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When biomedical crises strike, structural biologists worldwide respond by determining the structures of relevant proteins and their complexes, resulting in an avalanche of data that can be overwhelming without a resource designed to classify, annotate and validate them. An advanced information system is necessary to extract and infer knowledge from a deluge of uncurated and disjointed data and publications., As part of the global mobilization to combat the present pandemic, almost 100 000 COVID-19-related papers have been published and nearly a thousand models of macromolecules encoded by SARS-CoV-2 have been deposited in the Protein Data Bank within less than a year. The avalanche of new structural data has given rise to multiple resources dedicated to assessing the correctness and quality of structural data and models. Here, an approach to evaluate the massive amounts of such data using the resource https://covid19.bioreproducibility.org is described, which offers a template that could be used in large-scale initiatives undertaken in response to future biomedical crises. Broader use of the described methodology could considerably curtail information noise and significantly improve the reproducibility of biomedical research.

In 2014–2017 an investigation was carried out into the occurrence of fungal storage diseases of five apple varieties (Red Jonaprince, Gala, Golden Delicious, Gloster and Ligol) in the Sandomierz orchard region. The fruit was stored at a CA cold storage room with ULO controlled atmosphere for six months. Occurrence of eight storage diseases was found. The most frequently occurring disease was bull’s eye rot and the losses caused thereby were even 24% of the affected fruit. The cultivars most susceptible to this disease were the Golden Delicious and Ligol apples; the least susceptible were the Gloster ones. The apples were significantly less affected by the fungi that cause brown rot, grey mould rot, blue mould rot and apple scab. Very seldom were the symptoms of calyx end rot, mouldy core and core rot, and anthracnose. Varying severity of infection of the varieties was noted in each season of observation.

The COVID‐19 pandemic has triggered numerous scientific activities aimed at understanding the SARS‐CoV‐2 virus and ultimately developing treatments. Structural biologists have already determined hundreds of experimental X‐ray, cryo‐EM, and NMR structures of proteins and nucleic acids related to this coronavirus, and this number is still growing. To help biomedical researchers, who may not necessarily be experts in structural biology, navigate through the flood of structural models, we have created an online resource, covid19.bioreproducibility.org, that aggregates expert‐verified information about SARS‐CoV‐2‐related macromolecular models. In this paper, we describe this web resource along with the suite of tools and methodologies used for assessing the structures presented therein. This article is protected by copyright. All rights reserved.

Efficient and comprehensive data management is an indispensable component of modern scientific research and requires effective tools for all but the most trivial experiments. The LabDB system developed and used in our laboratory was originally designed to track the progress of a structure determination pipeline in several large National Institutes of Health (NIH) projects. While initially designed for structural biology experiments, its modular nature makes it easily applied in laboratories of various sizes in many experimental fields. Over many years, LabDB has transformed into a sophisticated system integrating a range of biochemical, biophysical, and crystallographic experimental data, which harvests data both directly from laboratory instruments and through human input via a web interface. The core module of the system handles many types of universal laboratory management data, such as laboratory personnel, chemical inventories, storage locations, and custom stock solutions. LabDB also tracks various biochemical experiments, including spectrophotometric and fluorescent assays, thermal shift assays, isothermal titration calorimetry experiments, and more. LabDB has been used to manage data for experiments that resulted in over 1200 deposits to the Protein Data Bank (PDB); the system is currently used by the Center for Structural Genomics of Infectious Diseases (CSGID) and several large laboratories. This chapter also provides examples of data mining analyses and warnings about incomplete and inconsistent experimental data. These features, together with its capabilities for detailed tracking, analysis, and auditing of experimental data, make the described system uniquely suited to inspect potential sources of irreproducibility in life sciences research.

The structure of serum albumin in complex with dexamethasone reveals why the drug may not always help COVID-19 patients., Dexamethasone, a widely used corticosteroid, has recently been reported as the first drug to increase the survival chances of patients with severe COVID-19. Therapeutic agents, including dexamethasone, are mostly transported through the body by binding to serum albumin. Here, the first structure of serum albumin in complex with dexamethasone is reported. Dexamethasone binds to drug site 7, which is also the binding site for commonly used nonsteroidal anti-inflammatory drugs and testosterone, suggesting potentially problematic binding competition. This study bridges structural findings with an analysis of publicly available clinical data from Wuhan and suggests that an adjustment of the dexamethasone regimen should be further investigated as a strategy for patients affected by two major COVID-19 risk factors: low albumin levels and diabetes.

The aim of the study was to determine the effect of the amount of nitrogen dosed in supercritical state to polymer melt on structure and physical properties of large-scale molded pieces from polyamide PA 6 GF30, obtained in the process of microcellular injection molding with gas dosage using MuCell® method. Apart from the reduction of the density at a level of about 15 % for samples with the highest nitrogen content, the increase of the impact resistance of samples PA 6 GF30 was observed. The other investigated mechanical properties were maintained at a good level.

The current-voltage curves of a double strand DNA nanowire connected to armchair graphene nanoribbon leads are investigated using the tight-binding Hamiltonian model and the Landauer-Buttiker formalism. The nonlinear behavior of the current-voltage for different lengths of the DNA nanowire is calculated and interpreted through the corresponding density of states and transmission probability. The behavior of the current-voltage changes as the temperature or width of the leads varies and is also affected by the randomization of parallel and perpendicular hopping terms in the DNA. Around zero bias of the leads, the current-voltage depends on the dimerization effects of parallel hopping in the device.

Vibrio cholerae, the causative pathogen of the life-threatening infection cholera, encodes two copies of β-ketoacyl-acyl carrier protein synthase III (vcFabH1 and vcFabH2). vcFabH1 and vcFabH2 are pathogenic proteins associated with fatty acid synthesis, lipid metabolism, and potential applications in biofuel production. Our biochemical assays characterize vcFabH1 as exhibiting specificity for acetyl-CoA and CoA thioesters with short acyl chains, similar to that observed for FabH homologs found in most gram-negative bacteria. vcFabH2 prefers medium chain-length acyl-CoA thioesters, particularly octanoyl-CoA, which is a pattern of specificity rarely seen in bacteria. Structural characterization of one vcFabH1 and six vcFabH2 structures determined in either apo form or in complex with acetyl-CoA/octanoyl-CoA indicate that the substrate-binding pockets of vcFabH1 and vcFabH2 are of different sizes, accounting for variations in substrate chain-length specificity. An unusual and unique feature of vcFabH2 is its C-terminal fragment that interacts with both the substrate-entrance loop and the dimer interface of the enzyme. Our discovery of the pattern of substrate specificity of both vcFabH1 and vcFabH2 can potentially aid the development of novel antibacterial agents against V. cholerae. Additionally, the distinctive substrate preference of FabH2 in V. cholerae and related facultative anaerobes conceivably make it an attractive component of genetically engineered bacteria used for commercial biofuel production.

The possibility of the s-wave superconductive state in armchair graphene nanoribbons is studied within the attractive Hubbard model, standard BCS theory, and Green’s function approach. Bogoliubov de Gennes equations are derived for this system in the singlet state. A nonzero critical temperature is found which depends on the width of the system. This critical temperature decreases as the width of the system increases and for sufficiently large widths, its value reaches a constant amount which could be taken as the critical temperature of the graphene sheet. The critical temperature also depends on band-filling which, around half band-filling, shows a maximum. However, at half-filling, it drops to a lower value less than its maximum.

In the framework of the Green's functions approach, random tight-binding model and using the coherent potential approximation, electronic characteristics of the bilayer graphene are investigated by exploring various forms of substitutional doping of a single or both layers of the system by either boron and (or) nitrogen atoms. The results for displacement of the Fermi level resemble the behavior of acceptor or donor doping in a conventional semiconductor, dependent on the impurity type and concentration. The particular pattern of doping of just one layer with one impurity type is most efficient for opening a gap within the energy bands which could be tuned directly by impurity concentration. Doping both layers at the same time, each with one impurity type, leads to an anomaly whereby the gap decreases with increasing impurity concentration.

The Kubo formula is used to extract the electrical conductivity (EC) of different diameters of doped zigzag carbon nanotubes and their corresponding unzipped armchair graphene nanoribbons, as a function of temperature and chemical potential, within the tight-binding Hamiltonian model and Green's functions approach. The results reveal more sensitivity to temperature for semiconducting systems in addition to a decrease in EC of all systems with increasing cross-sections.

Nearly 80% of all burns include the hands of affected individuals. Skin grafting is the gold standard in burns treatment, but in the case of the burn wound bed, it may require the necessity of utilising skin substitutes to facilitate closure. The aim of this study is to assess the impact of a porcine-derived wound dressing (Oasis™) for application to hand burns compared to a synthetic dressing (Suprathel™). Comparative assessments were made, including the time to heal, quality of healing and pain intensity. A retrospective, unblinded, matching pair case-control of hand burns was performed. A control group of 24 patients was treated with Suprathel dressing, and a study group of six patients underwent application of the Oasis dressing. The wound healing process was evaluated by taking histopathological specimens and also utilising the Bates-Jensen Wound Assessment Tool. A 10-cm Visual Analogue Scale (VAS) was used for pain assessment. Other parameters measured included dressing loss because of infection and the need of rehabilitation. The progress of wound healing on the fourth day in the study group was 30%. A decrease in the level of pain was recorded on the fourth day after surgery. There was a decrease of 5% in the risk of rehabilitation in the treatment group.

Through the Green's function formalism and tight-binding Hamiltonian model calculations, the temperature dependent electronic thermal conductivity (TC) for different diameters of zigzag carbon nanotubes and their corresponding unzipped armchair graphene nanoribbons is calculated. All functional temperature dependencies bear crossovers, for which, at higher temperatures, nanotubes have a slightly higher TC than their derived nanoribbons, while below that crossover, both systems exhibit a significant coincidence over a moderate range of lower temperatures. Noticeably, TC decreases with increasing the width or diameter of the corresponding systems. Also, at low temperatures TC is proportional to the density of states around the Fermi level, and thus increasing for metal or semiconductors of narrower gap cases.

The period 2000–2015 brought the advent of high-throughput approaches to protein structure determination. With the overall funding on the order of $2 billion (in 2010 dollars), the structural genomics (SG) consortia established worldwide have developed pipelines for target selection, protein production, sample preparation, crystallization, and structure determination by X-ray crystallography and NMR. These efforts resulted in the determination of over 13,500 protein structures, mostly from unique protein families, and increased the structural coverage of the expanding protein universe. SG programs contributed over 4,400 publications to the scientific literature. The NIH-funded Protein Structure Initiatives (PSI) alone have produced over 2,000 scientific publications, which to date have attracted more than 93,000 citations. Software and database developments that were necessary to handle high-throughput structure determination workflows have led to structures of better quality and improved integrity of the associated data. Organized and accessible data have a positive impact on the reproducibility of scientific experiments. Most of the experimental data generated by the SG centers are freely available to the community and has been utilized by scientists in various fields of research. SG projects have created, improved, streamlined, and validated many protocols for protein production and crystallization, data collection, and functional analysis, significantly benefiting biological and biomedical research.

