Miracle-dz/UniFAR-FTUs huggingface dataset:
Seasonal plasticity in the temperature sensitivity of microbial activity in three temperate forest soils

The temperature sensitivity of soil organic matter (SOM) decomposition has been a source of much debate, given the potential feedbacks with climate warming. Here, we evaluated possible seasonal variation in the temperature sensitivity of microbially mediated soil fluxes related to decomposition (net N mineralization, net nitrification, proteolysis, the maximum velocity (Vmax) of proteolysis, microbial respiration, and the Vmax of four soil exo-enzymes) across forests dominated by eastern hemlock (Tsuga canadensis), white ash (Fraxinus americana), and red oak (Quercus rubra) in central Massachusetts, USA. We asked two simple questions: (1) do temperature sensitivities vary across forest types or different steps of the decomposition process, and (2) do temperature sensitivities display plasticity on a seasonal time frame? We observed substantial variation in temperature sensitivities (Q10 and R10 values) across the different fluxes and forest types. The ash soils exhibited the strongest temperature sensitivities and the mineral-N fluxes exhibited higher temperature sensitivities relative to the proteolytic fluxes or microbial respiration. The Vmax of soil exo-enzymes varied considerably in an interactive manner across forests and time, and the response of some enzymes was consistent with the thermal plasticity. The enzymatic kinetic properties Vmax and Km (half-saturation constant) were strongly correlated with slopes that differed across enzymes, reflecting an enzyme-specific tradeoff between maximum catalytic rate and substrate-binding efficiency. Generally, Q10 values were largely constant, but R10 values varied in a manner consistent with distinct seasonal plasticity. There was a consistent seasonal shift in R10 values coincident with snowmelt, suggesting that the time following snowmelt is a particularly interesting and dynamic period of microbial activity in these temperate forests.
Classification-based symbolic indoor positioning over the Miskolc IIS Data-set

Abstract Determination of indoor position is vital for the creation of smart environments. Symbolic indoor positioning algorithms determine the location as a well-defined part of the building, such as a room, a corridor or a floor. Performance analysis of classification-based symbolic indoor positioning methods are presented in this paper. Symbolic positioning can be considered as a classification task, where position denotes the category and the attributes are the measured values. Evaluation and comparison of the selected classification methods are performed over a hybrid data-set which was recorded by the ILONA (Indoor Localisation and Navigation) System. These experiments were performed in RapidMiner and the Weka framework. Accuracy is the base of comparison and the following classification methods were used: k–NN, Naive Bayes, Decision Tree, Rule Induction and Artificial Neural Network. Comparison is used to recommend a classification-based symbolic indoor positioning method to be implemented in the ILONA System. Experimental results show that the k–NN, Naive Bayes with 1 kernel and ANN classifiers achieved better than 90% accuracy. As a result of our experiments, k–NN, Naive Bayes with 1 kernel- and ANN-based classification methods are recommended.
Contrast enhancement and feature extraction algorithms of finger knucle print image for personal recognition

The Finger-Knuckle-Print (FKP) which is defined with its rich texture is becoming a new challenge to identify persons. In this paper, we propose a new algorithm for personal recognition including two main steps. Firstly, an enhancement algorithm based on Adaptive Histogram Equalization (AHE) is considered to improve the contrast of input FKP images. Secondly, a new algorithm is proposed to extract minutiae from enhanced FKP image. Simulation results showed that our proposed algorithm performs better than others existing methods with an FAR close to 0% and FRR values ranging from 87.5% to 100%.
Observation of Acoustical Signal Fluctuations by Time-Frequency Analysis Methods

Summary In this paper, acoustic data collected in shallow water during INTIMATE00 sea trials are analysed with the aim to detect fluctuations in acoustical signals under the effect of a time-varying environment. Some time-frequency analysis methods, i.e., Gabor Expansion and Wigner-Ville Distribution, have resulted to be adequate to find out fluctuations in the received signals, in particular, such analyses reported different time-frequency behaviour in relation with different tidal conditions. The variations observed in the received acoustic data have been compared with the temperature data collected during the sea trials and the related sound-speed profiles: it has been reported that major fluctuations over time in the acoustic signal occur in correspondence of the sound-speed profile changes linked to environmental variability.
OS-SIFT: A Robust SIFT-Like Algorithm for High-Resolution Optical-to-SAR Image Registration in Suburban Areas

