Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors influencing CO 2 and CH 4 emissions from coastal wetlands in the Liaohe Delta, northeast China

Abstract. Many factors are known to influence greenhouse gas emissions from coastal wetlands, but it is still unclear which factors are most important under field conditions when they are all acting simultaneously. The objective of this study was to assess the effects of water table, salinity, soil temperature and vegetation on CH4 emissions and ecosystem respiration (Reco) from five coastal wetlands in the Liaohe Delta, Northeast China: two Phragmites australis (common reed) wetlands, two Suaeda salsa (sea blite) marshes and a rice (Oryza sativa) paddy. Throughout the growing season, the Suaeda wetlands were net CH4 sinks whereas the Phragmites wetlands and the rice paddy were net CH4 sources emitting 1.2–6.1 g CH4 m−2 yr−1. The Phragmites wetlands emitted the most CH4 per unit area and the most CH4 relative to CO2. The main controlling factors for the CH4 emissions were water table, temperature, soil organic carbon and salinity. The CH4 emission was accelerated at high and constant (or managed) water tables and decreased at water tables below the soil surface. High temperatures enhanced CH4 emissions, and emission rates were consistently low ( 18 ppt, the CH4 emission rates were always low (
Are We Wasting Our Talent? Overqualification and Overskilling Among PhD Graduates

Drawing on a very rich data set from a recent cohort of PhD graduates, we examine the correlates and consequences of qualification and skills mismatch. We show that job characteristics such as the economic sector and the main activity at work play a fundamental direct role in explaining the probability of being well matched. However, the effect of academic attributes seems to be mainly indirect, since it disappears once we control for the full set of work characteristics. We detected a significant earnings penalty for those who are both overqualified and overskilled and also showed that being mismatched reduces job satisfaction, especially for those whose skills are underutilized. Overall, the problem of mismatch among PhD graduates is closely related to demand-side constraints of the labor market. Increasing the supply of adequate jobs and broadening the skills PhD students acquire during training should be explored as possible responses.
CLAIRE: A modular support vector image indexing and classification system

Many users of image retrieval systems would prefer to express initial queries using keywords. However, manual keyword indexing is very time-consuming. Therefore, a content-based image retrieval system which can automatically assign keywords to images would be very attractive. Unfortunately, it has proved very challenging to build such systems, except where either the image domain is restricted or the keywords relate only to low-level concepts such as color. This article presents a novel image indexing and classification system, called CLAIRE (CLAssifying Images for REtrieval), composed of one image processing module and three modules of support vector machines for color, texture, and high-level concept classification for keyword assignment. The experimental prototype system described here assigns up to five keywords selected from a controlled vocabulary of 60 terms to each image. The system is trained offline by 1639 examples from the Corel stock photo library. For evaluation, five judges reviewed a sample of 800 unknown images to identify which automatically assigned keywords were actually relevant to the image. The system proved to have an 80% probability to assign at least one relevant keyword to an image.
Characterization of recovered walking patterns and motor control after contusive spinal cord injury in rats

Currently, complete recovery is unattainable for most individuals with spinal cord injury (SCI). Instead, recovery is typically accompanied by persistent sensory and motor deficits. Restoration of preinjury function will likely depend on improving plasticity and integration of these impaired systems. Eccentric muscle actions require precise integration of sensorimotor signals and are predominant during the yield (E2) phase of locomotion. Motor neuron activation and control during eccentric contractions is impaired across a number of central nervous system (CNS) disorders, but remains unexamined after SCI. Therefore, we characterized locomotor recovery after contusive SCI using hindlimb (HL) kinematics and electromyographic (EMG) recordings with specific consideration of eccentric phases of treadmill (TM) walking. Deficits in E2 and a caudal shift of locomotor subphases persisted throughout the 3‐week recovery period. EMG records showed notable deficits in the semitendinosus (ST) during yield. Unlike other HL muscles, recruitment of ST changed with recovery. At 7 days, the typical dual‐burst pattern of ST was lost and the second burst (ST2) was indistinct. By 21 days, the dual‐burst pattern returned, but latencies remained impaired. We show that ST2 burst duration is highly predictive of open field Basso, Beattie, Bresnahan (BBB) scores. Moreover, we found that simple changes in locomotor specificity which enhance eccentric actions result in new motor patterns after SCI. Our findings identify a caudal shift in stepping kinematics, irregularities in E2, and aberrant ST2 bursting as markers of incomplete recovery. These residual impairments may provide opportunities for targeted rehabilitation.
Ultrafast Laser-Driven Microlens to Focus and Energy-Select Mega-Electron Volt Protons

We present a technique for simultaneous focusing and energy selection of high-current, mega–electron volt proton beams with the use of radial, transient electric fields (107 to 1010 volts per meter) triggered on the inner walls of a hollow microcylinder by an intense subpicosecond laser pulse. Because of the transient nature of the focusing fields, the proposed method allows selection of a desired range out of the spectrum of the polyenergetic proton beam. This technique addresses current drawbacks of laser-accelerated proton beams, such as their broad spectrum and divergence at the source.
Renters' Housing Behaviour in Transitional Urban China

This paper aims to understand how renters make their housing decisions in urban China, where market mechanisms are being introduced into a previously welfare-oriented housing system. It is argued that while renters now enjoy more housing options, their choices are constrained not only by socio-economic factors as is the case in the West, but also by persisting socialist institutions such as the hukou system and work units. Using a national survey carried out in 1996 and a technique of multi-level modelling, the empirical study suggests that while private housing is becoming an important option, renters who can access public housing are still more likely to choose public housing because of heavy subsidies. Yet, renters with rural and temporary hukou, and those working in low-ranking work units are at a disadvantage in the housing market. In general they do not qualify for public housing, and private housing is their only option. Although the socialist housing system is under reform, it still defines renters' housing access and thus shapes their behaviour in the late 1990s.
Mass Transfer in Close, Rapidly Accreting Protobinaries: An Origin for Massive Twins?

Rapidly accreting massive protostars undergo a phase of deuterium shell burning during pre-main-sequence evolution that causes them to swell to tenths of an AU in radius. During this phase, those with close binary companions will overflow their Roche lobes and begin transferring mass. Since massive stars frequently have companions at distances well under 1 AU, this process may affect the early evolution of a substantial fraction of massive stars. We use a simple protostellar evolution model to determine the range in accretion rates, mass ratios, and orbital separations for which mass transfer will occur, and we compute approximately the stability and final outcome of the transfer process. We discuss how mass transfer affects the demographics of massive binaries and show that it provides a natural explanation for the heretofore unexplained population of massive "twins," high-mass binaries with mass ratios very close to unity.
Bluetooth Network Performance in Nakagami-m Fading Channels

The Bluetooth standard is considered a promising technology for implementing ad-hoc networks. This paper addresses issues related to Bluetooth performance in Nakagami-m fading channels. This distribution allows a better channel characterization for mobile and wireless communications. The throughput of Bluetooth links using asynchronous packets is investigated. Useful relations between packet error rate and node distances are derived. These results can be used as benchmarks for performance analysis or incorporated into Bluetooth network simulation tools. As an example, we evaluate by computer simulation the performance of a scatternet in a Nakagami fading scenario.
Automatic responses, target resistance, and the adaptation of compliance-seeking requests

Based upon politeness theory and a cognitive rules perspective, it is argued that anticipated resistance to a persuasive message should effect compliance-seeking message behavior. Using controlled interviews to elicit persuasive messages, results indicate that persuaders used a greater number of strategies when confronting a positively predisposed target who refused to comply. Beyond an initial opening gambit, negative sanctions were employed more extensively against positively predisposed targets. Overall, a pattern of compliance-gaining behaviors involving a gradual shift to negative sanctions was observed. The findings are explained in light of current perspectives on information processing, possibly shedding light on past failures to find evidence of strategic adaptation based on situational factors.
Fuzzy rule inference based human activity recognition

