Miracle-dz/UniFAR-FTUs huggingface dataset:
Krill, climate, and contrasting future scenarios for Arctic and Antarctic fisheries

Arctic and Antarctic marine systems have in common high latitudes, large seasonal changes in light levels, cold air and sea temperatures, and sea ice. In other ways, however, they are strikingly different, including their: age, extent, geological structure, ice stability, and foodweb structure. Both regions contain very rapidly warming areas and climate impacts have been reported, as have dramatic future projections. However, the combined effects of a changing climate on oceanographic processes and foodweb dynamics are likely to influence their future fisheries in very different ways. Differences in the life-history strategies of the key zooplankton species (Antarctic krill in the Southern Ocean and Calanus copepods in the Arctic) will likely affect future productivity of fishery species and fisheries. To explore future scenarios for each region, this paper: (i) considers differing characteristics (including geographic, physical, and biological) that define polar marine ecosystems and reviews known and projected impacts of climate change on key zooplankton species that may impact fished species; (ii) summarizes existing fishery resources; (iii) synthesizes this information to generate future scenarios for fisheries; and (iv) considers the implications for future fisheries management. Published studies suggest that if an increase in open water during summer in Arctic and Subarctic seas results in increased primary and secondary production, biomass may increase for some important commercial fish stocks and new mixes of species may become targeted. In contrast, published studies suggest that in the Southern Ocean the potential for existing species to adapt is mixed and that the potential for the invasion of large and highly productive pelagic finfish species appears low. Thus, future Southern Ocean fisheries may largely be dependent on existing species. It is clear from this review that new management approaches will be needed that account for the changing dynamics in these regions under climate change.
Trust Based Algorithm for Candidate Node Selection in Hybrid MANET-DTN

The hybrid MANET - DTN is a mobile network that enables transport of the data between groups of the disconnected mobile nodes. The network provides benefits of the Mobile Ad-Hoc Networks (MANET) and Delay Tolerant Network (DTN). The main problem of the MANET occurs if the communication path is broken or disconnected for some short time period. On the other side, DTN allows sending data in the disconnected environment with respect to higher tolerance to delay. Hybrid MANET - DTN provides optimal solution for emergency situation in order to transport information. Moreover, the security is the critical factor because the data are transported by mobile devices. In this paper, we investigate the issue of secure candidate node selection for transportation of the data in a disconnected environment for hybrid MANET- DTN. To achieve the secure selection of the reliable mobile nodes, the trust algorithm is introduced. The algorithm enables select reliable nodes based on collecting routing information. This algorithm is implemented to the simulator OPNET modeler.
The Effect of Air Pollution on Cardiovascular Mortality : Evidences from the 2008 Beijing Olympic Games

The exogenous air pollution variations induced by the 2008 Beijing Olympic Games provide a natural experiment to estimate the health effects of air pollution. This study finds that air pollution has a robust and significant effect on cardiovascular mortality in China. A 10 μg/m! (10 percent) decrease in PM! " mean concentrations decreases monthly cardiovascular mortality by 13.6 percent. The estimates are larger than conventional estimates in the cross-sectional studies and fixed-effects studies.
Deep Fully-Connected Part-Based Models for Human Pose Estimation

We propose a 2D multi-level appearance representation of the human body in RGB images, spatially modelled using a fully-connected graphical model. The appearance model is based on a CNN body part detector, which uses shared features in a cascade architecture to simultaneously detect body parts with different levels of granularity. We use a fully-connected Conditional Random Field (CRF) as our spatial model, over which approximate inference is efficiently performed using the Mean-Field algorithm, implemented as a Recurrent Neural Network (RNN). The stronger visual support from body parts with different levels of granularity, along with the fully-connected pairwise spatial relations, which have their weights learnt by the model, improve the performance of the bottom-up part detector. We adopt an end-to-end training strategy to leverage the potential of both our appearance and spatial models, and achieve competitive results on the MPII and LSP datasets.
A global population redistribution in a migrant shorebird detected with continent‐wide qualitative breeding survey data

Aim Over the last two decades, thousands of northward migrating ruffs (Philomachus pugnax) have disappeared from western European staging sites. These migratory ruffs were partly temperate breeding birds, but most individuals head towards the Eurasian Arctic tundras where 95% of the global population breeds. This regional decline may represent either: (1) local loss of breeding birds in western Europe, (2) a global decline, (3) shift(s) in distribution or (4) a combination of these.
Modelling forage potential for red deer: A case study in post-disturbance young stands of rowan

Recently, the red deer ( Cervus elaphus ) population has increased considerably and caused serious damage in forest stands in Slovakia as well as in other Central-European countries. Rowan ( Sorbus aucuparia L.) is the tree species that is most intensively browsed and stripped by deer, especially during young stages of tree development. Our research focuses on estimating rowan mass consumption by red deer in young stands which developed after large-scale wind disturbance that occurred in the Tatra National Park in 2004. New models were developed for estimating the mass of tree components that are potentially edible by red deer using tree-base diameter as an independent variable. The results showed that the mass contribution of particular tree components to accessible deer forage depended on tree size (tree-base diameter). At stand level, total forage potential increased with an increase in tree size. However, whereas the quantity of bark available for stripping increased with tree size, the total mass accessible for browsing (leader shoot and branches with foliage) decreased. For instance, the contribution of stem bark to total forage potential in stands with a mean tree-base diameter of 20 mm and 50 mm was 15% and 50%, respectively. Theoretically, if all tree mass potential is consumed by red deer, young rowan stands (considering tree coverage of 50%) growing within an area of 100 m2 might provide sufficient forage for one adult deer for ca. 10 days. It is suggested that rowan species should not be removed from forest stands in territories with a high deer population in order to decrease the potential damage of other, commercially important, tree species.
Unsupervised Pre-Training with Spiking Neural Networks in Semi-Supervised Learning

Semi-supervised learning appeared when people understood that ignoring the possibility of getting benefit from unlabeled data during supervised learning with little labeled data is not wise. It is difficult to overestimate the importance of this as much more unlabeled data exists than labeled and much easier it can be collected. Many semi-supervised learning methods were developed over the past decades. A new approach is presented in this paper, which is based on using spiking neural networks in the pre-training phase. Spiking neural networks are biologically plausible neural networks, which try to simulate the behavior of neurons and processes which occur in biological neural networks. Most of the learning rules used in spiking neural networks are unsupervised as unsupervised learning is thought to be a major drive for developmental plasticity in the brain. It is considered that it is hard for the brain to do supervised learning, things like doing math or classification. In a nutshell, the proposed method is a combination of the advantages of both spiking neuron networks (unsupervised learning) and classical artificial neural networks (supervised learning). We showed that such approach may increase the accuracy of the classifier when a small amount of labeled data is given.
Protein-Protein Interactions Extraction from Biomedical Literatures

