nils-herrmann/clean_scirepeval huggingface dataset:
Cell-permeable nanobodies for targeted immunolabelling and antigen manipulation in living cells.

Functional antibody delivery in living cells would enable the labelling and manipulation of intracellular antigens, which constitutes a long-thought goal in cell biology and medicine. Here we present a modular strategy to create functional cell-permeable nanobodies capable of targeted labelling and manipulation of intracellular antigens in living cells. The cell-permeable nanobodies are formed by the site-specific attachment of intracellularly stable (or cleavable) cyclic arginine-rich cell-penetrating peptides to camelid-derived single-chain VHH antibody fragments. We used this strategy for the non-endocytic delivery of two recombinant nanobodies into living cells, which enabled the relocalization of the polymerase clamp PCNA (proliferating cell nuclear antigen) and tumour suppressor p53 to the nucleolus, and thereby allowed the detection of protein-protein interactions that involve these two proteins in living cells. Furthermore, cell-permeable nanobodies permitted the co-transport of therapeutically relevant proteins, such as Mecp2, into the cells. This technology constitutes a major step in the labelling, delivery and targeted manipulation of intracellular antigens. Ultimately, this approach opens the door towards immunostaining in living cells and the expansion of immunotherapies to intracellular antigen targets.
Pharmacological study of the light/dark preference test in zebrafish (<ce:italic>Danio rerio</ce:italic>): Waterborne administration

Anxiety is a complex disorder; thus, its mechanisms remain unclear. Zebrafish (Danio rerio) are a promising pharmacological model for anxiety research. Light/dark preference test is a behaviorally validated measure of anxiety in zebrafish; however, it requires pharmacological validation. We sought to evaluate the sensitivity of the light/dark preference test in adult zebrafish by immersing them in drug solutions containing clonazepam, buspirone, imipramine, fluoxetine, paroxetine, haloperidol, risperidone, propranolol, or ethanol. The time spent in the dark environment, the latency time to first crossing, and the number of midline crossings were analyzed. Intermediate concentrations of clonazepam administered for 600s decreased the time spent in the dark and increased locomotor activity. Buspirone reduced motor activity. Imipramine and fluoxetine increased time spent in the dark and the first latency, and decreased the number of alternations. Paroxetine did not alter the time in the dark; however, it increased the first latency time and decreased locomotor activity. Haloperidol decreased the time spent in the dark at low concentrations. Risperidone and propranolol did not change any parameters. Ethanol reduced the time spent in the dark and increased the number of crossings at intermediate concentrations. These results corroborate the previous work using intraperitoneal drug administration in zebrafish and rodents, suggesting that water drug delivery in zebrafish can effectively be used as an animal anxiety model.
Insertion Sort is O(n log n)

Traditional Insertion Sort runs in O(n2) time because each insertion takes O(n) time. When people run Insertion Sort in the physical world, they leave gaps between items to accelerate insertions. Gaps help in computers as well. This paper shows that Gapped Insertion Sort has insertion times of O(log n) with high probability, yielding a total running time of O(n log n) with high probability.
Maintainability and safety indicators at design stage for mechanical products

With the industrial products competition, reliability, maintainability and safety are key characteristics for availability improvement. This is mainly true in industries like automotive, aeronautic, or NC machines tools. Therefore, it is crucial to predict these characteristics as soon as possible before the manufacturing starts. This paper presents an approach to provide indicators for maintainability and safety prediction at early stage of design. The assessment procedure uses the product CAD 3D model and an associated semantic matrix gathering information on the product components criticality and reliability. Using this information we calculate indicators for the product maintainability and safety. An academic application is developed to illustrate our approach and point out the interaction between maintainability and safety constraints to determine suitable solution. In conclusion, we state on possible extensions of this approach for evaluating other product lifecycle characteristics and we give guidelines on the implementation aspects.
Building Blocks for DNA Self-Assembly

DNA complexes, like the double crossover, are used as building blocks for the assembly of higher-order structures. Currently, the number of experimentally proven reliable complexes is small. We have begun work on expanding the collection of such complexes. Here we report on our design concepts and initial experiments. In particular, we present experimental evidence of two new complexes: quadruple crossovers and triangles. In principle, quadruple crossovers can be extended to three-dimensional, spacefilling lego brick complexes, while triangles are capable of hexagonally tiling the plane.
Towards User-Oriented RBAC Model

