Crystal structure of yellow meal worm alpha-amylase at 1.64 A resolution

The three-dimensional structure of the alpha-amylase from Tenebrio molitor larvae (TMA) has been determined by molecular replacement techniques using diffraction data of a crystal of space group P212121 (a=51.24 A; b=93.46 A; c=96.95 A). The structure has been refined to a crystallographic R-factor of 17.7% for 58,219 independent reflections in the 7.0 to 1.64 A resolution range, with root-mean-square deviations of 0.008 A for bond lengths and 1.482 degrees for bond angles. The final model comprises all 471 residues of TMA, 261 water molecules, one calcium cation and one chloride anion. The electron density confirms that the N-terminal glutamine residue has undergone a post-transitional modification resulting in a stable 5-oxo-proline residue. The X-ray structure of TMA provides the first three-dimensional model of an insect alpha-amylase. The monomeric enzyme exhibits an elongated shape approximately 75 Ax46 Ax40 A and consists of three distinct domains, in line with models for alpha-amylases from microbial, plant and mammalian origin. However, the structure of TMA reflects in the substrate and inhibitor binding region a remarkable difference from mammalian alpha-amylases: the lack of a highly flexible, glycine-rich loop, which has been proposed to be involved in a "trap-release" mechanism of substrate hydrolysis by mammalian alpha-amylases. The structural differences between alpha-amylases of various origins might explain the specificity of inhibitors directed exclusively against insect alpha-amylases.     

Circulating Exosomal miRNAs as Biomarkers for the Diagnosis and Prognosis of Colorectal Cancer

Colorectal cancer (CRC) is one of the most common malignant tumors in the gastrointestinal tract. It is a multifactorial disease that involves environmental factors, genetic factors, and lifestyle factors. Due to the absence of specific and sensitive biomarkers, CRC patients are usually diagnosed at an advanced stage and consequently suffer from a low 5-year overall survival rate. Despite improvements in surgical resection and adjuvant chemotherapy, the prognosis of patients with CRC remains unfavorable due to local and distant metastases. Several studies have shown that small noncoding RNAs, such as microRNAs packed in exosomes, are potential biomarkers in various types of cancers, including CRC, and that they can be detected in a stable form in both serum and plasma. In this review, we report the potential of circulating exosomal miRNAs to act as biomarkers for the diagnosis and prognosis of CRC.     

Social networks applied

Social networks have interesting properties. They influence our lives enormously without us being aware of the implications they raise. The authors investigate the following areas concerning social networks: how to exploit our unprecedented wealth of data and how we can mine social networks for purposes such as marketing campaigns; social networks as a particular form of influence, i.e.., the way that people agree on terminology and this phenomenon's implications for the way we build ontologies and the Semantic Web; social networks as something we can discover from data; the use of social network information to offer a wealth of new applications such as better recommendations for restaurants, trustworthy email senders, or (maybe) blind dates; investigation of the richness and difficulty of harvesting FOAF (friend-of-a-friend) information; and by looking at how information processing is bound to social context, the resulting ways that network topology's definition determines its outcomes.     

Tree Induction for Probability-Based Ranking

Tree induction is one of the most effective and widely used methods for building classification models. However, many applications require cases to be ranked by the probability of class membership. Probability estimation trees (PETs) have the same attractive features as classification trees (e.g., comprehensibility, accuracy and efficiency in high dimensions and on large data sets). Unfortunately, decision trees have been found to provide poor probability estimates. Several techniques have been proposed to build more accurate PETs, but, to our knowledge, there has not been a systematic experimental analysis of which techniques actually improve the probability-based rankings, and by how much. In this paper we first discuss why the decision-tree representation is not intrinsically inadequate for probability estimation. Inaccurate probabilities are partially the result of decision-tree induction algorithms that focus on maximizing classification accuracy and minimizing tree size (for example via reduced-error pruning). Larger trees can be better for probability estimation, even if the extra size is superfluous for accuracy maximization. We then present the results of a comprehensive set of experiments, testing some straightforward methods for improving probability-based rankings. We show that using a simple, common smoothing method—the Laplace correction—uniformly improves probability-based rankings. In addition, bagging substantially improves the rankings, and is even more effective for this purpose than for improving accuracy. We conclude that PETs, with these simple modifications, should be considered when rankings based on class-membership probability are required.     

