Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolution of spatially structured host–parasite interactions

Spatial structure has dramatic effects on the demography and the evolution of species. A large variety of theoretical models have attempted to understand how local dispersal may shape the coevolution of interacting species such as host–parasite interactions. The lack of a unifying framework is a serious impediment for anyone willing to understand current theory. Here, we review previous theoretical studies in the light of a single epidemiological model that allows us to explore the effects of both host and parasite migration rates on the evolution and coevolution of various life‐history traits. We discuss the impact of local dispersal on parasite virulence, various host defence strategies and local adaptation. Our analysis shows that evolutionary and coevolutionary outcomes crucially depend on the details of the host–parasite life cycle and on which life‐history trait is involved in the interaction. We also discuss experimental studies that support the effects of spatial structure on the evolution of host–parasite interactions. This review highlights major similarities between some theoretical results, but it also reveals an important gap between evolutionary and coevolutionary models. We discuss possible ways to bridge this gap within a more unified framework that would reconcile spatial epidemiology, evolution and coevolution.
Direct conversion of plant biomass to ethanol by engineered Caldicellulosiruptor bescii

Significance The ever-increasing demand for transportation fuels, the decrease in global petroleum reserves, and the negative impact of greenhouse gases resulting from burning petroleum make renewable and sustainable biofuels an imperative for the future. First-generation biofuels produced from food crops are limited by cost and competition with food supply. Considerable effort has been made to produce fuels from lignocellulosic biomass, but the need for chemical and enzymatic pretreatment to solubilize the biomass prior to microbial bioconversion is a major economic barrier to the development of an industrial process. Here we report the metabolic engineering of a bacterium, Caldicellulosiruptor bescii, that is capable of using unprocessed switchgrass, an abundant, environmentally desirable, and economically sustainable lignocellulosic plant biomass, as feedstock to produce ethanol. Ethanol is the most widely used renewable transportation biofuel in the United States, with the production of 13.3 billion gallons in 2012 [John UM (2013) Contribution of the Ethanol Industry to the Economy of the United States]. Despite considerable effort to produce fuels from lignocellulosic biomass, chemical pretreatment and the addition of saccharolytic enzymes before microbial bioconversion remain economic barriers to industrial deployment [Lynd LR, et al. (2008) Nat Biotechnol 26(2):169–172]. We began with the thermophilic, anaerobic, cellulolytic bacterium Caldicellulosiruptor bescii, which efficiently uses unpretreated biomass, and engineered it to produce ethanol. Here we report the direct conversion of switchgrass, a nonfood, renewable feedstock, to ethanol without conventional pretreatment of the biomass. This process was accomplished by deletion of lactate dehydrogenase and heterologous expression of a Clostridium thermocellum bifunctional acetaldehyde/alcohol dehydrogenase. Whereas wild-type C. bescii lacks the ability to make ethanol, 70% of the fermentation products in the engineered strain were ethanol [12.8 mM ethanol directly from 2% (wt/vol) switchgrass, a real-world substrate] with decreased production of acetate by 38% compared with wild-type. Direct conversion of biomass to ethanol represents a new paradigm for consolidated bioprocessing, offering the potential for carbon neutral, cost-effective, sustainable fuel production.
Cortisol and post-traumatic stress disorder in adults

Background Post-traumatic stress disorder (PTSD) has inconsistently been associated with lower levels of cortisol. Aims To compare basal cortisol levels in adults with current PTSD and in people without psychiatric disorder. Method Systematic review and meta-analysis. Standardised mean differences (SMD) in basal cortisol levels were calculated and random-effects models using inverse variance weighting were applied. Results Across 37 studies, 828 people with PTSD and 800 controls did not differ in cortisol levels (pooled SMD = −0.12, 95% C1= −0.32 to 0.080). Subgroup analyses revealed that studies assessing plasma or serum showed significantly lower levels in people with PTSD than in controls not exposed to trauma. Lower levels were also found in people with PTSD when females were included, in studies on physical or sexual abuse, and in afternoon samples. Conclusions Low cortisol levels in PTSD are only found under certain conditions. Future research should elucidate whether low cortisol is related to gender or abuse and depends on the measurement methods used.
A New Measurement Technique for Duct Characterization

