Signature of directed chaos in the conductance of a nanowire

We study the conductance of chaotic or disordered wires in a situation where equilibrium transport decomposes into biased diffusion and a counter-moving regular current. A possible realization is a semiconductor nanostructure with transversal magnetic field and suitably patterned surfaces. We find a non-trivial dependence of the conductance on the wire length which differs qualitatively from Ohm's law by the existence of a characteristic length scale and a finite saturation value

Directed chaos in a billiard chain with transversal magnetic field

In generic Hamiltonian systems with a mixed phase space chaotic transport may be directed and ballistic rather than diffusive. We investigate one particular model showing this behaviour, namely a spatially periodic billiard chain in which electrons move under the influence of a perpendicular magnetic field. We analyze the phase-space structure and derive an explicit expression for the chaotic transport velocity. Unlike previous studies of directed chaos our model has a parameter regime in which the dispersion of an ensemble of chaotic trajectories around its moving center of mass is essentially diffusive. We explain how in this limit the deterministic chaos reduces to a biased random walk in a billiard with a rough surface. The diffusion constant for this simplified model is calculated analytically

Stochastic boundary conditions for molecular dynamics simulations

In this paper we develop a stochastic boundary conditions (SBC) for event-driven molecular dynamics simulations of a finite volume embedded within an infinite environment. In this method, we first collect the statistics of injection/ejection events in periodic boundary conditions (PBC). Once sufficient statistics are collected, we remove the PBC and turn on the SBC. In the SBC simulations, we allow particles leaving the system to be truly ejected from the simulation, and randomly inject particles at the boundaries by resampling from the injection/ejection statistics collected from the current or previous simulations. With the SBC, we can measure thermodynamic quantities within the grand canonical ensemble, based on the particle number and energy fluctuations. To demonstrate how useful the SBC algorithm is, we simulated a hard disk gas and measured the pair distribution function, the compressibility and the specific heat, comparing them against literature values.Comment: 24 pages, 16 figure

Will the US Economy Recover in 2010? A Minimal Spanning Tree Study

We calculated the cross correlations between the half-hourly times series of the ten Dow Jones US economic sectors over the period February 2000 to August 2008, the two-year intervals 2002--2003, 2004--2005, 2008--2009, and also over 11 segments within the present financial crisis, to construct minimal spanning trees (MSTs) of the US economy at the sector level. In all MSTs, a core-fringe structure is found, with consumer goods, consumer services, and the industrials consistently making up the core, and basic materials, oil and gas, healthcare, telecommunications, and utilities residing predominantly on the fringe. More importantly, we find that the MSTs can be classified into two distinct, statistically robust, topologies: (i) star-like, with the industrials at the center, associated with low-volatility economic growth; and (ii) chain-like, associated with high-volatility economic crisis. Finally, we present statistical evidence, based on the emergence of a star-like MST in Sep 2009, and the MST staying robustly star-like throughout the Greek Debt Crisis, that the US economy is on track to a recovery.Comment: elsarticle class, includes amsmath.sty, graphicx.sty and url.sty. 68 pages, 16 figures, 8 tables. Abridged version of the manuscript presented at the Econophysics Colloquim 2010, incorporating reviewer comment

Application of Enhanced Recovery after Surgery Pathways in Patients Undergoing Laparoscopic Cholecystectomy With and Without Common Bile Duct Exploration: A systematic review and meta-analysis

Many researchers implemented enhanced recovery after surgery (ERAS) pathways for laparoscopic cholecystectomy (LC) and found it effective over conventional care. This review investigates the efficacy and safety of ERAS pathways implemented for LC over conventional practices. We searched PubMed/Medline, SCOPUS, CENTRAL, Ovid, and clinicaltrials.gov using relevant keywords to identify studies in which ERAS pathways in LC were compared with conventional pathways. The primary outcome was length of stay (LOS) from the day of surgery and the secondary outcomes were comparison of pain scores, postoperative nausea/vomiting (PONV), readmissions (within 30-days after surgery), complications (medical and surgical), time to first flatus, and cost. Out of 590 articles identified, 6 studies (n=1489 patients) fulfilled inclusion criteria and were used for qualitative and quantitative analysis. On pooled analysis, the LOS, time to first flatus, PONV, pain scores were significantly less in ERAS group than the conventional one. However, readmission and complications were comparable in both groups. Keywords: Cholecystectomy; Enhanced recovery After Surgery; Fast-track surgery; Laparoscopy; Meta-analysis; Perioperative care; Systematic review

Genetic transformation and genomic resources for next-generation precise genome engineering in vegetable crops

In the frame of modern agriculture facing the predicted increase of population and general environmental changes, the securement of high quality food remains a major challenge to deal with. Vegetable crops include a large number of species, characterized by multiple geographical origins, large genetic variability and diverse reproductive features. Due to their nutritional value, they have an important place in human diet. In recent years, many crop genomes have been sequenced permitting the identification of genes and superior alleles associated with desirable traits. Furthermore, innovative biotechnological approaches allow to take a step forward towards the development of new improved cultivars harboring precise genome modifications. Sequence-based knowledge coupled with advanced biotechnologies is supporting the widespread application of new plant breeding techniques to enhance the success in modification and transfer of useful alleles into target varieties. Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 system, zinc-finger nucleases, and transcription activator-like effector nucleases represent the main methods available for plant genome engineering through targeted modifications. Such technologies, however, require efficient transformation protocols as well as extensive genomic resources and accurate knowledge before they can be efficiently exploited in practical breeding programs. In this review, we revise the state of the art in relation to availability of such scientific and technological resources in various groups of vegetables, describe genome editing results obtained so far and discuss the implications for future applications

Double-Stranded RNA-Mediated Suppression of Trypsin-Like Serine Protease (t-SP) Triggers Over-Expression of Another t-SP Isoform in Helicoverpa armigera

High diversity of digestive proteases is considered to be the key factor in the evolution of polyphagy in Helicoverpa armigera. Serine proteases (SPs) contribute ~85% of the dietary protein digestion in H. armigera. We investigated the dynamics of SP regulation in the polyphagous pest, H. armigera using RNA interference (RNAi). HaTry1, an isoform of SP, expressed irrespective of the composition of the diet, and its expression levels were directly proportional to the larval growth rate. Therefore, HaTry1 was silenced by delivering 10 and 20 μg concentrations of double-stranded RNA through semi-synthetic diet. This led to a drastic reduction in the target gene transcript levels that manifested in a significant reduction in the larval weight initially, but the larvae recovered in later stages despite continuous dsRNA treatment. This was probably due to the compensatory effect by over-expression of HaTry13 (31-folds), another isoform of SP. Phylogenetic analysis of H. armigera SPs revealed that the over-expressed isoform was closely related to the target gene as compared to the other tested isoforms. Further, silencing of both the isoforms (HaTry1 and HaTry13) caused the highest reduction in the larval weight and there was no larval growth recovery. These findings provide a new evidence of the existence of compensatory effect to overcome the effect of silencing individual gene with RNAi. Hence, the study emphasizes the need for simultaneous silencing of multiple isoforms

Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine

Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine