to jointly estimate the system states as well as the unknow deception attacks, ensuring that the state estimation errors at each sensing node reside within required ellipsoidal regions with a pre-specified probability. With the help of certain convex optimization methods, we obtain sufficient conditions for the solvability of the addressed problem and the desired estimator gains can be iteratively computed by solving a series of matrix inequalities. On basis of the proposed framework, some optimization problems are presented to determine sub-optimal estimator parameters from different perspectives. Finally, the applicability of the developed algorithms is validated via a numerical simulation example.
Transmit Power Pool Design for Uplink IoT Networks with Grant-free NOMA

Grant-free non-orthogonal multiple access (GF-NOMA) is a potential multiple access framework for internet-of-things (IoT) networks to enhance connectivity. However, the resource allocation problem in GF-NOMA is challenging and the effectiveness of such a solution is limited due to the absence of closed-loop power control. In this paper, we design a prototype of layer-based transmit power pool by utilizing multi-agent reinforcement learning to provide open-loop power control and offload the computing tasks at the base station (BS) side. IoT users in each layer decide their own transmit power level from this layer-based power pool, instead of transmitting on the allocated sub-channel with allocated transmit power level. The proposed algorithm does not require any information exchange between IoT users and does not rely on any assistance from the BS. Numerical results confirm that the double deep Q network based GF-NOMA algorithm achieves high accuracy and finds out an accurate transmit power level for each layer. Moreover, the proposed GF-NOMA system outperforms the traditional GF with orthogonal multiple access techniques in terms of throughput.
FS-IDS: A Novel Few-Shot Learning Based Intrusion Detection System for SCADA Networks

Supervisory control and data acquisition (SCADA) networks provide high situational awareness and automation control for industrial control systems, whilst introducing a wide range of access points for cyber attackers. To address these issues, a line of machine learning or deep learning based intrusion detection systems (IDSs) have been presented in the literature, where a large number of attack examples are usually demanded. However, in real-world SCADA networks, attack examples are not always sufficient, having only a few shots in many cases. In this paper, we propose a novel few-shot learning based IDS, named FS-IDS, to detect cyber attacks against SCADA networks, especially when having only a few attack examples in the defenders’ hands. Specifically, a new method by orchestrating one-hot encoding and principal component analysis is developed, to preprocess SCADA datasets containing sufficient examples for frequent cyber attacks. Then, a few-shot learning based preliminary IDS model is designed and trained using the preprocessed data. Last, a complete FS-IDS model for SCADA networks is established by further training the preliminary IDS model with a few examples for cyber attacks of interest. The high effectiveness of the proposed FS-IDS, in detecting cyber attacks against SCADA networks with only a few examples, is demonstrated by extensive experiments on a real SCADA dataset.
Communication Security in VANETs based on the Physical Unclonable Function

In this paper we propose an alternative to the security protocols generally developed for VANETs (Vehicular Ad-hoc NETworks), which often rely on asymmetric keys and PKIs (Public Key Infrastructures). Instead, we propose a solution whose architecture is based on Physical Unclonable Functions (PUFs) inside the vehicles. We design a protocol with these PUF functions to securely spread a secret key by means of a Road Side Unit (RSU). This secret key will be used by the vehicles to warn neigboring vehicles of an emergency situation. The key is regularly changed by the RSU but its value evolves slowly over time in a random way. Thus a message using an old (but not too old) key can be accepted as valid. We define the whole protocol where a vehicle first identifies itself to the Road Side Unit for authentication. Then the RSU sends back a key which will be used to send alerts if an emergency situation occurs on the road. The RSU manages the evolution of this key by just randomly changing one bit at each change. Thus an alert can be accepted if the key used to issue an alert is "close" to the last key issued by the RSU. We investigate the advantages of this new scheme and evaluate the implications in terms of exchanges between the vehicles (and their PUFs) and the network connecting the RSUs.
Direct Synthesis of Multiband Bandpass Filters With Generalized Frequency Transformation Methods

This article presents a new generalized direct synthesis method to design multiband bandpass filters (MBPFs). Based on the proposed frequency transformation technique, it can be used to synthesize an arbitrary number of bandpass filters starting from a low-pass prototype filter. This synthesis allows determining analytically all the resonant angular frequencies and slope parameters of all the bandpass filters from the low and high cut-off angular frequencies used as initial specifications whatever the number of bands. To validate our method, triple-, quad-, and quint-band third-order Chebyshev bandpass filters are designed and implemented in microstrip technologies. Very good agreements were achieved between simulation responses and measurements.
Neighboring-Aware Caching in Heterogeneous Edge Networks by Actor-Attention-Critic Learning

With the development of network technology and the surge in demand, the speed and throughput of data and applications are leading to the skyrocketing increase in traffic. The communication and collaboration between heterogeneous edge servers are indispensable. In this scenario with heterogeneous edges, there is a common understanding on the fact that an effective edge caching algorithm could play the role of enabler to reduce the network resource consumption and content fetch delay. However, most of the existing studies on multi-agent caching methods focus more on the overall situation, while ignoring the mutual influence between different agents. In this context, we model the edge caching content replacement problem as a Markov process and deploy attention mechanism based on the Actor-Attention-Critic algorithm to realize a neighboring-aware edge caching (NAEC) strategy. The proposed method makes full use of the communication between base stations to exchange neighboring information, so that we can reduce the pressure on the backbone and further improve user satisfaction. The simulation results have verified the feasibility and effectiveness of the proposed algorithm.
3D face detection based on salient features extraction and skin colour detection using data mining

ABSTRACT Face detection has an essential role in many applications. In this paper, we propose an efficient and robust method for face detection on a 3D point cloud represented by a weighted graph. This method classifies graph vertices as skin and non-skin regions based on a data mining predictive model. Then, the saliency degree of vertices is computed to identify the possible candidate face features. Finally, the matching between non-skin regions representing eyes, mouth and eyebrows and salient regions is done by detecting collisions between polytopes, representing these two regions. This method extracts faces from situations where pose variation and change of expressions can be found. The robustness is showed through different experimental results. Moreover, we study the stability of our method according to noise. Furthermore, we show that our method deals with 2D images.
Green Multi-Stage Upgrade for Bundled-Links SDN/OSPF-ECMP Networks

This paper considers the problem of upgrading a legacy network into a Software Defined Network (SDN) over multiple stages and maximizing energy saving (ES) in the resulting upgraded network or hybrid SDN. In each stage, an operator needs to select and replace legacy switches with SDN switches and seek to switch off as many cables as possible over each link. This paper addresses the said problem where it considers (i) the available budget at each stage, (ii) maximum path delays, (iii) maximum link utilization, (iv) per-stage increase (decrease) in traffic size (upgrade cost), and (v) each non SDN switch must comply with the Open Shortest Path First (OSPF)-Equal Cost Multi-Path (ECMP) protocol. It outlines a Mixed Integer Program (MIP) and a heuristic algorithm called M-GMSU. The results show that (i) MIP and M-GMSU achieve ES of up to 71.93%, (ii) using a larger budget and/or number of stages increases ES, and (iii) the ES of M-GMSU is within 3.55% away from the optimal ES computed by MIP.
Enhancing Wake-Up Radio Range Through Minimum Energy Coding

A substantial part of the research on wireless sensor networks is focused on the optimization of the energy consumption through either hardware or protocol communication stacks. Wake-up Receivers (WuRs) represent a new paradigm that offers both ultra low power consumption and low latency through asynchronous communications. However, WuRs have a low sensitivity and thus can misinterpret the received signal inducing a performance degradation of the whole communicating system. To tackle this issue, low power channel coding techniques can be used and we propose in this work to apply Hamming coding and Minimum Energy Coding (ME) to enhance WuR range. A performance study of these two types of coding shows that ME coding outperforms Hamming code in reducing both bit error rate and energy consumption. At a range of 28 m, ME coding saves about 3 times the energy at a bit error rate of 10−3 compared to uncoded scheme. Furthermore, experimentation on the missed wake-ups when applying ME coding was done, showing a gain of 22% in reliability compared to uncoded scheme.