Miracle-dz/UniFAR-FTUs huggingface dataset:
Very-high-energy gamma-ray emission from GRB 201216C detected by MAGIC

Institute for Cosmic Ray Research, The University of Tokyo, Kashiwanoha 5-1-5, Kashiwa, Japan Max Planck Institut for Physics,Föhringer Ring 6, Munich, Germany INFN MAGIC Group: INFN Sezione di Bari and Dipartimento Interateneo di Fisica dell’Università e del Politecnico di Bari, I-70125, Bari, Italy Physics Program, Graduate School of Advanced Science and Engineering, Hiroshima University, 739-8526 Hiroshima, Japan National Institute for Astrophysics (INAF), Street number, Merate, Italy 5 Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia University of Trieste, Department of Physics, via Valerio 2, Trieste, Italy INFN, Sezione di Trieste, via Valerio 2, Trieste, Italy E-mail: fukami@icrr.u-tokyo.ac. jp
Speed Records in Network Flow Measurement on FPGA

Network traffic measurement keeps track of the amount of traffic sent by each flow in the network. It is a core functionality in applications such as traffic engineering and network intrusion detection. In high-speed networks, it is impossible to keep an exact count of the flow traffic, due to limitations with respect to memory and computational speed. Therefore, probabilistic data structures, such as sketches, are used. This paper proposes Approximate Count-Min sketch or ACM sketch, a novel variant of the Count-Min sketch algorithm that uses less memory and has a higher throughput compared to other FPGA-based sketch implementations. A-CM sketch relies on optimizations at two levels: (1) it uses approximate counters and the newly proposed Hardware-oriented Simple Active Counter algorithm to efficiently implement these counters; (2) it uses a distribution of the embedded memory, optimized towards maximum operating frequency. To the best of our knowledge, A-CM sketch outperforms all other FPGA-based sketch implementations.
Prevalence of aflatoxin M1 in pasteurized and ultra-high temperature (UHT) milk marketed in Dar es Salaam, Tanzania

The aim of this study was to determine the level of aflatoxin M1 (AFM1) in pasteurized and UHT milk marketed in Dar es Salaam, Tanzania. AFM1 in pasteurized milk samples (75) and ultra-high temperature (UHT) milk (43) was determined by using immuno-affinity high performance liquid chromatography. AFM1 was detected in 97% (115/118) of samples that consisted of 96% (72/75) of pasteurized milk samples and 100% (43/43) of UHT milk samples. About 82% of the contaminated pasteurized and UHT had AFM1 levels above EU acceptable levels (0.05 μg/L). However, none of the contaminated pasteurized and UHT milk sample had levels of AFM1 above the maximum recommended Codex limits (0.5 μg/L). The results indicate that the contamination of the samples with AFM1 at such level could pose a serious public health problem. Thus, regular monitoring of AFM1 levels in milk is important in order to protect consumers.
Enhancement of Antibiofilm Activity of Ciprofloxacin against Staphylococcus aureus by Administration of Antimicrobial Peptides

Staphylococcus aureus can develop resistance by mutation, transfection or biofilm formation. Resistance was induced in S. aureus by growth in sub-inhibitory concentrations of ciprofloxacin for 30 days. The ability of the antimicrobials to disrupt biofilms was determined using crystal violet and live/dead staining. Effects on the cell membranes of biofilm cells were evaluated by measuring release of dyes and ATP, and nucleic acids. None of the strains developed resistance to AMPs while only S. aureus ATCC 25923 developed resistance (128 times) to ciprofloxacin after 30 passages. Only peptides reduced biofilms of ciprofloxacin-resistant cells. The antibiofilm effect of melimine with ciprofloxacin was more (27%) than with melimine alone at 1X MIC (p < 0.001). Similarly, at 1X MIC the combination of Mel4 and ciprofloxacin produced more (48%) biofilm disruption than Mel4 alone (p < 0.001). Combinations of either of the peptides with ciprofloxacin at 2X MIC released ≥ 66 nM ATP, more than either peptide alone (p ≤ 0.005). At 2X MIC, only melimine in combination with ciprofloxacin released DNA/RNA which was three times more than that released by melimine alone (p = 0.043). These results suggest the potential use of melimine and Mel4 with conventional antibiotics for the treatment of S. aureus biofilms.
Transgenic reporter analysis of ChIP-Seq-defined enhancers identifies novel target genes for the terminal selector UNC-3/Collier/Ebf

