Miracle-dz/UniFAR-FTUs huggingface dataset:
Microhabitat preferences of the stone crayfish Austropotamobius torrentium (Schrank, 1803)

The stone crayfish Austropotamobius torrentium (Schrank, 1803) is a species that usually prefers small and medium-sized streams. It is mainly dependent on coarse grained substrata, which it uses as shelter, but has also been observed on clay in which it creates burrows, similarly to other species of crayfish. In this study, the microhabitat preferences of the stone crayfish in day-time were evaluated. In 2008 (June–November) we collected data on stone crayfish abundances in 1263 patches, recording their position in streams (in five categories: 1. current, 2. lee, 3. shoreline, 4. backwater, 5. rapids), dominant substratum (in seven categories: 1. mud, 2. sand, 3. fine gravel, 4. gravel, 5. pebbles, 6. stones, 7. boulders) in eight streams in the West Bohemia region. This data was evaluated using an index of microhabitat electivity (calculated as the difference between the expected (calculated from all the samples) and actual frequency of occurrence in habitats within a given environmental parameter) and multivariate analysis (redundancy detrended analysis). Substratum type was the major determinant of microhabitat preference for all size classes ( 90 mm). There was a generalised avoidance of fine-grained substrata and a preference for coarser ones, which was more evident in larger size groups. The substrata character explained 8.7% of the variability in distribution. The parameter “depth” explained 1% of the overall crayfish variability distribution. Nevertheless, an avoidance of greater depths by small stone crayfish individuals was found, as well as an avoidance of shallows by the largest size categories. No major preference was noted for different stream positions (which explained only 0.7% of the microhabitat use variability), but crayfish avoided rapids with higher water velocities.
Automated Interpretation and Reduction of In-Vehicle Network Traces at a Large Scale

In modern vehicles, high communication complexity requires cost-effective integration tests such as data-driven system verification with in-vehicle network traces. With the growing amount of traces, distributable Big Data solutions for analyses become essential to inspect massive amounts of traces. Such traces need to be processed systematically using automated procedures, as manual steps become infeasible due to loading and processing times in existing tools. Further, trace analyses require multiple domains to verify the system in terms of different aspects (e.g., specific functions) and thus, require solutions that can be parameterized towards respective domains. Existing solutions are not able to process such trace amounts in a flexible and automated manner. To overcome this, we introduce a fully automated and parallelizable end-to-end preprocessing framework that allows to analyze massive in-vehicle network traces. Being parameterized per domain, trace data is systematically reduced and extended with domain knowledge, yielding a representation targeted towards domain-specific system analyses. We show that our approach outperforms existing solutions in terms of execution time and extensibility by evaluating our approach on three real-world data sets from the automotive industry.
Towards multi-instrument drum transcription

Automatic drum transcription, a subtask of the more general automatic music transcription, deals with extracting drum instrument note onsets from an audio source. Recently, progress in transcription performance has been made using non-negative matrix factorization as well as deep learning methods. However, these works primarily focus on transcribing three drum instruments only: snare drum, bass drum, and hi-hat. Yet, for many applications, the ability to transcribe more drum instruments which make up standard drum kits used in western popular music would be desirable. In this work, convolutional and convolutional recurrent neural networks are trained to transcribe a wider range of drum instruments. First, the shortcomings of publicly available datasets in this context are discussed. To overcome these limitations, a larger synthetic dataset is introduced. Then, methods to train models using the new dataset focusing on generalization to real world data are investigated. Finally, the trained models are evaluated on publicly available datasets and results are discussed. The contributions of this work comprise: (i.) a large-scale synthetic dataset for drum transcription, (ii.) first steps towards an automatic drum transcription system that supports a larger range of instruments by evaluating and discussing training setups and the impact of datasets in this context, and (iii.) a publicly available set of trained models for drum transcription. Additional materials are available at this http URL
LEAD: Learning-enabled Energy-Aware Dynamic Voltage/frequency scaling in NoCs