Role mining recently has attracted much attention from the role-based access control (RBAC) research community as it provides a machine-operated means of discovering roles from existing permission assignments. While there is a rich body of literature on role mining, we find that user experience/perception one ultimate goal for any information system is surprisingly ignored by the existing works. This work is the first to study role mining from the end-user perspective. Specifically, based on the observation that end-users prefer simple role assignments, we propose to incorporate to the role mining process a user-role assignment sparseness constraint that mandates the maximum number of roles each user can have. Under this rationale, we formulate user-oriented role mining as two specific problems: one is user-oriented exact role mining problem (RMP), which is obliged to completely reconstruct the given permission assignments, and the other is user-oriented approximate RMP, which tolerates a certain amount of deviation from the complete reconstruction. The extra sparseness constraint poses a great challenge to role mining, which in general is already a hard problem. We examine some typical existing role mining methods to see their applicability to our problems. In light of their insufficiency, we present a new algorithm, which is based on a novel dynamic candidate role generation strategy, tailored to our problems. Experiments on benchmark datasets demonstrate the effectiveness of our proposed algorithm.
Plant-Derived Anti-Inflammatory Compounds: Hopes and Disappointments regarding the Translation of Preclinical Knowledge into Clinical Progress

Many diseases have been described to be associated with inflammatory processes. The currently available anti-inflammatory drug therapy is often not successful or causes intolerable side effects. Thus, new anti-inflammatory substances are still urgently needed. Plants were the first source of remedies in the history of mankind. Since their chemical characterization in the 19th century, herbal bioactive compounds have fueled drug development. Also, nowadays, new plant-derived agents continuously enrich our drug arsenal (e.g., vincristine, galantamine, and artemisinin). The number of new, pharmacologically active herbal ingredients, in particular that of anti-inflammatory compounds, rises continuously. The major obstacle in this field is the translation of preclinical knowledge into evidence-based clinical progress. Human trials of good quality are often missing or, when available, are frequently not suitable to really prove a therapeutical value. This minireview will summarize the current situation of 6 very prominent plant-derived anti-inflammatory compounds: curcumin, colchicine, resveratrol, capsaicin, epigallocatechin-3-gallate (EGCG), and quercetin. We will highlight their clinical potential and/or pinpoint an overestimation. Moreover, we will sum up the planned trials in order to provide insights into the inflammatory disorders that are hypothesized to be beneficially influenced by the compound.
Support slimming for single material based additive manufacturing

In layer-based additive manufacturing (AM), supporting structures need to be inserted to support the overhanging regions. The adding of supporting structures slows down the speed of fabrication and introduces artifacts onto the finished surface. We present an orientation-driven shape optimizer to slim down the supporting structures used in single material-based AM. The optimizer can be employed as a tool to help designers to optimize the original model to achieve a more self-supported shape, which can be used as a reference for their further design. The model to be optimized is first enclosed in a volumetric mesh, which is employed as the domain of computation. The optimizer is driven by the operations of reorientation taken on tetrahedra with ‘facing-down’ surface facets. We formulate the demand on minimizing shape variation as global rigidity energy. The local optimization problem for determining a minimal rotation is analyzed on the Gauss sphere, which leads to a closed-form solution. Moreover, we also extend our approach to create the functions of controlling the deformation and searching for optimal printing directions.
Help or Hinder ? Adjunct Professors and Student Outcomes

College and universities are increasingly using part-time, adjunct instructors on their faculties to facilitate greater fiscal flexibility. However, critics argue that the use of adjuncts is causing the quality of higher education to deteriorate. This paper addresses questions about the impact of adjuncts on student outcomes. Using a unique dataset of public four-year colleges in Ohio, we quantify how having adjunct instructors affects student persistence after the first year. Because students taking courses from adjuncts differ systematically from other students, we use an instrumental variable strategy to address concerns about biases. The findings suggest that, in general, students taking an "adjunct-heavy" course schedule in their first semester are adversely affected. They are less likely to persist into their second year. We reconcile these findings with previous research that shows that adjuncts may encourage greater student interest in terms of major choice and subsequent enrollments in some disciplines, most notably fields tied closely to specific professions. The authors are grateful for helpful suggestions from Ronald Ehrenberg and seminar participants at the NBER Labor Studies Meetings. The authors also thank the Ohio Board of Regents for their support during this research project. Rod Chu, Darrell Glenn, Robert Sheehan, and Andy Lechler provided invaluable access and help with the data. Amanda Starc, James Carlson, Erin Riley, and Suzan Akin provided excellent research assistance. All opinions and mistakes are our own. The authors worked equally on the project and are listed alphabetically.
Immunoliposome-PCR: a generic ultrasensitive quantitative antigen detection system

