Miracle-dz/UniFAR-FTUs huggingface dataset:
Metaphors in spoken academic discourse in German and English

Metaphors have been increasingly associated with cognitive functions, which means that metaphors structure how we think and express ourselves. Metaphors are embodied in our basic physical experience, which is one reason why certain abstract concepts are expressed in more concrete terms, such as visible entities, journeys, and other types of movement, spaces etc. This communicative relevance also applies to specialised, institutionalised settings and genres, such as those produced in or related to higher education institutions, among which is spoken academic discourse. A significant research gap has been identified regarding spoken academic discourse and metaphors therein, but also given the fact that with increasing numbers of students in higher education and international research and cooperation e.g. in the form of invited lectures, spoken academic discourse can be seen as nearly omnipresent. In this context, research talks are a key research genre. A mixed methods study has been conducted, which investigates metaphors in a corpus of eight fully transcribed German and English L1 speaker conference talks and invited lectures, totalling to 440 minutes. A wide range of categories and functions were identified in the corpus. Abstract research concepts, such as results or theories are expressed in terms of concrete visual entities that can be seen or shown, but also in terms of journeys or other forms of movement. The functions of these metaphors are simplification, rhetorical emphasis, theory-construction, or pedagogic illustration. For both the speaker and the audience or discussants, anthropomorphism causes abstract and complex ideas to become concretely imaginable and at the same time more interesting because the contents of the talk appear to be livelier and hence closer to their own experience, which ensures the audience’s attention. These metaphor categories are present in both the English and the German sub corpus of this study with similar functions.
Missense mutations in the extracellular domain of the human neural cell adhesion molecule L1 reduce neurite outgrowth of murine cerebellar neurons

Mutations in L1CAM, the gene encoding the transmembrane multifunctional neuronal adhesion molecule L1, are associated with neurodevelopmental disorders including X‐linked hydrocephalus and mental retardation. Some amino acid substitutions in various extracellular domains of L1 are known to affect posttranslational processing of the protein or its homophilic and heterophilic interactions. It is largely unknown, however, how these mutations result in neurodevelopmental disturbances and whether the effects of mutations on neurodevelopment can be modeled in vitro. We stably expressed full‐length human wild type L1 and the known pathogenic missense mutations I179S, R184W, Y194C, and C264Y in NIH‐3T3 cells. L1 protein synthesis, glycosylation pattern, and subcellular localization were analyzed. Neurite outgrowth of primary murine cerebellar neurons was measured after 23 hrs of co‐cultivation using transfected NIH‐3T3 cells as substrate. Like wild type L1, L1 protein with I179S or Y194C mutations was localized on the surface of the transfected substrate cells, but this was not the case with R184W or C264Y mutations. All four mutations were associated with reduced stimulation of neurite outgrowth. Measurement of neurite outgrowth on transfected substrate cells may be a suitable model for studying neurodevelopmental disturbances. © 2002 Wiley‐Liss, Inc.
Functional Geometry of the Permeation Pathway of Ca2+-activated Cl− Channels Inferred from Analysis of Voltage-dependent Block*

We examined the voltage-dependent block of Ca2+-activated Cl− channels by anthacene-9-carboxylic acid (A9C), diphenylamine-2-carboxylic acid (DPC), 4,4′-diisothiocyanostilbene-2,2′-disulfonic acid (DIDS), and niflumic acid (NFA) in excised inside-out and outside-out patches fromXenopus oocytes. The fraction of the voltage field (δ) experienced by the blocking drug was determined from the voltage dependence of block. All the drugs blocked by entering the channel from the outside. δ was 0.6 for A9C, 0.3 for DPC and DIDS, and <0.1 for NFA. Because the voltage dependence of the drugs differed, the order of potency was also voltage-dependent. At +100 mV the order of potency was NFA > A9C > DIDS > DPC (K i (μm) = 10.1, 18.3, 48, and 111, respectively). Because the drugs are hydrophobic, they can cross the bilayer when applied from the inside and block the channel from the outside. The equilibrium geometries of the blockers were determined by molecular modeling and compared with their blocking positions (δ). This analysis suggests that the channel is an elliptical cone with the largest opening facing the extracellular space. The selectivity filter has an apparent size of 0.33 × 0.75 nm, because C(CN)3 −, which has these dimensions, permeates. The external opening is at least 0.60 × 0.94 nm, because DPC has these dimensions and penetrates the channel ∼30%.
Immune-spectrum disease among female veterans: relations with posttraumatic stress disorder and maladaptive repetitive thought

BACKGROUND: Female veterans are at risk for stressr lated physical disorders given unique environmental stress factors, high rat es of trauma exposure and a heightened physiologic stress response. There is a need to identify modifiable risk factors which may help minimize the emergence and i mpact of veteran illness. RESEARCH QUESTION: The present study investigated t he contributions of posttraumatic stress symptoms, maladaptive repetiti v thought (MRT), depression, childhood trauma and health behaviors (sleep, alcoh ol use and smoking) to physical disease as operationalized by immune-mediated infla mm tory disease occurrence and related functional disability. METHOD: Female R serve or National Guard veterans ( N = 643) enrolled in a parent study conducted throug h the Iowa City Veteran’s Affairs Hospital completed a one-time computer-assi sted telephone interview. The current study examined self-report measures of post traumatic stress symptoms, MRT, depression, childhood trauma, smoking, alcohol use, sle p, inflammatory disease incidence and physical functioning. RESULTS: Propo sed models of primary hypotheses were tested using structural equation modeling. Re sults indicated that both physical disease and functional decline were greater in vete rans reporting a history of trauma. Physical disease was associated with greater depres sion and childhood trauma but lower levels of alcohol use after accounting for covariat es. Unexpectedly, greater MRT was associated with less physical disease, although it was only related to disease when depression was included as a covariate. Reduced s le p was linked with greater disease but only when depression was not included in the mo del, and depression was found to fully mediate the relationship between sleep and ph ysical disease. Smoking and the interaction between posttraumatic stress symptoms a nd MRT were generally unrelated to physical disease in this sample. CONCLUSIONS: Res ults of this study are consistent with the hypothesis that physical disorders and rel ated functional decline are greater in trauma-exposed individuals and that depression, chi ldhood trauma, repetitive thought and
Does theory influence the effectiveness of health behavior interventions? Meta-analysis.

OBJECTIVE To systematically investigate the extent and type of theory use in physical activity and dietary interventions, as well as associations between extent and type of theory use with intervention effectiveness. METHODS An in-depth analysis of studies included in two systematic reviews of physical activity and healthy eating interventions (k = 190). Extent and type of theory use was assessed using the Theory Coding Scheme (TCS) and intervention effectiveness was calculated using Hedges's g. Metaregressions assessed the relationships between these measures. RESULTS Fifty-six percent of interventions reported a theory base. Of these, 90% did not report links between all of their behavior change techniques (BCTs) with specific theoretical constructs and 91% did not report links between all the specified constructs with BCTs. The associations between a composite score or specific items on the TCS and intervention effectiveness were inconsistent. Interventions based on Social Cognitive Theory or the Transtheoretical Model were similarly effective and no more effective than interventions not reporting a theory base. CONCLUSIONS The coding of theory in these studies suggested that theory was not often used extensively in the development of interventions. Moreover, the relationships between type of theory used and the extent of theory use with effectiveness were generally weak. The findings suggest that attempts to apply the two theories commonly used in this review more extensively are unlikely to increase intervention effectiveness.
EDM of Ti-6Al-4V: Electrode and polarity selection for minimum tool wear rate and overcut

ABSTRACT Titanium and its alloys especially Ti-6Al-4V are being widely used numerous application areas. In addition to have iconic properties, Ti-6Al-4V is considered as challenging material in machining perspective that is why it has captured global research focus. In this research, electric discharge machining of Ti-6Al-4V has been carried out by employing four different types of electrode materials (graphite, aluminum, copper, and brass) assigned with two alternate polarities (positive and negative). Selection of the most appropriate tool material and electrode polarity is the important aspect needed to be explored for this alloy. In addition to polarity, discharge current, and pulse time ratio have been considered as process variables owing to have their direct influence in electric discharge machining. Taguchi L9 array has been employed for each of the four electrodes with positive polarity and similarly L9 with negative polarity. Thus, a total number of 72 experiments have been conducted. Tool wear rate and overcut (OC) around the machined surfaces are the response characteristics to be investigated in order to achieve minimum amounts of both of these two responses. Selection of the most suitable tool with common tool polarity has been carried out meeting the decision criteria of minimum tool wear and minimum OC.
Glutamatergic Inputs Contribute to Phasic Activity in Vasopressin Neurons

Many neurons in the CNS display rhythmic patterns of activity to optimize excitation–secretion coupling. However, the mechanisms of rhythmogenesis are only partially understood. Magnocellular vasopressin (VP) neurons in the hypothalamus display a phasic activity that consists of alternative bursts of action potentials and silent periods. Previous observations from acute slices of adult hypothalamus suggested that VP cell rhythmicity depends on intrinsic membrane properties. However, such activity in vivo is nonregenerative. Here, we studied the mechanisms of VP neuron rhythmicity in organotypic slice cultures that, unlike acute slices, preserve functional synaptic connections. Comparative analysis of phasic firing of VP neurons in vivo, in acute slices, and in the cultures revealed that, in the latter, the activity was closely related to that observed in vivo. It was synaptically driven, essentially from glutamatergic inputs, and did not rely on intrinsic membrane properties. The glutamatergic synaptic activity was sensitive to osmotic challenges and κ-opioid receptor activation, physiological stimuli known to affect phasic activity. Together, our data thus strongly suggest that phasic activity in magnocellular VP neurons is controlled by glutamatergic synaptic inputs rather than by intrinsic properties.
Towards publishing recommendation data with predictive anonymization

Recommender systems are used to predict user preferences for products or services. In order to seek better prediction techniques, data owners of recommender systems such as Netflix sometimes make their customers' reviews available to the public, which raises serious privacy concerns. With only a small amount of knowledge about individuals and their ratings to some items in a recommender system, an adversary may easily identify the users and breach their privacy. Unfortunately, most of the existing privacy models (e.g., k-anonymity) cannot be directly applied to recommender systems. In this paper, we study the problem of privacy-preserving publishing of recommendation datasets. We represent recommendation data as a bipartite graph, and identify several attacks that can re-identify users and determine their item ratings. To deal with these attacks, we first give formal privacy definitions for recommendation data, and then develop a robust and efficient anonymization algorithm, Predictive Anonymization, to achieve our privacy goals. Our experimental results show that Predictive Anonymization can prevent the attacks with very little impact to prediction accuracy.
Diffusion approximation for the components in critical inhomogeneous random graphs of rank 1.

Consider the random graph on n vertices 1,…,n. Each vertex i is assigned a type xi with x1,…,xn being independent identically distributed as a nonnegative random variable X. We assume that EX3< ∞. Given types of all vertices, an edge exists between vertices i and j independent of anything else and with probability \documentclass{article}\usepackage{mathrsfs, amsmath, amssymb}\pagestyle{empty}\begin{document}\begin{align*}\min \{1, \frac{x_ix_j}{n}\left(1+\frac{a}{n^{1/3}} \right) \}\end{align*} \end{document}. We study the critical phase, which is known to take place when EX2 = 1. We prove that normalized by n‐2/3the asymptotic joint distributions of component sizes of the graph equals the joint distribution of the excursions of a reflecting Brownian motion with diffusion coefficient \documentclass{article}\usepackage{mathrsfs, amsmath, amssymb}\pagestyle{empty}\begin{document}\begin{align*}\sqrt{{\textbf{ E}}X{\textbf{ E}}X^3}\end{align*} \end{document}and drift \documentclass{article}\usepackage{mathrsfs, amsmath, amssymb}\pagestyle{empty}\begin{document}\begin{align*}a-\frac{{\textbf{ E}}X^3}{{\textbf{ E}}X}s\end{align*} \end{document}. In particular, we conclude that the size of the largest connected component is of order n2/3. © 2013 Wiley Periodicals, Inc. Random Struct. Alg. , 43, 486–539, 2013
Plastome-encoded bacterial ribulose-1,5-bisphosphate carboxylase/oxygenase (RubisCO) supports photosynthesis and growth in tobacco

The efficiency with which crop plants use their resources of light, water, and fertilizer nitrogen could be enhanced by replacing their CO2-fixing enzyme, d-ribulose-1,5-bisphosphate carboxylase-oxygenase (RubisCO), with more efficient forms, such as those found in some algae, for example. This important challenge has been frustrated by failure of all previous attempts to substitute a fully functional, foreign RubisCO (efficient or inefficient) into higher plants. This failure could be caused by incompatibility between the plastid-encoded large subunits and the nucleus-encoded small subunits or by inability of the foreign RubisCO subunits to fold or assemble efficiently in the plastid. Mismatch between the regulatory requirements of the foreign RubisCO and conditions in the chloroplast also might render the substituted enzyme inactive but, previously, it has not been possible to test this. To answer the general question of whether a foreign RubisCO can support photosynthesis in a plant, we used plastid transformation to replace RubisCO in tobacco with the simple homodimeric form of the enzyme from the α-proteobacterium, Rhodospirillum rubrum, which has no small subunits and no special assembly requirements. The transplastomic plants so obtained are fully autotrophic and reproductive but require CO2 supplementation, consistent with the kinetic properties of the bacterial RubisCO. This establishes that the activity of a RubisCO from a very different phylogeny can be integrated into chloroplast photosynthetic metabolism without prohibitive problems.
Enhanced bioavailability of danazol nanosuspensions by wet milling and high-pressure homogenization

Introduction: The majority of drugs obtained through synthesis and development show poor aqueous solubility and dissolution velocity, resulting in reduced bioavailability of drugs. Most of these problems arise from formulation-related performance issues, and an efficient way to overcome these obstacles and to increase dissolution velocity is to reduce the particle size of drug substances to form drug nanosuspensions. Materials and Methods: Danazol nanosuspensions were prepared by wet milling (WM) and high-pressure homogenization (HPH) methods. The nanosuspensions obtained using these fabrication methods were analyzed for their particle size, surface charge, and the crystallinity of the product was assessed by X-ray diffraction (XRD) and differential scanning calorimetry techniques. To determine in vitro and in vivo performances of the prepared nanosuspensions, dissolution velocity, and bioavailability studies were performed. Results: Particle size and zeta potential analysis showed the formation of nanosized particles with a negative charge on the surface. XRD depicted the nanocrystalline nature of danazol with low diffraction intensities. With increased surface area and saturation solubility, the nanosuspensions showed enhanced dissolution velocity and oral bioavailability in rats when compared to the bulk danazol suspension. Conclusions: The results suggest that the preparation of nanosuspensions by WM or HPH is a promising approach to formulate new drugs or to reformulate existing drugs with poorly water-soluble properties.
StitchUp: Automatic control flow protection for high level synthesis circuits

Soft-error detection in FPGAs typically requires replication, doubling the required area. We propose an approach which distinguishes between tolerable errors in data-flow, such-as arithmetic, and intolerable errors in control-flow, such as branches and their data-dependencies. This approach is demonstrated in a new high-level synthesis compiler pass called StitchUp, which precisely identifies the control critical parts of the design, then automatically replicates only that part. We applied StitchUp to the CHStone benchmark suite and performed exhaustive hardware fault injection in each case, finding that all control-flow errors were detected while only requiring 1% circuit area overhead in the best case.
Propositional Density in Spoken and Written Language of Czech-Speaking Patients With Mild Cognitive Impairment.

