Miracle-dz/UniFAR-FTUs huggingface dataset:
The Rice α-Amylase Glycoprotein Is Targeted from the Golgi Apparatus through the Secretory Pathway to the Plastids[W][OA]

The well-characterized secretory glycoprotein, rice (Oryza sativa) α-amylase isoform I-1 (AmyI-1), was localized within the plastids and proved to be involved in the degradation of starch granules in the organelles of rice cells. In addition, a large portion of transiently expressed AmyI-1 fused to green fluorescent protein (AmyI-1-GFP) colocalized with a simultaneously expressed fluorescent plastid marker in onion (Allium cepa) epidermal cells. The plastid targeting of AmyI-1 was inhibited by both dominant-negative and constitutively active mutants of Arabidopsis thaliana ARF1 and Arabidopsis SAR1, which arrest endoplasmic reticulum-to-Golgi traffic. In cells expressing fluorescent trans-Golgi and plastid markers, these fluorescent markers frequently colocalized when coexpressed with AmyI-1. Three-dimensional time-lapse imaging and electron microscopy of high-pressure frozen/freeze-substituted cells demonstrated that contact of the Golgi-derived membrane vesicles with cargo and subsequent absorption into plastids occur within the cells. The transient expression of a series of C-terminal-truncated AmyI-1-GFP fusion proteins in the onion cell system showed that the region from Trp-301 to Gln-369 is necessary for plastid targeting of AmyI-1. Furthermore, the results obtained by site-directed mutations of Trp-302 and Gly-354, located on the surface and on opposite sides of the AmyI-1 protein, suggest that multiple surface regions are necessary for plastid targeting. Thus, Golgi-to-plastid traffic appears to be involved in the transport of glycoproteins to plastids and plastid targeting seems to be accomplished in a sorting signal–dependent manner.
Deqi Sensation in Placebo Acupuncture: A Crossover Study on Chinese Medicine Students

Objective. To evaluate the similarity of deqi sensation of real and noninvasive placebo acupuncture in healthy people with knowledge of Chinese medicine. Methods. In a crossover design, volunteers recruited from Chinese medicine college students were randomized to two groups to receive two phases of intervention with a one-week washout interval. In Group A, the participants were firstly treated by real acupuncture and then by sham needle, and the treatment sequence was reversed in Group B. VAS for pain intensity and deqi sensation was evaluated as outcomes. Results. Sixty-three volunteers were recruited and 60 were included and finished the study. In Group A, VAS was higher in Phase I than in Phase II (P = 0.017). Only treatment methods were selected as factor to VAS difference (P = 0.046) in ANOVA test. More positive deqi was reported in Group A in Phase I when treated by real acupuncture (P = 0.039), but the difference was not significant in Phase II (P = 0.301). Conclusion. The noninvasive placebo acupuncture device can effetely simulate the deqi sensation as real acupuncture, but it is less likely to evoke the active effect of deqi in real practice. This trial is registered with Chinese Clinical Trial Registry: ChiCTR-ORC-09000505.
IFOA4WSC: a quick and effective algorithm for QoS-aware servicecomposition

In the service-oriented environment, large-scale Service Composition SC has become an important research, where Quality of Service QoS has been applied widely. QoS is generally used to represent non-functional properties of web services and differentiate them with the same functionality. Studying how to select appropriate services for a composition from a very large number of similar candidate services is an NP-hard problem. However, there are limitations among existing methods, e.g. poor scalability and massive calculation in the exhaustion method, slow convergence speed and easy to fall into local optimum in the traditional evolutionary computation method. Thus, in this paper, an improved fruit fly optimisation algorithm called IFOA4WSC is proposed, which is quick and effective for web service composition. We analyse the convergence of fruit fly swarms in IFOA4WSC algorithm, and carry out a large number of simulation experiments in the common and random data sets. Experimental results show that IFOA4WSC is effective and highly efficient, and has better stability against classical PSO, GA and FOA.
Graph based User Interest Modeling in Twitter CS 224 W : Final Project , Group 41

Interest graph is a comparatively recent phenomenon in social media, building on the lines of Knowledge Graph1 and Social Graph2. Simply put, an interest graph is a representation of relationship between people and things that they are interested in. Companies like Netflix and Facebook are already leveraging Interest graph to power their recommendations. Additionally, these interest graphs fuel behavioral profiling based on interests for advertisement targeting and audience analytics. One social network that inherently presents itself as an interest graph is Twitter. In this project, we explore a graph based approach to user-interest modeling in Twitter.
Half Field Offense: An Environment for Multiagent Learning and Ad Hoc Teamwork

