Miracle-dz/UniFAR-FTUs huggingface dataset:
The Subjective Experience of Patients who Received Electroconvulsive Therapy

Objective: Despite the vast amount of scientific literature available on electroconvulsive therapy (ECT), there is little qualitative focus upon the patients' subjective experience of this procedure. Using an exploratory descriptive methodology, this study aims to provide a more unique insight into what certain patients actually think of ECT. Method: Semistructured interviews were conducted to explore eight patients' opinions and experiences of ECT. Interviews were subjected to analysis by a five-step framework approach that identified prominent themes in relation to five broad questions and in conjunction with issues raised by the subjects themselves. Results: Eleven major themes were identified. Four of these were chosen for discussion, not only as the most prevalent themes (in terms of how frequently they were mentioned by the subjects), but also as the most striking (in regards to the intensity of emotions evoked, or their influence on their perception of ECT as a future treatment option). The four themes are fear of ECT, attribution of cognitive decline and memory loss to ECT, positive ECT experiences, and patients' suggestions. Conclusions: Using such a qualitative approach, the depth of the information obtained has revealed new perspectives on how patients perceive the experience of ECT. Fears reported by patients present an opportunity to address specific areas of the procedure that generate the most angst. These were closely associated with recommendations that many patients proposed throughout the interviews. Patients' perceptions of the cognitive effects of ECT do not necessarily correspond with those commonly reported in the literature on ECT. Positive experiences with ECT were more complex than simply its efficacy. There is a need for future research in order to explore and address patients' experiences of ECT.
Cloudy with a Chance of Poaching: Adversary Behavior Modeling and Forecasting with Real-World Poaching Data

Wildlife conservation organizations task rangers to deter and capture wildlife poachers. Since rangers are responsible for patrolling vast areas, adversary behavior modeling can help more effectively direct future patrols. In this innovative application track paper, we present an adversary behavior modeling system, INTERCEPT (INTERpretable Classification Ensemble to Protect Threatened species), and provide the most extensive evaluation in the AI literature of one of the largest poaching datasets from Queen Elizabeth National Park (QENP) in Uganda, comparing INTERCEPT with its competitors; we also present results from a month-long test of INTERCEPT in the field. We present three major contributions. First, we present a paradigm shift in modeling and forecasting wildlife poacher behavior. Some of the latest work in the AI literature (and in Conservation) has relied on models similar to the Quantal Response model from Behavioral Game Theory for poacher behavior prediction. In contrast, INTERCEPT presents a behavior model based on an ensemble of decision trees (i) that more effectively predicts poacher attacks and (ii) that is more effectively interpretable and verifiable. We augment this model to account for spatial correlations and construct an ensemble of the best models, significantly improving performance. Second, we conduct an extensive evaluation on the QENP dataset, comparing 41 models in prediction performance over two years. Third, we present the results of deploying INTERCEPT for a one-month field test in QENP - a first for adversary behavior modeling applications in this domain. This field test has led to finding a poached elephant and more than a dozen snares (including a roll of elephant snares) before they were deployed, potentially saving the lives of multiple animals - including elephants.
Bioinformatische Datenintegration und Analyse der Eigenschaften molekularer Interaktionen

v Eigene Publikationen . vi Kapitel 1 Einleitung . 1 1.1 Protein-Protein-Interaktionen . 2 1.2 Protein-Wirkstoff-Interaktionen . 6 1.3 Zielsetzung der Arbeit . 8 Kapitel 2 Methoden und Ergebnisse . 10 2.1 Protein-Protein-Interaktionen . 10 2.1.1 Erstellen einer Bibliothek von Makromolekül-Interaktionsflächen . 10 Originalartikel: "JAIL: a Structure-Based Interface Library for Macromolecules" . 12 2.1.2 Analyse von Protein-Interaktionsflächen . 16 Originalartikel: "Structural Features and Evolution of Protein-Protein Interactions" . 18 2.1.3 Erstellen wissensbasierter Interaktionspotentiale . 28 Originalartikel: "Development of Immune-Specific Interaction Potentials and Their Application in the Multi-Agent-System VaccImm" . 30 2.2 Protein-Wirkstoff-Interaktionen . 41 2.2.1 Erstellen einer Ressource für Protein-Wirkstoff-Interaktionen . 41 Originalartikel: "PROMISCUOUS: a Database for Network-Based DrugRepositioning" . 43 2.2.2 Analyse promiskuitiver und nicht-promiskuitiver Wirkstoffe . 50 Eingereichtes Manuskript: "Polypharmacology of Drugs: Analysing Molecular and Network Properties" . 52 2.2. 3 Interaktive Visualisierung komplexer Netzwerke . 61
Myopia and choice: framing, screening and shopping

