Miracle-dz/UniFAR-FTUs huggingface dataset:
Game-themed programming assignments for faculty: a case study

Despite the proven success of using computer video games as a context for teaching introductory programming (CS1/2) courses, barriers including the lack of adoptable materials, required background expertise (in graphics/games), and institutional acceptance still prevent interested faculty members from experimenting with this approach. The Game-Themed programming Assignment (GTA) modules are designed specifically for these faculty members such that they can selectively pick and choose a subset to experiment with and gradually adopt the materials in their own classes. The design and academic merits of the GTA modules have been verified and presented previously [24]. This paper begins by describing results from GTA workshops for CS1/2 faculty and goes on to detail the results of our year-long project in adopting the GTA modules in classes. In this case, we have demonstrated that introductory programming concepts can be examined, practiced, and learned based on GTA modules when neither the faculty nor the students involved have backgrounds in graphics or games. More importantly, our results showed that it is straightforward to blend the GTA modules into existing classes with minimum alterations. In these ways, the GTA modules are excellent catalysts enabling faculty to begin exploring teaching with game-themed materials.
Cytochrome P450 Inhibitory Properties of Common Efflux Transporter Inhibitors

Drug transporter inhibitors are important tools to elucidate the contribution of transporters to drug disposition both in vitro and in vivo. These inhibitors are often unselective and affect several transporters as well as drug metabolizing enzymes, which can make experimental results difficult to interpret with confidence. We therefore tested 14 commonly used P-glycoprotein (P-gp), breast cancer resistance protein (BCRP), and multidrug-resistance associated protein (MRP) inhibitors as inhibitors of cytochrome P450 (P450) enzyme activities using recombinant enzymes. A subset of P-gp and/or CYP3A inhibitors were selected (cyclosporin A, elacridar, ketoconazole, quinidine, reserpine, and tacrolimus) for a comparison of P450 inhibition in human microsomes and hepatocytes. Most P-gp inhibitors showed CYP3A4 inhibition, with potencies often in a similar range as their P-gp inhibition, as well as less potent CYP2C19 inhibition. Other P450 enzymes were not strongly inhibited except a few cases of CYP2D6 inhibition. MRP and BCRP inhibitors showed limited P450 inhibition. Some inhibitors showed less P450 inhibition in human hepatocytes than human liver microsomes, for example, elacridar, probably due to differences in binding, permeability limitations, or active, P-gp mediated efflux of the inhibitor from the hepatocytes. Quinidine was a potent P450 inhibitor in hepatocytes but only showed weak inhibition in microsomes. Quinidine shows an extensive cellular uptake, which may potentiate intracellular P450 inhibition. Elacridar, described as a potent and selective P-gp inhibitor, displayed modest P450 inhibition in this study and is thus a useful model inhibitor to define the role of P-gp in drug disposition without interference with other processes.
Portfolio Credit Risk : A Model of Correlated Credit Losses Dynamics and the Inverse-Gamma Approximation

Top-down’ approach of portfolio credit risk lacks of transparency in terms of explicit implications at the ‘down’ level. ‘Bottom-up’ approach is less flexible to accommodate for modeling the dynamics of credit loss. In this paper, a hybrid approach is proposed, which combines both the modeling parsimony of ‘top-down’ models and the explicit implications of ‘bottom-up’ models in terms of default risk of single names. Correlated forward credit losses of the n constituents of the pool (or index) are modeled under a HJM-like framework. This allows for arbitrage-free dynamics of single names’ defaults matching the n single names’ CDS spread curves and an explicit characterization of a name-sensitive credit loss correlation structure. Under an explicit setup of correlated lognormal cumulated credit losses, it is argued that the Inverse-Gamma distribution (with time-varying shape and scaling parameters generated back at the ‘down’ level by the HJM-like model) approximates the credit pool loss distribution, which results in a simple closed-form solution of CDO spreads. Calibration is made efficiently thanks to a name grouping technique and shows a high fitting power of the model, particularly for the skew of the market CDO spreads. Calibration procedure, model extensions and numerical examples illustrating the impact of the subprime crisis are discussed. Furthermore, Monte Carlo methods for the simulation of the Inverse-Gamma pool loss dynamics are developed to price CDO derivatives with early-exercise-style and/or losstrigger features. JEL classifications: G13.
A Fast Incremental Algorithm for Maintaining Dispatchability of Partially Controllable Plans

