Exploring proteasome inhibition using atomic weighted vector indices and machine learning approaches.

Ubiquitin-proteasome system (UPS) is a highly regulated mechanism of intracellular protein degradation and turnover. The UPS is involved in different biological activities, such as the regulation of gene transcription and cell cycle. Several researchers have applied cheminformatics and artificial intelligence methods to study the inhibition of proteasomes, including the prediction of UPP inhibitors. Following this idea, we applied a new tool for obtaining molecular descriptors (MDs) for modeling proteasome Inhibition in terms of EC50 (µmol/L), in which a set of new MDs called atomic weighted vectors (AWV) and several prediction algorithms were used in cheminformatics studies. In the manuscript, a set of descriptors based on AWV are presented as datasets for training different machine learning techniques, such as linear regression, multiple linear regression (MLR), random forest (RF), K-nearest neighbors (IBK), multi-layer perceptron, best-first search, and genetic algorithm. The results suggest that these atomic descriptors allow adequate modeling of proteasome inhibitors despite artificial intelligence techniques, as a variant to build efficient models for the prediction of inhibitory activity.

Deficits of entropy modulation of the EEG: A biomarker for altered function in schizophrenia and bipolar disorder?

Background: The synchronized activity of distributed neural assemblies ­ reflected in the electroencephalogram (EEG) ­ underpins mental function. In schizophrenia, modulation deficits of EEG spectral content during a P300 task have been replicated. The effects of treatment, chronicity and specificity in these deficits and their possible relationship with anatomic connectivity remain to be explored. Methods: We assessed spectral entropy modulation of the EEG during a P300 task in 79 patients with schizophrenia (of those, 31 werein their first episode), 29 patients with bipolar disorder and 48 healthy controls. Spectral entropy values summarize EEG characteristics by quantifying the irregularity of spectral content. In a subsample, we calculated the network architecture of structural connectivity using diffusion tensor imaging and graph-theory parameters. Results: We found significant spectral entropy modulation deficits with task performance in patients with chronic or first-episode schizophrenia and in patients with bipolar disorder, without significant pre-stimulus spectral entropy differences. The deficits were unrelated to treatment doses, and spectral entropy modulation did not differ between patients taking or not taking antipsychotics, lithium, benzodiazepines or antidepressants. Structural connectivity values were unrelated to spectral entropy modulation. In patients with schizophrenia, spectral entropy modulation was inversely related to negative symptoms and directly related to verbal memory. Limitations: All patients were taking medication. Patients with bipolar disorder were euthymic and chronic. The cross-sectional nature of this study prevented a more thorough analysis of state versus trait criteria for spectral entropy changes. Conclusion: Spectral entropy modulation with task performance is decreased in patients with schizophrenia and bipolar disorder. This deficit was not an effect of psychopharmacological treatment or structural connectivity and might reflect a deficit in the synchronization of the neural assemblies that underlie cognitive activity.

When global and local molecular descriptors are more than the sum of its parts: Simple, But Not Simpler?

In this report, we introduce a set of aggregation operators (AOs) to calculate global and local (group and atom type) molecular descriptors (MDs) as a generalization of the classical approach of molecular encoding using the sum of the atomic (or fragment) contributions. These AOs are implemented in a new and free software denominated MD-LOVIs ( http://tomocomd.com/md-lovis ), which allows for the calculation of MDs from atomic weights vector and LOVIs (local vertex invariants). This software was developed in Java programming language and employed the Chemical Development Kit (CDK) library for handling chemical structures and the calculation of atomic weights. An analysis of the complexities of the algorithms presented herein demonstrates that these aspects were efficiently implemented. The calculation speed experiments show that the MD-LOVIs software has satisfactory behavior when compared to software such as Padel, CDKDescriptor, DRAGON and Bluecal software. Shannon's entropy (SE)-based variability studies demonstrate that MD-LOVIs yields indices with greater information content when compared to those of popular academic and commercial software. A principal component analysis reveals that our approach captures chemical information orthogonal to that codified by the DRAGON, Padel and Mold2 software, as a result of the several generalizations in MD-LOVIs not used in other programs. Lastly, three QSARs were built using multiple linear regression with genetic algorithms, and the statistical parameters of these models demonstrate that the MD-LOVIs indices obtained with AOs yield better performance than those obtained when the summation operator is used exclusively. Moreover, it is also revealed that the MD-LOVIs indices yield models with comparable to superior performance when compared to other QSAR methodologies reported in the literature, despite their simplicity. The studies performed herein collectively demonstrated that MD-LOVIs software generates indices as simple as possible, but not simpler and that use of AOs enhances the diversity of the chemical information codified, which consequently improves the performance of traditional MDs.

Reliability of OMERACT ultrasound elementary lesions in gout: results from a multicenter exercise.

The aim of this study was to evaluate the reliability of the outcome measures in rheumatology (OMERACT) definitions for ultrasound (US) elementary lesions in gout through an image reading exercise. Images from patients with gout (static images and videos) were collected. As an initial step, we carried out a image reading exercise within the experts of the Pan-American League of Associations for Rheumatology (PANLAR) US Study Group (n = 16). The following step consisted in a web-based exercise with the participation of larger number of sonographers (n = 63) from different centers. Images were rated evaluating the presence/absence of any US elementary lesion. Inter- and intra-reader reliabilities were analyzed using kappa coefficients. Participants were stratified according to their level of experience. In the first exercise, inter-reader kappa values were 0.45 for aggregates, 0.57 for tophus, 0.69 for erosions, and 0.90 for double contour (DC). Intra-reader kappa values were 0.86, 0.76, 0.80, and 0.90, respectively. The web-based exercise showed inter-reader kappa values for aggregates, tophus, erosions, and DC of 0.42, 0.49, 0.69, and 0.79, respectively. The intra-reader kappa values were 0.62, 0.69, 0.77, and 0.85, respectively. Reliability was not influenced by the sonographer's level of experience. The reliability of the new OMERACT US definitions for elementary lesions in gout ranged from moderate to excellent, depending on the type of lesion.

Association between electroencephalographic modulation, psychotic-like experiences and cognitive performance in the general population.

AIM: An association between deficit of electroencephalographic (EEG) modulation during an odd-ball task and psychotic symptoms has been described in clinical samples, in agreement with the proposed role for altered salience in psychosis. To discard the possible influence of medication, the relationship between psychotic-like experiences and EEG modulation in the general population was explored. METHODS: EEG and psychotic-like experiences were assessed in 194 healthy subjects during a P300 paradigm. EEG modulation was assessed as changes from pre-stimulus to response windows in spectral entropy (SE, a measurement of signal irregularity), median frequency (MF, a quantifier of the frequency distribution of oscillatory activity) and theta, alpha, beta-1, beta-2 and gamma relative power (RP, a summary of the distribution of spectral components). RESULTS: A significant widespread decrease in SE and MF from baseline to response was found, with a significant increase in RP for theta and a decrease for higher frequency bands, supporting an increase in EEG regularity and a slowing of brain oscillations during the response. Furthermore, a significant association was found between SE modulation and distress of negative psychotic-like experiences, as well as between verbal memory and RP modulation for beta-1. Performance in verbal fluency was associated with the increase in theta RP during the response. CONCLUSION: EEG irregularity of healthy subjects decreased at the expense of a larger contribution of theta RP and a decreased contribution of fast frequency bands. Subjects with smaller modulation showed poorer cognitive scores and greater distress of negative psychotic-like experiences.

Physico-Chemical and Structural Interpretation of Discrete Derivative Indices on N-Tuples Atoms.

This report examines the interpretation of the Graph Derivative Indices (GDIs) from three different perspectives (i.e., in structural, steric and electronic terms). It is found that the individual vertex frequencies may be expressed in terms of the geometrical and electronic reactivity of the atoms and bonds, respectively. On the other hand, it is demonstrated that the GDIs are sensitive to progressive structural modifications in terms of: size, ramifications, electronic richness, conjugation effects and molecular symmetry. Moreover, it is observed that the GDIs quantify the interaction capacity among molecules and codify information on the activation entropy. A structure property relationship study reveals that there exists a direct correspondence between the individual frequencies of atoms and Hückel's Free Valence, as well as between the atomic GDIs and the chemical shift in NMR, which collectively validates the theory that these indices codify steric and electronic information of the atoms in a molecule. Taking in consideration the regularity and coherence found in experiments performed with the GDIs, it is possible to say that GDIs possess plausible interpretation in structural and physicochemical terms.

Metabolic correction: a functional biochemical mechanism against disease--Part 1: concept and historical background.

Human physiology depends on countless biochemical reactions, numerous of which are co-dependent and interrelated. The speed and level of completion of reactions usually depend on the availability of precursors and enzymes. The enzymatic activity depends on the bioavailability of micronutrient cofactors such as vitamins and minerals. In order to achieve a healthy physiological state, the organism requires that biochemical reactions occur at a controlled rate. To achieve this state it is required that metabolic reactions reach what can be considered an optimal metabolic equilibrium. A combination of genetic makeup, dietary patterns, trauma, disease, toxins, medications, and environmental stressors can elevate the demand for the nutrients needed to reach this optimal metabolic equilibrium. In this, part 1, the general concept of metabolic correction is presented with an elaboration explaining how this concept is increasing in importance as we become aware of the presence of genetic variants that affect enzymatic reactions causing metabolic disturbances that themselves favor or promote the disease state. In addition, part 1 reviews how prominent scientists have contributed in fundamental ways to our understanding of the importance of micronutrients in health and disease and in the development of the metabolic correction concept.

Experimental Lab Tests on Rabbits for the Optimization and Redesign of Low-Cost Equipment for Automated Peritoneal Dialysis.

This work shows the experiences acquired by the experimental test performed to validate an automated peritoneal dialysis machine using rabbits with kidney damage to find improvements that can be made for future advances. These are listed to understand the direction of the development of the machine. The article shows the device's background and previous tests using a testbed. The rabbit anatomy was prepared for nephrectomy surgery. The tests were practiced by checking all of the APD machine's subsystems. The data were analyzed to develop improvements in the process. The results indicate the importance of the DPA machine as an alternative by implementing peristaltic pumps to substitute disposable cassettes. The identified improvements are the main objectives for research to continue improving the technology.

Relations frequency hypermatrices in mutual, conditional and joint entropy-based information indices.

Graph-theoretic matrix representations constitute the most popular and significant source of topological molecular descriptors (MDs). Recently, we have introduced a novel matrix representation, named the duplex relations frequency matrix, F, derived from the generalization of an incidence matrix whose row entries are connected subgraphs of a given molecular graph G. Using this matrix, a series of information indices (IFIs) were proposed. In this report, an extension of F is presented, introducing for the first time the concept of a hypermatrix in graph-theoretic chemistry. The hypermatrix representation explores the n-tuple participation frequencies of vertices in a set of connected subgraphs of G. In this study we, however, focus on triple and quadruple participation frequencies, generating triple and quadruple relations frequency matrices, respectively. The introduction of hypermatrices allows us to redefine the recently proposed MDs, that is, the mutual, conditional, and joint entropy-based IFIs, in a generalized way. These IFIs are implemented in GT-STAF (acronym for Graph Theoretical Thermodynamic STAte Functions), a new module of the TOMOCOMD-CARDD program. Information theoretic-based variability analysis of the proposed IFIs suggests that the use of hypermatrices enhances the entropy and, hence, the variability of the previously proposed IFIs, especially the conditional and mutual entropy based IFIs. The predictive capacity of the proposed IFIs was evaluated by the analysis of the regression models, obtained for physico-chemical properties the partition coefficient (Log P) and the specific rate constant (Log K) of 34 derivatives of 2-furylethylene. The statistical parameters, for the best models obtained for these properties, were compared to those reported in the literature depicting better performance. This result suggests that the use of the hypermatrix-based approach, in the redefinition of the previously proposed IFIs, avails yet other valuable tools beneficial in QSPR studies and diversity analysis.

Spark of Life: Role of Electrotrophy in the Emergence of Life.

The emergence of life has been a subject of intensive research for decades. Different approaches and different environmental "cradles" have been studied, from space to the deep sea. Since the recent discovery of a natural electrical current through deep-sea hydrothermal vents, a new energy source is considered for the transition from inorganic to organic. This energy source (electron donor) is used by modern microorganisms via a new trophic type, called electrotrophy. In this review, we draw a parallel between this metabolism and a new theory for the emergence of life based on this electrical electron flow. Each step of the creation of life is revised in the new light of this prebiotic electrochemical context, going from the evaluation of similar electrical current during the Hadean, the CO2 electroreduction into a prebiotic primordial soup, the production of proto-membranes, the energetic system inspired of the nitrate reduction, the proton gradient, and the transition to a planktonic proto-cell. Finally, this theory is compared to the two other theories in hydrothermal context to assess its relevance and overcome the limitations of each. Many critical factors that were limiting each theory can be overcome given the effect of electrochemical reactions and the environmental changes produced.

Derivatives in discrete mathematics: a novel graph-theoretical invariant for generating new 2/3D molecular descriptors. I. Theory and QSPR application.

In this report, we present a new mathematical approach for describing chemical structures of organic molecules at atomic-molecular level, proposing for the first time the use of the concept of the derivative ([Formula: see text]) of a molecular graph (MG) with respect to a given event (E), to obtain a new family of molecular descriptors (MDs). With this purpose, a new matrix representation of the MG, which generalizes graph's theory's traditional incidence matrix, is introduced. This matrix, denominated the generalized incidence matrix, Q, arises from the Boolean representation of molecular sub-graphs that participate in the formation of the graph molecular skeleton MG and could be complete (representing all possible connected sub-graphs) or constitute sub-graphs of determined orders or types as well as a combination of these. The Q matrix is a non-quadratic and unsymmetrical in nature, its columns (n) and rows (m) are conditions (letters) and collection of conditions (words) with which the event occurs. This non-quadratic and unsymmetrical matrix is transformed, by algebraic manipulation, to a quadratic and symmetric matrix known as relations frequency matrix, F, which characterizes the participation intensity of the conditions (letters) in the events (words). With F, we calculate the derivative over a pair of atomic nuclei. The local index for the atomic nuclei i, Δ(i), can therefore be obtained as a linear combination of all the pair derivatives of the atomic nuclei i with all the rest of the j's atomic nuclei. Here, we also define new strategies that generalize the present form of obtaining global or local (group or atom-type) invariants from atomic contributions (local vertex invariants, LOVIs). In respect to this, metric (norms), means and statistical invariants are introduced. These invariants are applied to a vector whose components are the values Δ(i) for the atomic nuclei of the molecule or its fragments. Moreover, with the purpose of differentiating among different atoms, an atomic weighting scheme (atom-type labels) is used in the formation of the matrix Q or in LOVIs state. The obtained indices were utilized to describe the partition coefficient (Log P) and the reactivity index (Log K) of the 34 derivatives of 2-furylethylenes. In all the cases, our MDs showed better statistical results than those previously obtained using some of the most used families of MDs in chemometric practice. Therefore, it has been demonstrated to that the proposed MDs are useful in molecular design and permit obtaining easier and robust mathematical models than the majority of those reported in the literature. All this range of mentioned possibilities open "the doors" to the creation of a new family of MDs, using the graph derivative, and avail a new tool for QSAR/QSPR and molecular diversity/similarity studies.

Neurobiological underpinnings of cognitive subtypes in psychoses: A cross-diagnostic cluster analysis.

Schizophrenia and bipolar disorder include patients with different characteristics, which may hamper the definition of biomarkers. One of the dimensions with greater heterogeneity among these patients is cognition. Recent studies support the identification of different patients' subgroups along the cognitive domain using cluster analysis. Our aim was to validate clusters defined on the basis of patients' cognitive status and to assess its relation with demographic, clinical and biological measurements. We hypothesized that subgroups characterized by different cognitive profiles would show differences in an array of biological data. Cognitive data from 198 patients (127 with chronic schizophrenia, 42 first episodes of schizophrenia and 29 bipolar patients) were analyzed by a K-means cluster approach and were compared on several clinical and biological variables. We also included 155 healthy controls for further comparisons. A two-cluster solution was selected, including a severely impaired group and a moderately impaired group. The severely impaired group was associated with higher illness duration and symptoms scores, lower thalamus and hippocampus volume, lower frontal connectivity and basal hypersynchrony in comparison to controls and the moderately impaired group. Moreover, both patients' groups showed lower cortical thickness and smaller functional connectivity modulation than healthy controls. This study supports the existence of different cognitive subgroups within the psychoses with different neurobiological underpinnings.

Identificacion of MRI-based psychosis subtypes: Replication and refinement.

The identification of the cerebral substrates of psychoses such as schizophrenia and bipolar disorder is likely hampered by its biological heterogeneity, which may contribute to the low replication of results in the field. In this study we aimed to replicate in a completely new sample and supplement the results of a previous study with additional data on this topic. In the aforementioned study we identified a schizophrenia cluster characterized by high mean cortical curvature and low cortical thickness, subcortical hypometabolism and progressive negative symptoms. Here, we have used magnetic resonance images from 61 schizophrenia and 28 bipolar patients, as well as 51 healthy controls and a cluster analysis to search for possible subgroups primarily characterized by cerebral structural data. Diffusion tensor imaging (fractional anisotropy, FA), cognition, clinical data and electroencephalographic (EEG) modulation during a P300 task were used to validate the possible clusters. Two clusters of patients were identified. The first cluster (29 schizophrenia and 18 bipolar patients) showed decreased cortical thickness and area values, as well as lower subcortical volumes and higher cortical curvature in some regions, as compared to the second cluster. This first cluster also showed decreased FA in frontal lobe connections and worse cognitive performance. Although this cluster also showed longer illness duration, there were first episode patients in both clusters and treatment doses and types were not different between clusters. Both clusters of patients showed decreased EEG task-related modulation. In conclusion, our data give additional support to a distinct biologically based cluster encompassing schizophrenia and bipolar disorder patients with cortical and subcortical alterations, hampered cortical connectivity and lower cognitive performance.

Higher-Order and Mixed Discrete Derivatives such as a Novel Graph- Theoretical Invariant for Generating New Molecular Descriptors.

BACKGROUND: Recently, some authors have defined new molecular descriptors (MDs) based on the use of the Graph Discrete Derivative, known as Graph Derivative Indices (GDI). This new approach about discrete derivatives over various elements from a graph takes as outset the formation of subgraphs. Previously, these definitions were extended into the chemical context (N-tuples) and interpreted in structural/physicalchemical terms as well as applied into the description of several endpoints, with good results. OBJECTIVE: A generalization of GDIs using the definitions of Higher Order and Mixed Derivative for molecular graphs is proposed as a generalization of the previous works, allowing the generation of a new family of MDs. METHODS: An extension of the previously defined GDIs is presented, and for this purpose, the concept of Higher Order Derivatives and Mixed Derivatives is introduced. These novel approaches to obtaining MDs based on the concepts of discrete derivatives (finite difference) of the molecular graphs use the elements of the hypermatrices conceived from 12 different ways (12 events) of fragmenting the molecular structures. The result of applying the higher order and mixed GDIs over any molecular structure allows finding Local Vertex Invariants (LOVIs) for atom-pairs, for atoms-pairs-pairs and so on. All new families of GDIs are implemented in a computational software denominated DIVATI (acronym for Discrete DeriVAtive Type Indices), a module of KeysFinder Framework in TOMOCOMD-CARDD system. RESULTS: QSAR modeling of the biological activity (Log 1/K) of 31 steroids reveals that the GDIs obtained using the higher order and mixed GDIs approaches yield slightly higher performance compared to previously reported approaches based on the duplex, triplex and quadruplex matrix. In fact, the statistical parameters for models obtained with the higher-order and mixed GDI method are superior to those reported in the literature by using other 0-3D QSAR methods. CONCLUSION: It can be suggested that the higher-order and mixed GDIs, appear as a promissory tool in QSAR/QSPRs, similarity/dissimilarity analysis and virtual screening studies.

Regional moderate hyperthermia for mild-to-moderate COVID-19 (TherMoCoV study): a randomized controlled trial.