Protein-protein interactions (PPI) play a key role in various aspects of the structural and functional organization of the cell. Knowledge about them unveils the molecular mechanisms of biological processes. A number of databases such as MINT (Zanzoni et al., 2002), BIND (Bader et al., 2003), and DIP (Xenarios et al., 2002) have been created to store protein interaction information in structured and standard formats. However, the amount of biomedical literature regarding protein interactions is increasing rapidly and it is difficult for interaction database curators to detect and curate protein interaction information manually. Thus, most of the protein interaction information remains hidden in the text of the papers in the literature. Therefore, automatic extraction of protein interaction information from biomedical literature has become an important research area. Existing PPI works can be roughly divided into three categories: Manual pattern engineering approaches, Grammar engineering approaches and Machine learning approaches. Manual pattern engineering approaches define a set of rules for possible textual relationships, called patterns, which encode similar structures in expressing relationships. The SUISEKI system uses regular expressions, with probabilities that reflect the experimental accuracy of each pattern to extract interactions into predefined frame structures (Blaschke & Valencia, 2002). Ono et al. manually defined a set of rules based on syntactic features to preprocess complex sentences, with negation structures considered as well (Ono et al., 2001). The BioRAT system uses manually engineered templates that combine lexical and semantic information to identify protein interactions (Corney et al., 2004). Such manual pattern engineering approaches for information extraction are very hard to scale up to large document collections since they require labor-intensive and skilldependent pattern engineering. Grammar engineering approaches use manually generated specialized grammar rules that perform a deep parse of the sentences. Sekimizu et al. used shallow parser, EngCG, to generate syntactic, morphological, and boundary tags (Sekimizu et al., 1998). Based on the tagging results, subjects and objects were recognized for the most frequently used verbs. Fundel et al. proposed RelEx based on the dependency parse trees to extract relations (Fundel et al., 2007). Machine learning techniques for extracting protein interaction information have gained interest in the recent years. In most recent work on machine learning for PPI extraction, the PPI extraction task is casted as learning a decision function that determines for each
Robot guidance by estimating the force-image interaction matrix

Abstract This paper describes an uncalibrated visual-force control system which does not require any kinematic calibration to develop the task. An important aspect of these kinds of control systems is the necessity to maintain the coherence between the control actions obtained from each sensorial system. To do so, the paper proposes to modify the image trajectory from the information obtained from the force sensor by using the concept of force-image interaction matrix. This matrix relates changes in the image space with changes in the interaction forces. In order to estimate the value of this matrix this paper suggests the use of a Gauss-Newton method.
IN VITRO ANTIFUNGAL SUSCEPTIBILITY REVEALS OCCURRENCE OF AZOLE RESISTANCE AMONG CLINICAL ISOLATES OF CANDIDA ALBICANS

Infections due to fungal pathogens are on the rise. Systematic studies on antifungal drug susceptibility which may be useful in deciding clinical strategies are not routinely done in India and other developing countries. Aim of this study was testing sensitivity of clinical isolates of Candida albicans to frequently prescribed antifungal drugs, fluconazole, ketoconazole, clotrimazole, amphotericin B and terbinafine. Thirty one strains of C. albicans were isolated from patients visiting a tertiary care urban hospital in Maharashtra, India. Presumptive identification of C. albicans was done on HiCHROM agar-Candida. Confirmation was done by Germ tube formation assay, carbohydrate assimilation test and Corn meal agar test. Isolates were tested in vitro for susceptibility to five antifungal agents, by using standard broth micro dilution method. All the experiments were performed in triplicates. Results of this study revealed dose dependent susceptibility to fluconazole in 16% of isolates. Resistance to ketoconazole and clotrimazole was observed in 23 % and 10 % isolates, respectively. All the isolates were sensitive to amphotericin B (a polyene) and terbinafine (an allylamine). One isolate was found resistant to ketoconazole and clotrimazole, at the same time S-DD to fluconazole. Occurrence of resistance to ketoconazole and clotrimazole, as well as dose dependent susceptibility to fluconazole, alarms emergence of multiple drug resistance among Indian clinical isolates of C. albicans. Although fluconazole could still be used effectively, S-DD isolates may become resistant upon repeated exposure to the drug. Hence, caution need to be taken while prescribing these azoles.
TRANSITION OF GROWTH MODE IN ELECTROCHEMICAL DEPOSITION OF COPPER: ATOMIC FORCE MICROSCOPY ANALYSIS AND SIMULATION

The scaling behavior of roughness in electrochemical deposition of copper under stable growth conditions was studied by atomic force microscopy and numerical simulation. An abrupt transition in the growth mode was observed as the concentration of “brightener” (organic additive) in the plating solution was increased. The two growth modes are characterized by the roughness exponents α = 0.87 and 0.63 for low and high concentrations of organic additive, respectively. The transition can be reproduced numerically via a Kardar-Parisi-Zhang (KPZ) -like equation with an additional term proportional to the surface height. The effect of current density on the transition is also investigated.
The Outbreak of Koi Herpesvirus (KHV) in Koi (Cyprinus carpio koi) from Chiang Mai Province, Thailand

