Miracle-dz/UniFAR-FTUs huggingface dataset:
Demographic variability among subpopulations of two coastal fishes around the South Island of New Zealand

ii Acknowledgments . iv Table of contents . vi List of Tables . viii List of Figures . ix Chapter 1: General introduction . 1 1.1 Fish as a resource . 1 1.1.1 Increased demand . 1 1.2 Fish population structure . 2 1.3 Marine Management . 7 1.4 Study species . 8 1.4. 1 Blue cod. 8 1.4. 2 Sea perch . 11 1.5 Thesis aims . 14 1.5. 1 Population structure of blue cod. 14 1.5. 2 Subpopulations of sea perch within the South Island of New Zealand . 15 1.5. 3 Maternal investment in sea perch . 15 Chapter 2: Size frequency distribution: Biological consequences for a size truncated blue cod (Parapercis colias) population . 16 2.
Acceptance and use of a social robot by elderly users in a domestic environment

The study presented in this article aims to improve our understanding of how people use zoomorphic robots in a health related setting in their domestic environments in general and, in particular, whether people are able to build (long- term) relationships with these robots. The influences of social and hedonic factors were examined, in addition to the normally studied utilitarian factors of the Technology Acceptance Model (TAM). Three elderly participants interacted with the Nabaztag, a zoomorphic robot, for 10 days to improve their overall health condition. Hedonic factors were not found to be important for the acceptance of the Nabaztag. However, these factors seemed to be important for building a relationship with the Nabaztag. Social factors were found to be important for the acceptance of robots, but not for building a relationship with the Nabaztag. The results yielded some interesting findings that need more study: (1) the relationship between the place of the Nabaztag and acceptance and use, (2) the relationship between naming the Nabaztag and building a relationship with it and (3) the relationship between using verbal/non-verbal communication and building a relationship with it.
Aspects of large-scale in-core linear programming

Unconventional methods for matricial compression indicate that large linear programming constraint matrices may comfortably remain core-resident during optimization. Minor changes in the computational aspects of the simplex algorithm coupled with efficient inverse matrix representation show that the major portion of the inverse in product form of a basis may be embedded in the constraint matrix. A method for generating a sparse inverse matrix is presented.
Social influence computation and maximization in signed networks with competing cascades

Often in marketing, political campaigns and social media, two competing products or opinions propagate over a social network. Studying social influence in such competing cascades scenarios enables building effective strategies for maximizing the propagation of one process by targeting the most "influential" nodes in the network. The majority of prior work however, focuses on unsigned networks where individuals adopt the opinion of their neighbors with certain probability. In real life, relationships between individuals can be positive (e.g., friend-of relationship) or negative (e.g. connection between "foes"). According to social theory, people tend to have similar opinions to their friends but opposite of their foes. In this work, we study the problem of competing cascades on signed networks, which has been relatively unexplored. Particularly, we study the progressive propagation of two competing cascades in a signed network under the Independent Cascade Model, and provide an approximate analytical solution to compute the probability of infection of a node at any given time. We leverage our analytical solution to the problem of competing cascades in signed networks to develop a heuristic for the influence maximization problem. Unlike prior work, we allow the seed-set to be initialized with populations of both cascades with the end goal of maximizing the spread of one cascade. We validate our approach on several large-scale real-world and synthetic networks. Our experiments demonstrate that our influence maximization heuristic significantly outperforms state-of-the-art methods, particularly when the network is dominated by distrust relationships.
Palliative systemic therapy for women with recurrent epithelial ovarian cancer: current options

Objectives To review the available systemic treatments for women with recurrent ovarian cancer. Methods A literature review was conducted for recurrent ovarian cancer articles in English, including randomized trials, Phase II trials, or reviews. Results We discuss the efficacy and toxicity outcomes associated with systemic therapy for platinum-sensitive and platinum-resistant ovarian cancer. Clearly, platinum-based combination systemic therapy shows a prolonged progression-free interval compared with single-agent chemotherapy with a low toxicity profile. No clear superior management strategy exists for platinum-resistant/refractory disease. Novel targeted antiangiogenic agents (eg, bevacizumab), angiopoeitin inhibitors (eg, AMG 386), and poly ADP ribose polymerase inhibitors (eg, olaparib) are reviewed. Conclusion Although combination platinum-based chemotherapy has shown benefits for women with platinum-sensitive recurrent ovarian cancer, the optimal treatment strategy for those with platinum-resistant or platinum-refractory disease is not clear. Molecular and genetic targeted therapies may provide opportunities for those women with tumor profiles that show sensitivity for specific agents.
Speech Recognition in Multisource Reverberant Environments with Binaural Inputs

