Silicon Analogues of the RXR‐Selective Retinoid Agonist SR11237 (BMS649): Chemistry and Biology

C/Si switch : Twofold sila‐substitution (C/Si exchange) in the RXR‐selective retinoids 4 a (SR11237) and 5 a leads to 4 b (disila‐SR11237) and 5 b , respectively. Chemistry and biology of the C/Si pairs are reported.

     

200 ns pulse high-voltage supply for terahertz field emission

We present a method of generating 200 ns high-voltage (up to 40 kV) pulses operating at repetition rates of up to 100 kHz, which may be synchronized with laser pulses. These supplies are simple to make and were developed for ultrafast terahertz pulse generation from GaAs photoconductive antennas using a high-repetition-rate regeneratively amplified laser. We also show an improvement in signal-to-noise ratio over a continuous dc bias field and application of the supply to terahertz pulse generation.     

Efficient breadth-first mining of frequent pattern with monotone constraints

The key point of this article is that, in frequent pattern mining, the most appropriate way of exploiting monotone constraints in conjunction with frequency is to use them in order to reduce the input data; this reduction in turn induces a stronger pruning of the search space of the problem. Following this intuition, we introduce ExAMiner, a breadth-first algorithm that exploits the real synergy of antimonotone and monotone constraints: the total benefit is greater than the sum of the two individual benefits. ExAMiner generalizes the basic idea of the preprocessing algorithm ExAnte (Bonchi et al. 2003(b)), embedding such ideas at all levels of an Apriori-like computation. The resulting algorithm is the generalization of the Apriori algorithm when a conjunction of monotone constraints is conjoined to the frequency antimonotone constraint. Experimental results confirm that this is, so far, the most efficient way of attacking the computational problem in analysis.     

Development of a biomimetic miniature robotic crawler

The paper presents the development of segmented artificial crawlers endowed with passive hook-shaped frictional microstructures. The goal is to find design rules for fabricating biomimetic, adaptable and mobile machines mimicking segmented animals with hydrostatic skeleton, and intended to move effectively along unstructured substrates. The paper describes the mechanical model, the design and the fabrication of a SMA-actuated segmented microrobot, whose locomotion is inspired by the peristaltic motion of Annelids, and in particular of earthworms (Lumbricus Terrestris). Experimental locomotion performance are compared with theoretical performance predicted by a purposely developed friction model -taking into account design parameters such as number of segments, body mass, special friction enhancement appendixes—and with locomotion performance of real earthworms as presented in literature. Experiments indicate that the maximum speed of the crawler prototype is 2.5 mm/s, and that 3-segment crawlers have almost the same velocity as earthworms having the same weight (and about 330% their length), whereas 4-segment crawlers have the same velocity, expressed as body lengths/s, as earthworms with the same mass (and about 270% their length). Arianna Menciassi (MS, 1995; PhD, 1999) joined the CRIM Lab of the Scuola Superiore Sant’Anna (Pisa, Italy) as a Ph.D. student in Bioengineering with a research program on the micromanipulation of mechanical and biological micro-objects. The main results of the activity on micromanipulation were presented at the IEEE International Conference on Robotics & Automation (May 2001, Seoul) in a paper titled “Force Feedback-based Microinstrument for Measuring Tissue Properties and Pulse in Microsurgery”, which won the “ICRA2001 Best Manipulation Paper Award”. In the year 2000, she was offered a position of Assistant Professor in Biomedical Robotics at the Scuola Superiore Sant’Anna and in June 2006 she obtained a promotion to Associate Professor. Her main research interests are in the field of biomedical microrobotics, biomimetics, microfabrication technologies, micromechatronics and microsystem technologies. She is working on several European projects and international projects for the development of minimally invasive instrumentation for medical applications and for the exploitation of micro- and nano-technologies in the medical field. Samuele Gorini received his Laurea Degree in Mechanical Engineering (with honors) from the University of Pisa, Italy, in 2001. In 2005 he obtained the Ph.D. in Microsystem Engineering with a thesis on locomotion methods and systems for miniaturised endoscopic devices. Since 2000, he has been working at the CRIM Lab of the Scuola Superiore Sant’Anna in Pisa, Italy. His research interests are in the field of biomedical robotics with a special focus on actuation technologies. Starting from the year 2004 he has been president of Era Endoscopy S.r.l., a start-up company of Scuola Superiore Sant’Anna developing novel devices for endoscopy. Dino Accoto (MS 1998, PhD 2002) is Assistant Professor of Biomedical Engineering at Scuola Sant’Anna (Pisa, Italy). He received the Laurea degree in Mechanical Engineering from the University of Pisa (cum laude) in 1998, the diploma in Engineering from the Scuola Sant’Anna (cum laude) in 1999 and the PhD degree from the Scuola Sant’Anna in 2002. From October 2001 to September 2002 he has been visiting scholar at the RPL-Lab, Stanford University (Ca, USA). Since 2004 he cooperates with the Biomedical Robotics & EMC Lab at Campus Bio-Medico University in Rome. His main research field is the modelling and development of small electromechanical systems, with a special attention to multi-physics and multi-domain approaches. The research, often inspired by the analysis of natural mechanisms, has been mainly applied to hybridizing microtechnologies, including microfluidics, and robotics. He has co-authored more than 30 papers, appeared in international journals and conference proceedings. Paolo Dario received his Dr. Eng. Degree in Mechanical Engineering from the University of Pisa, Italy, in 1977. He is currently a Professor of Biomedical Robotics at the Scuola Superiore Sant’Anna in Pisa.. He also teaches courses at the School of Engineering of the University of Pisa and at the Campus Biomedico University in Rome. He has been Visiting Professor at Brown University, Providence, RI, USA, at the Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland, at Waseda University, Tokyo, Japan, at the College de France, Paris, and at the Ecole Normale Superieure de Cachan, France. He was the founder of the ARTS (Advanced Robotics Technologies and Systems) Laboratory and is currently the Co-ordinator of the CRIM (Center for the Research in Microengineering) Laboratory of the Scuola Superiore Sant’Anna, where he supervises a team of about 70 researchers and Ph.D. students. His main research interests are in the fields of medical robotics, bio-robotics, mechatronics and micro/nanoengineering, and specifically in sensors and actuators for the above applications, and in robotics for rehabilitation. He is the coordinator of many national and European projects, the editor of two books on the subject of robotics, and the author of more than 200 scientific papers (75 on ISI journals). He is Editor-in-Chief, Associate Editor and member of the Editorial Board of many international journals. Prof. Dario has served as President of the IEEE Robotics and Automation Society in the years 2002–2003. He has been the General Chair of the IEEE RAS-EMBS BioRob’06 Conference and he is the General Co-Chair of ICRA 2007 Conference. Prof. Dario is an IEEE Fellow, a Fellow of the European Society on Medical and Biological Engineering, and a recipient of many honors and awards, such as the Joseph Engelberger Award. He is also a member of the Board of the International Foundation of Robotics Research (IFRR).     

