Miracle-dz/UniFAR-FTUs huggingface dataset:
Viability of dietary substitution of live microalgae with dry Ulva rigida in broodstock conditioning of the Pacific oyster (Crassostrea gigas)

ABSTRACT The current study evaluated the microalgae replacement by dry macroalgae (Ulva rigida) in the reproductive success and biochemical composition of the Pacific oyster (Crassostrea gigas) during broodstock conditioning. Five nutritional regimes were tested: 100% macroalgae (diet 1), 50% macroalgae+50% microalgae (diet 2), 25% macroalgae+75% microalgae (diet 3) and 100% microalgae (diet 4). An unfed group was used as a negative control. The microalgae blend was composed of 33% Isochrysis galbana and 67% diatoms (75% Skeletonema costatum+25% Chaetoceros calcitrans). Gonadal maturation was reflected in the physiological condition of the individuals. All treatments, except diet 1, showed an increase in condition index and were fully matured at the end of the trial, with the best physiological condition observed in oysters fed diet 3 and diet 4. Protein and total lipid content increased during the conditioning period, whereas glycogen content decreased. Oysters conditioned with diet 3 had higher protein and total lipid content and lower glycogen content than the other treatments. In addition, diet 3 showed the highest percentage of viable veliger larvae. The current study demonstrated that it is possible to replace 25% of microalgae with macroalgae in the broodstock conditioning, minimizing the operative cost in bivalve hatcheries. This article has an associated First Person interview with the first author of the paper. Summary: The use of an alternative diet with 25% microalgae replacement by dry macroalgae is beneficial during broodstock conditioning and allows the operation costs of bivalve hatcheries to be minimized.
Can Energy-Aware Routing Improve the Energy Savings of Energy-Efficient Ethernet?

Telecommunication networks are over-provisioned with redundant resources in order to cope with traffic load during peak hours and to quickly recover from failures. However, much of the resources are underutilized during long periods of time, but still consuming full energy. With the growing concerns of energy waste and greenhouse gas emissions, the network design principles tend to shift towards allocation of resources on-demand for energy-efficiency. In this paper, we analyze and evaluate the performance of two different energy-saving techniques, namely energy saving topology control (ESTOP) and energy-efficient Ethernet (EEE). We investigate the energy-saving characteristics of ESTOP+EEE; the combination of ESTOP and EEE. The evaluation is conducted in OMNet++ with realistic and synthetic network topologies under varying traffic conditions. The results indicate that the combination has a significant potential for saving energy, compared to running ESTOP or EEE alone, but that the amount of energy savings depends on topology, traffic load, and the chosen target connectivity level for ESTOP. In particular, the results show that the target connectivity level needs to be carefully matched to the topology and the current traffic situation, suggesting that ESTOP+EEE would be suitable where the target connectivity level is dynamically adjusted according to traffic variations.
Metabolism of vesatolimod in rat, dog, and human liver microsomes: Metabolic stability assessment, metabolite identification, and interspecies comparison.

Vesatolimod (GS-9620) is an agonist of toll-like receptor (TLR7) 7, which has been developed as an anti-hepatitis B virus (HBV) agent. The focus of the present study is on the metabolic stability evaluation and metabolite identification of GS-9620 in rat, dog, and human liver microsomes, as well as interspecies comparison. The average observed in vitro T1/2 values were 3.06, 13.06, and 15.56 minutes in rat, dog, and human liver microsomes, respectively. The findings suggested that GS-9620 was rapidly metabolized in the presence of reductive nicotinamide adenine dinucleotide phosphate (NADPH) in rat liver microsomes (RLM), and moderately metabolized in dog liver microsomes (DLM) and human liver microsomes (HLM). Subsequently, the metabolites were characterized using an ultra-high performance liquid chromatography coupled with linear ion trap orbitrap tandem mass spectrometer (UHPLC-LTQ-Orbitrap-MS) with dd-MS2 on-line data acquisition mode. Under the current conditions, a total of 18 metabolites were detected and their identities were proposed by comparing their accurate masses, fragmental ions, and retention times with those of GS-9620. Three metabolites (M2, M4, and M18) were authentically identified by using reference standards. In RLM, 16 metabolites were identified with M2 being the most abundant metabolite. M4, M5, and M9 were rat-specific. In DLM, 12 minor metabolites were identified with a dog-specific metabolite (M6). In HLM, GS-9620 showed similar metabolic profiles to that in DLM, and 11 minor metabolites were detected with M12 being human-specific. Based on the identified metabolites, the metabolic pathways of GS-9620 were proposed, including hydroxylation, bis-hydroxylation, dehydrogenation, and oxidative deamination.
Ultrafast Synthesis of Urchin-Like Rutile TiO2 by Single-Step Microwave-Assisted Method