Network on Chips (NoCs) are the interconnect fabric of choice for multicore processors due to their superiority over traditional buses and crossbars in terms of scalability. While NoC’s offer several advantages, they still suffer from high static and dynamic power consumption. Dynamic Voltage and Frequency Scaling (DVFS) is a popular technique that allows dynamic energy to be saved, but it can potentially lead to loss in throughput. In this paper, we propose LEAD - Learning-enabled Energy-Aware Dynamic voltage/frequency scaling for NoC architectures wherein we use machine learning techniques to enable energy-performance trade-offs at reduced overhead cost. LEAD enables a proactive energy management strategy that relies on an offline trained regression model and provides a wide variety of voltage/frequency pairs (modes). LEAD groups each router and the router’s outgoing links locally into the same V/F domain, allowing energy management at a finer granularity without additional timing complications and overhead. Our simulation results using PARSEC and Splash-2 benchmarks on a 4 × 4 concentrated mesh architecture show an average dynamic energy savings of 17% with a minimal loss of 4% in throughput and no latency increase.
Sacubitril/valsartan: from a large clinical trial to clinical practice

&NA; The Prospective Comparison of Angiotensin Receptor Antagonist and Neprilysin Inhibitor with Angiotensin-Converting Enzyme Inhibitor to Determine Impact on Global Mortality and Morbidity in Heart Failure Trial (PARADIGM-HF) has shown a reduction in the risk of death and heart failure hospitalizations with sacubitril/valsartan, compared with enalapril, in patients with heart failure and reduced ejection fraction. Guidelines now recommend the substitution of angiotensin-converting enzyme inhibitors or AT1 blockers with sacubitril/valsartan in patients with heart failure and reduced ejection fraction. The aim of this review is to discuss factors that may have an impact on the implementation of these guidelines into clinical practice. The main limitation is that, based on the inclusion criteria of PARADIGM-HF, sacubitril/valsartan is not indicated in patients with heart failure and preserved ejection fraction, although they may be the majority of the patients with heart failure. The trial enrolled ambulatory patients and thus start of sacubitril/valsartan is not indicated in those hospitalized for heart failure. A drug's tolerability may be limited by hypotension with, however, a lower rate of renal dysfunction, compared with enalapril. The cost of the new treatment is also an issue. Similarly to what occurred when other neurohormonal antagonists have been introduced in clinical practice, increased awareness of poor heart failure outcomes and better patients’ management programs may be of utmost importance for the implementation of this new agent.
Revisiting of Channel Access Mechanisms in Mobile Wireless Networks through Exploiting Physical Layer Technologies

The wireless local area networks (WLANs) have been widely deployed with the rapid development of mobile devices and have further been brought into new applications with infrastructure mobility due to the growth of unmanned aerial vehicles (UAVs). However, the WLANs still face persistent challenge on increasing the network throughput to meet the customer’s requirement and fight against the node mobility. Interference is a well-known issue that would degrade the network performance due to the broadcast characteristics of the wireless signals. Moreover, with infrastructure mobility, the interference becomes the key obstacle in pursuing the channel capacity. Legacy interference management mechanism through the channel access control in the MAC layer design of the 802.11 standard has some well-known drawbacks, such as exposed and hidden terminal problems, inefficient rate adaptation, and retransmission schemes, making the efficient interference management an everlasting research topic over the years. Recently, interference management through exploiting physical layer mechanisms has attracted much research interest and has been proven to be a promising way to improve the network throughput, especially under the infrastructure mobility scenarios which provides more indicators for node dynamics. In this paper, we introduce a series of representative physical layer techniques and analyze how they are exploited for interference management to improve the network performance. We also provide some discussions about the research challenges and give potential future research topics in this area.
IP Network Stack in Ada 2012 and the Ravenscar Profile

