Miracle-dz/UniFAR-FTUs huggingface dataset:
The algal chloroplast as a platform for synthesis of lytic enzymes targeting Gram-negative pathogens

The rapid rise of antibiotic resistance and shortage of new antibiotics in the drug development pipeline is reaching crisis point, and the scientific response must include a supporting infrastructure for delivery and production. A primary threat to human health is Gram-negative bacteria, which are naturally more resistant to antimicrobial drugs due to the presence of an outer membrane acting as an additional protective barrier. A growing body of research promotes the use of endolysins as novel antibiotics. In their native context, these enzymes lyse the peptidoglycan layer of the bacterial cell wall to release phage progeny from within the host bacterium at the end of the infection cycle. It has been shown that they can also lyse bacteria “from without”, holding great promise as next-generation antibiotics by targeting molecules essential for bacterial viability. This work demonstrates that the eukaryotic microalgae Chlamydomonas reinhardtii can be modified to produce endolysins that target Gram-negative bacteria, offering a new photosynthetic platform with limitless potential for sustainable production. The C. reinhardtii chloroplast presents an excellent compartment for the expression of endolysins as it has a prokaryotic origin, mimicking the environment in which endolysins are naturally produced. Furthermore, it does not contain PG, so the accumulation of endolysin proteins does not pose a deleterious effect on cell viability due to the lack of substrate. Transgenic lines of C. reinhardtii expressing the globular endolysin LysAB2 targeting Acinetobacter baumannii were produced by chloroplast transformation, and recombinant protein accumulation and antimicrobial activity were demonstrated. Enzymatic and antibacterial assays showed clear endolysin activity in algal crude extract against reference strains and multidrug resistant clinical isolates of A. baumannii. Synthetic enzymes known as Artilysins – endolysins fused with antimicrobial peptides so that they act more effectively against Gram-negative pathogens – were also expressed in the C. reinhardtii chloroplast.
Enhanced properties of SPEEK with incorporating of PFSA and barium strontium titanate nanoparticles for application in DMFCs

Novel blend nanocomposite proton‐exchange membranes were prepared using sulfonated poly (ether ether ketone) (SPEEK), perfluorosulfonic acid (PFSA), and Ba0.9Sr0. 1TiO3 (BST) doped‐perovskite nanoparticles. The membranes were evaluated by attenuated total reflection, X‐ray diffraction spectroscopy, water uptake, proton conductivity, methanol permeability, and direct methanol fuel cell test. The effect of two additives, PFSA and BST, were investigated. Results indicated that both proton conductivity and methanol barrier of the blend nanocomposite membranes improved compared with pristine SPEEK and the as‐prepared blend membranes. The methanol permeability and the proton conductivity of the blend membrane containing 6 wt% BST obtained 3.56 × 10−7 cm2 s−1 (at 25 °C) and 0.110 S cm−1 (at 80 °C), respectively. The power density value for the optimum blend nanocomposite membrane (15 wt% PFSA and 6 wt% BST) (54.89 mW cm‐2) was higher than that of pristine SPEEK (31.34 mW cm‐2) and SPF15 blend membrane (36.12 mW cm‐2).
Reference Model and Simulation Framework for Semantic P2P Networks and its Application to Fault-tolerant Semantic P2P Networks

