Data on soil physicochemical properties and chemical composition of rainfall and of throughfall and stemflow generated by Turkey oak trees (Quercus cerris L.) in acid and sub-alkaline soils.

We report data on the physicochemical properties of soils collected in two adjacent areas, one acid and one sub-alkaline, both developed on sequential beds of Plio-pleistocene marine sediments, and on the chemical composition of ecological solutions (rainfall, throughfall and stemflow) separately collected in the two areas. Throughfall and stemflow were generated by Turkey oak trees (Quercus cerris L.), which was the dominant tree species in both study areas. These data are related to the original article "Soil affects throughfall and stemflow under Turkey oak (Quercus cerris L.)" (Corti et al., 2019) [1].

Neural assemblies revealed by inferred connectivity-based models of prefrontal cortex recordings.

We present two graphical model-based approaches to analyse the distribution of neural activities in the prefrontal cortex of behaving rats. The first method aims at identifying cell assemblies, groups of synchronously activating neurons possibly representing the units of neural coding and memory. A graphical (Ising) model distribution of snapshots of the neural activities, with an effective connectivity matrix reproducing the correlation statistics, is inferred from multi-electrode recordings, and then simulated in the presence of a virtual external drive, favoring high activity (multi-neuron) configurations. As the drive increases groups of neurons may activate together, and reveal the existence of cell assemblies. The identified groups are then showed to strongly coactivate in the neural spiking data and to be highly specific of the inferred connectivity network, which offers a sparse representation of the correlation pattern across neural cells. The second method relies on the inference of a Generalized Linear Model, in which spiking events are integrated over time by neurons through an effective connectivity matrix. The functional connectivity matrices inferred with the two approaches are compared. Sampling of the inferred GLM distribution allows us to study the spatio-temporal patterns of activation of neurons within the identified cell assemblies, particularly their activation order: the prevalence of one order with respect to the others is weak and reflects the neuron average firing rates and the strength of the largest effective connections. Other properties of the identified cell assemblies (spatial distribution of coactivation events and firing rates of coactivating neurons) are discussed.

ACE: adaptive cluster expansion for maximum entropy graphical model inference.

MOTIVATION: Graphical models are often employed to interpret patterns of correlations observed in data through a network of interactions between the variables. Recently, Ising/Potts models, also known as Markov random fields, have been productively applied to diverse problems in biology, including the prediction of structural contacts from protein sequence data and the description of neural activity patterns. However, inference of such models is a challenging computational problem that cannot be solved exactly. Here, we describe the adaptive cluster expansion (ACE) method to quickly and accurately infer Ising or Potts models based on correlation data. ACE avoids overfitting by constructing a sparse network of interactions sufficient to reproduce the observed correlation data within the statistical error expected due to finite sampling. When convergence of the ACE algorithm is slow, we combine it with a Boltzmann Machine Learning algorithm (BML). We illustrate this method on a variety of biological and artificial datasets and compare it to state-of-the-art approximate methods such as Gaussian and pseudo-likelihood inference. RESULTS: We show that ACE accurately reproduces the true parameters of the underlying model when they are known, and yields accurate statistical descriptions of both biological and artificial data. Models inferred by ACE more accurately describe the statistics of the data, including both the constrained low-order correlations and unconstrained higher-order correlations, compared to those obtained by faster Gaussian and pseudo-likelihood methods. These alternative approaches can recover the structure of the interaction network but typically not the correct strength of interactions, resulting in less accurate generative models. AVAILABILITY AND IMPLEMENTATION: The ACE source code, user manual and tutorials with the example data and filtered correlations described herein are freely available on GitHub at https://github.com/johnbarton/ACE CONTACTS: jpbarton@mit.edu, cocco@lps.ens.frSupplementary information: Supplementary data are available at Bioinformatics online.

Transglutaminase 2 ablation leads to mitophagy impairment associated with a metabolic shift towards aerobic glycolysis.

Macroautophagy selectively degrades dysfunctional mitochondria by a process known as mitophagy. Here we demonstrate the involvement of transglutaminase 2 (TG2) in the turnover and degradation of damaged mitochondria. In TG2-ablated cells we observed the presence of a large number of fragmented mitochondria that display decreased membrane potential, downregulation of IF1 along with increased Drp1 and PINK1 levels, two key proteins regulating the mitochondrial fission. Of note, we demonstrate that in healthy mitochondria, TG2 interacts with the dynamic proteins Drp1 and Fis1; interestingly, their interaction is largely reduced upon induction of the fission process by carbonyl cyanide m-chlorophenyl hydrazine (CCCP). In keeping with these findings, mitochondria lacking TG2 are more susceptible to CCCP treatment. As a consequence of accumulation of damaged mitochondria, cells lacking TG2 increased their aerobic glycolysis and became sensitive to the glycolytic inhibitor 2-deoxy-D-glucose (2-DG). In contrast, TG2-proficient cells are more resistant to 2-DG-induced apoptosis as the caspase 3 is inactivated through the enzyme's crosslinking activity. The data presented in this study show that TG2 plays a key role in cellular dynamics and consequently influences the energetic metabolism.

