Miracle-dz/UniFAR-FTUs huggingface dataset:
Biotic and Abiotic Factors Controlling Respiration Rates of Above- and Belowground Woody Debris of Fagus crenata and Quercus crispula in Japan

As a large, long-term pool and source of carbon and nutrients, woody litter is an important component of forest ecosystems. The objective of this study was to estimate the effect of the factors that regulate the rate of decomposition of coarse and fine woody debris (CFWD) of dominant tree species in a cool-temperate forest in Japan. Respiration rates of dead stems, branches, and coarse and fine roots of Fagus crenata and Quercus crispula felled 4 years prior obtained in situ ranged from 20.9 to 500.1 mg CO2 [kg dry wood]–1 h–1 in a one-time measurement in summer. Respiration rate had a significant negative relationship with diameter; in particular, that of a sample of Q. crispula with a diameter of >15 cm and substantial heartwood was low. It also had a significant positive relationship with moisture content. The explanatory variables diameter, [N], wood density, and moisture content were interrelated. The most parsimonious path model showed 14 significant correlations among 8 factors and respiration. Diameter and [C] had large negative direct effects on CFWD respiration rate, and moisture content and species had medium positive direct effects. [N] and temperature did not have direct or indirect effects, and position and wood density had indirect effects. The model revealed some interrelationships between controlling factors. We discussed the influence of the direct effects of explanatory variables and the influence especially of species and position. We speculate that the small R 2 value of the most parsimonious model was probably due to the omission of microbial biomass and activity. These direct and indirect effects and interrelationships between explanatory variables could be used to develop a process-based CFWD decomposition model.
A Time Domain Tracking Method for Limited-Rate Feedback Precoding in MIMO Systems

In this paper, a novel time domain tracking approach with limited-rate feedback is proposed. It can reduce the feedback rate by tracking the precoding matrices on Grassmann manifold according to the time domain correlation feature of channel fading. And the main idea of the proposed approach is to adjust the feedback overhead to the varying of channel fading adaptively. It makes sure that the optimal precoding matrices can be chosen by the precoder. Therefore, the degradation of performance can be neglected. The contribution of this paper is to propose an adaptive tracking algorithm on time domain. Compared with the limited feedback precoding, the proposed algorithm can reduce the feedback overhead to the half. In this paper, auto-regressive model is used for temporal correlative channel. Moreover, the numerical simulation results have been demonstrated and some critical factors have been analyzed.
Posterior dopamine D2/3 receptors and brain network functional connectivity

Recent studies suggest that dopaminergic tone influences resting state activity in multiple brain networks. Although dopamine receptors and transporters have been identified in the posteromedial and parietal cortices, which are linked to functional networks such as the default mode network (DMN), the relationship between dopamine receptor distribution in these posterior regions and resting‐state connectivity has yet to be explored. Here, we used a multi‐modal neuroimaging strategy, combining resting‐state functional magnetic resonance imaging (rsfMRI) and [18 F]‐fallypride high‐resolution positron emission tomography (PET), to examine the association between within‐network functional connectivity and the dopamine D2/3 receptor distribution in the posterior portion of the brain in 13 healthy adults. Our results indicate that the posterior distribution of D2/3 receptors coincides primarily with the posterior portion of the DMN. Furthermore, in the posterior portion of the brain, the level of [18 F]‐fallypride binding in the posteromedial cortex correlated positively with the functional connectivity strength of the DMN and sensorimotor network, and negatively with the functional connectivity strength of the dorsal attention network, the salience network, and a network that included the anterior part of the temporo‐parietal junction. On the basis of these findings, we propose that posterior brain dopamine influences the configuration of the posterior DMN and several other functional brain networks. The posterior distribution of D2/3 receptors binding (hot colour spectrum) coincides with the functional connectivity of the posterior portion of the default mode network (green colour spectrum). The mean BPND in a posteromedial cortex and the mean ICA‐Z score in the precuneus showed significant positive correlation.
Motor-commands decoding using peripheral nerve signals: a review

