Miracle-dz/UniFAR-FTUs huggingface dataset:
Language formation experiments in virtual worlds

Virtual Worlds are computer-mediated environments, in which we can monitor the interactions among our tools, the human users of the tools, and the humans themselves. They thus provide a powerful new experimental platform for studying issues that were heretofore very difficult. In this paper, we describe an experiment to study two claims about language formation: (1) all communication requires shared semantics (and not shared syntax), and (2) shared semantics requires shared experience. The key technical contributor is a new data structure called a Conceptual Category, which allows us to deal with representational and other semiotic issues within the program.
Physiological and Proteomics Analyses Reveal Low-Phosphorus Stress Affected the Regulation of Photosynthesis in Soybean

Previous studies have revealed a significant genetic relationship between phosphorus (P)-efficiency and photosynthesis-related traits in soybean. In this study, we used proteome profiling in combination with expression analysis, biochemical investigations, and leaf ultrastructural analysis to identify the underlying physiological and molecular responses. The expression analysis and ultrastructural analysis showed that the photosynthesis key genes were decreased at transcript levels and the leaf mesophyll and chloroplast were severely damaged after low-P stress. Approximately 55 protein spots showed changes under low-P condition by mass spectrometry, of which 17 were involved in various photosynthetic processes. Further analysis revealed the depression of photosynthesis caused by low-P stress mainly involves the regulation of leaf structure, adenosine triphosphate (ATP) synthesis, absorption and transportation of CO2, photosynthetic electron transport, production of assimilatory power, and levels of enzymes related to the Calvin cycle. In summary, our findings indicated that the existence of a stringent relationship between P supply and the genomic control of photosynthesis in soybean. As an important strategy to protect soybean photosynthesis, P could maintain the stability of cell structure, up-regulate the enzymes’ activities, recover the process of photosystem II (PSII), and induce the expression of low-P responsive genes and proteins.
Variability in common synaptic input to motor neurons modulates both force steadiness and pegboard time in young and older adults

The fluctuations in force during a steady isometric contraction (force steadiness) are associated with oscillations in common synaptic input to the involved motor neurons. Decreases in force steadiness are associated with increases in pegboard times in older adults, although a mechanism for this link has not been established. We used a state‐space model to estimate the variability in common synaptic input to motor neurons during steady, isometric contractions. The estimate of common synaptic input was derived from the discharge times of motor units as recorded with high‐density surface electrodes. We found that the variability in common synaptic input to motor neurons modulates force steadiness for young and older adults, as well as pegboard time for older adults.
GP-RVM: Genetic Programing-Based Symbolic Regression Using Relevance Vector Machine

This paper proposes a hybrid basis function construction method (GP-RVM) for Symbolic Regression problem, which combines an extended version of Genetic Programming called Kaizen Programming and Relevance Vector Machine to evolve an optimal set of basis functions. Different from traditional evolutionary algorithms where a single individual is a complete solution, our method proposes a solution based on linear combination of basis functions built from individuals during the evolving process. RVM which is a sparse Bayesian kernel method selects suitable functions to constitute the basis. RVM determines the posterior weight of a function by evaluating its quality and sparsity. The solution produced by GP-RVM is a sparse Bayesian linear model of the coefficients of many non-linear functions. Our hybrid approach is focused on nonlinear white-box models selecting the right combination of functions to build robust predictions without prior knowledge about data. Experimental results show that GP-RVM outperforms conventional methods, which suggest that it is an efficient and accurate technique for solving SR. The computational complexity of GP-RVM scales in O(M3), where M is the number of functions in the basis set and is typically much smaller than the number N of training patterns.
Organic matter preserved in 3-billion-year-old mudstones at Gale crater, Mars

