Gulf War Syndrome: A role for organophosphate induced plasticity of locus coeruleus neurons

Gulf War syndrome is a chronic multi-symptom illness that has affected about a quarter of the deployed veterans of the 1991 Gulf War. Exposure to prolonged low-level organophosphate insecticides and other toxic chemicals is now thought to be responsible. Chlorpyrifos was one commonly used insecticide. The metabolite of chlorpyrifos, chlorpyrifos oxon, is a potent irreversible inhibitor of acetylcholinesterase, much like the nerve agent Sarin. To date, the target brain region(s) most susceptible to the neuroactive effects of chlorpyrifos oxon have yet to be identified. To address this we tested ability of chlorpyrifos oxon to influence neuronal excitability and induce lasting changes in the locus coeruleus, a brain region implicated in anxiety, substance use, attention and emotional response to stress. Here we used an ex vivo rodent model to identify a dramatic effect of chlorpyrifos oxon on locus coeruleus noradrenergic neuronal activity. Prolonged exposure to chlorpyrifos oxon caused acute inhibition and a lasting rebound excitatory state expressed after days of exposure and subsequent withdrawal. Our findings indicate that the locus coeruleus is a brain region vulnerable to chlorpyrifos oxon-induced neuroplastic changes possibly leading to the neurological symptoms affecting veterans of the Gulf War

Optical control of the spin state of two Mn atoms in a quantum dot

We report on the optical spectroscopy of the spin of two magnetic atoms (Mn) embedded in an individual quantum dot interacting with either a single electron, a single exciton and single trion. As a result of their interaction to a common entity, the Mn spins become correlated. The dynamics of this process is probed by time resolved spectroscopy, that permits to determine the optical orientation time in the range of a few tens of $ns$. In addition, we show that the energy of the collective spin states of the two Mn atoms can be tuned through the optical Stark effect induced by a resonant laser field

Spin-phonon coupling in single Mn doped CdTe quantum dot

The spin dynamics of a single Mn atom in a laser driven CdTe quantum dot is addressed theoretically. Recent experimental results\cite{Le-Gall_PRL_2009,Goryca_PRL_2009,Le-Gall_PRB_2010}show that it is possible to induce Mn spin polarization by means of circularly polarized optical pumping. Pumping is made possible by the faster Mn spin relaxation in the presence of the exciton. Here we discuss different Mn spin relaxation mechanisms. First, Mn-phonon coupling, which is enhanced in the presence of the exciton. Second, phonon-induced hole spin relaxation combined with carrier-Mn spin flip coupling and photon emission results in Mn spin relaxation. We model the Mn spin dynamics under the influence of a pumping laser that injects excitons into the dot, taking into account exciton-Mn exchange and phonon induced spin relaxation of both Mn and holes. Our simulations account for the optically induced Mn spin pumping.Comment: 17 pages, 11 figures, submitted to PR

Change detection in categorical evolving data streams

Detecting change in evolving data streams is a central issue for accurate adaptive learning. In real world applications, data streams have categorical features, and changes induced in the data distribution of these categorical features have not been considered extensively so far. Previous work on change detection focused on detecting changes in the accuracy of the learners, but without considering changes in the data distribution. To cope with these issues, we propose a new unsupervised change detection method, called CDCStream (Change Detection in Categorical Data Streams), well suited for categorical data streams. The proposed method is able to detect changes in a batch incremental scenario. It is based on the two following characteristics: (i) a summarization strategy is proposed to compress the actual batch by extracting a descriptive summary and (ii) a new segmentation algorithm is proposed to highlight changes and issue warnings for a data stream. To evaluate our proposal we employ it in a learning task over real world data and we compare its results with state of the art methods. We also report qualitative evaluation in order to show the behavior of CDCStream

Noise of Kondo dot with ac gate: Floquet-Green's function and Noncrossing Approximation Approach

The transport properties of an ac-driving quantum dot in the Kondo regime are studied by the Floquet-Green's function method with slave-boson infinite-$U$ noncrossing approximation. Our results show that the Kondo peak of the local density of states is robust against weak ac gate modulation. Significant suppression of the Kondo peak can be observed when the ac gate field becomes strong. The photon-assisted noise of Kondo resonance as a function of dc voltage does not show singularities which are expected for noninteracting resonant quantum dot. These findings suggest that one may make use of the photon-assisted noise measurement to tell apart whether the resonant transport is via noninteracting resonance or strongly-correlated Kondo resonance

