Miracle-dz/UniFAR-FTUs huggingface dataset:
Direct from solid natural products to pure compounds in a single step: Coupling online extraction with high-speed counter-current chromatography.

Extraction is the most important step in the purification of bioactive compounds from natural products. This study introduces a simple online extraction strategy coupled with high-speed counter-current chromatography for efficient extraction and purification of bioactive components from solid natural products. For online extraction strategy, 1.0 g of ground Mangnolia officinalis or Piper nigrum was loaded into a guard column, which was then positioned on the manual injection valve instead of the sample loop. Bioactive components were directly extracted by the mobile phase of high-speed counter-current chromatography, and then transferred into high-speed counter-current chromatography for purification. In addition, the compatibility of the developed methodology for direct purification of bioactive components from fresh M. officinalis was successfully demonstrated. Obviously, in comparison with traditional offline heat-reflux extraction, online extraction avoided the instrument, time, solvent, and energy consumption, and purified two phenolic compounds (honokiol and magnolol) from M. officinalis and three alkaloids (piperyline, piperine, and piperanine) from P. nigrum with high extraction efficiency. The superiority of the developed methodology is to establish an easy, rapid, and efficient technique for the purification of a wide variety of bioactive components from solid natural products.
Enabling Identity-Based Integrity Auditing and Data Sharing With Sensitive Information Hiding for Secure Cloud Storage

With cloud storage services, users can remotely store their data to the cloud and realize the data sharing with others. Remote data integrity auditing is proposed to guarantee the integrity of the data stored in the cloud. In some common cloud storage systems such as the electronic health records system, the cloud file might contain some sensitive information. The sensitive information should not be exposed to others when the cloud file is shared. Encrypting the whole shared file can realize the sensitive information hiding, but will make this shared file unable to be used by others. How to realize data sharing with sensitive information hiding in remote data integrity auditing still has not been explored up to now. In order to address this problem, we propose a remote data integrity auditing scheme that realizes data sharing with sensitive information hiding in this paper. In this scheme, a sanitizer is used to sanitize the data blocks corresponding to the sensitive information of the file and transforms these data blocks’ signatures into valid ones for the sanitized file. These signatures are used to verify the integrity of the sanitized file in the phase of integrity auditing. As a result, our scheme makes the file stored in the cloud able to be shared and used by others on the condition that the sensitive information is hidden, while the remote data integrity auditing is still able to be efficiently executed. Meanwhile, the proposed scheme is based on identity-based cryptography, which simplifies the complicated certificate management. The security analysis and the performance evaluation show that the proposed scheme is secure and efficient.
Low-Latency Neural Speech Translation

Through the development of neural machine translation, the quality of machine translation systems has been improved significantly. By exploiting advancements in deep learning, systems are now able to better approximate the complex mapping from source sentences to target sentences. But with this ability, new challenges also arise. An example is the translation of partial sentences in low-latency speech translation. Since the model has only seen complete sentences in training, it will always try to generate a complete sentence, though the input may only be a partial sentence. We show that NMT systems can be adapted to scenarios where no task-specific training data is available. Furthermore, this is possible without losing performance on the original training data. We achieve this by creating artificial data and by using multi-task learning. After adaptation, we are able to reduce the number of corrections displayed during incremental output construction by 45%, without a decrease in translation quality.
Deep Learning for Radio Resource Allocation in Multi-Cell Networks

The increased complexity and heterogeneity of emerging 5G and B5G wireless networks will require a paradigm shift from traditional resource allocation mechanisms. Deep learning (DL) is a powerful tool where a multi-layer neural network can be trained to model a resource management algorithm using network data.Therefore, resource allocation decisions can be obtained without intensive online computations which would be required otherwise for the solution of resource allocation problems. In this context, this article focuses on the application of DL to obtain solutions for the radio resource allocation problems in multi-cell networks. Starting with a brief overview of a DNN as a DL model, relevant DNN architectures and the data training procedure, we provide an overview of existing state-of-the-art applying DL in the context of radio resource allocation. A qualitative comparison is provided in terms of their objectives, inputs/outputs, learning and data training methods. Then, we present a supervised DL model to solve the sub-band and power allocation problem in a multi-cell network. Using the data generated by a genetic algorithm, we first train the model and then test the accuracy of the proposed model in predicting the resource allocation solutions. Simulation results show that the trained DL model is able to provide the desired optimal solution 86.3 percent of the time.
Timing of Rotavirus Vaccine Doses and Severe Rotavirus Gastroenteritis Among Vaccinated Infants in Low- and Middle-income Countries