Purpose Propositional density (PD) is a measure of content richness in language production that declines in normal aging and more profoundly in dementia. The present study aimed to develop a PD scoring system for Czech and use it to compare PD in language productions of older people with amnestic mild cognitive impairment (aMCI) and control participants matched on age, gender, and education. Method Groups of patients with aMCI and cognitively healthy control participants (N = 20 each) provided short spoken and written language samples. Two samples were elicited for each modality, 1 describing recent events and 1 describing childhood memories. Series of neuropsychological tests were administered. The groups were compared using t-tests and the relations between measures using correlation coefficients. Results PD was lower in spoken productions of patients with aMCI, compared with control participants, but only in language samples using remote memories. PD in these samples was related to verbal fluency and education but not to working memory. PD in written samples did not differ between participants with aMCI and control participants. Conclusions PD in spoken language reflects the cognitive decline in people with aMCI, but the effect is relatively mild. The results support the existing findings that PD is related to verbal fluency.
An Improved SINR Estimation Method for Heterogeneous Networks

Distributed resource management becomes a popular concept in next generation cellular network. It however also results in less information available for each eNB to make appropriate decisions. Signal to Interference plus Noise Ratio (SINR), as one of the important inputs for eNBs when considering allocating appropriate resource blocks (RBs) to User Equipment (UE), is designed as the function of CQI (Channel Quality Indicator) reported from UE. However, too much signaling overhead and estimation error is induced with CQI mechanism. This paper presents an improved downlink SINR estimation method together with a corresponding resource allocation scheme for Low Power Nodes (LPNs) in Heterogeneous Networks (HetNets). Cognitive Radio (CR) is introduced to construct a distributed adaptive channel evaluation model. The work makes use of the Frequency Domain Packet Scheduling (FDPS) implementation in resource block level without CQI report from UEs. Simulation results show that the proposed scheme can achieve considerable system performance improvement in OFDMA based HetNets.
Greedy Decoding for Statistical Machine Translation in Almost Linear Time

We present improvements to a greedy decoding algorithm for statistical machine translation that reduce its time complexity from at least cubic (O(n6) when applied naively) to practically linear time1 without sacrificing translation quality. We achieve this by integrating hypothesis evaluation into hypothesis creation, tiling improvements over the translation hypothesis at the end of each search iteration, and by imposing restrictions on the amount of word reordering during decoding.
Exploiting structure defined by data in machine learning : some new analyses

This thesis offers some new analyses and presents some new methods for learning in the context of exploiting structure defined by data – for example, when a data distribution has a submanifold support, exhibits cluster structure or exists as an object such as a graph. 1. We present a new PAC-Bayes analysis of learning in this context, which is sharp and in some ways presents a better solution than uniform convergence methods. The PAC-Bayes prior over a hypothesis class is defined in terms of the unknown true risk and smoothness of hypotheses w.r. t. the unknown data-generating distribution. The analysis is “localized” in the sense that complexity of the model enters not as the complexity of an entire hypothesis class, but focused on functions of ultimate interest. Such bounds are derived for various algorithms including SVMs. 2. We consider an idea similar to the p-norm Perceptron for building classifiers on graphs. We define p-norms on the space of functions over graph vertices and consider interpolation using the pnorm as a smoothness measure. The method exploits cluster structure and attains a mistake bound logarithmic in the diameter, compared to a linear lower bound for standard methods. 3. Rademacher complexity is related to cluster structure in data, quantifying the notion that when data clusters we can learn well with fewer examples. In particular we relate transductive learning to cluster structure in the empirical resistance metric. 4. Typical methods for learning over a graph do not scale well in the number of data points – often a graph Laplacian must be inverted which becomes computationally intractable for large data sets. We present online algorithms which, by simplifying the graph in principled way, are able to exploit the structure while remaining computationally tractable for large datasets. We prove state-of-the-art performance guarantees.