Miracle-dz/UniFAR-FTUs huggingface dataset:
Isotopic signatures induced by upwelling reveal regional fish populations in Lake Tanganyika

Lake Tanganyika’s pelagic fish sustain the second largest inland fishery in Africa and are under pressure from heavy fishing and global warming related increases in stratification. Only little is known about whether basin-scale hydrodynamics – including a more stratified north and an upwelling-driven south – induce ecological and genetic differences among populations of highly mobile, pelagic fish inhabiting these different areas. Here, we examine whether the basin-scale dynamics leave distinct isotopic imprints in the pelagic fish of Lake Tanganyika, which may reveal differences in habitat, diet, or lipid content. We conducted two lake-wide campaigns during different seasons and collected physical, nutrient, chlorophyll, phytoplankton and zooplankton data. Additionally, we analyzed the pelagic fish – the clupeids Stolothrissa tanganicae, Limnothrissa miodon and four Lates species – for their isotopic and elemental carbon (C) and nitrogen (N) compositions. The δ13C values were significantly higher in the productive south after the upwelling/mixing period across all trophic levels, implying that the fish have regional foraging grounds, and thus record these latitudinal isotope gradients. By combining our isotope data with genetics, we demonstrate that the fish form regional populations on a seasonal to multiannual time scale. Based on δ15N and C:N ratios, we found no strong evidence for varying diets or lipid contents between those regional populations. Additional analyses revealed that isotopic variations between specimens from the same location are not linked to genetic differences. We suggest that the development of basinscale ecological differences in response to the prevailing hydrodynamic regimes may be inhibited by lake-wide gene flow on the long term. Our findings show that the pelagic fish species are genetically adapted to the whole lake, but they form regional populations on short time scales. This implies that sustainable management strategies may adopt basin-scale fishing quotas.
In planta dynamics, transport-biases and endogenous functions of mobile siRNAs in Arabidopsis

In RNA interference (RNAi), small-interfering (si)RNAs processed from double-stranded RNA guide ARGONAUTE(AGO) proteins to silence sequence-complementary RNA/DNA. Plant RNAi can propagate locally and systemically, but despite recent mechanistic advances, basic questions/hurdles remain unaddressed. For instance, RNAi is inferred to diffuse through plasmodesmata, yet how its dynamics in planta compares with that of established symplastic-diffusion markers remains unknown. Also unknown is why select siRNA species, or size-classes thereof, are recovered in RNAi-recipient tissues, yet only under some experimental settings. Finally, RNAi shootward movement in micro-grafted Arabidopsis – necessary to study its presumptive transgenerational effects– has not been achieved thus far and endogenous functions of mobile RNAi remain scarcely documented. Focusing on non-amplified RNAi in Arabidopsis, we show here that (i) transgenic RNAi-movement, although symplasmic, only partially recapitulates the diffusion pattern of free GFP in planta, (ii) the presence/absence of specific AGOs in incipient/traversed/recipient tissues likely explains the apparent siRNA-selectivity observed during vascular movement, (iii) stress application allows endo-siRNA translocation against the shoot-to-root phloem flow, and (iv) mobile endo-siRNAs generated from a single inverted-repeat(IR) locus, have the potential to regulate hundreds of transcripts. Our results close important knowledge-gaps, rationalize previously-noted inconsistencies between mobile RNAi settings, and provide a framework for functional endo-siRNA studies.
Intracellular Delivery of Antibodies for Selective Cell Signaling Interference

Many intracellular signaling events remain poorly characterized due to a general lack of tools to interfere with “undruggable” targets. Antibodies have the potential to elucidate intracellular mechanisms via targeted disruption of cell signaling cascades because of their ability to bind to a target with high specificity and affinity. However, due to their size and chemical composition, antibodies cannot innately cross the cell membrane, and thus access to the cytosol with these macromolecules has been limited. Here, we describe strategies for accessing the intracellular space with recombinant antibodies mediated by cationic lipid nanoparticles to selectively disrupt intracellular signaling events. To enable such investigations, we first produced a series of antibody constructs, known as scFv-Fcs, containing additional, genetically encoded negative charges located at the C-termini of the constructs. Preparing proteins with negatively charged motifs has previously been shown to enhance intracellular protein delivery with cationic lipids, but usually for the purpose of genome editing or targeted cell death. We started by generating derivatives of scFv-Fc17, an antibody construct previously reported to bind specifically to signal transducer and activator of transcription 3 (STAT3) phosphorylated at Tyr705 (pYSTAT3). We screened a small number of lipids from our combinatorial lipid library with flow cytometry and found that PBA-Q76-O16B facilitated the most efficient delivery of scFv-Fcs under the conditions tested. In HepG2 cells, we observed up to 60.5% delivery efficacy, while in a STAT3-luciferase reporter cell line up to 71.5% delivery efficacy was observed. These results demonstrated the feasibility of accessing the intracellular space with scFv-Fcs. However, we also note that no more than modest changes were observed upon changing the numbers of negative charges in these constructs during delivery. Characterization of the cytotoxicity, size, and encapsulation efficiency of scFv-Fcs with PBA-Q76-O16B revealed that the constructs were generally well-behaved, with addition of differing quantities of negative charge resulting in at most modest effects. Importantly, functional assays monitoring transcriptional activity in luciferase reporter cell lines and HepG2 cells demonstrated significant reduction of gene expression downstream of pYSTAT3 following delivery of scFv-Fc17 constructs. Together, our results establish the use of recombinantly produced antibodies to selectively interfere with cell signaling events driven by a single posttranslational modification. Efficient intracellular delivery of engineered antibodies opens up possibilities for modulation of previously “undruggable” targets, including for potential therapeutic applications.
Single cell profiling in COVID-19 associated acute kidney injury reveals patterns of tubule injury and repair in human

