A bioinspired peptide scaffold with high antibiotic activity and low in vivo toxicity

Bacterial resistance to almost all available antibiotics is an important public health issue. A major goal in antimicrobial drug discovery is the generation of new chemicals capable of killing pathogens with high selectivity, particularly multi-drug-resistant ones. Here we report the design, preparation and activity of new compounds based on a tunable, chemically accessible and upscalable lipopeptide scaffold amenable to suitable hit-to-lead development. Such compounds could become therapeutic candidates and future antibiotics available on the market. The compounds are cyclic, contain two D-amino acids for in vivo stability and their structures are reminiscent of other cyclic disulfide-containing peptides available on the market. The optimized compounds prove to be highly active against clinically relevant Gram-negative and Gram-positive bacteria. In vitro and in vivo tests show the low toxicity of the compounds. Their antimicrobial activity against resistant and multidrug-resistant bacteria is at the membrane level, although other targets may also be involved depending on the bacterial strain.

Wood as a model material for medical biomaterials. In vivo and in vitro studies with bone and Betula pubescens Ehrh

Novel biomaterials are needed to fill the demand of tailored bone substitutes required by an ever‐expanding array of surgical procedures and techniques. Wood, a natural fiber composite, modified with heat treatment to alter its composition, may provide a novel approach to the further development of hierarchically structured biomaterials. The suitability of wood as a model biomaterial as well as the effects of heat treatment on the osteoconductivity of wood was studied by placing untreated and heat‐treated (at 220 C , 200 degrees and 140 degrees for 2 h) birch implants (size 4 x 7mm) into drill cavities in the distal femur of rabbits. The follow‐up period was 4, 8 and 20 weeks in all in vivo experiments. The flexural properties of wood as well as dimensional changes and hydroxyl apatite formation on the surface of wood (untreated, 140 degrees C and 200 degrees C heat‐treated wood) were tested using 3‐point bending and compression tests and immersion in simulated body fluid. The effect of premeasurement grinding and the effect of heat treatment on the surface roughness and contour of wood were tested with contact stylus and non‐contact profilometry. The effects of heat treatment of wood on its interactions with biological fluids was assessed using two different test media and real human blood in liquid penetration tests. The results of the in vivo experiments showed implanted wood to be well tolerated, with no implants rejected due to foreign body reactions. Heat treatment had significant effects on the biocompatibility of wood, allowing host bone to grow into tight contact with the implant, with occasional bone ingrowth into the channels of the wood implant. The results of the liquid immersion experiments showed hydroxyl apatite formation only in the most extensively heat‐treated wood specimens, which supported the results of the in vivo experiments. Parallel conclusions could be drawn based on the results of the liquid penetration test where human blood had the most favorable interaction with the most extensively heat‐treated wood of the compared materials (untreated, 140 degrees C and 200 degrees C heat‐treated wood). The increasing biocompatibility was inferred to result mainly from changes in the chemical composition of wood induced by the heat treatment, namely the altered arrangement and concentrations of functional chemical groups. However, the influence of microscopic changes in the cell walls, surface roughness and contour cannot be totally excluded. The heat treatment was hypothesized to produce a functional change in the liquid distribution within wood, which could have biological relevance. It was concluded that the highly evolved hierarchical anatomy of wood could yield information for the future development of bulk bone substitutes according to the ideology of bioinspiration. Furthermore, the results of the biomechanical tests established that heat treatment alters various biologically relevant mechanical properties of wood, thus expanding the possibilities of wood as a model material, which could include e.g. scaffold applications, bulk bone applications and serving as a tool for both mechanical testing and for further development of synthetic fiber reinforced composites.

An Hp-Adaptive Hierarchical Formulation for the Boundary Element Method Applied to Elasticity in Two Dimensions

This paper presents an HP-Adaptive Procedure with Hierarchical formulation for the Boundary Element Method in 2-D Elasticity problems. Firstly, H, P and HP formulations are defined. Then, the hierarchical concept, which allows a substantial reduction in the dimension of equation system, is introduced. The error estimator used is based on the residual computation over each node inside an element. Finally, the HP strategy is defined and applied to two examples.