A role for intracellular zinc in glioma alteration of neuronal chloride equilibrium.

Glioma patients commonly suffer from epileptic seizures. However, the mechanisms of glioma-associated epilepsy are far to be completely understood. Using glioma-neurons co-cultures, we found that tumor cells are able to deeply influence neuronal chloride homeostasis, by depolarizing the reversal potential of γ-aminobutyric acid (GABA)-evoked currents (EGABA). EGABA depolarizing shift is due to zinc-dependent reduction of neuronal KCC2 activity and requires glutamate release from glioma cells. Consistently, intracellular zinc loading rapidly depolarizes EGABA in mouse hippocampal neurons, through the Src/Trk pathway and this effect is promptly reverted upon zinc chelation. This study provides a possible molecular mechanism linking glioma invasion to excitation/inhibition imbalance and epileptic seizures, through the zinc-mediated disruption of neuronal chloride homeostasis.

Large pseudocounts and L2-norm penalties are necessary for the mean-field inference of Ising and Potts models.

The mean-field (MF) approximation offers a simple, fast way to infer direct interactions between elements in a network of correlated variables, a common, computationally challenging problem with practical applications in fields ranging from physics and biology to the social sciences. However, MF methods achieve their best performance with strong regularization, well beyond Bayesian expectations, an empirical fact that is poorly understood. In this work, we study the influence of pseudocount and L(2)-norm regularization schemes on the quality of inferred Ising or Potts interaction networks from correlation data within the MF approximation. We argue, based on the analysis of small systems, that the optimal value of the regularization strength remains finite even if the sampling noise tends to zero, in order to correct for systematic biases introduced by the MF approximation. Our claim is corroborated by extensive numerical studies of diverse model systems and by the analytical study of the m-component spin model for large but finite m. Additionally, we find that pseudocount regularization is robust against sampling noise and often outperforms L(2)-norm regularization, particularly when the underlying network of interactions is strongly heterogeneous. Much better performances are generally obtained for the Ising model than for the Potts model, for which only couplings incoming onto medium-frequency symbols are reliably inferred.

Stochastic ratchet mechanisms for replacement of proteins bound to DNA.

Experiments indicate that unbinding rates of proteins from DNA can depend on the concentration of proteins in nearby solution. Here we present a theory of multistep replacement of DNA-bound proteins by solution-phase proteins. For four different kinetic scenarios we calculate the dependence of protein unbinding and replacement rates on solution protein concentration. We find (1) strong effects of progressive "rezipping" of the solution-phase protein onto DNA sites liberated by "unzipping" of the originally bound protein, (2) that a model in which solution-phase proteins bind nonspecifically to DNA can describe experiments on exchanges between the nonspecific DNA-binding proteins Fis-Fis and Fis-HU, and (3) that a binding specific model describes experiments on the exchange of CueR proteins on specific binding sites.

From rainfall to throughfall in a maritime vineyard.

This study deals with the characteristics of throughfall produced by vine (Vitis vinifera L.) in one of the most common pedoclimatic conditions for grape production: a soil derived from marine sediments under a temperate Mediterranean climate, and located rather close to the seacoast. To distinguish the contribution of the plant from that of the atmospheric deposition, the throughfall was collected for more than one year under real and artificial (plastic) vines; for the same period, also the bulk precipitation was collected. The solution collected were analysed for pH, electrical conductivity, and concentration of cations and anions. For each event, the ionic fluxes of bulk precipitation and throughfall were calculated. Results indicated that the chemical composition of the bulk precipitation was strongly influenced by the proximity of the seashore and, to a lesser extent, by local anthropic activities and windblown material coming from distant areas. The chemical composition of the throughfall was affected by the same factors of bulk precipitation, but also by solubilisation of dry deposition trapped by the canopies, agronomic practices, plant, and living-on-the-leaves microorganisms. The comparison of the characteristics of the throughfall of the real with the artificial vines revealed that the vines are a source of Mg and K. During winter season, the reduction of Ca, NH(4) and PO(4) from bulk precipitation to throughfall was ascribed to the formation of biogenic minerals on the plant surface. The presence of these minerals was proved by X-ray diffraction on the powders collected during the winter season on the surface of cordons and fruiting canes. We conclude that an approach to the estimation of the nutritional potentiality of the soil that includes the contribution of the throughfall is functional to the management of the agro-ecosystem.