We present a binaural solution to robust speech recognition in multi-source reverberant environments. We employ the notion of an ideal time-frequency binary mask, which selects the target if it is stronger than the interference in a local time-frequency (T-F) unit. Our system estimates this ideal binary mask at the output of a target cancellation module implemented using adaptive filtering. This mask is used in conjunction with a missing-data algorithm to decode the target utterance. A systematic evaluation in terms of automatic speech recognition (ASR) performance shows substantial improvements over the baseline performance and better results over related two-microphone approaches
Representations in constraint programming

Constraint programming is a powerful and general purpose tool which is used to solve a large range of combinatorial and real-world problems. CP solvers combine a number of powerful and generic algorithms, which when used together can often solve problems infeasable in other frameworks. The major practical issue which stalls adoption of constraint programming by a wider audience is that transforming a problem from a high-level description into a format suitable for a constraint solver, called modelling, is more of an art than a science. Hence, modelling can only be accomplished well by expert practitioners. Often small changes, which to the untrained user may appear useless, can lead to huge reductions or increases in the time taken to solve a problem. The first, and arguably most important, decision when modelling a problem is to choose the type of variables which will be used. Most constraint systems provide only a small number of variable types; usually integers, matrices and possibly sets. Users must transform the high-level abstract types from their problem into these basic types, and then map the constraints down onto these types. All this must be accomplished using only the set of constraints provided by their solver. This thesis provides the first complete, generic method for comparing all the representations of a particular variable. In doing so it provides a number of key insights which improve the state of the art in automating the modelling process. Choosing the “best” representation of a high-level variable, such as a timetable or partition, in CP is extremely difficult; with many possible conflicting definitions of best. In general keeping the number of variables and constraints used small will allow the solver to work faster. Designing the representation so that propagators for global constraints can be used can also improve performance. Often different representations have different strengths,
Spatial Predictive Modeling of Fish Distributions Incorporating Habitat Utilization and Behavior in the Context of Marine Protection

The U.S. is adopting a Marine Spatial Planning (MSP) approach to address conflicting objectives of conservation and resource development and usage in marine spaces. At this time MSP remains primarily as a concept rather than a well-defined framework, however expanding anthropogenic impacts on coastal and marine areas reinforce the need to adopt an MSP approach to manage societal demands while preserving the marine environment. The development of theory and methods to implement MSP are on the rise across the nation to address coastal and marine environmental challenges. Critical components of marine spatial planning are (1) spatial data collection, (2) data management, (3) data analysis, and (4) decision support systems. Advances in geotechnology have increased access to spatial data enabling the development of decision support tools to organize, analyze, and inform the MSP process by projecting future scenarios. A review of the current literature reveals the available technological and methodological tools that are best suited for marine spatial planning, as well as suggests areas for further research in order to better inform this process in the U.S.
Pengaruh Latihan Plyometric Depth Jump Terhadap Daya Ledak Otot Tungkai Dan Kadar Enzim Keratinfosokinase (CPK) Pemain Bola Basket

