Miracle-dz/UniFAR-FTUs huggingface dataset:
Metastatic Potential and Normal Tissues Expression of Five Cathepsins in Murine Melanomas of Varying Updated

The relative levels of mRNAs for cathepsins B, D, H, L, and S in eight normal murine tissues and three murine melanoma variants, B16Fl, B16-F10, and B16a, have been analyzed by RNA dot blot and densitometry. A direct correlation was observed between the levels of cathepsin B mKNA and the metastatic potentials of these three melanoma variants. The relative amount of cathepsin B mRNA in BIda, which is the melanoma variant with the highest metastatic potential, was at least 3 times greater than that found in any of the normal murine tissues surveyed. Similar results were obtained in analyses of either solid tumors or of cultures of tumor cells, confirming that the tumor cells themselves were the source for the elevated expression of cathepsin B mRNA. Northern blot analysis revealed the presence of three cathepsin B tran scripts of 5.0,4.0, and 2.2 kilobases in the melanoma variants, while only the 2.2-kilobase transcript was seen in the normal murine tissues. Con currently with the mRNA analysis, enzyme assays for cathepsin B activity were also performed using synthetic peptide substrates. The assays revealed increased cathepsin B activities in the melanoma variants, corresponding well with the increased cathepsin B mRNA levels, and in addition demonstrated that all three of the melanoma variants secreted a latent form of cathepsin B into conditioned medium, which could be activated by limited proteolysis with pepsin. The levels of the latent enzyme released by the murine melanoma variants correlated well with the levels of cathepsin B mRNA and with the metastatic potentials as determined by spontaneous metastasis from a s.c. site.
Library of Adaptive Regular Arrays for Convolution-like Computations

The recent years have shown increasing interest in configurable or adaptive computing [13]. This approach aims at implementing algorithms in a computational space consisting of a hudge number of elementary computing cells, which can be configured, or adapted, for solving a given problem. In this case, we say, an algorithm is implemented in space and time in contrast to the conventional computers where an algorithm is implemented in time, i.e. is carried out step by step on a single processor. The adaptive computing approach integrates the flexibility of programming conventional computers, with the efficiency of dedicated hardware devices on ASICs. This new computing platform demands new design tools and methods [5, 7, 12]. One way to improve the efficiency of designers is to develop libraries of programs for adaptive computing devises [6]. The new, adaptive computing platform presents new requirements for such libraries. For example, a conventional program module for matrix multiplication is parameterized with respect to the problem size parameters, i.e. with respect to the size of matrices. In the case of adaptive computing new parameters should be added. In different applications, the matrix multiplication program is needed to be implemented with different performances [18]. Thus, the parameters that describe the performance and the available area should be added. The performance depends on a number of elementary computing cells allocated to the program, i.e. depends on the size of a space on an adaptive computing platform that is assigned to the particular program at a particular instance. Utilization of different number of computing cells requires a different degree of parallelism in implementation of a given program. In general, the program modules for adaptive computing platform must be adaptable to fit the space-time restrictions of a particular application and an instance of use. For example, the Fig. 1 represents an adaptive computational space where a program module, A, is implemented using different space-time configurations. Configurations A1 and A2 have the same number of inputs/outputs and the same performance, but different space shapes and different input/output positioning. Configurations A3 and A4 have an increased, but different performance achieved by an increased number of inputs/outputs, which have different positioning. The current approaches in developing libraries for reconfigurable computing platform introduce modules that are parameterized with respect to the problem size and the word size parameters [6, 7]. A1 ❄ ✲ A2 ✲ ✲
Influence of Social Networks on the Effect of Retirement on Cognition

English Abstract: This paper examines the role of social networks as a potential mechanism in the relationship between retirement and cognitive decline. In a first step, I analyze the effect of retirement on different social network characteristics using novel panel data of 19,999 respondents on social networks from wave 4 and wave 6 of the Survey of Health, Ageing and Retirement in Europe (SHARE). In a second step, I estimate the effect of retirement on cognition under consideration of changing social network sizes. Applying instrumental variable fixed effects regressions based on country-specific statutory eligibility ages allows controlling for unobserved individual heterogeneity and endogeneity of the retirement decision. The results suggest that retirement leads to an increase in the number of close family members named as confidants indicating that the social network becomes more kin-oriented after retirement. However, adding close family members to the social network does not have a significant effect on cognition. In turn, adding non-family members like friends or colleagues to the social network has a positive impact on the cognitive performance. Since I do not find that retirement impacts the number of friends or colleagues significantly, I cannot claim social networks to be the explaining underlying mechanism in the relationship between retirement and cognitive decline. German Abstract: Ich untersuche die Rolle von sozialen Netzwerken als moglichen Mechanismus in dem Zusammenhang zwischen Ruhestand und dem Ruckgang kognitiver Fahigkeiten. Im ersten Schritt analysiere ich den Effekt von Ruhestand auf verschiedene Eigenschaften des sozialen Netzwerkes mithilfe von Langsschnittdaten 19.999 Befragten des Survey of Health, Ageing and Retirement in Europe (SHARE). Im zweiten Schritt untersuche ich den Effekt von Ruhestand auf Kognition unter Berucksichtigung der Grosenanderung des sozialen Netzwerkes. Durch die Anwendung eines Paneldatenmodells mit fixen Individualeffekten und durch den Einsatz von Instrumentvariablen kann fur Endogenitatsprobleme kontrolliert werden. Die Ergebnisse zeigen, dass Ruhestand zu einem Anstieg in der Anzahl der nahen Familienmitglieder im sozialen Netzwerk fuhrt. Allerdings hat dieser Anstieg keinen signifikanten Einfluss auf die kognitiven Fahigkeiten. Im Gegensatz dazu finde ich einen signifikant positiven Effekt auf Kognition, wenn Nicht-Familienmitglieder wie Freunde oder Kollegen neu in das soziale Netzwerk aufgenommen werden. Allerdings ist dieser Anstieg von Nicht-Familienmitgliedern nicht mit dem Eintritt in den Ruhestand verbunden, sodass ich die sozialen Netzwerke nicht als den erklarenden Mechanismus zwischen Ruhestand und Kognition herausstellen kann.
MEAN-FIELD THEORY OF TEMPERATURE CYCLING EXPERIMENTS IN SPIN GLASSES

We study analytically the effect of temperature cyclings in mean-field spin glasses. In accordance with real experiments, we obtain a strong reinitialization of the dynamics on decreasing the temperature combined with memory effects when the original high temperature is restored. The same calculation applied to mean-field models of structural glasses shows no such reinitialization, again in accordance with experiments. In this context, we derive some relations between experimentally accessible quantities and propose experimental protocols. Finally, we briefly discuss the effect of field cyclings during isothermal aging.
Spaced Formula Learning

Abstract Spaced learning was applied to solve a very challenging aspect of the traditional approach in learningscientific formulas. Learning formulas, starting from their memorization to their application and the attitudes towardthem, was asserted to be achieved using the spaced learning approach. A sequential, explanatory, mixed methodsresearch design was implemented to see the effects of massed and spaced learning on learning formulas in three intactgroups of early childhood teacher candidates. Lessons were designed to resemble the teaching carried out in mosttraditional physics classrooms and convince physics teachers that there is an approach to solve the problems theyencounter in formula learning. Although teacher candidates think positively about repetition, and while massedlearning was found to be boring, massed learning is also more efficient in achieving positive attitudes towards formulasand results in greater retention than spaced learning. Discussions concerning the results were subsequently held.
An ELFN-based adaptive probing strategy to improve TCP performance in ad hoc networks