Although the scale-invariant feature transform (SIFT) algorithm has been successfully applied to both optical image registration and synthetic aperture radar (SAR) image registration, SIFT-like algorithms have failed to register high-resolution (HR) optical and SAR images due to large geometric differences and intensity differences. In this paper, to perform optical-to-SAR (OS) image registration, we proposed an advanced SIFT-like algorithm (OS-SIFT) that consists of three main modules: keypoint detection in two Harris scale spaces, orientation assignment and descriptor extraction, and keypoint matching. Considering the inherent properties of SAR images and optical images, the multiscale ratio of exponentially weighted averages and multiscale Sobel operators are used to calculate consistent gradients for the SAR images and optical images on the basis of which, as a result, two Harris scale spaces can be constructed. Keypoints are detected by finding the local maxima in the scale space followed by a localization refinement method based on the spatial relationship of the keypoints. Moreover, gradient location orientation histogram-like descriptors are extracted using multiple image patches to increase the distinctiveness. The experimental results on simulated images and several HR satellite images show that the proposed OS-SIFT algorithm gives a robust registration result for optical-to-SAR images and outperforms other state-of-the-art algorithms in terms of registration accuracy.
Link Prediction in Sparse Networks by Incidence Matrix Factorization

Link prediction plays an important role in multiple areas of artificial intelligence, including social network analysis and bioinformatics; however, it is often negatively affected by the data sparsity problem. In this paper, we present and validate our hypothesis, i.e., for sparse networks, incidence matrix factorization (IMF) could perform better than adjacency matrix factorization (AMF), the latter used in many previous studies. A key observation supporting our hypothesis here is that IMF models a partially observed graph more accurately than AMF. Unfortunately, a technical challenge we face in validating our hypothesis is that there is not an obvious method for making link prediction using a factorized incidence matrix, unlike the AMF approach. To this end, we developed an optimization-based link prediction method. Then we have conducted thorough experiments using both synthetic and real-world datasets to investigate the relationship between the sparsity of a network and the predictive performance of the aforementioned two factorization approaches. Our experimental results show that IMF performed better than AMF as networks became sparser, which validates our hypothesis.
Plasma Species Interacting with Nickel Surfaces: Toward an Atomic Scale Understanding of Plasma-Catalysis

The adsorption probability and reaction behavior of CHx plasma species on various nickel catalyst surfaces is investigated by means of reactive molecular dynamics (MD) simulations using the ReaxFF potential. Such catalysts are used in the reforming of hydrocarbons and in the growth of carbon nanotubes, and further insight in the underlying mechanisms of these processes is needed to increase their applicability. Single and consecutive impacts of CHx radicals (x={1,2,3}) were performed on four different Ni surfaces, at a temperature of 400 K. The adsorption probability is shown to be related to the number of free electrons, i.e. a higher number leads to more adsorptions, and the steric hindrance caused by the hydrogen atoms bonded to the impacting CHx species. Furthermore, some of the C–H bonds break after adsorption, which generally leads to diffusion of the hydrogen atom over the surface. Additionally, these adsorbed H-atoms can be used in reactions to form new molecules, such as CH4 and C2Hx, although th.
Understanding anatomy classification through attentive response maps

One of the main challenges for broad adoption of deep learning based models such as convolutional neural networks (CNN), is the lack of understanding of their decisions. In many applications, a simpler, less capable model that can be easily understood is favorable to a black-box model that has superior performance. In this paper, we present an approach for designing CNNs based on visualization of the internal activations of the model. We visualize the model's response through attentive response maps obtained using a fractional stride convolution technique and compare the results with known imaging landmarks from the medical literature. We show that sufficiently deep and capable models can be successfully trained to use the same medical landmarks a human expert would use. Our approach allows for communicating the model decision process well, but also offers insight towards detecting biases.
Loss of Elongation-Like Factor 1 Spontaneously Induces Diverse, RNase H-Related Suppressor Mutations in Schizosaccharomyces pombe

A healthy individual may carry a detrimental genetic trait that is masked by another genetic mutation. Such suppressive genetic interactions, in which a mutant allele either partially or completely restores the fitness defect of a particular mutant, tend to occur between genes that have a confined functional connection. Here we investigate a self-recovery phenotype in Schizosaccharomyces pombe, mediated by suppressive genetic interactions that can be amplified during cell culture. Cells without Elf1, an AAA+ family ATPase, have severe growth defects initially, but quickly recover growth rates near to those of wild-type strains by acquiring suppressor mutations. elf1Δ cells accumulate RNAs within the nucleus and display effects of genome instability such as sensitivity to DNA damage, increased incidence of lagging chromosomes, and mini-chromosome loss. Notably, the rate of phenotypic recovery was further enhanced in elf1Δ cells when RNase H activities were abolished and significantly reduced upon overexpression of RNase H1, suggesting that loss of Elf1-related genome instability can be resolved by RNase H activities, likely through eliminating the potentially mutagenic DNA–RNA hybrids caused by RNA nuclear accumulation. Using whole genome sequencing, we mapped a few consistent suppressors of elf1Δ including mutated Cue2, Rpl2702, and SPBPJ4664.02, suggesting previously unknown functional connections between Elf1 and these proteins. Our findings describe a mechanism by which cells bearing mutations that cause fitness defects and genome instability may accelerate the fitness recovery of their population through quickly acquiring suppressors. We propose that this mechanism may be universally applicable to all microorganisms in large-population cultures.
Adversarial approach to diagnostic quality volumetric image enhancement