ABSTRACT Devices such as silencers or filters are commonly used by the automotive industry to reduce the propagation of noise in ducts. The acoustical characterization of these devices is usually performed on transfer matrix measurement rigs equipped with microphone doublets. One drawback of this widespread technique is the difficulty of fitting of the test specimen to the test rig duct. It is required that the shape and cross-section of the test rig duct be as alike as possible as that of the test specimen. Such requirement complicates the testing and can become a source of uncertainty should the fitting be inadequate. When the qualification procedure does not require an air flow to blow through the test specimen, an alternate measurement technique is proposed. This measurement technique allows avoiding the issue of the mechanical fitting, and also requires a simplified test rig calibration phase. The measurement duration is then reduced. Said technique uses an acoustic impedance sensor developed by LAUM and CTTM. First the principles of this alternate technique are reminded, then some experimental results are discussed in order to highlight the pros and cons of both methods.
SNTP time synchronization accuracy measurements

Time synchronization is a key requirement in many automation systems. There are currently two predominant technologies for time synchronization in automation systems: SNTP (Simple Network Time Protocol) and PTP (Precision Time Protocol). PTP is the preferred technology and has several advantages over SNTP including far better accuracy in most if not all situations. However, legacy systems in many situations have to rely on SNTP. This is a challenge since the requirements on accuracy increase while the systems become larger and more complex. Thus, it is of interest to investigate what kind of accuracy level can be expected with the use of SNTP in modern switched networks. This paper shows that accuracies at the milliseconds level can be achieved for small networks. It is also analyzed how the network infrastructure and the end nodes contribute to the measured inaccuracy.
Operational monitoring of SO2 emissions using the GOME-2 satellite instrument

Satellite-based remote sensing measurements of atmospheric sulphur dioxide (SO2) provide valuable information on anthropogenic pollution and volcanic activity. Sensors like GOME-2 on MetOp-A make it possible to monitor SO2 emissions on a global scale and daily basis. SO2 total column amounts are retrieved in near-real time using the UV range of backscattered sunlight making it possible to detect and track volcanic eruption plumes. Trajectory matching is applied to relate detected SO2 to particular volcanoes and to estimate eruption parameters such as the height of the volcanic plume. Taking this information as input, dispersion modelling is used to forecast the motion of the volcanic plume. The high sensitivity of the GOME-2 instrument to SO2 allows measuring anthropogenic SO2 in the boundary layer and volcanic SO2 from non-eruptive degassing. This can provide critical information for early warning of volcanic hazards as changes in the SO2 emissions can indicate increased volcanic activity.
Evaluation of the correlation between KRAS mutated allele frequency and pathologist tumorous nuclei percentage assessment in colorectal cancer suggests a role for zygosity status

Evaluation of molecular tumour heterogeneity relies on the tumorous nuclei percentage (TNP) assessment by a pathologist, which has been criticised for being inaccurate and suffering from interobserver variability. Based on the ‘Big Bang theory’ which states that KRAS mutation in colorectal cancer is mostly homogeneous, we investigated this issue by performing a critical analysis of the correlation of the KRAS mutant allele fraction with the TNP in 99 colorectal tumour samples with a positive KRAS mutation status as determined by next-generation sequencing. Our results yield indirect evidence that the KRAS zygosity status influences the correlation between these parameters and we show that a well-trained pathologist is indeed capable of accurately assessing TNP. Our findings indicate that tumour zygosity, a feature which has largely been neglected until now, should be taken into account in future studies on (colorectal) molecular tumour heterogeneity.
THE “BIRD IN THE HAND” IS NOT A FALLACY: A MODEL OF DIVIDENDS BASED ON HIDDEN SAVINGS