The RoboCup 2D simulation domain has served as a platform for research in AI, machine learning, and multiagent systems for more than two decades. However, for the researcher looking to quickly prototype and evaluate different algorithms, the full RoboCup task presents a cumbersome prospect, as it can take several weeks to set up the desired testing environment. The complexity owes in part to the coordination of several agents, each with a multi-layered control hierarchy, and which must balance offensive and defensive goals. This paper introduces a new open source benchmark, based on the Half Field Offense (HFO) subtask of soccer, as an easy-to-use platform for experimentation. While retaining the inherent challenges of soccer, the HFO environment constrains the agent’s attention to decision-making, providing standardized interfaces for interacting with the environment and with other agents, and standardized tools for evaluating performance. The resulting testbed makes it convenient to test algorithms for single and multiagent learning, ad hoc teamwork, and imitation learning. Along with a detailed description of the HFO environment, we present benchmark results for reinforcement learning agents on a diverse set of HFO tasks. We also highlight several other challenges that the HFO environment opens up for future research.
Fractal Modulation Effects

Fractal modulation is obtained by forming a power weighted superposition of scaled and modulated versions of the signal. The resulting signal is self-similar with fractal characteristics. In this paper we explore fractal modulation as a powerful method to generate rich signals, useful both for the synthesis of complex sounds, like the sounds from natural events or ecological sounds, or as control functions of audio effects. The wavelet transform can be used as an efficient tool in order to generate a subset of fractal modulated signals that are power homogeneous. Any signal used as a seed for fractal modulation is transformed into a multiscale sound by means of a tree-structured multirate filter bank. Moreover, by superimposing a structured modulation scheme one can generate pseudo-periodic sounds whose partials have fractal behavior.
Illumination recognition in field environments based on Gaussian Mixture Models

Terrain classification in field environment for mobile robot is affected by weather conditions among which illumination diversification plays a major role. With the changes in feature extraction method, classification result will vary significantly in the changing environment. This paper introduces a method of illumination recognition by analyzing the illumination distribution in visual space and using Gaussian Mixture Models to describe the illumination distribution. Illumination recognition provides a way to find suitable feature set for different illumination condition. The experiment result shows the illumination recognition with GMMs is accurate and efficient, and improves the performance of terrain classification.
Characterizing and predicting community members from evolutionary and heterogeneous networks

Mining different types of communities from web data have attracted a lot of research efforts in recent years. However, none of the existing community mining techniques has taken into account both the dynamic as well as heterogeneous nature of web data. In this paper, we propose to characterize and predict community members from the evolution of heterogeneous web data. We first propose a general framework for analyzing the evolution of heterogeneous networks. Then, the academic network, which is extracted from 1 million computer science papers, is used as an example to illustrate the framework. Finally, two example applications of the academic network are presented. Experimental results with a real and very large heterogeneous academic network show that our proposed framework can produce good results in terms of community member recommendation. Also, novel knowledge and insights can be gained by analyzing the community evolution pattern.
Radar characteristics of a tornadic low topped mini-supercell in Finland

An average of 10 tornado cases occurs in Finland yearly, but only few of them have been studied. Both non-supercell and supercell tornadoes have been observed in Finland. Severe thunderstorm warnings are issued if severe wind gusts or large hail are expected, but tornado nowcasting has been considered too challenging task for the forecaster. Hence, a tornado warning has never been issued in Finland. Recently, however, significant tornadoes in Finland have been documented to occur with supercells with distinct severe storm radar features well before tornado formation (Teittinen 2002). These storms, or even tornadoes, could be warned for, if the forecaster was given the proper tools and education. Forecasters need to understand how to interpret radarand environmentally-based tornado precursor signatures in order to be skillful in issuing tornado warnings. These signatures often contain ambiguities which create challenges for forecasters faced with making a tornado warning decision. The objective of this study is to try to understand why a tornado developed within this particular storm in an environment which is not known to favor tornadogenesis. A particular interest is to find out what kind of severe storm and tornado radar signatures the storm had before tornado formation to help the future warning process.
Combining Statistical Models for POS Tagging using Finite-State Calculus

We introduce a framework for POS tagging which can incorporate a variety of different information sources such as statistical models and hand-written rules. The information sources are compiled into a set of weighted finite-state transducers and tagging is accomplished using weighted finite-state algorithms. Our aim is to develop a fast and flexible way for trying out different tagger designs and combining them into hybrid systems. We test the applicability of the framework by constructing HMM taggers with augmented lexical models for English and Finnish. We compare our taggers with two existing statistical taggers TnT and Hunpos and find that we achieve superior accuracy.
Constrained Tensor Factorization with Accelerated AO-ADMM