Oil and gas pipeline failure prediction system using long range ultrasonic transducers and Euclidean-Support Vector Machines classification approach

This paper presents an intelligent failure prediction system for oil and gas pipeline using long range ultrasonic transducers and Euclidean-Support Vector Machines classification approach. Since the past decade, the incidents of oil and gas pipeline leaks and failures which happened around the world are becoming more frequent and have caused loss of life, properties and irreversible environmental damages. This situation is due to the lack of a full-proof method of inspection on the condition of oil and gas pipelines. Onset of corrosion and other defects are undetected which cause unplanned shutdowns and disruption of energy supplies to consumers. Existing failure prediction systems for pipeline which use non-destructive testing (NDTs) methods are accurate, but they are deployed at pre-determined intervals which can be several months apart. Hence, a full-proof and reliable inspection method is required to continuously monitor the condition of oil and gas pipeline in order to provide sufficient information and time to oil and gas operators to plan and organize shutdowns before failures occur. Permanently installed long range ultrasonic transducers (LRUTs) offer a solution to this problem by providing an inspection platform that continuously monitor critical pipeline sections. Data are acquired in real-time and processed to make decision based on the condition of the pipe. The continuous nature of the data requires an automatic decision making software rather than manual inspection by operators. Support Vector Machines (SVMs) classification approach has been increasingly used in a multitude of domains including LRUT and has shown better performance than other classification algorithms. SVM is heavily dependent on the choice of kernel functions as well as fine tuning of the kernel and soft margin parameters. Hence it is unsuitable to be used in continuous monitoring of pipeline data where constant modifications of kernels and parameters are not unrealistic. This paper proposes a novel classification technique, namely Euclidean-Support Vector Machines (Euclidean-SVM), to make a decision on the integrity of the pipeline in a continuous monitoring environment. The results show that the classification accuracy of the Euclidean-SVM approach is not dependent on the choice of the kernel function and parameters when classifying data from pipes with simulated defects. Irrespective of the kernel function and parameters chosen, classification accuracy of the Euclidean-SVM is comparable and also higher in some cases than using conventional SVM. Hence, the Euclidean-SVM approach is ideally suited for classifying data from the oil and gas pipelines which are continuously monitored using LRUT.     