Measuring martian organics and methane The Curiosity rover has been sampling on Mars for the past 5 years (see the Perspective by ten Kate). Eigenbrode et al. used two instruments in the SAM (Sample Analysis at Mars) suite to catch traces of complex organics preserved in 3-billion-year-old sediments. Heating the sediments released an array of organics and volatiles reminiscent of organic-rich sedimentary rock found on Earth. Most methane on Earth is produced by biological sources, but numerous abiotic processes have been proposed to explain martian methane. Webster et al. report atmospheric measurements of methane covering 3 martian years and found that the background level varies with the local seasons. The seasonal variation provides an important clue for determining the origin of martian methane. Science, this issue p. 1096, p. 1093; see also p. 1068 Complex organic compounds may have been detected by the Curiosity rover in ancient martian sedimentary rocks. Establishing the presence and state of organic matter, including its possible biosignatures, in martian materials has been an elusive quest, despite limited reports of the existence of organic matter on Mars. We report the in situ detection of organic matter preserved in lacustrine mudstones at the base of the ~3.5-billion-year-old Murray formation at Pahrump Hills, Gale crater, by the Sample Analysis at Mars instrument suite onboard the Curiosity rover. Diverse pyrolysis products, including thiophenic, aromatic, and aliphatic compounds released at high temperatures (500° to 820°C), were directly detected by evolved gas analysis. Thiophenes were also observed by gas chromatography–mass spectrometry. Their presence suggests that sulfurization aided organic matter preservation. At least 50 nanomoles of organic carbon persists, probably as macromolecules containing 5% carbon as organic sulfur molecules.
The RNA polymerase clamp interconverts dynamically among three states and is stabilized in a partly closed state by ppGpp

RNA polymerase (RNAP) contains a mobile structural module, the “clamp,” that forms one wall of the RNAP active-center cleft and that has been linked to crucial aspects of the transcription cycle, including loading of promoter DNA into the RNAP active-center cleft, unwinding of promoter DNA, transcription elongation complex stability, transcription pausing, and transcription termination. Crystal structures and single-molecule FRET studies establish that the clamp can adopt open and closed conformational states; however, the occurrence, pathway, and kinetics of transitions between clamp states have been unclear. Using single-molecule FRET (smFRET) on surface-immobilized RNAP molecules, we show that the clamp in RNAP holoenzyme exists in three distinct conformational states: the previously defined open state, the previously defined closed state, and a previously undefined partly closed state. smFRET time-traces show dynamic transitions between open, partly closed, and closed states on the 0.1-1 second time-scale. Similar analyses of transcription initiation complexes confirm that the RNAP clamp is closed in the catalytically competent transcription initiation complex and in initial transcribing complexes (RPITC), including paused initial transcribing complexes, and show that, in these complexes, in contrast to in RNAP holoenzyme, the clamp does not interconvert between the closed state and other states. The stringent-response alarmone ppGpp selectively stabilizes the partly-closed-clamp state, inhibiting interconversion between the partly closed state and the open state. The methods of this report should allow elucidation of clamp conformation and dynamics during all phases of transcription. SIGNIFICANCE STATEMENT The clamp forms a pincer of the RNA polymerase “crab-claw” structure, and adopts many conformations with poorly understood function and dynamics. By measuring distances within single surface-attached molecules, we observe directly the motions of the clamp and show that it adopts an open, a closed, and a partly closed state; the last state is stabilized by a sensor of bacterial starvation, linking the clamp conformation to the mechanisms used by bacteria to counteract stress. We also show that the clamp remains closed in many transcription steps, as well as in the presence of a specific antibiotic. Our approach can monitor clamp motions throughout transcription and offers insight on how antibiotics can stop pathogens by blocking their RNA polymerase movements.
Multi-Source Neural Machine Translation with Missing Data