An animal experimental study of porous magnesium scaffold degradation and osteogenesis

Our objective was to observe the biodegradable and osteogenic properties of magnesium scaffolding under in vivo conditions. Twelve 6-month-old male New Zealand white rabbits were randomly divided into two groups. The chosen operation site was the femoral condyle on the right side. The experimental group was implanted with porous magnesium scaffolds, while the control group was implanted with hydroxyapatite scaffolds. X-ray and blood tests, which included serum magnesium, alanine aminotransferase (ALT), creatinine (CREA), and blood urea nitrogen (BUN) were performed serially at 1, 2, and 3 weeks, and 1, 2, and 3 months. All rabbits were killed 3 months postoperatively, and the heart, kidney, spleen, and liver were analyzed with hematoxylin and eosin (HE) staining. The bone samples were subjected to microcomputed tomography scanning (micro-CT) and hard tissue biopsy. SPSS 13.0 (USA) was used for data analysis, and values of P<0.05 were considered to be significant. Bubbles appeared in the X-ray of the experimental group after 2 weeks, whereas there was no gas in the control group. There were no statistical differences for the serum magnesium concentrations, ALT, BUN, and CREA between the two groups (P>0.05). All HE-stained slices were normal, which suggested good biocompatibility of the scaffold. Micro-CT showed that magnesium scaffolds degraded mainly from the outside to inside, and new bone was ingrown following the degradation of magnesium scaffolds. The hydroxyapatite scaffold was not degraded and had fewer osteoblasts scattered on its surface. There was a significant difference in the new bone formation and scaffold bioabsorption between the two groups (9.29±1.27 vs 1.40±0.49 and 7.80±0.50 vs 0.00±0.00 mm3, respectively; P<0.05). The magnesium scaffold performed well in degradation and osteogenesis, and is a promising material for orthopedics.

Intelligence acquisition methods in cyber domain : examining the circumstantial applicability of cyber intelligence acquisition methods using a hierarchical model

Phenomena in cyber domain, especially threats to security and privacy, have proven an increasingly heated topic addressed by different writers and scholars at an increasing pace – both nationally and internationally. However little public research has been done on the subject of cyber intelligence. The main research question of the thesis was: To what extent is the applicability of cyber intelligence acquisition methods circumstantial? The study was conducted in sequential a manner, starting with defining the concept of intelligence in cyber domain and identifying its key attributes, followed by identifying the range of intelligence methods in cyber domain, criteria influencing their applicability, and types of operatives utilizing cyber intelligence. The methods and criteria were refined into a hierarchical model. The existing conceptions of cyber intelligence were mapped through an extensive literature study on a wide variety of sources. The established understanding was further developed through 15 semi-structured interviews with experts of different backgrounds, whose wide range of points of view proved to substantially enhance the perspective on the subject. Four of the interviewed experts participated in a relatively extensive survey based on the constructed hierarchical model on cyber intelligence that was formulated in to an AHP hierarchy and executed in the Expert Choice Comparion online application. It was concluded that Intelligence in cyber domain is an endorsing, cross-cutting intelligence discipline that adds value to all aspects of conventional intelligence and furthermore that it bears a substantial amount of characteristic traits – both advantageous and disadvantageous – and furthermore that the applicability of cyber intelligence methods is partly circumstantially limited.

A sequential analysis of therapist scaffolding and child concept formation in narrative therapy

Narrative therapy is a postmodern therapy that takes the position that people create self-narratives to make sense of their experiences. To date, narrative therapy has compiled virtually no quantitative and very little qualitative research, leaving gaps in almost all areas of process and outcome. White (2006a), one of the therapy's founders, has recently utilized Vygotsky's (1934/1987) theories of the zone of proximal development (ZPD) and concept formation to describe the process of change in narrative therapy with children. In collaboration with the child client, the narrative therapist formalizes therapeutic concepts and submits them to increasing levels of generalization to create a ZPD. This study sought to determine whether the child's development proceeds through the stages of concept formation over the course of a session, and whether therapists' utterances scaffold this movement. A sequential analysis was used due to its unique ability to measure dynamic processes in social interactions. Stages of concept formation and scaffolding were coded over time. A hierarchical log-linear analysis was performed on the sequential data to develop a model of therapist scaffolding and child concept development. This was intended to determine what patterns occur and whether the stated intent of narrative therapy matches its actual process. In accordance with narrative therapy theory, the log-linear analysis produced a final model with interactions between therapist and child utterances, and between both therapist and child utterances and time. Specifically, the child and youth participants in therapy tended to respond to therapist scaffolding at the corresponding level of concept formation. Both children and youth and therapists also tended to move away from earlier and toward later stages of White's scaffolding conversations map as the therapy session advanced. These findings provide support for White's contention that narrative therapists promote child development by scaffolding child concept formation in therapy.

