nils-herrmann/clean_scirepeval huggingface dataset:
Cell Cycle Control and DNA Damage Signaling in Mammals

Normal cellular functions, development, and disease prevention depend on genome stability maintenance. To safeguard the accurate transmission of genetic information, mammalian cells utilize cell cycle mechanisms coordinating DNA replication and mitosis. Upon DNA damage, diverse pathways are activated to promote cell cycle arrest and DNA damage repair. Collectively, these pathways are known as the DNA damage response, the major guardian of genomic integrity. The activation of DNA damage induced cell cycle checkpoints (termed DNA damage checkpoints) allows the completion of DNA repair before cell cycle progression continues. In case the DNA damage is beyond repair, the damaged cell commits to apoptosis or senescence. Here, we summarize the regulation of the mammalian cell cycle in the context of DNA damage signaling and DNA damage repair mechanisms. Since DNA damage checkpoints can prevent cancer development and influence somatic and stem cell physiology, we also discuss some molecular aspects in the context of disease.
Prioritizing qualitative research in surgery: A synthesis and analysis of publication trends.
Venue: Surgery

Over the past 2 decades, researchers have recognized the value of qualitative research. Little has been done to characterize its application to surgery. We describe characteristics and overall prevalence of qualitative surgical research.  We searched PubMed and CINAHL using "surgery" and 7 qualitative methodology terms. Four researchers extracted information; a fifth researcher reviewed 10% of abstracts for inter rater reliability.  A total of 3,112 articles were reviewed. Removing duplicates, 28% were relevant (N 878; k 0.70) Common qualitative methodologies included phenomenology (34.3% and grounded theory (30.2% Interviews were the most common data collection method (81.9% of patients (64% within surgical oncology (15.4% Postdischarge was the most commonly studied topic (30.8% Overall, 41% of studies were published in nursing journals, while 8% were published in surgical journals. More than half of studies were published since 2011.  Results suggest qualitative surgical research is gaining popularity. Most is published in nonsurgical journals, however, utilizing only 2 methodologies (phenomenology, grounded theory) The surgical journals that have published qualitative research had study topics restricted to a handful of surgical specialties. Additional surgical qualitative research should take advantage of a greater variety of approaches to provide insight into rare phenomena and social context.
Sparse Bilingual Word Representations for Cross lingual Lexical Entailment
Venue: NAACL

We introduce the task of cross lingual lexical entailment, which aims to detect whether the meaning of a word in one language can be inferred from the meaning of a word in another language. We construct a gold standard for this task, and propose an unsupervised solution based on distributional word representations. As commonly done in the monolingual setting, we assume a worde entails a wordf if the prominent context features of e are a subset of those of f To address the challenge of comparing contexts across languages, we propose a novel method for inducing sparse bilingual word representations from monolingual and parallel texts. Our approach yields an Fscore of 70% and significantly outperforms strong baselines based on translation and on existing word representations.
Using WordNet Based Word Sense Disambiguation to Improve MT Performance

We report on a series of experiments aimed at improving the machine translation of ambiguous lexical items by using WordNet based unsupervised Word Sense Disambiguation (WSD) and comparing its results to three MT systems. Our experiments are performed for the English Slovene language pair using UKB, a freely available graph based word sense disambiguation system. Since the fine granularity of WordNet is often reported as problematic, we compare the performance of UKB using all WordNet senses with using sense clusters. Results are evaluated in three ways: a manual evaluation of WSD performance from MT perspective, an analysis of agreement between the WSD proposed equivalent and those suggested by the three systems, and finally by computing BLEU, NIST and METEOR scores for all translation versions. Our results show that WSD performs with a MT relevant precision of 71 and that 21 of sense related MT errors could be prevented by using unsupervised WSD. We also show that sense clusters improve MT relevant precision.
Multiple kernel visual auditory representation learning for retrieval
Venue: Multimedia Tools and Applications

Cross media data representation, which focuses on semantics understanding of multimedia data in different modalities, is a rising hot topic in web media data analysis. The most challenging issues for cross media data representation include: how to find underlying content level data correlations and how to use such correlations in the representation model. Most traditional web media data analysis works are based on single modality data sources, such as Flickr images or YouTube videos, leaving cross media data representation and semantics understanding wide open. In this paper, we propose a multiple kernel visual auditory representation learning approach, which learns cross media correlations from visual and auditory feature spaces with multiple kernel strategies. Besides, we give cross media distance measure for image audio retrieval in the mutual subspace of co occurrence. Experiment results on the collected image audio database are encouraging, and show that the performance of our approach is effective from multiple perspectives.
Association between soluble lectin like oxidized low density lipoprotein receptor 1 levels and coronary slow flow phenomenon
Venue: Archives of medical science AMS