High-dimensional inference with the generalized Hopfield model: principal component analysis and corrections.

We consider the problem of inferring the interactions between a set of N binary variables from the knowledge of their frequencies and pairwise correlations. The inference framework is based on the Hopfield model, a special case of the Ising model where the interaction matrix is defined through a set of patterns in the variable space, and is of rank much smaller than N. We show that maximum likelihood inference is deeply related to principal component analysis when the amplitude of the pattern components ξ is negligible compared to √N. Using techniques from statistical mechanics, we calculate the corrections to the patterns to the first order in ξ/√N. We stress the need to generalize the Hopfield model and include both attractive and repulsive patterns in order to correctly infer networks with sparse and strong interactions. We present a simple geometrical criterion to decide how many attractive and repulsive patterns should be considered as a function of the sampling noise. We moreover discuss how many sampled configurations are required for a good inference, as a function of the system size N and of the amplitude ξ. The inference approach is illustrated on synthetic and biological data.

Adaptive cluster expansion for inferring boltzmann machines with noisy data.

We introduce a procedure to infer the interactions among a set of binary variables, based on their sampled frequencies and pairwise correlations. The algorithm builds the clusters of variables contributing most to the entropy of the inferred Ising model and rejects the small contributions due to the sampling noise. Our procedure successfully recovers benchmark Ising models even at criticality and in the low temperature phase, and is applied to neurobiological data.

Inferring DNA sequences from mechanical unzipping data: the large-bandwidth case.

The complementary strands of DNA molecules can be separated when stretched apart by a force; the unzipping signal is correlated to the base content of the sequence but is affected by thermal and instrumental noise. We consider here the ideal case where opening events are known to a very good time resolution (very large bandwidth), and study how the sequence can be reconstructed from the unzipping data. Our approach relies on the use of statistical Bayesian inference and of Viterbi decoding algorithm. Performances are studied numerically on Monte Carlo generated data, and analytically. We show how multiple unzippings of the same molecule may be exploited to improve the quality of the prediction, and calculate analytically the number of required unzippings as a function of the bandwidth, the sequence content, and the elasticity parameters of the unzipped strands.

Inference of DNA sequences from mechanical unzipping: an ideal-case study.

The performances of Bayesian inference to predict the sequence of DNA molecules from fixed-force unzipping experiments are investigated. We show that the probability of misprediction decreases exponentially with the amount of collected data. The decay rate is calculated as a function of biochemical parameters (binding free energies), the sequence content, the applied force, the elastic properties of a DNA single strand, and time resolution.

Loops in DNA: an overview of experimental and theoretical approaches.

DNA loop formation plays a central role in many cellular processes. The aim of this paper is to present the state of the art and open problems regarding the experimental and theoretical approaches to DNA looping. A particular attention is devoted to the effects of the protein bridge size and of protein induced sharp DNA bending on DNA loop formation enhancement.

Comment on the paper "Comparison of the measured phase diagrams in the force-temperature plane for the unzipping of two different natural DNA sequences" by C.H. Lee, C. Danilowicz, V.W. Coljee, and M. Prentiss.

The paper by Lee et al. describes the experiments on the unzipping of lambda DNA sequences as a function of force and temperature. This comment aims to stress that the unzipping takes place out of equilibrium due to high sequence-dependent free-energy barriers. The force at which a heterogeneous sequence is unzipped therefore depends on the experimental waiting time.

[Intellectual quotient and environmental exposure to low doses of lead]. / Quoziente intellettivo ed esposizione ambientale a basse dosi di piombo.

The effects of low-level lead exposure on children's intelligence quotient (IQ) were investigated in 64 Sardinian adolescents (13-16 years old). To estimate potential early adverse effects on the Central Nervous System (CNS) due to very low-level lead, the Intelligence Quotient (IQ) of adolescents with present blood lead concentrations (PbB) generally below 10 microg/dl was measured. We analyzed blood lead concentration and individual IQ of 32 Sardinian children living in Portoscuso, a town 2 Km far from a lead smelter, and of other 32 controls living in S. Antioco, a town about 15 Km far from the same smelter. The Wechsler Intelligence Scale for Children Revised (WISC-R) was administered. The relation between IQ and blood lead concentration was estimated by linear multivariate models adjusting for several potential confounders, such as the educational and socio-economic level of the parents. The blood lead concentration was in average significantly higher in the Portoscuso group compared to controls. The linear model applied to the total population studied (n 64) showed that the blood lead concentration was inversely and significantly associated with IQ, with an extrapolated decline of 1.29 points in total IQ for each microg/dl increase of lead blood concentration. According to the recent scientific literature on this topic, results of our pilot study suggest the need to further lower the blood lead concentration for children to a threshold significantly below 10 microg/dl, value till now considered "safe" for the children's CNS.