Scalable High‐Throughput Production of Modular Microgels for In Situ Assembly of Microporous Tissue Scaffolds

Hydrogel scaffolds that template the regeneration of tissue structures are widely explored; however, there is often a trade‐off between material properties, such as stiffness and interconnected pore size, that may be equally important in supporting tissue growth. Microporous annealed particle scaffolds are introduced to address this trade‐off while maintaining a flowable precursor; however, manufacturing throughput, reproducibility, and flexibility of hydrogel microparticle building blocks are limited, hindering widespread adoption. The scalable high‐throughput production of bioactive microgels for the formation of microporous tissue scaffolds in situ is presented. Using a parallelized step emulsification device, scalable high‐throughput generation of monodisperse microgels is achieved. Crosslinking is initiated downstream of droplet generation using pH modulation via proton acceptors dissolved in the oil phase. This approach enables continuous production of microgels for over 12 h while ensuring highly uniform physicochemical properties. Using this platform, the effects of local matrix stiffness on cell growth orthogonal to scaffold porosity are studied. Formation of injectable cell‐laden mechanically heterogeneous microporous scaffolds is also demonstrated. This approach is particularly suited for the formation of modular, multimaterial scaffolds in situ, which could be applied to 3D bioprinting or to form more complex scaffolds to enhance regeneration of irregular wounds.     

Time-focused clustering of trajectories of moving objects

Spatio-temporal, geo-referenced datasets are growing rapidly, and will be more in the near future, due to both technological and social/commercial reasons. From the data mining viewpoint, spatio-temporal trajectory data introduce new dimensions and, correspondingly, novel issues in performing the analysis tasks. In this paper, we consider the clustering problem applied to the trajectory data domain. In particular, we propose an adaptation of a density-based clustering algorithm to trajectory data based on a simple notion of distance between trajectories. Then, a set of experiments on synthesized data is performed in order to test the algorithm and to compare it with other standard clustering approaches. Finally, a new approach to the trajectory clustering problem, called temporal focussing, is sketched, having the aim of exploiting the intrinsic semantics of the temporal dimension to improve the quality of trajectory clustering. The authors are members of the Pisa KDD Laboratory, a joint research initiative of ISTI-CNR and the University of Pisa: .     

Integrating induction and deduction for finding evidence of discrimination

We present a reference model for finding (prima facie) evidence of discrimination in datasets of historical decision records in socially sensitive tasks, including access to credit, mortgage, insurance, labor market and other benefits. We formalize the process of direct and indirect discrimination discovery in a rule-based framework, by modelling protected-by-law groups, such as minorities or disadvantaged segments, and contexts where discrimination occurs. Classification rules, extracted from the historical records, allow for unveiling contexts of unlawful discrimination, where the degree of burden over protected-by-law groups is evaluated by formalizing existing norms and regulations in terms of quantitative measures. The measures are defined as functions of the contingency table of a classification rule, and their statistical significance is assessed, relying on a large body of statistical inference methods for proportions. Key legal concepts and reasonings are then used to drive the analysis on the set of classification rules, with the aim of discovering patterns of discrimination, either direct or indirect. Analyses of affirmative action, favoritism and argumentation against discrimination allegations are also modelled in the proposed framework. Finally, we present an implementation, called LP2DD, of the overall reference model that integrates induction, through data mining classification rule extraction, and deduction, through a computational logic implementation of the analytical tools. The LP2DD system is put at work on the analysis of a dataset of credit decision records.     

Defining the relationship between fetal Doppler indices,abdominal circumference and growth rate in severe fetal growth restriction using functional linear discriminant analysis

The relationship between Doppler measurements, size and growth rate in fetal growth restriction has not been defined. We used functional linear discriminant analysis (FLDA) to investigate these parameters taking account of the difficulties inherent in exploring relationships between repeated observations from a small number of cases. In 40 fetuses with severe growth restriction, serial abdominal circumference (AC), umbilical, middle cerebral artery (MCA) and ductus venosus Doppler pulsatility index measurements were recorded. In 11 singleton fetuses with normal growth, umbilical artery pulsatility index only was measured. Data were expressed as z-scores in relation to gestation and analysed longitudinally using FLDA. In severe growth restriction, the Spearman correlation coefficients between umbilical artery pulsatility index and AC z-score, MCA pulsatility index and AC z-score and ductus venosus pulsatility index z-score and AC z-score were, respectively: −0.36, p = 4.4 × 10⁻⁷; 0.70, p = 1.1 × 10⁻¹⁷ and −0.50, p = 8.1 × 10⁻⁴. No relationship was seen between Doppler parameters and growth rate. There was no relationship between umbilical artery pulsatility index and AC nor growth rate in normally grown fetuses. In severe fetal growth restriction, Doppler changes are related to absolute fetal AC size, not growth rate.