We develop a model where cash is only observable by the manager and she can save the cash. Our model is similar to Fudenberg and Tirole (1995) but extended to account for an infinite time horizon. We get consistent results about the interpretation of dividends as a good signal and that dividends are less volatile than the underlying cash flow of the firm (smoothnees). Empirical evidence support the idea that there is an asymmetric reaction in prices of shares after the announcement of initiations and omissions of dividend payment respectively. The jumpdown is about two times bigger in absolute value than the jump-up. Existing models about dividends are not able to explain this asymmetry. With infinite time horizon we are able to explain the asymmetric reaction of the price of a share following the announcement of initiations or omissions of dividend payment. The model predicts that firm value will increase over time if there is no news about the firm. We also find that the existence of a manager with discretionary funds increase firm value. José Antonio Espín Northwestern University joseaespin@gmail.com
How to Build a Functional Connectomic Biomarker for Mild Cognitive Impairment From Source Reconstructed MEG Resting-State Activity: The Combination of ROI Representation and Connectivity Estimator Matters

Our work aimed to demonstrate the combination of machine learning and graph theory for the designing of a connectomic biomarker for mild cognitive impairment (MCI) subjects using eyes-closed neuromagnetic recordings. The whole analysis based on source-reconstructed neuromagnetic activity. As ROI representation, we employed the principal component analysis (PCA) and centroid approaches. As representative bi-variate connectivity estimators for the estimation of intra and cross-frequency interactions, we adopted the phase locking value (PLV), the imaginary part (iPLV) and the correlation of the envelope (CorrEnv). Both intra and cross-frequency interactions (CFC) have been estimated with the three connectivity estimators within the seven frequency bands (intra-frequency) and in pairs (CFC), correspondingly. We demonstrated how different versions of functional connectivity graphs single-layer (SL-FCG) and multi-layer (ML-FCG) can give us a different view of the functional interactions across the brain areas. Finally, we applied machine learning techniques with main scope to build a reliable connectomic biomarker by analyzing both SL-FCG and ML-FCG in two different options: as a whole unit using a tensorial extraction algorithm and as single pair-wise coupling estimations. We concluded that edge-weighed feature selection strategy outperformed the tensorial treatment of SL-FCG and ML-FCG. The highest classification performance was obtained with the centroid ROI representation and edge-weighted analysis of the SL-FCG reaching the 98% for the CorrEnv in α1:α2 and 94% for the iPLV in α2. Classification performance based on the multi-layer participation coefficient, a multiplexity index reached 52% for iPLV and 52% for CorrEnv. Selected functional connections that build the multivariate connectomic biomarker in the edge-weighted scenario are located in default-mode, fronto-parietal, and cingulo-opercular network. Our analysis supports the notion of analyzing FCG simultaneously in intra and cross-frequency whole brain interactions with various connectivity estimators in beamformed recordings.
Cerebellar tDCS does not affect performance in the N-back task

ABSTRACT The N-back task is widely used in cognitive research. Furthermore, the cerebellum’s role in cognitive processes is becoming more widely recognized. Studies using transcranial direct current stimulation (tDCS) have demonstrated effects of cerebellar stimulation on several cognitive tasks. Therefore, the aim of this study was to investigate the effects of cerebellar tDCS on cognitive performance by using the N-back task. The cerebellum of 12 participants was stimulated during the task. Moreover, the cognitive load was manipulated in N = 2, N = 3, and N = 4. Every participant received three tDCS conditions (anodal, cathodal, and sham) divided over three separated days. It was expected that anodal stimulation would improve performance on the task. Each participant performed 6 repetitions of every load in which correct responses, false alarms, and reaction times were recorded. We found significant differences between the three levels of load in the rate of correct responses and false alarms, indicating that subjects followed the expected pattern of performance for the N-back task. However, no significant differences between the three tDCS conditions were found. Therefore, it was concluded that in this study cognitive performance on the N-back task was not readily influenced by cerebellar tDCS, and any true effects are likely to be small. We discuss several limitations in task design and suggest future experiments to address such issues.
In silico prediction of essential enzymes : Application to E . coli and Mycoplasmas