Current research directions in semantic peer-to-peer (SP2P) networks are evolving to combine two complementary technologies: peer-to-peer (P2P) networks and formallystructured information (ontology). SP2P systems represent the next step in the evolution of P2P networks because SP2P systems incorporate several additional features not present in P2P networks. Ontologies are advantageous in P2P networks because they provide the possibility for improving search and content retrieval. The current SP2P research efforts have generated many and diverse realizations and architectures. This diversity in implementation and architecture in turn has led to an ambiguity and incompatibility in defining domain abstracts and concepts. Progress in this area is hampered by a lack of commonality between these approaches, which makes their comparison and translation into practical implementations difficult. In this study, we describe a reference model for SP2P systems in an effort to model the emerging decentralized computing paradigm in a generic and high level abstraction. An SP2P simulation framework based on the reference model has been built. The framework is generic which enables instantiating different existing SP2P systems. A particular system can be considered an instance of the framework. This facilitates examining the effect of architecture changes, i.e., introducing different component implementations, on the behavior of the existing system. Further, the framework enables testing newly developed solutions to existing problems in SP2P systems. Simulation models are derived from the framework for studying the lack of fault-tolerance in the current SP2P systems. Two novel solutions are developed for reliability problem in the SP2P systems, and the effect of the developed solution on existing SP2P system behaviors are studied.
Comparison of coded modulation schemes for the AWGN and the Rayleigh fading channel

Coded modulation schemes based on convolutional codes as well as block codes, in particular turbo codes, are considered on AWGN and flat Rayleigh fading channels. It turns out that codes based on Gray mapping are robust to channel variations, whereas Ungerbock mapping is suitable only if the channel is a-priori known. The most power efficient scheme based on convolutional codes is multilevel coding with parallel decoding. For block codes, however, bit interleaved coded modulation gains by increased codeword length and outperforms the other schemes.
One-Step Hydrothermal Preparation of 1D α-MoO3 Nanobelt Electrode Material for Supercapacitor

1D [Formula: see text]-MoO3 nanobelts were prepared using ammonium heptamolybdate tetrahydrate [(NH[Formula: see text]Mo7O[Formula: see text]H2O] as raw material by one-step hydrothermal method without template or guide agent at 180∘C. The layered [Formula: see text]-MoO3 nanobelt electrode has favorable electrochemical performance, and displays a fairly high specific capacitance, which can be up to 445[Formula: see text]F/g at a current density of 0.5[Formula: see text]A/g.
A Hash-based Hierarchical Algorithm for Massive Text Clustering

Text clustering is the process of segmenting a particular collection of texts into subgroups including content based similar ones. The purpose of text clustering is to meet human interests in information searching and understanding. This study proposes a new fast hierarchical text clustering algorithm HBSH (Hash-based Structure Hierarchical Clustering), which is suitable for massive text clustering. This algorithm uses hash table instead of numerical vectors as its input data. Compared with the other clustering algorithms, the HBSH performs the text clustering process without setting clustering center number and has minor space complexity in advance, which can achieve better performance. The experimental results illustrate that the average time of HBSH is faster than that of traditional text clustering algorithms. Index Terms—hierarchical, text clustering, hash table
Relating PLI Data Across Scales

it g lie d d e r H e lm h o lt z -G e m e in s c h a ft Relating PLI Data Across Scales Sven Strohmer, Julia Reckfort, Melanie Dohmen, Anh-Minh Huynh and Markus Axer 1 Julich Supercomputing Centre (JSC), Institute for Advanced Simulation, Forschungszentrum Julich GmbH, 52425 Julich, Germany 2 Simulation Lab Neuroscience – Bernstein Facility for Simulation and Database Technology, Institute for Advanced Simulation, Julich Aachen Research Alliance, Forschungszentrum Julich GmbH, 52425 Julich, Germany 3 Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Julich GmbH, 52425 Julich, Germany
Contourlet Transform Based Shot Boundary Detection

Shot boundary detection is used to summarize the video in the video content management system so that particular content or required scene can be extracted from a given video and also observers or users can easily find summary of the given video in short time instead of time spending in seeing full length video. Also shot boundaries can be used for video indexing, browsing, and retrieval. So to extract such key frames (shot boundaries) from a given video needs a robust technique with high accuracy and low error rate. In this research work, we proposed a robust technique to find shot boundary using feature vectors, which are obtained from features calculated for each sub-band in the contour let transform domain. The feature vector has the energy, standard deviation, and histogram similarity as the features of each sub-band. The experimental results proved that this novel and robust method had more accuracy rate and low error rate.
The Emerging Role of p38 Alpha in Cancer Specific Metabolism and Therapy: Analysis of Autophagic and Apoptotic Pathways in Response to Its Inhibition

Cancer is one of the leading cause of death in the world. Since tumorigenesis was described as a multistep mechanism in 1958 by Foulds, important progresses are obtained in the research field. Through these years many altered mechanisms were discovered revealing a very intricate picture of this disease. Mutations, epigenetic changes, aneuploidy imply severe modifications in naive cellular pathways involved in every feature of cell life. p38 MAPK is a family of kinases composed by four isoforms: alpha, beta, delta and gamma. These kinases are involved in several important cellular pathways, from the differentiation of muscle cells to inflammation and also in cancer progression. The pharmacologic and genetic inhibition of p38 alpha in colorectal cancer cells induced cell cycle arrest, autophagy and then cell death with autophagic features. My PhD project is focused on the understanding the role of p38 alpha in the autophagic activation in colorectal cancer cell lines, finding that its inhibition induced the decrease of HIF-1 alpha protein levels and its glycolytic transcriptional program. Moreover, p38 alpha inhibition trigger the activation of FoxO3A-dependent transcription, which is related to cell cycle arrest, autopahgy and cell death. We checked for other HIF-1 alpha-dependent tumors which shown also overactivation of p38 alpha, such as ovarian cancer and prostate cancer. In these kind of tumors we obtained the same encouraging results. However, in the DU 145 prostate cancer cell line, the inhibition of p38 alpha failed to activate autophagic pathway due to the lack of LKB1 kinase, which is the upstream activator of AMPK. In this cell line the inhibition of p38 alpha triggered apoptotic pathway. Chemoresistance is one of the main obstacle in the treatment of cancer. A part of my PhD project is based on the study of p38 alpha role in cisplatin chemoresistance in colorectal cancer cells. Surprisingly, we found that its inhibition, together with the administration of cisplatin, induced apoptotic cell death in the resistant HT29 cell line and increased the effect of cell death in the responsive HCT116 cell line, through the activation of FoxO3A. All these evidences indicated that inhibition of p38 alpha could be used as a promising therapeutic approaches for colorectal cancer and other malignancy, through the activation of FoxO3A-dependent transcription program.
Study of Local Kernel with Fuzzy C Mean Algorithm

Remote sensing has become one of a new and interesting area in the field of computer science. Today we have to handle a large amount of remote sensing images to obtain information concerning land cover mapping. Image classification is one of the important process in this field. Effectual use of different features of remotely sensed data and the selection of suitable classification method are especially important for improving classification accuracy. This paper explains how remote sensing data with uncertainty are dealt with fuzzy based classification using kernel approach. Here we mainly study FCM with local kernels.
Energy Limits of Message-Passing Error Control Decoders

Modern message-passing error control decoders are studied in regards to the processing energy required to extract digital information from a noisy received signal. It is shown that fundamental charge-based computational models, together with limits of error-free message passing along the processors’ communications network, imply a lower limit to the energy efficiency achievable for such decoders in modern and future VLSI implementation technologies. The limiting energy of node processing is estimated for belief propagation decoding of LDPC codes, using estimates of nodes’ internal processing activity. The limiting energy of message passing is estimated by using an energy-annotated density evolution procedure. For the class of decoders studied, the minimum energy is found to be on the order of 0.4 fJ per bit for node processing, and 9.86 aJ per bit for message passing.
The Linguistics of Sentiment Analysis