We propose a method based on the generative adversarial networks to obtain diagnostic-quality volumetric images (magnetic resonance imaging (MRI) and computed tomography (CT)) from fast data acquisition protocols and lower radiation dosage respectively. These types of acquisition protocols are beneficial to patients, but often inadequate for diagnostic purposes due to the loss in image quality. This work extends the approach presented in [1] to volumetric medical imaging by using perceptual and clinically meaningful loss function. The proposed loss function consists of adversarial and perceptual loss components. The adversarial loss is trained to differentiate between the estimated and the diagnostic quality image while the perceptual loss incorporates perceptual similarity instead of pixel-wise similitude. The adversarial architecture with the perceptual loss was separately trained on 40 CT and 40 MR images, respectively and subsequently used to enhance low resolution scans to obtain diagnostic quality image data. The proposed framework produced an average peak signal-to-noise ratio (PSNR) of 32.09 ± 0.24 dB and 33.76 ± 0.28 dB for CT and MR images respectively in our experiments. The quality obtained is greater than the minimum discernible PSNR threshold of 31.7 dB of the human visual system, indicating high quality for diagnosis [2].
Assisting Movement Training and Execution With Visual and Haptic Feedback

In the practice of motor skills in general, errors in the execution of movements may go unnoticed when a human instructor is not available. In this case, a computer system or robotic device able to detect movement errors and propose corrections would be of great help. This paper addresses the problem of how to detect such execution errors and how to provide feedback to the human to correct his/her motor skill using a general, principled methodology based on imitation learning. The core idea is to compare the observed skill with a probabilistic model learned from expert demonstrations. The intensity of the feedback is regulated by the likelihood of the model given the observed skill. Based on demonstrations, our system can, for example, detect errors in the writing of characters with multiple strokes. Moreover, by using a haptic device, the Haption Virtuose 6D, we demonstrate a method to generate haptic feedback based on a distribution over trajectories, which could be used as an auxiliary means of communication between an instructor and an apprentice. Additionally, given a performance measurement, the haptic device can help the human discover and perform better movements to solve a given task. In this case, the human first tries a few times to solve the task without assistance. Our framework, in turn, uses a reinforcement learning algorithm to compute haptic feedback, which guides the human toward better solutions.
Achieving Secure and Privacy-Preserving Incentive in Vehicular Cloud Advertisement Dissemination

The recent research trend of extending cloud computing to vehicles by leveraging under-utilized on-board capabilities [also known as vehicular cloud (VC)] is unsurprising, partly due to the increasing popularity of intelligent vehicles (with on-board computing equipment) and cloud computing. However, VC deployment is still challenging: how to securely verify responding vehicles, how to recruit competent vehicles, and how to achieve privacy preservation, are among the remaining research challenges. In this paper, we propose a secure and privacy-preserving incentive mechanism under the vehicular advertisement dissemination setting, which enables vehicles to opportunistically perform on-demand advertisement dissemination tasks and (financially) benefit from the completed task. Specifically, the road side unit (RSU) representing the advertisement dissemination server first announces a dissemination task, and performs the privacy-preserving access control of the responding vehicles. Then the RSU selects the participating vehicles among a pool of verified competent vehicles, and acknowledges the participation of selected vehicles with a secure secret sharing scheme; meanwhile, the selected vehicles performs the dissemination task with incentives. Finally, we prove the security of the proposed scheme in terms of secure access control and privacy preservation, and demonstrate its efficiency via simulation results (i.e., improves the amount of advertisement dissemination and brings incentives to participating vehicles).
Dysphoria, dopaminergic medication and spatial memory in Parkinsons disease

Depression is considered a potential risk factor for developing cognitive deficits and dementia during the progression of Parkinson’s disease (PD). Depression and dysthymia are also among the most frequent neuropsychiatric comorbidities, especially in later stages of PD. Regardless of the depression diagnosis, clinical and subsyndromal depressive symptoms in PD patients are associated with decreased daily functioning. However, very little is known about subsyndromal depressive symptoms and their relations to cognitive function and dopamine medication in PD. Here we investigated depressive symptoms and spatial memory performance in 34 early PD patients compared to 36 matched healthy controls in a pharmacobehavioral cross-over study.
Multi-objective job shop scheduling using i-NSGA-III