Background: Altering rotavirus vaccine schedules may improve vaccine performance in low- and middle-income countries. We analyzed data from clinical trials of the monovalent (RV1) and pentavalent (RV5) rotavirus vaccines in low- and middle-income countries to understand the association between vaccine dose timing and severe rotavirus gastroenteritis incidence. Methods: We assessed the association between variations in rotavirus vaccine administration schedules and severe rotavirus gastroenteritis risk. We used the complement of the Kaplan–Meier survival estimator to estimate risk differences for different schedules. To adjust risk differences (RDs) for confounding, we calibrated estimates in the vaccinated arm using estimates from the placebo arm. Results: There were 3,114 and 7,341 children included from the RV1 and RV5 trials, respectively. The 18-month adjusted severe rotavirus gastroenteritis risk was 4.0% (95% confidence interval [CI] = 1.1, 7.1) higher for those receiving their first RV5 dose at <6 versus ≥6 weeks. For RV1, there was a 4.0% (95% CI = 0.0, 8.2) increase in 12-month adjusted risk for a 4- versus 6-week interval between doses. Further analysis revealed those receiving their first RV5 dose at 3–4 and 5–7 weeks had 2.9% (95% CI = 0.8, 5.3) and 1.3% (95% CI = −0.3, 3.0), respectively, higher risk compared with those at 9–12 weeks. Those receiving their first dose at 8 weeks had the lowest risk (RD: −2.6% [95% CI = −5.4, −0.1]) compared with those at 9–12 weeks. Conclusions: A modest delay in rotavirus vaccination start and increase in interval between doses may be associated with lower severe rotavirus gastroenteritis risk in low- and middle-income countries.
Bx 2015 — Bidirectional Transformations: Proceedings of the 4th International Workshop on Bidirectional Transformations

As part of an ongoing project to unify the treatment of symmetric lenses (of various kinds) as equivalence classes of spans of asymmetric lenses (of corresponding kinds) we relate the symmetric delta lenses of Diskin et al, with spans of asymmetric delta lenses. Because delta lenses are based on state spaces which are categories rather than sets there is further structure that needs to be accounted for and one of the main findings in this paper is that the required equivalence relation among spans is compatible with, but coarser than, the one expected. The main result is an isomorphism of categories between a category whose morphisms are equivalence classes of symmetric delta lenses (here called fb-lenses) and the category of spans of delta lenses modulo the new equivalence.
A Low-Frequency Dual-Band Operational Microphone Mimicking the Hearing Property of Ormia Ochracea

This paper introduces a directional MEMS microphone designed for hearing aid applications appropriate to low-frequency hearing impairment, inspired by the hearing mechanism of a fly, the female Ormia ochracea. It uses both piezoelectric and capacitive sensing schemes. In order to obtain a high sensitivity at low frequency bands, the presented microphone is designed to have two resonance frequencies below the threshold of low-frequency hearing loss at approximately 2 kHz. One is around 500 Hz and the other is slightly above 2 kHz. The novel dual sensing mechanism allows for optimization of the microphone sensitivity at both frequencies, with a maximum open-circuit (excluding pre-amplification) acoustic response captured via differential piezoelectric sensing at approximately −46 dB (V) ref. 94 dB (SPL) at the resonance frequencies. The corresponding minimum detectable sound pressure level is just below −12 dB. The comb finger capacitive sensing was employed due to a lower electrical response generated from a ground referenced single-ended output by the piezoelectric sensing at the first resonance frequency compared with the second resonance frequency. The capacitive sensing mechanism, connected to a charge amplifier, generates a −28.4 dB (V) ref. 94 dB (SPL) acoustic response when the device is excited at either of the two resonance frequencies. Due to the asymmetric geometry and the $400~\mu \text{m}$ thick substrate, the device is predicted to perform as a bi-directional microphone below 3 kHz, which is shown by the measured directional polar patterns. [2018-0036]
An Effective Scheduling Algorithm for Coverage Control in Underwater Acoustic Sensor Network

Coverage maintenance is a bottleneck restricting the development of underwater acoustic sensor networks (UASNs). Since the energy of the nodes is limited, the coverage of UASNs may gradually decrease as the network operates. Thus, energy-saving coverage control is crucial for UASNs. To solve the above problems, this paper proposes a coverage-control strategy (referred to as ESACC) that establishes a sleep–wake scheduling mechanism based on the redundancy of deployment nodes. The strategy has two main parts: (1) Node sleep scheduling based on a memetic algorithm. To ensure network monitoring performance, only some nodes are scheduled to work, with redundant nodes in a low-power hibernation state, reducing energy consumption and prolonging the network lifetime. The goal of node scheduling is to find a minimum set of nodes that can cover the monitoring area, and a memetic algorithm can solve this problem. (2) Wake-up scheme. During network operation, sleeping nodes are woken to cover the dead nodes and maintain high coverage. This scheme not only reduces the network energy consumption but takes into account the monitoring coverage of the network. The experimental data show that ESACC performs better than current algorithms, and can improve the network life cycle while ensuring high coverage.
Identifying exogenous and endogenous activity in social media