The cellular mechanisms of kidney tubule repair are poorly characterized in human. Here, we applied single-nucleus RNA sequencing to analyze the kidney in the first days after acute injury in 5 critically ill patients with COVID-19. We identified abnormal proximal tubule cell states associated with injury, characterized by altered functional and metabolic profiles and by pro-fibrotic properties. Tubule repair involved the plasticity of mature tubule cells in a process of cell de-differentiation and re-differentiation, which displayed substantial similarities between mouse and man. In addition, in man we identified a peculiar tubule reparative response determining the expansion of progenitor-like cells marked by PROM1 and following a differentiation program characterized by the sequential activation of the WNT, NOTCH and HIPPO signaling pathways. Taken together, our analyses reveal cell state transitions and fundamental cellular hierarchies underlying kidney injury and repair in critically ill patients.
GoDetector: Detecting Concurrent Bug in Go

Go provides a new concurrency mechanism based on message-passing, which brings a series of new concurrency bugs. The existing tools are highly dependent on model-checking methods in which they take the source program as the input of model-checking tools to detect the potential concurrent bugs. However, these methods can only qualitatively obtain the concurrency of the program but not obtain the exact number of concurrency bugs in the program. Meanwhile, these approaches are not sensitive to dead codes, which are prone to false negatives. Furthermore, few works have been done on detecting WaitGroup-related bugs in Go concurrent programming, which may cause deadlock. To this end, this paper proposes a novel approach GoDetector to detect concurrent bugs. GoDetector uses a detecting algorithm and a pushdown automaton to verify the channel-related concurrent bugs and WaitGroup-related concurrent bugs separately. To support the evaluation of the tool, 30 benchmarks are collected from the existing tools. GoDetector reports 2 channel safety errors, 29 deadlocks, and 11 WaitGroup-related errors. Compared with the existing tool Godel, GoDetector has the capacity to report more concurrency bugs and fewer false positives. GoDetector also offers additional support for the detection of WaitGroup variables. The experimental results indicate that GoDetector is more efficient at detecting the concurrency in regards to accuracy and diversity.
Discriminative Alignment of Projected Belief Networks

The projected belief network (PBN) is a deep layered generative network with tractable likelihood function (LF) and can be used as a Bayesian classifier by training a separate model on each data class, and classifying based on maximum likelihood (ML). Unlike other generative models with tractable LF, the PBN can share an embodiment with a feed-forward classifier network. By training a PBN with a cost function that combines LF with classifier cross-entropy, its network weights can be “aligned” to the decision boundaries separating the data class from other classes. This results in a Bayesian classifier that rivals state of the art discriminative classifiers. These claims are backed up by classification experiments involving spectrograms of spoken keywords and handwritten characters.
Simulation of Resident Demand Response Considering User Response Uncertainty

Residential load in the smart grid is a kind of potential demand response (DR) resource. The parameters under a given distribution law can be determined by parameter estimation methods. The information in the dataset including load curves, historical participation characteristics, and historical response capabilities of DR events is used as the basis for modeling. Simulation resident users are generated according to the distribution rules to simulate resident load characteristics and their behavior in DR event. This article uses simulated users as the object to simulate a DR event with specific conditions. The simulation obtains the maximum potential of a certain number of resident users.
Transforming procedures to web applications using IFML: The new Greek Citizenship Test

The Covid-19 pandemic accelerated the digital transformation in many areas in the Public Sector and Greece made remarkable progress towards that goal. This also increased the need for fast built user friendly web solutions. In this paper we make an initial functional requirement analysis of the new exam procedure for the Certificate of Knowledge Competence for Naturalization (CKCN) and propose the web modeling of one of its phases using IFML. We briefly present the IFML structure, its advantages and discuss how this approach will be extended in the future since it is still a work in progress.
An Improved Twin Support Vector Regression Machine Based on Adjusted Cosine Similarity for Load Prediction