TCP performance degrades considerably in ad hoc network due to its distinguishing characteristics, such as high channel error rate and link failure. Various schemes have been proposed to improve TCP performance in ad hoc network. ELFN (Explicit Link Failure Notification) is one of mechanisms to handle link failures with ceasing TCPpsilas exponential back-off behavior. Though ELFN improves TCP performance considerably, it uses fixed probing technique to check link status. Hence performance of TCP with ELFN depends on probing interval. In this paper, we propose an ELFN-based adaptive probing strategy that eliminates problem due to fixed probing interval. This strategy uses rapid probing initially so as to continue use of channel when link failure is due to channel contention. Our simulation results, using ns-2 simulator, indicate that the adaptive probing can handle link failures in better way, and improves TCP performance considerably.
Automated identification of diabetic retinal exudates in digital colour images

Aim: To identify retinal exudates automatically from colour retinal images. Methods: The colour retinal images were segmented using fuzzy C-means clustering following some key preprocessing steps. To classify the segmented regions into exudates and non-exudates, an artificial neural network classifier was investigated. Results: The proposed system can achieve a diagnostic accuracy with 95.0% sensitivity and 88.9% specificity for the identification of images containing any evidence of retinopathy, where the trade off between sensitivity and specificity was appropriately balanced for this particular problem. Furthermore, it demonstrates 93.0% sensitivity and 94.1% specificity in terms of exudate based classification. Conclusions: This study indicates that automated evaluation of digital retinal images could be used to screen for exudative diabetic retinopathy.
Unique genetic and environmental determinants of prolonged fatigue: a twin study

Background. Prolonged fatigue syndromes have been proposed as prevalent and disabling forms of distress that occur independently of conventional notions of anxiety and depression. Methods. To investigate the genetic and environmental antecedents of common forms of psychological and somatic distress, we measured fatigue, anxiety, depression and psychological distress in 1004 normal adult twin pairs (533 monozygotic (MZ), 471 dizygotic (DZ)) over 50 years of age. Results. Familial aggregation of psychological distress, anxiety and fatigue appeared to be due largely to additive genetic factors (MZ[ratio ]DZ ratios of 2·12–2·69). The phenotypic correlations between the psychological measures (distress, anxiety and depression) were moderate (0·67–0·79) and higher than that between fatigue and psychological distress (0·38). Multivariate genetic modelling revealed a common genetic factor contributing to the development of all the observed phenotypes (though most strongly for the psychological forms), a second independent genetic factor also influenced anxiety and depression and a third independent genetic factor made a major contribution to fatigue alone. In total, 44% (95% CI 25–60%) of the genetic variance for fatigue was not shared by the other forms of distress. Similarly, the environmental factor determining psychological distress made negligible contributions to fatigue, which was underpinned largely by its own independent environmental factor. Conclusion. This study supports the aetiological independence of prolonged fatigue and, therefore, argues strongly for its inclusion in classification systems in psychiatry.
Identification of the Elusive Pyruvate Reductase of Chlamydomonas reinhardtii Chloroplasts

Under anoxic conditions the green alga Chlamydomonas reinhardtii activates various fermentation pathways leading to the creation of formate, acetate, ethanol and small amounts of other metabolites including d-lactate and hydrogen. Progress has been made in identifying the enzymes involved in these pathways and their subcellular locations; however, the identity of the enzyme involved in reducing pyruvate to d-lactate has remained unclear. Based on sequence comparisons, enzyme activity measurements, X-ray crystallography, biochemical fractionation and analysis of knock-down mutants, we conclude that pyruvate reduction in the chloroplast is catalyzed by a tetrameric NAD+-dependent d-lactate dehydrogenase encoded by Cre07.g324550. Its expression during aerobic growth supports a possible function as a ‘lactate valve’ for the export of lactate to the mitochondrion for oxidation by cytochrome-dependent d-lactate dehydrogenases and by glycolate dehydrogenase. We also present a revised spatial model of fermentation based on our immunochemical detection of the likely pyruvate decarboxylase, PDC3, in the cytoplasm.
Towards Automatic Measurement of Probabilistic Processes

