nils-herrmann/clean_scirepeval huggingface dataset:
DNAzyme Based Logic Gate Mediated DNA Self Assembly.
Venue: Nano letters

Controlling DNA self assembly processes using rationally designed logic gates is a major goal of DNA based nanotechnology and programming. Such controls could facilitate the hierarchical engineering of complex nanopatterns responding to various molecular triggers or inputs. Here, we demonstrate the use of a series of DNAzyme based logic gates to control DNA tile self assembly onto a prescribed DNA origami frame. Logic systems such as "YES," "OR," "AND," and "logic switch" are implemented based on DNAzyme mediated tile recognition with the DNA origami frame. DNAzyme is designed to play two roles: (1) as an intermediate messenger to motivate downstream reactions and (2) as a final trigger to report fluorescent signals, enabling information relay between the DNA origami framed tile assembly and fluorescent signaling. The results of this study demonstrate the plausibility of DNAzyme mediated hierarchical self assembly and provide new tools for generating dynamic and responsive self assembly systems.
Window design in architecture: Analysis of energy savings for lighting and visual comfort in residential spaces

Window design is decisive in providing appropriate visual comfort for occupants and sufficient energy savings in electric lighting. In daylighting, visual comfort is dependent on the maximum daylight autonomy. Moreover, energy consumption in electric lighting relies on daylight autonomy. The aim of this research is to quantify these metrics in a residential room for different window models and analyze the results obtained. The surface reflectance and the geometry of the window are variable. DaySim 3.2 lighting program provides the simulations of the room model according to different orientations and weather conditions. Following the trials, it was concluded that daylight autonomy is proportional to the glass surface and reflectance of surfaces at the back of the room, while its influence near the facade is negligible. However, energy consumption does not depend on window shape. It is also concluded that windows located higher up result in higher illuminance at the back of the room than those in centered locations. The conclusions of this research are contrasted with the analysis of daylight metrics for different locations and orientations of the window.
Theory of Mind and Executive Control Deficits in Typically Developing Adults and Adolescents with High Levels of Autism Traits
Venue: Journal of autism and developmental disorders

Autism spectrum disorder (ASD) is characterised by profound difficulties in empathic processing and executive control. Whilst the links between these processes have been frequently investigated in populations with autism, few studies have examined them at the subclinical level. In addition, the contribution of alexithymia, a trait characterised by impaired interoceptive awareness and empathy, and elevated in those with ASD, is currently unclear. The present two part study employed a comprehensive battery of tasks to examine these processes. Findings support the notion that executive function and theory of mind are related abilities. They also suggest that individuals with elevated levels of autism like traits experience a partially similar pattern of social and executive function difficulties to those diagnosed with ASD, and that these impairments are not explained by co occurring alexithymia.
The Role of Immigrant Concentration Within and Beyond Residential Neighborhoods in Adolescent Alcohol Use
Venue: Journal of youth and adolescence

Neighborhoods are salient contexts for youth that shape adolescent development partly through informal social controls on their behavior. This research examines how immigrant concentration within and beyond the residential neighborhood influences adolescent alcohol use. Residential neighborhood immigrant concentration may lead to a cohesive, enclave like community that protects against adolescent alcohol use. But heterogeneity in the immigrant concentrations characterizing the places residents visit as they engage in routine activities outside of the neighborhood where they live may weaken the social control benefits of the social ties and shared cultural orientations present in enclave communities. This study investigates whether the protective influence of residential neighborhood immigrant concentration on adolescent alcohol consumption diminishes when youth live in communities where residents collectively are exposed to areas with more diverse immigrant concentrations. This study tests this contention by analyzing survey and geographic routine activity space data from the Los Angeles Family and Neighborhood Survey, and the 2000 census. The sample includes 793 adolescents (48.7 female, 16.5 foreign born Latino, 42.5 US born Latino, 11.0 black, 30 white/other) between the ages of 12 and 17 who live in 65 neighborhoods in Los Angeles County. Immigrant concentration among these neighborhoods derives primarily from Latin America. The results from multilevel models show that immigrant concentration protects against adolescent alcohol use only when there is low neighborhood level diversity of exposures to immigrant concentration among the contexts residents visit outside of their residential neighborhood. This research highlights the importance of considering the effects of aggregate exposures to non home contexts on adolescent wellbeing.
Targeting Ibrutinib Resistant BTK C481S Mutation with ARQ 531, a Reversible Non Covalent Inhibitor of BTK

The B cell receptor (BCR) signaling pathway is a central determinant of B cell fate and function and Bruton tyrosine kinase (BTK) is a critical component of this signaling cascade. BTK, a nonreceptor tyrosine kinase, plays a significant role in B cell development and is a unique therapeutic target in B cell malignancies. Targeting BTK with the irreversible, inhibitor ibrutinib achieved an impressive objective response rate in patients with CLL. However, disease progression occurs more frequently in these patients with high risk genomic features either shortly after the start of ibrutinib therapy or later with progressive CLL. Disease progression is often associated with resistance to ibrutinib with acquired BTK mutation on C481 residue which prevent covalent binding of ibrutinib to BTK. Here we present discovery and preclinical development of potent reversible BTK inhibitor, ARQ 531, capable of inhibiting activation of both wild type and the C481S mutant of BTK and subsequent downstream oncogenic signaling in various hematological malignancies.
Risk taking on the road and in the mind: behavioural and neural patterns of decision making between risky and safe drivers
Venue: Ergonomics

Drivers' risk taking is a key issue of road safety. This study explored individual differences in drivers' decision making, linking external behaviours to internal neural activity, to reveal the cognitive mechanisms of risky driving. Twenty four male drivers were split into two groups (risky vs. safe drivers) via the Drivier Behaviour Questionnaire violation. The risky drivers demonstrated higher preference for the risky choices in the paradigms of Iowa Gambling Task and Balloon Analogue Risk Task. More importantly, the risky drivers showed lower amplitudes of feedback related negativity (FRN) and loss minus gain FRN in both paradigms, which indicated their neural processing of error detection. A significant difference of P300 amplitudes was also reported between groups, which indicated their neural processing of reward evaluation and were modified by specific paradigm and feedback. These results suggested that the neural basis of risky driving was the decision patterns less revised by losses and more motivated by rewards.  Risk taking on the road is largely determined by inherent cognitive mechanisms, which can be indicated by the behavioural and neural patterns of decision making. In this regard, it is feasible to quantize drivers' riskiness in the cognitive stage before actual risky driving or accidents, and intervene accordingly.
Convolutional bidirectional long short term memory hidden Markov model hybrid system for polyphonic sound event detection

In this study, we propose a polyphonic sound event detection method based on a hybrid system of Convolutional Bidirectional Long Short Term Memory Recurrent Neural Network and Hidden Markov Model (CBLSTM HMM) Inspired by the state of the art approach to integrating neural networks to HMM in speech recognition, the proposed method develops the hybrid system using CBLSTM to estimate the HMM state output probability, making it possible to model sequential data while handling its duration change. The proposed hybrid system is capable of detecting a segment of each sound event without post processing, such as a smoothing process of detection results over multiple frames, usually required in the frame wise detection methods. Moreover, we can easily apply it to a multi label classification problem to achieve polyphonic sound event detection. We conduct experimental evaluations using the DCASE2016 task two dataset to compare the performance of the proposed method to that of the conventional methods, such as non
Event Driven Emotion Cause Extraction with Corpus Construction
Venue: EMNLP

In this paper, we present our work in emotion cause extraction. Since there is no open dataset available, the lack of annotated resources has limited the research in this area. Thus, we first present a dataset we built using SINA city news. The annotation is based on the scheme of the W3C Emotion Markup Language. Second, we propose a 7 tuple definition to describe emotion cause events. Based on this general definition, we propose a new event driven emotion cause extraction method using multi kernel SVMs where a syntactical tree based approach is used to represent events in text. A convolution kernel based multikernel SVM are used to extract emotion causes. Because traditional convolution kernels do not use lexical information at the terminal nodes of syntactic trees, we modify the kernel function with a synonym based improvement. Even with very limited training data, we can still extract sufficient features for the task. Evaluations show that our approach achieves 11.6% higher F measure compared to referenced methods. The contributions of our work include resource construction, concept definition and algorithm development.
DASPfind: new efficient method to predict drug target interactions
Venue: Journal of Cheminformatics

BackgroundIdentification of novel drug target interactions (DTIs) is important for drug discovery. Experimental determination of such DTIs is costly and time consuming, hence it necessitates the development of efficient computational methods for the accurate prediction of potential DTIs. To date, many computational methods have been proposed for this purpose, but they suffer the drawback of a high rate of false positive predictions.ResultsHere, we developed a novel computational DTI prediction method, DASPfind. DASPfind uses simple paths of particular lengths inferred from a graph that describes DTIs, similarities between drugs, and similarities between the protein targets of drugs. We show that on average, over the four gold standard DTI datasets, DASPfind significantly outperforms other existing methods when the single top ranked predictions are considered, resulting in 46.17 of these predictions being correct, and it achieves 49.22 correct single top ranked predictions when the set of all DTIs for a single drug is tested. Furthermore, we demonstrate that our method is best suited for predicting DTIs in cases of drugs with no known targets or with few known targets. We also show the practical use of DASPfind by generating novel predictions for the Ion Channel dataset and validating them manually.ConclusionsDASPfind is a computational method for finding reliable new interactions between drugs and proteins. We show over six different DTI datasets that DASPfind outperforms other state of the art methods when the single top ranked predictions are considered, or when a drug with no known targets or with few known targets is considered. We illustrate the usefulness and practicality of DASPfind by predicting novel DTIs for the Ion Channel dataset. The validated predictions suggest that DASPfind can be used as an efficient method to identify correct DTIs, thus reducing the cost of necessary experimental verifications in the process of drug discovery. DASPfind can be accessed online at: http:/www.cbrc.kaust.edu.sa/daspfind.Graphical abstractThe conceptual workflow for predicting drug target interactions using DASPfind
Virtual rational Betti numbers of nilpotent by abelian groups