During the last few decades, substantial scientific and technological efforts have been focused on the development of neuroprostheses. The major emphasis has been on techniques for connecting the human nervous system with a robotic prosthesis via natural-feeling interfaces. The peripheral nerves provide access to highly processed and segregated neural command signals from the brain that can in principle be used to determine user intent and control muscles. If these signals could be used, they might allow near-natural and intuitive control of prosthetic limbs with multiple degrees of freedom. This review summarizes the history of neuroprosthetic interfaces and their ability to record from and stimulate peripheral nerves. We also discuss the types of interfaces available and their applications, the kinds of peripheral nerve signals that are used, and the algorithms used to decode them. Finally, we explore the prospects for future development in this area.
Resisting Skew-Accumulation for Time-Stepped Applications in the Cloud via Exploiting Parallelism

Time-stepped applications are pervasive in scientific computing domain but perform poorly in the cloud because these applications execute in discrete time-step or tick and use logical synchronization barriers at tick boundaries to ensure correctness. As a result, the accumulated computational skew and communication skew that were unsolved in each tick can slow down time-stepped applications significantly. However, the existing solutions have focused only on the skew in each tick and thus cannot resist the accumulation of skew. To fill in this gap, an efficient approach to resisting the accumulation of skew is proposed in this paper via fully exploiting parallelism among ticks. This new approach allows the user to decompose much computational part (also called asynchronous part) of the processing for an object, into several asynchronous sub-processes which are dependent on one data object. Each sub-process from different ticks can then proceed in advance using the idle time whenever the needed data object is available, redressing the negative effects caused by accumulated unsolved computational and communication skew. To efficiently support such an approach, a data-centric programming model and also a runtime system, namely AsyTick, coupled with an ad hoc scheduler are developed. Experimental results show that the proposed approach can improve the performance of time-stepped applications over a state-of-the-art computational skew-resistant approach up to 2.53 times.
DepQBF: A Dependency-Aware QBF Solver

We present DepQBF 0.1, a new search-based solver for quantified boolean formulae (QBF). It integrates compact dependency graphs to overcome the restrictions imposed by linear quantifier prefixes of QBFs in prenex conjunctive normal form (PCNF). DepQBF 0.1 was placed first in the main track of QBFEVAL’10 in a score-based ranking. We provide a general system overview and describe selected orthogonal features such as restarts and removal of learnt constraints.
A thermal resilient integration of many-core microprocessors and main memory by 2.5D TSI I/Os

One memory-logic-integration design platform is developed in this paper with thermal reliability analysis provided for 2.5D through-silicon-interposer (TSI) and 3D through-silicon-via (TSV) based integrations. Temperature-dependent delay and power models have been developed at microarchitecture level for 2.5D and 3D integrations of many-core microprocessors and main memory, respectively. Experiments are performed by general-purpose benchmarks from SPEC CPU2006 and also cloud-oriented benchmarks from Phoenix with the following observations. The memory-logic integration by 3D RC-interconnected TSV I/Os can result in thermal runaway failures due to strong electrical-thermal couplings. On the other hand, the one by 2.5D transmission-line-interconnected TSI I/Os has shown almost the same energy efficiency and better thermal resilience.
Convolutional 2D LDA for Nonlinear Dimensionality Reduction

Representing high-volume and high-order data is an essential problem, especially in machine learning field. Although existing two-dimensional (2D) discriminant analysis achieves promising performance, the single and linear projection features make it difficult to analyze more complex data. In this paper, we propose a novel convolutional two-dimensional linear discriminant analysis (2D LDA) method for data representation. In order to deal with nonlinear data, a specially designed Convolutional Neural Networks (CNN) is presented, which can be proved having the equivalent objective function with common 2D LDA. In this way, the discriminant ability can benefit from not only the nonlinearity of Convolutional Neural Networks, but also the powerful learning process. Experiment results on several datasets show that the proposed method performs better than other state-of-the-art methods in terms of classification accuracy.
Asymmetric cross-view dictionary learning for person re-identification

Person re-identification is a critical yet challenging task in video surveillance which intends to match people over non-overlapping cameras. Most metric learning algorithms for person re-identification use symmetric matrix to project feature vectors into the same subspace to compute the similarity while ignoring the discrepancy between views. To solve this problem, we proposed an asymmetric cross-view matching algorithm with dictionary learning to alleviate the variations in human appearance across different views. Not only the views' dictionaries but also the persons' dictionary codes are constrained. Moreover, the ‘between-class’ and the ‘within-class’ distance are taken into consideration which makes the forming dictionary codes more robust and discriminative than the original feature vectors. The effectiveness of our approach is validated on the VIPeR and CUHK01 datasets. Experimental results show the proposed algorithm achieves compelling performance and asymmetric model plays an important role in the proposed approach.
Typing Textual Entities and M2T/T2M Transformations in a Model Management Environment

Global Model Management (GMM) is a model-based approach for managing large sets of interrelated heterogeneous and complex MDE artifacts. Such artifacts are usually represented as models, however as many Domain Specific Languages have a textual concrete syntax, GMM also supports textual entities and model-to-text/text-to-model transformations which are projectors that bridge the MDE technical space and the Grammarware technical space. As the transformations supported by GMM are executable artifacts, typing is critical for preventing type errors during execution. We proposed the cGMM calculus which formalizes the notion of typing in GMM. In this work, we extend cGMM with new types and rules for supporting textual entities and projectors. With such an extension, those artifacts may participate in transformation compositions addressing larger transformation problems. We illustrate the new constructs in the context of an interoperability case study.
Duality codes and the integrality gap bound for index coding

This paper considers a base station that delivers packets to multiple receivers through a sequence of coded transmissions. All receivers overhear the same transmissions. Each receiver may already have some of the packets as side information, and requests another subset of the packets. This problem is known as the index coding problem and can be represented by a bipartite digraph. An integer linear program is developed that provides a lower bound on the minimum number of transmissions required for any coding algorithm. Conversely, its linear programming relaxation is shown to provide an upper bound that is achievable by a simple form of vector linear coding. Thus, the information theoretic optimum is bounded by the integrality gap between the integer program and its linear relaxation. In the special case when the digraph has a planar structure, the integrality gap is shown to be zero, so that exact optimality is achieved. Finally, for non-planar problems, an enhanced integer program is constructed that provides a smaller integrality gap. The dual of this problem corresponds to a more sophisticated partial clique coding strategy that time-shares between Reed-Solomon erasure codes. This work illuminates the relationship between index coding, duality, and integrality gaps between integer programs and their linear relaxations.
Voluntary Modulation of Hemodynamic Responses in Swallowing Related Motor Areas: A Near-Infrared Spectroscopy-Based Neurofeedback Study

In the present study, we show for the first time that motor imagery of swallowing, which is defined as the mental imagination of a specific motor act without overt movements by muscular activity, can be successfully used as mental strategy in a neurofeedback training paradigm. Furthermore, we demonstrate its effects on cortical correlates of swallowing function. Therefore, N = 20 healthy young adults were trained to voluntarily increase their hemodynamic response in swallowing related brain areas as assessed with near-infrared spectroscopy (NIRS). During seven training sessions, participants received either feedback of concentration changes in oxygenated hemoglobin (oxy-Hb group, N = 10) or deoxygenated hemoglobin (deoxy-Hb group, N = 10) over the inferior frontal gyrus (IFG) during motor imagery of swallowing. Before and after the training, we assessed cortical activation patterns during motor execution and imagery of swallowing. The deoxy-Hb group was able to voluntarily increase deoxy-Hb over the IFG during imagery of swallowing. Furthermore, swallowing related cortical activation patterns were more pronounced during motor execution and imagery after the training compared to the pre-test, indicating cortical reorganization due to neurofeedback training. The oxy-Hb group could neither control oxy-Hb during neurofeedback training nor showed any cortical changes. Hence, successful modulation of deoxy-Hb over swallowing related brain areas led to cortical reorganization and might be useful for future treatments of swallowing dysfunction.
Numerical biaxial tensile test for sheet metal forming simulation of aluminium alloy sheets based on the homogenized crystal plasticity finite element method