The band structure, density of states (DOS), and electrical conductivity (EC) of different configurations of a double-strand poly-GC-poly-AT DNA-like nanowire are investigated within the tight-binding Hamiltonian model and the Green's function formalism. Three different configurations of the double-strand DNA-like nanowire were considered: infinite, finite, and cyclic. The change of behavior in the band structure due to the introduction of dimerization effects of the longitudinal hopping terms was studied. Without dimerization, the DNA-like nanowire behaves as a conductor and with dimerization, a semiconductor behavior was observed. This behavior change was also verified through the calculation of the DOS via the Green's function formalism. The effects of the finite size of the DNA-like nanowire and also the effect of length increase on the EC of the system were studied using Kubo formula. There exists a direct relationship between EC and length of DNA-like nanowire. As length increases, so does the EC of the DNA-like nanowire. This information can be useful in both theoretical research and experimental development of DNA-based molecular electronics.

We examine a magnetic moment subject to a static external magnetic field and a time dependent driving magnetic field. Taking the system in the highly anisotropic limit, like that of a thin film, we lower the complexity of the system so that it may be analyzed for transitions to chaos analytically using the Melnikov method. Guided by the calculation of the Melnikov bifurcation functions we computationally calculated the Lyapunov characteristic exponents for several lines in parameter space and find that the Melnikov method is a good predictor of chaotic motion for sufficiently small external fields. Motion plots and Poincare sections are also plotted for several parameter space points to observe the transitions to and from chaos around the calculated Melnikov bifurcations functions.

The electronic behavior of the long disordered DNA nanowires in both dry and wet conditions is investigated through the band structure and density of states of a tight-binding Hamiltonian model for π-electrons of the backbone, using Green׳s functions approach. For a chosen set of parameters in the dry case, semiconducting behavior is reproduced. It is also shown that for sufficiently long strands, the order of the base pairs has no noticeable effect on the energy band-gap. Moreover, this semiconducting duplex shows metallic tendencies when interacting with the environment of polar molecules.

Structural biology, like many other areas of modern science, produces an enormous amount of primary, derived, and "meta" data with a high demand on data storage and manipulations. Primary data come from various steps of sample preparation, diffraction experiments, and functional studies. These data are not only used to obtain tangible results, like macromolecular structural models, but also to enrich and guide our analysis and interpretation of various biomedical problems. Herein we define several categories of data resources, (a) Archives, (b) Repositories, (c) Databases, and (d) Advanced Information Systems, that can accommodate primary, derived, or reference data. Data resources may be used either as web portals or internally by structural biology software. To be useful, each resource must be maintained, curated, as well as integrated with other resources. Ideally, the system of interconnected resources should evolve toward comprehensive "hubs", or Advanced Information Systems. Such systems, encompassing the PDB and UniProt, are indispensable not only for structural biology, but for many related fields of science. The categories of data resources described herein are applicable well beyond our usual scientific endeavors.

According to the research carried out in 1998-2000 it was found that small particles of the shoots left in the orchard after pruning are the place of the numerous fungi development. It was observed that the greatest number of species appeared 12 months afier winter pruning. The species colonising fragments of bark and wood included typical patogenic species, pathogens of weakness, and saprotrophs. Some of isolated species may in favourable conditions become the cause of bark and wood diseases as well as apple rot.

The low reproducibility of published experimental results in many scientific disciplines has recently garnered negative attention in scientific journals and the general media. Public transparency, including the availability of `raw' experimental data, will help to address growing concerns regarding scientific integrity. Macromolecular X-ray crystallography has led the way in requiring the public dissemination of atomic coordinates and a wealth of experimental data, making the field one of the most reproducible in the biological sciences. However, there remains no mandate for public disclosure of the original diffraction data. The Integrated Resource for Reproducibility in Macromolecular Crystallography (IRRMC) has been developed to archive raw data from diffraction experiments and, equally importantly, to provide related metadata. Currently, the database of our resource contains data from 2920 macromolecular diffraction experiments (5767 data sets), accounting for around 3% of all depositions in the Protein Data Bank (PDB), with their corresponding partially curated metadata. IRRMC utilizes distributed storage implemented using a federated architecture of many independent storage servers, which provides both scalability and sustainability. The resource, which is accessibleviathe web portal at http://www.proteindiffraction.org, can be searched using various criteria. All data are available for unrestricted access and download. The resource serves as a proof of concept and demonstrates the feasibility of archiving raw diffraction data and associated metadata from X-ray crystallographic studies of biological macromolecules. The goal is to expand this resource and include data sets that failed to yield X-ray structures in order to facilitate collaborative efforts that will improve protein structure-determination methods and to ensure the availability of `orphan' data left behind for various reasons by individual investigators and/or extinct structural genomics projects.