Due to the rapid increase of power data resources on the consumer side, using of large-scale data to achieve fast and high-precision load prediction has become an urgent problem. This paper proposes an improved twin support vector regression machine for load forecasting based on adjusted cosine similarity. The data sparsity of the improved twin support vector regression machine is better than the twin support vector regression machine which is the novel machine learning method. The training speed of the improved twin support vector regression machine is faster than the support vector regression machine which is the classic machine learning method. After verification of the actual grid data, this method has good prediction accuracy and prediction speed compared with the twin support vector regression machine and the support vector regression machine.
A Distributed Method for Fast Mining Frequent Patterns From Big Data

In recent years, knowledge discovery in databases provides a powerful capability to discover meaningful and useful information. For numerous real-life applications, frequent pattern mining and association rule mining have been extensively studied. In traditional mining algorithms, data are centralized and memory-resident. As a result of the large amount of data, bandwidth limitation, and energy limitations when applying these methods to distributed databases, especially in this era of big data, the performance is not effective enough. Hence, data mining on distributed environments has emerged as an important research area. To improve the performance, we propose a set of algorithms based on FP growth that discover FPs that are capable of providing fast and scalable service in distributed computing environments and a brief data structure to store items and counts to minimize the data for transmission on the network. To ensure completeness and execution capability, DistEclat and BigFIM were considered for the experiment comparison. Experiments show that the proposed method has superior cost-effectiveness for processing massive datasets and good capabilities under various experiment conditions. The proposed method on average required only 33% of the execution time and 45% of the transmission cost of DistEclat. Compared to BigFIM, The proposed method on average required 23.3% of the execution time and 14.2% of the transmission cost of BigFIM.
Accurate Detection of False Data Injection Attacks in Renewable Power Systems Using Deep Learning

The rapid development of technology in the past decades created a society heavily dependent on electricity, where even short disturbances in the power supply can result in grave socio-economic consequences. Therefore, assuring a safe and reliable operation of the power system has become of utmost importance. False data injection attacks (FDIAs) represent a class of cyber-attacks targeting the power system state estimation. FDIAs alter the perspective of the power system’s state which can lead to inappropriate control actions. Thus, a reliable method for detecting FDIAs represents the main prerequisite to the safe operation of the power system in the context of cybersecurity. Noticing the scarce literature analyzing the detection of FDIAs in power systems with a high share of renewable energy sources, this paper demonstrates that the performance of the existing methods deteriorates when faced with the volatile nature of renewable energy sources. This paper presents a deep learning approach for detecting stealthy FDIAs concerning the power systems with high penetration of renewable energy sources. The performance of the proposed method is validated through different scenarios based on the modified versions of the IEEE 14-bus system and the IEEE 118-bus system. The proposed method is able to detect most of the attacks under different test scenarios, outperforming the benchmark techniques with an average detection rate of 99% for the IEEE 14-bus system and 97% for the IEEE 118-bus system.
Mass Vaccine Administration under Supply Uncertainty

The insurgence of COVID-19 requires fast mass vaccination, hampered by scarce availability and uncertain supply of vaccine doses and a tight schedule for boosters. In this paper, we analyze planning strategies for the vaccination campaign to vaccinate as many people as possible while meeting the booster schedule. We compare a conservative strategy and q-days-ahead strategies against the clairvoyant strategy. The conservative strategy achieves the best trade-off between utilization and compliance with the booster schedule. Q-days-ahead strategies with q < 7 provide a larger utilization but run out of stock in over 30% of days.
Match-Extracting Chirplet Transform With Application to Bearing Fault Diagnosis

As a typical nonstationary signal, rolling bearing fault signals exhibit dynamic characteristics and low signal-to-noise ratio, which lead to fuzzy spectrograms from traditional time–frequency analysis (TFA) methods. It is a challenging task to accurately represent its instantaneous frequency (IF) trajectory. To solve this problem, this article proposes a TFA method which is called match-extracting chirplet transform for analyzing multicomponent dynamic signals. It generates a time–frequency (TF) representation of high energy concentration by constructing a basis function that matches the chirp rate with the original signal at any TF point. A matching extraction operator is built to extract the best matching ridges on the IF trajectory to further improve the quality of the spectrum. The proposed method can effectively characterize the dynamic characteristics of multicomponent signals and reduce the energy divergence. Meanwhile the algorithm retains signal reconstruction and can be used to recover significant components by adaptively searching modal ridges. Numerical verification shows that this method has a better performance in TF location and noise robustness. Finally, the validity of this method in mechanical fault diagnosis is verified by applying it to actual bearing signals.
Statistical Delay and Error-Rate Bounded QoS Provisioning for SWIPT Over CF M-MIMO 6G Mobile Wireless Networks Using FBC

