Sustainable bioethanol production combining biorefinery principles using combined raw materials from wheat undersown with clover-grass

To obtain the best possible net energy balance of the bioethanol production the biomass raw materials used need to be produced with limited use of non-renewable fossil fuels. Intercropping strategies are known to maximize growth and productivity by including more than one species in the crop stand, very often with legumes as one of the components. In the present study clover-grass is undersown in a traditional wheat crop. Thereby, it is possible to increase input of symbiotic fixation of atmospheric nitrogen into the cropping systems and reduce the need for fertilizer applications. Furthermore, when using such wheat and clover-grass mixtures as raw material, addition of urea and other fermentation nutrients produced from fossil fuels can be reduced in the whole ethanol manufacturing chain. Using second generation ethanol technology mixtures of relative proportions of wheat straw and clover-grass (15:85, 50:50, and 85:15) were pretreated by wet oxidation. The results showed that supplementing wheat straw with clover-grass had a positive effect on the ethanol yield in simultaneous saccharification and fermentation experiments, and the effect was more pronounced in inhibitory substrates. The highest ethanol yield (80% of theoretical) was obtained in the experiment with high fraction (85%) of clover-grass. In order to improve the sugar recovery of clover-grass, it should be separated into a green juice (containing free sugars, fructan, amino acids, vitamins and soluble minerals) for direct fermentation and a fibre pulp for pretreatment together with wheat straw. Based on the obtained results a decentralized biorefinery concept for production of biofuel is suggested emphasizing sustainability, localness, and recycling principle

Completeness for a First-order Abstract Separation Logic

Existing work on theorem proving for the assertion language of separation logic (SL) either focuses on abstract semantics which are not readily available in most applications of program verification, or on concrete models for which completeness is not possible. An important element in concrete SL is the points-to predicate which denotes a singleton heap. SL with the points-to predicate has been shown to be non-recursively enumerable. In this paper, we develop a first-order SL, called FOASL, with an abstracted version of the points-to predicate. We prove that FOASL is sound and complete with respect to an abstract semantics, of which the standard SL semantics is an instance. We also show that some reasoning principles involving the points-to predicate can be approximated as FOASL theories, thus allowing our logic to be used for reasoning about concrete program verification problems. We give some example theories that are sound with respect to different variants of separation logics from the literature, including those that are incompatible with Reynolds's semantics. In the experiment we demonstrate our FOASL based theorem prover which is able to handle a large fragment of separation logic with heap semantics as well as non-standard semantics.Comment: This is an extended version of the APLAS 2016 paper with the same titl

Evidence for Efimov quantum states in an ultracold gas of cesium atoms

Systems of three interacting particles are notorious for their complex physical behavior. A landmark theoretical result in few-body quantum physics is Efimov's prediction of a universal set of bound trimer states appearing for three identical bosons with a resonant two-body interaction. Counterintuitively, these states even exist in the absence of a corresponding two-body bound state. Since the formulation of Efimov's problem in the context of nuclear physics 35 years ago, it has attracted great interest in many areas of physics. However, the observation of Efimov quantum states has remained an elusive goal. Here we report the observation of an Efimov resonance in an ultracold gas of cesium atoms. The resonance occurs in the range of large negative two-body scattering lengths, arising from the coupling of three free atoms to an Efimov trimer. Experimentally, we observe its signature as a giant three-body recombination loss when the strength of the two-body interaction is varied. We also detect a minimum in the recombination loss for positive scattering lengths, indicating destructive interference of decay pathways. Our results confirm central theoretical predictions of Efimov physics and represent a starting point with which to explore the universal properties of resonantly interacting few-body systems. While Feshbach resonances have provided the key to control quantum-mechanical interactions on the two-body level, Efimov resonances connect ultracold matter to the world of few-body quantum phenomena.Comment: 18 pages, 3 figure

3-D Ultrastructure of O. tauri: Electron Cryotomography of an Entire Eukaryotic Cell

The hallmark of eukaryotic cells is their segregation of key biological functions into discrete, membrane-bound organelles. Creating accurate models of their ultrastructural complexity has been difficult in part because of the limited resolution of light microscopy and the artifact-prone nature of conventional electron microscopy. Here we explored the potential of the emerging technology electron cryotomography to produce three-dimensional images of an entire eukaryotic cell in a near-native state. Ostreococcus tauri was chosen as the specimen because as a unicellular picoplankton with just one copy of each organelle, it is the smallest known eukaryote and was therefore likely to yield the highest resolution images. Whole cells were imaged at various stages of the cell cycle, yielding 3-D reconstructions of complete chloroplasts, mitochondria, endoplasmic reticula, Golgi bodies, peroxisomes, microtubules, and putative ribosome distributions in-situ. Surprisingly, the nucleus was seen to open long before mitosis, and while one microtubule (or two in some predivisional cells) was consistently present, no mitotic spindle was ever observed, prompting speculation that a single microtubule might be sufficient to segregate multiple chromosomes

Open Problems on Central Simple Algebras

We provide a survey of past research and a list of open problems regarding central simple algebras and the Brauer group over a field, intended both for experts and for beginners.Comment: v2 has some small revisions to the text. Some items are re-numbered, compared to v

Brain changes associated with cognitive and emotional factors in chronic pain : a systematic review

An emerging technique in chronic pain research is MRI, which has led to the understanding that chronic pain patients display brain structure and function alterations. Many of these altered brain regions and networks are not just involved in pain processing, but also in other sensory and particularly cognitive tasks. Therefore, the next step is to investigate the relation between brain alterations and pain related cognitive and emotional factors. This review aims at providing an overview of the existing literature on this subject. Pubmed, Web of Science and Embase were searched for original research reports. Twenty eight eligible papers were included, with information on the association of brain alterations with pain catastrophizing, fear-avoidance, anxiety and depressive symptoms. Methodological quality of eligible papers was checked by two independent researchers. Evidence on the direction of these associations is inconclusive. Pain catastrophizing is related to brain areas involved in pain processing, attention to pain, emotion and motor activity, and to reduced top-down pain inhibition. In contrast to pain catastrophizing, evidence on anxiety and depressive symptoms shows no clear association with brain characteristics. However, all included cognitive or emotional factors showed significant associations with resting state fMRI data, providing that even at rest the brain reserves a certain activity for these pain-related factors. Brain changes associated with illness perceptions, pain attention, attitudes and beliefs seem to receive less attention in literature. Significance: This review shows that maladaptive cognitive and emotional factors are associated with several brain regions involved in chronic pain. Targeting these factors in these patients might normalize specific brain alterations

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur