nils-herrmann/clean_scirepeval huggingface dataset:
Factors and Processes Shaping Land Cover and Land Cover Changes Along the Wisconsin River

Land use can exert a powerful influence on ecological systems, yet our understanding of the natural and social factors that influence land use and land-cover change is incomplete. We studied land-cover change in an area of about 8800 km2 along the lower part of the Wisconsin River, a landscape largely dominated by agriculture. Our goals were (a) to quantify changes in land cover between 1938 and 1992, (b) to evaluate the influence of abiotic and socioeconomic variables on land cover in 1938 and 1992, and (c) to characterize the major processes of land-cover change between these two points in time. The results showed a general shift from agricultural land to forest. Cropland declined from covering 44% to 32% of the study area, while forests and grassland both increased (from 32% to 38% and from 10% to 14% respectively). Multiple linear regressions using three abiotic and two socioeconomic variables captured 6% to 36% of the variation in land-cover categories in 1938 and 9% to 46% of the variation in 1992. Including socioeconomic variables always increased model performance. Agricultural abandonment and a general decline in farming intensity were the most important processes of land-cover change among the processes considered. Areas characterized by the different processes of land-cover change differed in the abiotic and socioeconomic variables that had explanatory power and can be distinguished spatially. Understanding the dynamics of landscapes dominated by human impacts requires methods to incorporate socioeconomic variables and anthropogenic processes in the analyses. Our method of hypothesizing and testing major anthropogenic processes may be a useful tool for studying the dynamics of cultural landscapes.
Chemopreventive effect of raw and cooked lentils (Lens culinaris L) and soybeans (Glycine max) against azoxymethane-induced aberrant crypt foci.

Although lentils (Lens culinaris L) contain several bioactive compounds that have been linked to the prevention of cancer, the in vivo chemopreventive ability of lentils against chemically induced colorectal cancer has not been examined. Our present study examined the hypothesis that lentils could suppress the early carcinogenesis in vivo by virtue of their bioactive micro- and macroconstituents and that culinary thermal treatment could affect their chemopreventive potential. To accomplish this goal, we used raw whole lentils (RWL), raw split lentils (RSL), cooked whole lentils (CWL), and cooked split lentils (CSL). Raw soybeans (RSB; Glycine max) were used for the purpose of comparison with a well-studied chemopreventive agent. Sixty weanling Fischer 344 male rats, 4 to 5 weeks of age, were randomly assigned to 6 groups (10 rats/group): the control group (C) received AIN-93G diet, and treatment leguminous groups of RWL, CWL, RSL, CSL, and RSB received the treatment diets containing AIN-93G+5% of the above-mentioned legumes. After acclimatization for 1 week (at 5th to 6th week of age), all animals were put on the control and treatment diets separately for 5 weeks (from 6th to 11th week of age). At the end of the 5th week of feeding (end of 11th week of age), all rats received 2 subcutaneous injections of azoxymethane carcinogen at 15 mg/kg rat body weight per dose once a week for 2 consecutive weeks. After 17 weeks of the last azoxymethane injection (from 12th to 29th week of age), all rats were euthanized. Chemopreventive ability was assessed using colonic aberrant crypt foci and activity of hepatic glutathione-S-transferases. Significant reductions (P < .05) were found in total aberrant crypt foci number (mean +/- SEM) for RSB (27.33 +/- 4.32), CWL (33.44 +/- 4.56), and RSL (37.00 +/- 6.02) in comparison with the C group (58.33 +/- 8.46). Hepatic glutathione-S-transferases activities increased significantly (P < .05) in rats fed all treatment diets (from 51.38 +/- 3.66 to 67.94 +/- 2.01 micromol mg(-1) min(-1)) when compared with control (C) diet (26.13 +/- 1.01 micromol mg(-1) min(-1)). Our findings indicate that consumption of lentils might be protective against colon carcinogenesis and that hydrothermal treatment resulted in an improvement in the chemopreventive potential for the whole lentils.
Pericyclic reactions catalyzed by chorismate-utilizing enzymes.