The simulation of the stretch forming of A5182-O aluminum alloy sheet with a spherical punch is performed using the crystal plasticity (CP) finite element method based on the mathematical homogenization theory. In the simulation, the CP constitutive equations and their parameters calibrated by the numerical and experimental biaxial tensile tests with a cruciform specimen are used. The results demonstrate that the variation of the sheet thickness distribution simulated show a relatively good agreement with the experimental results.
Toward dynamic lumbar puncture guidance using needle-based single-element ultrasound imaging

Abstract. Lumbar punctures (LPs) are interventional procedures that are used to collect cerebrospinal fluid. Since the target window is small, physicians have limited success conducting the procedure. The procedure is especially difficult for obese patients due to the increased distance between bone and skin surface. We propose a simple and direct needle insertion platform, enabling image formation by sweeping a needle with a single ultrasound element at the tip. The needle-shaped ultrasound transducer can not only sense the distance between the tip and a potential obstacle, such as bone, but also visually locate the structures by combining transducer location tracking and synthetic aperture focusing. The concept of the system was validated through a simulation that revealed robust image reconstruction under expected errors in tip localization. The initial prototype was built into a 14 G needle and was mounted on a holster equipped with a rotation shaft allowing one degree-of-freedom rotational sweeping and a rotation tracking encoder. We experimentally evaluated the system using a metal-wire phantom mimicking high reflection bone structures and human spinal bone phantom. Images of the phantoms were reconstructed, and the synthetic aperture reconstruction improved the image quality. These results demonstrate the potential of the system to be used as a real-time guidance tool for improving LPs.
Low-Complexity and High-Resolution DOA Estimation for Hybrid Analog and Digital Massive MIMO Receive Array

A large-scale fully digital receive antenna array can provide very high-resolution direction of arrival (DOA) estimation, but resulting in a significantly high RF-chain circuit cost. Thus, a hybrid analog and digital (HAD) structure is preferred. Two phase alignment (PA) methods, HAD PA (HADPA) and hybrid digital and analog PA (HDAPA), are proposed to estimate DOA based on the parametric method. Compared to analog PA (APA), they can significantly reduce the complexity in the PA phases. Subsequently, a fast root multiple signal classification HDAPA (root-MUSIC-HDAPA) method is proposed specially for this hybrid structure to implement an approximately analytical solution. Due to the HAD structure, there exists the effect of direction-finding ambiguity. A smart strategy of maximizing the average receive power is adopted to delete those spurious solutions and preserve the true optimal solution by linear searching over a set of limited finite candidate directions. This results in a significant reduction in computational complexity. Eventually, the Cramer–Rao lower bound (CRLB) of finding emitter direction using the HAD structure is derived. Simulation results show that our proposed methods, root-MUSIC-HDAPA and HDAPA, can achieve the hybrid CRLB with their complexities being significantly lower than those of pure linear searching-based methods, such as APA.
Detection and activity of 11 beta hydroxylase (CYP11B1) in the bovine ovary.