The preparations of crystal titanium dioxide (TiO2) are often time-consuming multistep processes involving high temperature. Rapid and efficient methods to obtain TiO2 with anatase or rutile phase are desirable. In this paper, we describe an ultrafast single-step method to obtain urchin-like rutile TiO2 particles via microwave irradiation. In the procedure, TiCl4 aqueous solution was used as a reactant and toluene was used as a solvent. It takes only a few minutes without any further heat treatment. The samples were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and thermal gravimetric analysis (TGA). The effect of temperature, irradiation time and the ratio of precursor to solvent on the morphology and crystal structure were studied. The results show urchin-like rutile TiO2 with high stability is formed after only 5 min microwave irradiation at 135 °C.
Use of meteorological data for improved estimation of risk in capacity adequacy studies

This paper investigates the use of additional meteorological (temperature, wind) data, to obtain more reliable estimates of risk metrics for capacity adequacy in systems with significant wind generation. The advantage of this approach, which requires the careful verification of a number of statistical conditions (well satisfied in practice), is that a considerably longer dataset may be used to estimate the distribution of wind generation, and so also that of demand-net-of-wind, thereby overcoming the otherwise serious problem of a lack of data in the critical region of high demand and low wind. A further advantage is that a block-bootstrapping procedure may be used to assess the remaining uncertainty associated with wind generation, and the effect of that uncertainty on desired risk metrics. This is in contrast to the hindcast approach, where no such uncertainty estimation is possible and where there may be serious concerns about the calculated values of the risk metrics.
The myostatin gene: an overview of mechanisms of action and its relevance to livestock animals

Myostatin, also known as growth differentiation factor 8, a member of the transforming growth factor-beta super-family, is a negative regulator of muscle development. Myostatin acts at key points during pre- and post-natal life of amniotes that ultimately determine the overall muscle mass of an animal. Mutations have already demonstrated the impact of attenuating myostatin activity on muscle development. A number of large animals, including cattle, sheep, dogs and humans, display the 'double muscled' phenotype due to mutations in the myostatin gene. Here, we firstly give an overview of the molecular pathways regulated by myostatin that control muscle development. Then we describe the natural mutations and their associated phenotypes as well as the physiological influence of altering myostatin expression in livestock animals (cattle, sheep, goat, horse, pig, rabbit and chicken). Knowledge of null alleles and polymorphisms in the myostatin gene are of great interest in the animal breeding field, and it could be utilized to improve meat production in livestock animals.
A Research Program on Implementing Integrated Care for Older Adults with Complex Health Needs (iCOACH): An International Collaboration