Rigorous decimation-based construction of ground pure states for spin-glass models on random lattices.

A constructive scheme for determining pure states at very low temperature in the 3-spins glass model on a random lattice is provided, in full agreement with Parisi's one step replica symmetry breaking (RSB) scheme. Proof is based on the analysis of a partial decimation procedure and of the statistical properties of its output, i.e., a reduced Hamiltonian acting on a subset of the initial spins. The number of ground states (GS) in each state, the number of states, and the distances between GS are calculated and correspond to RSB predictions.

[Cognitive and performance capacity among adolescents living near a lead and zinc smelter]. / Capacità cognitive e di performance in adolescenti residenti in prossimità di una fonderia di piombo e zinco.

To estimate potential early adverse effects on the Central Nervous System (CNS) due to very low exposure to inorganic lead, the Intelligence Quotient (IQ) of adolescents with present blood lead concentrations (PhB) generally below 10 micrograms/dl was measured. We analyzed blood lead concentration and individual IQ of 32 Sardinian adolescents living in Portoscuso, a town 2 Km far from a lead smelter, and of other 32 controls living in S. Antioco, a town about 15 Km far from the same smelter. The Wechsler Intelligence Scale for Children Revised (WISC-R) was administered. The relation between IQ and blood lead concentration was estimated by linear multivariate models adjusting for several potential confounders, such as the educational and socio-economic level of the parents. The blood lead concentration was in average significantly higher in the Portoscuso group compared to controls. The linear model applied to the total population studied (n 64) showed that the blood lead concentration was inversely and significantly associated with IQ, with an extrapolated decline of 1.29 points in total IQ for each microgram/dl increase of blood concentration. According to the recent scientific literature on this topic, results of our pilot study suggest the need to further lower the definition of an elevated blood lead concentration for children to a threshold significantly below 10 micrograms/dl, value till now considered "safe" for the children's CNS.

Slow nucleic acid unzipping kinetics from sequence-defined barriers.

Recent experiments on unzipping of RNA helix-loop structures by force have shown that approximately 40-base molecules can undergo kinetic transitions between two well-defined "open" and "closed" states, on a timescale approximately 1 sec [Liphardt et al., Science 297, 733-737 (2001)]. Using a simple dynamical model, we show that these phenomena result from the slow kinetics of crossing large free energy barriers which separate the open and closed conformations. The dependence of barriers on sequence along the helix, and on the size of the loop(s) is analyzed. Some DNA and RNA sequences that could show dynamics on different time scales, or three(or more)-state unzipping, are proposed. Our dynamical model is also applied to the unzipping of long (kilo-basepair) DNA molecules at constant force.

Force-extension behavior of folding polymers.

The elastic response of flexible polymers made of elements which can be either folded or unfolded, having different lengths in these two states, is discussed. These situations are common for biopolymers as a result of folding interactions intrinsic to the monomers, or as a result of binding of other smaller molecules along the polymer length. Using simple flexible-chain models, we show that even when the energy epsilon associated with maintaining the folded state is comparable to k(B) T, the elastic response of such a chain can mimic usual polymer linear elasticity, but with a force scale enhanced above that expected from the flexibility of the chain backbone. We discuss recent experiments on single-stranded DNA, chromatin fiber and double-stranded DNA with proteins weakly absorbed along its length which show this effect. Effects of polymer semiflexiblity and torsional stiffness relevant to experiments on proteins binding to dsDNA are analyzed. We finally discuss the competition between electrostatic self-repulsion and folding interactions responsible for the complex elastic response of single-stranded DNA.

Vitamin A deficiency produces spatial learning and memory impairment in rats.

Vitamin A and its derivatives (retinoids) play important roles in many physiological processes. The recent finding of high levels of cellular retinol-binding protein type 1 immunoreactivity, cellular retinoic acid-binding protein type 1 immunoreactivity and the presence of nuclear retinoid receptors in the central nervous system of adult rodents suggests that retinoids may carry out important roles in the adult brain. In consideration of the role of the hippocampus in spatial learning and memory we evaluated the effect of vitamin A deprivation in adult rats on these functions. Following 12 weeks of vitamin A-free diet, rats were trained to acquire a radial-arm maze task. Results show that this diet induced a severe deficit in the spatial learning and memory task. The cognitive impairment was fully restored when vitamin A was replaced in the diet. We also found a significant decrease in hippocampal acetylcholine release induced by scopolamine, assessed using microdialysis technique, and a reduction in the size of hippocampal nuclei of CA1 region in vitamin-deficient rats, compared to rats fed with a vitamin A-sufficient diet. These results demonstrate that vitamin A has a critical role in the learning and memory processes linked to a proper hippocampal functioning.