Autonomous systems operating in real-world environments must be able to plan, schedule, and execute missions while robustly adapting to uncertainty and disturbances. Previous work on dispatchable execution increases the efficiency of plan execution under uncertainty by introducing a temporal plan dispatcher that schedules events dynamically in response to disturbances, and a compiler that reduces a plan to a dispatchable form that enables real-time scheduling. However, this work does not address the situation where response requires modifying the plan in real-time. In these situations, after the autonomous system replans, compilation to dispatchable form must occur in near real-time. The key contribution of this paper is a fast Incremental Dynamic Control algorithm (IDC) for maintaining the dispatchability of a partially controllable plan, in response to incremental plan modifications by an online planner. IDC is developed as a set of incremental update rules that exploit the structure of the plan in order to efficiently propagate the effects of constraint loosening and tightening throughout the plan. IDC exhibits an order of magnitude improvement in compile time over the state of the art non-incremental algorithm applied to randomly generated problems. Its practicality is demonstrated on plans for coordinating rovers within the authors' hardware test-bed.
BOND OF EPOXY-COATED REINFORCEMENT TO CONCRETE: SPLICES. FINAL REPORT

The effects of epoxy coating and transverse reinforcement on the splice strength of reinforcing bars in concrete are described. Tests included 65 beam and slab splice specimens for members containing No. 6 and No. 8 bars. The average coating thickness ranged from 6 to 11 mils. Three deformation patterns were used in the study. All but one group of specimens contained Class B ACI/Class C AASHTO splices. The results of the current study are analyzed, along with the results of 48 specimens from earlier studies and used to develop improved development length modification factors for use with epoxy-coated bars. Epoxy coatings are found to significantly reduce splice strength. However, the extent of the reduction is less than used to select the development length modification factors in the 1989 AASHTO Bridge Specifications and 1989 ACI Building Code. The percentage decrease in splice strength caused by epoxy coating is independent of the degree of confining reinforcement, which provides approximately the same percentage increase in the strength of splices of both coated and uncoated reinforcement. A development length modification factor of 1.35 is applicable for design with epoxy-coated reinforcement. An alternate factor of 1.20 is applicable for epoxy-coated bars with a minimum amount of transverse reinforcement, if the positive effects of that transverse reinforcement are not already taken into account in the design provisions. The 1.20 factor is, thus, not applicable to the ACI Building Code but is applicable to the AASHTO Bridge Specifications. This report is the third in a continuing series describing research at the University of Kansas to gain a better understanding and develop accurate design procedures that reflect the changes in bond strength caused by the use of epoxy coating on reinforcing bars.
Crystal Structure of Carbapenemase OXA-58 from Acinetobacter baumannii

ABSTRACT Class D β-lactamases capable of hydrolyzing last-resort carbapenem antibiotics represent a major challenge for treatment of bacterial infections. Wide dissemination of these enzymes in Acinetobacter baumannii elevated this pathogen to the category of most deadly and difficult to treat. We present here the structure of the OXA-58 β-lactamase, a major class D carbapenemase of A. baumannii, determined to 1.30-Å resolution. Unlike two other Acinetobacter carbapenemases, OXA23 and OXA-24, the OXA-58 enzyme lacks the characteristic hydrophobic bridge over the active site, despite conservation of the residues which participate in its formation. The active-site residues in OXA-58 are spatially conserved in comparison to those in other class D β-lactamases. Lys86, which activates water molecules during the acylation and deacylation steps, is fully carboxylated in the OXA-58 structure. In the absence of a substrate, a water molecule is observed in the active site of the enzyme and is positioned in the pocket that is usually occupied by the 6α-hydroxyethyl moiety of carbapenems. A water molecule in this location would efficiently deacylate good substrates, such as the penicillins, but in the case of carbapenems, it would be expelled by the 6α-hydroxyethyl moiety of the antibiotics and a water from the surrounding medium would find its way to the vicinity of the carboxylated Lys86 to perform deacylation. Subtle differences in the position of this water in the acyl-enzyme complexes of class D β-lactamases could ultimately be responsible for differences in the catalytic efficiencies of these enzymes against last-resort carbapenem antibiotics.
Illusory Motion and Mislocalization of Temporally Offset Target in Apparent Motion Display

When a visual target briefly appears in a display containing visual motion information, the perceived position of the target is mislocalized forward along its direction of motion. This phenomenon is assumed to be caused by the interaction between the transient onset signal of the target and motion information. However, while transient onset and offset signals are important for the establishment of our perceptual awareness, it has not been examined whether transient offset signals could be also effective for target mislocalization. Here, we demonstrate that shifts in perceived position occurred for a visual target containing a temporally transient offset signal in an apparent motion (AM) display. First, with horizontal AM, we found that illusory motion was perceived when a static target transiently and repeatedly blinked at a fixed position. The perceived direction of the illusory motion was in counter-phase with that of the AM stimuli. Further, we confirmed that illusory motion was frequently perceived when (1) the eccentricity of the target was larger, (2) offset duration was longer, and (3) smoother AM was perceived. Illusory motion was not perceived unless AM stimuli were presented after the offset signal, while illusory motion still occurred when the AM stimuli disappeared before the offset signal. In addition, we found that mislocalization of the target’s perceived position actually occurred in a direction opposite to AM. These findings suggest that a transient offset signal could trigger perceptual mislocalization of static visual stimuli by interacting with motion information in a postdictive manner.
System pharmacology: Application of network theory in predicting potential adverse drug reaction based on gene expression data