The coronary slow flow phenomenon (CSFP) has been associated with myocardial ischemia, myocardial infarction, life threatening arrhythmias, sudden cardiac death and increased cardiovascular mortality similar to coronary artery disease (CAD) Possible underlying mechanisms of CSFP are endothelial dysfunction, chronic inflammation, microvascular dysfunction and diffuse atherosclerosis. Soluble lectin like oxidized low density lipoprotein receptor 1 (sLOX 1) seems to play an important role in the pathogenesis of atherosclerosis. We hypothesized that sLOX 1 might be associated with CSFP, and aimed to research the relationship between sLOX 1 and CSFP.  Forty patients with angiographically proven CSFP and 43 patients with a normal coronary flow pattern (NCFP) were included in this study. Coronary blood flow was measured according to the Thrombolysis In Myocardial Infarction (TIMI) frame count method. sLOX 1 levels were measured in all study subjects.  Serum levels of sLOX 1 were significantly higher in the CSFP group than the NCFP group (1061.80 422.20 ng/ml vs. 500.043 282.97 ng/ml, p 0.001, respectively) Multivariate logistic regression analysis including sLOX 1, MPV, GGT and uric acid levels revealed a significant association between sLOX 1 levels and CSFP (Exp (B)/OR: 1.006, 95% CI: 1.002 1.010, p 0.001)  The present study demonstrated that serum sLOX 1 levels were significantly higher in patients with CSFP and there was a strong association between high sLOX 1 levels and CSFP. High serum sLOX 1 levels may have an important role in the pathogenesis of CSFP. Future studies are needed to confirm these results.
Deep Dynamic Neural Networks for Multimodal Gesture Segmentation and Recognition
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence

This paper describes a novel method called Deep Dynamic Neural Networks (DDNN) for multimodal gesture recognition. A semi supervised hierarchical dynamic framework based on a Hidden Markov Model (HMM) is proposed for simultaneous gesture segmentation and recognition where skeleton joint information, depth and RGB images, are the multimodal input observations. Unlike most traditional approaches that rely on the construction of complex handcrafted features, our approach learns high level spatiotemporal representations using deep neural networks suited to the input modality: a Gaussian Bernouilli Deep Belief Network (DBN) to handle skeletal dynamics, and a 3D Convolutional Neural Network (3DCNN) to manage and fuse batches of depth and RGB images. This is achieved through the modeling and learning of the emission probabilities of the HMM required to infer the gesture sequence. This purely data driven approach achieves a Jaccard index score of 0.81 in the ChaLearn LAP gesture spotting challenge. The performance is on par with a variety of state of the art hand tuned feature based approaches and other learning based methods, therefore opening the door to the use of deep learning techniques in order to further explore multimodal time series data.
The Basal Forebrain and Motor Cortex Provide Convergent yet Distinct Movement Related Inputs to the Auditory Cortex
Venue: Neuron

Cholinergic inputs to the auditory cortex from the basal forebrain (BF) are important to auditory processing and plasticity, but little is known about the organization of these synapses onto different auditory cortical neuron types, how they influence auditory responsiveness, and their activity patterns during various behaviors. Using intersectional tracing, optogenetic circuit mapping, and in vivo calcium imaging, we found that cholinergic axons arising from the caudal BF target major excitatory and inhibitory auditory cortical cell types, rapidly modulate auditory cortical tuning, and display fast movement related activity. Furthermore, the BF and the motor cortex another source of movement related activity provide convergent input onto some of the same auditory cortical neurons. Cholinergic and motor cortical afferents to the auditory cortex display distinct activity patterns and presynaptic partners, indicating that the auditory cortex integrates bottom up cholinergic signals related to ongoing movements and arousal with top down information concerning impending movements and motor planning.
Causal contribution of primate auditory cortex to auditory perceptual decision making
Venue: Nature Neuroscience

Auditory perceptual decisions are thought to be mediated by the ventral auditory pathway. However, the specific and causal contributions of different brain regions in this pathway, including the middle lateral (ML) and anterolateral (AL) belt regions of the auditory cortex, to auditory decisions have not been fully identified. To identify these contributions, we recorded from and microstimulated ML and AL sites while monkeys decided whether an auditory stimulus contained more low frequency or high frequency tone bursts. Both ML and AL neural activity was modulated by the frequency content of the stimulus. But, only the responses of the most stimulus sensitive AL neurons were systematically modulated by the monkeys' choices. Consistent with this observation, microstimulation of AL, but not ML, systematically biased the monkeys' behavior toward the choice associated with the preferred frequency of the stimulated site. Together, these findings suggest that AL directly and causally contributes sensory evidence to form this auditory decision.
Functional Topography of Human Auditory Cortex
Venue: The Journal of Neuroscience

