Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating bottom properties with a vector sensor array during MakaiEx 2005

Nowadays, vector sensors which measure both acoustic pressure and particle velocity begin to be available in underwater acoustic systems, normally configured as vector sensor arrays (VSA). The spatial filtering capabilities of a VSA can be used, with advantage over traditional pressure only hydrophone arrays, for estimating acoustic field directionality as well as arrival times and spectral content, which could open up the possibility for its use in bottom properties' estimation. An additional motivation for this work is to test the possibility of using high frequency probe signals (say above 2 kHz) for reducing size and cost of actual sub bottom profilers and current geoacoustic inversion methods. This work studies the bottom related structure of the VSA acquired signals, regarding the emitted signal waveform, frequency band and source-receiver geometry in order to estimate bottom properties, specially bottom reflection coefficient characteristics. Such a system was used during the Makai 2005 experiment, off Kauai I. , Hawai (USA) to receive precoded signals in a broad frequency band from 8 up to 14 kHz. The agreement between the observed and the modelled acoustic data is discussed and preliminary results on the bottom reflection estimation are presented.
A novel de novo CDH1 germline variant aids in the classification of carboxy-terminal E-cadherin alterations predicted to escape nonsense-mediated mRNA decay

Most truncating cadherin 1 (CDH1) pathogenic alterations confer an elevated lifetime risk of diffuse gastric cancer (DGC) and lobular breast cancer (LBC). However, transcripts containing carboxy-terminal premature stop codons have been demonstrated to escape the nonsense-mediated mRNA decay pathway, and gastric and breast cancer risks associated with these truncations should be carefully evaluated. A female patient underwent multigene panel testing because of a personal history of invasive LBC diagnosed at age 54, which identified the germline CDH1 nonsense alteration, c.2506G>T (p.Glu836*), in the last exon of the gene. Subsequent parental testing for the alteration was negative and additional short tandem repeat analysis confirmed the familial relationships and the de novo occurrence in the proband. Based on the de novo occurrence, clinical history, and rarity in general population databases, this alteration was classified as a likely pathogenic variant. This is the most carboxy-terminal pathogenic alteration reported to date. Additionally, this alteration contributed to the classification of six other upstream CDH1 carboxy-terminal truncating variants as pathogenic or likely pathogenic. Identifying the most distal pathogenic alteration provides evidence to classify other carboxy-terminal truncating variants as either pathogenic or benign, a fundamental step to offering presymptomatic screening and prophylactic procedures to the appropriate patients.
Human-Mediated Introgression of Haplotypes in a Modern Dairy Cattle Breed

Domestic animals provide a robust model system to understand complex evolutionary processes and their genomic consequences. Zhang et al. use full re-sequenced genomes of modern dairy cattle to identify haplotypes in the genomes of an admixed. Domestic animals can serve as model systems of adaptive introgression and their genomic signatures. In part, their usefulness as model systems is due to their well-known histories. Different breeding strategies such as introgression and artificial selection have generated numerous desirable phenotypes and superior performance in domestic animals. The modern Danish Red Dairy Cattle is studied as an example of an introgressed population. It originates from crossing the traditional Danish Red Dairy Cattle with the Holstein and Brown Swiss breeds, both known for high milk production. This crossing happened, among other things due to changes in the production system, to raise milk production and overall performance. The genomes of modern Danish Red Dairy Cattle are heavily influenced by regions introgressed from the Holstein and Brown Swiss breeds and under subsequent selection in the admixed population. The introgressed proportion of the genome was found to be highly variable across the genome. Haplotypes introgressed from Holstein and Brown Swiss contained or overlapped known genes affecting milk production, as well as protein and fat content (CD14, ZNF215, BCL2L12, and THRSP for Holstein origin and ITPR2, BCAT1, LAP3, and MED28 for Brown Swiss origin). Genomic regions with high introgression signals also contained genes and enriched QTL associated with calving traits, body confirmation, feed efficiency, carcass, and fertility traits. These introgressed signals with relative identity-by-descent scores larger than the median showing Holstein or Brown Swiss introgression are mostly significantly correlated with the corresponding test statistics from signatures of selection analyses in modern Danish Red Dairy Cattle. Meanwhile, the putative significant introgressed signals have a significant dependency with the putative significant signals from signatures of selection analyses. Artificial selection has played an important role in the genomic footprints of introgression in the genome of modern Danish Red Dairy Cattle. Our study on a modern cattle breed contributes to an understanding of genomic consequences of selective introgression by demonstrating the extent to which adaptive effects contribute to shape the specific genomic consequences of introgression.
Performance analysis of variable length sliding window network coding in MANETs

Network coding (NC) is a forward-looking technology of coding technology, which can rapidly improve the network throughput and bandwidth available in Mobile Ad hoc NETwork (MANET). Sliding window network coding is an improved encoding of network coding, which increases the network throughput of TCP to MANETs by increasing the sliding mechanism. In this paper, we propose a Variable Length Sliding Window Network Coding algorithm in MANETs (VLSWNC). The performance of the VLSWNC is experiments studied by using NS2 and evaluated in terms of the network throughput, packet loss probability, and decoding delay when data packet is transmitted. Simulation results show that VLSWNC can significantly improve network throughput and achieve better network performance.
Human cytomegalovirus (HCMV)‐specific T cell but not neutralizing or IgG binding antibody responses to glycoprotein complexes gB, gHgLgO, and pUL128L correlate with protection against high HCMV viral load reactivation in solid‐organ transplant recipients

Immune correlates of protection against human cytomegalovirus (HCMV) infection are still debated. This study aimed to investigate which arm of the immune response plays a major role in protection against HCMV infection in kidney transplant recipients (n = 40) and heart transplant recipients (n = 12). Overall, patients were divided into 2 groups: one including 37 patients with low viral load (LVL), and the other including 15 patients with high viral load (HVL). All LVL patients resolved the infection spontaneously, whereas HVL patients were all treated with one or more courses of antivirals. In HVL patients, viral DNAemia, which was more than 100 times higher than LVL, appeared and peaked at significantly earlier times, but disappeared much later than in LVL patients. During a 1‐year follow‐up, all LVL patients had levels of HCMV‐specific CD4+ (and CD8+) T cells significantly higher than HVL patients. On the contrary, titers of neutralizing antibodies and enzyme‐linked immunosorbent assay‐IgG antibodies to gB, gHgLgO, and pentamer gHgLpUL128L were overlapping in the 2 patient groups. In conclusion, while a valid HCMV‐specific T‐cell response was detected in more than 90% of LVL patients, >90% of HVL patients lacked an adequate T‐cell response. Antibody responses did not appear to be associated directly or indirectly with protection.
Ethical Community-Engaged Research : A Literature

Health research has relied on ethical principles, such as those of the Belmont Report, to protect the rights and well-being of research participants. Community-based participatory research (CBPR), however, must also consider the rights and wellbeing of communities. This requires additional ethical considerations that have been extensively discussed but not synthesized in the CBPR literature. We conducted a comprehensive thematic literature review and summarized empirically grounded discussions of ethics in CBPR, with a focus on the value of the Belmont principles in CBPR, additional essential components of ethical CBPR, the ethical challenges CBPR practitioners face, and strategies to ensure that CBPR meets ethical standards. Our study provides a foundation for developing a working definition and a conceptual model of ethical CBPR. (Am J Public Health. 2013;103:e7–e14. doi:10. 2105/AJPH.2013.301605) Lisa Mikesell, PhD, Elizabeth Bromley, MD, PhD, and Dmitry Khodyakov, PhD
A review on home energy management systems

This paper proposes a review on available solutions for Home Energy Management Systems (HEMS) which is dedicated for homes using three sources of energy (Grid, Photovoltaic and battery). Used HEMS tools consider home loads as identical in priority. Hence, a new approach is suggested to improve the actual approaches. In fact, variable priority of loads according to their service categories is taken into account. Such improvement should allow the necessary sources to supply the appropriate equipment while maintaining the user comfort.
Mining Reveals the Secret of Success : Identification of Sales Determinants Hidden in Customers ' Opinions

Nowadays, in the Big Data era, Business Intelligence Departments collect, store, process, calculate, and monitor massive amounts of data. Nevertheless, sometimes hundreds of metrics built on the structured data are inefficient to explain why the offered deal sold better or worse than expected. The answer might be found in text data that every company owns and yet is not aware of its possible usage or neglects its value. This project shows text mining methods, implemented in SAS® Text Miner 12.1, that enable the determination of a deal's success or failure factors based on in-house or Internet-scattered customers' views and opinions. The study is conducted on data gathered from Groupon Sp. z o.o. (Polish business unit) e-commerce company, as it is assumed that the market is by and large a customer-driven environment.
Water Masses and Depth Structure Prokaryotic and T4-Like Viral Communities Around Hydrothermal Systems of the Nordic Seas

The oceanographic features of the Nordic Seas, situated between Iceland and Svalbard, have been extensively studied over the last decades. As well, the Nordic Seas hydrothermal systems situated on the Arctic Mid-Ocean Ridge System have received an increasing interest. However, there is very little knowledge on the microbial communities inhabiting the water column of the Nordic Seas, and nothing is known about the influence of the different water masses and hydrothermal plumes on the microbial community structures. In this study, we aimed at characterizing the impact of hydrothermal plumes on prokaryotic and T4-like viral communities around the island of Jan Mayen. To this end, we used 16S rRNA-gene and g23-gene profiling as well as flow cytometry counts to examine prokaryotic and viral communities in 27 samples obtained from different water masses in this area. While Thaumarchaeota and Marine group II Archaea dominated the waters deeper than 500 m, members of Flavobacteria generally dominated the shallower waters. Furthermore, extensive chemical and physical characteristics of all samples were obtained, including temperature measurements and concentrations of major ions and gases. The effect of these physiochemical variables on the communities was measured by using constrained and unconstrained multivariate analyzes, Mantel tests, network analyzes, phylogenetic analyzes, taxonomic analyzes and temperature-salinity (Θ-S) plots. Our results suggest that hydrothermal activity has little effect on pelagic microbial communities in hydrothermal plumes of the Nordic Seas. However, we provide evidences that observed differences in prokaryotic community structure can largely be attributed to which water mass each sample was taken from. In contrast, depth was the major factor structuring the T4-like viral communities. Our results also show that it is crucial to include water masses when studying the influence of hydrothermal plumes on microbial communities, as it could prevent to falsely associate a change in community structure with the presence of a plume.
Antioxidant capacity and larvicidal activity of essential oil and extracts from Lippia grandis

The leaves and thin branches of Lippia grandis Schauer, Verbenaceae, are used for flavoring of food in the Brazilian Amazon, as substitute for oregano. In this study the constituents of the essential oil were identified and the antioxidant capacity and larvicidal activity of the oil and methanol extract and its sub-fractions were evaluated. A sensory evaluation was determined in view of absence of toxicity. The oil showed a yield of 2.1% and its main constituents were thymol (45.8%), p-cymene (14.3%), γ-terpinene (10.5%), carvacrol (9.9%) and thymol methyl ether (4.8%), totalizing 85%. The DPPH radical scavenging activity showed values for the EC50 between 9.0 and 130.5 µg mL-1 and the TEAC/ABTS values varied from 131.1 to 336.0 mg TE/g, indicating significant antioxidant activity for the plant. The total phenolic content ranged from 223.0 to 761.4 mg GAE/g, contributing to the antioxidant activity observed. The crude extracts inhibited the bleaching of β-carotene and the oil showed the greatest inhibition (42.5%). The oil (LgO, 7.6±2.4 µg mL-1) showed strong larvicidal activity against the brine shrimp bioassay. The sensory evaluation was highly satisfactory in comparison to oregano. The results are very promising for the use of L. grandis in seasoning and antioxidant products.
Best-First Enumeration Based on Bounding Conflicts, and its Application to Large-scale Hybrid Estimation