Understanding the architecture of the cell biochemical network is a fundamental problem in modern science. Recent studies of metabolic networks have focused on the relevance of specific metabolites [1]. However, from a practical and biological point of view, it is important to investigate the enzymatic influence on the network since metabolites are not encoded in the genome, while enzymes are subject to evolution and can be genetically engineered to change the metabolism. A critical issue is the determination of the important enzymes that can be used as targets for drugs. We have presented a novel graph analysis of the metabolism that determines the most important (essential) enzymes of an organism and applied it to E. coli [2]. Essential enzymes are those that in their absence the organism is not viable. In the present work we apply the same method to identify the set of most important enzymes of the metabolism of Mycoplasmas. Enzyme importance is determined in terms of the damage the removal of the enzyme causes to the simulated network of the organism. Damage d is defined as the number of metabolites whose production is prevented in the absence of the enzyme. To calculate the damage d, we use a special graph representation (bipartite graph) for the metabolism [2]. The graph was constructed based on the list of reactions catalyzed by the enzymes involved in the small molecules metabolism of Mycoplasmas available in the KEGG database [3]. The algorithm is defined as follows: Initially, we choose an enzyme and remove the metabolites exclusively produced by that enzyme. The damage spreads preventing the occurrence of other reactions and the production of other metabolites. The total number of deleted metabolites is then counted to obtain d. Using our method we predicted a small set (9%) of the total enzymes of E. coli that cause the highest damage. According to experimental results, this set contains 50% of all essential enzymes giving support to our method [2]. Applying the same analysis to Mycoplasmas, we identified a set of enzymes that cause the highest damage. Additional applications of our method include, the previous indication of essential enzymes for gene knock out experiments and bioengineering of organisms.
Rehabilitation for improved cognition in patients with stress-related exhaustion disorder: RECO – a randomized clinical trial

Abstract Stress-related exhaustion has been associated with selective and enduring cognitive impairments. However, little is known about how to address cognitive deficits in stress rehabilitation and how this influences stress recovery over time. The aim of this open-label, parallel randomized controlled trial (ClinicalTrials.gov: NCT03073772) was to investigate the long-term effects of 12 weeks cognitive or aerobic training on cognitive function, psychological health, and work ability for patients diagnosed with exhaustion disorder (ED). One-hundred-and-thirty-two patients (111 women) participating in multimodal stress rehabilitation were randomized to receive additional cognitive training (n = 44), additional aerobic training (n = 47), or no additional training (n = 41). Treatment effects were assessed before, immediately after and one-year post intervention. The primary outcome was global cognitive function. Secondary outcomes included domain-specific cognition, self-reported burnout, depression, anxiety, fatigue and work ability, aerobic capacity, and sick-leave levels. Intention-to-treat analysis revealed a small but lasting improvement in global cognitive functioning for the cognitive training group, paralleled by a large improvement on a trained updating task. The aerobic training group showed improvements in aerobic capacity and episodic memory immediately after training, but no long-term benefits. General improvements in psychological health and work ability were observed, with no difference between interventional groups. Our findings suggest that cognitive training may be a viable method to address cognitive impairments for patients with ED, whereas the effects of aerobic exercise on cognition may be more limited when performed during a restricted time period. The implications for clinical practice in supporting patients with ED to adhere to treatment are discussed.
Design Space Exploration of Microring Resonators in Silicon Photonic Interconnects: Impact of the Ring Curvature