Functional and anatomical studies have clearly demonstrated that auditory cortex is populated by multiple subfields. However, functional characterization of those fields has been largely the domain of animal electrophysiology, limiting the extent to which human and animal research can inform each other. In this study, we used high resolution functional magnetic resonance imaging to characterize human auditory cortical subfields using a variety of low level acoustic features in the spectral and temporal domains. Specifically, we show that topographic gradients of frequency preference, or tonotopy, extend along two axes in human auditory cortex, thus reconciling historical accounts of a tonotopic axis oriented medial to lateral along Heschl's gyrus and more recent findings emphasizing tonotopic organization along the anterior posterior axis. Contradictory findings regarding topographic organization according to temporal modulation rate in acoustic stimuli, or "periodotopy," are also addressed. Although isolated subregions show a preference for high rates of amplitude modulated white noise (AMWN) in our data, large scale "periodotopic" organization was not found. Organization by AM rate was correlated with dominant pitch percepts in AMWN in many regions. In short, our data expose early auditory cortex chiefly as a frequency analyzer, and spectral frequency, as imposed by the sensory receptor surface in the cochlea, seems to be the dominant feature governing large scale topographic organization across human auditory cortex.  In this study, we examine the nature of topographic organization in human auditory cortex with fMRI. Topographic organization by spectral frequency (tonotopy) extended in two directions: medial to lateral, consistent with early neuroimaging studies, and anterior to posterior, consistent with more recent reports. Large scale organization by rates of temporal modulation (periodotopy) was correlated with confounding spectral content of amplitude modulated white noise stimuli. Together, our results suggest that the organization of human auditory cortex is driven primarily by its response to spectral acoustic features, and large scale periodotopy spanning across multiple regions is not supported. This fundamental information regarding the functional organization of early auditory cortex will inform our growing understanding of speech perception and the processing of other complex sounds.
Cross Modal Functional Reorganization of Visual and Auditory Cortex in Adult Cochlear Implant Users Identified with fNIRS
Venue: Neural plasticity

Cochlear implant (CI) users show higher auditory evoked activations in visual cortex and higher visual evoked activation in auditory cortex compared to normal hearing (NH) controls, reflecting functional reorganization of both visual and auditory modalities. Visual evoked activation in auditory cortex is a maladaptive functional reorganization whereas auditory evoked activation in visual cortex is beneficial for speech recognition in CI users. We investigated their joint influence on CI users' speech recognition, by testing 20 postlingually deafened CI users and 20 NH controls with functional near infrared spectroscopy (fNIRS) Optodes were placed over occipital and temporal areas to measure visual and auditory responses when presenting visual checkerboard and auditory word stimuli. Higher cross modal activations were confirmed in both auditory and visual cortex for CI users compared to NH controls, demonstrating that functional reorganization of both auditory and visual cortex can be identified with fNIRS. Additionally, the combined reorganization of auditory and visual cortex was found to be associated with speech recognition performance. Speech performance was good as long as the beneficial auditory evoked activation in visual cortex was higher than the visual evoked activation in the auditory cortex. These results indicate the importance of considering cross modal activations in both visual and auditory cortex for potential clinical outcome estimation.
Neural Relation Extraction with Selective Attention over Instances
Venue: ACL

Distant supervised relation extraction has been widely used to find novel relational facts from text. However, distant supervision inevitably accompanies with the wrong labelling problem, and these noisy data will substantially hurt the performance of relation extraction. To alleviate this issue, we propose a sentence level attention based model for relation extraction. In this model, we employ convolutional neural networks to embed the semantics of sentences. Afterwards, we build sentence level attention over multiple instances, which is expected to dynamically reduce the weights of those noisy instances. Experimental results on real world datasets show that, our model can make full use of all informative sentences and effectively reduce the influence of wrong labelled instances. Our model achieves significant and consistent improvements on relation extraction as compared with baselines. The source code of this paper can be obtained from https: /github.com/thunlp/NRE.
Neural Sentiment Classification with User and Product Attention
Venue: EMNLP

Document level sentiment classification aims to predict user's overall sentiment in a document about a product. However, most of existing methods only focus on local text information and ignore the global user preference and product characteristics. Even though some works take such information into account, they usually suffer from high model complexity and only consider wordlevel preference rather than semantic levels. To address this issue, we propose a hierarchical neural network to incorporate global user and product information into sentiment classification. Our model first builds a hierarchical LSTM model to generate sentence and document representations. Afterwards, user and product information is considered via attentions over different semantic levels due to its ability of capturing crucial semantic components. The experimental results show that our model achieves significant and consistent improvements compared to all state of theart methods. The source code of this paper can be obtained from https:/github. com/thunlp/NSC.
ENGAGING UNMOTIVATED EFL LEARNERS IN CLASSROOM TALK