Human activity recognition plays an essential role in e-health applications, such as automatic nursing home systems, human-machine interface, home care system, and smart home applications. Many of human activity recognition systems only used the posture of an image frame to classify an activity. But transitional relationships of postures embedded in the temporal sequence are important information for human activity recognition. In this paper, we combine temple posture matching and fuzzy rule reasoning to recognize an action. Firstly, a fore-ground subject is extracted and converted to a binary image by a statistical background model based on frame ratio, which is robust to illumination changes. For better efficiency and separability, the binary image is then trans-formed to a new space by eigenspace and canonical space transformation, and recognition is done in canonical space. A three image frame sequence, 5:1 down sampling from the video, is converted to a posture sequence by template matching. The posture sequence is classified to an action by fuzzy rules inference. Fuzzy rule approach can not only combine temporal sequence information for recognition but also be tolerant to variation of action done by different people. In our experiment, the proposed activity recognition method has demonstrated higher recognition accuracy of 91.8% than the HMM approach by about 5.4 %.
Multiple time-span feature fusion for deep neural network modeling

In this paper, we exploit long term information from multiple time-spans for automatic speech recognition. The multiple time-span information is encoded into three different feature streams: speaker-adaptation-transformed features, deep bottleneck features and deep hierarchical bottleneck features. By combining three different time-spans in discriminative acoustic modeling, the character/syllable error rate improves for Mandarin and Vietnamese conversational telephone speech recognition. We obtain 0.8% and 1.9% absolute over DNN-HMM baselines in character error rate and syllable error rate for Mandarin and Vietnamese, respectively. Further analysis also suggests that our proposed feature fusion approach is able to encode finer-grain temporal information than directly using input features of long time-spans in DNN-HMM baselines.
Utilization of Chinese tallow tree and bagasse for medium density fiberboard

The objective of this research was to investigate various adhesive systems and determine the best composite formulation for selected mechanical and physical properties of medium density fiberboard (MDF) made from wood and bagasse fibers. This study investigated opportunities ofbiomass utilization for natural fiber-based composites from agricultural (bagasse) and Chinese tallow tree (Sapium sebiferum) fibers. The mixing ratios were 100:0, 75:25, 50:50, 25:75, and 0:100 ofbagasse and tallow tree fiber, and the furnish moisture content (MC) was 4 percent. The resin systems used were 8 percent urea formaldehyde (UF), 2.5 percent MDI (4,4'-diphenylmethane diisocyanate), and a mixed resin system of 1 percent MDI and 4 percent UF. Panels containing 100 percent bagasse furnish were also prepared with either 3.5 percent or 4.5 percent M DI at a furnish MC of 0 percent, 4 percent, and 8 percent. Two mixing combinations (50:50 and 25:75) of bagasse/tallow tree fibers yielded mechanical and physical properties which were not statistically different from higher proportions of Sapium fibers and provided the maximum utilization ofbagasse fibers into the panels. The MC ofthe furnish and additional moisture from the resin applications were significant factors influencing the mechanical properties of the composites. MDF made from 8 percent MC bagasse fibers obtained a 63 percent increase in modulus ofrupture (MOR) and a 30 percent in modulus of elasticity (MOE) compared to composites manufactured with 0 percent MC furnish. Panels at all fiber combination ratios with the mixed resin system performed superior to all furnish mixes with 4.5 percent MDI for MOR and MOE. Internal bond (IB) test results showed that the mixed resin system yielded slightly lower IB mean values than panels produced with 4.5 percent MDI.
Nonlinear Model Reduction via Discrete Empirical Interpolation

A dimension reduction method called discrete empirical interpolation is proposed and shown to dramatically reduce the computational complexity of the popular proper orthogonal decomposition (POD) method for constructing reduced-order models for time dependent and/or parametrized nonlinear partial differential equations (PDEs). In the presence of a general nonlinearity, the standard POD-Galerkin technique reduces dimension in the sense that far fewer variables are present, but the complexity of evaluating the nonlinear term remains that of the original problem. The original empirical interpolation method (EIM) is a modification of POD that reduces the complexity of evaluating the nonlinear term of the reduced model to a cost proportional to the number of reduced variables obtained by POD. We propose a discrete empirical interpolation method (DEIM), a variant that is suitable for reducing the dimension of systems of ordinary differential equations (ODEs) of a certain type. As presented here, it is applicable to ODEs arising from finite difference discretization of time dependent PDEs and/or parametrically dependent steady state problems. However, the approach extends to arbitrary systems of nonlinear ODEs with minor modification. Our contribution is a greatly simplified description of the EIM in a finite-dimensional setting that possesses an error bound on the quality of approximation. An application of DEIM to a finite difference discretization of the one-dimensional FitzHugh-Nagumo equations is shown to reduce the dimension from 1024 to order 5 variables with negligible error over a long-time integration that fully captures nonlinear limit cycle behavior. We also demonstrate applicability in higher spatial dimensions with similar state space dimension reduction and accuracy results.
Catalytic Water Co-Existing with a Product Peptide in the Active Site of HIV-1 Protease Revealed by X-Ray Structure Analysis

Background It is known that HIV-1 protease is an important target for design of antiviral compounds in the treatment of Acquired Immuno Deficiency Syndrome (AIDS). In this context, understanding the catalytic mechanism of the enzyme is of crucial importance as transition state structure directs inhibitor design. Most mechanistic proposals invoke nucleophilic attack on the scissile peptide bond by a water molecule. But such a water molecule coexisting with any ligand in the active site has not been found so far in the crystal structures. Principal Findings We report here the first observation of the coexistence in the active site, of a water molecule WAT1, along with the carboxyl terminal product (Q product) peptide. The product peptide has been generated in situ through cleavage of the full-length substrate. The N-terminal product (P product) has diffused out and is replaced by a set of water molecules while the Q product is still held in the active site through hydrogen bonds. The position of WAT1, which hydrogen bonds to both the catalytic aspartates, is different from when there is no substrate bound in the active site. We propose WAT1 to be the position from where catalytic water attacks the scissile peptide bond. Comparison of structures of HIV-1 protease complexed with the same oligopeptide substrate, but at pH 2.0 and at pH 7.0 shows interesting changes in the conformation and hydrogen bonding interactions from the catalytic aspartates. Conclusions/Significance The structure is suggestive of the repositioning, during substrate binding, of the catalytic water for activation and subsequent nucleophilic attack. The structure could be a snap shot of the enzyme active site primed for the next round of catalysis. This structure further suggests that to achieve the goal of designing inhibitors mimicking the transition-state, the hydrogen-bonding pattern between WAT1 and the enzyme should be replicated.
Tat-Induced FOXO3a Is a Key Mediator of Apoptosis in HIV-1-Infected Human CD4+ T Lymphocytes1