This article presents Ada Embedded Network, a small network stack intended to be used by small embedded Ada applications running on ARM. It implements the standard ARP, IPv4, UDP, DNS and DHCP protocols on top of an Ethernet driver. Its memory efficient design allows it to run on the STM32F746 board. The article presents the components from the Ada implementation point of view. It highlights the Ada features that have been used and shows some benefits of the Ravenscar profile that have helped the project.
Genetic Analyses of the rbcL and psaA Genes From Single Cells Demonstrate a Rhodophyte Origin of the Prey in the Toxic Benthic Dinoflagellate Ostreopsis

Phagotrophy of the harmful benthic dinoflagellates of the genus Ostreopsis has long been inferred based on observations of food particles present inside the cell, but the prey was not identified yet. This study aimed to investigate seasonal dynamics of the benthic dinoflagellates Ostreopsis spp.in temperate Korean coastal sites, with special emphasis on their phagotrophy. To further identify the prey, the ingested food particles were extracted from single Ostreopsis cells and their rbcL and psaA genes sequences were determined. High concentration of Ostreopsis cells was observed between June and October at all sites, when water temperatures were higher than 19oC, exhibiting a marked temporal seasonality. The percentage of Ostreopsis cells containing the ingested food particles exhibited the large spatial and temporal variations among sampling sites, ranging from undetectable level to 29.5%, and was not always associated with Ostreopsis cell abundance. Phylogenetic analyses using both plastid-encoded rbcL and psaA genes revealed that all sequences obtained from the ingested food particles of Ostreopsis cells grouped within the class Florideophyceae, Rhodophyta. Our result clearly demonstrates that Ostreopsis species consume various macroalgae from the rhodophyta, not protists which have long been thought to be potential prey. The result from this study provides a basis for better understanding mixotrophic behavior and nutritional ecology of the benthic harmful dinoflagellate Ostreopsis species.
Compressive Behavior and Constitutive Model of Austenitic Stainless Steel S30403 in High Strain Range

Material anisotropy for tension and compression is a significant characteristic of austenitic stainless steel compared to carbon steel. Due to limitations during the testing of the restrained jig, the maximum strain value of compressive experiments of austenitic stainless steel is around 2%. This value cannot satisfy the requirements of accurate finite simulation on austenitic stainless steel columns and beams in the high compressive strain range. In this study, a new type of compressive specimen that satisfies the high compressive strain test was designed. The stress-strain response of austenitic stainless steel S30403 (JISCO, Gansu, China) was investigated in the high compressive strain range up to 10%, and constitutive models were compared with the experimental data. It was found that the new type specimen with length-to-diameter ratio of 1:1 can reliably obtain the stress-strain response of austenitic stainless steel S30403 in the high compressive strain range. It was found that the material anisotropy of austenitic stainless steel S30403 is remarkable in the high compressive strain range up to 10%. The strain-hardening curve of the austenitic stainless steel S30403 can be represented by a straight line in the high compressive strain range. Our study also found that the Quach constitutive model accurately describes the two-stage strain-hardening phenomenon in the high compressive strain range up to 10%.
Toward Battery-Free Wearable Devices

Recent years have witnessed the prevalence of wearable devices. Wearable devices are intelligent and multifunctional, but they rely heavily on batteries. This greatly limits their application scope, where replacement of battery or recharging is challenging or inconvenient. We note that wearable devices have the opportunity to harvest energy from human motion, as they are worn by the users as long as being functioning. In this article, we propose a battery-free sensing platform for wearable devices in the form factor of shoes. It harvests the kinetic energy from walking or running to supply devices with power for sensing, processing, and wireless communication, covering all the functionalities of commercial wearable devices. We achieve this goal by enabling the whole system running on the harvested energy from two feet. Each foot performs separate tasks and two feet are coordinated by ambient backscatter communication. We instantiate this idea by building a prototype, containing energy harvesting insoles, power management circuits, and ambient backscatter module. Evaluation results demonstrate that the system can wake up shortly after several seconds’ walk and have sufficient Bluetooth throughput for supporting many applications. We believe that our framework can stir a lot of useful applications that were infeasible previously.
Serine metabolism in the brain regulates starvation-induced sleep suppression in Drosophila melanogaster

