bluuebunny/crossref_metadata_2025_split huggingface dataset:
Boric Acid Cross-linked 3D Polyvinyl Alcohol Gel Beads by NaOH-Titration Method as a Suitable Biomass Immobilization Matrix
Authors: Sun Li , Wang Jinxing , Liang Jidong , Li Gaigai 
Published: December 2019

AbstractGranule-base immobilization of biomass is a potential method for a decent quality granular sludge cultivation. In this study, 3D polyvinyl alcohol (PVA) gel beads were chemically cross-linked via a simple NaOH-titration method. The PVA gel beads’ porous morphology was characterized using scanning electron microscope (SEM) and Brunauer–Emmette–Teller (BET), and their mechanical properties were evaluated by swelling rate and compressive stress tests. When cross-linking time was 10 min, high quality gel beads (P10) were synthesized, which demonstrated a homogeneous porous structure, good swelling rate, and high compressive strength. A mechanism for synthesis of the gel beads was proposed based on the results of Fourier transform infrared (FTIR) and X-ray diffractometer (XRD) analysis. Briefly, the intermolecular hydrogen bonds of PVA were firstly broken by NaOH to generate active bond of O–Na, which easily reacted with B(OH)4− to produce the PVA-boric acid gel beads. P10 showed excellent biocompatibility for anaerobic ammonia oxidation (anammox) biomass’ immobilization. After incubation for three months, well granule-base immobilized sludge on P10 was developed in up-flow reactor. The sludge had high abundance of anammox biomass and was in balance with other functional bacteria. This work provides a simple method for the rapid preparation of 3D PVA gel beads and verifies their potential in granule-base immobilization of biomass.
High-Order Sequential Simulation via Statistical Learning in Reproducing Kernel Hilbert Space
Authors: Yao Lingqing , Dimitrakopoulos Roussos , Gamache Michel 
Published: December 2019

AbstractThe present work proposes a new high-order simulation framework based on statistical learning. The training data consist of the sample data together with a training image, and the learning target is the underlying random field model of spatial attributes of interest. The learning process attempts to find a model with expected high-order spatial statistics that coincide with those observed in the available data, while the learning problem is approached within the statistical learning framework in a reproducing kernel Hilbert space (RKHS). More specifically, the required RKHS is constructed via a spatial Legendre moment (SLM) reproducing kernel that systematically incorporates the high-order spatial statistics. The target distributions of the random field are mapped into the SLM-RKHS to start the learning process, where solutions of the random field model amount to solving a quadratic programming problem. Case studies with a known data set in different initial settings show that sequential simulation under the new framework reproduces the high-order spatial statistics of the available data and resolves the potential conflicts between the training image and the sample data. This is due to the characteristics of the spatial Legendre moment kernel and the generalization capability of the proposed statistical learning framework. A three-dimensional case study at a gold deposit shows practical aspects of the proposed method in real-life applications.
Evaluation of genomic selection and marker-assisted selection in Miscanthus and energycane
Authors: Olatoye Marcus O. , Clark Lindsay V. , Wang Jianping , Yang Xiping , Yamada Toshihiko , Sacks Erik J. , Lipka Alexander E. 
Published: December 2019

AbstractAlthough energycane (Saccharum spp.hybrids) is widely used as a source of lignocellulosic biomass for bioethanol, breeding this crop for disease resistance is challenging due to its narrow genetic base. Therefore, efforts are underway to introgress novel sources of genetic resistance fromMiscanthusinto energycane. Given that disease resistance in energycane could be either qualitative or quantitative in nature, careful examination of a wide variety of genomic-enabled breeding approaches will be crucial to the success of such an undertaking. Here we examined the efficiency of both genomic selection (GS) and marker-assisted selection (MAS) for traits simulated under different genetic architectures in F1and BC1populations ofMiscanthus×Miscanthusand sugarcane × sugarcane crosses. We observed that the performance of MAS was comparable and sometimes superior to GS for traits simulated with four quantitative trait nucleotides (QTNs). In contrast, as the number of simulated QTN increased, all four GS models that were evaluated tended to outperform MAS, select more phenotypically optimal F1individuals, and accurately predict simulated trait values in subsequent BC1generations. We therefore conclude that GS is preferable to MAS for introgressing genetic sources of horizontal disease resistance fromMiscanthusto energycane, while MAS remains a suitable option for introgressing vertical disease resistance.
Kant and the forms of realism
Authors: Heidemann Dietmar 
Published: December 2019

AbstractRealism takes many forms. The aim of this paper is to show that the “Critique of pure Reason” is the founding document of realism and that to the present-day Kant’s discussion of realism has shaped the theoretical landscape of the debates over realism. Kant not only invents the now common philosophical term ‘realism’. He also lays out the theoretical topography of the forms of realism that still frames our understanding of philosophical questions concerning reality. The paper explores this by analysis of Kant’s methodological procedure to distinguish between empirical (i.e. nonmetaphysical) and transcendental (metaphysical) realism. This methodological procedure is still of great help in contemporary philosophy, although it has its limits.
The Truth of Dharma and the Dharma of Truth: Reflections on Hinduism as a Dharmic Faith
Authors: Lipner Julius 
Published: December 2019

AbstractThis article discusses what it might mean to characterize traditional Hinduism as a dharmic faith in relation to the concepts of truth (satyam) and its opposite (anṛtam), without however expatiating on supposed contrasts between Hinduism and the “Abrahamic” faiths. The argument is conducted by recognizing two senses toanṛtam, namely, “non-truth” and “falsehood,” in contrast tosatyam; and the method used is inductive in that a historically well-known episode of theMahābhārata—the story of Kauśika and the bandits—and its authoritative interpretation by the deity Kṛṣṇa in terms ofsatyamandanṛtam(in theKarṇaparvan, Book 8, of the text) are analyzed heuristically to indicate howdharmais viewed in the traditionquaethical concept. The conclusion is drawn that Hindudharmais understood as that whose objective is ultimately to bring about the welfare of the world (lokasaṅgraha). In relation to truth (and its opposite),materially,dharmais invariably contextual in connotation (in contrast to what might be a Kantian or absolutist reading of moral imperatives), whereasformally, at the hands of one commentator or other, not least in modern times,dharmais that which defines what it means to be Hindu. The material and formal connotations ofdharmathus analyzed and taken together set Hinduism apartquadharmic faith.
Biomass Recalcitrance in Willow Under Two Biological Conversion Paradigms: Enzymatic Hydrolysis and Anaerobic Digestion
Authors: Ohlsson Jonas A. , Harman-Ware Anne E. , Sandgren Mats , Schnürer Anna 
Published: December 2019

AbstractBiomass recalcitrance, the inherent resistance of plants towards deconstruction, negatively affects the viability of biorefineries. This trait is not only dictated by the properties of the biomass but also by the conversion system used and its interactions with specific features of the biomass. Here, biomass recalcitrance to anaerobic digestion (AD) was assessed using a biomethanation potential (BMP) assay. Plant material (n = 94) was selected from a large population of natural Salix viminalis accessions, previously evaluated for biomass recalcitrance using hydrothermal pretreatment–enzymatic hydrolysis. Correlations between yields from the two biological conversion systems were evaluated, as well as the influence of biomass compositional features, analyzed by pyrolysis-molecular beam mass spectrometry (py-MBMS), and other biomass physical properties on conversion performance. BMP values averaged 198.0 Nml CH4/g biomass after 94 days, ranging from 28.6 to 245.9. S lignin and carbohydrate-derived spectral features were positively correlated with performance under both systems, whereas G lignin, p-coumaric acid, and ferulic acid-derived ions were negatively correlated with yields and rates. Most spectral features were more strongly correlated with enzymatic hydrolysis yields compared to methane production. For early-stage methane production and rate, recalcitrance factors were similar compared to enzymatic hydrolysis, with weaker correlations observed at later timepoints. The results suggest that although variation in methane potential was considerably lower than enzymatic hydrolysis yields, a reduced recalcitrance under this system will still be of importance to improve early conversion rates. Spectral features of low methane-producing samples indicate the presence of inhibitory substances, warranting further study.
CanAssist Breast Impacting Clinical Treatment Decisions in Early-Stage HR+ Breast Cancer Patients: Indian Scenario
Authors: Sankaran Satish , Dikshit Jyoti Bajpai , Prakash SV Chandra , Mallikarjuna SE , Somashekhar SP , Patil Shekhar , Kumar Rajeev , Prasad Krishna , Shet Dinesh , Bakre Manjiri M. 
Published: December 2019

