Miracle-dz/UniFAR-FTUs huggingface dataset:
Generating synthetic signaling networks for in silico modeling studies

Predictive models of signaling pathways have proven to be difficult to develop. The reasons for this include the uncertainty in the number of species, the complexity of species interactions, and the sparseness and uncertainty in experimental data. Traditional approaches to developing mechanistic models rely on collecting experimental data and fitting a single model to that data. This approach works for simple systems but has proven unreliable for complex systems such as biological signaling networks. Thus, there is a need to develop new approaches to create predictive mechanistic models of complex systems. However, to determine the effectiveness of any new algorithm, a baseline model is needed to test the performance of any new algorithm. To meet this need, we developed a method for generating artificial signaling networks that are reasonably realistic and thus could be treated as ground truth models. These synthetic models could then be used to generate synthetic data for developing and testing algorithms designed to recover the underlying network topology and associated parameters. To determine whether our generated signaling networks display meaningful behavior, we compared them with signaling networks from the BioModels Database. This comparison indicated that our generated signaling networks had high topological similarities with BioModel signaling networks with respect to the reaction degree and distance distributions. In addition, our synthetic signaling networks had similar behavioral dynamics with respect to both steady states and oscillations, suggesting that our method generated synthetic signaling networks comparable with BioModel ones and thus could be useful for building network evaluation tools. Highlights We provide a Julia script to generate synthetic random signaling networks. Our generated synthetic random signaling networks had high topological similarities with signaling networks from the BioModels Database. The behavioural dynamics of our synthetic signaling networks are similar to the dynamical behaviors of signaling networks from the BioModels Database.
On the relationship between C-band interferometric coherence and crop phenology: case study of Metaponto plain (Basilicata, Italy)

In this paper we discuss the use of Sentinel-1 interferometric Synthetic Aperture Radar (SAR) coherence to study the crop phenology. This research activity has been proposed within the project “On Demand Services for Smart Agriculture” (ODESSA)”, funded by the European Regional Development Fund (ERDF), Axis I Research and Innovation, Action 1B.1. 2.1.
Bulk crystal growth of electronic, optical & optoelectronic materials

Series Preface. Preface. Acknowledgments. List of Contributors. Abbreviations. 1. Silicon (T. Hibiya and K. Hoshikawa). 2. Growth of Gallium Arsenide (M.R. Brozel and I.R. Grant). 3. Computer Modelling of Bulk Crystal Growth (A. Yeckel and J.J. Derby). 4. Indium Phosphide Crystal Growth (I.R. Grant). 5. The Bulk Growth of InSb & Related Ternary Alloys (W.F.H. Micklethwaite). 6. GaN Bulk Substrates Grown Under Pressure from Solution in Gallium (I. Grzegory, M. Bochowski and S. Porowski). 7. Bulk Growth of Cadmium Mercury Telluride (CMT) (P. Capper). 8. Bulk Growth of CdZnTe/CdTe Crystals (R. Hirano and H. Kurita). 9. Bulk Crystal Growth of Wide-gap II Vls (M. Isshiki and J.F. Wang). 10. Sapphire Crystals Growth and Application (V.A. Tatartchenko). 11. Crystal Growth of Flourides (P.P. Fedorov and V.V. Osiko). 12. Scintillators. Crystal Growth and Scintillator Performance (Gektin). 13. Growth of Quartz Crystals (K. Byrappa). 14. Crystal Growth of Diamond (H. Kanda). 15. Growth of Silicone Carbide (T.S. Sudarshan, D. Cherednichenko and R. Yakimova). 16. Photovoltaic Silicon Crystal Growth (T.F. Ciszek). 17. Bulk Crystal Growth under Microgravity Conditions (T. Duffar).
Phylogenomic Analysis of Campylobacter fetus Reveals a Clonal Structure of Insertion Element ISCfe1 Positive Genomes

Subspecies of the species Campylobacter fetus are associated with specific host niches including mammals and reptiles. Campylobacter fetus subsp. fetus is a zoonotic pathogen infecting humans. Infections can vary from an acute intestinal illness to severe systemic infections, with sheep and cattle as major reservoirs. In contrast, Campylobacter fetus subsp. venerealis causes bovine genital campylobacteriosis, which leads to abortion in cattle and a high economic burden for the farmers. Therefore, high-quality molecular subtyping is indispensable for interventional epidemiology. We used whole-genome sequencing (WGS) data of 283 Campylobacter fetus strains from 18 countries and compared several methods for Campylobacter fetus subtyping, including WGS, multilocus sequence typing, PCR assays, and the presence of the insertion element ISCfe1. We identified a highly clonal clade (designated as clade 1) that harbors the insertion sequence ISCfe1. The presence of this insertion sequence is an essential diagnostic tool for the identification of the subspecies Campylobacter fetus subsp. venerealis, serving as a target for several PCR assays. However, we have found a high sequence variability for the ISCfe1 besides the presence of ISCfe1-paralogues in certain other genomes (n = 7) which may cause incorrect diagnostic results. Clade 1 seems to be the cattle-specific clade of this species. We propose that only this clade might be designated as Campylobacter fetus subsp. venerealis as it harbors the ISCfe1 marker sequence, which is a major target for molecular methods currently used for Campylobacter fetus subspecies identification. Fostering this proposal, we defined eleven stable nucleotide markers specific for this clade. Additionally, we developed a bioinformatics toolbox for the fast identification of this clade based on WGS data. In conclusion, our results demonstrate that WGS can be used for Campylobacter fetus subtyping overcoming limitations of current PCR and MLST protocols.
Fabrication of High‐Power Li‐Ion Hybrid Supercapacitors by Enhancing the Exterior Surface Charge Storage