Significance Foraging and sleep are two conflicting behaviors in starved animals; however, it remains elusive how metabolic status governs sleep drive. In this study, we show that a biosynthetic pathway for the amino acid serine is transcriptionally up-regulated by starvation in adult fly brains. The behavioral response to genetic manipulation of key enzymes involved in serine metabolism supports the sleep-suppressing effect of serine in response to starvation. In a society where daily diet is becoming increasingly important to the sleep quality of individuals, our study defines an amino acid metabolic pathway that underlies adaptive sleep behaviors upon dietary stress. Sleep and metabolism are physiologically and behaviorally intertwined; however, the molecular basis for their interaction remains poorly understood. Here, we identified a serine metabolic pathway as a key mediator for starvation-induced sleep suppression. Transcriptome analyses revealed that enzymes involved in serine biosynthesis were induced upon starvation in Drosophila melanogaster brains. Genetic mutants of astray (aay), a fly homolog of the rate-limiting phosphoserine phosphatase in serine biosynthesis, displayed reduced starvation-induced sleep suppression. In contrast, a hypomorphic mutation in a serine/threonine-metabolizing enzyme, serine/threonine dehydratase (stdh), exaggerated starvation-induced sleep suppression. Analyses of double mutants indicated that aay and stdh act on the same genetic pathway to titrate serine levels in the head as well as to adjust starvation-induced sleep behaviors. RNA interference-mediated depletion of aay expression in neurons, using cholinergic Gal4 drivers, phenocopied aay mutants, while a nicotinic acetylcholine receptor antagonist selectively rescued the exaggerated starvation-induced sleep suppression in stdh mutants. Taken together, these data demonstrate that neural serine metabolism controls sleep during starvation, possibly via cholinergic signaling. We propose that animals have evolved a sleep-regulatory mechanism that reprograms amino acid metabolism for adaptive sleep behaviors in response to metabolic needs.
Comparison of Safety and Efficacy of Methadone vs Morphine for Treatment of Neonatal Abstinence Syndrome: A Randomized Clinical Trial

Importance Although opioids are used to treat neonatal abstinence syndrome (NAS), the best pharmacologic treatment has not been established. Objective To compare the safety and efficacy of methadone and morphine in NAS. Design, Setting, and Participants In this randomized, double-blind, intention-to-treat trial, term infants from 8 US newborn units whose mothers received buprenorphine, methadone, or opioids for pain control during pregnancy were eligible. A total of 117 infants were randomized to receive methadone or morphine from February 9, 2014, to March 6, 2017. Mothers who declined randomization could consent to data collection and standard institutional treatment. Interventions Infants were assessed with the Finnegan Neonatal Abstinence Scoring System every 4 hours and treated with methadone or placebo every 4 hours or morphine every 4 hours. Infants with persistently elevated Finnegan scores received dose increases. Infants who exceeded a predetermined opioid dose received phenobarbital. Dose reductions occurred every 12 to 48 hours when signs of NAS were controlled with therapy, stopping at 20% of the original dose. Main Outcomes and Measures The primary end point was length of hospital stay (LOS). The secondary end points were LOS attributable to NAS and length of drug treatment (LOT). Results A total of 183 mothers consented to have their infants in the study; 117 infants required treatment. Because 1 parent withdrew consent, data were analyzed on 116 infants (mean [SD] gestational age, 39.1 [1.1] weeks; mean [SD] birth weight, 3157 [486] g; 58 [50%] male). Demographic variables and risk factors were similar except for more prenatal cigarette exposure in infants who received methadone. Adjusting for study site and maternal opioid type, methadone was associated with decreased mean number of days for LOS by 14% (relative number of days, 0.86; 95% CI, 0.74-1.00; P = .046), corresponding to a difference of 2.9 days; 14% reduction in LOS attributable to NAS (relative number of days, 0.86; 95% CI, 0.77-0.96; P = .01), corresponding to a difference of 2.7 days; and 16% reduction in LOT (relative number of days, 0.84; 95% CI, 0.73-0.97; P = .02), corresponding to a difference of 2.3 days. Methadone was also associated with reduced median LOS (16 vs 20 days, P = .005), LOS attributable to NAS (16 vs 19 days, P = .005), and LOT (11.5 vs 15 days, P = .009). Study infants had better short-term outcomes than 170 nonrandomized infants treated with morphine per standard institutional protocols. Conclusions and Relevance With use of weight- and sign-based treatment for NAS, short-term outcomes were better in infants receiving methadone compared with morphine. Assessment of longer-term outcomes is ongoing. Trial Registration ClinicalTrials.gov Identifier: NCT01958476
Synthesis and molecular modelling studies of pyrimidinones and pyrrolo[3,4-d]-pyrimidinodiones as new antiplasmodial compounds