In this paper we propose a metric for finite processes in a probabilistic extension of CSP. The kernel of the metric corresponds to trace equivalence and most of the operators in the process algebra is shown to satisfy non-expansiveness property with respect to this metric. We also provide an algorithm to calculate the distance between two processes to a prescribed discount factor in polynomial time. The algorithm has been implemented in a tool that helps us to measure processes automatically.
Tensor global and local discriminant embedding for SAR target configuration recognition

Tensor linear discriminant analysis (LDA) is an effective feature extraction method for images, but it just considers the globally discriminative information of the data and neglects to preserve the local structure. In this paper, we propose a feature extraction approach based on tensor globally and locally discriminative information preserving projections for SAR target configuration recognition. We first represent SAR images as second-order tensors, and then use the known aspect angles to construct two local adjacent graphs to represent the local structure because SAR images are very sensitive to aspect angles. Finally an optimization problem is obtained which can be solved with the eigenvalue decomposition method by combining the local structure preservation with tensor LDA. Experiments are carried out on Moving and Stationary Target Acquisition and Recognition (MSTAR) public database to evaluate the performance of the proposed method. Experimental results demonstrate the effectiveness of the proposed method.
Fast emotion detection from EEG using asymmetric spatial filtering

The injection of emotional intelligence in human-computer interfaces is necessary for computer applications to appear intelligent when interacting with people. With the recent development of brain imaging techniques and brain-computer interfaces, computers can actually take a look inside users' head to observe their emotional states. This paper presents an EEG-based emotion detection system which detects emotional states based on short EEG segments of 1s. A novel feature extraction algorithm termed asymmetric spatial filtering is proposed to extract features from high dimensional EEG data. The effectiveness of the proposed method is tested for two types of emotion detection problems on data from five subjects.
Defective Complements in Tree Adjoining Grammar 1

Syntactic theory has long made use of the idea that verbs can t ake complements of different sizes, such as as subject-raising or ECM verbs taking IP complements instead of full CP complements. By increasing options for the size of the complement, this approach has been used fo r “reduced constructions” in Romance or German, in which, e.g., a clitic can sometimes appear in a hig her clause than that to which it is semantically associated. Going back to at least Strozer (1977), various l nguists have suggested that clitic climbing takes place when the complement is “defective”, that is, smaller t han it might be otherwise In this work we examine the role of defective complements in s y tactic theory from the perspective of Tree Adjoining Grammar (TAG) (Kroch and Joshi 1985, Frank 20 02). A TAG derivation uses the adjoining operation to compose independently derived clausal struct ures, calledelementary trees . While movement can apply within an elementary tree, there is no movement acr oss elementary trees, and apparent interclausal movement instead results from the use of adjoining. This allows the factoring out of recursion from the localization of dependencies, and forces the substanti ve theory of syntax to be restricted to the domain of the elementary trees. The defective complement analysis cannot be applied in TAG a s it is in other frameworks, due to the way that dependencies are localized. At the same time, the ad joining operation is also insufficient to derive reduced constructions. Understanding what a defective com ple ent analysis means in the context of TAG leads to a modification of the adjoining operation. This in tu rn leads to a unified view of the locality
Aging and the Social Cognitive Determinants of Physical Activity Behavior and Behavior Change: Evidence from the Guide to Health Trial