Terminal selector-type transcription factors are key regulators of neuronal identity and function (Hobert and Kratsios, 2019; Kratsios and Hobert, 2018). Mechanistically, terminal selectors are thought to act directly through binding at the cis-regulatory region of genes (termed “terminal identity genes”) that encode, among others, neurotransmitter [NT] synthesis proteins, ion channels, neuropeptides, and cell adhesion molecules (Hobert and Kratsios, 2019; Kratsios and Hobert, 2018). Although dozens of terminal selectors have been described thus far for individual neuron types of the nematode C. elegans (Hobert, 2016), the identification of their target genes has primarily relied on candidate approaches and availability of markers for neuronal terminal identity. Hence, unbiased methods are needed to identify the full spectrum of terminal selector target genes in individual neuron types. This study focuses on the phylogenetically conserved terminal selector UNC-3/Ebf (member of the Collier/Olf/Ebf family), which controls cholinergic motor neuron (MN) identity in the ventral nerve cord of the nematode C. elegans. To identify novel UNC-3 target genes, we took advantage of the genome-wide binding map of UNC-3 from our previous Chromatin Immunoprecipitation followed by Sequencing (ChIP-Seq) analysis (Li et al., 2020). We generated transgenic reporter lines for ten putative terminal identity genes (pxd-1, cal-2, lgc-4, ldb-1, nep-21, D2007.2, dmsr-2, ncs-2, npr-29, drn-1), whose expression patterns were largely unknown in C. elegans. Six of these reporter lines showed expression in ventral nerve cord MNs (nep-21, D2007.2, dmsr-2, ncs-2, npr-29, drn-1), whereas the remaining four (pxd-1, cal-2, lgc-4, ldb-1) showed expression in head and tail neurons, as well as some non-neuronal cells. Importantly, the number of ventral nerve cord MNs showing expression of the nep-21, D2007.2, and dmsr-2 reporters was significantly reduced in unc-3 null mutant animals, thereby expanding the repertoire of known UNC-3 target genes in these cells. Altogether, this study demonstrates that transgenic reporter analysis guided by ChIP-Seq results is a relatively efficient approach for the identification and validation of transcription factor target genes.
Integration and qualification of the Mini-EUSO telescope on board the ISS

Università degli studi di Roma Tor Vergata, Dipartimento di Fisica Roma, Italy Faculty of Physics, M.V. Lomonosov Moscow State University Moscow, Russia Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University Moscow, Russia KTH Royal Institute of Technology Stockholm, Sweden INFN, Sezione di Roma Tor Vergata Roma, Italy Riken Wako, Japan INFN-LNF Frascati, Italy E-mail: giorgio.cambie@roma2.infn. it
Android Malware Classification Using Deep Learning CNN with Co-occurrence Matrix Feature 

Recently, deep learning has been widely applying to speech and image recognition. Convolutional neural network (CNN) is one of the main categories to do image classifications with very high accuracy. In Android malware classification field, many works have been trying to convert Android malwares into “images” to make them well-matched with the CNN input to take advantage of the CNN model. The performance, however, is not significantly improved because simply converting malwares into images may lack several important features of the malwares. This paper proposes a method for improving the feature set of Android malware classification based on co-concurrence matrix (co-matrix). The co-matrix is established based on a list of raw features extracted from . apk files. The proposed feature can take the advantage of CNN while remaining important features of the Android malwares. Experimental results of CNN model conducted on a very popular Android malware dataset, Drebin, prove the feasibility of our proposed co-matrix feature.
Intraperitoneal Chemotherapy after Cytoreductive Surgery Improves Survival in Epithelial Ovarian Cancer: A Systematic Review and Meta-Analysis