BACKGROUND Malaria is responsible for 429,000 deaths per year worldwide, and more than 200 million cases were reported in 2015. Increasing parasite resistance has imposed restrictions to the currently available antimalarial drugs. Thus, the search for new, effective and safe antimalarial drugs is crucial. Heterocyclic compounds, such as dihydropyrimidinones (DHPM), synthesised via the Biginelli multicomponent reaction, as well as bicyclic compounds synthesised from DHPMs, have emerged as potential antimalarial candidates in the last few years. METHODS Thirty compounds were synthesised employing the Biginelli multicomponent reaction and subsequent one-pot substitution/cyclisation protocol; the compounds were then evaluated in vitro against chloroquine-resistant Plasmodium falciparum parasites (W2 strain). Drug cytotoxicity in baseline kidney African Green Monkey cells (BGM) was also evaluated. The most active in vitro compounds were evaluated against P. berghei parasites in mice. Additionally, we performed an in silico target fishing approach with the most active compounds, aiming to shed some light into the mechanism at a molecular level. RESULTS The synthetic route chosen was effective, leading to products with high purity and yields ranging from 10-84%. Three out of the 30 compounds tested were identified as active against the parasite and presented low toxicity. The in silico study suggested that among all the molecular targets identified by our target fishing approach, Protein Kinase 3 (PK5) and Glycogen Synthase Kinase 3β (GSK-3β) are the most likely molecular targets for the synthesised compounds. CONCLUSIONS We were able to easily obtain a collection of heterocyclic compounds with in vitro anti-P. falciparum activity that can be used as scaffolds for the design and development of new antiplasmodial drugs.
Registry Cohort Study to Determine Risk for Multiple Sclerosis after Vaccination for Pandemic Influenza A(H1N1) with Arepanrix, Manitoba, Canada

To investigate a potential risk for multiple sclerosis (MS) after vaccination with Arepanrix, the GlaxoSmithKline AS03-adjuvanted influenza A(H1N1)pdm09 vaccine, we used the provincewide immunization registry for Manitoba, Canada, to match 341,347 persons vaccinated during the 2009 pandemic to 485,941 unvaccinated persons on age, sex, address, and a propensity score measuring the probability of vaccination. We used a previously validated algorithm to identify MS cases from provincial hospital, physician, and prescription drug claims databases. After 12 months of follow-up, the age-adjusted incidence rate of MS was 17.7 cases per 100,000 person-years in the Arepanrix cohort and 24.2 per 100,000 in the unvaccinated cohort. The corresponding adjusted hazard ratio was 0.9. We observed similar patterns when we measured incidence over the entire follow-up period. The AS03 adjuvant, a candidate for inclusion in future pandemic vaccines, does not appear to increase the short-term risk for MS when included in influenza vaccines.