Miracle-dz/UniFAR-FTUs huggingface dataset:
Bacterial density as an unexpected factor regulating decomposition by soil oligotrophs

Bacterial decomposition of organic matter in soils is generally believed to be mainly controlled by the accessibility of bacteria to their substrate. The influence of bacterial metabolic traits on this control has however received little attention in highly heterogeneous spatial conditions under advective-dispersive transport of bacteria and substrates. Here, we develop a biochemical transport model to screen the interactive impacts of dispersion and metabolic traits on mineralization. We compare the model results with two sets of previously performed cm-scale soil-core experiments in which the mineralization of the pesticide 2,4-D was measured under well-controlled initial distributions and transport conditions. Bacterial dispersion away from the source of substrate induced a significant increase in 2,4-D mineralization, revealing the existence of a control of decomposition by the bacterial density, in addition to the accessibility to the substrate. This regulation of degradation by density becomes dominant for bacteria with an efficient uptake of substrate at low substrate concentrations (i.e. oligotrophs). The model output suggests that the distance between bacteria adapted to oligotrophic environments is a stronger regulator of degradation than the distance between substrate source and these bacteria. Such oligotrophs, commonly found in soils, compete with each other for substrate even under remarkably low population densities. The ratio-dependent Contois growth model, which includes a density regulation in the expression of the uptake efficiency, appears more versatile and accurate than the substrate-dependent Monod model. In view of their strong interactions, biochemical and transport processes cannot be handled independently but should be integrated, in particular when biochemical processes of interest are carried out by oligotrophs. Highlights – Biodegradation in soils results from strong biochemical and transport couplings – Biodegradation depends on bacterial density, in addition to substrate accessibility – Bacterial density regulation counterbalances substrate accessibility regulation – Density regulation is enhanced for oligotrophic bacteria – The ratio-dependent Contois model is relevant to represent this double regulation
Proximity Measures as Graph Convolution Matrices for Link Prediction in Biological Networks

Motivation Link prediction is an important and well-studied problem in computational biology, with a broad range of applications including disease gene prioritization, drug-disease associations, and drug response in cancer. The general principle in link prediction is to use the topological characteristics and the attributes–if available– of the nodes in the network to predict new links that are likely to emerge/disappear. Recently, graph representation learning methods, which aim to learn a low-dimensional representation of topological characteristics and the attributes of the nodes, have drawn increasing attention to solve the link prediction problem via learnt low-dimensional features. Most prominently, Graph Convolution Network (GCN)-based network embedding methods have demonstrated great promise in link prediction due to their ability of capturing non-linear information of the network. To date, GCN-based network embedding algorithms utilize a Laplacian matrix in their convolution layers as the convolution matrix and the effect of the convolution matrix on algorithm performance has not been comprehensively characterized in the context of link prediction in biomedical networks. On the other hand, for a variety of biomedical link prediction tasks, traditional node similarity measures such as Common Neighbor, Ademic-Adar, and other have shown promising results, and hence there is a need to systematically evaluate the node similarity measures as convolution matrices in terms of their usability and potential to further the state-of-the-art. Results We select 8 representative node similarity measures as convolution matrices within the single-layered GCN graph embedding method and conduct a systematic comparison on 3 important biomedical link prediction tasks: drug-disease association (DDA) prediction, drug–drug interaction (DDI) prediction, protein–protein interaction (PPI) prediction. Our experimental results demonstrate that the node similarity-based convolution matrices significantly improves GCN-based embedding algorithms and deserve more attention in the future biomedical link prediction Availability Our method is implemented as a python library and is available at githublink Contact mustafa.coskun@agu.edu. tr Supplementary information Supplementary data are available at Bioinformatics online.
Common virulence gene expression in naïve and severe malaria cases

