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We present the activities of the \"New Physics\" working group for the \"Physics at TeV Colliders\" workshop (Les Houches, France, 3--21 June, 2013). Our report includes new computational tool developments, studies of the implications of the Higgs boson discovery on new physics, important signatures for searches for natural new physics at the LHC, new studies of flavour aspects of new physics, and assessments of the interplay between direct dark matter searches and the LHC.

We present the activities of the \"New Physics\" working group for the \"Physics at TeV Colliders\" workshop (Les Houches, France, 30 May-17 June, 2011). Our report includes new agreements on formats for interfaces between computational tools, new tool developments, important signatures for searches at the LHC, recommendations for presentation of LHC search results, as well as additional phenomenological studies.

A bstract The Higgs discovery and the lack of any other hint for new physics favor a description of non-standard Higgs physics in terms of an effective field theory. We present an implementation of a general Higgs effective Lagrangian containing operators up to dimension six in the framework of F eyn R ules and provide details on the translation between the mass and interaction bases, in particular for three- and four-point interaction vertices involving Higgs and gauge bosons. We illustrate the strengths of this implementation by using the UFO interface of F eyn R ules capable to generate model files that can be understood by the M ad G raph 5 event generator and that have the specificity to contain all interaction vertices, without any restriction on the number of external legs or on the complexity of the Lorentz structures. We then investigate several new physics effects in total rates and differential distributions for different Higgs production modes, including gluon fusion, associated production with a gauge boson and di-Higgs production. We finally study contact interactions of gauge and Higgs bosons to fermions.

Submerged aquatic vegetation (SAV) plays important roles in shallow lakes. In addition to its refuge effect for zooplankton, one key role of SAV is to provide diverse ecological niches to these organisms. The reduction of habitat complexity due to loss of SAV might thus have huge effects on zooplankton communities. The objective of this study was to investigate the relationship between SAV abundance and composition and zooplankton functional diversity and community structure. We used as model system the littoral zone of Lake St. Pierre (Québec, Canada), a shallow fluvial lake experiencing dramatic changes in SAV cover. Our sampling protocol allowed us to analyse the relationship between SAV and zooplankton along a gradient of SAV abundance. We showed that SAV abundance explained 41% of the variation in the zooplankton community structure and 25% of the variation in zooplankton functional diversity. Our results also indicated that the presence of the benthic cyanobacterium Gloeotrichia sp. in SAV beds had a structuring effect within the dominant family of Chydoridae, being negatively correlated to the abundance of relatively large-bodied species. Our findings suggest that loss in SAV biomass and complexity can affect both community structure and functional diversity of zooplankton in shallow fluvial lakes.

Aim: We tested the energy and metabolic theories for explaining diversity patterns of crustacean Zooplankton in Canadian lakes, and evaluated the influence of regional and local environments on community structure. Location: The 1665 studied lakes are distributed across Canada in 47 ecoprovinces. Methods: Our database included the occurrence of 83 pelagic crustacean species. The regional species richness in each ecoprovince was estimated using the average local species richness per lake and the first-order jackknife diversity index. Using a principal component plot and forward selection in a multiple regression we identified the most important predictors of regional species richness estimates. We tested the predictions of the species richness-energy hypothesis using climatic variables at regional scale, and of the metabolic theory using the inverse of air temperature. To evaluate the influence of regional and local environmental drivers, we carried out a redundancy analysis between crustacean species occurrences and regional climate and lake environmental factors on a subset of 458 lakes. Results: Estimates of pelagic crustacean species richness in Canadian ecoprovinces varied from 3 to 10 species per lake (average local species richness) or 8 to 52 species per ecoprovince (Jackknife diversity index). Our study fully supports the species richness-energy hypothesis and partially the metabolic theory. Mean daily global solar radiation was the most important regional predictor, explaining 51% of the variation in the regional species richness among ecoprovinces. Together, regional climate and local lake environment accounted for 31% of the total variation in community structure. Regional-scale energy variables accounted for 24% of the total explained variation, whereas local-scale lake conditions had less influence (2%). Main conclusions: The richness-energy theory explains diversity patterns of freshwater crustacean zooplankton in Canadian ecoprovinces. Solar radiation is the best predictor explaining regional species richness in ecoprovinces and community structure of pelagic crustaceans in Canadian lakes.