The explosion of the size of the universe of known protein sequences has stimulated two complementary approaches to structural mapping of these sequences: theoretical structure prediction and experimental determination by structural genomics (SG). In this work, we assess the accuracy of structure prediction by two automated template-based structure prediction metaservers (genesilico.pl and bioinfo.pl) by measuring the structural similarity of the predicted models to corresponding experimental models determined a posteriori. Of 199 targets chosen from SG programs, the metaservers predicted the structures of about a fourth of them "correctly." (In this case, "correct" was defined as placing more than 70 % of the alpha carbon atoms in the model within 2 Å of the experimentally determined positions.) Almost all of the targets that could be modeled to this accuracy were those with an available template in the Protein Data Bank (PDB) with more than 25 % sequence identity. The majority of those SG targets with lower sequence identity to structures in the PDB were not predicted by the metaservers with this accuracy. We also compared metaserver results to CASP8 results, finding that the models obtained by participants in the CASP competition were significantly better than those produced by the metaservers.

The macroscopic observations of apples showing symptoms of sooty blotch have indicated considerable differences in spot shape, colour, diameter and margin. The largest spots were observed on Florina apples, while the smallest on the cultivar Boiken. For 5 apple cultivars under investigation 4 types of thallus produced by fungi causing sooty blotch were found. The ramose pattern was observed most often on diseased fruits. The punctate thallus was reported to be less frequent, while the rimate and fuliginous types were the rarest ones. The distribution of mycelial types on lesions was diversified among apple cultuvars under examination.

The Landau–Lifshitz–Gilbert equation is analyzed in the case of a configuration involving easy plane isotropy under the influence of a sinusoidally oscillating magnetic field and a demagnetizing field. Through the use of numerical techniques, chaotic behavior is found and analyzed. By reducing the system to a discrete map (numerically), bifurcation diagrams for the system are computed. The system is found to exhibit a period doubling cascade route to chaos, and it obeys certain convergence rules for chaotic transitions outlined by Feigenbaum. A connection is drawn between the route to chaos and the geometry of the system, and comparisons are made with similar systems. Within the chaotic regime, windows of arbitrarily large period are suspected to exist, and explicitly illustrated and discussed for a period three window.

We examine the motion of a spin subject to a static external field, dynamic demagnetizing fields, and a relatively weak oscillatory driving field. More specifically, we study the initial transient behavior just after all these fields are turned on and present analytic calculations based on an approximation scheme for the motion of the spin. We find that two modes – a mode at the driving frequency and a mode at the natural resonant frequency – play an important role in the initial transient. Because of the nonlinear nature of the spin equations of motion, one also finds, only in the initial transient, modes with doubled frequencies of the initial two modes and modes involving the sum and difference frequencies of the initial two modes. We also examine large amplitude transients in specific cases through numerical calculations.