Background: To prevent COVID-19 progression, low-cost alternatives that are available to all patients are needed. Diverse forms of thermotherapy have been proposed to prevent progression to severe/critical COVID-19. Objective: The aim of this study is to evaluate the efficacy and safety of local thermotherapy to prevent disease progression in hospitalized adult patients with mild-to-moderate COVID-19. Methods: A multicenter, open-label, parallel-group, randomized, adaptive trial is used to evaluate the efficacy and safety of local thermotherapy to prevent disease progression in hospitalized adult patients with mild-to-moderate COVID-19. Eligible hospitalized adult patients with symptoms of COVID-19 with ≤5 days from symptom onset, meeting criteria for mild or moderate COVID-19, were randomly assigned to the intervention consisting of local thermotherapy via an electric heat pad in the thorax (target temperature range 39.5­42°C) continuously for 90 min, twice daily, for 5 days, or standard care. The main outcome was the proportion of patients who progressed to severe-to-critical COVID-19 or death. Patients were randomized in a 1:1 ratio through a centralized computer-generated sequence of minimization with a random component of 20%. Participants and medical staff were not blinded to the intervention. Results: One-hundred and five participants (thermotherapy n = 54, control n = 51) with a median age of 53 (IQR: 41­64) years were included for analysis after the early cessation of recruitment due to the closure of all temporal COVID-19 units (target sample size = 274). The primary outcome of disease progression occurred in 31.4% (16/51) of patients in the control group vs. 25.9% (14/54) of those receiving thermotherapy (risk difference = 5.5%; 95%CI: −11.8­22.7, p = 0.54). Thermotherapy was well tolerated with a median total duration of thermotherapy of 900 (IQR: 877.5­900) min. Seven (13.7%) patients in the control group and seven (12.9%) in the thermotherapy group had at least one AE (p = 0.9), none of which were causally attributed to the intervention. No statistically significant differences in serum cytokines (IL-1ß, IL-6, IL-8, IL-10, IL-17, and IFN-γ) were observed between day 5 and baseline among groups. Conclusion: Local thermotherapy was safe and well-tolerated. A non-statistically significant lower proportion of patients who experienced disease progression was found in the thermotherapy group compared to standard care. Local thermotherapy could be further studied as a strategy to prevent disease progression in ambulatory settings.Clinical Trial registration: www.clinicaltrials.gov, identifier: NCT04363541.

Prediction of aquatic toxicity of benzene derivatives using molecular descriptor from atomic weighted vectors.

Several descriptors from atom weighted vectors are used in the prediction of aquatic toxicity of set of organic compounds of 392 benzene derivatives to the protozoo ciliate Tetrahymena pyriformis (log(IGC50)-1). These descriptors are calculated using the MD-LOVIs software and various Aggregation Operators are examined with the aim comparing their performances in predicting aquatic toxicity. Variability analysis is used to quantify the information content of these molecular descriptors by means of an information theory-based algorithm. Multiple Linear Regression with Genetic Algorithms is used to obtain models of the structure-toxicity relationships; the best model shows values of Q2=0.830 and R2=0.837 using six variables. Our models compare favorably with other previously published models that use the same data set. The obtained results suggest that these descriptors provide an effective alternative for determining aquatic toxicity of benzene derivatives.

Novel pre-treatment of zeolite materials for the removal of sodium ions: potential materials for coal seam gas co-produced wastewater.

Coal seam gas (CSG) is the extraction of methane gas that is desorbed from the coal seam and brought to the surface using a dewatering and depressurisation process within the saturated coalbed. The extracted water is often referred to as co-produced CSG water. In this study, co-produced water from the coal seam of the Bowen Basin (QLD, Australia) was characterised by high concentration levels of Na(+) (1156 mg/L), low concentrations of Ca(2+) (28.3 mg/L) and Mg(2+) (5.6 mg/L), high levels of salinity, which are expected to cause various environmental problems if released to land or waters. The potential treatment of co-produced water using locally sourced natural ion exchange (zeolite) material was assessed. The zeolite material was characterized for elemental composition and crystal structure. Natural, untreated zeolite demonstrated a capacity to adsorb Na(+) ions of 16.16 mEq/100 g, while a treated zeolite using NH4 (+) using a 1.0 M ammonium acetate (NH4C2H3O2) solution demonstrated an improved 136 % Na(+) capacity value of 38.28 mEq/100 g after 720 min of adsorption time. The theoretical exchange capacity of the natural zeolite was found to be 154 mEq/100 g. Reaction kinetics and diffusion models were used to determine the kinetic and diffusion parameters. Treated zeolite using a NH4 (+) pre-treatment represents an effective treatment to reduce Na(+) concentration in coal seam gas co-produced waters, supported by the measured and modelled kinetic rates and capacity.

[Relationship between subclinical psychotic symptoms and cognitive performance in the general population]. / Relaciones entre síntomas psicóticos subclínicos y rendimiento cognitivo en la población general.