Glucocorticoids (GCs) such as cortisol and corticosterone are important steroid hormones with different functions in intermediate metabolism, development, cell differentiation, immune response and reproduction. In response to physiological and immunological stress, adrenocorticotropic hormone (ACTH) acts on the adrenal gland by stimulating the synthesis and secretion of GCs. However, there is increasing evidence that GCs may also be synthesized by extra-adrenal tissues. Here, we examined the gene and protein expression of the enzyme 11β-hydroxylase P450c11 (CYP11B1), involved in the conversion of 11-deoxycortisol to cortisol, in the different components of the bovine ovary and determined the functionality of CYP11B1 in vitro CYP11B1 mRNA was expressed in granulosa and theca cells in small, medium and large antral ovarian follicles, and CYP11B1 protein was expressed in medium and large antral follicles. After stimulation by ACTH, we observed an increased secretion of cortisol by the wall of large antral follicles. We also observed a concentration-dependent decrease in the concentration of cortisol in response to metyrapone, an inhibitor of CYP11B1. This decrease was significant at 10-5 µM metyrapone. In conclusion, this study demonstrated for the first time the presence of CYP11B1 in the bovine ovary. This confirms that there could be a local synthesis of GCs in the bovine ovary and therefore a potential endocrine responder to stress through these hormones.
Agile Workflows for Climate Impact Risk Assessment based on the ci:grasp Platform and the jABC Modeling Framework

: Analyzing and assessing potential impacts of climate change are critical and challenging tasks that require the processing of large and heterogeneous datasets. These analyses are particularly demanding because of the multi-scale and multi-objective nature of environmental modeling for climate change impact assessment. The Climate Impacts: Global and Regional Adaptation Support Platform (ci:grasp) is a web-based climate information service for exploring climate change related information in its geographical context. In this paper, we show how the agile workﬂow development style supported by the jABC process modeling and execution framework permits us to leverage the processes implemented in the ci:grasp platform to a user-accessible level, and thus to enable users to easily deﬁne and perform multi-objective workﬂows tailored to their speciﬁc needs. Concretely, we discuss how we decomposed the ci:grasp’s original assessment of the impact of sea level rise into basic computational services, which we then used as workﬂow building blocks. Together with additional functionality from the jABC, we built a ﬂexible library of directly executable workﬂows for this type of analysis that is especially suitable to increase the productivity of researchers working on the analysis processes.
Multiplexed Elimination of Wild-Type DNA and High-Resolution Melting Prior to Targeted Resequencing of Liquid Biopsies.

BACKGROUND The use of clinical samples and circulating cell-free DNA (cfDNA) collected from liquid biopsies for diagnostic and prognostic applications in cancer is burgeoning, and improved methods that reduce the influence of excess wild-type (WT) portion of the sample are desirable. Here we present enrichment of mutation-containing sequences using enzymatic degradation of WT DNA. Mutation enrichment is combined with high-resolution melting (HRM) performed in multiplexed closed-tube reactions as a rapid, cost-effective screening tool before targeted resequencing. METHODS We developed a homogeneous, closed-tube approach to use a double-stranded DNA-specific nuclease for degradation of WT DNA at multiple targets simultaneously. The No Denaturation Nuclease-assisted Minor Allele Enrichment with Probe Overlap (ND-NaME-PrO) uses WT oligonucleotides overlapping both strands on putative DNA targets. Under conditions of partial denaturation (DNA breathing), the oligonucleotide probes enhance double-stranded DNA-specific nuclease digestion at the selected targets, with high preference toward WT over mutant DNA. To validate ND-NaME-PrO, we used multiplexed HRM, digital PCR, and MiSeq targeted resequencing of mutated genomic DNA and cfDNA. RESULTS Serial dilution of KRAS mutation-containing DNA shows mutation enrichment by 10- to 120-fold and detection of allelic fractions down to 0.01%. Multiplexed ND-NaME-PrO combined with multiplexed PCR-HRM showed mutation scanning of 10-20 DNA amplicons simultaneously. ND-NaME-PrO applied on cfDNA from clinical samples enables mutation enrichment and HRM scanning over 10 DNA targets. cfDNA mutations were enriched up to approximately 100-fold (average approximately 25-fold) and identified via targeted resequencing. CONCLUSIONS Closed-tube homogeneous ND-NaME-PrO combined with multiplexed HRM is a convenient approach to efficiently enrich for mutations on multiple DNA targets and to enable prescreening before targeted resequencing.