Programming by Demonstration Using Version Space Algebra

Programming by demonstration enables users to easily personalize their applications, automating repetitive tasks simply by executing a few examples. We formalize programming by demonstration as a machine learning problem: given the changes in the application state that result from the user's demonstrated actions, learn the general program that maps from one application state to the next. We present a methodology for learning in this space of complex functions. First we extend version spaces to learn arbitrary functions, not just concepts. Then we introduce the version space algebra, a method for composing simpler version spaces to construct more complex spaces. Finally, we apply our version space algebra to the text-editing domain and describe an implemented system called SMARTedit that learns repetitive text-editing procedures by example. We evaluate our approach by measuring the number of examples required for the system to learn a procedure thatworks on the remainder of examples, and by an informal user study measuring the effort users spend using our system versus performing the task by hand. The results show that SMARTedit is capable of generalizing correctly from as few as one or two examples, and that users generally save a significant amount of effort when completing tasks with SMARTedit's help.     

Autografting in chronic myeloid leukemia: an overview

Autografting could become a promising treatment for patients with chronic myeloid leukemia (CML) who cannot undergo allogeneic bone marrow transplantation or failed to respond to recombinant alpha-interferon (IFN). In this review, we analyze the results which have been published for patients transplanted in chronic phase and which suggest that autografting could prolong survival, at least in some patients. We also discuss the different methods of purging whose clinical efficacy remains to be assessed.     

Automated extraction of lysergic acid diethylamide (LSD) and N-demethyl-LSD from blood, serum, plasma, and urine samples using the Zymark RapidTrace with LC/MS/MS confirmation

A forensic procedure for the quantitative confirmation of lysergic acid diethylamide (LSD) and the qualitative confirmation of its metabolite, N-demethyl-LSD, in blood, serum, plasma, and urine samples is presented. The Zymark RapidTrace was used to perform fully automated solid-phase extractions of all specimen types. After extract evaporation, confirmations were performed using liquid chromatography (LC) followed by positive electrospray ionization (ESI+) mass spectrometry/mass spectrometry (MS/MS) without derivatization. Quantitation of LSD was accomplished using LSD-d3 as an internal standard. The limit of quantitation (LOQ) for LSD was 0.05 ng/mL. The limit of detection (LOD) for both LSD and N-demethyl-LSD was 0.025 ng/mL. The recovery of LSD was greater than 95% at levels of 0.1 ng/mL and 2.0 ng/mL. For LSD at 1.0 ng/mL, the within-run and between-run (different day) relative standard deviation (RSD) was 2.2% and 4.4%, respectively.     

Quantitative brain magnetic resonance imaging in attention-deficit hyperactivity disorder

BACKGROUND: Anatomic magnetic resonance imaging (MRI) studies of attention-deficit hyperactivity disorder (ADHD) have been limited by small samples or measurement of single brain regions. Since the neuropsychological deficits in ADHD implicate a network linking basal ganglia and frontal regions, 12 subcortical and cortical regions and their symmetries were measured to determine if these structures best distinguished ADHD. METHODS: Anatomic brain MRIs for 57 boys with ADHD and 55 healthy matched controls, aged 5 to 18 years, were obtained using a 1.5-T scanner with contiguous 2-mm sections. Volumetric measures of the cerebrum, caudate nucleus, putamen, globus pallidus, amygdala, hippocampus, temporal lobe, cerebellum; a measure of prefrontal cortex; and related right-left asymmetries were examined along with midsagittal area measures of the cerebellum and corpus callosum. Interrater reliabilities were .82 or greater for all MRI measures. RESULTS: Subjects with ADHD had a 4.7% smaller total cerebral volume (P = .02). Analysis of covariance for total cerebral volume demonstrated a significant loss of normal right > left asymmetry in the caudate (P = .006), smaller right globus pallidus (P = .005), smaller right anterior frontal region (P = .02), smaller cerebellum (P = .05), and reversal of normal lateral ventricular asymmetry (P = .03) in the ADHD group. The normal age-related decrease in caudate volume was not seen, and increases in lateral ventricular volumes were significantly diminished in ADHD. CONCLUSION: This first comprehensive morphometric analysis is consistent with hypothesized dysfunction of right-sided prefrontal-striatal systems in ADHD.     

Psilocybin induces schizophrenia-like psychosis in humans via a serotonin-2 agonist action

Psilocybin, an indoleamine hallucinogen, produces a psychosis-like syndrome in humans that resembles first episodes of schizophrenia. In healthy human volunteers, the psychotomimetic effects of psilocybin were blocked dose-dependently by the serotonin-2A antagonist ketanserin or the atypical antipsychotic risperidone, but were increased by the dopamine antagonist and typical antipsychotic haloperidol. These data are consistent with animal studies and provide the first evidence in humans that psilocybin-induced psychosis is due to serotonin-2A receptor activation, independently of dopamine stimulation. Thus, serotonin-2A overactivity may be involved in the pathophysiology of schizophrenia and serotonin-2A antagonism may contribute to therapeutic effects of antipsychotics.