Miracle-dz/UniFAR-FTUs huggingface dataset:
Community Structure and Function of Epiphytic Bacteria Associated With Myriophyllum spicatum in Baiyangdian Lake, China

Epiphytic bacteria on the surfaces of submerged macrophytes play important roles in the growth of the host plant, nutrient cycling, and the conversion of pollutants in aquatic systems. A knowledge of the epiphytic bacterial community structure could help us to understand these roles. In this study, the abundance, diversity, and functions of the epiphytic bacterial community of Myriophyllum spicatum collected from Baiyangdian Lake in June, August, and October 2019 were studied using quantitative PCR (qPCR), high-throughput sequencing, and the prediction of functions. An analysis using qPCR showed that the epiphytic bacteria were the most abundant in October and the least abundant in August. High-throughput sequencing revealed that Proteobacteria, Gammaproteobacteria, and Aeromonas were the dominant phylum, class, and genus in all the samples. The common analyses of operational taxonomic units (OTUs), NMDS, and LDA showed that the epiphytic bacterial communities were clustered together based on the seasons. The results of a canonical correlation analysis (CCA) showed that the key water quality index that affected the changes of epiphytic bacterial community of M. spicatum was the total phosphorus (TP). The changes in abundance of Gammaproteobacteria negatively correlated with the TP. Predictive results from FAPROTAX showed that the predominant biogeochemical cycle functions of the epiphytic bacterial community were chemoheterotrophy, nitrate reduction, and fermentation. These results suggest that the epiphytic bacterial community of M. spicatum from Baiyangdian Lake varies substantially with the seasons and environmental conditions.
Ion Mobility Coupled to a Time-of-Flight Mass Analyzer Combined With Fragment Intensity Predictions Improves Identification of Classical Bioactive Peptides and Small Open Reading Frame-Encoded Peptides

Bioactive peptides exhibit key roles in a wide variety of complex processes, such as regulation of body weight, learning, aging, and innate immune response. Next to the classical bioactive peptides, emerging from larger precursor proteins by specific proteolytic processing, a new class of peptides originating from small open reading frames (sORFs) have been recognized as important biological regulators. But their intrinsic properties, specific expression pattern and location on presumed non-coding regions have hindered the full characterization of the repertoire of bioactive peptides, despite their predominant role in various pathways. Although the development of peptidomics has offered the opportunity to study these peptides in vivo, it remains challenging to identify the full peptidome as the lack of cleavage enzyme specification and large search space complicates conventional database search approaches. In this study, we introduce a proteogenomics methodology using a new type of mass spectrometry instrument and the implementation of machine learning tools toward improved identification of potential bioactive peptides in the mouse brain. The application of trapped ion mobility spectrometry (tims) coupled to a time-of-flight mass analyzer (TOF) offers improved sensitivity, an enhanced peptide coverage, reduction in chemical noise and the reduced occurrence of chimeric spectra. Subsequent machine learning tools MS2PIP, predicting fragment ion intensities and DeepLC, predicting retention times, improve the database searching based on a large and comprehensive custom database containing both sORFs and alternative ORFs. Finally, the identification of peptides is further enhanced by applying the post-processing semi-supervised learning tool Percolator. Applying this workflow, the first peptidomics workflow combined with spectral intensity and retention time predictions, we identified a total of 167 predicted sORF-encoded peptides, of which 48 originating from presumed non-coding locations, next to 401 peptides from known neuropeptide precursors, linked to 66 annotated bioactive neuropeptides from within 22 different families. Additional PEAKS analysis expanded the pool of SEPs on presumed non-coding locations to 84, while an additional 204 peptides completed the list of peptides from neuropeptide precursors. Altogether, this study provides insights into a new robust pipeline that fuses technological advancements from different fields ensuring an improved coverage of the neuropeptidome in the mouse brain.
Short- to Medium-Term Sea Surface Height Prediction in the Bohai Sea Using an Optimized Simple Recurrent Unit Deep Network