Computational linguistics is a field that was founded by linguists, but more recently is the domain of more computer scientists than linguists. Use of data­driven and machine learning methods for computational linguistics applications is now more common than hand­written linguistic rules. In order for a linguist to enter the field, it is essential that he or she be familiar with methods and techniques from computer science. The purpose of this paper is two­fold. The first is to serve as a linguist's introduction to concepts from outside of linguistics that are used in computational linguistics. The second purpose is to illustrate the use of linguistic features for a specific task known as sentiment analysis. This task involves determining the sentiment of a piece of text. By way of examining linguistics within sentiment analysis, this paper will begin to gesture at the potential role for linguists in the modern field of computational linguistics as a whole. The goal is to encourage and enable linguists to re­engage with computational linguistics by providing a suitable introductory work. Table of
Optimal collaborative caching: Theory and applications

On most modern computers, most memory accesses happen in cache, and its usage increasingly affects the performance, stability, and energy consumption of the whole system. The traditional solution divides the memory problem and conquers the pieces separately: software to improve cache locality and hardware to improve cache speed and capacity. A recent approach called collaborative caching breaks the rigid division by allowing software to provide cache hints to influence hardware cache management. In traditional caching, the hardware infers the “importance” of data and manages cache based on the inferred priority. In collaborative caching, software specifies the “importance” of data and changes the priority in which the hardware manages the data. The dissertation presents the theory and applications of optimal collaborative caching. Through software-hardware collaboration, the goal is not to improve existing solutions but to obtain the optimal solution. Toward this goal, I tackle both theoretical and practical issues. I show in theory that efficient hardware similar to the existing cache is sufficient to produce optimal results. Based on the theory, I describe practical techniques on the software side and evaluate the combined solution on both simulated and real hardware.
Successfully Treated Case of Non-Typhoidal Salmonella Meningitis in an Otherwise Healthy 5 Months Old Infant: A Case Report

Acute bacterial meningitis in infants is a medical emergency requiring prompt diagnosis and early institution of empirical antibiotic therapy. Non-typhoidal salmonella (NTS) is a major cause of uncomplicated infectious diarrhoea worldwide; however NTS meningitis is extremely uncommon beyond the neonatal period with very few cases being reported in the literature and has been associated with increased mortality and morbidity with mortality rates of up to 40-70% reported in recent studies. NTS being a facultative intracellular organism does not respond to conventional antibiotic therapy and therefore failure and relapse rates are higher particularly with meningitis. We report a rare case of a five month old infant with non-typhoidal salmonella meningitis who was successfully treated with prolonged duration of antibiotic therapy.
A Descriptive Classification of Causes of Data Quality Problems in Data Warehousing

Data warehousing is gaining in eminence as organizations become awake of the benefits of decision oriented and business intelligence oriented data bases. However, there is one key stumbling block to the rapid development and implementation of quality data warehouses, specifically that of warehouse data quality issues at various stages of data warehousing. Specifically, problems arise in populating a warehouse with quality data. Over the period of time many researchers have contributed to the data quality issues, but no research has collectively gathered all the causes of data quality problems at all the phases of data warehousing Viz. 1) data sources, 2) data integration & data profiling, 3) Data staging and ETL, 4) data warehouse modeling & schema design. The state-of-the-art purpose of the paper is to identify the reasons for data deficiencies, non-availability or reach ability problems at all the aforementioned stages of data warehousing and to formulate descriptive classification of these causes. We have identified possible set of causes of data quality issues from the extensive literature review and with consultation of the data warehouse practitioners working in renowned IT giants on India. We hope this will help developers & Implementers of warehouse to examine and analyze these issues before moving ahead for data integration and data warehouse solutions for quality decision oriented and business intelligence oriented applications.
Kinetic mechanisms of fast glutamate sensing by fluorescent protein probes