The accurate quantification of antigens at low concentrations over a wide dynamic range is needed for identifying biomarkers associated with disease and detecting protein interactions in high-throughput microarrays used in proteomics. Here we report the development of an ultrasensitive quantitative assay format called immunoliposome polymerase chain reaction (ILPCR) that fulfills these requirements. This method uses a liposome, with reporter DNA encapsulated inside and biotin-labeled polyethylene glycol (PEG) phospholipid conjugates incorporated into the outer surface of the liposome, as a detection reagent. The antigenic target is immobilized in the well of a microplate by a capture antibody and the liposome detection reagent is then coupled to a biotin-labeled second antibody through a NeutrAvidin bridge. The liposome is ruptured to release the reporter DNA, which serves as a surrogate to quantify the protein target using real-time PCR.



A liposome detection reagent was prepared, which consisted of a population of liposomes ~120 nm in diameter with each liposome possessing ~800 accessible biotin receptors and ~220 encapsulated reporters. This liposome detection reagent was used in an assay to quantify the concentration of carcinoembryonic antigen (CEA) in human serum. This ILPCR assay exhibited a linear dose-response curve from 10-10 M to 10-16 M CEA. Within this range the assay coefficient of variance was <6 % for repeatability and <2 % for reproducibility. The assay detection limit was 13 fg/mL, which is 1,500-times more sensitive than current clinical assays for CEA. An ILPCR assay to quantify HIV-1 p24 core protein in buffer was also developed.



The ILPCR assay has several advantages over other immuno-PCR methods. The reporter DNA and biotin-labeled PEG phospholipids spontaneously incorporate into the liposomes as they form, simplifying preparation of the detection reagent. Encapsulation of the reporter inside the liposomes allows nonspecific DNA in the assay medium to be degraded with DNase I prior to quantification of the encapsulated reporter by PCR, which reduces false-positive results and improves quantitative accuracy. The ability to encapsulate multiple reporters per liposome also helps overcome the effect of polymerase inhibitors present in biological specimens. Finally, the biotin-labeled liposome detection reagent can be coupled through a NeutrAvidin bridge to a multitude of biotin-labeled probes, making ILPCR a highly generic assay system.
The UNET-2 modem — An extensible tool for underwater networking research

Several decades of research in underwater communication and networking has resulted in novel and innovative solutions to combat challenges such as long delay spread, rapid channel variation, significant Doppler, high levels of non-Gaussian noise, limited bandwidth and long propagation delays. Many of the physical layer solutions can be tested by transmitting carefully designed signals, recording them after passing through the underwater channel, and then processing them offline using appropriate algorithms. However some solutions requiring online feedback to the transmitter cannot be tested without real-time processing capability in the field. Protocols and algorithms for underwater networking also require real-time communication capability for experimental testing. Although many modems are commercially available, they provide limited flexibility in physical layer signaling and sensing. They also provide limited control over the exact timing of transmission and reception, which can be critical for efficient implementation of some networking protocols with strict time constraints. To aid in our physical and higher layer research, we developed the UNET-2 software-defined modem with flexibility and extensibility as primary design objectives. We present the hardware and software architecture of the modem, focusing on the flexibility and adaptability that it provides researchers with. We describe the network stack that the modem uses, and show how it can also be used as a powerful tool for underwater network simulation. We illustrate the flexibility provided by the modem through a number of practical examples and experiments.
Automated Bug Finding in Video Games: A Case Study for Runtime Monitoring

Runtime verification is the process of observing a sequence of events generated by a running system and comparing it to some formal specification for potential violations. We show how the use of a runtime monitor can greatly speed up the testing phase of a video game under development by automating the detection of bugs when the game is being played. We take advantage of the fact that a video game, contrarily to generic software, follows a special structure that contains a “game loop.” This game loop can be used to centralize the instrumentation and generate events based on the game's internal state. We report on experiments made on a sample of six real-world video games of various genres and sizes by successfully instrumenting and efficiently monitoring various temporal properties over their execution, including actual bugs reported in the games' bug tracking database in the course of their development.
Ultra-wearable capacitive coupled and common electrode-free ECG monitoring system

Nowadays, transfer of the health care from ambulance to patient's home needs higher demand on patient's mobility, comfort and acceptance of the system. Therefore, the goal of this study is to proof the concept of a system which is ultra-wearable, less constraining and more suitable for long term measurements than conventional ECG monitoring systems which use conductive electrolytic gels for low impedance electrical contact with skin. The developed system is based on isolated capacitive coupled electrodes without any galvanic contact to patient's body and does not require the common right leg electrode. Measurements performed under real conditions show that it is possible to acquire well known ECG waveforms without the common electrode when the patient is sitting and even during walking. Results of the validation process demonstrate that the system performance is comparable to the conventional ECG system while the wearability is increased.
Community detection in content-sharing social networks