Global warming has intensified the rise in sea levels and has caused severe ecological disasters in shallow coastal waters such as the Northeastern China's Bohai Sea. The prediction of the sea surface height anomaly (SSHA) has great significance in the context of monitoring changes in sea levels. However, the non-linearity of SSHA due to the occurrence of dynamic physical phenomena poses a challenge to current methods(e.g., ROMS, MITgcm) that aim to provide accurate predictions of SSHA. In this study, we have developed an optimized Simple Recurrent Unit (SRU) deep network for the short- to medium-term prediction of the SSHA using Archiving Validation and International of Satellites Oceanographic (AVISO) data. Thanks to the parallel structure of the SRU, the computational complexity of the deep network can be reduced to a considerable extent and this makes the short- to medium-term prediction more efficient. To avoid over-fitting and a vanishing gradient, a skip-connection strategy has been utilized for model optimization, and this improves significantly the accuracy of prediction. Detailed experiments were carried out in the Bohai Sea to evaluate the proposed model and it was demonstrated that the proposed framework (i) outperformed significantly the current deep learning methods such as the BP (Backpropagation), the RNN (Recurrent Neural Network), the LSTM (Long Short-term Memory), and the GRU (Gated Recurrent Unit) algorithms for 1, 5, 20, and 300-day prediction; (ii) can predict the short-term trend in the SSHA (for the next day or 2 days) in real time; and (iii) achieves medium-term prediction in seconds for the next 5–20 days and shows great potential for applications requiring medium- to long-term predictions. To the best of our knowledge, this is the first paper that investigates the effectiveness of the SRU deep learning model for short- to medium-term SSHA predictions.
FOCUS :I ON THERMOCHEMISTRY The Thermochemistry of Group 15 Tetrachloride Anions

Bond strengths for a series of Group 15 tetrachloride anions ACl4 (A P, As, Sb, and Bi) have been determined by measuring thresholds for collision-induced dissociation of the anions in a flowing afterglow-tandem mass spectrometer. The central atoms in these systems have ten electrons, which violates the octet rule: the bond dissociation energies for ACl4 help to clarify the effect of the central atom on hypervalent bond strengths. The 0 K bond energies in kJ mol 1 are D(Cl3A-Cl ) 90 7, 115 7, 161 8, and 154 15, respectively. Computational results using the B3LYP/LANL2DZpd level of theory are higher than the experimental bond energies. Calculations give a geometry for BiCl4 that is essentially tetrahedral rather than the see-saw observed for the other tetrachlorides. NBO calculations predict that the phosphorus and arsenic systems have 3C– 4E bonds, while the antimony and bismuth systems are more ionic. (J Am Soc Mass Spectrom 2002, 13, 469 – 476) © 2002 American Society for Mass Spectrometry
Arbuscular mycorrhiza confers lead tolerance and uptake in Miscanthus sacchariflorus

ABSTRACT The effects of an arbuscular mycorrhizal fungi (AMF) association on the growth, survival capabilities, nutrients and lead (Pb) uptake of Miscanthus sacchariflorus under different Pb concentrations were studied in the form of pot cultures. The treatments comprised inoculation or non-inoculation of the AMF, Gigaspora margarita, and the addition of three Pb concentrations to the soil (0, 100 and 1000 mg kg−1). The addition of Pb significantly decreased mycorrhizal colonisation. The inoculation of AMF with Pb increased chlorophyll content, Fv/Fm, total dry mass, indole-3-acetic acid (IAA), total nitrogen, and total phosphorus, whereas H2O2 level, indole-3-acetic acid oxidase (IAAO) activity, and peroxidase (POD) activity were low compared to those in the non-inoculated treatments. Moreover, the application of AMF together with Pb doses induces concentrations of Pb in the plant, where the higher dose of Pb (1000 mg kg−1) induces a lower content of Pb in the aerial part of the plant but a higher content in the root. G. margarita enhanced the tolerance of M. sacchariflorus against Pb toxicity, and facilitated the accumulation of Pb in the plant roots, whereas translocation to the shoots was inhibited at the highest dose Pb (1000 mg kg−1). However, in contaminated soil, the Pb removal capability of M. sacchariflorus with AMF was remarkable.
Olisipo: A Probabilistic Approach to the Adaptable Execution of Deterministic Temporal Plans