The aim of this paper is to demonstrate a teacher's efforts in engaging her unmotivated EFL learners in classroom talk. The paper identifies the teacher's elicitation techniques during the third turn of triadic exchanges for extending turn taking. It also identifies the types of learner initiatives that created learning opportunities. The teacher and author of this paper introduced eight Saudi EFL learners (aged 18) to a short mystery story, before requiring them to take turns in classroom talk. 40 minutes of classroom interaction were recorded and analysed qualitatively. Informed by the principles of Conversation Analysis (CA) methodology, the paper shows that the teacher's role moved beyond giving negative or positive feedback. Her role involved extending learners' responses by inviting disagreement, suggesting alternatives, paraphrasing and shaping learners' responses, linking learners' contributions and increasing wait time. These efforts engaged learners in different types of turn taking, including presenting new ideas, building on classmates' ideas, disagreeing with others and extending one's turn. It was found that learners not only engaged in turn taking during interaction but also learned how to use the new language. These findings add to the small body of available literature on elicitation techniques that involve learners in extended turns when creating space for learning. It is hoped that the findings discussed in this paper will inform EFL teaching practices. More investigation is needed to shed light on how to engage unmotivated EFL learners in classroom interaction using appropriate teaching methods. Keywords unmotivated learners, EFL, elicitation techniques, turn taking, learner initiatives, learning opportunities
Improved Agricultural Technology Adoption in Zambia: Are Women Farmers Being Left Behind?

The use of modern seed varieties and other improved technologies is essential for farmers to significantly increase their crop harvest and improve their livelihoods. All over Sub Saharan Africa, agriculture productivity growth has remained very low over many decades irrespective of gender of the farmer. However, studies have shown that women farmers fare worse than the male counterparts in terms of adoption of improved technology and productivity. This gender gap in technology adoption curtails agricultural development because women in developing countries such as Zambia play a significant role in agriculture and food production.
Cytoplasmic protein aggregates interfere with nucleocytoplasmic transport of protein and RNA
Venue: Science

Location, location, location Aggregates of certain disease associated proteins are involved in neurodegeneration. Woerner et al. now show that the exact location of these aggregates in the cell may be the key to their pathology (see the Perspective by Da Cruz and Cleveland) An artificial aggregate prone protein caused problems when expressed in the cytoplasm but not when expressed in the nucleus. Cytoplasmic aggregates interfered with nucleocytoplasmic import and export. Perhaps if we can shunt pathological aggregates to the nucleus in the future, we will be able to ameliorate some forms of degenerative disease. Science, this issue p. 173; see also p. 125 Protein aggregates in the cytoplasm soak up accessory factors needed for transport of other proteins and RNA across the nuclear envelope.  Aggregation in the cytoplasm interfered with nucleocytoplasmic protein and RNA transport. In contrast, the same proteins did not inhibit transport when forming inclusions in the nucleus at or around the nucleolus. Protein aggregation in the cytoplasm, but not the nucleus, caused the sequestration and mislocalization of proteins containing disordered and low complexity sequences, including multiple factors of the nuclear import and export machinery. Thus, impairment of nucleocytoplasmic transport may contribute to the cellular pathology of various aggregate deposition diseases.
Quantum sort algorithm based on entanglement qubits {00, 11}
Venue: 2016 IEEE Long Island Systems, Applications and Technology Conference (LISAT)

Quantum algorithms have gained a lot of consideration especially for the need of searching unsorted database in a faster way rather than using the classical search mechanisms. This paper introduces a new quantum sort algorithm by employing the quantum features to expedite the searching process in conventional environments. The proposed efficient sorting algorithm emphasizes entanglement qubits property to sort N positive integers.
StraightTaint: Decoupled offline symbolic taint analysis
Venue: 2016 31st IEEE/ACM International Conference on Automated Software Engineering (ASE)

Taint analysis has been widely applied in ex post facto security applications, such as attack provenance investigation, computer forensic analysis, and reverse engineering. Unfortunately, the high runtime overhead imposed by dynamic taint analysis makes it impractical in many scenarios. The key obstacle is the strict coupling of program execution and taint tracking logic code. To alleviate this performance bottleneck, recent work seeks to offload taint analysis from program execution and run it on a spare core or a different CPU. However, since the taint analysis has heavy data and control dependencies on the program execution, the massive data in recording and transformation overshadow the benefit of decoupling. In this paper, we propose a novel technique to allow very lightweight logging, resulting in much lower execution slowdown, while still permitting us to perform full featured offline taint analysis. We develop StraightTaint, a hybrid taint analysis tool that completely decouples the program execution and taint analysis. StraightTaint relies on very lightweight logging of the execution information to reconstruct a straight line code, enabling an offline symbolic taint analysis without frequent data communication with the application. While StraightTaint does not log complete runtime or input values, it is able to precisely identify the causal relationships between sources and sinks, for example. Compared with traditional dynamic taint analysis tools, StraightTaint has much lower application runtime overhead.