Methods: A literature search of PubMed, Embase, MEDLINE and ClinicalTrials.gov between January 1990 and January 2018 was conducted to identify relevant RCTs comparing IPC vs. intravenous chemotherapy (IV CT) after cytoreductive surgery in women with a new diagnosis of primary EOC. Progression-Free Survival (PFS) and OS were primary outcomes and secondary outcomes were Complete Pathological Response (CPR) and adverse effects. Pooled Hazard Ratios (HRs) with 95% Confidence Intervals (CI) were calculated with fixed or random-effect models using the generic inverse-variance method. Statistical heterogeneity and publication bias were analyzed.
Pulse Shape Discrimination for Online Data Acquisition in Water Cherenkov Detectors Based on FPGA/SoC

(ICTP-MLAB) The Abdus Salam International Centre for Theoretical Physics Strada Costiera, 11, 34151 Trieste Italy (UNITS) Università degli studi di Trieste, Department of Engineering and Architecture Piazzale Europa, 1, 34127 Trieste, Italy (USAC-ICFM) Instituto de Investigación en Ciencias Físicas y Matemáticas, Universidad de San Carlos de Guatemala Ciudad Universitaria, Zona 12, Guatemala, Guatemala (UNSL-LEIS) Departamento de electrónica, Universidad Nacional de San Luis Av. Ejército de los Andes 950, D5700, San Luis, Argentina E-mail: lgarcia1@ictp.it
Bayesian streaming sparse Tucker decomposition

Tucker decomposition is a classical tensor factorization model. Compared with the most widely used CP decomposition, Tucker model is much more ﬂexible and interpretable in that it accounts for every possible (multiplicative) interaction be-tween the factors in different modes. However, this also brings in the risk of overﬁtting and computational challenges, especially in the case of fast streaming data. To address these issues, we develop BASS-Tucker, a BAyesian Streaming Sparse Tucker decomposition method. We place a spike-and-slab prior over the core tensor elements to automatically select meaningful factor interactions so as to prevent overﬁtting and to further enhance the interpretability. To enable efﬁcient streaming factorization, we use conditional moment matching and delta method to develop one-shot incremental update of the latent factors and core tensor upon receiving each streaming batch. Thereby, we avoid processing the data points one by one as in the standard assumed density ﬁltering, which needs to update the core tensor for each point and is quite inefﬁcient. We explicitly introduce and up-date a sparse prior approximation in the running posterior to fulﬁll effective sparse estimation in the streaming inference. We show the advantage of BASS-Tucker in several real-world applications.
Missense mutation of a conserved residue in UNC-112 (kindlin) eliminates binding to PAT-4 (ILK)

C. elegans UNC-112 (kindlin) is required for muscle sarcomere assembly, and is one component of a conserved four-protein complex that associates with the cytoplasmic tail of integrin at the base of integrin adhesion complexes in muscle. The four-protein complex consists of UNC-112 (kindlin), PAT-4 (integrin linked kinase; ILK), PAT-6 (alpha-parvin), and UNC-97 (PINCH). UNC-112 is comprised of 720 amino acid residues and contains FERM and PH domains. The N-terminal half of UNC-112 (1-396 aa) can bind to the C-terminal half of UNC-112 (397-720 aa), and this interaction is inhibited by the association of PAT-4 (ILK) to the N-terminal half of UNC-112. In support of this model, previously, we reported identification of a D382V mutation that results in lack of binding to PAT-4. However, this residue is not conserved in human Kindlins. Here, we report identification of a novel UNC-112 mutation of a conserved residue that cannot bind to PAT-4. UNC-112 E302G cannot bind to PAT-4 and does not localize to integrin adhesion complexes in muscle.
Hydrogel-Based Delivery for Antineoplastic Drugs and Vascular Scaffolding