There is an increasing desire for autonomous systems to have high levels of robustness and safety, attained through continuously planning and self-repairing online. Underlying this is the need to accurately estimate the system state and diagnose subtle failures. Estimation methods based on hybrid discrete and continuous state models have emerged as a method of precisely computing these estimates. However, existing methods have difficulty scaling to systems with more than a handful of components. Discrete, consistency based state estimation capabilities can scale to this level by combining best-first enumeration and conflict-directed search. While best-first methods have been developed for hybrid estimation, conflict-directed methods have thus far been elusive as conflicts learn inconsistencies from constraint violation, but probabilistic hybrid estimation is relatively unconstrained. In this paper we present an approach to hybrid estimation that unifies best-first enumeration and conflict-directed search through the concept of "bounding" conflicts, an extension of conflicts that represent tighter bounds on the cost of regions of the search space. This paper presents a general best-first enumeration algorithm based on bounding conflicts (A*BC) and a hybrid estimation method using this enumeration algorithm. Experiments show that an A*BC powered state estimator produces estimates up to an order of magnitude faster than the current state of the art, particularly on large systems.
Benchmarking of Routing Algorithms in 3D MANETs

Unmanned aerial vehicles (UAV), or commonly referred to as Drones, have captured our imagination bringing new and challenging service provisioning schemes into the networking scenario. We might indeed be reaching the day where communication-enabled drones might be employed in synergy with deployed public networks offering alternate service access means. In this context, we contribute with quantitative analysis of classic routing algorithms in a 3D mobile environment, shedding some light on their performance tradeoffs.
On the Effectiveness of the National Do-Not-Call Registries

Voice spam is a significant problem worldwide. In this paper, we observe some aspects of the voice spam ecosystem in Europe using a telephony honeypot with 800 phone numbers from 8 European countries over a period of 3 years. Unlike previous honeypots, the numbers were never assigned to a real user before being added to the honeypot. The specific nature of this honeypot allows us to focus on illegitimate calls. Indeed, as the numbers were never used by real users, no consent to telemarketing has ever been given. In particular, we present an experiment we conduct on National Do-Not-Call (DNC) lists and the effects of registering to such lists. We find that the phone numbers added to DNC lists receive a lower number of spam calls both in Spain and the UK. However, we also show anecdotal evidence that the DNC list in the UK is abused by spammers. Finally, we discuss the effectiveness of DNC lists by reviewing various implementation challenges and comparing the existing approaches.
Deep learning and neutrino physics

The Particle Physics Project Prioritization Panel (“P5”) report [1] highlights the physics of neutrino mass as one it it’s five Science Drivers for organizing the activities of high energy physics (HEP). Because neutrino masses are so anomalously small, they may provide a window to the highest energy scales in nature, allowing access to regimes far beyond what we may currently test directly. Cornerstones of this program are the efforts to establish whether neutrinos and antineutrinos oscillate differently (so-called “CP violation”) and to understand the nature and ordering of the neutrino masses. Novel tools are needed to fully exploit the enormous amount of data being generated now and to be produced in the future to address these programs. Furthermore, neutrino experiments, especially in the large far detectors characteristic of long-baseline experiments, are in the interesting position of having enormous data volumes, but very few events in the flagship oscillation analysis. We face a serious problem not only in developing algorithms capable of sifting through huge volumes of data, but we also must develop algorithms that are maximally efficient to fully utilize the investments required to produce neutrino interactions in far detectors. The past several years have witnessed a revolution in computing and machine learning [2, 3]. Using new hardware advances, particularly more capable Graphics Processing Units (GPUs), and the algorithms associated with “deep learning” (the use of neural networks with many hidden layers), computers have surpassed humans in certain pattern recognition exercises, particularly in computer vision and image recognition problems, but tremendous leaps are being made in many endeavors. These techniques have clear application to HEP event reconstruction as modern neutrino detectors are effectively imaging devices. Early results [4–6] indicate that deep learning will significantly improve the physics reach at running neutrino experiments. There is even the intriguing possibility to use deep learning and other forms of machine intelligence to discover entirely new phenomena in our data, although this is still a speculative exercise. While this paper will largely discuss deep learning in HEP from perspective of neutrino physics, it is doubtlessly true that many of the items discussed apply just as well at colliders. Finding common synergies between needs at neutrino, collider, and other HEP experiments will be an important and valuable task in the very near future.
Associations between dietary risk factors and ischemic stroke: a comparison of regression methods using data from the Multi-Ethnic Study of Atherosclerosis