One of the fundamental questions of enzymology is how catalytic power is derived. This review focuses on recent developments in the structure--function relationships of chorismate-utilizing enzymes involved in siderophore biosynthesis to provide insight into the biocatalysis of pericyclic reactions. Specifically, salicylate synthesis by the two-enzyme pathway in Pseudomonas aeruginosa is examined. The isochorismate-pyruvate lyase is discussed in the context of its homologues, the chorismate mutases, and the isochorismate synthase is compared to its homologues in the MST family (menaquinone, siderophore, or tryptophan biosynthesis) of enzymes. The tentative conclusion is that the activities observed cannot be reconciled by inspection of the active site participants alone. Instead, individual activities must arise from unique dynamic properties of each enzyme that are tuned to promote specific chemistries.
Policy Shaping and Generalized Update Equations for Semantic Parsing from Denotations

Semantic parsing from denotations faces two key challenges in model training: (1) given only the denotations (e.g., answers), search for good candidate semantic parses, and (2) choose the best model update algorithm. We propose effective and general solutions to each of them. Using policy shaping, we bias the search procedure towards semantic parses that are more compatible to the text, which provide better supervision signals for training. In addition, we propose an update equation that generalizes three different families of learning algorithms, which enables fast model exploration. When experimented on a recently proposed sequential question answering dataset, our framework leads to a new state-of-theart model that outperforms previous work by 5.0% absolute on exact match accuracy.
Lifted Relational Variational Inference

Hybrid continuous-discrete models naturally represent many real-world applications in robotics, finance, and environmental engineering. Inference with large-scale models is challenging because relational structures deteriorate rapidly during inference with observations. The main contribution of this paper is an efficient relational variational inference algorithm that factors largescale probability models into simpler variational models, composed of mixtures of iid (Bernoulli) random variables. The algorithm takes probability relational models of largescale hybrid systems and converts them to a close-to-optimal variational models. Then, it efficiently calculates marginal probabilities on the variational models by using a latent (or lifted) variable elimination or a lifted stochastic sampling. This inference is unique because it maintains the relational structure upon individual observations and during inference steps.
The contribution of attentional bias to worry: Distinguishing the roles of selective engagement and disengagement

This study investigated the effect on worry of biased attentional engagement and disengagement. Variants of a novel attention modification paradigm were developed, designed to induce a group difference either in participants' tendency to selectively engage with, or disengage from, threatening meanings. An index of threat bias, reflecting relative speeding to process threat word compared to non-threat word content, confirmed that both procedures were effective in inducing differential attentional bias. Importantly, when the induced group difference in attentional bias followed the procedure designed to influence selective engagement with threat meanings, it also gave rise to a corresponding group difference in worry. This was not the case when it was induced by the procedure designed to influence selective disengagement from threat meanings. These findings suggest that facilitated attentional engagement with threat meanings may causally contribute to variability in worry.
A Phylogenetic Analysis of the Brassicales Clade Based on an Alignment-Free Sequence Comparison Method

Phylogenetic analyses reveal the evolutionary derivation of species. A phylogenetic tree can be inferred from multiple sequence alignments of proteins or genes. The alignment of whole genome sequences of higher eukaryotes is a computational intensive and ambitious task as is the computation of phylogenetic trees based on these alignments. To overcome these limitations, we here used an alignment-free method to compare genomes of the Brassicales clade. For each nucleotide sequence a Chaos Game Representation (CGR) can be computed, which represents each nucleotide of the sequence as a point in a square defined by the four nucleotides as vertices. Each CGR is therefore a unique fingerprint of the underlying sequence. If the CGRs are divided by grid lines each grid square denotes the occurrence of oligonucleotides of a specific length in the sequence (Frequency Chaos Game Representation, FCGR). Here, we used distance measures between FCGRs to infer phylogenetic trees of Brassicales species. Three types of data were analyzed because of their different characteristics: (A) Whole genome assemblies as far as available for species belonging to the Malvidae taxon. (B) EST data of species of the Brassicales clade.
Brain computer interface systems using non-invasive electroencephalogram signal: A literature review

Systems based on Brain Computer Interface (BCI) have been developed from the past three decades for assisting locked-in state patients. Researchers across the globe are developing new techniques to increase the BCI accuracy. In 1924 Dr. Hans Berger recorded the first EEG signal. The number of experimental measurements of brain activity has been done using human control commands. The main function of BCI is to convert and transmit human intentions into appropriate motion commands for the wheelchairs, robots, devices, and so forth. BCI allows improving the quality of life of disabled patients and letting them interact with their environment. Since the BCI signals are non-stationary, the main challenges in the non-invasive BCI system are to accurately detect and classify the signals. This paper reviews the State of Art of BCI and techniques used for feature extraction and classification using electroencephalogram (EEG) and highlights the need of adaptation concept.
Multi-modal and Multi-spectral Registration for Natural Images

Images now come in different forms – color, near-infrared, depth, etc. – due to the development of special and powerful cameras in computer vision and computational photography. Their cross-modal correspondence establishment is however left behind. We address this challenging dense matching problem considering structure variation possibly existing in these image sets and introduce new model and solution. Our main contribution includes designing the descriptor named robust selective normalized cross correlation (RSNCC) to establish dense pixel correspondence in input images and proposing its mathematical parameterization to make optimization tractable. A computationally robust framework including global and local matching phases is also established. We build a multi-modal dataset including natural images with labeled sparse correspondence. Our method will benefit image and vision applications that require accurate image alignment.
Light guiding in biological tissue due to scattering.