Sequestration of Plasmodium falciparum-infected erythrocytes to host endothelium through the parasite-derived PfEMP1 adhesion proteins, is central to the development of malaria pathogenesis. PfEMP1 proteins have diversified and expanded to encompass many sequence variants conferring the same array of human endothelial receptor binding phenotypes. Here, we analyzed RNA-seq profiles of parasites isolated from 32 infected travelers returning to Germany. Patients were categorized into either malaria naïve (n=15) or pre-exposed (n=17), and into severe (n=8) or non-severe (n=24) cases. Expression analysis of PfEMP1-encoding var genes showed that severe malaria was associated with expression of PfEMP1 containing the endothelial protein C receptor (EPCR)-binding CIDRα1 domain, whereas CD36-binding PfEMP1 was linked to non-severe malaria outcomes. In addition, gene expression-guided determination of parasite age, suggested that circulating parasites from non-severe malaria patients were older than parasites from severe malaria patients. First-time infected patients were also more likely to develop severe symptoms and tended to be infected for a longer period, which thus appeared to select for parasites with more sequestration-efficiency and therefore more pathogenic PfEMP1 variants.
A Recombinant Protein Biomarker DDA Library Increases DIA Coverage of Low Abundance Plasma Proteins

Credible detection and quantification of low abundance proteins from human blood plasma is a major challenge in precision medicine biomarker discovery when using mass spectrometry (MS). Here, we employed a mixture of recombinant proteins in DDA libraries to subsequently detect cancer-associated low abundance plasma proteins using SWATH/DIA. The exemplar DDA recombinant protein spectral library (rPSL) was derived from tryptic digestion of 36 human recombinant proteins that had been previously implicated as possible cancer biomarkers in both our own and other studies. The rPSL was then used to identify proteins from non-depleted colorectal cancer (CRC) plasmas by SWATH-MS. Most (32/36) of the proteins in the rPSL were reliably identified in plasma samples, including 8 proteins (BTC, CXCL10, IL1B, IL6, ITGB6, TGFα, TNF, TP53) not previously detected using high-stringency MS in human plasmas according to PeptideAtlas. The rPSL SWATH-MS protocol was compared to DDA-MS using MARS-depleted and post-digestion peptide fractionated plasmas (here referred to as a human plasma DDA library). Of the 32 proteins identified using rPSL SWATH, only 12 were identified using DDA-MS. The 20 additional proteins exclusively identified by using the rPSL approach with SWATH were mostly lower abundance (i.e., <10ng/ml) plasma proteins. To mitigate FDR concerns, and replicating a more typical approach, the DDA rPSL was also merged into a human plasma DDA library. When SWATH identification was repeated using this merged library, the majority (33/36) of low abundance plasma proteins from the rPSL could still be identified using high-stringency HPP Guidelines v3.0 protein inference criteria.
Costly mate preferences can promote reproductive isolation along ecological gradients

Species often replace each-other spatially along ecological or environmental gradients. In models of parapatric speciation driven by assortative mating, delayed mating when females are too choosy about mates has so far been ignored. Yet, this generates a cost of choosiness, which should cause direct sexual selection against female choosiness. In our spatially-explicit individual-based model, disruptive viability selection leads to divergence of an ecological trait in a population distributed along an ecological gradient. Additionally, female choosiness (following a ‘matching mating rule’ based on the same ecological trait) can evolve at the risk of delaying mating, and can limit gene flow between diverging populations. We show that, along ecological gradients, a cost of choosiness associated with delayed mating modifies the genotypic frequencies on which viability selection acts. This cost can even remove much of the viability selection acting indirectly against choosiness at the ends of the gradient, thereby favouring the evolution of strong choosiness. A cost of choosiness can therefore promote reproductive isolation in parapatry, depending on the characteristics of the ecological gradient.
Phylogenetic analysis of quorum sensing systems in bacteria