Dr. M. Yang, Y. R. Zhong, J. J. Ren, X. L. Zhou, Prof. J. P. Wei, Prof. Z. Zhou Tianjin Key Laboratory of Metal and Molecule Based Material Chemistry Key Laboratory of Advanced Energy Materials Chemistry (Ministry of Education) Institute of New Energy Material Chemistry School of Materials Science and Engineering Collaborative Innovation Center of Chemical Science and Engineering (Tianjin) Nankai University Tianjin 300071 , China E-mail: zhouzhen@nankai.edu. cn
Thermal boundary layer structure in convection with and without rotation

The thermal boundary layer is identified and studied using numerical simulations of Rayleigh-B\'enard convection. Different methods of defining the thermal boundary layer are investigated when applied to fixed temperature or fixed heat-flux boundary conditions. The crossover in advective and conductive heat flux is a robust way to define the thermal boundary layer.
Prediction of Network Traffic Through Light-Weight Machine Learning

The evolving nature of network traffic challenges existing learning-based models, as frequently re-training the hyper-parameters is required to adaptively learn and predict its behavior. Benefiting from discovering the sparsity and self-similarity of network traffic, it becomes possible to develop compact algorithms with high accuracy and low computational complexity. However, how to properly make use of these properties still remains to be explored. In this article, we establish a light-weight learning framework for network traffic prediction based on sparse representation, and try to take the full advantage of these properties to enhance the capability of tracking its highly evolving characteristics. Specifically, 1). The strict causality constraint makes it difficult to equip the conventional sparse representation with predictive capability. To solve this issue, we make use of the self-similarity and train the representative/predictive dictionaries in a joint manner, such that the query point is embedded in terms of a sparse combination of dictionary atoms, and jointly coded with its T + 1 time slot behind counterpart, which proves to be optimal in the concatenated representative/predictive feature space. Then, the query point can be estimated through iterative projection method. 2). The consideration of the sparsity constraint loosens the upper bound of the time averaged prediction error. To address this problem, we slightly modify the sparse representation-based prediction by adopting Lyapunov optimization, and try to minimize the time averaged prediction error. Finally, the simulation results verify the performance improvements.
UNIT: Unifying Tensorized Instruction Compilation

Because of the increasing demand for intensive computation in deep neural networks, researchers have developed both hardware and software mechanisms to reduce the compute and memory burden. A widely adopted approach is to use mixed precision data types. However, it is hard to benefit from mixed precision without hardware specialization because of the overhead of data casting. Recently, hardware vendors offer tensorized instructions specialized for mixed-precision tensor operations, such as Intel VNNI, Nvidia Tensor Core, and ARM DOT. These instructions involve a new computing idiom, which reduces multiple low precision elements into one high precision element. The lack of compilation techniques for this emerging idiom makes it hard to utilize these instructions. In practice, one approach is to use vendor-provided libraries for computationally-intensive kernels, but this is inflexible and prevents further optimizations. Another approach is to manually write hardware intrinsics, which is error-prone and difficult for programmers. Some prior works tried to address this problem by creating compilers for each instruction. This requires excessive efforts when it comes to many tensorized instructions. In this work, we develop a compiler framework, UNIT, to unify the compilation for tensorized instructions. The key to this approach is a unified semantics abstraction which makes the integration of new instructions easy, and the reuse of the analysis and transformations possible. Tensorized instructions from different platforms can be compiled via UNIT with moderate effort for favorable performance. Given a tensorized instruction and a tensor operation, UNIT automatically detects the applicability of the instruction, transforms the loop organization of the operation, and rewrites the loop body to take advantage of the tensorized instruction. According to our evaluation, UNIT is able to target various mainstream hardware platforms. The generated end-to-end inference model achieves 1.3 x speedup over Intel oneDNN on an x86 CPU, 1.75x speedup over Nvidia cuDNN on an Nvidia GPU, and 1.13x speedup over a carefully tuned TVM solution for ARM DOT on an ARM CPU.
Gut-Brain Axis Cross-Talk and Limbic Disorders as Biological Basis of Secondary TMAU

Background: Trimethylaminuria (TMAU) is a rare metabolic syndrome characterized by the accumulation and the excretion of trimethylamine (TMA), a volatile diet compound produced by gut microbiota. Gut microbiota alterations are mainly involved in the secondary TMAU, whose patients show also different psychiatric conditions. We hypothesized that the biological activity of several molecules acting as intermediate in TMA metabolic reaction might be at the basis of TMAU psychiatric comorbidities. Methods: To corroborate this hypothesis, we performed the analysis of microbiota of both psychiatric suffering secondary TMAU patients and TMAU “mentally ill” controls, comparing the alteration of metabolites produced by their gut bacteria possibly involved in neurotransmission and, in the same time, belonging to biochemical pathways leading to TMA accumulation. Results: Microbiota analyses showed that Clostridiaceae, Lachnospiraceae and Coriobacteriaceae alterations represented the bacterial families with highest variations. This results in an excessive release of serotonin and an hyperactivation of the vagus nerve that might determine the widest spectrum of psychiatric disorders shown by affected patients. These metabolites, as short chain fatty acids, lactate and neurotransmitter precursors, are also related to TMA accumulation. Conclusions: Knowledge of microbiota-gut-brain axis may become a potential new strategy for improving metabolic diseases and to treat linked psychiatric disorders.
Self-supervised Learning of 3D Object Understanding by Data Association and Landmark Estimation for Image Sequence

In this paper, we propose a self-supervised learningmethod for multi-object pose estimation. 3D object under-standing from 2D image is a challenging task that infers ad-ditional dimension from reduced-dimensional information.In particular, the estimation of the 3D localization or orien-tation of an object requires precise reasoning, unlike othersimple clustering tasks such as object classification. There-fore, the scale of the training dataset becomes more cru-cial. However, it is challenging to obtain large amount of3D dataset since achieving 3D annotation is expensive andtime-consuming. If the scale of the training dataset can beincreased by involving the image sequence obtained fromsimple navigation, it is possible to overcome the scale lim-itation of the dataset and to have efficient adaptation tothe new environment. However, when the self annotation isconducted on single image by the network itself, trainingperformance of the network is bounded to the self perfor-mance. Therefore, we propose a strategy to exploit multipleobservations of the object in the image sequence in orderto surpass the self-performance: first, the landmarks for theglobal object map are estimated through network predic-tion and data association, and the corrected annotation fora single frame is obtained. Then, network fine-tuning is con-ducted including the dataset obtained by self-annotation,thereby exceeding the performance boundary of the networkitself. The proposed method was evaluated on the KITTIdriving scene dataset, and we demonstrate the performanceimprovement in the pose estimation of multi-object in 3D space.
Can the addition of biochar improve the performance of biogas digesters operated at 45°C?

Biogas from anaerobic digestion (AD) may contribute towards a green energy scene in the future. Despite many benefits, AD plant operators are still challenged by limitations of the technology. This study aimed at evaluating the effect of biochar addition to AD reactors operated at 45°C to optimize productivity, efficiency and stability of the process. Biochar is characterized by a large surface area and may therefore facilitate the creation of biofilms on the one hand and remove environmental contaminants on the other hand. Moreover, biochar may serve as pH control agent due to its alkaline properties, counteracting reactor acidification. Our results showed that the addition of biochar reduced efficiently the amount of H2S that initially occurred in the headspace during reactor start-up. Nevertheless, no impact on biogas- and methane yields was observed. This might be explained with an already well-working AD system without any severe disturbances, where the balancing nature of biochar did hardly count. Future studies, however, need to evaluate the effect on stressed systems. The microbiome analysis provided a detailed view of the microbial dynamics during the start-up phase, revealing Methanosarcina as dominant methanogen in the end. The addition of biochar did not alter the microbial community composition.
Financial Knowledge Graph Based Financial Report Query System

Annual Financial Reports are the core in the Banking Sector to publish its financial statistics. Extracting useful information from these complex and lengthy reports involves manual process to resolve the financial queries, resulting in delays and ambiguity in investment decisions. One of the major reasons is the lack of any standardization in the format and vocabulary used in the reports. An automated system for resolution of intelligent financial queries is therefore difficult to design. Several works have been proposed to overcome these problems using Information Extraction; however, they do not address the semantic interoperability of the reports across different institutions. This work proposed an automated querying engine to answer the financial queries using Ontology based Information Extraction. For Semantic modeling of financial reports, a Financial Knowledge Graph, assisted by Financial Ontology, has been proposed. The nodes are populated with entities, while links are populated with relationships using Information Extraction applied on annual reports. Two benefits have been provided by this system to stakeholders through automation: decision making through queries and generation of custom financial stories. The work can further be extended to other domains including healthcare and academia where physical reports are used for communication.
Permian palynoevents in the circum-Arctic region