For a description of light propagation in biological tissue it is usually assumed that tissue is a random medium. We report a pronounced light guiding effect in cubes of human dentin that cannot be described by this standard model. Monte Carlo simulations which consider the microstructure of dentin are performed and successfully compared to experiments. Contrary to explanations so far, we show that light guiding is due to scattering by the tissue's microstructure. Exploiting this concept, light can be guided in arbitrary directions or locations without involving reflections or wave effects.
A new age in AquaMedicine: unconventional approach in studying aquatic diseases

Marine and aquaculture industries are important sectors of the food production and global trade. Unfortunately, the fish food industry is challenged with a plethora of infectious pathogens. The freshwater and marine fish communities are rapidly incorporating novel and most up to date techniques for detection, characterization and treatment strategies. Rapid detection of infectious diseases is important in preventing large disease outbreaks.


MAIN TEXT
One hundred forty-six articles including reviews papers were analyzed and their conclusions evaluated in the present paper. This allowed us to describe the most recent development research regarding the control of diseases in the aquatic environment as well as promising avenues that may result in beneficial developments. For the characterization of diseases, traditional sequencing and histological based methods have been augmented with transcriptional and proteomic studies. Recent studies have demonstrated that transcriptional based approaches using qPCR are often synergistic to expression based studies that rely on proteomic-based techniques to better understand pathogen-host interactions. Preventative therapies that rely on prophylactics such as vaccination with protein antigens or attenuated viruses are not always feasible and therefore, the development of therapies based on small nucleotide based medicine is on the horizon. Of those, RNAi or CRISPR/Cas- based therapies show great promise in combating various types of diseases caused by viral and parasitic agents that effect aquatic and fish medicine.



In our modern times, when the marine industry has become so vital for feed and economic stability, even the most extreme alternative treatment strategies such as the use of small molecules or even the use of disease to control invasive species populations should be considered.
Microwave mixers based on a novel zero bias diode

A Ku-band mixer based on a zero bias diode (ZBD) and its application is described. The diode can easily be implemented in an MMIC technology if enhancement FET or HEMT devices are available in the MMIC-process. It was experimentally verified that a star mixer realized with zero bias diodes instead of ordinary gate-Schottky diodes requires less LO-power and result in lower conversion loss. The specific application described is intended for space use and the reliability of the mixer is expected to be improved due to a smaller gate current.
Thermal analysis of a segmented stator winding design

This paper presents a thermal analysis of a segmented stator winding design. As the thermal performance is one of the main factors limiting a machine's output capability, a thermal test on a complete prototype machine is an essential part of the design process. However, for the segmented stator winding design a test-informed thermal analysis on a single stator tooth can be performed prior to the manufacture of the full machine. This approach allows for a rapid and inexpensive assessment of the thermal performance of the complete machine and early identification of design modifications needed. The research has been applied to the design of a highly efficient and compact permanent magnet (PM) traction motor. A thermal model for a single tooth was developed and supported by tests to identify key heat transfer coefficients. A number of winding assemblies were compared and the most promising was selected for the final motor prototype. The results from the approach are compared with thermal test results from the complete machine.
Understanding Representations and Reducing their Redundancy in Deep Networks

Neural networks in their modern deep learning incarnation have achieved state of the art performance on a wide variety of tasks and domains. A core intuition behind these methods is that they learn layers of features which interpolate between two domains in a series of related parts. The first part of this thesis introduces the building blocks of neural networks for computer vision. It starts with linear models then proceeds to deep multilayer perceptrons and convolutional neural networks, presenting the core details of each. However, the introduction also focuses on intuition by visualizing concrete examples of the parts of a modern network. The second part of this thesis investigates regularization of neural networks. Methods like dropout and others have been proposed to favor certain (empirically better) solutions over others. However, big deep neural networks still overfit very easily. This section proposes a new regularizer called DeCov, which leads to significantly reduced overfitting (difference between train and val performance) and greater generalization, sometimes better than dropout and other times not. The regularizer is based on the cross-covariance of hidden representations and takes advantage of the intuition that different features should try to represent different things, an intuition others have explored with similar losses. Experiments across a range of datasets and network architectures demonstrate reduced overfitting due to DeCov while almost always maintaining or increasing generalization performance and often improving performance over dropout.
Identification of a rice APETALA3 homologue by yeast two-hybrid screening