The occurrence of new events in a system is typically driven by external causes and by previous events taking place inside the system. This is a general statement, applying to a range of situations including, more recently, to the activity of users in Online social networks (OSNs). Here we develop a method for extracting from a series of posting times the relative contributions of exogenous, e.g. news media, and endogenous, e.g. information cascade. The method is based on the fitting of a generalized linear model (GLM) equipped with a self-excitation mechanism. We test the method with synthetic data generated by a nonlinear Hawkes process, and apply it to a real time series of tweets with a given hashtag. In the empirical dataset, the estimated contributions of exogenous and endogenous volumes are close to the amounts of original tweets and retweets respectively. We conclude by discussing the possible applications of the method, for instance in online marketing.
Forest Learning From Data and its Universal Coding

This paper considers structure learning from data with <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> samples of <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> variables, assuming that the structure is a forest, using the Chow–Liu algorithm. Specifically, for incomplete data, we construct two model selection algorithms that complete in <inline-formula> <tex-math notation="LaTeX">$O(p^{2})$ </tex-math></inline-formula> steps: one obtains a forest with the maximum posterior probability given the data and the other obtains a forest that converges to the true one as <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> increases. We show that the two forests are generally different when some values are missing. In addition, we present estimations for benchmark data sets to demonstrate that both algorithms work in realistic situations. Moreover, we derive the conditional entropy provided that no value is missing, and we evaluate the per-sample expected redundancy for the universal coding of incomplete data in terms of the number of non-missing samples.
Bifocal Design Procedure for Dual-Reflectarray Antennas in Offset Configurations

This letter presents a new bifocal design procedure for dual-reflectarray antennas (DRAAs) in offset configurations. The technique starts by considering an axially symmetric geometry with the reflectarrays placed in parallel planes, which allows the rotation of a two-dimensional bifocal design around the symmetry axis. To reach a more compact configuration and to obtain smoother phase distributions, the reflectarrays are tilted and their phases are adjusted by means of a ray-tracing routine. The technique has been validated by numerical simulations through the comparison with a previous center-fed dual-reflectarray prototype. Finally, the simulations of an offset DRAA with tilted reflectarrays are presented, providing 0.56° beam spacing at 20 GHz for multispot satellite applications in Ka-band.
Incremental Rule Splitting in Generalized Evolving Fuzzy Systems for Autonomous Drift Compensation

Gradual drifts in data streams are usually hard to detect and often do not necessarily trigger the evolution of new fuzzy rules during model adaptation steps in order to represent the new, drifted data distribution(s) appropriately in the fuzzy model. Over time, they thus lead to oversized rules with untypically large local errors (typically also worsening the global model error), as representing joint local data distributions before and after a drift happened likewise. We therefore propose an incremental rule splitting concept for generalized fuzzy rules in order to autonomously compensate these negative effects of gradual drifts. Our splitting condition is based on the local error of rules measured in terms of a weighted contribution to the whole model error and on the size of the rules measured in terms of the volume of the associated clusters. We use the concept of statistical process control in order to omit an extra threshold parameter in our splitting condition. The splitting technique relies on the eigendecomposition of the rule covariance matrix to adequately manipulate the largest eigenvector and eigenvalues in order to retrieve the new centers and contours of the two split rules. Furthermore, we guarantee sufficient flexibility in adapting the shapes and consequents of the split rules to the new drifted situation in the stream by integrating a specific dynamic and smooth forgetting concept of older samples, which formed the original (nonsplit) rules. Robustness against outliers is guaranteed by the realization of a two-layer model building process, where one layer represents the cluster partition and the other layer the rule partition: Only clusters becoming significant over time are accepted as rules in the fuzzy model. The splitting concepts are integrated in the generalized smart evolving learning engine for fuzzy systems (termed as Gen-Smart-EFS) and successfully tested on two real-world application scenarios, engine test benches and rolling mills, the latter including a real-occurring gradual drift (whose position in the data is known). Results show clearly improved error trend lines over time when splitting is applied, compared to the case when it is not applied: reduction of the mean absolute model error by about one third (rolling mills) and about one half (engine test benches).
A Novel Mutation of OsPPDKB, Encoding Pyruvate Orthophosphate Dikinase, Affects Metabolism and Structure of Starch in the Rice Endosperm