Hydrogels, hydrophilic polymeric compounds, have been recently put under investigation as regenerative medicine applications and delivery systems for antineoplastic drugs, particularly chemotherapeutics (anthracyclines, alkylating agents), target drugs (trastuzumab) and immunotherapies. Porosity, conductivity, biodegradability and physical states are some of the peculiarities that render hydrogels suitable for therapies implementation. Chemically-modifying agents and enzymes can be also coupled to hydrogels for pharmacokinetical parameters improvement and side effects avoidance. Cardiotoxicity is in fact one of the major issues for oncological patients after treatment efficacy. Heart failure, myocarditis and hypertension are causes of morbidity and mortality that can possibly be avoided. Specific reaching of the target tumor site has been achieved by several authors in preclinical in vivo studies but clinical studies are currently under design processes. Polydioxanone, a hydrogel-mimicking agent, is capable to interact with the elastic properties of pulmonary artery. An advantageous characteristic is that can be also reabsorbed within biological systems and can cause a remodeling process of the vessel wall. Hydrogels currently represent a strong topic of interest for researchers and probably will guide future clinical investigations and practice.
COSM: Controlled Over-Sampling Method. A Methodological Proposal to Overcome the Class Imbalance Problem in Data Mining

The class imbalance problem is widespread in Data Mining and it can reduce the general performance of a classification model. Many techniques have been proposed in order to overcome it, thanks to which a model able to handling rare events can be trained. The methodology presented in this paper, called Controlled Over-Sampling Method (COSM), includes a controller model able to reject new synthetic elements for which there is no certainty of belonging to the minority class. It combines the common Machine Learning method for holdout with an oversampling algorithm, for example the classic SMOTE algorithm. The proposal explained and designed here represents a guideline for the application of oversampling algorithms, but also a brief overview on techniques for overcoming the problem of the unbalanced class in Data Mining.
Genome-Scale Transcription-Translation Mapping Reveals Features of <named-content content-type='genus-species'>Zymomonas mobilis</named-content> Transcription Units and Promoters

Zymomonas mobilis is an ethanologenic alphaproteobacterium with promise for the industrial conversion of renewable plant biomass into fuels and chemical bioproducts. Limited functional annotation of the Z. mobilis genome is a current barrier to both fundamental studies of Z. mobilis and its development as a synthetic biology chassis. To gain insight, we collected sample-matched multiomics data, including RNA sequencing (RNA-seq), transcription start site (TSS) sequencing (TSS-seq), termination sequencing (term-seq), ribosome profiling, and label-free shotgun proteomic mass spectrometry, across different growth conditions and used these data to improve annotation and assign functional sites in the Z. mobilis genome. Proteomics and ribosome profiling informed revisions of protein-coding genes, which included 44 start codon changes and 42 added proteins. We developed statistical methods for annotating transcript 5= and 3= ends, enabling the identification of 3,940 TSSs and their corresponding promoters and 2,091 transcription termination sites, which were distinguished from RNA processing sites by the lack of an adjacent RNA 5= end. Our results revealed that Z. mobilis A 35 and 10 promoter elements closely resemble canonical Escherichia coli 35 and 10 elements, with one notable exception: the Z. mobilis 10 element lacks the highly conserved 7 thymine observed in E. coli and other previously characterized A promoters. The A promoters of another alphaproteobacterium, Caulobacter crescentus, similarly lack the conservation of 7 thymine in their 10 elements. Our results anchor the development of Z. mobilis as a platform for synthetic biology and establish strategies for empirical genome annotation that can complement purely computational
Graph-based semi-supervised learning through the lens of safety

Graph-based semi-supervised learning (G-SSL) algorithms have witnessed rapid development and widespread usage across a variety of applications in recent years. However, the theoretical characterisation of the efﬁcacy of such algorithms has remained an under-explored area. We introduce a novel algorithm for G-SSL, CSX, whose objective function extends those of Label Propagation and Expander, two popular G-SSL algorithms. We provide data-dependent generalisation error bounds for all three aforementioned algorithms when they are applied to graphs drawn from a partially labelled extension of a versatile latent space graph generative model. The bounds we obtain enable us to characterise the predictive performance as measured by accuracy in terms of homophily and label quantity. Building on this we develop a key notion of GLM-safety which enables us to compare G-SSL algorithms on the basis of the range of graphs on which they obtain a guaranteed accuracy. We show that the proposed algorithm CSX has a better GLM-safety proﬁle than Label Propagation and Expander while achieving comparable or better accuracy on synthetic as well as real-world benchmark networks.
Women‟s Wearable Security and Safety Device