In drug development process, adverse drug reaction (ADR) is one of the biggest challenges to evaluate the drug safety for passing to the market. Genomic expression data following in vitro drug treatments and thus have become widely used in ADR identification and prediction. In this research, we develop the prediction method by using system pharmacology-based study. We performed the proteomic, small molecular compounds — protein interaction and ADR data based on Connectivity Map database. A major protein-drug-side effect (PDS) network and a protein-drug (PD) network were obtained and analyzed by followed network centrality study, which allows for selection of side effects that are defined as central nodes. From the result, the top ranking of novel side effects was identified. In a case study, we established prediction models for 2,3, 7, 8-tetra-chlorodibenzo-p-dioxin (TCDD) in breast cancer treatment adverse events. In conclusion, the network-based approach provided the relationship between protein targets network and side effects based on the gene expression profiles and can predict the potential side effects for new a combinatory drug in the drug development process.
Eye Contact Is Crucial for Referential Communication in Pet Dogs

Dogs discriminate human direction of attention cues, such as body, gaze, head and eye orientation, in several circumstances. Eye contact particularly seems to provide information on human readiness to communicate; when there is such an ostensive cue, dogs tend to follow human communicative gestures more often. However, little is known about how such cues influence the production of communicative signals (e.g. gaze alternation and sustained gaze) in dogs. In the current study, in order to get an unreachable food, dogs needed to communicate with their owners in several conditions that differ according to the direction of owners' visual cues, namely gaze, head, eyes, and availability to make eye contact. Results provided evidence that pet dogs did not rely on details of owners' direction of visual attention. Instead, they relied on the whole combination of visual cues and especially on the owners' availability to make eye contact. Dogs increased visual communicative behaviors when they established eye contact with their owners, a different strategy compared to apes and baboons, that intensify vocalizations and gestures when human is not visually attending. The difference in strategy is possibly due to distinct status: domesticated vs wild. Results are discussed taking into account the ecological relevance of the task since pet dogs live in human environment and face similar situations on a daily basis during their lives.
Retrieval of Remote Sensing Images with Pattern Spectra Descriptors

The rapidly increasing volume of visual Earth Observation data calls for effective content based image retrieval solutions, specifically tailored for their high spatial resolution and heterogeneous content. In this paper, we address this issue with a novel local implementation of the well-known morphological descriptors called pattern spectra. They are computationally efficient histogram-like structures describing the global distribution of arbitrarily defined attributes of connected image components. Besides employing pattern spectra for the first time in this context, our main contribution lies in their dense calculation, at a local scale, thus enabling their combination with sophisticated visual vocabulary strategies. The Merced Landuse/Landcover dataset has been used for comparing the proposed strategy against alternative global and local content description methods, where the introduced approach is shown to yield promising performances.
Guide to best practices for ocean acidification research and data reporting

Ocean acidiﬁcation is an undisputed fact. The ocean presently takes up one-fourth of the carbon CO2 emitted to the atmosphere from human activities. As this CO2 dissolves in the surface ocean, it reacts with seawater to form carbonic acid, increasing ocean acidity and shifting the partitioning of inorganic carbon species towards increased CO2 and dissolved inorganic carbon, and decreased concentration of carbonate ion. While our understanding of the possible consequences of ocean acidiﬁcation is still rudimentary, both the scientific community and the society at large are increasingly concerned about the possible risks associated with ocean acidiﬁcation for marine organisms and ecosystems. As this new and pressing ﬁeld of marine research gains momentum, many in our community, including representatives of coordinated research projects, international scientific organisations, funding agencies, and scientists in this ﬁeld felt the need to provide guidelines and standards for ocean acidiﬁcation research. To initiate this process, the European Project on Ocean Acidiﬁcation (EPOCA) and the International Oceanographic Commission (IOC) jointly invited over 40 leading scientists active in ocean acidiﬁcation research to a meeting at the Leibniz Institute of Marine Science (IFM-GEOMAR) in Kiel, Germany on 19-21 November 2008. At the meeting, which was sponsored by EPOCA, IOC, the Scientific Council on Oceanic Research (SCOR), the U.S. Ocean Carbon and Biogeochemistry Project (OCB) and the Kiel Excellence Cluster “The Future Ocean”, the basic structure and contents of the guide was agreed upon and an outline was drafted. In the following months, the workshop participants and additional invited experts prepared draft manuscripts for each of the sections, which were subsequently reviewed by independent experts and revised according to their recommendations. Starting 15 May 2009, the guide was made publicly available for an open community review.
Dynamic Analysis of the Conditional Oscillator Underlying Slow Waves in Thalamocortical Neurons