Contemporary accounts of customer behaviour emphasise reductionism and rationality. They are myopic since they construct customers as homogenous, or reducible to types, and motivated by discrete, separable forces. In Levitt's terms, current research into customer behaviour has been more concerned with the mechanics of selling than marketing, and this is a cause and consequence of contemporary 'marketing myopia'. Taking retail as our context, we advocate greater diversity in representing the complexities of customer behaviour. We summarise contemporary thinking on choice in the customer behaviour literature and critique two commonly used ways of representing choice: trait/type accounts, and stimulus – organism - response models. We then propose and outline an alternative framework for thinking about shopper choice based on Beach's (1990, 1998) image theory. The paper draws out implications for merchandising, and proposes a research agenda.
Comparison of active renin concentration and plasma renin activity for the diagnosis of primary hyperaldosteronism in patients with an adrenal mass.

OBJECTIVE Plasma aldosterone concentration (PAC) to plasma renin activity (PRA) ratio is an established screening test for primary hyperaldosteronism. Due to the increased recognition of adrenal incidentalomas, reliable parameters are required. Determination of active renin concentration (ARC) in contrast to PRA offers advantages with regard to processing and standardization. The present study compared PRA and ARC under random conditions to establish thresholds for the diagnosis of primary hyperaldosteronism. DESIGN AND METHODS Fifty patients with various adrenal tumors, including ten patients with aldosterone-secreting adrenal adenomas, as well as ten hypertensive patients and 23 normotensive volunteers were studied. PAC and PRA were measured by radioimmunoassay. ARC was determined by an immunoluminometric assay. RESULTS Receiver operating curve (ROC) analysis suggested a PAC to ARC ratio threshold of 90 ((ng/l)/(ng/l)) (sensitivity 100%, specificity 98.6%) and a ratio threshold of 62 by additional consideration of PAC > or =200 ng/l (sensitivity 100%, specificity 100%) for the diagnosis of aldosterone-secreting adrenal adenomas. CONCLUSIONS A PAC to ARC ratio of > or =62 in patients with PAC levels > or =200 ng/l is a reliable screening method for primary hyperaldosteronism in patients with an aldosterone-producing adenoma under random conditions. Because of its advantages with regard to probe processing and its independence from endogenous angiotensinogen levels, ARC may be preferred to PRA.
Building an EEG-fMRI Multi-Modal Brain Graph: A Concurrent EEG-fMRI Study

The topological architecture of brain connectivity has been well-characterized by graph theory based analysis. However, previous studies have primarily built brain graphs based on a single modality of brain imaging data. Here we develop a framework to construct multi-modal brain graphs using concurrent EEG-fMRI data which are simultaneously collected during eyes open (EO) and eyes closed (EC) resting states. FMRI data are decomposed into independent components with associated time courses by group independent component analysis (ICA). EEG time series are segmented, and then spectral power time courses are computed and averaged within 5 frequency bands (delta; theta; alpha; beta; low gamma). EEG-fMRI brain graphs, with EEG electrodes and fMRI brain components serving as nodes, are built by computing correlations within and between fMRI ICA time courses and EEG spectral power time courses. Dynamic EEG-fMRI graphs are built using a sliding window method, versus static ones treating the entire time course as stationary. In global level, static graph measures and properties of dynamic graph measures are different across frequency bands and are mainly showing higher values in eyes closed than eyes open. Nodal level graph measures of a few brain components are also showing higher values during eyes closed in specific frequency bands. Overall, these findings incorporate fMRI spatial localization and EEG frequency information which could not be obtained by examining only one modality. This work provides a new approach to examine EEG-fMRI associations within a graph theoretic framework with potential application to many topics.
Making historical preservation in Chicago: discourse and spatiality in neo‐liberal times

This article examines an underground but emergent neo‐liberal initiative in US cities, historic preservation. A focus on Chicago's programme highlights this as a complex cultural project. The focus is on how narrativists of this programme use imagined mental spaces in discourse to constitute a programme‐ and neo‐liberal‐supporting cultural content for common consumption. The results suggst that this neo‐liberal programme offers elaborate renditions of spatially infused heroes, villains, victims and unmistakable forces that function as cultural frame. This constituting, the study concludes, is crucial to bolstering assertions about the importance of this programme and the need for neo‐liberal restructuring
Mixed excitation linear prediction coding of wideband speech at 8 kbps

This paper presents our study on the feasibility and effectiveness of using the MELP (mixed excitation linear prediction) model for coding wideband (7 kHz) speech signals at a transmission bit rate of 8 kbps. In order to achieve a reasonably good subjective quality for the decoded speech while maintaining a low operating bit rate at the same time, modifications to the pitch estimation, LP analysis/synthesis and post filtering stages of the original MELP model are discussed. Informal listening tests show that the subjective quality of the decoded speech of the proposed coder is rated to be slightly better than the MPEG-4 CELP coder operating at 14.4 kbps for both male and female utterances. The subjective quality of the decoded female utterances from the proposed coder operating at 8.4 kbps is rated to be comparable to that produced by the ITU G.722 coder operating at 48 kbps.
‘The word isn't there!’: a Foucauldian approach to power negotiation in an instructional interaction across linguistic and cultural boundaries

Power is often stabilized as status, role, and expertise that create discourse variations in existing studies on nonnative speakers' (NNSs) interactions with native speakers (NSs). The ethnolinguistic approach views NSs as the powerful and dominant group due to their linguistic status. In contrast, the discourse domain approach argues that NNSs are capable of giving a better discourse performance than NSs if they have the expertise on the topic. Drawing on Foucault's conceptualization of power, the present study argues that power is not monolithic and static, but dynamic and negotiable. The discourse under consideration represents an instructional interaction between an NNS teacher and a NS college student. A close analysis of the discourse reveals that neither the NS student's linguistic status nor the NNS teacher's expertise is taken for granted as a given privilege, but both are actually negotiated. Power negotiation involves the use of language resources, interactional skills, and contextual knowledge in the participants' attempts to achieve their respective situational goals.
Frequency Selective Surface Intelligent Topology Design Based on Machine Learning Technique

To efficiently and conveniently realize the topology design, this work proposes an intelligent topological design method based on machine learning for frequency selective surface (FSS) structures from a novel perspective. In the proposed method, the input is a set of desired $\vert \text{S}11\vert$ curves, and the output is the predicted FSS structure presented as an image to directly reveal the distribution of binary pixels in the modeling domain. A numerical example of single-layer double-layer FSS structures involving large numbers of binary variables is employed to validate the effectiveness of the proposed method.
POLYGENIC TRAITS AND PARASITE LOCAL ADAPTATION

Abstract The extent to which parasites are locally adapted to their hosts has important implications for human health and agriculture. A recently developed conceptual framework—the geographic mosaic theory of coevolution—predicts that local maladaptation should be common and largely determined by the interplay between gene flow and spatially variable reciprocal selection. Previous investigation of this theory has predominately focused on genetic systems of infection and resistance characterized by few genes of major effect and particular forms of epistasis. Here we extend existing theory by analyzing mathematical models of host–parasite interactions in which host resistance to parasites is mediated by quantitative traits with an additive polygenic basis. In contrast to previous theoretical studies predicated upon major gene mechanisms, we find that parasite local maladaptation is quite uncommon and restricted to one specific functional form of host resistance. Furthermore, our results show that local maladaptation should be rare or absent in studies that measure local adaptation using reciprocal transplant designs conducted in natural environments. Our results thus narrow the scope over which the predictions of the geographic mosaic theory are likely to hold and provide novel and readily testable predictions about when and where local maladaptation is expected.
Autophagy delays sulindac sulfide-induced apoptosis in the human intestinal colon cancer cell line HT-29.