Twenty-two sick koi with cumulative mortality showed clinical signs of depression, brachial hemorrhage and necrosis, focal hemorrhage, pale patches and ulcers on the skin. Clinical examination showed the heavy infection by opportunistic organisms such as Dactylogyrus sp. , Gyrodactylus sp. and Saprolegnia sp. of the skin, gill and fins. Histopathologically, the gill showed severe acute diffuse necrosis of the branchial epithelial cells and severe diffuse lymphocytic-monocytic interstitial nephritis with necrosis of the tubular epithelial cells were predominantly found in most sick fish. The presence of homogeneous pale basophilic intranuclear viral inclusions was observed. Transmission electron microscopy showed herpesvirus-like particles in the nuclei of gill epithelium. The positive result at 290 base pairs DNA product of KHV was confirmed from the gill samples using the polymerase chain reaction (PCR). In conclusion, this report confirmed the evidence of the outbreak of KHV in koi from Chiang Mai using pathology and molecular method.
Memory-systems challenges in realizing monolithic computers

This paper presents the notion of a monolithic computer, a future computer architecture in which a CPU and a high-capacity main memory system are integrated in a single die. Such computers will become possible in the near future due to emerging non-volatile memory technology. In particular, we consider using resistive random access memory, or ReRAM, from Crossbar Incorporated. Crossbar's ReRAM is dense, fast, and consumes zero static power. Also, it can be fabricated in a standard CMOS logic process, allowing it to be integrated into a CPU's die. The ReRAM cells are manufactured in between metal wires and do not employ per-cell access transistors, so the bulk of the transistors underneath the ReRAM arrays are vacant. This means a CPU can be implemented using a die's logic transistors (minus transistors for access circuits), while the ReRAM can be implemented "in the wires" above the CPU. This will enable massive memory parallelism, as well as high performance and power efficiency. We discuss several challenges that must be overcome in order to realize monolithic computers. First, there is a physical design challenge of merging ReRAM access circuits with CPU logic. Second, while Crossbar's ReRAM technology exists today, it is currently targeted for storage. There is a device challenge to redesign Crossbar's ReRAM so that it is more optimized for CPUs. And third, there is an architecture challenge to expose the massive memory-level parallelism that will be available in the on-die ReRAM. This will require highly parallel network-on-chip and memory controller designs, and a CPU architecture that can issue memory requests at a sufficiently high rate to make use of the memory parallelism.
Developing New Strategies for the Treatment of Tuberculosis Employing Ruthnium(II)Quaterpyridyl Compexes

A series of ruthenium(II)quaterpyridyl complexes has been synthesized as prototypes for mycobacterial channel blockers. These Ru(II)complexes show distinct changes in their luminescence spectra when bound to the porin MspA from M. smegmatis, which is a non-pathogenic relative of M. tuberculosis. By using HPLC, we have determined binding constants of the Ru(II)-complexes to MspA in phosphate buffer (0.05M, pH = 6.8) ranging from 5.2 x 10 9 M -1 (Ru-C2) to 1.8 x 10 9 M -1 (Ru-C4). Our findings indicate that channel blocking is a promising treatment strategy for mycobacterial infections. It appears to be also a viable approach towards luminescent nanostructures, because MspA features extraordinary stability. Keywords: Ru(II)complexe, porin MspA, mycobacterial infection, tuberculosis DOI: 10.3126/jncs.v23i0. 2091 Journal of Nepal Chemical Society, Vol. 23, 2008/2009 Page:2-10 .
Prediction of diffuse axonal injury with a strain measure from an analytical model