INTRODUCTION: Subclinical psychotic symptoms are associated to negative life outcomes in the general population, but their relationship with cognitive performance is still not well understood. Assessing the relationship between performance in cognitive domains and subclinical psychotic symptoms in the general population may also help understand the handicap attributed to clinical psychosis, in which these alterations are present. METHODS: Subclinical and cognitive assessments were obtained in 203 participants from the general population by means of the Community Assessment of Psychic Experiences, the Brief Assessment of Cognition in Schizophrenia, the Wechsler Adults Intelligence Scale and the Wisconsin Card Sorting Test. The positive and negative subclinical symptoms and their relationship with age and cognition were examined, followed by assessing the influence of subclinical depression scores on the possible relationships between those subclinical psychotic symptoms and cognitive deficits. RESULTS: Inverse relationships were found between frequency in the Community Assessment of Psychic Experiences positive dimension and motor speed, and frequency and distress in the Community Assessment of Psychic Experiences negative dimension and motor speed. A direct relationship was also found between distress scores of the positive dimension and executive functions. Both positive and negative subclinical symptoms were related to depression scores. CONCLUSIONS: Psychotic symptoms, similar to those in the clinical population, may be associated with cognitive deficits in the general population.

Modulation of brain network parameters associated with subclinical psychotic symptoms.

OBJECTIVE: Static deficits in small-world properties of brain networks have been described in clinical psychosis, but task-related modulation of network properties has been scarcely studied. Our aim was to assess the modulation of those properties and its association with subclinical psychosis and cognition in the general population. METHOD: Closeness centrality and small-worldness were compared between pre-stimulus baseline and response windows of an odd-ball task in 200 healthy individuals. The correlation between modulation of network parameters and clinical (scores in the Community Assessment of Psychological Experiences) and cognitive measures (performance in the dimensions included in the Brief Assessment of Cognition in Schizophrenia battery) was analyzed, as well as between these measures and the corresponding network parameters during baseline and response windows during task performance. RESULTS: In the theta band, closeness centrality decreased and small-worldness increased in the response window. Centrality and small-worldness modulation were, respectively, directly and inversely associated with subclinical symptoms. CONCLUSIONS: A widespread modulation of network properties in theta band was observed, with a transient increase of small-worldness during the response window, compatible with a transiently more integrated cortical activity associated to cognition. This supports the relevance of electroencephalography to study of normal and altered cognition and its substrates. A relative deficit in the ability to reorganize brain networks may contribute to subclinical psychotic symptoms.

Generalized Molecular Descriptors Derived From Event-Based Discrete Derivative.

In the present study, a generalized approach for molecular structure characterization is introduced, based on the relation frequency matrix (F) representation of the molecular graph and the subsequent calculation of the corresponding discrete derivative (finite difference) over a pair of elements (atoms). In earlier publications (22- 24), an unique event, named connected subgraphs, (based on the Kier-Hall's subgraphs) was systematically employed for the computation of the matrix F. The present report is a generalization of this notion, in which eleven additional events are introduced, classified in three categories, namely, topological (terminal paths, vertex path incidence, quantum subgraphs, walks of length k, Sach's subgraphs), fingerprints (MACCs, E-state and substructure fingerprints) and atomic contributions (Ghose and Crippen atom-types for hydrophobicity and refractivity) for F generation. The events are intended to capture diverse information by the generation or search of different kinds of substructures from the graph representation of a molecule. The discrete derivative over duplex atom relations are calculated for each event, and the resulting derivatives, local vertex invariants (LOVIs) are finally obtained. These LOVIs are subsequently employed as the basis for the calculation of global and local indices over groups of atoms (heteroatoms, halogens, methyl carbons, etc.), by using norms, means, statistics and classical algorithms as aggregator (fusion) operators. These indices were implemented in our house software DIVATI (Derivative Type Indices, a new module of TOMOCOMDCARDD system). DIVATI provides a friendly and cross-platform graphical user interface, developed in the Java programming language and is freely available at: http: //www.tomocomd.com. Factor analysis shows that the presented events are rather orthogonal and collect diverse information about the chemical structure. Finally, QSPR models were built to describe the logP and logK of 34 furylethylenes derivatives using the eleven events. Generally, the equations obtained according to these events showed high correlations, with the Sach's sub-graphs and Multiplicity events showing the best behavior in the description of logK (Q2 LOO value of 99.06%) and logP (Q2 LOO value of 98.1 %), respectively. These results show that these new eventbased indices constitute a powerful approach for chemoinformatics studies.