In this paper we study virtual rational Betti numbers of a nilpotent by abelian group $G$ where the abelianization $N/N' of its nilpotent part $N$ satisfies certain tameness property. More precisely, we prove that if $N/N' is $2(c(n 1) 1) tame as a $G/N$ module, $c$ the nilpotency class of $N$ then \mathrm{vb}_j(G)\sup_{M\in\mathcal{A}_G}\dim_\mathbb{Q} H_j(M,\mathbb{Q} is finite for all $0\leq j\leq n$ where \mathcal{A}_G$ is the set of all finite index subgroups of $G$
Self Enforcing Online Dispute Resolution: Lessons from Bitcoin

The enforcement of outcomes in online dispute resolution (ODR) is a delicate problem. Since disputes arising out of e commerce transactions are typically low in value, the traditional channels of coercive enforcement are often not a viable option. The article argues that the Bitcoin system can be used as a source of inspiration to devise new models of self enforcement. The article describes the legal framework of ODR and argues that the goal of self enforcement can be attained through the use of technology. It then describes the relevant features of the Bitcoin system, underlining its potential as a new forum for the expression of private autonomy. It then investigates the features of Bitcoin adjudication, before arguing that Bitcoin must be regarded as an original and self contained system of dispute resolution, whose characteristics can be used to theorise new models of self enforcement. Next, it compares four alternative models of self enforcement, two of which take Bitcoin adjudication as an example. Finally, it puts forth recommendations for all actors involved in the implementation of self enforcing ODR mechanisms and argues that different models should be left free to compete.
Microparticles and cancer thrombosis in animal models.
Venue: Thrombosis research

Cancer associated venous thromboembolism (VTE) constitutes the second cause of death after cancer. Many risk factors for cancer associated VTE have been identified, among them soluble tissue factor and microparticles (MPs) Few data are available about the implication of MPs in cancer associated VTE through animal model of cancer. The objective of the present review was to report the state of the current literature about MPs and cancer associated VTE in animal model of cancer. Fourteen series have reported the role of MPs in cancer associated VTE, through three main mouse models: ectopic or orthotopic tumor induction, experimental metastasis by intravenous injection of tumor cells into the lateral tail vein of the mouse. Pancreatic cancer is the most used animal model, due to its high rate of cancer associated VTE. All the series reported that tumor cell derived MPs can promote thrombus formation in TF dependent manner. Some authors reported also the implication of phosphatidylserine and PSGL1 in the generation of thrombin. Moreover, MPs seem to be implicated in cancer progression through a coagulation dependent mechanism secondary to thrombocytosis, or a mechanism implicating the regulation of the immune response. For these reasons, few authors have reported that antiplatelet and anticoagulant treatments may prevent tumor progression and the formation of metastases in addition of coagulopathy.
Modeling Soil Salinity in Greenhouse Cultivations Under a Changing Climate With SALTMED: Model Modification and Application in Timpaki, Crete

Soil salinity is a major soil degradation threat especially for arid coastal environments where it hinders agricultural production, thus imposing a desertification risk. In the prospect of a changing climate, soil salinity caused by brackish water irrigation introduces additional uncertainties regarding the viability of deficit irrigation and intensive cultivation practices such as greenhouse cropping. Here, we propose a modification of the SALTMED leaching requirement model to account for greenhouse cultivation conditions. The model is applied in the RECARE Project Case Study of Timpaki, a semiarid region in south central Crete, Greece, where greenhouse horticulture is an important land use. Excessive groundwater abstractions toward irrigation have resulted in a drop of the groundwater level in the coastal part of the aquifer, thus leading to seawater intrusion and in turn to soil salinization. Crop yield and soil profile electrical conductivity (EC) sensitivity to initial soil EC (up to 2 dS m 1) and irrigation water EC (up to 3 dS m 1) are modeled for the locally popular horticultural crops of Solanum lycopersicum, Solanum melongena, and Capsicum annuum. Climate model data obtained from nine general circulation models for the "worst case" representative concentration pathway of 8.5 W m 2 of the fifth phase of the Coupled Model Intercomparison Project are corrected for bias against historical observations with the Multisegment Statistical Bias Correction method and used to estimate crop yield and soil profile EC sensitivity in a warmer future. Results show that the effects of climate change on S. lycopersicum greenhouse cultivations of Timpaki will be detrimental, whereas S. melongena and C. annuum cultivations may show greater resilience.
Improving the accounting of field emissions in the carbon footprint of agricultural products: a comparison of default IPCC methods with readily available medium effort modeling approaches
Venue: The International Journal of Life Cycle Assessment