The robust execution of a temporal plan in a perturbed environment is a problem that remains to be solved. Perturbed environments, such as the real world, are non-deterministic and filled with uncertainty. Hence, the execution of a temporal plan presents several challenges and the employed solution often consists of replanning when the execution fails. In this paper, we propose a novel algorithm, named Olisipo , which aims to maximise the probability of a successful execution of a temporal plan in perturbed environments. To achieve this, a probabilistic model is used in the execution of the plan, instead of in the building of the plan. This approach enables Olisipo to dynamically adapt the plan to changes in the environment. In addition to this, the execution of the plan is also adapted to the probability of successfully executing each action. Olisipo was compared to a simple dispatcher and it was shown that it consistently had a higher probability of successfully reaching a goal state in uncertain environments, performed fewer replans and also executed fewer actions. Hence, Olisipo offers a substantial improvement in performance for disturbed environments. 2012 ACM
Semantic Interoperability Issues in the Master’s Curriculum in Arti ﬁ cial Intelligence at So ﬁ a University

In today’s world of digital transformation and big data the issues related to the semantic interoperability of different information systems that support the decision making activities in the same or similar areas are becoming increasingly important. It is therefore natural for such issues to be in the focus of the education at master’s level in a variety of professional ﬁ elds. The paper discusses the experience in this regard of the Master’s program in Arti ﬁ cial Intelligence at the Faculty of Mathematics and Informatics at So ﬁ a University, focusing on some good examples of student projects.
Improved Adsorption of the Antimicrobial Agent Poly (Hexamethylene) Biguanide on Ti-Al-V Alloys by NaOH Treatment and Impact of Mass Coverage and Contamination on Cytocompatibility

Unlike the native surface of the implant material (Ti6Al4V), oxidation with H2O2 leads to increased binding of the effective antimicrobial agent poly(hexamethylene) biguanide [PHMB]. However, treating with NaOH instead results in an even higher PHMB mass coverage. After oxidation with H2O2, strong differences in the PHMB adsorption capability between polished and corundum-blasted surfaces appear, indicating a roughness dependence. After NaOH treatment, no such effect was observed. The wetting properties of specimens treated with either H2O2 or NaOH prior to PHMB exposure clearly varied. To unravel the nature of this interaction, widespread in silico and in vitro experiments were performed. Methods: By X-ray photoelectron spectroscopy, scanning electron microscopy, water contact angle measurements and MD simulations, we characterized the interplay between the polycationic antimicrobial agent and the implant surface. A theoretical model for PHMB micelles is tested for its wetting properties and compared to carbon contaminated TiO2. In addition, quantitation of anionic functional group equivalents, the binding properties of PHMB with blocked amino end-group, and the ability to bind chlorhexidine digluconate (CHG) were investigated. Ultimately, the capability of osteoblasts to build calcium apatite, and the activity of alkaline phosphatase on PHMB coated specimens, were determined. Results: Simulated water contact angles on carbon contaminated TiO2 surfaces and PHMB micelle models reveal little influence of PHMB on the wetting properties and point out the major influence of remaining and recovering contamination from ambient air. Testing PHMB adsorption beyond the critical micelle concentration and subsequent staining reveals an island-like pattern with H2O2 as compared to an evenly modified surface with NaOH. Both CHG and PHMB, with blocked amino end groups, were adsorbed on the treated surfaces, thus negating the significant influence of PHMB’s terminal groups. The ability of osteoblasts to produce calcium apatite and alkaline phosphatase is not negatively impaired for PHMB mass coverages up to 8 μg/specimen. Conclusion: Differences in PHMB adsorption are triggered by the number of anionic groups and carbon contaminants, both of which depend on the specimen pre-treatment. With more PHMB covering, the implant surface is protected against the capture of new contamination from the ambient air, thus building a robust antimicrobial and biocompatible surface coating.
Maternal education and infant mortality decline: The evidence from Indonesia, 1980–2015

BACKGROUND Better maternal education has been credited with making a major contribution to infant mortality decline. Most of the evidence is based on cross-sectional analyses, which show a strong correlation between maternal education and infant mortality. However, crosssectional analyses do not provide an estimate of the contribution of maternal education to infant mortality decline. OBJECTIVE The major objective is to obtain a more accurate estimate of the contribution of maternal education to infant mortality decline. METHODS Pooling data from all available phases of the Demographic and Health Survey, this article presents a longitudinal, individual-level analysis of the determinants of trends in infant mortality in Indonesia. RESULTS Better maternal education explains 15% of the infant mortality decline in Indonesia from 1980 to 2015. CONTRIBUTION The article presents the results of the largest individual-level study of its kind in terms of length of the period covered and number of infants involved. 1 Hebrew University of Jerusalem, Israel. Email: jona@mail.huji. ac.il. Schellekens: Maternal education and infant mortality decline: The evidence from Indonesia, 1980–2015 808 https://www.demographic-research.org
Deliberation-Based Multi-Pass Speech Synthesis

Sequence-to-sequence (seq2seq) models have achieved state-of-the-art performance in a wide range of tasks including Neural Machine Translation (NMT) and Text-To-Speech (TTS). These models are usually trained with teacher forcing, where the reference back-history is used to predict the next token. This makes training efﬁcient, but limits performance, because during inference the free-running back-history must be used. To address this problem, deliberation-based multi-pass seq2seq has been used in NMT. Here the output sequence is generated in multiple passes, each one conditioned on the initial input and the free-running output of the previous pass. This paper investigates, and compares, deliberation-based multi-pass seq2seq for TTS and NMT. For NMT the simplest form of multi-pass approaches, where the free-running ﬁrst-pass output is combined with the initial input, improves performance. However, applying this scheme to TTS is challenging: the multi-pass model tends to converge to the standard single-pass model, ignoring the previous output. To tackle this issue, a guided attention loss is added, enabling the system to make more extensive use of the free-running output. Experimental results conﬁrm the above analysis and demonstrate that the proposed TTS model outperforms a strong baseline.
Hygroscopicity of Fresh and Aged Salt Mixtures from Saline Lakes

The high hygroscopicity of salt aerosol particles makes the particles active in aerosol and cloud formations. Inland saline lakes are an important and dynamic source of salt aerosol. The salt particles can be mixed with mineral dust and transported over long distances. During transportation, these particles participate in atmospheric heterogeneous chemistry and further impact the climate and air quality on a global scale. Despite their importance and potential, relatively little research has been done on saline lake salt mixtures from atmospheric perspectives. In this study, we use experimental and model methods to evaluate the hygroscopic properties of saline lake brines, fresh salt aerosol particles, and aged salt aerosol particles. Both original samples and literature data are investigated. The original brine samples are collected from six salt lakes in Shanxi and Qinghai provinces in China. The ionic compositions of the brines are determined and the hygroscopicity measurements are performed on crystallized brines. The experimental results agree well with theoretical deliquescence relative humidity (DRH) values estimated by a thermodynamic model. The correlations between DRHs of different salt components and the correlations between DRHs and ionic concentrations are presented and discussed. Positive matrix factorization (PMF) analysis is performed on the ionic concentrations data and the hygroscopicity results, and the solutions are interpreted and discussed. The fresh and aged salt aerosol particles are analyzed in the same way as the brines, and the comparison shows that the aged salt aerosol particles completely alter their hygroscopic property, i.e., transferring from MgCl2− governed to NH4NO3− governed.
Scaling up Lloyd’s algorithm: stochastic and parallel block-wise optimization perspectives

We found, contrary to common belief, previously developed online and mini-batch Lloyd’s variants are not truly stochastic versions of the batch algorithm and we characterize when they are stochastically descending in k -means objective. Sub-sequently, we cast Lloyd’s and its scaled variants as block coordinate minimization algorithms on a k-means loss function with enlarged solution space, and examined whether they converge to the same stopping points, given the same initialization. Our work suggests that current scaled Lloyd’s variants exhibit complicated convergence properties even when the problem of initialization is ignored and that they should be used with great caution in terms of the choice of different parameters.
An Engineering Perspective on the Bacterial Flagellum: Part 2 – Analytic View

Systems biology employs methodology and techniques typical of systems engineering. Similarly, reverse engineering of the features of biological organisms leverages both biology and engineering disciplines. The systems perspective on the bacterial flagellum detailed below studies the purpose, functions, components, and structure of a typical bacterial flagellum. The dynamic operation and control of this organelle and the flagellum’s assembly stages are also studied. The bacterial flagellum is a well-researched bacterial subsystem [1,2,3,4] in biology. However, this three-part engineering study takes two essentially independent approaches. First was a constructive approach, which was discussed in detail in Part 1 [5]; the other is an analytical approach, which is discussed in detail herein. The first, constructive approach was a top-down specification. That is, Part 1 started with specifying the purpose of a bacterial motility organelle, the environment of a bacterium, its existing resources, its existing constitution, and its physical limits, all within the relevant aspects of physics and molecular chemistry. From that, the constructive approach derived the logically necessary functional requirements, the constraints, the assembly needs, and the hierarchical relationships within the functionality. The functionality included a required control subsystem to properly direct the operation of a propulsion subsystem. Those functional requirements and constraints then suggested the few—and very limited—viable implementation schemata for a bacterial propulsion system. The details of one schema were then set forth. A sincere attempt was made to keep the elaboration of this constructive approach logical and as independent as possible from knowledge of the actual flagellar structure. The second, analytical approach employed here in Part 2 is the converse of the first approach; it is a bottom-up analysis. This Part 2 presents the constituent proteins, observed structure, assembly, and resultant behavior of a typical bacterium. This knowledge has been acquired by microscopic observation, by gene sequencing, by disabling component proteins Abstract
Reduced-Memory Kalman Based Stochastic Gradient Descent

We develop a limited-memory method for online linear regression problems named the reduced Kalman-based Stochastic Gradient Descent. Limited-memory methods are intriguing since they address the computational challenges of second order methods and show more robustness to different parameter choices than ﬁrst order methods. However, current limited-memory methods, such as adaption of L-BFGS to the stochastic case, suffer from one or more of the following drawbacks: (1) assumptions made on objective functions that rule out least squares problems; (2) samples with size larger than the dimensional of problem required when estimating Hessian; (3) less robustness due to new hyper-parameters introduced. Moreover, all the methods do not incorporate the latest observation in the Hessian estimate for the next iterate due to the lack of a relevant proof technique. The standard approach used in current methods requires a conditional independence between the Hessian estimate and the gradient estimate. We tackle those problems by introducing a new limited-memory method that avoids those drawbacks by construction. We give the theoretical guarantee and experimentally demonstrate our method. Importantly, in our method, we fully exploit the up-to-the-moment information in the Hessian estimate and the gradient estimate. We develop a new analysis strategy that allows us to study the convergence when the Hessian and gradient are dependent. Furthermore, this strategy can be adapted for a series of procedures that include dependent Hessian and gradient estimate since no specialties of least squares problems are utilized in our analysis scheme.
Intermediate Phase Evolution in YBCO Superconducting Film Fabricated by Fluorine Free MOD Method

: The second generation of high temperature superconducting tape is considered to be a promising mate-rial for future power and magnet applications. Among the fabrication processes, Fluorine Free-Metal Organic Deposition (FF-MOD) method has received much attention in recent years because of its low equipment require-ments, high crystal growth rate, and environmental friendliness. In present study, yttrium barium copper oxide (YBCO) films were prepared on the LAO single crystals and CeO 2 /IBAD-MgO/Y 2 O 3 /Al 2 O 3 /Hastelloy C276 technical substrates by the FF-MOD technique. The samples were quenched at different temperatures and then charac-terized by X-ray diffraction, scanning electron microscope and ATR-FT-IR to inveitigate the phase evolution process of YBCO and BaCO 3 . The results demonstrate that YBCO crystal does not formed directly by reaction between BaCO 3 and Y/Cu oxides. And the formed YBCO crystals are randomly oriented at first, and then gradually convert into the biaxially textured structure during the heat treatment process at about 800 ℃ .