The complexity of job shop scheduling problems is related to many factors, such as a large number of jobs, the number of objectives and constraints. Evolutionary algorithms are a natural fit to search for the optimum schedules in complex job shop scheduling problems with multiple objectives. This paper extends the authors' i-NSGA-In algorithm to tackle a manufacturing job shop scheduling problem with multiple objectives. One of the complex objectives is to pair jobs with similar properties to increase the overall cost savings. The genetic operators in i-NSGA-III are replaced with novel problem-specific crossover and mutation operators. The proposed approach is validated by comparing against the enumeration technique for problems with 5 to 10 jobs. Unlike the enumeration technique, the proposed methodology shows competence in terms of computation time and ability to schedule a large number of jobs with a high number of objectives. Further comparisons with NSGA-III demonstrate the superiority of i-NSGA-III for problems with 30, 40, and 50 jobs.
Prevalence of Some Factors Associated to Metabolic Syndrome at Mexico Hospital

Objective: To determine associated factors to metabolic syndrome in the populations that partici-pated in the Mexico hospital health fair in February 2007. Materials and Methods: A retrospective, descriptive transverse and analytic study of the population interviewed during the Health Fair held from February 19 to 22. A descriptive analysis was done as well as a single-variable and mul-tivariate analysis of the main variables. Results: San Jose Province reported a higher percentage of cases with 40.7%, followed by Heredia with 26.0% and Alajuela with 24.9%. In the case of Lemon, the percentage was 0.4% only. Most of the interviewed ones are Costa Ricans, representing 94.0%. The female predominance was notorious with 72.6%. The single-variable and multivariate analysis presented an association among blood pressure, glucose, besides of the body mass index related to the metabolic syndrome. Conclusion: The blood pressure, the glucose, and the body mass index are to be considered as associated factors to metabolic syndrome.
Etoposide-induced DNA damage affects multiple cellular pathways in addition to DNA damage response

DNA damage response (DDR) coordinates lesion repair and checkpoint activation. DDR is intimately connected with transcription. However, the relationship between DDR and transcription has not been clearly established. We report here RNA-sequencing analyses of MCF7 cells containing double-strand breaks induced by etoposide. While etoposide does not apparently cause global changes in mRNA abundance, it altered some gene expression. At the setting of fold alteration ≥ 2 and false discovery rate (FDR) ≤ 0.001, FDR < 0.05, or p < 0.05, etoposide upregulated 96, 268, or 860 genes and downregulated 41, 133, or 503 genes in MCF7 cells. Among these differentially expressed genes (DEGs), the processes of biogenesis, metabolism, cell motility, signal transduction, and others were affected; the pathways of Ras GTPase activity, RNA binding, cytokine-mediated signaling, kinase regulatory activity, protein binding, and translation were upregulated, and those pathways related to coated vesicle, calmodulin binding, and microtubule-based movement were downregulated. We further identified RABL6, RFTN2, FAS-AS1, and TCEB3CL as new DDR-affected genes in MCF7 and T47D cells. By metabolic labelling using 4-thiouridine, we observed dynamic alterations in the transcription of these genes in etoposide-treated MCF7 and T47D cells. During 0-2 hour etoposide treatment, RABL6 transcription was robustly increased at 0.5 and 1 hour in MCF7 cells and at 2 hours in T47D cells, while FAS-AS1 transcription was dramatically and steadily elevated in both cell lines. Taken together, we demonstrate dynamic alterations in transcription and that these changes affect multiple cellular processes in etoposide-induced DDR.
Skilled or educated? Educational reforms, human capital and earnings

We use OECD-PIAAC data to estimate the earnings eff ects of education and numeracy skills. Our identification strategy is based instrumental varia bles exploiting differential exposure to educationa l reforms across birth cohorts and countries. We find that education has the strongest earnings effect. A one standard deviation increase in education rais es earnings by almost 20 percentage points (p.p.) –corresponding to a 6 p.p. per year of education—wh ich compares with a 11 p.p. return to an equivalent increase in numeracy skills. Also, most of the endogeneity of skills appears to reflect the endogeneity in education, suggesting that it is the same set of unobservables that favours human capital accumulation in both dimensions. OLS estima tes underestimate returns to human capital, consistent with the idea that educational reforms f avour the human capital acquisition of able children from disadvantaged parental background. Wh en we look at the effects at the tails of the earnings distribution, we show that while skills co mpress earnings from the bottom and are therefore inequality-reducing, returns to education are inequality-enhancing as their effects is concentrated in the top quartile of the earnings di stribution.