Quorum sensing (QS) is a cell-to-cell communication system that enables bacteria to coordinate their gene expression depending on their population density, via the detection of small molecules called autoinducers. In this way bacteria can act collectively to initiate processes like bioluminescence, virulence and biofilm formation. Autoinducers are detected by receptors, some of which are part of Two Component Signal Transduction Systems (TCS), which comprise of a sensor histidine kinase (usually membrane-bound) and a cognate response regulator. Different QS systems are used by different bacterial taxa, and their relative evolutionary relationships have not been extensively studied. To address this, we used the KEGG database to identify all the QS receptors and response regulators that are part of TCS and collected their amino acid sequences from different species. In order to discover their evolutionary relationships: (i) we compared the combinations of the highly conserved domains in the different receptors and response regulators using the SMART and KEGG databases, and (ii) we constructed and compared phylogenetic trees, based on neighbor-joining and maximum likelihood methods. For both the QS receptors and the response regulators, our analysis indicates certain close evolutionary relationships, highlight a common evolutionary history, and which can inform future applications, such as the design of novel inhibitors for pathogenic QS systems.
Distributed Spatiotemporal Trajectory Query Processing in SQL

Nowadays, the collection of moving object data is significantly increasing due to the ubiquity of GPS-enabled devices. Managing and analyzing this kind of data is crucial in many application domains, including social mobility, pandemics, and transportation. In previous work, we have proposed the MobilityDB moving object database system. It is a production-ready system, that is built on top of PostgreSQL and PostGIS. It accepts SQL queries and offers most of the common spatiotemporal types and operations. In this paper, to address the scalability requirement of big data, we provide an architecture and an implementation of a distributed moving object database system based on MobilityDB. More specifically, we define: (1) an architecture for deploying a distributed MobilityDB database on a cluster using readily available tools, (2) two alternative trajectory data partitioning and index partitioning methods, and (3) a query optimizer that is capable of distributing spatiotemporal SQL queries over multiple MobilityDB instances. The overall outcome is that the cluster is managed in SQL at the run-time and that the user queries are transparently distributed and executed. This is validated with experiments using a real dataset, which also compares MobilityDB with other relevant systems.
Constrained Consensus-based Iterative Algorithm for Economic Dispatch in Power Systems

This paper considers the distributed economic dispatch problem in power systems. A novel constrained consensus-based iterative algorithm is proposed to cooperatively search the optimal incremental cost. The algorithm is carried out by alternately executing a continuous-time finite-time consensus algorithm and employing local projection operations only when consensus variables reach agreement. At each iteration, the initial values of consensus variables are restored locally based on the deviation between the consensus value in the last iteration and its projection on local constraints. Compared to existing methods, our method only needs a single consensus algorithm and does not require continuous projection operations. Thus each node in our algorithm only transmits a single variable to neighbours and the search time of the optimal solution is significantly reduced. Convergence analysis is given, and numerical examples are presented to show the effectiveness of our method with comparison to some existing results.
"Reading" cities with computer vision: a new multi-spatial scale urban fabric dataset and a novel convolutional neural network solution for urban fabric classification tasks

This paper builds on the proven track record of CNN-based pattern recognition and feature extraction methods, and reports a novel model that classifies urban fabric samples of metropolitan areas in terms of (1) which city they belong to, (2) what types of urban fabric they belong to, and (3) which historic period they originate from. Currently, such tasks require intensive manual work by senior professionals, and even then, inconsistencies and errors occur. Our work is based on a novel urban fabric dataset of four metropolitan areas with distinct typologies (linear development, open block, gated compound, medieval region, irregular grid and orthogonal gird), which consist of high resolution 3-dimensional built form data and hierarchical street networks. The classification model presented in this paper is the first that is capable of predicting the city origin, urban fabric pattern type and construction period. The novelty is also characterised by jointly considering urban fabric features across multiple spatial scales. The experiments demonstrate that this multi-scale approach can capture a wide range of urban fabric features across cities, urban fabric pattern types and development periods. We further find that the effectiveness can be enhanced by appending an auxiliary network for identifying the most appropriate combinations of the multiple spatial scales in line with the classification task. The dataset and model can massively scale up the productivity of researchers and professionals working on cities.
Research on Robot's Indoor Object Finding Strategy Based on Semantic Relatedness

