Targeting T cells against brain tumors with a bispecific ligand-antibody conjugate

Haemophilus influenzae is a bacterium of medical interest of which the entire genome has been sequenced. The proteome of the microorganism has been analyzed by two-dimensional gel electrophoresis, during which immobilized pH 3-10 gradient strips were used and approximately 300 proteins were identified. In order to detect additional, basic proteins, we analyzed the soluble protein fraction of H. influenzae and the proteins of fractions collected from affinity chromatography on heparin, by two-dimensional gel electrophoresis, using for the first-dimensional separation immobilized pH gradient strips comprising the pH region of 6-11. The protein spots were analyzed by matrix-assisted laser desorption ionization-mass spectrometry. One hundred and two proteins were identified, of which 58 were identified for the first time. A large percentage of the basic proteins represent nucleic acid binding and, in particular, ribosomal proteins. The locations of the identified basic proteins of H. influenzae are indicated in a two-dimensional map.     

Successful ATM Network Implementations

ATM promises increased functionality and performance for users and decreased costs for corporate voice and data networks. the tips provided here focus on the vendor selection and installation processes, which are critical to the successful design and implementation of an ATM network.     

Assessing Optimal Digital Elevation Model Selection for Active River Area Delineation Across Broad Regions

Water Resources Management - The Active River Area (ARA) is a spatial approach for identifying the extent of functional riparian area. Given known limitations in terms of input elevation data...     

Population effect of a large‐scale stream restoration effort on Chinook salmon in the Pahsimeroi River,Idaho

Stream habitat restoration is an important tool for fisheries management in impaired lotic systems. Although small‐scale benefits of stream habitat restoration are commonly investigated, it is difficult to demonstrate population effects. The Pahsimeroi River Chinook salmon Oncorhynchus tshawytscha population was previously restricted to the lower portion of the river by multiple irrigation structures. To address fish passage issues, a combination of restoration projects was initiated including barrier removals, instream flow enhancements and installation of fish screens on diversions. The largest barrier was removed in 2009, more than doubling the amount of accessible linear habitat. We hypothesized restoration efforts would expand the distribution of spawning salmon in the Pahsimeroi River watershed, leading to a broader distribution of juveniles. We also hypothesized a broader juvenile distribution would have population effects by reducing the prevalence of density‐dependent growth and survival. Redds were documented in newly accessible habitat immediately following barrier removal and accounted for a median of 42% of all redds in the Pahsimeroi River watershed during 2009–2015. Snorkel surveys also documented juvenile rearing in newly accessible habitat. Juvenile productivity increased from a median of 64 smolts/female spawner for brood years 2002–2008 to 99 smolts/female spawner for brood years 2009–2014. Overall, results suggested increased habitat accessibility in the Pahsimeroi River broadened the distribution of spawning adult and rearing juvenile salmon and reduced the effects of density‐dependent survival. Large‐scale stream restoration efforts can have a population effect. Despite the large‐scale effort and response, habitat restoration alone is likely not sufficient to restore this population.     

Artificial intelligence capability: Conceptualization,measurement calibration,and empirical study on its impact on organizational creativity and firm performance

Artificial intelligence (AI) has been heralded by many as the next source of business value. Grounded on the resource-based theory of the firm and on recent work on AI at the organizational context, this study (1) identifies the AI-specific resources that jointly create an AI capability and provides a definition, (2) develops an instrument to capture the AI capability of the firms, and (3) examines the relationship between an AI capability and organizational creativity and performance. Findings empirically support the suggested theoretical framework and corresponding instrument and provide evidence that an AI capability results in increased organizational creativity and performance.     

Gaze tutor: A gaze-reactive intelligent tutoring system

We developed an intelligent tutoring system (ITS) that aims to promote engagement and learning by dynamically detecting and responding to students' boredom and disengagement. The tutor uses a commercial eye tracker to monitor a student's gaze patterns and identify when the student is bored, disengaged, or is zoning out. The tutor then attempts to reengage the student with dialog moves that direct the student to reorient his or her attentional patterns towards the animated pedagogical agent embodying the tutor. We evaluated the efficacy of the gaze-reactive tutor in promoting learning, motivation, and engagement in a controlled experiment where 48 students were tutored on four biology topics with both gaze-reactive and non-gaze-reactive (control condition) versions of the tutor. The results indicated that: (a) gaze-sensitive dialogs were successful in dynamically reorienting students’ attentional patterns to the important areas of the interface, (b) gaze-reactivity was effective in promoting learning gains for questions that required deep reasoning, (c) gaze-reactivity had minimal impact on students’ state motivation and on self-reported engagement, and (d) individual differences in scholastic aptitude moderated the impact of gaze-reactivity on overall learning gains. We discuss the implications of our findings, limitations, future work, and consider the possibility of using gaze-reactive ITSs in classrooms.     