Cognitive Behavior Therapy for Obsessive-Compulsive Behavior in Children with Autism Spectrum Disorder: A Comparison of Hierarchical Linear Modelling and Visual Analysis

Behavioral researchers commonly use single subject designs to evaluate the effects of a given treatment. Several different methods of data analysis are used, each with their own set of methodological strengths and limitations. Visual inspection is commonly used as a method of analyzing data which assesses the variability, level, and trend both within and between conditions (Cooper, Heron, & Heward, 2007). In an attempt to quantify treatment outcomes, researchers developed two methods for analysing data called Percentage of Non-overlapping Data Points (PND) and Percentage of Data Points Exceeding the Median (PEM). The purpose of the present study is to compare and contrast the use of Hierarchical Linear Modelling (HLM), PND and PEM in single subject research. The present study used 39 behaviours, across 17 participants to compare treatment outcomes of a group cognitive behavioural therapy program, using PND, PEM, and HLM on three response classes of Obsessive Compulsive Behaviour in children with Autism Spectrum Disorder. Findings suggest that PEM and HLM complement each other and both add invaluable information to the overall treatment results. Future research should consider using both PEM and HLM when analysing single subject designs, specifically grouped data with variability.

Development of Hierarchical Clustering Techniques for Gridded Data from Mixed Data Sequences

Knowledge discovery in databases is the non-trivial process of identifying valid, novel potentially useful and ultimately understandable patterns from data. The term Data mining refers to the process which does the exploratory analysis on the data and builds some model on the data. To infer patterns from data, data mining involves different approaches like association rule mining, classification techniques or clustering techniques. Among the many data mining techniques, clustering plays a major role, since it helps to group the related data for assessing properties and drawing conclusions. Most of the clustering algorithms act on a dataset with uniform format, since the similarity or dissimilarity between the data points is a significant factor in finding out the clusters. If a dataset consists of mixed attributes, i.e. a combination of numerical and categorical variables, a preferred approach is to convert different formats into a uniform format. The research study explores the various techniques to convert the mixed data sets to a numerical equivalent, so as to make it equipped for applying the statistical and similar algorithms. The results of clustering mixed category data after conversion to numeric data type have been demonstrated using a crime data set. The thesis also proposes an extension to the well known algorithm for handling mixed data types, to deal with data sets having only categorical data. The proposed conversion has been validated on a data set corresponding to breast cancer. Moreover, another issue with the clustering process is the visualization of output. Different geometric techniques like scatter plot, or projection plots are available, but none of the techniques display the result projecting the whole database but rather demonstrate attribute-pair wise analysis

Hierarchical Object Recognition Using Libraries of Parameterized Model Sub-Parts

This thesis describes the development of a model-based vision system that exploits hierarchies of both object structure and object scale. The focus of the research is to use these hierarchies to achieve robust recognition based on effective organization and indexing schemes for model libraries. The goal of the system is to recognize parameterized instances of non-rigid model objects contained in a large knowledge base despite the presence of noise and occlusion. Robustness is achieved by developing a system that can recognize viewed objects that are scaled or mirror-image instances of the known models or that contain components sub-parts with different relative scaling, rotation, or translation than in models. The approach taken in this thesis is to develop an object shape representation that incorporates a component sub-part hierarchy- to allow for efficient and correct indexing into an automatically generated model library as well as for relative parameterization among sub-parts, and a scale hierarchy- to allow for a general to specific recognition procedure. After analysis of the issues and inherent tradeoffs in the recognition process, a system is implemented using a representation based on significant contour curvature changes and a recognition engine based on geometric constraints of feature properties. Examples of the system's performance are given, followed by an analysis of the results. In conclusion, the system's benefits and limitations are presented.