Health and social care systems across western developed nations are being challenged to meet the needs of an increasing number of people aging with multiple complex health and social needs. Community based primary health care (CBPHC) has been associated with more equitable access to services, better population level outcomes and lower system level costs. Itmay be well suited to the increasingly complex needs of populations; however the implementation of CBPHC models of care faces many challenges. This paper describes a program of research by an international, multi-university, multidisciplinary research team who are seeking to understand how to scale up and spread models of Integrated CBPHC (ICBPHC). The key question being addressed is “What are the steps to implementing innovative integrated community-based primary health care models that address the health and social needs of older adults with complex care needs? ” and will be answered in three phases. In the first phase we identify and describe exemplar models of ICBPHC and their context in relation to relevant policies and performance across the three jurisdictions (New Zealand, Ontario and Québec, Canada). The second phase involves a series of theory-informed, mixed methods case studies from which we shall develop a conceptual framework that captures not only the attributes of successful innovative ICBPHC models, but also how these models are being implemented. In the third phase, we aim to translate our research into practice by identifying emerging models of ICBPHC in advance, and working alongside policymakers to inform the development and implementation of these models in each jurisdiction. The final output of the program will be a comprehensive guide to the design, implementation and scaling-up of innovative models of ICBPHC.
AN EFFICIENT RECOMMENDER SYSTEM BY INTEGRATING NON-NEGATIVE MATRIX FACTORIZATION WITH TRUST AND DISTRUST RELATIONSHIPS

Matrix factorization (MF) has been proved to be an effective approach to build a successful recommender system. However, most current MF-based recommenders cannot obtain high prediction accuracy due to the sparseness of user-item matrix. Moreover, these methods suffer from the scalability issues when applying on large-scale real-world tasks. To tackle these issues, in this paper a social regularization method called TrustRSNMF is proposed that incorporates the social trust information of users in nonnegative matrix factorization framework. The proposed method integrates trust statements along with user-item ratings as an additional information source into the recommendation model to deal with the data sparsity and cold-start issues. In order to evaluate the effectiveness of the proposed method, a number of experiments are performed on two real-world datasets. The obtained results demonstrate significant improvements of the proposed method compared to state-of-the-art recommendation methods.
HyperOXN: A Novel Data Center Topology Driven by Machine Learning

Driven by emerging applications such as machine learning, cloud computing, and big data, modern data center network architecture has been evolving to meet the challenging requirements, like scalability, agility, energy efficiency, and high performance. In the meantime, artificial intelligent applications are expediting the convergence of highperformance computing and data centers. To address the challenges noted above, we investigate communication patterns for emerging applications and find that the dynamic and diverse *-cast traffic play a significant impact on the performance of new applications. Inspired by Hypermeshes topology, this paper presents HyperOXN, a novel cost- efficient topology for exascale DCNs. HyperOXN takes advantage of passive optical wavelength division multiplexing technologies. We show that HyperOXN outperforms other topologies in latency, cost, and scalability.
Multicellularity Drives the Evolution of Sexual Traits

From the male peacock’s tail plumage to the floral displays of flowering plants, traits related to sexual reproduction are often complex and exaggerated. Why has sexual reproduction become so complicated? Why have such exaggerated sexual traits evolved? Early work posited a connection between multicellularity and sexual traits such as anisogamy (i.e., the evolution of small sperm and large eggs). Anisogamy then drives the evolution of other forms of sexual dimorphism. Yet the relationship between multicellularity and the evolution of sexual traits has not been empirically tested. Given their extensive variation in both multicellular complexity and sexual systems, the volvocine green algae offer a tractable system for understanding the interrelationship of multicellular complexity and sex. Here we show that species with greater multicellular complexity have a significantly larger number of derived sexual traits, including anisogamy, internal fertilization, and secondary sexual dimorphism. Our results demonstrate that anisogamy repeatedly evolved from isogamous multicellular ancestors and that anisogamous species are larger and produce larger zygotes than isogamous species. In the volvocine algae, the evolution of multicellularity likely drives the evolution of anisogamy, and anisogamy subsequently drives secondary sexual dimorphism. Multicellularity may set the stage for the overall diversity of sexual complexity throughout the Tree of Life.
An Anomaly Detection Approach Based on Machine Learning and SCADA Data for Condition Monitoring of Wind Turbines