Physics systems are becoming increasingly complex and require more and more computing time. Quantum computing, which has shown its efficiency on some problems, such as the factorisation of a number with Shor's algorithm, may be the solution to reduce these computation times. Here, the authors propose two quantum numerical schemes for the simulation of physics phenomena, based on the finite difference method. The aim is to see if quantum versions of standard numerical schemes offer an advantage over their classical counterparts, either in accuracy, stability or computation time. First, the authors will present the different phenomena studied as well as the classical solution methods chosen. The authors will then describe the implementation of the quantum numerical schemes and present some results obtained on the different physics phenomena beforehand and then compare both approaches, classical and quantum. The authors propose two quantum numerical schemes for the simulation of physics phenomena, based on the finite difference method. The aim is to see if quantum versions of standard numerical schemes offer an advantage over their classical counterparts, whether in accuracy, stability or computation time.

Accumulation of glycogen in the kidney and liver is the main feature of Fanconi–Bickel Syndrome (FBS), a rare disorder of carbohydrate metabolism inherited in an autosomal recessive manner due to SLC2A2 gene mutations. Missense, nonsense, frame-shift (fs), in-frame indels, splice site, and compound heterozygous variants have all been identified in SLC2A2 gene of FBS cases. Approximately 144 FBS cases with 70 different SLC2A2 gene variants have been reported so far. SLC2A2 encodes for glucose transporter 2 (GLUT2) a low affinity facilitative transporter of glucose mainly expressed in tissues playing important roles in glucose homeostasis, such as renal tubular cells, enterocytes, pancreatic β-cells, hepatocytes and discrete regions of the brain. Dysfunctional mutations and decreased GLUT2 expression leads to dysglycaemia (fasting hypoglycemia, postprandial hyperglycemia, glucose intolerance, and rarely diabetes mellitus), hepatomegaly, galactose intolerance, rickets, and poor growth. The molecular mechanisms of dysglycaemia in FBS are still not clearly understood. In this review, we discuss the physiological roles of GLUT2 and the pathophysiology of mutants, highlight all of the previously reported SLC2A2 mutations associated with dysglycaemia, and review the potential molecular mechanisms leading to dysglycaemia and diabetes mellitus in FBS patients.

Organic fertilizers and especially microbial biomass, also known as microbial fertilizer, can enable a paradigm shift to the conventional fertilizer-to-food chain, particularly when produced on secondary resources. Microbial fertilizers are already common practice (e.g. Bloom ® and Synagro); yet microbial fertilizer blends to align the nutrient release profile to the plant’s needs are, thus far, unexplored. Moreover, most research only focuses on direct fertilization effects without considering added value properties, such as disease prevention. This study has explored three promising types of microbial fertilizers, namely dried biomass from a consortium of aerobic heterotrophic bacteria, a microalga ( Arthrospira platensis ) and a purple non-sulfur bacterium ( Rhodobacter sphaeroides ). Mineralization and nitrification experiments showed that the nitrogen mineralization profile can be tuned to the plant’s needs by blending microbial fertilizers, without having toxic ammonium peaks. In a pot trial with perennial ryegrass ( Lolium perenne L.), the performance of microbial fertilizers was similar to the reference organic fertilizer, with cumulative dry matter yields of 5.6–6.7 g per pot. This was confirmed in a pot trial with tomato ( Solanum lycopersicum L.), showing an average total plant length of 90–99 cm after a growing period of 62 days for the reference organic fertilizer and the microbial fertilizers. Moreover, tomato plants artificially infected with powdery mildew ( Oidium neolycopersici ), a devastating disease for the horticultural industry, showed reduced disease symptoms when A . platensis was present in the growing medium. These findings strengthen the application potential of this novel class of organic fertilizers in the bioeconomy, with a promising match between nutrient mineralization and plant requirements as well as added value in crop protection.

The increasing world population and living standards urgently necessitate the transition towards a sustainable food system. One solution is microbial protein, i.e. using microbial biomass as alternative protein source for human nutrition, particularly based on renewable electron and carbon sources that do not require arable land. Upcoming green electrification and carbon capture initiatives enable this, yielding new routes to H2, CO2 and CO2‐derived compounds like methane, methanol, formic‐ and acetic acid. Aerobic hydrogenotrophs, methylotrophs, acetotrophs and microalgae are the usual suspects for nutritious and palatable biomass production on these compounds. Interestingly, these compounds are largely un(der)explored for purple non‐sulfur bacteria, even though these microbes may be suitable for growing aerobically and phototrophically on these substrates. Currently, selecting the best strains, metabolisms and cultivation conditions for nutritious and palatable microbial food mainly starts from empirical growth experiments, and mostly does not stretch beyond bulk protein. We propose a more target‐driven and efficient approach starting from the genome‐embedded potential to tuning towards, for instance, essential amino‐ and fatty acids, vitamins, taste,... Genome‐scale metabolic models combined with flux balance analysis will facilitate this, narrowing down experimental variations and enabling to get the most out of the ‘best’ combinations of strain and electron and carbon sources.