Formal Multilevel Hierarchical Verification of Synchronous MOS Circuits

I have designed and implemented a system for the multilevel verification of synchronous MOS VLSI circuits. The system, called Silica Pithecus, accepts the schematic of an MOS circuit and a specification of the circuit's intended digital behavior. Silica Pithecus determines if the circuit meets its specification. If the circuit fails to meet its specification Silica Pithecus returns to the designer the reason for the failure. Unlike earlier verifiers which modelled primitives (e.g., transistors) as unidirectional digital devices, Silica Pithecus models primitives more realistically. Transistors are modelled as bidirectional devices of varying resistances, and nodes are modelled as capacitors. Silica Pithecus operates hierarchically, interactively, and incrementally. Major contributions of this research include a formal understanding of the relationship between different behavioral descriptions (e.g., signal, boolean, and arithmetic descriptions) of the same device, and a formalization of the relationship between the structure, behavior, and context of device. Given these formal structures my methods find sufficient conditions on the inputs of circuits which guarantee the correct operation of the circuit in the desired descriptive domain. These methods are algorithmic and complete. They also handle complex phenomena such as races and charge sharing. Informal notions such as races and hazards are shown to be derivable from the correctness conditions used by my methods.

A Hierarchical Cache Coherent Protocol

As the number of processors in distributed-memory multiprocessors grows, efficiently supporting a shared-memory programming model becomes difficult. We have designed the Protocol for Hierarchical Directories (PHD) to allow shared-memory support for systems containing massive numbers of processors. PHD eliminates bandwidth problems by using a scalable network, decreases hot-spots by not relying on a single point to distribute blocks, and uses a scalable amount of space for its directories. PHD provides a shared-memory model by synthesizing a global shared memory from the local memories of processors. PHD supports sequentially consistent read, write, and test- and-set operations. This thesis also introduces a method of describing locality for hierarchical protocols and employs this method in the derivation of an abstract model of the protocol behavior. An embedded model, based on the work of Johnson[ISCA19], describes the protocol behavior when mapped to a k-ary n-cube. The thesis uses these two models to study the average height in the hierarchy that operations reach, the longest path messages travel, the number of messages that operations generate, the inter-transaction issue time, and the protocol overhead for different locality parameters, degrees of multithreading, and machine sizes. We determine that multithreading is only useful for approximately two to four threads; any additional interleaving does not decrease the overall latency. For small machines and high locality applications, this limitation is due mainly to the length of the running threads. For large machines with medium to low locality, this limitation is due mainly to the protocol overhead being too large. Our study using the embedded model shows that in situations where the run length between references to shared memory is at least an order of magnitude longer than the time to process a single state transition in the protocol, applications exhibit good performance. If separate controllers for processing protocol requests are included, the protocol scales to 32k processor machines as long as the application exhibits hierarchical locality: at least 22% of the global references must be able to be satisfied locally; at most 35% of the global references are allowed to reach the top level of the hierarchy.

A Detailed Look at Scale and Translation Invariance in a Hierarchical Neural Model of Visual Object Recognition

The HMAX model has recently been proposed by Riesenhuber & Poggio as a hierarchical model of position- and size-invariant object recognition in visual cortex. It has also turned out to model successfully a number of other properties of the ventral visual stream (the visual pathway thought to be crucial for object recognition in cortex), and particularly of (view-tuned) neurons in macaque inferotemporal cortex, the brain area at the top of the ventral stream. The original modeling study only used ``paperclip'' stimuli, as in the corresponding physiology experiment, and did not explore systematically how model units' invariance properties depended on model parameters. In this study, we aimed at a deeper understanding of the inner workings of HMAX and its performance for various parameter settings and ``natural'' stimulus classes. We examined HMAX responses for different stimulus sizes and positions systematically and found a dependence of model units' responses on stimulus position for which a quantitative description is offered. Interestingly, we find that scale invariance properties of hierarchical neural models are not independent of stimulus class, as opposed to translation invariance, even though both are affine transformations within the image plane.

Hierarchical Mixtures of Experts and the EM Algorithm

We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM's). Learning is treated as a maximum likelihood problem; in particular, we present an Expectation-Maximization (EM) algorithm for adjusting the parameters of the architecture. We also develop an on-line learning algorithm in which the parameters are updated incrementally. Comparative simulation results are presented in the robot dynamics domain.

Global and dominance with concentric hierarchical stimuli and orientation classification task. 'Dominancia global y local con est??mulos conc??ntricos y tareas de clasificaci??n de la orientaci??n'

Resumen tomado de la publicaci??n. Resumen tambi??n en ingl??s