AbstractCanAssist Breast (CAB) has thus far been validated on a retrospective cohort of 1123 patients who are mostly Indians. Distant metastasis–free survival (DMFS) of more than 95% was observed with significant separation (P < 0.0001) between low-risk and high-risk groups. In this study, we demonstrate the usefulness of CAB in guiding physicians to assess risk of cancer recurrence and to make informed treatment decisions for patients. Of more than 500 patients who have undergone CAB test, detailed analysis of 455 patients who were treated based on CAB-based risk predictions by more than 140 doctors across India is presented here. Majority of patients tested had node negative, T2, and grade 2 disease. Age and luminal subtypes did not affect the performance of CAB. On comparison with Adjuvant! Online (AOL), CAB categorized twice the number of patients into low risk indicating potential of overtreatment by AOL-based risk categorization. We assessed the impact of CAB testing on treatment decisions for 254 patients and observed that 92% low-risk patients were not given chemotherapy. Overall, we observed that 88% patients were either given or not given chemotherapy based on whether they were stratified as high risk or low risk for distant recurrence respectively. Based on these results, we conclude that CAB has been accepted by physicians to make treatment planning and provides a cost-effective alternative to other similar multigene prognostic tests currently available.
D-Lactic acid fermentation performance and the enzyme activity of a novel bacterium Terrilactibacillus laevilacticus SK5–6
Authors: Prasirtsak Budsabathip , Thitiprasert Sitanan , Tolieng Vasana , Assabumrungrat Suttichai , Tanasupawat Somboon , Thongchul Nuttha 
Published: December 2019

Abstract
Purpose
The aim of this study was to prove that Terrilactibacillus laevilacticus SK5-6, a novel D-lactate producer, exhibited a good fermentation performance comparing to the reference D-lactate producer Sporolactobacillus sp.

Methods
Glucose bioconversion for D-lactate production and the activity of five key enzymes including phosphofructokinase (PFK), pyruvate kinase (PYK), D-lactate dehydrogenase (D-LDH), L-lactate dehydrogenase (L-LDH), and lactate isomerase (LI) were investigated in the cultivation of T. laevilacticus SK5–6 and S. laevolacticus 0361T.

Results
T. laevilacticus SK5–6 produced D-lactate at higher yield, productivity, and optical purity compared with S. laevolacticus 0361T. T. laevilacticus SK5–6, the catalase-positive isolate, simultaneously grew and produced D-lactate without lag phase while delayed growth and D-lactate production were observed in the culture of S. laevolacticus 0361T. The higher production of D-lactate in T. laevilacticus SK5–6 was due to the higher growth rate and the higher specific activities of the key enzymes observed at the early stage of the fermentation. The low isomerization activity was responsible for the high optical purity of D-lactate in the cultivation of T. laevilacticus SK5–6.

Conclusion
The lowest specific activity of PFK following by PYK and D/L-LDHs, respectively, indicated that the conversion of fructose-6-phosphate was the rate limiting step. Under the well-optimized conditions, the activation of D/L-LDHs by fructose-1,6-phosphate and ATP regeneration by PYK drove glucose bioconversion toward D-lactate. The optical purity of D-lactate was controlled by D/L-LDHs and the activation of isomerases. High D-LDH with limited isomerase activity was preferable during the fermentation as it assured the high optical purity.
Identification and probiotic properties of lactobacilli isolated from two different fermented beverages
Authors: Angelescu Iulia-Roxana , Zamfir Medana , Stancu Mihaela-Marilena , Grosu-Tudor Silvia-Simona 
Published: December 2019

Abstract
Purpose
Scientific information regarding the microbial content and functional aspects of fermented beverages traditionally produced in certain parts of Europe are scarce. However, such products are believed to have some health benefits and might contain functional bacterial strains, such as probiotics. The aim of the study was to identify such lactic acid bacteria strains isolated from water kefir and, for the first time, from braga, a Romanian fermented beverage made of cereals.

Methods
Lactic acid bacteria (LAB) were identified to species level based on (GTG)5-PCR fingerprinting and 16S rRNA gene sequencing. Selected strains were screened for their antibacterial activity and probiotic potential.