For the scene of finding things, people will go directly to the area where the purpose of the target object is most closely related, but current robots cannot understand the use relevance of commonly used objects in daily life. In order to give robots an understanding of this content, this article designs an object-finding strategy based on semantic information, which is primarily divided into two parts: visual learning and semantic learning. Visual learning mainly obtains more realistic corpus through the recognition of a large number of actual pictures. Semantic learning mainly processes the corpus and obtains the semantic similarity ranking of objects that are more in line with daily life. Through the verification of the training set and the test set, the semantic similarity ranking obtained in this paper has a good performance.
Deep Neural Networks Based Key Concealment Scheme

To keep the Internet-of-things (IoT) environment secure, employing a cryptographic function to various IoT devices has become vital. An important factor to consider is how to store a cryptographic key (or passwords) securely. A popular method is to store the key in the storage protected by some hardware-based security functions. This paper presents a novel concept to conceal cryptographic keys into deep neural networks (DNNs), named DNNs-based key concealment scheme. In this scheme, a key can be concealed into a proper deep neural network model which is trained with secret input data. We demonstrate the practical applicability of our concept by presenting an instance and a use-case scenario of the DNNs-based key concealment scheme and show its correctness. To prove its robustness, two fundamental security evaluation methods are proposed for investigating the security of the instantiation. To the best of our knowledge, this is the first attempt of its kind.
An enhanced dynamic identification method for 6-DOF industrial robot based on time-variant and weighted Genetic algorithm

This paper presents an identification method which is based on genetic algorithm (GA) and its improved method to estimate dynamic parameters of industrial robots without load. The procedure consists of the following steps: 1) derivation of the linear form of the dynamic model of the robot according to the Lagrange equation; 2) designing of the excitation trajectory in the form of fifth order Fourier series as exciting trajectory; 3) identification, where genetic algorithm is used to find the global optimal parameters through the genetic exchange between the groups and the survival of the fittest mechanism with the minimum variance between the theoretical torque and the actual torque as the optimization criteria; 4) model validation; 5) analysis of the factors influencing the accuracy of the results in the identification process; 6) proposal of improved method. The experimental results show that the predicted torque and the measured torque obtained by the identification algorithm have a high matching degree, and the model can reflect the actual dynamic characteristics of the robot.
An Open-loop Digitally Controlled Supply Modulator for Wideband Envelope Tracking

Envelope tracking (ET) is one of the important ways to increase the efficiency of power amplifier (PA). The supply modulator (SM) is critical to fulfil the ET strategy. This paper provides a ripple tracking method based on an open-loop digital controller for SM with one switching amplifier (SA) to efficiently track the envelope of PA. The digital controller combines the strong abilities of caching the envelope signals in advance and the powerful envelope shaping as well as the filtering capability to conquer the intrinsic obstacle of close-loop delay, accelerating the tracking speed and decreasing the operation frequency of SM. The performance of SM is highly enhanced based on the proposed circuit model with error correction technique. The proposed ripple tracking method realizes excellent average power tracking (APT) performance compared with that of multi-level SM structure and decreases the hardware requirements, significantly. A test SA chip is fabricated with a standard 0.18 μm CMOS technology and the test platform of whole SM system is established. The measurement results show that the proposed platform achieves very high efficiency of 88% in tracking the envelope of 10-MHz ~ 100-MHz OFDM baseband signals.
PAK 4 regulates stemness and progression in endocrine resistant ER-positive metastatic breast cancer

