Trick or treat?: uneven understanding of mind and emotion and executive dysfunction in "hard-to-manage" preschoolers

It is widely recognised that impaired social relations are characteristic of school-aged children with behavioural disorders, and predict a poor long-term outcome (Parker & Asher, 1987). However, little is known about the early antecedents of social impairment in behaviourally disturbed children. The aim of the present study was to explore three areas of potential dysfunction in younger children: theory of mind, emotion understanding, and executive function. Forty preschoolers, rated by their parents on the Strengths and Difficulties Questionnaire (Goodman, 1994) as "hard to manage" (H2M) were compared with a control group on a set of: (1) theory of mind tasks (including an emotion prediction task involving either a nice or a nasty surprise); (2) emotion understanding stories (that required affective perspective-taking skills as well as situational understanding); and (3) simple executive function tasks (adapted for preschoolers, and tapping inhibitory control, attentional set-shifting, and working memory). Small but significant group differences were found in all three cognitive domains. In particular, hard-to-manage preschoolers showed poor understanding of emotion and executive control, poor prediction or recall of a false belief, and better understanding of the belief-dependency of emotion in the context of a trick than a treat. Moreover, executive function was associated with performance on the theory of mind tasks for the hard-to-manage group alone, suggesting both direct and indirect links between executive dysfunction and disruptive behaviour.     

Model selection for covariance structures analysis in nursing research

Covariance structures analysis is often used in nursing research to appraise statistical models reflecting complex human health processes. The model selection approach in covariance structures analysis is designed to select the "best" model from a specified set of theoretically defensible, competing alternatives, all of which are viewed as approximations. Model selection criteria explicitly incorporate both model misfit in the population and sampling error to evaluate the set of models. The result is that interpretability of model parameters and goodness-of-fit are enhanced simultaneously. Relative merits of the model selection approach are identified in light of technical concerns, parsimony, and use of scientific theory in nursing.     

The effects of refractive index heterogeneity within kidney tissue on multiphoton fluorescence excitation microscopy

Although multiphoton fluorescence excitation microscopy has improved the depth at which useful fluorescence images can be collected in biological tissues, the reach of multiphoton fluorescence excitation microscopy is nonetheless limited by tissue scattering and spherical aberration. Scattering can be reduced in fixed samples by mounting in a medium whose refractive index closely matches that of the fixed material. Using optical 'clearing', the effects of refractive index heterogeneity on signal attenuation with depth are investigated. Quantitative measurements show that by mounting kidney tissue in a high refractive index medium, less than 50% of signal attenuates in 100 μm of depth.     

Metabolic mapping of the effects of oral alcohol self-administration in rats

A method for the quantitation of protein carbonyls, which have been widely employed as markers of protein oxidative damage, is described. Protein carbonyls were derivatized with tritiated sodium borohydride and the tritiated proteins were separated on SDS-PAGE. Protein bands, visualized by Coomassie blue staining, were then excised and incubated in 30% H2O2 at 60 degrees C for 48 h. Tritium, incorporated into the proteins, was quantitated by liquid scintillation counting after gel solubilization by H2O2. This method can be applied to the measurement of carbonylation of specific proteins as it employs SDS-PAGE and has the advantage that unreacted NaB3H4 in the labeling reaction mixture need not be removed. The present method, when combined with immunochemical detection of protein carbonyls, should be very useful in the quantitation of oxidative damage to individual proteins.     

Characterization of the Basal and mTOR-Dependent Acute Pulmonary and Systemic Immune Response in a Murine Model of Combined Burn and Inhalation Injury

Severe burn injury leads to a cascade of local and systemic immune responses that trigger an extreme state of immune dysfunction, leaving the patient highly susceptible to acute and chronic infection. When combined with inhalation injury, burn patients have higher mortality and a greater chance of developing secondary respiratory complications including infection. No animal model of combined burn and inhalation injury (B+I) exists that accurately mirrors the human clinical picture, nor are there any effective immunotherapies or predictive models of the risk of immune dysfunction. Our earlier work showed that the mechanistic/mammalian target of rapamycin (mTOR) pathway is activated early after burn injury, and its chemical blockade at injury reduced subsequent chronic bacterial susceptibility. It is unclear if mTOR plays a role in the exacerbated immune dysfunction seen after B+I injury. We aimed to: (1) characterize a novel murine model of B+I injury, and (2) investigate the role of mTOR in the immune response after B+I injury. Pulmonary and systemic immune responses to B+I were characterized in the absence or presence of mTOR inhibition at the time of injury. Data describe a murine model of B+I with inhalation-specific immune phenotypes and implicate mTOR in the acute immune dysfunction observed.     

A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters

Abstract

Two fuzzy versions of the k-means optimal, least squared error partitioning problem are formulated for finite subsets X of a general inner product space. In both cases, the extremizing solutions are shown to be fixed points of a certain operator T on the class of fuzzy, k-partitions of X, and simple iteration of T provides an algorithm which has the descent property relative to the least squared error criterion function. In the first case, the range of T consists largely of ordinary (i.e. non-fuzzy) partitions of X and the associated iteration scheme is essentially the well known ISODATA process of Ball and Hall. However, in the second case, the range of T consists mainly of fuzzy partitions and the associated algorithm is new; when X consists of k compact well separated (CWS) clusters, X_i , this algorithm generates a limiting partition with membership functions which closely approximate the characteristic functions of the clusters X_i . However, when X is not the union of k CWS clusters, the limiting partition is truly fuzzy in the sense that the values of its component membership functions differ substantially from 0 or 1 over certain regions of X. Thus, unlike ISODATA, the “fuzzy” algorithm signals the presence or absence of CWS clusters in X. Furthermore, the fuzzy algorithm seems significantly less prone to the “cluster-splitting” tendency of ISODATA and may also be less easily diverted to uninteresting locally optimal partitions. Finally, for data sets X consisting of dense CWS clusters embedded in a diffuse background of strays, the structure of X is accurately reflected in the limiting partition generated by the fuzzy algorithm. Mathematical arguments and numerical results are offered in support of the foregoing assertions.     