Permian palynofloras are recorded around the present-day Arctic and are typically dominated by taeniate and non-taeniate pollen, with intervals of spore domination. The assemblages show close similarities around the Arctic. Based on the published record, we present a compilation of 23 last occurrences (LOs), first occurrences (FOs), and some abundance events. These are anticipated to have regional correlation potential. In general, the Permian palynofloras of the Arctic have not been extensively studied, and the resolution is low due to a general lack of independent age control. doi:10.4138/atlgeol.2021. 004 ATLANTIC GEOLOGY 57, 57–69 (2021) 0843-5561|21|0057–0069$2.95|0 (see for example Utting and Piasecki 1995). Utting (1989, p. 234) did not use the Kungurian stage. Utting (1994, p. 9) correlated the Roadian to the “Ufimian (? )” and the Wordian to the “Kazanian (? )” (Fig. 1). See Lucas and Shen (2016) for further discussion on Permian chronostratigraphy. Palynomorphs are the most common, and often the only, fossil group recorded from the Permian, particularly the upper Permian. Palynology is therefore a critical tool for dating and refining stratigraphic correlations of Permian rocks (Stephenson 2016). However, different taxonomic approaches and stratigraphic methods have made comparisons difficult between regions. Especially problematic in the Arctic is the limited communication between Russian and western palynologists; the development especially of a shared taxonomy would likely lead to greatly improved correlations. Permian palynomorphs are primarily pollen and spores (miospores), although acritarchs, algae and foraminiferal linings also occur. For the Cisuralian, fusulinids and conodonts provide independent age determinations for some of the published palynostratigraphic zones. None of the Permian INTRODUCTION This article is a contribution to the Circum-Arctic Palynological Events (CAPE) project, providing a scheme of selected events for the Permian Period. The Permian Period extended from 298.89 to 251.90 Ma according to Gradstein et al. (in press). The Permian is divided into three epochs, Cisuralian, Guadalupian (with a base at 272.3 Ma) and Lopingian (with a base at 259.8 Ma). A major part of the palynological work from the Permian successions in the Arctic was carried out using the chronostratigraphic scale of Harland et al. (1982). Since stratigraphic standards using Global Stratigraphic Sections and Points was not applied until the late 1990s, correlation and comparison with the earlier palynological charts is difficult; this problem has been com-pounded for the Arctic because a variety of Permian region-al stages have been applied there. This is particularly true around the lower/upper Permian boundary (now Cisuralian/Guadalupian boundary) where, for example, the Roadian stage has been correlated to either the upper Kungurian or to the Ufimian (for example Utting et al. 1997), and the Wordian has been correlated to the Kazanian
Theory meets Practice at the Median: A Worst Case Comparison of Relative Error Quantile Algorithms

Estimating the distribution and quantiles of data is a foundational task in data mining and data science. We study algorithms which provide accurate results for extreme quantile queries using a small amount of space, thus helping to understand the tails of the input distribution. Namely, we focus on two recent state-of-the-art solutions: t-digest and ReqSketch. While t-digest is a popular compact summary which works well in a variety of settings, ReqSketch comes with formal accuracy guarantees at the cost of its size growing as new observations are inserted. In this work, we provide insight into which conditions make one preferable to the other. Namely, we show how to construct inputs for t-digest that induce an almost arbitrarily large error and demonstrate that it fails to provide accurate results even on i.i. d. samples from a highly non-uniform distribution. We propose practical improvements to ReqSketch, making it faster than t-digest, while its error stays bounded on any instance. Still, our results confirm that t-digest remains more accurate on the "non-adversarial" data encountered in practice.
Diode-capacitor voltage multipliers combined with boost-converters: topologies and characteristics

Various topological modifications of diode–capacitor voltage multipliers are considered. All the topologies are based on two known schemes: Cockcroft–Walton and Dickson. These topologies are built combined with a boost-converter, operating at a high switching frequency. Such a solution allows reducing the values of the capacitors. The proposed topologies can also be used by feeding them directly from a three-phase network through the regular rectifier; in this case the influence of such circuits on a supply network is reduced. A number of novel modifications of multipliers, having their specific features, are obtained. The procedure of calculating the output voltage, depending on the capacitor values and load parameters is proposed and the design formulas for the output voltage of some of the schemes are developed. It is shown that a decrease in the output voltage is caused by some sort of internal resistance. The essentially similar operating modes of different topologies are characterised by a different value of such a resistance, and accordingly by a different internal voltage drop. Dynamic models for some of the proposed topologies are also developed. The computer simulation and the experimental results proved the theoretical expectations.