Part one of this study investigated the effect of aging on social-cognitive characteristics related to physical activity (PA) among adults in the baseline phase of a health promotion intervention. Participants' questionnaire responses and activity logs indicated PA levels and self-efficacy declined with age, while social support and the use of self-regulatory behaviors (e.g., goal setting, planning, and keeping track) increased. With age participants were also less likely to expect PA to interfere with their daily routines and social obligations. Part two of the study was among overweight/obese, inactive participants completing the intervention; it examined whether improvements in psychosocial variables might counteract declining PA associated with age. After treatment, participants were more active and decreased body weight regardless of age, and improved self-efficacy, outcome expectations, and self-regulatory behaviors. In a causal model, increases in self-efficacy at 7-months lead to increased PA levels and, albeit marginally, weight loss at 16 months; increased PA was associated with greater weight loss. Aging adults who were more confident exercised more and as a result lost more weight. This longitudinal study suggests interventions that offset the effect of aging on self-efficacy may be more successful in helping older participants become more active and avoid weight gain.
A study on optimal hierarchy in multi-level hierarchical mobile IPv6 networks

Hierarchical mobile IPv6 (HMIPv6) is an enhanced mobile IPv6 in order to reduce signaling overhead and to support seamless handoff in IP-based wireless/mobile networks. To support more scalable services, HMIPv6 can be organized as a multilevel hierarchy architecture (i.e, tree structure). However, since the multi-level HMIPv6 results in additional packet processing overhead, it is necessary to consider the overall cost and to find the optimal level to minimize the overall cost. In this paper, we investigate this problem, namely the design of a multi-level HMIPv6 with optimal hierarchy. To do this, we formulate the location update cost and the packet delivery cost in the multi-level HMIPv6. Based on the formulated cost functions, we present the optimal hierarchy level in the multi-level HMIPv6 to minimize the total cost. In addition, we investigate the effects of session-to-mobility ratio (SMR) on the total cost and the optimal hierarchy. The numerical results, which show various relationships among network size, optimal hierarchy, and SMR, can be utilized to design an optimal HMIPv6 network.
Routage dans les réseaux ( k , r )-cellulaires

Résumé—Fraigniaud et al. [1] et indépendamment Thorup et al. [2] ont montr é que tout arbre à n nœuds poss̀ ede un sch́ema de routage de plus courts chemins utilisant des adresses, de s ntêtes et des tables de routage de O(log n) bits. Il est toujours ouvert de savoir si ce ŕesultat optimal peut être étendu ne seraitce qu’aux réseaux de largeur arborescente deux dont la meilleure borne connue, dueà Peleg [3] estO(log n) bits. Dans cet article, nouśetendons ce ŕ esultat optimal aux réseaux dits (k, r)-cellulaires, c’est-̀a-dire aux réseaux ne contenant pas K2,r comme mineur et dont les composantes 2-connexes peuvent être rendues planaire-ext́ erieures par la suppression dek sommets. Par exemple, les ŕ eseaux (2,4)-cellulaires contiennent les arbres, les graphes planaire-ext́ erieurs et plus ǵenéralement tous les graphes sans mineurK2,4.
Need for a transport aPI in 5G for global orchestration of cloud and networks through a virtualized infrastructure manager and planner [invited]

The new 5G paradigm seeks for a scalable architecture that is able to efficiently manage the increasing volume of traffic generated by smart devices to be processed in a distributed cloud infrastructure. To this end, coordinated management of the network and the cloud resources forming an end-to-end system is of great importance. Software defined networking and network function virtualization architectures are the key enablers for integrating network and cloud resources, enabling cross optimization on both sides. This optimization requires efficient resource allocation algorithms, which take into account computing and network resources. In this paper, we propose an end-to-end orchestration architecture for distributed cloud and network resources aligned with the European Telecommunications Standards Institute management and orchestration architecture. The proposed architecture includes the virtual infrastructure manager and planner (VIMaP) component to enable dynamic resource allocation for interconnected virtual instances in distributed cloud locations. A heuristic algorithm for dynamic virtual machine graphs resource allocation is included to validate the VIMaP architecture and exploit its functionalities. Moreover, the control orchestration protocol is included between the architecture components to offer end-to-end transport services. Finally, the proposed architecture is experimentally validated, and the heuristic algorithm performance is evaluated.