Despite the effectiveness of endocrine therapies to treat estrogen receptor-positive (ER+) breast tumours, two thirds of patients will eventually relapse due to de novo or acquired resistance to these agents. Cancer Stem-like Cells (CSCs), a rare cell population within the tumour, accumulate after anti-estrogen treatments and are likely to contribute to their failure. Here we studied the role of p21-activated kinase 4 (PAK4) as a promising target to overcome endocrine resistance and disease progression in ER + breast cancers. PAK4 predicts for resistance to tamoxifen and poor prognosis in 2 independent cohorts of ER + tumours. We observed that PAK4 strongly correlates with CSC activity in metastatic patient-derived samples irrespective of breast cancer subtype. However, PAK4-driven mammosphere-forming CSC activity increases alongside progression only in ER + metastatic samples. PAK4 activity increases in ER + models of acquired resistance to endocrine therapies. Targeting PAK4 with either CRT PAKi, a small molecule inhibitor of PAK4, or with specific siRNAs abrogates CSC activity/self-renewal in clinical samples and endocrine-resistant cells. Together, our findings establish that PAK4 regulates stemness during disease progression and that its inhibition reverses endocrine resistance in ER + breast cancers.
Multi-Mode Self-Driving EV Control for Industrial Sites and Logistic Stores

The paper presents an experimental prototype of multi-mode autonomous self-driving electric vehicle controlled by Raspberry PI card based on Python language. The developed system provides several self-operating modes of operation using different types of sensors (ultrasound distance sensors and infrared sensors). According to multiple experimental testing, the developed system operates successfully under different modes of operation achieving satisfactory response and good tracking accuracy. The developed modes would be employed in industrial sites and logistic stores as autonomous shuttle service.
Avoiding DC link oscillations in drives

Variable speed drives often contains a DC link with a passive filter to decrease the Total Harmonic Distortion of the input currents. Unfortunately the interaction of the motor with this filter may yield an undesirable sustained oscillation in the DC link. We propose a simple yet effective strategy to avoid this phenomenon, by suitably manipulating the inverter duty cycles. The stability analysis, based on the mathematical theory of averaging, is interesting in itself as a means to study oscillations in power systems.
Electric Vehicle Charging Based on Inductive Power Transfer Employing Variable Compensation Capacitance for Optimum Load Matching

In inductive power transfer applications, it is possible to ensure high efficiency of the main coils by operating at the optimum load. Since the optimum load depends on the coupling between the main coils, the operation needs to be adapted to match this case at different alignment conditions. This paper proposes a method to keep the optimum load constant by varying the natural resonant frequency of both the primary and secondary circuits of a S-S compensation network. This is possible by changing the value of the compensation capacitors at different alignments. This strategy differs from the ones found in the literature, where the input and the output voltage are changed to always match the optimum load. The proposed concept is proven through circuit simulations of an 11 kW EV battery charging system, and several strategies for the implementation of the variable capacitance are discussed.
Airborne Ultrasonic Emission Based on Asymmetric Vibration

This paper proposes a structure of an electrostatically driven thin transducer for high-intensity airborne ultrasound. The proposed transducer generates ultrasound by asymmetric vibration. The air between a diaphragm and a backplate is used as a nonlinear spring. The diaphragm is driven by converting electrostatic energy into acoustic energy of the air. In the experiment, the diaphragm velocity reached 0.27 m/s in the spatially averaged value and 4.5 m/s in the maximum value in the diaphragm. The vibration velocity of 0.27 m/s is equivalent to 112 Pa. However, we could not observe a continuous ultrasound since the velocity decelerated in a shorter duration than we expected.
Development of a Novel Flywheel Energy Storage System Topology

This paper presents a Flywheel Energy Storage System (FESS) concept based on the use of Reluctance Magnetic Gear (RMG) and Superconducting Magnetic Bearing (SMB). A review of these parts used in the FESS and some results related to their behavior are presented. The design is assessed for its performance and experimental work on different components highlight the issues involved with the operation of the FESS.