Protein-based fluorescent glutamate sensors have the potential for real-time monitoring of synaptic and cellular glutamate concentration changes, however even the fastest currently available sensors’ response times of 2-3 ms are too slow for accurate reporting of the post-synaptic AMPA receptor function in physiological conditions. We have developed probes based on the bacterial periplasmic glutamate/aspartate binding protein with either an endogenously fluorescent protein or a synthetic fluorophore as the indicator of glutamate binding: affinity variants of iGluSnFR termed iGluh, iGlum and iGlul covering a range of Kd−s (5.8 μM, 2.1 mM and 50 mM, respectively) and a novel fluorescently labelled indicator, Fl-GluBP with a Kd of 9.7 μM are presented. The fluorescence response kinetics of all the probes are consistent with two-step mechanisms involving ligand binding and rate limiting isomerisation, however the contribution in each step to the total fluorescence enhancement and kinetic paths to the final state are diverse. In contrast to the previously characterised ultrafast indicators iGluu and iGluf, for which fluorescence enhancement occurred only in the rate limiting isomerisation step, the sensors described here all have biphasic binding kinetics with a significant fraction of the fluorescence increase evoked by glutamate binding which, in the case of iGluh and Fl-GluBP, occurs with a diffusion limited rate constant. The above genetically encoded and chemically labelled fluorescent glutamate sensor variants demonstrate how single amino acid changes around the binding site introduce structural heterogeneity affecting the kinetic mechanism of interactions with glutamate. Through their broad affinity range and mechanistic variety, the probes contribute to a novel toolkit for monitoring processes of glutamate neurotransmission and cellular homeostasis. STATEMENT OF SIGNIFICANCE Glutamate is a major excitatory neurotransmitter, important in synaptic plasticity e.g. memory formation. Although predicted to clear rapidly from the synaptic cleft following presynaptic release, optical monitoring of glutamate neurotransmission has only become possible with the advent of fluorescent, protein-based indicators. Understanding their biophysical properties is important for quantification of the observed processes. Here we report the biophysical characterisation of a number of glutamate indicator variants based on the bacterial periplasmic glutamate/aspartate binding protein, revealing the subtle differences in their kinetic pathway caused by structural alteration of the glutamate binding protein by point mutations. Diffusion limited glutamate binding indicated by a novel chemically labelled probe hints at the mechanism that underlies the rapid response of the AMPA receptor.
A 50 MHz high-Q bandpass CMOS filter

A high-speed bandpass continuous-time CMOS biquad filter based on the Gm-C technique is presented. The filter is to be used in a bandpass Sigma-Delta modulator with central frequency 50 MHz. The quality factor Q of the filter is about 50. One of the most important specifications is the Spurious Free Dynamic Range (SFDR) which is a measure of the filter's immunity to spurious signals from third-order nonlinearities. Simulation results predict a SFDR of about 34 dB with Q=50 and power dissipation 12.5 mW. The design is based on a triode-region transconductor with negative resistance load and is designed for high tunability.
Iterative learning control of electromechanical camless valve actuator

In this paper we consider the control of the valve trajectory in a gasoline engine equipped with an electromechanical camless valvetrain (EMCV) actuator. Precise control of the voltage applied to the coils is required to avoid high contact velocities during valve closing and opening, i.e., to ensure soft landing. In our previous work we designed a tracking controller that consists of a constant preset voltage augmented by a voltage command based on a linear feedback and modified by an Iterative Learning Controller (ILC). The new results reported here involve the preset voltage in the learning process. This yields good results in the presence of unknown gas flow forces through the valves. As a consequence, a non-square learning algorithm is used to calculate more input values than available error values. We demonstrate the closed loop performance through simulations with an unknown acting gas flow force that emulates a realistic varying engine load.
Kernel-Mapping Recommender: Scalable, Accurate, Hybrid Recommendation Algorithms

Recommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given item. In this paper, we propose a new algorithm we call the kernel-mapping recommender (KMR) which uses a novel structure learning technique. This paper makes contributions to the following: we show how (1) user-based and item-based versions of the KMR algorithms can be built; (2) user-based and item-based versions can be combined; (3) more information— features, genre, etc.—can be employed using kernels and how it a ects the final results; and (4) to make reliable recommendations under sparse, cold-start, and long-tail scenarios. By extensive experimental results on five di erent datasets, we show that the proposed algorithms outperform or give comparable results to other state-of-the-art algorithms.