Multi-source translation is an approach to exploit multiple inputs (e.g. in two different languages) to increase translation accuracy. In this paper, we examine approaches for multi-source neural machine translation (NMT) using an incomplete multilingual corpus in which some translations are missing. In practice, many multilingual corpora are not complete due to the difficulty to provide translations in all of the relevant languages (for example, in TED talks, most English talks only have subtitles for a small portion of the languages that TED supports). Existing studies on multi-source translation did not explicitly handle such situations. This study focuses on the use of incomplete multilingual corpora in multi-encoder NMT and mixture of NMT experts and examines a very simple implementation where missing source translations are replaced by a special symbol . These methods allow us to use incomplete corpora both at training time and test time. In experiments with real incomplete multilingual corpora of TED Talks, the multi-source NMT with the tokens achieved higher translation accuracies measured by BLEU than those by any one-to-one NMT systems.
A Multi-Task Cascaded Network for Prediction of Affect, Personality, Mood and Social Context Using EEG Signals

This paper presents a multi-task cascaded deep neural network which jointly predicts people's affective levels (valence and arousal) and personal factors using EEG signals recorded in response to presentation of affective multimedia content. Studied personal factors are the Big-five personality traits, mood (Positive Affect and Negative Affect Schedules) and social context (individual vs group). The cascaded network consists of two levels of prediction. The first level consists of a hybrid network designed to predict affective levels for individual video segments by combining the capabilities of CNN and RNN units. The second level consists of an RNN unit designed to predict personal factors based on the sequence of affective levels predictions of consecutive video segments. The first level reduces the dimensionality of the input while keeping affective information. The fusion of decisions of RNN and CNN networks produces an increase in performance of 3% and 4% f1-score respectively for valence and arousal recognition. And, our results for personal factors recognition, on average, outperform baseline studies by at least 2:7% mean f1-score.
Modelling and forecasting of hydrological variables using artificial neural networks: the Kafue River sub-basin

Abstract A simple reservoir routing scheme is applied to the available hydrometeorlogical data from the Kafue River sub-drainage basin in Zambia to derive flow contributions from the ungauged parts of the basin. The derived flow series and the time series of historic flow measured at the Kafue Hook Bridge (KHB) are separately modelled using artificial neural networks (ANNs). For each of these two flow series, relevant input variables are determined with the help of input—output intercorrelations, where inputs are given to a host of three-layer feedforward back-propagation (FF-BP) ANNs to predict the current, derived flow or KHB flow. A couple of ANN models selected on the basis of defined criteria are then used to forecast the flows at m time steps ahead. To evaluate the forecasting performance of the best ANN models, comparison with best autoregressive moving average models with exogenous inputs, ARMAX, is made. In both cases the ANNs give more robust forecasts over long terms than the ARMAX models, thereby making ANNs a viable alternative in flow forecasting.
The temporal dynamics of structure and content in the language network

Humans have a striking capacity: the ability to combine words into sentences that express new meanings. Previous research has identified key brain regions involved in this capacity, but little is known about the timecourse of activity in these regions. We performed a parallel fMRI and MEG experiment in the same subjects, finding that brain regions showing similar effects for structure and content in fMRI show distinct temporal activation patterns in MEG. The temporoparietal junction showed increased activity for both structure and content near the end of the sentence, consistent with a role in incremental interpretation of event semantics. The posterior temporal lobe, a region not often associated with syntax, showed a strong early effect of structure, consistent with predictive parsing models, and both structural and semantic context effects on function words. By contrast, the anterior temporal lobe and inferior frontal gyrus, regions traditionally associated with syntax, showed minimal effects of sentence structure. All three of these regions showed effects of semantic content: increased activation for real words relative to nonwords. Our results provide novel support for the posterior temporal lobe as a key region for lexicalized syntactic processing.
The Role of Retinal Vascular Density as a Screening Tool for Ageing and Stroke