Penelitian ini bertujuan untuk mengetahui pengaruh latihan plyometric depth jump terhadap daya ledak otot tungkai dan kadar enzim keratinfosokinase (CPK) pada atlet basket. Latihan plyometric depth jump adalah tipe latihan yang predominan metabolisme anaerobik yang dilakukan dalam intensitas tinggi sehingga dapat menimbulkan cedera pada sel otot. Metode penelitian ini adalah eksperimental dengan subjek penelitian 22 atlet bola basket putra dengan rata-rata usia 19 tahun selama 6 minggu. Sebelum pemberian latihan dilakukan pengukuran daya ledak otot tungkai dan kadar (CPK) diukur 24 jam setelah melakukan latihan pertama. Hasil penelitian menunjukkan peningkatan daya ledak otot tungkai (54,05±5,00 vs 58,32±4,17 p=0,00) sebesar 9,56% dan penurunan kadar CPK (626,32±506,98 vs 260,68 ±155,02 p=0,00) sebesar 27,88% setelah melakukan latihan plyometic depth jump selama 6 minggu. Simpulan penelitian menunjukkan bahwa latihan plyometric depth jump meningkatkan daya ledak otot tungkai dan menurunkan kadar CPK pada pemain bola basket setelah melakukan latihan selama 6 minggu. Kata Kunci: Plyometrik Depth Jump, Daya Ledak Otot Tungkai, Enzim Keratinfosokinase (CPK) ABSTRACT This study aims to find out effect of plyometric depth jump exercises on the explosive power of the limb muscles and levels of the enzyme keratinfosokinase in basketball athletes Plyometric depth jump exercises are types of exercises that predominant anaerobic metabolism performed in high intensity so as to cause injury to muscle cells.This research method was experimental with the study subjects of 22 male basketball athletes with an average age of 19 years a 6-week plyometric depth jump exercise. Prior to the administration of exercise, measurements of limb muscle explosives and keratinfosokinase (CPK) enzyme levels were measured 24 hours after the first exercise. The results showed an increase in the explosive power of the limb muscles (54.05±5.00 vs. 58.32±4.17 p=0.00) by 9.56% and a decrease in CPK levels (6 26.32±506.98 vs 260.68 ±155.02 p=0.00) by 27.88% after doing plyometic depth jump exercises for 6 weeks.The study concluded that plyometric depth jump exercises increase the explosiveness of the limb muscles and lower CPK levels in basketball players after 6 weeks of exercise. Keywords: Plyometric Depth Jump, Limb Muscle Explosive Power, Enzyme Keratinfosokinase (CPK)
Solving Sudoku puzzles with node based Coincidence algorithm

In Evolutionary computation, Sudoku puzzles are categorized as hard combinatorial problems. It is almost impossible to solve these puzzles using only native operations of genetic algorithms. This article presents an application of Coincidence algorithm, which is an Estimation of distribution algorithms in the class of evolutionary computation that can outperform traditional algorithms on several combinatorial problems. It makes use of both positive and negative knowledge for solving problems. The proposed method is compared with the current best known method. It significantly outperforms problem-specific GA to solve easy, medium, and hard level of Sudoku puzzles.
Role of pleiotropy during adaptation of TEM-1 β-lactamase to two novel antibiotics

Pleiotropy is a key feature of the genotype–phenotype map, and its form and extent have many evolutionary implications, including for the dynamics of adaptation and the evolution of specialization. Similarly, pleiotropic effects of antibiotic resistance mutations may affect the evolution of antibiotic resistance in the simultaneous or fluctuating presence of different antibiotics. Here, we study the role of pleiotropy during the in vitro adaptation of the enzyme TEM‐1 β‐lactamase to two novel antibiotics, cefotaxime (CTX) and ceftazidime (CAZ). We subject replicate lines for four rounds of evolution to selection with CTX and CAZ alone, and in their combined and fluctuating presence. Evolved alleles show positive correlated responses when selecting with single antibiotics. Nevertheless, pleiotropic constraints are apparent from the effects of single mutations and from selected alleles showing smaller correlated than direct responses and smaller responses after simultaneous and fluctuating selection with both than with single antibiotics. We speculate that these constraints result from structural changes in the oxyanion pocket surrounding the active site, where accommodation of CTX and the larger CAZ is balanced against their positioning with respect to the active site. Our findings suggest limited benefits from the combined or fluctuating application of these related cephalosporins for containing antibiotic resistance.
Biodiversity assessment in Western Mediterranean marine protected areas (MPAs): Porifera of Posidonia oceanica meadows (Asinara Island MPA) and marine caves (Capo Caccia–Isola Piana MPA) of Sardinia