As a new and dominating type of time-sensitive traffic over 6G wireless networks, massive ultra-reliable and low latency communications (mURLLC) has attracted considerable research attention, while raising several new challenges, including massive connectivity, ultra-low latency, super-reliability, and high energy efficiency. Several promising 6G enablers, including statistical delay and error-rate bounded quality-of-service (QoS) provisioning, cell-free (CF) massive multi-input multi-output (m-MIMO), simultaneous wireless information and power transfer (SWIPT), etc., have been developed to support mURLLC. Specifically, CF m-MIMO can significantly enhance QoS performance of SWIPT by boosting data rate and energy efficiency. On the other hand, finite blocklength coding (FBC) has been proposed to support various massive access techniques while reducing access latency and guaranteeing stringent QoS. However, how to efficiently integrate SWIPT with CF m-MIMO using FBC for statistical delay and error-rate bounded QoS to support mURLLC has posed many new challenges not encountered before. To overcome these difficulties, in this paper we develop FBC based statistical delay and error-rate bounded QoS provisioning schemes over SWIPT-enabled CF m-MIMO 6G wireless networks. First, we establish SWIPT-enabled CF m-MIMO system models using FBC. Then, we optimize the tradeoffs between $\epsilon$-effective capacity and harvested energy for our proposed statistical QoS provisioning. Finally, our simulated results validate and evaluate our developed schemes.
Review: Application of Convolutional Neural Network in Defect Detection of 3C Products

Based on the rapid development of semiconductors, integrated circuits and the Internet. 3C products such as computers, tablets, mobile phones and smart TVs have become an indispensable part of people’s lives. With the prosperity and development of the 3C product market, the demand for the quality of display panels and related detection technologies are increasing. As the iconic network of deep learning, has been extensively studied in the field of image recognition and defect detection. Based on the development of CNN, this article summarizes the defect detection method of 3C products by CNN with different depths. First, we reviewed the origin of CNN and its structural components, then introduced the upgrade and improvement of important components, and finally introduced and compared the applications of CNN with different depths in defect detection. Through the comparison and summary of the effect of defect detection, we analyze the opportunities and challenges of different CNN frameworks, and exhibit the strategies for different application scenarios.
Directional Magnitude Local Hexadecimal Patterns: A Novel Texture Feature Descriptor for Content-based Image Retrieval

Social media platforms such as Twitter, Facebook, and Flicker, and the evolution of digital image capturing devices have resulted in the generation of a massive number of images. Thus, we experienced exponential growth in digital image repositories in the last decade. Content-based image retrieval (CBIR) has been extensively employed to reduce the dependency on textual annotations for image searching. Effective feature descriptor is mandatory to retrieve the most relevant images from the repository. Additionally, CBIR methods often experience the semantic gap problem, which must be addressed. In this paper, we propose a novel texture feature descriptor, Directional Magnitude Local Hexadecimal Patterns (DMLHP), based on texture orientation and magnitude to retrieve the most relevant images. The objective of the proposed feature descriptor is to examine the relationship of neighboring pixels with their adjacent neighbors based on texture orientation and magnitude. Our DMLHP texture descriptor is capable of capturing the texture and semantic information of the images effectively with the same visual content. Furthermore, the proposed method employs a learning-based approach to lessen the semantic gap problem and to improve the understanding of the contents of query images to retrieve the relevant images. The presented descriptor provides remarkable results by achieving the average retrieval precision (ARP) of 66%, 92%, 83%, average retrieval recall (ARR) of 66%, 92%, 83%, average retrieval specificity (ARS) of 99%, 99%, 76%, and average retrieval accuracy (ARA) of 98%, 99%, 85% on the AT&T, MIT Vistex, and Brodatz Texture image repositories, respectively. Our experiments reveal that the proposed DMLHP descriptor achieves far better performance, i.e., 95% on AT&T, 92% on BT, and 99% on MIT Vistex, when used with a learning-based approach over a non-learning-based approach (similarity measure). Experimental results show that the proposed texture descriptor outperforms state-of-the-art descriptors such as LNIP, LTriDP, LNDP, LDGP, LEPSEG, and CSLBP for CBIR.
Low-Complexity Construction of Polar Codes Based on Genetic Algorithm

How to efficiently rank the reliability order of the channels is critical for polar code construction. A new polar code construction framework tailored to a given decoding algorithm based on the genetic algorithm has been proposed to achieve an error correction performance gain. However, the time complexity of this scheme is extremely high. In this letter, a scheme based on a hash table is proposed to generally reduce the computational complexity of the repeated candidates in the genetic algorithm-based system. A two-step method is proposed to improve the crossover process when the system has prior knowledge. It is shown from the numeric results that the proposed scheme achieves computational complexity reduction of the repeated candidates. An error correction performance gain is achieved over the original scheme under successive cancellation list decoding.