Low-rank sparse tensor factorization is a populartool for analyzing multi-way data and is used in domainssuch as recommender systems, precision healthcare, and cybersecurity.Imposing constraints on a factorization, such asnon-negativity or sparsity, is a natural way of encoding priorknowledge of the multi-way data. While constrained factorizationsare useful for practitioners, they can greatly increasefactorization time due to slower convergence and computationaloverheads. Recently, a hybrid of alternating optimization andalternating direction method of multipliers (AO-ADMM) wasshown to have both a high convergence rate and the ability tonaturally incorporate a variety of popular constraints. In thiswork, we present a parallelization strategy and two approachesfor accelerating AO-ADMM. By redefining the convergencecriteria of the inner ADMM iterations, we are able to splitthe data in a way that not only accelerates the per-iterationconvergence, but also speeds up the execution of the ADMMiterations due to efficient use of cache resources. Secondly,we develop a method of exploiting dynamic sparsity in thefactors to speed up tensor-matrix kernels. These combinedadvancements achieve up to 8 speedup over the state-of-the art on a variety of real-world sparse tensors.
Evolving Relative Importance of the Southern Ocean and North Atlantic in Anthropogenic Ocean Heat Uptake

Ocean uptake of anthropogenic heat over the past 15 years has mostly occurred in the Southern Ocean, based on Argo float observations. This agrees with historical simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5), where the Southern Ocean (south of 30°S) accounts for 72% ± 28% of global heat uptake, while the contribution from the North Atlantic north of 30°N is only 6%. Aerosols preferentially cool the Northern Hemisphere, and the effect on surface heat flux over the subpolar North Atlantic opposes the greenhouse gas (GHG) effect in nearly equal magnitude. This heat uptake compensation is associated with weakening (strengthening) of the Atlantic meridional overturning circulation (AMOC) in response to GHG (aerosol) radiative forcing. Aerosols are projected to decline in the near future, reinforcing the greenhouse effect on the North Atlantic heat uptake. As a result, the Southern Ocean, which will continue to take up anthropogenic heat largely through the mean upwelling of water from depth, will be joined by increased relative contribution from the North Atlantic because of substantial AMOC slowdown in the twenty-first century. In the RCP8.5 scenario, the percentage contribution to global uptake is projected to decrease to 48% ± 8% in the Southern Ocean and increase to 26% ± 6% in the northern North Atlantic. Despite the large uncertainty in the magnitude of projected aerosol forcing, our results suggest that anthropogenic aerosols, given their geographic distributions and temporal trajectories, strongly influence the high-latitude ocean heat uptake and interhemispheric asymmetry through AMOC change.
Emotional health, support, and self‐efficacy in young adults with a history of language impairment

Children and adolescents with language impairment (LI) are at risk of emotional health difficulties. However, less is known about whether these difficulties continue into adulthood for this group, or about the potential role of environmental resources (e.g., social support) or internal resources (e.g., self‐efficacy). This study investigates emotional health in 81 adults with a history of developmental LI (aged 24) compared with 87 age‐matched peers (AMPs) using Beck Inventories. Social support and self‐efficacy measures were examined as predictors. The results were fourfold: (1) adults with LI had higher levels of emotional health problems; (2) whilst the availability of social support was similar across groups, people with LI received more help from others compared to peers; (3) social support was not significantly related to emotional health in those with LI – in contrast, for AMPs, uptake of support indicated poorer emotional health; (4) self‐efficacy was the strongest predictor of emotional health in both groups and fully mediated the relationship between language and emotional health (no moderation by group). This cross‐sectional study has implications for concurrent factors that might affect emotional health outcomes for children and young people with and without LI.
Sequential Split Vector Quantization of LSF Parameters using Conditional Pdf

A better performing product code vector quantization (VQ) method is proposed for coding the line spectrum frequency (LSF) parameters; the method is referred to as sequential split vector quantization (SeSVQ). The split sub-vectors of the full LSF vector are quantized in sequence and thus uses conditional distribution derived from the previous quantized sub-vectors. Unlike the traditional split vector quantization (SVQ) method, SeSVQ exploits the inter sub-vector correlation and thus provides improved rate-distortion performance, but at the expense of higher memory. We investigate the quantization performance of SeSVQ over traditional SVQ and transform domain split VQ (TrSVQ) methods. Compared to SVQ, SeSVQ saves 1 bit and nearly 3 bits, for telephone-band and wide-band speech coding applications respectively.
Eurographics/ Acm Siggraph Symposium on Computer Animation (2006) Local Constraint Methods for Deformable Objects

We present an efficient scheme to enforce non-conflicting geometric constraints for deformable objects. Iterative solvers and stabilization techniques are avoided. The method is not subject to numerical drift and all constraints are accurately met at each simulation step. The approach does not require any pre-processing and dynamically changing constraints can be handled efficiently. Experiments indicate that thousands of constraints can be processed at interactive rates. Although our approach is restricted to non-conflicting constraints, experiments illustrate the versatility of the method in the context of deformable objects. Details can be found in [GBT06].
Substrate assistance in the mechanism of family 18 chitinases: theoretical studies of potential intermediates and inhibitors.

Based on first principles and molecular mechanics calculations, we conclude that the mechanism of hevamine (a family 18 chitinase) involves an oxazoline ion intermediate stabilized by the neighboring C2' acetamido group. In this intermediate, the acetamido carbonyl oxygen atom forms a covalent bond to C1' of N-acetyl-glucosamine and has a transferred positive charge from the pyranose ring onto the acetamido nitrogen atom, leading to an anchimeric stabilization of 38.1 kcal/mol when docked with hevamine. This double displacement mechanism involving an oxazoline intermediate distinguishes the family 18 chitinase (which have one acidic residue near the active site) from family 19 chitinase and from hen egg-white lysozyme, which have two acidic residues near the active site. The structural and electronic properties of the oxazoline intermediate are similar to the known chitinase inhibitor allosamidin, suggesting that allosamidins act as transition state analogs of an oxazoline intermediate. Structural and electronic features of the oxazoline ion likely to be important in the design of new chitinase inhibitors are discussed.
Art and Science: A Case Study of their Interconnectedness in the Marine Natural Sciences

Science and art are interrelated in the form of scientific illustration: the act of observing a subject and translating the gained knowledge into a visual form. Humans have found inspiration from the natural world since our beginnings, but the practice of accurately portraying it arose with the growing interest in natural science in the 15th century. This paper explores three questions: what is scientific illustration, what is its role in scientific research, and how has it changed through history? Modern scientific illustration thrives as an effective method to teach the general public environmental issues. I present a case study using my own personal artwork to illustrate a literature review of conducted scientific research investigating the effects of ocean acidification on echinoderms. All echinoderms have calcified structures, ranging from larval support structures to endoskeletons made of ossicles embedded in the skin of sea stars, sea cucumbers, and sea urchins, and these structures may be impacted by acidified conditions. Larval echinoderms are especially at risk. The reviewed literature suggests that adults exhibit the ability to acclimate to short term acidification, but the long term impacts are not well understood. To more accurately predict the impact of ocean acidification on echinoderms, additional research should study long-term exposure and incorporate impacts on community dynamics as well as individuals.
TrSDB: a proteome database of transcription factors

TrSDB-TranScout Database-(http://ibb.uab.es/trsdb) is a proteome database of eukaryotic transcription factors based upon predicted motifs by TranScout and data sources such as InterPro and Gene Ontology Annotation. Nine eukaryotic proteomes are included in the current version. Extensive and diverse information for each database entry, different analyses considering TranScout classification and similarity relationships are offered for research on transcription factors or gene expression.
Extract-classify 1 Problem

When we have available a corpus of data which has been partitioned into a set of distinct classes, how can we make decisions about similar data which has not been labeled? 2 Context In many applications, there exists a large body of pre-analyzed data; each datum has been given a label according to some disjoint partitioning of the corpus. Frequently, one would like to use these labeled examples to make guesses as to which labels should be applied to previously unseen data. For example, given a set of emails which have been marked as " Spam " or " Not Spam " , we would like to design an email filter which will identify future emails as " Spam " or " Not Spam ". Another example, more consistent with the examples we will use in the rest of this pattern: given an image, determine whether it is of some particular object. Many computer vision benchmarks have exactly this flavor. The well-known Caltech-101, Caltech-256, and Pascal VOC datasets are designed for precisely this object recognition task. However, in most cases the data is not inherently structured. That is, there is not a trivial or obvious representation of the data which lends itself to the classification task. In the example of spam-detection, each email is an arbitrary-length sequence of characters: this representation itself carries no information directly useful to the task of spam detectin. Likewise in the object recognition task, the image is an arbitrary-sized 2-D array of 8-bit pixels. This representation carries no useful inherent structure. In either case, however, the information necessary to make the classification decision is clearly present: a human can easily determine whether an email is spam, or distinguish an image of a car from an image of an elephant. Many problems can be successfully understood through this application pattern. For people who are new to these fields, we hope to give them some insights about the problems, some basic solutions, and how we can help them develop their program by the Pattern Language and Design Patterns. In the following sections, we introduce feature extraction and classification in the field of computer vision, as motivation for this application pattern. Extract vs Classify: Since both feature extraction and classification need to be performed for all the data we have, one can trade-off the complexity of one with the other. We can use complex features coupled with a …
Strong Baselines for Cross-Lingual Entity Linking

We describe several context-independent baselines for tackling the cross-lingual entity linking task. Our methods are quite basic, reducing to efficient look-ups in static, pre-computed tables. Despite their simplicity, however, such approaches scored well in a recent knowledgebase population competition. Moreover, these language-independent techniques still perform strongly on English entity linking tasks.