Autophagy is a major catabolic process allowing the renewal of intracellular organelles by which cells maintain their homeostasis. We have previously shown that autophagy is controlled by two transduction pathways mediated by a heterotrimeric Gi3 protein and phosphatidylinositol 3-kinase activities in the human colon cancer cell line HT-29. Here, we show that 3-methyladenine, an inhibitor of autophagy, increases the sensitivity of HT-29 cells to apoptosis induced by sulindac sulfide, a nonsteroidal anti-inflammatory drug which inhibits the cyclooxygenases. Similarly, HT-29 cells overexpressing a GTPase-deficient mutant of the G(alpha i3) protein (Q204L), which have a low rate of autophagy, were more sensitive to sulindac sulfide-induced apoptosis than parental HT-29 cells. In both cell populations we did not observe differences in the expression patterns of COX-2, Bcl-2, Bcl(XL), Bax, and Akt/PKB activity. However, the rate of cytochrome c release was higher in Q204L-overexpressing cells than in HT-29 cells. These results suggest that autophagy could retard apoptosis in colon cancer cells by sequestering mitochondrial death-promoting factors such as cytochrome c.
Dietary NaCl affects bleomycin-induced lung fibrosis in mice

ABSTRACT Purpose: High levels of NaCl in the diet are associated with both cardiac and renal fibrosis, but whether salt intake affects pulmonary fibrosis has not been examined. Aim of the Study: To test the hypothesis that salt intake might affect pulmonary fibrosis. Materials and Methods: Mice were fed low, normal, or high salt diets for 2 weeks, and then treated with oropharyngeal bleomycin to induce pulmonary fibrosis, or oropharyngeal saline as a control. Results: As determined by collagen staining of lung sections, and protein levels and cell numbers in the bronchoalveolar lavage (BAL) fluid at 21 days after bleomycin, the high salt diet did not exacerbate bleomycin-induced fibrosis, while the low salt diet attenuated fibrosis. For the bleomycin-treated mice, staining of the post-BAL lung sections indicated that compared to the regular salt diet, high salt increased the number of Ly6c-positive macrophages and decreased the number of CD11c and CD206-positive macrophages and dendritic cells. The low salt diet caused bleomycin-induced leukocyte numbers to be similar to control saline-treated mice, but reduced numbers of CD45/collagen-VI positive fibrocytes. In the saline controls, low dietary salt decreased CD11b and CD11c positive cells in lung sections, and high dietary salt increased fibrocytes. Conclusions: Together, these data suggest the possibility that a low salt diet might attenuate pulmonary fibrosis.
Malaria: Disease Impacts and Long-Run Income Differences

The World Health Organization (WHO) reports that malaria, a parasitic disease transmitted by mosquitoes, causes over 300 million episodes of "acute illness" and more than one million deaths annually. Most of the deaths occur in poor countries of the tropics, and especially sub-Saharan Africa. Most of the countries with high rates of malaria prevalence are also poor, and some researchers have suggested a direct link from malaria to poverty. This paper explores the potential impact of malaria on national income levels, using a dynamic general equilibrium framework with epidemiological features. We find that if there is no feasible prevention or control, malaria can have a significant impact on income levels. However, if people have any effective way of avoiding infection, the disease impacts on income levels are likely to be small. This is true even where preventive measures are costly.
Classification method of brain functional connections in deaf children’s tone processing based on one-dimensional convolutional network