Starch, as a main energy storage substance, plays an important role in plant growth and human life. Despite the fact that several enzymes and regulators involved in starch biosynthesis have been identified, the regulating mechanism of starch synthesis is still unclear. In this study, we isolated a rice floury endosperm mutant M14 from a mutant pool induced by 60Co. Both total starch content and amylose content in M14 seeds significantly decreased, and starch thermal and pasting properties changed. Compound starch granules were defected in the floury endosperm of M14 seeds. Map-based cloning and a complementation test showed that the floury endosperm phenotype was determined by a gene of OsPPDKB, which encodes pyruvate orthophosphate dikinase (PPDK, EC 2.7.9.1). Subcellular localization analysis demonstrated that PPDK was localized in chloroplast and cytoplasm, the chOsPPDKB highly expressed in leaf and leaf sheath, and the cyOsPPDKB constitutively expressed with a high expression in developing endosperm. Moreover, the expression of starch synthesis-related genes was also obviously altered in M14 developing endosperm. The above results indicated that PPDK played an important role in starch metabolism and structure in rice endosperm.
The putative ceramide-conjugation protein Cwh43 regulates G0 quiescence, nutrient metabolism and lipid homeostasis in fission yeast

ABSTRACT Cellular nutrient states control whether cells proliferate, or whether they enter or exit quiescence. Here, we report characterizations of fission yeast temperature-sensitive (ts) mutants of the evolutionarily conserved transmembrane protein Cwh43, and explore its relevance to utilization of glucose, nitrogen source and lipids. GFP-tagged Cwh43 localizes at ER associated with the nuclear envelope and the plasma membrane, as in budding yeast. We found that cwh43 mutants failed to divide in low glucose and lost viability during quiescence under nitrogen starvation. In cwh43 mutants, comprehensive metabolome analysis demonstrated dramatic changes in marker metabolites that altered under low glucose and/or nitrogen starvation, although cwh43 cells apparently consumed glucose in the culture medium. Furthermore, we found that cwh43 mutant cells had elevated levels of triacylglycerols (TGs) and coenzyme A, and that they accumulated lipid droplets. Notably, TG biosynthesis was required to maintain cell division in the cwh43 mutant. Thus, Cwh43 affects utilization of glucose and nitrogen sources, as well as storage lipid metabolism. These results may fit a notion developed in budding yeast stating that Cwh43 conjugates ceramide to glycosylphosphatidylinositol (GPI)-anchored proteins and maintains integrity of membrane organization. Summary: A characterization of fission yeast temperature-sensitive mutants of the putative ceramide-conjugation protein Cwh43, is presented in order to explore its relevance to utilization of glucose, nitrogen source and lipids.
Sargassum Fusiforme Polysaccharide SFP-F2 Activates the NF-κB Signaling Pathway via CD14/IKK and P38 Axes in RAW264.7 Cells

Sargassum fusifrome is considered a “longevity vegetable” in Asia. Sargassum fusifrome polysaccharides exhibit numerous biological activities, specially, the modulation of immune response via the NF-κB signaling pathway. However, the precise mechanisms by which these polysaccharides modulate the immune response through the NF-κB signaling pathway have not been elucidated. In this study, we purified and characterized a novel fraction of Sargassum fusifrome polysaccharide and named it SFP-F2. SFP-F2 significantly upregulated the production of the cytokines TNF-α, IL-1β and IL-6 in RAW264.7 cells. It also activated the NF-κB signaling pathway. Data obtained from experiments carried out with specific inhibitors (PDTC, BAY 11-7082, IKK16 and SB203580) suggested that SFP-F2 activated the NF-κB signaling pathway via CD14/IKK and P38 axes. SFP-F2 could therefore potentially exert an immune-enhancement effect through inducing the CD14/IKK/NF-κB and P38/NF-κB signaling pathways.
Comparative Analysis of Pigments, Phenolics, and Antioxidant Activity of Chinese Jujube (Ziziphus jujuba Mill.) during Fruit Development

Chinese jujube (Ziziphus jujuba Mill.) fruit are much admired for their unique flavor and high nutritional value. This study tracks changes in skin color and antioxidant activity over six stages (S1 … S6) of fruit development in two cultivars of jujube, ‘Junzao’ and the color mutant ‘Tailihong’. The study records the changing levels of chlorophylls, carotenoids, anthocyanins, and phenolic compounds during fruit development. Contents of chlorophylls, β-carotenes and anthocyanins decreased throughout the developmental stages in two jujube cultivars, while lutein contents decreased at first and then increased to a maximum at S6. The levels of total phenolics, total flavonoids, total flavanols, total anthocyanins, procyanidin B1, procyanidin B2, procyanidin B3, (+)-catechin, quercetin, and ferulic acid are significantly higher in ‘Tailihong’ than in ‘Junzao’ before the onset ripening (before S3). However, after S3 the level differences of these components in the two cultivars are not significant. In both cultivars, antioxidant activity reduces gradually throughout fruit development. Our findings indicate how the skin color of jujube fruit during maturation is due to changes in the levels of flavonoids, carotenoids, and anthocyanins. The color changes are also associated with changes in antioxidant activity.