While three dimensional structures have long been used to search for new drug targets, only a fraction of new drugs coming to the market has been developed with the use of a structure-based drug discovery approach. However, the recent years have brought not only an avalanche of new macromolecular structures, but also significant advances in the protein structure determination methodology only now making their way into structure-based drug discovery. In this paper, we review recent developments resulting from the Structural Genomics (SG) programs, focusing on the methods and results most likely to improve our understanding of the molecular foundation of human diseases. SG programs have been around for almost a decade, and in that time, have contributed a significant part of the structural coverage of both the genomes of pathogens causing infectious diseases and structurally uncharacterized biological processes in general. Perhaps most importantly, SG programs have developed new methodology at all steps of the structure determination process, not only to determine new structures highly efficiently, but also to screen protein/ligand interactions. We describe the methodologies, experience and technologies developed by SG, which range from improvements to cloning protocols to improved procedures for crystallographic structure solution that may be applied in “traditional” structural biology laboratories particularly those performing drug discovery. We also discuss the conditions that must be met to convert the present high-throughput structure determination pipeline into a high-output structure-based drug discovery system.

A nonredundant set of 9081 protein crystal structures in the Protein Data Bank was used to examine the solvent content, the number of polypeptide chains, and the oligomeric states of proteins in crystals as a function of crystal symmetry (as classified by crystal systems and space groups). It was found that there is a correlation between solvent content and crystal symmetry. Surprisingly, proteins crystallizing in lower symmetry systems have lower solvent content compared to those crystallizing in higher symmetry systems. Nevertheless, there is no universal correlation between solvent content and preferences of macromolecules to crystallize in certain space groups. Crystal symmetry as a function of oligomeric state was examined, where trimers, tetramers, and hexamers were found to prefer to crystallize in systems where the oligomer symmetry could be incorporated in the crystal symmetry. Our analysis also shows that the frequency distribution within the enantiomorphous pairs of space groups does not differ significantly, in contrast to previous reports.

Structural characterization of the protein universe is the main mission of Structural Genomics (SG) programs. However, progress in gene sequencing technology, set in motion in the 1990s, has resulted in rapid expansion of protein sequence space — a twelvefold increase in the past seven years. For the SG field, this creates new challenges and necessitates a reassessment of its strategies. Nevertheless, despite the growth of sequence space, at present nearly half of the content of the Swiss-Prot database and over 40% of Pfam protein families can be structurally modeled based on structures determined so far, with SG projects making an increasingly significant contribution. The SG contribution of new Pfam structures nearly doubled from 27.2% in 2003 to 51.6% in 2006.

Structural genomics (SG) initiatives are currently attempting to achieve the high-throughput determination of protein structures on a genome-wide scale. Here we analyze the SG target data that have been publicly released over a period of 16 months to assess the potential of the SG initiatives. We use statistical techniques most commonly applied in epidemiology to describe the dynamics of targets through the experimental SG pipeline. There is no clear bottleneck among the key stages of cloning, expression, purification and crystallization. An SG target will progress through each of these steps with a probability of approximately 45%. Around 80% of targets with diffraction data will yield a crystal structure, and 20% of targets with HSQC spectra will yield an NMR structure. We also find the overlaps among SG targets: 61% of SG protein sequences share at least 30% sequence identity with one or more other SG targets. There is no significant difference in average structure quality among SG structures and other structures in the PDB determined by 'traditional' methods, but on average SG structures are deposited to the PDB twice as quickly after X-ray data collection. Copyright 2004 Wiley-Liss, Inc, English15211505

Modern high-throughput structural biology laboratories produce vast amounts of raw experimental data. The traditional method of data reduction is very simple-results are summarized in peer-reviewed publications, which are hopefully published in high-impact journals. By their nature, publications include only the most important results derived from experiments that may have been performed over the course of many years. The main content of the published paper is a concise compilation of these data, an interpretation of the experimental results, and a comparison of these results with those obtained by other scientists.Due to an avalanche of structural biology manuscripts submitted to scientific journals, in many recent cases descriptions of experimental methodology (and sometimes even experimental results) are pushed to supplementary materials that are only published online and sometimes may not be reviewed as thoroughly as the main body of a manuscript. Trouble may arise when experimental results are contradicting the results obtained by other scientists, which requires (in the best case) the reexamination of the original raw data or independent repetition of the experiment according to the published description of the experiment. There are reports that a significant fraction of experiments obtained in academic laboratories cannot be repeated in an industrial environment (Begley CG & Ellis LM, Nature 483(7391):531-3, 2012). This is not an indication of scientific fraud but rather reflects the inadequate description of experiments performed on different equipment and on biological samples that were produced with disparate methods. For that reason the goal of a modern data management system is not only the simple replacement of the laboratory notebook by an electronic one but also the creation of a sophisticated, internally consistent, scalable data management system that will combine data obtained by a variety of experiments performed by various individuals on diverse equipment. All data should be stored in a core database that can be used by custom applications to prepare internal reports, statistics, and perform other functions that are specific to the research that is pursued in a particular laboratory.This chapter presents a general overview of the methods of data management and analysis used by structural genomics (SG) programs. In addition to a review of the existing literature on the subject, also presented is experience in the development of two SG data management systems, UniTrack and LabDB. The description is targeted to a general audience, as some technical details have been (or will be) published elsewhere. The focus is on "data management," meaning the process of gathering, organizing, and storing data, but also briefly discussed is "data mining," the process of analysis ideally leading to an understanding of the data. In other words, data mining is the conversion of data into information. Clearly, effective data management is a precondition for any useful data mining. If done properly, gathering details on millions of experiments on thousands of proteins and making them publicly available for analysis-even after the projects themselves have ended-may turn out to be one of the most important benefits of SG programs.

The MapReduce programming model is recently getting a lot of attention from both academic and business researchers. Systems based on this model hide communication and synchronization issues from the user and allow processing of high volumes of data on thousands of commodity computers. In this paper we are interested in applying MR to processing hierarchical data with nested collections such as stored in JSON or XML formats but with restricted nesting depth as is usual in the nested relational model. The current data analytics systems now often propose ad-hoc formalisms to represent query evaluation plans and to optimize their execution. In this paper we will argue that the Nested Relation Calculus provides a general, elegant and effective way to describe and investigate these optimizations. It allows to describe and combine both classical optimizations and MapReduce-specific optimizations. We demonstrate this by showing that MapReduce programs can be expressed and represented straightforwardly in NRC by adding syntactic short-hands. In addition we show that optimizations in existing systems can be readily represented in this extended formalism.

The energy-exchange process between the fundamental and its second-harmonic fields copropagating in an optical medium is facilitated by the spatially periodic dc poling field. The nonlinear dynamics of this system is investigated in the context of an effective Hamiltonian formalism. The explicitly included cross-modulation and self-phase-modulation nonlinear terms lead to the bifurcation instability interpreted as a new switching phenomenon in which both up- and down-conversion efficiencies can be precisely controlled by the external field. Experiments testing predicted effects are suggested

The LabDB laboratory information management system (LIMS) tracks, organizes and analyzes data from chemical and solution management, protein production, crystallization, diffraction, structure solution, and in vitro biochemical and biophysical experiments. The system is comprised of multiple modules specialized for different tasks, such as the Xtaldb system for crystallization or the hkldb module of the HKL-3000 suite for diffraction data collection and structure solution. The biochemical/biophysical experiments tracked by LabDB include spectrophotometric binding and kinetics, thermal shift binding, isothermal titration calorimetry (ITC) and protein quantitation. These tools associate functional and structural experiments, for example, for selecting likely substrates for co-crystallization and soaking experiments. Whenever possible, the system harvests data with no or minimal user intervention from laboratory hardware. Devices that may connect to or import data into LabDB include crystal observation (Rigaku Minstrel HT and Formulatrix Rock Imager), liquid handling (Formulatrix Rock Maker and Emerald Opti-Matrix Maker), chromatography (GE Healthcare AKTA), quantitation (Caliper LabChip GX II and Bio-Rad Gel Doc EZ), RT-PCR (Applied Biosystems 7900HT and Bio-Rad C1000/CFX96) and ITC (MicroCal iTC-200) systems. LabDB is used by two high-throughput PSI:Biology centers (MCSG and NYSGRC) as well as other major NIH consortia (the Center for Structural Genomics of Infectious Diseases and the Enzyme Function Initiative), and track millions of experiments on tens of thousands of targets.[1] The system also provides extensive data mining and analysis tools for translating raw experimental data into information and knowledge. We present examples of analyses generated by the system useful in designing new experiments.