During non-REM sleep the EEG shows characteristics waves that are generated by the dynamic interactions between cortical and thalamic oscillators. In thalamic neurons, low-threshold T-type Ca2+ channels play a pivotal role in almost every type of neuronal oscillations, including slow (< 1 Hz) waves, sleep spindles and delta waves. The transient opening of T channels gives rise to the low threshold spikes (LTSs), and associated high frequency bursts of action potentials, that are characteristically present during sleep spindles and delta waves, whereas the persistent opening of a small fraction of T channels, (i.e., ITwindow) is responsible for the membrane potential bistability underlying sleep slow oscillations. Surprisingly thalamocortical (TC) neurons express a very high density of T channels that largely exceed the amount required to generate LTSs and therefore, to support certain, if not all, sleep oscillations. Here, to clarify the relationship between T current density and sleep oscillations, we systematically investigated the impact of the T conductance level on the intrinsic rhythmic activities generated in TC neurons, combining in vitro experiments and TC neuron simulation. Using bifurcation analysis, we provide insights into the dynamical processes taking place at the transition between slow and delta oscillations. Our results show that although stable delta oscillations can be evoked with minimal T conductance, the full range of slow oscillation patterns, including groups of delta oscillations separated by Up states (“grouped-delta slow waves”) requires a high density of T channels. Moreover, high levels of T conductance ensure the robustness of different types of slow oscillations.
Ranking by Dependence - A Fair Criteria

Estimating the dependences between random variables, and ranking them accordingly, is a prevalent problem in machine learning. Pursuing frequentist and information-theoretic approaches, we first show that the p-value and the mutual information can fail even in simplistic situations. We then propose two conditions for regularizing an estimator of dependence, which leads to a simple yet effective new measure. We discuss its advantages and compare it to well-established model-selection criteria. Apart from that, we derive a simple constraint for regularizing parameter estimates in a graphical model. This results in an analytical approximation for the optimal value of the equivalent sample size, which agrees very well with the more involved Bayesian approach in our experiments.
Need for Cognition and Psychosocial Adjustment in Prostate Cancer Patients and Partners

Abstract Individual differences in cognitive style, specifically need for cognition (NFC), may play an important role in facilitating communication and psychosocial adjustment to cancer during the presurgical period, a time marked by distress and the need to process diseaserelated information. This study examines the relations between NFC, adjustment, and communication in 106 prostate cancer patients and their partners within 2 weeks prior to radical prostatectomy. High NFC was significantly associated with better psychological adjustment for partners only, whereas for patients, communication with the medical team played a more important role. High NFC patients who were partnered with high NFC partners reported better dyadic communication compared with those who were partnered with low NFC partners. This study indicates that predictors of adjustment may differ for patients and partners, who are likely differentially affected by the disease process.
Active Learning With Gaussian Process Classifier for Hyperspectral Image Classification

Gaussian process (GP) classifiers represent a powerful and interesting theoretical framework for the Bayesian classification of hyperspectral images. However, the collection of labeled samples is time consuming and costly for hyperspectral data, and the training samples available are often not enough for an adequate learning of the GP classifier. Moreover, the computational cost of performing inference using GP classifiers scales cubically with the size of the training set. To address the limitations of GP classifiers for hyperspectral image classification, reducing the label cost and keeping the training set in a moderate size, this paper introduces an active learning (AL) strategy to collect the most informative training samples for manual labeling. First, we propose three new AL heuristics based on the probabilistic output of GP classifiers aimed at actively selecting the most uncertain and confusing candidate samples from the unlabeled data. Moreover, we develop an incremental model updating scheme to avoid the repeated training of the GP classifiers during the AL process. The proposed approaches are tested on the classification of two realworld hyperspectral data. Comparison with random sampling method reveals a better accuracy gain and faster convergence with the number of queries, and comparison with recent active learning approaches shows a competitive performance. Experimental results also verified the efficiency of the incremental model updating scheme.
Texture Analysis in Lung HRCT Images

Automatic classification of lung tissue patterns in high resolution computed tomography images of patients with interstitial lung diseases is an important stage in the construction of a computer-aided diagnosis system. To this end, a novel approach is proposed using two sets of overcomplete wavelet filters, namely discrete wavelet frames (DWF) and rotated wavelet frames (RWF), to extract the features which best characterizes the lung tissue patterns. Support vector machines learning algorithm is then applied to perform the pattern classification. Four different lung patterns (ground glass, honey combing, reticular, and normal) selected from a database of 340 images are classified using the proposed method. The overall multiclass accuracy reaches 90.72%, 95.85%, and 96.81% for DWF, RWF, and their combination, respectively. These results prove that RWF is superior to DWF, due to its orientation selectivity. However, best results are obtained by the combination of two filter banks which shows that the two set of filters are complementary.