A hyperheuristic approach to examination timetabling problems: benchmarks and a new problem from practice

Many researchers studying examination timetabling problems focus on either benchmark problems or problems from practice encountered in their institutions. Hyperheuristics are proposed as generic optimisation methods which explore the search space of heuristics rather than direct solutions. In the present study, the performance of tournament-based hyperheuristics for the exam timetabling problem are investigated. The target instances include both the Toronto and ITC 2007 benchmarks and the examination timetabling problem at KAHO Sint-Lieven (Ghent, Belgium). The Toronto and ITC 2007 benchmarks are post-enrolment-based examination timetabling problems, whereas the KAHO Sint-Lieven case is a curriculum-based examination timetabling problem. We drastically improve the previous (manually created) solution for the KAHO Sint-Lieven problem by generating a timetable that satisfies all the hard and soft constraints. We also make improvements on the best known results in the examination timetabling literature for seven out of thirteen instances for the To ronto benchmarks. The results are competitive with those of the finalists of the examination timetabling track of the International Timetabling Competition.     

Identifying the criterion spontaneously minimized during the take-off phase of a sub-maximal long jump through optimal synthesis

Optimal synthesis of human movement or the prediction of the kinematics of a new movement require not only that the multi-body system be modeled but also that a performance criterion is specified. For sub-maximal movements the selection of a suitable performance criterion, able to generate realistic dynamic behavior is difficult. A two-dimensional simulation model of the take-off phase of a sub-maximal long jump was developed to study the effect of criterion choice on the realism of simulated movements. A parametric optimization technique was employed to obtain solutions to the constrained equations of motion. Seven different criteria were evaluated, by comparing simulated movements with an actual performance, to identify the criterion which most closely approximated that spontaneously minimized by the athlete. Synthesis of the take-off phase of a sub-maximal long jump was found to be sensitive to the chosen criterion, with a criterion based on minimizing joint intersegmental forces found to perform well.     

A code tagging approach to software product line development

Software product line engineering seeks to systematise reuse when developing families of similar software systems so as to minimise development time, cost and defects. To realise variability at the code level, product line methods classically advocate usage of inheritance, components, frameworks, aspects or generative techniques. However, these might require unaffordable paradigm shifts for developers if the software was not thought at the outset as a product line. Furthermore, these techniques can be conflicting with a company’s coding practices or external regulations. These concerns were the motivation for the industry–university collaboration described in this paper in which we developed a minimally intrusive coding technique based on tags. The approach was complemented with traceability from code to feature diagrams which were exploited for automated configuration. It is supported by a toolchain and is now in use in the partner company for the development of flight-grade satellite communication software libraries.     

Learning local linear Jacobians for flexible and adaptive robot arm control

Successful planning and control of robots strongly depends on the quality of kinematic models, which define mappings between configuration space (e.g. joint angles) and task space (e.g. Cartesian coordinates of the end effector). Often these models are predefined, in which case, for example, unforeseen bodily changes may result in unpredictable behavior. We are interested in a learning approach that can adapt to such changes—be they due to motor or sensory failures, or also due to the flexible extension of the robot body by, for example, the usage of tools. We focus on learning locally linear forward velocity kinematics models by means of the neuro-evolution approach XCSF. The algorithm learns self-supervised, executing movements autonomously by means of goal-babbling. It preserves actuator redundancies, which can be exploited during movement execution to fulfill current task constraints. For detailed evaluation purposes, we study the performance of XCSF when learning to control an anthropomorphic seven degrees of freedom arm in simulation. We show that XCSF can learn large forward velocity kinematic mappings autonomously and rather independently of the task space representation provided. The resulting mapping is highly suitable to resolve redundancies on the fly during inverse, goal-directed control.