The high mutation rate of HIV is linked to the generation of viruses expressing proteins with altered function whose impact on disease progression is unknown. We investigated how HIV-1 viruses lacking Env, Vpr, and Nef affect CD4+ T cell survival. We found that in the absence of these proteins, HIV-1-infected CD4+ primary T cells progress to the G0 phase of the cell cycle and to cell death, indicating that viruses expressing inactive forms of these proteins can contribute to the CD4+ T cell decline as the wild-type virus, suggesting that other HIV proteins are responsible for inducing apoptosis. Apoptosis in these cells is triggered by the alteration of the Egr1-PTEN-Akt (early growth response-1/phosphate and tensin homolog deleted on chromosome 10/Akt) and p53 pathways, which converge on the FOXO3a (Forkhead box transcription factor O class 3a) transcriptional activator. The FOXO3a target genes Fas ligand and TRAIL, involved in the extrinsic apoptotic pathway, and PUMA, Noxa, and Bim, which are part of the intrinsic apoptotic pathway, were also up-regulated, indicating that HIV infection leads to apoptosis by the engagement of multiple apoptotic pathways. RNAi-mediated knockdown of Egr1 and FOXO3a resulted in reduced apoptosis in HIV-infected HeLa and CD4+ T cells, providing further evidence for their critical role in HIV-induced apoptosis and G0 arrest. We tested the possibility that Tat is responsible for the T cell apoptosis observed with these mutant viruses. The induction of Egr1 and FOXO3a and its target genes was observed in Jurkat cells transduced by Tat alone. Tat-dependent activation of the Egr1-PTEN-FOXO3a pathway provides a mechanism for HIV-1-associated CD4+ T cell death.
LOCAS – A Low Coverage Assembly Tool for Resequencing Projects

Motivation Next Generation Sequencing (NGS) is a frequently applied approach to detect sequence variations between highly related genomes. Recent large-scale re-sequencing studies as the Human 1000 Genomes Project utilize NGS data of low coverage to afford sequencing of hundreds of individuals. Here, SNPs and micro-indels can be detected by applying an alignment-consensus approach. However, computational methods capable of discovering other variations such as novel insertions or highly diverged sequence from low coverage NGS data are still lacking. Results We present LOCAS, a new NGS assembler particularly designed for low coverage assembly of eukaryotic genomes using a mismatch sensitive overlap-layout-consensus approach. LOCAS assembles homologous regions in a homology-guided manner while it performs de novo assemblies of insertions and highly polymorphic target regions subsequently to an alignment-consensus approach. LOCAS has been evaluated in homology-guided assembly scenarios with low sequence coverage of Arabidopsis thaliana strains sequenced as part of the Arabidopsis 1001 Genomes Project. While assembling the same amount of long insertions as state-of-the-art NGS assemblers, LOCAS showed best results regarding contig size, error rate and runtime. Conclusion LOCAS produces excellent results for homology-guided assembly of eukaryotic genomes with short reads and low sequencing depth, and therefore appears to be the assembly tool of choice for the detection of novel sequence variations in this scenario.
Large sample asymptotics for the ensemble Kalman filter

The ensemble Kalman filter (EnKF) has been proposed as a Monte Carlo, derivative-free, alternative to the extended Kalman filter, and is now widely used in sequential data assimilation, where state vectors of huge dimension (e.g. resulting from the discretization of pressure and velocity fields over a continent, as considered in meteorology) should be estimated from noisy measurements (e.g. collected at sparse in-situ stations). Even if the state and measurement equations are linear with additive Gaussian white noise, computing and storing the error covariance matrices involved in the Kalman filter is practically impossible, and it has been proposed to represent the filtering distribution with a sample (ensemble) of a few elements and to think of the corresponding empirical covariance matrix as an approximation of the intractable error covariance matrix. Extensions to nonlinear state equations have also been proposed. Surprisingly, very little is known about the asymptotic behaviour of the EnKF, whereas on the other hand, the asymptotic behaviour of many different classes of particle filters is well understood, as the number of particles goes to infinity. Interpreting the ensemble elements as a population of particles with mean-field interactions (and not merely as an instrumental device producing the ensemble mean value as an estimate of the hidden state), we prove the convergence of the EnKF, with the classical rate 1/\sqrt{N}, as the number N of ensemble elements increases to infinity. In the linear case, the limit of the empirical distribution of the ensemble elements is the usual (Gaussian distribution associated with the) Kalman filter, as expected, but in the more general case of a nonlinear state equation with linear observations, this limit differs from the usual Bayesian filter. To get the correct limit in this case, the mechanism that generates the elements in the EnKF should be interpreted as a proposal importance distribution, and appropriate importance weights should be assigned to the ensemble elements.