OBJECTIVES We analyzed dietary patterns using reduced rank regression (RRR), and assessed how well the scores extracted by RRR predicted stroke in comparison to the scores produced by partial least squares and principal component regression models. METHODS Dietary data at baseline were used to extract dietary patterns using the 3 methods, along with 4 response variables: body mass index, fibrinogen, interleukin-6, and low-density lipoprotein cholesterol. The analyses were based on 5,468 males and females aged 45-84 years who had no clinical cardiovascular disease, using data from the Multi-Ethnic Study of Atherosclerosis. RESULTS The primary factor derived by RRR was positively associated with stroke incidence in both models. The first model was adjusted for sex and race and the second model was adjusted for the variables in model 1 as well as smoking, physical activity, family and sibling history of stroke, the use of any lipid-lowering medication, the use of any anti-hypertensive medication, hypertension, and history of myocardial infarction (model 1: hazard ratio [HR], 7.49; 95% confidence interval [CI], 1.66 to 33.69; p for trend=0.01; model 2: HR, 6.83; 95% CI, 1.51 to 30.87 for quintile 5 compared with the reference category; p for trend=0.02). CONCLUSIONS Based primarily on RRR, we identified that a dietary pattern high in fats and oils, poultry, non-diet soda, processed meat, tomatoes, legumes, chicken, tuna and egg salad, and fried potatoes and low in dark-yellow and cruciferous vegetables may increase the incidence of ischemic stroke.
Temporal variability of larval drift of tropical Amphidromous gobies along a watershed in Reunion Island

Freshwater gobies of tropical islands are amphidromous: adults reproduce in rivers and larvae passively drift down to the sea immediately after hatching. Describing the phenology of this larval drift is essential to understanding the population dynamics of amphidromous gobies and to developing ecologically based recommendations for managing the watersheds. The larval drift patterns of two amphidromous gobies of Reunion Island, Sicyopterus lagocephalus and Cotylopus acutipinnis, were studied on a monthly basis for 1 year using plankton nets at two sites on the Mât River, located 20 km and 7 km from the river mouth. Genetic analyses showed that larval assemblages were dominated at 90% by S. lagocephalus, although the proportion of C. acutipinnis increased during early summer. Our findings highlighted a marked larval drift peak in full austral summer. The diel larval drift dynamic was described using periodic linear models. A diel pattern was only observed downstream, peaking a few hours after sunset. Finall.
An economic analysis of farm risk under water stress production environment in Namakkal district of Tamil Nadu

Agriculture is the single largest private sector occupation and is also considered to be the riskiest business since it mostly depends on the vagaries of nature. It is important for all decision makers to know the degree of risk involved in each activity. Once the risk corresponding to an a ainable level of expected return is known, depending on the risk taking ability, different farm plans having different level of risk can be taken up by different farmer. In this context, the present study a empts to analyze the agricultural production risk in Namakkal district because of the availability of dry farms. In this study, survey was conducted in four blocks of the same district. The overall objective of the study is to analyze the farm risk to derive optimal input use and optimal cropping pa ern under the conditions of risk by an appropriate risk programming models. In the case of cost of cultivation of selected crops, total cost was high i.e. 62208 for banana and followed by tomato i.e. 38976. Banana and tomato generated higher returns viz. 97976 and 49516 when compared to maize and groundnut. The results of quadratic production function concluded that when risk level increases, optimum quantity of inputs used for the production was decreased. The results showed that even though decline trend in income, the resources which were used in the cultivation as an efficiency factor.