Diffuse axonal injury (DAI) is one of the most devastating types of traumatic brain injury and frequently occurs from automobile crashes. Strain deformation induced by shear force from sudden acceleration of the human head during impact has been recognized as a major cause of DAI. Recent mathematical studies on strain damage of brain tissue are dominated by finite element (FE) modeling work. Although offering detailed modeling of anatomical structures, FE models have not been fully validated due to the lack of complete knowledge of the material properties of brain tissue. Based on a well-established analytical brain injury model, which was validated against the most advanced experimental brain motion data and compared with high-fidelity FE results under low-severity impacts, a measure of strain for DAI prediction is presented. Increasing with higher accelerations under more severe impact, the maximum shear strain was selected as the injury indicator of DAI because DAI most likely results from shear force. This strain measure was applied to the frontal crash tests using the acceleration data collected with dummies through the New Car Assessment Program (NCAP) conducted by the National Highway Traffic Safety Administration (NHTSA). The results were compared with the Head Injury Criterion (HIC) and the Cumulative Strain Damage Measure (CSDM) in the SIMon FE head model developed by NHTSA. It shows that the simple measure of strain has the capabilities of predicting brain injuries from automobile crashes. The maximum shear strain from the analytical model can be used as a critical element to predict DAI. Including both linear and angular accelerations, this analytical model tends to fill the gap between HIC and the more complex SIMon, where HIC predicts injuries based only on linear accelerations while the SIMon prediction is dominated by angular accelerations. This paper has not been screened for accuracy nor refereed by any body of scientific peers and should not be referenced in the open literature
The Absolute Bioavailability of Desvenlafaxine in Healthy Subjects

Background and Objective: Desvenlafaxine (administered as desvenlafaxine succinate) is approved for the treatment of major depressive disorder (MDD). If eliminated by the kidney, desvenlafaxine may have more favorable pharmacokinetic or drug-drug interaction profiles compared to its parent compound, venlafaxine, which depends primarily on the CYP2D6 enzyme system. Therefore, the pharmacokinetics and bioavailability of desvenlafaxine was assessed in healthy human subjects. Methods: In a single-dose, open-label, crossover study, subjects were randomly assigned to 100 mg/d of oral desvenlafaxine or intravenous (50 mg/1 hr) desvenlafaxine. Plasma and urine were collected for 72 hours postdosing and assayed to determine pharmacokinetics and bioavailability of (R)-, (S)-, and (R+S)-desvenlafaxine and N,O-didesmethylvenlafaxine. Results and Discussion: Pharmacokinetic parameters for (R)- and (S)-desvenlafaxine enantiomers were approximately equivalent for the oral and intravenous formulations of desvenlafaxine. Compared with 50 mg intravenous desvenlafaxine, 100 mg oral desvenlafaxine had a higher area under the plasma concentration-time curve and an absolute bioavailability of 80.5%. Urinary excretion of total desvenlafaxine and N,O-didesmethylvenlafaxine accounted for 69% of the orally administered desvenlafaxine dose, with the majority of a dose being excreted unchanged or as the glucuronide conjugate (66%). Conclusion: Desvenlafaxine has high oral bioavailability and provides an evenly balanced enantiomeric ratio.
Exploring College Students' Cultural View from a Knowledge Creation Perspective

The purpose of this study is to investigate college students’ cultural views. To this end, an exploratory study was implemented, and data mainly came from students’ essay writing (via individual reflective activities) and focused group discussion (via collective reflective activities). The participants were 176 college students taking a culture-related course. Content analysis was employed to analyze data, and a coding scheme highlighting three general cultural views (i.e. transmission, exchange, and innovation) was developed. Findings indicate that college students typically hold a more conservative, transmission-oriented view that sees culture mainly as a legacy left by previous generations. They did not also see culture as something that should be further improved or innovated (an innovation-oriented view). In addition, students’ discussion also showed that they had a superficial understanding of why culture is important for the overall progress of a society. Some implications for curricular and instructional designs for promoting cultural awareness and multiculturism were discussed.