Objectives: To measure the density of retinal vessels from digitized fundus photographs in patients with recent stroke and age-matched controls. To investigate whether the parameter retinal vascular density (RVD) served as a quantitative marker for cerebrovascular events. Methods: Digitized fundus photographs of n = 158 subjects with stroke or transient ischemic attack within 1 year at the time of examination and n = 1,250 age-matched controls without any remarkable medical history were examined. Sex, hypertension, and diabetes were considered to be cofactors. Measurement of RVD was performed with a computer-aided image-analyzing program by segmenting automatically all visible retinal vessels and measuring areas of vessels in distinct circles around the optic disk. Results: In controls RVD dwindles with increasing distance from the optic disk. RVD decreased significantly with age (p = 0.000). Stroke patients showed significantly lower values of RVD of –15% in comparison to age-matched controls. In old subjects, stroke in combination with hypertension is associated with a significant decreased RVD, and in middle-aged subjects diabetes and stroke are associated with a significant decreased RVD (p = 0.01). Conclusion: Age and stroke are significant risk factors for decreased RVD. Diabetes and arterial hypertension are additional significant risk factors in patients with stroke with respect to RVD.
Enhancing AlexNet for Arabic Handwritten words Recognition Using Incremental Dropout

Currently, the growth of mobile technologies, lead to a necessity to develop handwritten recognition applications. While the recognition of handwritten Latin and Chinese has been extensively investigated using various techniques, so little works have been done on Arabic handwritten recognition, and none of the existing techniques is accurate enough for practical application. Over the past few years, deeper convolutional neural networks (CNNs) have widely been employed for improving handwritten recognition performance. In this paper, we enhance the popular AlexNet for Arabic Handwritten Words Recognition (HWR). By adopting a dropout regularization, we prevented our system against overfitting problem and reduced the error recognition rate. We also investigated ReLU and tanH activation functions performance in the fully connected layers. Through several settings of experiments using the benchmarking IFN/ENIT Database, we achieved a new state-of-the-art classification accuracy of 92.13% and 92.55%. Lastly, we compared our best result to those of previous state-of-the-art.
Initial Evidence for the Validity of the California Bullying Victimization Scale (CBVS-R) as a Retrospective Measure for Adults

Childhood bullying is an important predictor of psychological and health outcomes in adulthood; however, validated retrospective measures of childhood bullying are lacking. This study investigates the psychometric properties of an adult retrospective version of the California Bullying Victimization Scale (CBVS). The CBVS self-report measure was developed for use with children and adolescents to assess the three definitional characteristics of bullying (aggression that is chronic, intentional, and involves an imbalance of power), without using the term “bullying. ” In the current study, we evaluate patterns of retrospective reports of bullying victimization, and compare results to a common definition-first measure of bullying. Concurrent validity and 4-year stability are addressed. In the fall of 2012, entering first-year students at 4 universities in the United States (N = 1,209; 65.2% female) completed the California Bullying Victimization Scale–Retrospective (CBVS–R) as part of an online survey. In spring of 2016, participants at 2 universities who provided contact information (N = 175) completed a 4-year follow-up survey. Results support the validity of the CBVS-R as a retrospective self-report measure of bullying victimization experienced in childhood. In particular, the percent of respondents classified as being bullied (27.9%) and age- and gender-related patterns of victimization were consistent with known patterns of childhood bullying. In addition, respondents reporting childhood victimization indicated increased psychological distress in adulthood. However, stability of reports across a 4-year follow-up period were lower than expected (&kgr; = .38). Implications for the use of retrospective reports of childhood bullying victimization are discussed.
Prognostic of RUL based on Echo State Network Optimized by Artificial Bee Colony

Prognostic is an engineering technique used to predict the future health state or behavior of an equipment or system. In this work, a data-driven hybrid approach for prognostic is presented. The approach based on Echo State Network (ESN) and Artificial Bee Colony (ABC) algorithm is used to predict machine’s Remaining Useful Life (RUL). ESN is a new paradigm that establishes a large space dynamic reservoir to replace the hidden layer of Recurrent Neural Network (RNN). Through the application of ESN is possible to overcome the shortcomings of complicated computing and difficulties in determining the network topology of traditional RNN. This approach describes the ABC algorithm as a tool to set the ESN with optimal parameters. Historical data collected from sensors are used to train and test the proposed hybrid approach in order to estimate the RUL. To evaluate the proposed approach, a case study was carried out using turbofan engine signals show that the proposed method can achieve a good collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). The experimental results using the engine data from NASA Ames Prognostics Data Repository RUL estimation precision. The performance of this model was compared using prognostic metrics with the approaches that use the same dataset. Therefore, the ESNABC approach is very promising in the field of prognostics of the RUL.
A novel method for high precision aortic constriction that allows for generation of specific cardiac phenotypes in mice

Aims Generation of reproducible cardiac disease phenotypes in mice is instrumental for investigating mechanisms leading to heart failure (HF). For decades, suture-based thoracic aortic constriction has been the preferred method for increasing left ventricular (LV) afterload in rodents, but the degree of stenosis resulting from this method is variable. In an effort to improve this methodology, we subjected mice to constriction of the ascending aorta using o-rings with fixed inner diameters (IDs). Methods and results Mice of C57BL/6J and FVB/N background were subjected to constriction of the ascending aorta using o-rings with fixed IDs of 0.71, 0.66, and 0.61 mm. O-ring aortic banding resulted in 98.7% survival 2 weeks post-surgery, with very low intra- and inter-surgeon variation. When using the narrowest o-ring (0.61 mm), mice developed hypertrophy within 1 week. Over 20 weeks, the mice gradually developed reduced LV ejection fraction (LVEF) and dilatation with increased left atrial dimensions and lung weight, indicating congestion. When using o-rings with IDs of 0.66 mm and 0.71 mm, the mice developed hypertrophy, but maintained a compensated state with stabilized LVEF 8-20 weeks post-surgery. The up-regulation of signature genes associated with HF, hypertrophy, fibrosis, and the level of activation of MAPK and NFAT signalling pathways corresponded to the degree of stenosis. Conclusion Here, we introduce a novel method for high precision aortic constriction in mice with high intra- and inter-surgeon reproducibility and low post-operative mortality that allows generation of specific cardiac disease phenotypes.
Biosynthesis of Fluorescent β Subunits of C-Phycocyanin from Spirulina subsalsa in Escherichia coli, and Their Antioxidant Properties

Phycocyanin, which covalently binds phycocyanobilin chromophores, is not only a candidate fluorescent probe for biological imaging, but also a potential antioxidative agent for healthcare. Herein, a plasmid harboring two cassettes was constructed, with cpcB from Spirulina subsalsa in one cassette and the fusion gene cpcS::ho1::pcyA in the other, and then expressed in Escherichia coli. PCB-CpcB(C-82), a fluorescent phycocyanin β subunit, was biosynthesized in E. coli, exhibiting an absorption maximum at 620 nm and fluorescence emission maximum at 640 nm. When cpcS was replaced by cpcT, PCB-CpcB(C-153), another fluorescent phycocyanin β subunit, was produced, exhibiting an absorption maximum at 590 nm and fluorescence emission maximum at 620 nm. These two fluorescent biliproteins showed stronger scavenging activity toward hydroxyl and DPPH free radicals than apo-CpcB. The IC50 values for hydroxyl radical scavenging by PCB-CpcB(C-82), PCB-CpcB(C-153), and apo-CpcB were 38.72 ± 2.48 µg/mL, 51.06 ± 6.74 µg/mL, and 81.82 ± 0.67 µg/mL, respectively, and the values for DPPH radical scavenging were 201.00 ± 5.86 µg/mL, 240.34 ± 4.03 µg/mL, and 352.93 ± 26.30 µg/mL, respectively. The comparative antioxidant capacities of the proteins were PCB-CpcB(C-82) > PCB-CpcB(C-153) > apo-CpcB, due to bilin binding. The two fluorescent biliproteins exhibited a significant effect on relieving the growth of E. coli cells injured by H2O2. The results of this study suggest that the fluorescent phycocyanin β subunits of S. subsalsa were reconstructed by one expression vector in E. coli, and could be developed as potential antioxidants.