Nuptial gift chemistry reveals convergent evolution correlated with antagonism in mating systems of harvestmen (Arachnida, Opiliones)

Nuptial gifts are material donations given from male to female before or during copulation and are subject to sexual selection in a wide variety of taxa. The harvestman genus Leiobunum has emerged as a model system for understanding the evolution of reproductive morphology and behavior, as transitions between solicitous and antagonistic modes of courtship have occurred multiple times within the lineage and are correlated with convergence in genital morphology. We analyzed the free amino acid content of nuptial gift secretions from five species of Leiobunum using gas chromatography–mass spectrometry. Multivariate analysis of the free amino acid profiles revealed that, rather than clustering based on phylogenetic relationships, nuptial gift chemical composition was better predicted by genital morphology and behavior, suggesting that convergent evolution has acted on the chemical composition of the nuptial gift. In addition, we found that, species with solicitous courtship produce gifts consisting of a 19% larger proportion of essential amino acids as compared to those with more antagonistic courtship interactions. This work represents the first comparative study of nuptial gift chemistry within a phylogenetic framework in any animal group and as such contributes to our understanding of the evolution of reproductive diversity and the participant role of nuptial gift chemistry in mating system transitions

Critical Behavior of Hadronic Fluctuations and the Effect of Final-State Randomization

The critical behaviors of quark-hadron phase transition are explored by use of the Ising model adapted for hadron production. Various measures involving the fluctuations of the produced hadrons in bins of various sizes are examined with the aim of quantifying the clustering properties that are universal features of all critical phenomena. Some of the measures involve wavelet analysis. Two of the measures are found to exhibit the canonical power-law behavior near the critical temperature. The effect of final-state randomization is studied by requiring the produced particles to take random walks in the transverse plane. It is demonstrated that for the measures considered the dependence on the randomization process is weak. Since temperature is not a directly measurable variable, the average hadronic density of a portion of each event is used as the control variable that is measurable. The event-to-event fluctuations are taken into account in the study of the dependence of the chosen measures on that control variable. Phenomenologically verifiable critical behaviors are found and are proposed for use as a signature of quark-hadron phase transition in relativistic heavy-ion collisions.Comment: 17 pages (Latex) + 24 figures (ps file), submitted to Phys. Rev.

Table2Vec-automated universal representation learning of enterprise data DNA for benchmarkable and explainable enterprise data science.

Enterprise data typically involves multiple heterogeneous data sources and external data that respectively record business activities, transactions, customer demographics, status, behaviors, interactions and communications with the enterprise, and the consumption and feedback of its products, services, production, marketing, operations, and management, etc. They involve enterprise DNA associated with domain-oriented transactions and master data, informational and operational metadata, and relevant external data. A critical challenge in enterprise data science is to enable an effective 'whole-of-enterprise' data understanding and data-driven discovery and decision-making on all-round enterprise DNA. Accordingly, here we introduce a neural encoder Table2Vec for automated universal representation learning of entities such as customers from all-round enterprise DNA with automated data characteristics analysis and data quality augmentation. The learned universal representations serve as representative and benchmarkable enterprise data genomes (similar to biological genomes and DNA in organisms) and can be used for enterprise-wide and domain-specific learning tasks. Table2Vec integrates automated universal representation learning on low-quality enterprise data and downstream learning tasks. Such automated universal enterprise representation and learning cannot be addressed by existing enterprise data warehouses (EDWs), business intelligence and corporate analytics systems, where 'enterprise big tables' are constructed with reporting and analytics conducted by specific analysts on respective domain subjects and goals. It addresses critical limitations and gaps of existing representation learning, enterprise analytics and cloud analytics, which are analytical subject, task and data-specific, creating analytical silos in an enterprise. We illustrate Table2Vec in characterizing all-round customer data DNA in an enterprise on complex heterogeneous multi-relational big tables to build universal customer vector representations. The learned universal representation of each customer is all-round, representative and benchmarkable to support both enterprise-wide and domain-specific learning goals and tasks in enterprise data science. Table2Vec significantly outperforms the existing shallow, boosting and deep learning methods typically used for enterprise analytics. We further discuss the research opportunities, directions and applications of automated universal enterprise representation and learning and the learned enterprise data DNA for automated, all-purpose, whole-of-enterprise and ethical machine learning and data science