A cDNA clone OsMADS16 was isolated from the rice young inflorescence cDNA expression library by the yeast two-hybrid screening method with OsMADS4 as bait. We have previously shown that the OsMADS4 gene is a member of the PI family and that the MADS-box gene is involved in controlling development of the second and third whorls of rice flowers. The sequence comparison indicated that OsMADS16 belongs to the AP3 family. The OsMADS16 protein contains a PI-derived motif, FAFRVVPSQPNLH, that is a conserved sequence in AP3 family genes at the C-terminal region. In addition, OsMADS16 contains a paleoAP3 motif, YGGNHDLRLG, downstream of the PI-derived motif. The paleoAP3 motif is a consensus sequence in the C-terminal region of the AP3 family genes of lower eudicot and magnolid dicot species. RNA blot analysis showed that the OsMADS16 gene was expressed in the second and third whorls, whereas the OsMADS4 transcripts were present in the second, third, and fourth whorls. These expression patterns of the OsMADS16 and OsMADS4 genes are very similar to those of AP3 and PI, respectively. In the yeast two-hybrid system, OsMADS4 interacted only with OsMADS16 among several rice MADS genes investigated, suggesting that OsMADS4 and OsMADS16 function as a heterodimer in specifying sepal and petal identities. The OsMADS16 protein displayed transcription activation ability in yeast, whereas AP3 did not. It was also shown in yeast that OsMADS16 interacted with PI whereas OsMADS4 did not interact with AP3. These differences between OsMADS16 and AP3 indicate that the functions of the AP3 family genes of monocots and dicots diverged during molecular evolution processes of the B function genes. Deletion analysis showed that the 155–200 amino acid region of the OsMADS16 protein plays an important role in the transcription activation ability.
Vertical Integration in Building Automation Systems

Building automation systems (BAS) are widely deployed in modern buildings. They are typically engineered adhering to the classical, hierarchical 3-layer model, which has served well in the past but is reaching its limits in complex BAS. The move to more integrated building services also requires tighter integration of the mostly heterogeneous technologies. Vertical integration promises seamless communication from the individual sensor up to IT systems. The hierarchy levels are eliminated in favor of a service-oriented model. Several approaches promise to accomplish this goal: integration of network infrastructure, convergence of different protocols, distribution of services and integration to IT systems.
The role of sirtuins in modulating redox stressors.

For much of the time since their discovery, the sirtuin family of deacetylase enzymes has been associated with extension of life span. This longevity-promoting capacity in numerous model systems has enabled the sirtuins to gain "celebrity status" in the field of aging research. However, the mechanisms underpinning these changes remain incompletely defined. A general phenotype long associated with aging is the dysregulation of biological systems, which partly occurs via the accumulation of damage over time. One of the major sources of this damage is oxidative stress, which can harm both biological structures and the mechanisms with which they are repaired. It is now becoming clear that the beneficial life-span effects of sirtuins, along with many of their other functions, are closely linked to their ability to regulate systems that control the redox environment. Here we investigate the links between sirtuins and their oxidative/redox environment and review the control mechanisms that are regulated by the activity of sirtuin deacetylase proteins.
STRUM: structure-based prediction of protein stability changes upon single-point mutation

Mutations in human genome are mainly through single nucleotide polymorphism, some of which can affect stability and function of proteins, causing human diseases. Several methods have been proposed to predict the effect of mutations on protein stability; but most require features from experimental structure. Given the fast progress in protein structure prediction, this work explores the possibility to improve the mutation-induced stability change prediction using low-resolution structure modeling.



We developed a new method (STRUM) for predicting stability change caused by single-point mutations. Starting from wild-type sequences, 3D models are constructed by the iterative threading assembly refinement (I-TASSER) simulations, where physics- and knowledge-based energy functions are derived on the I-TASSER models and used to train STRUM models through gradient boosting regression. STRUM was assessed by 5-fold cross validation on 3421 experimentally determined mutations from 150 proteins. The Pearson correlation coefficient (PCC) between predicted and measured changes of Gibbs free-energy gap, ΔΔG, upon mutation reaches 0.79 with a root-mean-square error 1.2 kcal/mol in the mutation-based cross-validations. The PCC reduces if separating training and test mutations from non-homologous proteins, which reflects inherent correlations in the current mutation sample. Nevertheless, the results significantly outperform other state-of-the-art methods, including those built on experimental protein structures. Detailed analyses show that the most sensitive features in STRUM are the physics-based energy terms on I-TASSER models and the conservation scores from multiple-threading template alignments. However, the ΔΔG prediction accuracy has only a marginal dependence on the accuracy of protein structure models as long as the global fold is correct. These data demonstrate the feasibility to use low-resolution structure modeling for high-accuracy stability change prediction upon point mutations.



http://zhanglab.ccmb.med.umich.edu/STRUM/ CONTACT: qiang@suda.edu.cn and zhng@umich.edu



Supplementary data are available at Bioinformatics online.