This paper presents an anomaly detection approach using machine learning to achieve condition monitoring for wind turbines. The approach applies the information in supervisory control and data acquisition systems as data input. First, machine learning is used to estimate the temperature signals of the gearbox component. Then the approach analyzes the deviations between the estimated values and the measurements of the signals. Finally, the information of alarm logs is integrated with the previous analysis to determine the operation states of wind turbines. The proposed approach has been tested with the data experience of a 2MW wind turbine in Sweden. The result demonstrates that the approach can detect possible anomalies before the failure occurrence. It also certifies that the approach can remind operators of the possible changes inside wind turbines even when the alarm logs do not report any alarms.
Universal Image Manipulation Detection using Deep Siamese Convolutional Neural Network

Detection of different types of image editing operations carried out on an image is an important problem in image forensics. It gives the information about the processing history of an image, and also can expose forgeries present in an image. There have been few methods proposed to detect different types of image editing operations in a single framework. However, all the operations have to be known a priori in the training phase. But, in real-forensics scenarios it may not be possible to know about the editing operations carried out on an image. To solve this problem, we propose a novel deep learning-based method which can differentiate between different types of image editing operations. The proposed method classifies image patches in a pair-wise fashion as either similarly or differently processed using a deep siamese neural network. Once the network learns feature that can discriminate between different image editing operations, it can differentiate between different image editing operations not present in the training stage. The experimental results show the efficacy of the proposed method in detecting/discriminating different image editing operations.
Pro-resolving mediator maresin 1 ameliorates pain hypersensitivity in a rat spinal nerve ligation model of neuropathic pain

Background Pro-resolving mediators (PRMs) are considered as emerging analgesics for chronic pain. Maresin 1 (MaR1) is a newly identified member of PRMs, and recent studies implicate its potential role in some pain conditions. As the function of MaR1 in neuropathic pain remains unclear, we investigated the effects of MaR1 on pain hypersensitivity and the underlying mechanism using a rat spinal nerve ligation (SNL) model of neuropathic pain. Materials and methods MaR1 (100 ng/10 μL) or commensurable artificial cerebrospinal fluid was delivered via intrathecal catheter from days 3 to 5 post-SNL followed by assessment of mechanical allodynia and thermal hyperalgesia. Ipsilateral L4–L5 spinal cord tissue was collected on day 7 post-SNL and assessed by Western blotting, enzyme-linked immunosorbent assay or immunohistochemistry. Results Intrathecal MaR1 significantly attenuated mechanical allodynia and thermal hyperalgesia from day 5 to day 7 post-SNL, which was associated with decreased spinal levels of glial markers, GFAP and IBA1. It was also found that intrathecal MaR1 downregulated phosphorylation levels of NF-κB p65 and its nuclear translocation, as well as decreased protein levels of pro-inflammatory cytokines, TNF-α, IL-1β and IL-6. Further, MaR1 treatment restored PSD95 and synapsin II levels, suggesting that MarR1 also protected synaptic integrity. Conclusion Our results indicate that MaR1 ameliorates the SNL-induced neuropathic pain by regulating glial activities and pro-inflammatory cytokines release. The present study offers insight into the potential of MaR1 as a novel intervention to ameliorate neuropathic pain.
Single-View Place Recognition under Seasonal Changes

Single-view place recognition, that we can define as finding an image that corresponds to the same place as a given query image, is a key capability for autonomous navigation and mapping. Although there has been a considerable amount of research in the topic, the high degree of image variability (with viewpoint, illumination or occlusions for example) makes it a research challenge. One of the particular challenges, that we address in this work, is weather variation. Seasonal changes can produce drastic appearance changes, that classic low-level features do not model properly. Our contributions in this paper are twofold. First we pre-process and propose a partition for the Nordland dataset, frequently used for place recognition research without consensus on the partitions. And second, we evaluate several neural network architectures such as pre-trained, siamese and triplet for this problem. Our best results outperform the state of the art of the field. A video showing our results can be found in this https URL. The partitioned version of the Nordland dataset at this http URL.
Electrochemical measurements to predict the antifungal properties of nanostructured thin films