Abstract This paper focuses on a faunistic inventory and the biogeographic pattern of Sardinian Porifera in a scarcely investigated Italian sea belonging to the Eastern Algero-Provençal Basin. The composition and structure of sponge fauna were studied in two priority habitats in two Marine Protected Areas (MPAs) of the North-Western Sardinian Sea. Shallow-water surveys investigated Posidonia oceanica meadows at five sites in the Asinara Island MPA and three karstic submerged caves in the Capo Caccia–Isola Piana MPA. The annotated checklist shows notably high values of taxonomic richness, confirming that P. oceanica meadows and karstic caves are key habitats for Porifera assemblages. Indeed, the sponge taxa recorded (n = 77) with 59 cave-dwelling and 45 taxa in seagrass meadows represent over 10% of the total Mediterranean sponge fauna, despite the small areas investigated, restricted biotopes and low sampling effort. This suggests that species richness is underestimated in circum-Sardinia seas. Internationally protected species (n = 6), rare species (n = 5) and Mediterranean endemic species (n = 14) were recorded. Previously investigated Posidonia meadows and caves share comparable values of species richness and endemicity with the Sardinian biotopes. The geographic range of species indicates that the biogeographic affinity of the NW Sardinian Sea sponge fauna is dominated by Atlanto-Mediterranean species. The three caves harbour a notably homogeneous sponge community, as indicated by the ca. 50% of shared species and high similarity index (> 83%). By contrast, the composition varies markedly among the five Posidonia meadows, which share only five species and display similarity values ranging ca. 20–60%. Unexpectedly when comparing these contrasting biotopes, faunal similarity is relatively low (< 40% shared species); indeed, Posidonia meadows harbour a notable number of cave-dwelling species in intermatte shaded microhabitats.
Artificial intelligence for diagnostic purposes: principles, procedures and limitations

Abstract Background: Back propagation (BP) artificial neural networks are a distribution-free method for data analysis based on layers of artificial neurons that transduce imputed information. It has been recognized as having a number of advantages compared to traditional methods including the possibility to process imperfect data, and complex non-linear data. The objective of this study was to review the principles, procedures, and limitations of BP artificial neural networks for a non-mathematical readership. Methods: A real data sample of weight, height and measured body surface area from 90 individuals was used as an example. SPSS 17.0 with neural network add-on was used for the analysis. The predicted body surface from a two hidden layer BP neural network was compared to the body surface calculated by the Haycock equation. Results: Both the predicted values from the neural network and from the Haycock equation were close to the measured values. A linear regression analysis with neural network as predictor produced an r2-value of 0.983, while the Haycock equation produced an r2-value of 0.995 (r2>0.95 is a criterion for accurate diagnostic testing). Conclusions: BP neural networks may, sometimes, predict clinical diagnoses with accuracies similar to those of other methods. However, traditional statistical procedures, such as regression analyses need to be added for testing their accuracies against alternative methods. Nonetheless, BP neural networks have great potential through their ability to learn by example instead of learning by theory. Clin Chem Lab Med 2010;48:159–65.
Anticancer Activity of Green Tea Polyphenols in Prostate Gland

Numerous evidences from prevention studies in humans, support the existence of an association between green tea polyphenols consumption and a reduced cancer risk. Prostate cancer is one of the most frequently diagnosed male neoplasia in the Western countries, which is in agreement with this gland being particularly vulnerable to oxidative stress processes, often associated with tumorigenesis. Tea polyphenols have been extensively studied in cell culture and animal models where they inhibited tumor onset and progression. Prostate cancer appears a suitable target for primary prevention care, since it grows slowly, before symptoms arise, thus offering a relatively long time period for therapeutic interventions. It is, in fact, usually diagnosed in men 50-year-old or older, when even a modest delay in progression of the disease could significantly improve the patients quality of life. Although epidemiological studies have not yet yielded conclusive results on the chemopreventive and anticancer effect of tea polyphenols, there is an increasing trend to employ these substances as conservative management for patients diagnosed with less advanced prostate cancer. Here, we intend to review the most recent observations relating tea polyphenols to human prostate cancer risk, in an attempt to outline better their potential employment for preventing prostate cancer.
PHYSICOCHEMICAL EFFECT OF OZONE IN ATMOSPHERE OF GREATER CAIRO AREA