We investigate the tunneling of carriers in a quasi-one-dimensional array of semimagnetic quantum dots. The large magnetopolaron effects due to the exchange interaction of carriers with magnetic ions result in a transmission via solitonlike electronic states. The intensity-dependent transmission coefficient shows the opening of intensity-dependent gaps in the transmission spectrum.

We consider a superlattice built of an array of narrow layers of a narrow gap material (quantum wells) sandwiched hy thick layers of a wide gap material (barriers). The electromagnetic wave with frequency below the band gap of the wells results in optical nonlinearities confined to narrow layers. We model this system by an array of equally spaced, infinitesimally thin layers. characterized by Kerr nonlinearity. It is shown that the electromagnetic wave induces intensity dependent gaps in the spectrum giving rise to a multivalued transmission coefficient. The origin of gaps, their connection to chaos, and a possible application for bistable devices are discussed.

We investigate electronic states and the far-infrared absorption spectrum of a two-dimensional (2D) hydrogenic impurity in a parabolic quantum wire in a magnetic field. The problem is mapped into the problem of interacting nonlinear harmonic oscillators. The evolution of the energy levels and level statistics from a 2D to a 1D effective hydrogen atom is investigated in the quantum and classically chaotic regimes. The ground-state energy reflects a delicate balance between a blueshift due to confinement and a redshift due to an increase in binding energy. In the absence of a magnetic field, the model reduces to the well-known problem of quantum chaos in a 3D hydrogen atom in a magnetic field in a zero angular-momentum channel. The presence of a magnetic field in the wire breaks the scaling behavior inherent in the 3D hydrogen problem.

Many interesting phenomena associated with the propagation of electromagnetic waves in spatially periodic nonlinear media have recently been studied. Beginning with Delyon et al. 1, followed by Mills and Trullinger2, as well as Grabowski and Hawrylak3, these investigations led to fairly complete understanding of the regular (periodic), localized (solitons), and chaotic behavior of monochromatic waves in one-dimensional geometries. However, the full picture of spatio-temporal nonlinear phenomena is still missing.

The propagation of electronic and electromagnetic waves in a periodic, nonlinear medium is described in terms of a discrete dynamical system represented by a universal, area-preserving map of a plane onto itself. The map is characterized by two control parameters: the wave vector k and the current density j. The dynamics of this Hamiltonian map is very complex admitting periodic, quasiperiodic, and chaotic orbits bifurcating and resonating at various points of the two-dimensional parameter space (k,j). The analysis of this dynamical system is based on the pattern of strong resonances and then applied to the problems of electromagnetic-wave propagation through a superlattice characterized by a strong excitonic nonlinearity and the ballistic transport of electrons in spatially periodic media.

A possible novel mechanism for superconductivity in heavy fermion systems is proposed. The Coqblin-Schrieffer model for a Kondo-like impurity is studied in the limit of large spin degeneracy. The effective dynamical interaction between conduction electrons mediated by spin fluctuations is calculated and shown to be attractive at finite frequencies.

We consider a semiconductor superlattice built of quasi-two-dimensional layers of narrow-band-gap material sandwiched by barriers of wide-band-gap material. We show that when the electromagnetic wave with frequency below the band gap of the superlattice propagates along the superlattice axis, it induces intensity-dependent gaps in the spectrum. The connection between these unusual gaps and spatial chaos is explored. The possibility of bistable devices is discussed.