In today’s world, one of the major issues that women all across the globe face is threats to their safety and security. This paper presents a quick safety device that responds immediately when women are in danger. The device is equipped with a button and on pressing the button, a “HELP”message is sent along with the location coordinatesto the trusted contacts via an SMS. The device is also capable of capturing an image and a video which can then be analyzed to identify the perpetuator. When a woman feels threatened or she finds herself in a compromising situation, she can use this device for her safety.
Barriers to product innovation among the manufacturing micro, small and medium enterprises in Malawi

Most micro, small, medium enterprises in Malawi struggle to grow and survive despite their critical role in the socio-economic development of the country. One major contributing factor is their inability to innovate. This study was therefore aimed at investigating the innovation barriers affecting the manufacturing MSMEs. The study was conducted among the 45 manufacturing MSMEs operating in the Malawi’s commercial city of Blantyre. The study adopted a case study approach and employed both quantitative and descriptive approaches. Empirical data was collected using a semi-structured questionnaire and one- on- one interview. The results of the study revealed that factors such as market competition, difficulty in accessing loans, inadequate government support, labour laws, taxes and regulations in the external environment, and inadequate financial resources, lack of qualified personnel, and poor financial performance within the enterprises hamper innovation activities within the MSMEs. Results of the study also revealed that despite government efforts to promote MSMEs innovativeness, most MSMEs do not participate in such government interventions and worse still some are not even aware of the existence of such interventions. The study therefore recommends that government should intensify its efforts to provide financial support in form of soft loans, entrepreneurial training to MSMEs and promote innovation in MSMEs through relevant policies. Key words: Micro small medium enterprises (MSMEs), innovation, barriers, manufacturing.
Possibilistic preference elicitation by minimax regret

Identifying the preferences of a given user through elicitation is a central part of multi-criteria decision aid (MCDA) or preference learning tasks. Two classical ways to perform this elicitation is to use either a robust or a Bayesian approach. However, both have their shortcoming: the robust approach has strong guarantees through very strong hypotheses, but cannot integrate uncertain information. While the Bayesian approach can integrate uncer-tainties, but sacriﬁces the previous guarantees and asks for stronger model assumptions. In this paper, we propose and test a method based on possibility theory, which keeps the guarantees of the robust approach without needing its strong hypotheses. Among other things, we show that it can detect user errors as well as model misspeciﬁcation.
Phylogenetic Signal of the Nuclear Gene GA20ox1 in Seed Plants: The Relationship Between Monocots and Eudicots

: This study investigated the phylogenetic signal of the nuclear gene GA20ox1 in seed plants focusing in the relationship between Monocots and Eudicots. Sequences were obtained from GenBank and analyzed using the maximum likelihood and the maximum parsimony approaches. A maximum likelihood tree was built using sequences of the rbcL plastid gene in order to enable comparison of the results. The GA20ox1 gene presents neutral evolution, levels of homoplasy equivalent to that observed in chloroplast sequences and generated well-resolved phylogenetic relationships. The relationship between Mocots and Eudicots based on the GA20ox1 gene was clear resolved, revealing the evolution of both groups. All these characteristics taken together make the GA20ox1 gene a promissory marker to corroborate as well as to complement and resolve phylogenetic relationships among species within one to several genera.
Pricing Exotic Options Using Some Lattice Procedures

In this work, we discuss some very simple and extremely efficient lattice models, namely, Binomial tree model (BTM) and Trinomial tree model (TTM) for valuing some types of exotic barrier options in details. For both these models, we consider the concept of random walks in the simulation of the path which is followed by the underlying stock price. Our main objective is to estimate the value of barrier options by using BTM and TTM for different time steps and compare these with the exact values obtained by the benchmark Black-Scholes model (BSM). Moreover, we analyze the convergence of these lattice models for these exotic options. All the results have been shown numerically as well as graphically. GANITJ. Bangladesh Math. Soc.41. 1 (2021) 26-40
SMART FAKE NEWS PREDICTION USING MACHINE LEARNING FOR SOCIAL MEDIA

- Fake news are described with an intention to misdirect or to delude the reader. We have presented a response for the task for fake news, individuals are clashing if not by large poor locators of fake news. For this reason new system is generated for fake news identification. The result of this project determines the actual fake news detection for social networks using machine learning.