A detailed analysis of fundamental tradeoffs between ring radius and coupling gap size is presented to draw realistic borders of the possible design space for microring resonators (MRRs). The coupling coefficient for the ring-waveguide structure is estimated based on an integration of the nonuniform gap between the ring and the waveguide. Combined with the supermode analysis of two coupled waveguides, this approach is further expanded into a closed-form equation that describes the coupling strength. This equation permits to evaluate how the distance separating a waveguide from a ring resonator, and the ring radius, affect coupling. The effect of ring radius on the bending loss of the ring is furthermore modeled based on the measurements for silicon MRRs with different radii. These compact models for coupling and loss are subsequently used to derive the main optical properties of MRRs, such as 3-dB optical bandwidth, extinction ratio of resonance, and insertion loss, hence identifying the design space. Our results indicate that the design space for add-drop filters in a wavelength division multiplexed link is currently limited to 5–10 $\mu$m in radius and gap sizes ranging from 120 to 210 nm. The good agreement between the results from the proposed compact model for coupling and the numerical FDTD and experimental measurements indicate the application of our approach in realizing fast and efficient design space exploration of MRRs in silicon photonic interconnects.
B-Cell Responses to Pregnancy-Restricted and -Unrestricted Plasmodium falciparum Erythrocyte Membrane Protein 1 Antigens in Ghanaian Women Naturally Exposed to Malaria Parasites

ABSTRACT Protective immunity to Plasmodium falciparum malaria acquired after natural exposure is largely antibody mediated. IgG-specific P. falciparum EMP1 (PfEMP1) proteins on the infected erythrocyte surface are particularly important. The transient antibody responses and the slowly acquired protective immunity probably reflect the clonal antigenic variation and allelic polymorphism of PfEMP1. However, it is likely that other immune-evasive mechanisms are also involved, such as interference with formation and maintenance of immunological memory. We measured PfEMP1-specific antibody levels by enzyme-linked immunosorbent assay (ELISA) and memory B-cell frequencies by enzyme-linked immunosorbent spot (ELISPOT) assay in a cohort of P. falciparum-exposed nonpregnant Ghanaian women. The antigens used were a VAR2CSA-type PfEMP1 (IT4VAR04) with expression restricted to parasites infecting the placenta, as well as two commonly recognized PfEMP1 proteins (HB3VAR06 and IT4VAR60) implicated in rosetting and not pregnancy restricted. This enabled, for the first time, a direct comparison in the same individuals of immune responses specific for a clinically important parasite antigen expressed only during well-defined periods (pregnancy) to responses specific for comparable antigens expressed independent of pregnancy. Our data indicate that PfEMP1-specific B-cell memory is adequately acquired even when antigen exposure is infrequent (e.g., VAR2CSA-type PfEMP1). Furthermore, immunological memory specific for VAR2CSA-type PfEMP1 can be maintained for many years without antigen reexposure and after circulating antigen-specific IgG has disappeared. The study provides evidence that natural exposure to P. falciparum leads to formation of durable B-cell immunity to clinically important PfEMP1 antigens. This has encouraging implications for current efforts to develop PfEMP1-based vaccines.
Piecing Together Large Puzzles, Efficiently: Towards Scalable Loading Into Graph Database Systems

Many applications rely on network analysis to extract business intelligence from large datasets, requiring specialized graph tools such as processing frameworks (e.g. Apache Giraph, Gradoop), database systems (e.g. Neo4j, JanusGraph) or applications/libraries (e.g. NetworkX, nvGraph). A recent survey reports scalability, particularly for loading, as the foremost practical challenge faced by users. In this paper we consider the design space of tools for efficient and scalable graph bulk loading. For this we implement a prototypical loader for a property graph DBMS, using a distributed message bus. With our implementation we evaluate the impact and limits of basic optimizations. Our results confirm the expectation that bulk loading can be best supported as a server-side process. We also find, for our specific case, gains from batching writes (up to 64x speedups in our evaluation), uniform behavior across partitioning strategies, and the need for careful tuning to find the optimal configuration of batching and partitioning. In future work we aim to study loading into alternative physical storages with GeckoDB, an HTAP database system developed in our group.