Fungi and bacterial infections are an important problem in modern medicine and in hospitals. Even though specific drugs are available to treat these infections, a preventive solution to avoid the proliferation of such bacteria would be extremely interesting in many biotechnological applications. Silver and copper metals have antifungal and bacteriostatic properties, however a real challenge will be to obtain a continuous release of metal species in a controlled way. In this work metal/polymer nanocomposite coatings were deposited by reactive magnetron sputtering; Cu nanoparticles were incorporated in a SiOx thin film to obtain a large effective surface for metal ion release. The nanocomposite coatings were characterised by Electrochemical Impedance Spectroscopy and ion release measurements were performed to assess their bioactive behaviour against fungi. Specific tests have been performed by using both a direct measurement of the released ions amount and by means of an electrochemical impedance measurement. Both tests confirmed the possibility of obtaining such a release for more then ten days by using extremely thin coatings of the order of 100 nm, which could easily be added to any implant.
Endogenous and Exogenous Multi-Modal Layers in Context Aware Recommendation Systems for Health

People care more about the solutions to their problems rather than data alone. Inherently, this means using data to generate a list of recommendations for a given situation. The rapid growth of multi-modal wearables and sensors have not made this jump effectively in the domain of health. Modern user content consumption and decision making in both cyber (e.g. entertainment, news) and physical (eg. food, shopping) spaces rely heavily on targeted personalized recommender systems. The utility function is the primary ranking method to predict what a given person would explicitly prefer. In this work we describe two unique layers of user and context modeling that can be coupled to traditional recommender system approaches. The exogenous layer incorporates factors outside of the person's body (eg. location, weather, social context), while the endogenous layer integrates data to estimate the physiologic or innate needs of the user. This is accomplished through multi-modal sensor data integration applied to domain-specific utility functions, filters and re-ranking weights. We showcase this concept through a nutrition guidance system focused on controlling sodium intake at a personalized level, dramatically improving upon the fixed recommendations.
An X-band RF-input Outphasing Power Amplifier

This work presents an RF-input outphasing power amplifier (RFIO PA) operating at X-band. A simple passive “inphasing” network implemented with PIN diodes is described that produces the required phase- and amplitude-modulated signals needed to drive an existing outphasing PA MMIC. The approach eliminates the need for multiple modulated input signals to drive the outphasing PA, particularly important at X band where phased-array applications demand low-complexity PA solutions. The proof-of-concept X-band RFIO PA is demonstrated using pulsed CW signals to have a peak drain efficiency of 63 % at 35.05 dBm output power. Dynamic performance is characterized using modulated RADAR signals for enhanced spectral confinement with 10μs pulse duration and 100μs repetition period, and achieves −10 dB improvement on the first sidelobe of the spectrum when digital predistortion is applied. Average efficiency of the RFIO PA is measured at 51.8% for a conventional rectangular RF pulse and 18.3% for an amplitude-modulated pulse with raised-cosine profile.
Filtering Power Divider with Wide Stopband Using Open-Stub Loaded Coupled-Line and Hybrid Microstrip T-Stub/DGS Cell

In this paper, a compact filtering Wilkinson power divider exhibiting the high isolation with an ultra-wideband spurious suppression is proposed. The open-stub loaded coupled-line (OSLCL) is introduced for the power divider design to obtain the passband enhancement with two transmission zeros close to center frequency of $f_{0}$. Besides, hybrid microstrip T-stub/defected ground structure (HMT/DGS) cells are employed to introduce intrinsic slow-wave effect, which leads to the performance of high isolation and rejection level within a wide stopband. To verify the mechanism mentioned above, a filtering power divider operating at $f_{0}$ of 2.31 GHz with 3-dB FBW of 17.7% is implemented and fabricated. The measurement exhibits more than 28 dB stopband isolation and 30 dB stopband rejection level up to 20 GHz (i.e., 8.66 $f_{0})$.