Algorithm for tissue ischemia estimation based on electrical impedance spectroscopy

The purpose of this paper is to present an algorithm developed for real-time estimation of skeletal muscle ischemia, based on parameters extracted from in vivo obtained electrical impedance spectra. A custom impedance spectrometer was used to acquire data sets: complex impedance spectra measured at 27 frequencies in the range of 100 Hz-1 MHz, and tissue pH. Twenty-nine in vivo animal studies on rabbit anterior tibialis muscle were performed to gather data on the behavior of tissue impedance during ischemia. An artificial neural network (ANN) was used to quantitatively describe the relationship between the parameters of complex tissue impedance spectra and tissue ischemia via pH. The ANN was trained on 1249, and tested on 946 ischemic tissue impedance data sets. A correlation of 94.5% and a standard deviation of 0.15 pH units was achieved between the ANN estimated pH and measured tissue pH values.     

A Practical Cross-Layer Mechanism For Fairness in 802.11 Networks

Many companies, organizations and communities are providing wireless hotspots that provide networking access using 802.11b wireless networks. Since wireless networks are more sensitive to variations in bandwidth and environmental interference than wired networks, most networks support a number of transmission rates that have different error and bandwidth properties. Access points can communicate with multiple clients running at different rates, but this leads to unfair bandwidth allocation. If an access point communicates with a mix of clients using both 1 Mb/s and 11 Mb/s transmission rates, the faster clients are effectively throttled to 1 Mb/s as well. This happens because the 802.11 MAC protocol approximate “station fairness”, with each station given an equal chance to access the media. We provide a solution to provide “rate proportional fairness”, where the 11 Mb/s stations receive more bandwidth than the 1 Mb/s stations. Unlike previous solutions to this problem, our mechanism is easy to implement, works with common operating systems and requires no change to the MAC protocol or the stations. Joseph Dunn received an M.S. in computer science from the University of Colorado at Boulder in 2003, and B. S. in coputer science and mathematics from the University of Arizona in 2001. His research interests are in the general area of computer systems, primarily focusing on security and scalability in distributed systems. He is currently working on his Ph.D. in computer science from the University of Colorado at Boulder. Michael Neufeld received a Ph.D. in Computer Science from the University of Colorado at Boulder in December of 2004, having previously received an M.S. in Computer Science from the University of Colorado at Boulder in 2000 and an A.B. in Computer Science from Princeton University in 1993. His research interests are in the general area of computer system, specifically concentrating on wireless networking, software defind/cognitive radio, and streerable antennas. He is currently a postdoc in the Computer Science department at the University of Calorado at Boulder pursuing research related to software defined radio and new MAC protocols for steerable phase array antennas. Anmol Sheth is a Ph.D. student in Computer Science at the University of Colorado at Boulder. He received his B.S. in Computer Science from the University of Pune, India in 2001. He has been co-leading the development of the MANTIS operating system. He has co-authored three papers include MAC layer protocol design, energy-efficient wireless communication, and adapting communications to mobility. Dirk Grunwald received his Ph.D. from the University of Illinois in 1989 and joined the University of Colorado the same year. His work addresses research and teaching in the broad area of “computer systems”, which includes computer architecture, operating systems, networks, and storage systems. His interests also include issues in pervasive computing, novel computing models, and enjoying the mountains. He is currently an Associate Professor in the Department of Computer Science and in Electrical and Computer Engineering and is also the Director of the Colorado Center for Information Storage. John Bennett is a Professor of Computer Science with a joint appointment in Electrical and Computer Engineering at the University of Colorado at Boulder. He also serves as Associate Dean for Education in the College of Engineering and Applied Science. He joined the CU-Boulder faculty in 2000, after serving on the faculty of Rice University for 11 years. While at Rice, Bennett pioneered a course in engineering design for both engineering and non-engineering students that has been emulated at several universities and high schools. In addition to other teaching awards, Bennett received the Keck Foundation National Award for Engineering Teaching Excellence for his work on this course. Bennett received his Ph.D. in 1988 from the University of Washington. Prior to completing his doctoral studies, he was a U.S. Naval Officer for several years and founded and served as President of Pacific Mountain Research, Inc., where he supervised the design and development of a number of commercial computing systems. Bennett's primary research interests are broadly focused in the area of distributed systems, and more narrowly in distributed information management and distributed robotic macrosensors.     

超短脉冲光电导开关的全波分析

本文采用时域有限差分法直接求解包含传导电流的三维麦克斯韦方程组,对超短脉冲光电导开关进行全波分析,分析中考虑了静电场影响,采用了比较精确的光电导数学模型。本文还讨论了开关输出信号的模式组成和激光脉冲能量、脉宽对开关性能的影响,并给出了光电导开关响应在不同时刻的三维图形。     

Adaptive sigma-delta modulation for use in DACs

Adapting the loop filter in a sigma-delta modulator (SDM) increases the signal/noise ratio (SNR) at low input amplitudes, and also allows stable operation at high amplitudes where a fixed system would normally be unstable. Adaption is easily implemented in SDM digital/analogue converters (DACs)