PurposeThe estimations of greenhouse gas (GHG) field emissions from fertilization and soil carbon changes are challenges associated with calculating the carbon footprint (CFP) of agricultural products. At the regional level, the IPCC Guidelines for National Greenhouse Gas Inventories (2006a) Tier 1 approach, based on default emission factors, insufficiently accounts for emission variability resulting from pedo climatic conditions or management practices. However, Tier 2 and 3 approaches are usually considered too complex to be practicable. In this paper, we discuss different readily available medium effort methods to improve the accuracy of GHG emission estimates.MethodsWe present four case studies two wheat crops in Germany and two peach orchards in Italy to test the performance of Tier 1, 2, and 3 methodologies and compare the estimated results with available field measurements. The methodologies selected at Tier 2 and Tier 3 level are characterized by simple implementation and data collection, for which only a medium level of effort for stakeholders is required. The Tier 2 method consists of calculating direct and indirect N2O, emissions from fertilization with a multivariate empirical model which accounts for pedo climatic and crop management conditions. The Tier 3 method entails simulation of soil carbon stock change using the Rothamsted carbon model.Results and discussionRelevant differences were found among the tested methodologies: in all case studies, the Tier 1 approach exceeded the Tier 2 estimations for fertilizer induced emissions (up to +50 and the measurements. Using this higher Tier approach reduced the estimated CFP calculation of annual crops by 4 and 21 and that of the perennial crop by 7 Removals related to positive soil carbon change calculated using the Tier 1 approach also exceeded the Tier 3 calculations for the studied annual crops (up to +90 but considerably underrated the Tier 3 estimations and measurements for perennial crops 75 In this case, the impact of the selected Tier method on the final CFP results was even more relevant: an increase of 194 and 88 for the studied annual crops and a decrease of 67 for the perennial crop case study.ConclusionsThe use of higher Tiers for the estimation of land based emissions is strongly recommended to improve the accuracy of the CFP results. The suggested medium effort methods tested in this study represent a good compromise between complexity reduction and accuracy improvement and can be considered reliable for the assessment of GHG mitigation potentials.
Fully Anonymous Transferable Ecash
Venue: IACR Cryptol. ePrint Arch.

Numerous electronic cash schemes have been proposed over the years ranging from Ecash, Mondex to Millicent. However none of these schemes have been adopted by the financial institutions as an alternative to traditional paper based currency. The Ecash scheme was the closest to a system that mimicked fiat currency with the property that it provided anonymity for users when buying coins from the Bank and spending them at a merchant premises (from the Bank's perspective) However Ecash lacked one crucial element present in current fiat based systems i.e. the ability to continuously spend or transfer coins within the system. In this paper we propose an extension to the Ecash scheme which allows for the anonymous transfer of coins between users without the involvement of a trusted third party. We make use of a powerful technique which allows for distributed decision making within the network namely the Bitcoin blockchain protocol. Combined with the proof of work technique and the classical discrete logarithm problem we are able to continuously reuse coins within our system, and also prevent double spending of coins without revealing the identities of the users.
Optimality and Modularity in Human Movement: From Optimal Control to Muscle Synergies
Venue: Biomechanics of Anthropomorphic Systems

In this chapter, we review recent work related to the optimal and modular control hypotheses for human movement. Optimal control theory is often thought to imply that the brain continuously computes global optima for each motor task it faces. Modular control theory typically assumes that the brain explicitly stores genuine synergies in specific neural circuits whose combined recruitment yields task effective motor inputs to muscles. Put this way, these two influential motor control theories are pushed to extreme positions. A more nuanced view, framed within Marr's tri level taxonomy of a computational theory of movement neuroscience, is discussed here. We argue that optimal control is best viewed as helping to understand "why" certain movements are preferred over others but does not say much about how the brain would practically trigger optimal strategies. We also argue that dimensionality reduction found in muscle activities may be a by product of optimality and cannot be attributed to neurally hardwired synergies stricto sensu, in particular when the synergies are extracted from simple factorization algorithms applied to electromyographic data; their putative nature is indeed strongly dictated by the methodology itself. Hence, more modeling work is required to critically test the modularity hypothesis and assess its potential neural origins. We propose that an adequate mathematical formulation of hierarchical motor control could help to bridge the gap between optimality and modularity, thereby accounting for the most appealing aspects of the human motor controller that robotic controllers would like to mimic: rapidity, efficiency, and robustness.