This study aims to investigate differences in brain functional connectivity between deaf children and hearing children when processing three distinct tones (first tone, second tone, third tone) using deep learning methods. Additionally, the study seeks to identify disparities in brain activation regions during tone processing between deaf and hearing children, along with variations in activated brain regions during tone processing between deaf children and typically developing children. The participant pool consisted of five deaf children and two typically developing children. Resting-state functional magnetic resonance imaging (fMRI) scans were conducted using a functional MRI scanner, followed by data preprocessing and fMRI data analysis. A one-dimensional convolutional network was employed to extract features and classify the input brain functional connectivity. Through a series of theoretical and comparative analyses, it was determined that the one-dimensional convolutional network is adept at capturing local patterns within sequential data. It can effectively extract information pertinent to connectivity patterns from the one-dimensional sequence of connection weights, rendering it particularly suitable for processing brain functional connectivity data in deaf children.
Identification and Population Dynamics of Yeasts in Sourdough Fermentation Processes by PCR-Denaturing Gradient Gel Electrophoresis

ABSTRACT Four sourdoughs (A to D) were produced under practical conditions, using a starter obtained from a mixture of three commercially available sourdough starters and baker's yeast. The doughs were continuously propagated until the composition of the microbiota remained stable. A fungi-specific PCR-denaturing gradient gel electrophoresis (DGGE) system was established to monitor the development of the yeast biota. The analysis of the starter mixture revealed the presence of Candida humilis, Debaryomyces hansenii, Saccharomyces cerevisiae, and Saccharomyces uvarum. In sourdough A (traditional process with rye flour), C. humilis dominated under the prevailing fermentation conditions. In rye flour sourdoughs B and C, fermented at 30 and 40°C, respectively, S. cerevisiae became predominant in sourdough B, whereas in sourdough C the yeast counts decreased within a few propagation steps below the detection limit. In sourdough D, which corresponded to sourdough C in temperature but was produced with rye bran, Candida krusei became dominant. Isolates identified as C. humilis and S. cerevisiae were shown by randomly amplified polymorphic DNA-PCR analysis to originate from the commercial starters and the baker's yeast, respectively. The yeast species isolated from the sourdoughs were also detected by PCR-DGGE. However, in the gel, additional bands were visible. Because sequencing of these PCR fragments from the gel failed, cloning experiments with 28S rRNA amplicons obtained from rye flour were performed, which revealed Cladosporium sp. , Saccharomyces servazii, S. uvarum, an unculturable ascomycete, Dekkera bruxellensis, Epicoccum nigrum, and S. cerevisiae. The last four species were also detected in sourdoughs A, B, and C.
Pathogen Recognition Receptor Signaling Accelerates Phosphorylation-Dependent Degradation of IFNAR1

An ability to sense pathogens by a number of specialized cell types including the dendritic cells plays a central role in host's defenses. Activation of these cells through the stimulation of the pathogen-recognition receptors induces the production of a number of cytokines including Type I interferons (IFNs) that mediate the diverse mechanisms of innate immunity. Type I IFNs interact with the Type I IFN receptor, composed of IFNAR1 and IFNAR2 chains, to mount the host defense responses. However, at the same time, Type I IFNs elicit potent anti-proliferative and pro-apoptotic effects that could be detrimental for IFN-producing cells. Here, we report that the activation of p38 kinase in response to pathogen-recognition receptors stimulation results in a series of phosphorylation events within the IFNAR1 chain of the Type I IFN receptor. This phosphorylation promotes IFNAR1 ubiquitination and accelerates the proteolytic turnover of this receptor leading to an attenuation of Type I IFN signaling and the protection of activated dendritic cells from the cytotoxic effects of autocrine or paracrine Type I IFN. In this paper we discuss a potential role of this mechanism in regulating the processes of innate immunity.
A Fixed-Complexity MIMO Detector Based on the Complex Sphere Decoder

A new detection algorithm for uncoded multiple input-multiple output (MIMO) systems based on the complex version of the sphere decoder (SD) is presented in this paper. The algorithm performs a fixed number of operations to detect the symbols, independent of the noise level. The algorithm achieves this by combining a novel channel matrix preprocessing with a search through a small subset of the complete receive constellation. Simulation results show it has only a very small bit error ratio (BER) degradation compared to the original SD while being suited for a fully-pipelined hardware implementation due to its fixed complexity