Results
Eight isolates belonging to seven Lactobacillus species were recovered from the two drinks. The identification of LAB involved in the fermentation of braga (Lactobacillus plantarum, Lactobacillus fermentum, and Lactobacillus delbrueckii) is firstly reported here. Five of the Lactobacillus isolates showed antibacterial activity against pathogenic bacteria, including Listeria monocytogenes, Escherichia coli, Staphylococcus aureus, and Salmonella enterica. Moreover, most of them showed a good resistance to pH 2.5 and some survived at high concentrations of bile salts (up to 2%). Two L. plantarum isolates were able to inhibit all the indicator strains, and showed the best viability (about 70%) after a sequential treatment simulating the passage through the gastrointestinal tract.

Conclusion
Based on the results, the most promising candidates for designing new probiotic products are: L. plantarum BR9 from braga and L. plantarum CR1 from water kefir.
WEDeepT3: predicting type III secreted effectors based on word embedding and deep learning
Authors: Fu Xiaofeng , Yang Yang 
Published: December 2019

BackgroundThe type III secreted effectors (T3SEs) are one of the indispensable proteins in the growth and reproduction of Gram‐negative bacteria. In particular, the pathogenesis of Gram‐negative bacteria depends on the type III secreted effectors, and by injecting T3SEs into a host cell, the host cell’s immunity can be destroyed. The high diversity of T3SE sequences and the lack of defined secretion signals make it difficult to identify and predict. Moreover, the related study of the pathological system associated with T3SE remains a hot topic in bioinformatics. Some computational tools have been developed to meet the growing demand for the recognition of T3SEs and the studies of type III secretion systems (T3SS). Although these tools can help biological experiments in certain procedures, there is still room for improvement, even for the current best model, as the existing methods adopt hand‐designed feature and traditional machine learning methods.MethodsIn this study, we propose a powerful predictor based on deep learning methods, called WEDeepT3. Our work consists mainly of three key steps. First, we train word embedding vectors for protein sequences in a large‐scale amino acid sequence database. Second, we combine the word vectors with traditional features extracted from protein sequences, like PSSM, to construct a more comprehensive feature representation. Finally, we construct a deep neural network model in the prediction of type III secreted effectors.ResultsThe feature representation of WEDeepT3 consists of both word embedding and position‐specific features. Working together with convolutional neural networks, the new model achieves superior performance to the state‐of‐the‐art methods, demonstrating the effectiveness of the new feature representation and the powerful learning ability of deep models.ConclusionWEDeepT3 exploits both semantic information of k‐mer fragments and evolutional information of protein sequences to accurately differentiate between T3SEs and non‐T3SEs. WEDeepT3 is available at bcmi.sjtu.edu.cn/~yangyang/WEDeepT3.html.
Neonatal Outcome After Hexoprenaline Compared with Atosiban After Preterm Premature Rupture of Membranes
Authors: Ulrich Daniela , Schneider Verena , Pichler Gerhard , Haas Josef , Culea Valeriu , Joksch Maike , Mager Corinna , Schmied Christian , Lakovschek Ioana-Claudia , Lang Uwe , Reif Philipp 
Published: December 2019

AbstractPreterm premature rupture of membranes (PPROM) occurs in up to 3% of all pregnancies. Only few comparative studies have investigated potential risks and benefits between different tocolytic substances in women with PPROM. The aim of this study was to compare the neonatal short term outcome after tocolysis with Atosiban or Hexoprenaline in women with PPROM. This is a retrospective observational cohort study of women with PPROM between 24 and 32 weeks of gestation comparing neonatal and maternal outcome after tocolysis with atosiban or hexoprenaline. Outcome parameters were short term neonatal outcome, maternal tocolytic efficacy, effectiveness and tolerability and neonatal neurodevelopmental long-term outcome. Continuous variables were compared using t-Test or Mann–Whitney U test, as appropriate. For categorical variables Chi-square after Pearson and Fisher exact-test were used to compare the two groups. 93 women were included into this study with 42 women receiving hexoprenaline and 51 women receiving atosiban as primary tocolytic treatment. Mean gestational age was 29 weeks in both groups at the time PPROM. No differences were found for any short term neonatal outcome parameters, tocolytic efficacy, effectiveness and tolerability and neonatal neurodevelopmental long-term outcome. Both hexoprenaline and atosiban do not affect the short and long term neonatal outcome in women with PPROM for the time of lung maturation.
Improved method for correcting sample Mahalanobis distance without estimating population eigenvalues or eigenvectors of covariance matrix
Authors: Kobayashi Yasuyuki 
Published: December 2019

AbstractThe recognition performance of the sample Mahalanobis distance (SMD) deteriorates as the number of learning samples decreases. Therefore, it is important to correct the SMD for a population Mahalanobis distance (PMD) such that it becomes equivalent to the case of infinite learning samples. In order to reduce the computation time and cost for this main purpose, this paper presents a correction method that does not require the estimation of the population eigenvalues or eigenvectors of the covariance matrix. In short, this method only requires the sample eigenvalues of the covariance matrix, number of learning samples, and dimensionality to correct the SMD for the PMD. This method involves the summation of the SMD’s principal components (each of which is divided by its expectation obtained using the delta method), Lawley’s bias estimation, and the variances of the sample eigenvectors. A numerical experiment demonstrates that this method works well for various cases of learning sample number, dimensionality, population eigenvalues sequence, and non-centrality. The application of this method also shows improved performance of estimating a Gaussian mixture model using the expectation–maximization algorithm.
Distress in delirium: causes, assessment and management
Authors: Williams Sophie T. , Dhesi Jugdeep K. , Partridge Judith S. L. 
Published: December 2019

Abstract
Purpose
Delirium is a common clinical syndrome associated with increased physical and psychological morbidity, mortality, inpatient stay and healthcare costs. There is growing interest in understanding the delirium experience and its psychological impact, including distress, for patients and their relatives, carers and healthcare providers.

Methods
This narrative review focuses on distress in delirium (DID) with an emphasis on its effect on older patients. It draws on qualitative and quantitative research to describe patient and environmental risk factors and variations in DID across a number of clinical settings, including medical and surgical inpatient wards and end of life care. The article provides an overview of the available distress assessment tools, both for clinical and research practice, and outlines their use in the context of delirium. This review also outlines established and emerging management strategies, focusing primarily on prevention and limitation of distress in delirium.

Results
Both significant illness and delirium cause distress. Patients who recall the episode of delirium describe common experiential features of delirium and distress. Relatives who witness delirium also experience distress, at levels suggested to be greater than that experienced by patients themselves. DID results in long-term psychological sequelae that can last months and years. Preventative actions, such pre-episode educational information for patients and their families in those at risk may reduce distress and psychological morbidity.

Conclusions
Improving clinicians’ understanding of the experience and long term psychological harm of delirium will enable the development of targeted support and information to patients at risk of delirium, and their families or carers.
Superconvergence and the Numerical Flux: a Study Using the Upwind-Biased Flux in Discontinuous Galerkin Methods
Authors: Frean Daniel J. , Ryan Jennifer K. 
Published: December 2019

AbstractOne of the beneficial properties of the discontinuous Galerkin method is the accurate wave propagation properties. That is, the semi-discrete error has dissipation errors of order $$2k+1$$2k+1 ($$\le Ch^{2k+1}$$≤Ch2k+1) and order $$2k+2$$2k+2 for dispersion ($$\le Ch^{2k+2}$$≤Ch2k+2). Previous studies have concentrated on the order of accuracy, and neglected the important role that the error constant, C, plays in these estimates. In this article, we show the important role of the error constant in the dispersion and dissipation error for discontinuous Galerkin approximation of polynomial degree k, where $$k=0,1,2,3.$$k=0,1,2,3. This gives insight into why one may want a more centred flux for a piecewise constant or quadratic approximation than for a piecewise linear or cubic approximation. We provide an explicit formula for these error constants. This is illustrated through one particular flux, the upwind-biased flux introduced by Meng et al., as it is a convex combination of the upwind and downwind fluxes. The studies of wave propagation are typically done through a Fourier ansatz. This higher order Fourier information can be extracted using the smoothness-increasing accuracy-conserving (SIAC) filter. The SIAC filter ties the higher order Fourier information to the negative-order norm in physical space. We show that both the proofs of the ability of the SIAC filter to extract extra accuracy and numerical results are unaffected by the choice of flux.