The surface Ozone has been determined to be human risk factor in urban environments, as well as a contributor to the formation of photochemical oxidants. Where the surface ozone is not a primary pollutant (i.e. not emitted directly by human activity), but it is produced through the action of sunlight on primary pollutants such as nitrogen oxides and hydrocarbons. Concentrations of ozone were measured at five monitoring sites located in the greater Cairo area, Egypt, as a part of the air pollution study for the effect of the volatile organic compounds and nitrogen oxides on surface ozone. For each site, during the period from March 2003 to March 2004, we collected forty eight Ozone samples per day (every half hours), daily samples of nitrogen oxides (Nitrogen oxide & Nitrogen dioxide) and 22 abundant volatile organic compounds (VOCs) three times per day (7-9 in the morning, 2-4 afternoon and 8-10 evening). The average ozone concentrations were found to be higher in the afternoon than in morning and nights. The morning rush hours are marked by an increase in the primary emission of NO and hydrocarbons. In the evening and night, ozone is destroyed by NO generated locally by unburned hydrocarbons from industry, traffic and thermal power plants.
About cases of pathology of the dental system In Archidiskodon meridionalis meridionalis (Nesti, 1825) from Georgievsk sand pit (Stavropol Territory, Russia)

Two cases of the dental system’s dysfunction in Archidiskodon meridionalis meridionalis found in the Georgievsk sand pit (Stavropol Territory, Russia; Early Pleistocene, Psekups Faunal complex) are described. One of the tusks is missing in one of the elephant’s skull, and its alveolus is overgrown. Probably, the tusk was broken off already in the alveolus at the adult animal, after which its growth process did not renew. On the skull from a skeleton of the second animal on the left upper jaw there is a disturbance of the M2 teeth change on M3 teeth. Tooth M2 is somewhat displaced lingually because of its falling delay. The anterior part of tooth M3 is beveled buccally relatively to the axis of the crown approximately at 104°. At the same time, the medial half of the first six enamel plates were significantly turned out laterally. On the other teeth noticeable changes were not noted. An obvious cause of this anomaly was a disruption of M3 growth rate and slowing of the rate of crown wearing and resorption of two posterior roots of M2. The described pathology was probably not a consequence of genetic predispositions, infection, or trauma. There are signs of osteodystrophy at separate bones of the skeleton.
Evaluation of antimicrobial susceptibility patterns in Campylobacter spp isolated from dairy cattle and farms managed organically and conventionally in the midwestern and northeastern United States.

OBJECTIVE To describe antimicrobial susceptibility patterns in Campylobacter spp isolated from dairy cattle and farms managed organically and conventionally in the midwestern and northeastern United States. DESIGN Longitudinal study. SAMPLE POPULATION 128 farms. PROCEDURE Samples and data were collected every 2 months from August 2000 to October 2001. Fecal samples were collected from calves and cows. Milk samples were obtained from the bulk tank and milk line filters. Environmental samples were obtained from a water source, feed bunks of lactating cows, and cattle housing areas. Campylobacter identification and antimicrobial susceptibility testing were performed at a central laboratory by use of microbroth dilution with 2 customized antimicrobial susceptibility panels. RESULTS 460 and 1,570 Campylobacter isolates were obtained from organic and conventional dairy farms, respectively. Most isolates from both farm types were susceptible to most antimicrobial agents tested, and antimicrobial susceptibility of conventional dairy isolates was decreased, compared with organic dairy isolates. Low proportions of isolates resistant to ampicillin (< 10%) and moderate proportions resistant (30% to 60%) to kanamycin, sulfamethoxazole, and tetracycline were observed on both farm types. The proportion of isolates resistant to tetracycline was higher for conventional than organic farms. CONCLUSIONS AND CLINICAL RELEVANCE Campylobacter isolates from dairy cattle and farms managed organically and conventionally had similar patterns of antimicrobial resistance; the proportion of resistant isolates was higher for conventional than organic farms.