Network structure and content in microblogging sites like Twitter influence each other ---user A on Twitter follows user B for the tweets that B posts on the network, and A may then re-tweet the content shared by B to his/her own followers. In this paper, we propose a probabilistic model to jointly model link communities and content topics by leveraging both the social graph and the content shared by users. We model a community as a distribution over users, use it as a source for topics of interest, and jointly infer both communities and topics using Gibbs sampling. While modeling communities using the social graph, or modeling topics using content have received a great deal of attention, a few recent approaches try to model topics in content-sharing platforms using both content and social graph. Our work differs from the existing generative models in that we explicitly model the social graph of users along with the user-generated content, mimicking how the two entities co-evolve in content-sharing platforms. Recent studies have found Twitter to be more of a content-sharing network and less a social network, and it seems hard to detect tightly knit communities from the follower-followee links. Still, the question of whether we can extract Twitter communities using both links and content is open. In this paper, we answer this question in the affirmative. Our model discovers coherent communities and topics, as evinced by qualitative results on sub-graphs of Twitter users. Furthermore, we evaluate our model on the task of predicting follower-followee links. We show that joint modeling of links and content significantly improves link prediction performance on a sub-graph of Twitter (consisting of about 0.7 million users and over 27 million tweets), compared to generative models based on only structure or only content and paths-based methods such as Katz.
Structured Weight Matrices-Based Hardware Accelerators in Deep Neural Networks: FPGAs and ASICs

Both industry and academia have extensively investigated hardware accelerations. To address the demands in increasing computational capability and memory requirement, in this work, we propose the structured weight matrices (SWM)-based compression technique for both Field Programmable Gate Array (FPGA) and application-specific integrated circuit (ASIC) implementations. In the algorithm part, the SWM-based framework adopts block-circulant matrices to achieve a fine-grained tradeoff between accuracy and compression ratio. The SWM-based technique can reduce computational complexity from O(n2) to O(nlog n) and storage complexity from O(n2) to O(n) for each layer and both training and inference phases. For FPGA implementations on deep convolutional neural networks (DCNNs), we achieve at least 152X and 72X improvement in performance and energy efficiency, respectively using the SWM-based framework, compared with the baseline of IBM TrueNorth processor under same accuracy constraints using the data set of MNIST, SVHN, and CIFAR-10. For FPGA implementations on long short term memory (LSTM) networks, the proposed SWM-based LSTM can achieve up to 21X enhancement in performance and 33.5X gains in energy efficiency compared with the ESE accelerator. For ASIC implementations, the proposed SWM-based ASIC design exhibits impressive advantages in terms of power, throughput, and energy efficiency. Experimental results indicate that this method is greatly suitable for applying DNNs onto both FPGAs and mobile/IoT devices.
Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learning

Recent advances in combining deep neural network architectures with reinforcement learning techniques have shown promising potential results in solving complex control problems with high dimensional state and action spaces. Inspired by these successes, in this paper, we build two kinds of reinforcement learning algorithms: deep policy-gradient and value-function based agents which can predict the best possible traffic signal for a traffic intersection. At each time step, these adaptive traffic light control agents receive a snapshot of the current state of a graphical traffic simulator and produce control signals. The policy-gradient based agent maps its observation directly to the control signal, however the value-function based agent first estimates values for all legal control signals. The agent then selects the optimal control action with the highest value. Our methods show promising results in a traffic network simulated in the SUMO traffic simulator, without suffering from instability issues during the training process.
A Mobile Vision System for Urban Detection with Informative Local Descriptors

We present a computer vision system for the detection and identification of urban objects from mobile phone imagery, e.g., for the application of tourist information services. Recognition is based on MAP decision making over weak object hypotheses from local descriptor responses in the mobile imagery. We present an improvement over the standard SIFT key detector [7] by selecting only informative (i-SIFT) keys for descriptor matching. Selection is applied first to reduce the complexity of the object model and second to accelerate detection by selective filtering. We present results on the MPG-20 mobile phone imagery with severe illumination, scale and viewpoint changes in the images, performing with ≈ 98% accuracy in identification, efficient (100%) background rejection, efficient (0%) false alarm rate, and reliable quality of service under extreme illumination conditions, significantly improving standard SIFT based recognition in every sense, providing - important for mobile vision - runtimes which are ≈ 8 (≈24) times faster for the MPG-20 (ZuBuD) database.