Miracle-dz/UniFAR-FTUs huggingface dataset:
A power series beta Weibull regression model for predicting breast carcinoma

The postmastectomy survival rates are often based on previous outcomes of large numbers of women who had a disease, but they do not accurately predict what will happen in any particular patient's case. Pathologic explanatory variables such as disease multifocality, tumor size, tumor grade, lymphovascular invasion, and enhanced lymph node staining are prognostically significant to predict these survival rates. We propose a new cure rate survival regression model for predicting breast carcinoma survival in women who underwent mastectomy. We assume that the unknown number of competing causes that can influence the survival time is given by a power series distribution and that the time of the tumor cells left active after the mastectomy for metastasizing follows the beta Weibull distribution. The new compounding regression model includes as special cases several well‐known cure rate models discussed in the literature. The model parameters are estimated by maximum likelihood. Further, for different parameter settings, sample sizes, and censoring percentages, some simulations are performed. We derive the appropriate matrices for assessing local influences on the parameter estimates under different perturbation schemes and present some ways to assess local influences. The potentiality of the new regression model to predict accurately breast carcinoma mortality is illustrated by means of real data. Copyright © 2015 John Wiley & Sons, Ltd.
Comparative performance analysis of feature sets for abandoned object classification

Accurate classification of abandoned objects is crucial in video surveillance systems. In this paper, we experiment with different validation techniques (hold-out and 10-fold cross validation), with the aim of determining which feature set proves more useful for accurate object classification in a video surveillance context (scale invariant image transform (SIFT) keypoints vs. geometric primitive features). Moreover, we show how the resulting features affect classification performance across different classifiers. We also further analyze the best performing classifier in order to have better understanding of its classification results. Objects are classified into four different categories: bag (s), person (s), trolley (s), and group (s) of people. Our experimental results show that the highest recognition accuracy and the lowest false alarm rate are achieved by building a classifier based on our proposed set of statistics of geometric primitives' features. This set of features maximizes inter-class separation and simplifies the classification process. Classification based on this set of features thus outperforms the second best approach based on SIFT keypoint histograms by providing on average 22% higher recognition accuracy and 7% lower false alarm rate.
“Learn Something from This!”

America' s Next Top Model (ANTM), the popular reality television show produced by and starring Tyra Banks, has garnered a sizeable audience over its thirteen seasons. Synergistically marketed to readers of Young People and Teen Magazine, ANTM enjoys an audience of five million viewers mostly from the 18–35 year-old female demographic. This essay explores representations of ethnic and gendered identities constructed through the visual and discursive rhetoric of ANTM. ANTM judges define “model” femininity as those contestants whose look is “a blank palette” or “androgynous,” descriptors that signal unmarked whiteness, while nonwhite women are most often marked as “exotic” or eliminated for being “too ethnic. ” This essay argues that despite Banks's expressed desire to help more women of color into the modeling business, ANTM participates in emerging, neoliberal understandings of racial and gendered identities, which, characterized by a hegemonic postfeminist and postrace worldview, obscure the operating of privilege in the young women's “choices” of how and when to perform their ethnicities. Rejecting the ANTM judges' claims to objectivity, this essay attempts to situate the gaze of the fashion industry and its aesthetic knowledge through its unspoken reliance on the notion of “optional” ethnic identity.
Morphologic, phenotypic, and transcriptomic characterization of classically and alternatively activated canine blood-derived macrophages in vitro

Macrophages are a heterogeneous cell population playing a pivotal role in tissue homeostasis and inflammation, and their phenotype strongly depends on the micromilieu. Despite its increasing importance as a translational animal model for human diseases, there is a considerable gap of knowledge with respect to macrophage polarization in dogs. The present study comprehensively investigated the morphologic, phenotypic, and transcriptomic characteristics of unstimulated (M0), M1- (GM-CSF, LPS, IFNγ-stimulated) and M2- (M-CSF, IL-4-stimulated)-polarized canine blood-derived macrophages in vitro. Scanning electron microscopy revealed distinct morphologies of polarized macrophages with formation of multinucleated cells in M2-macrophages, while immunofluorescence employing literature-based prototype-antibodies against CD16, CD32, iNOS, MHC class II (M1-markers), CD163, CD206, and arginase-1 (M2-markers) demonstrated that only CD206 was able to discriminate M2-macrophages from both other phenotypes, highlighting this molecule as a promising marker for canine M2-macrophages. Global microarray analysis revealed profound changes in the transcriptome of polarized canine macrophages. Functional analysis pointed out that M1-polarization was associated with biological processes such as “respiratory burst”, whereas M2-polarization was associated with processes such as “mitosis”. Literature-based marker gene selection revealed only minor overlaps in the gene sets of the dog compared to prototype markers of murine and human macrophages. Biomarker selection using supervised clustering suggested latexin (LXN) and membrane-spanning 4-domains, subfamily A, member 2 (MS4A2) to be the most powerful predicting biomarkers for canine M1- and M2-macrophages, respectively. Immunofluorescence for both markers demonstrated expression of both proteins by macrophages in vitro but failed to reveal differences between canine M1 and M2-macrophages. The present study provides a solid basis for future studies upon the role of macrophage polarization in spontaneous diseases of the dog, a species that has emerging importance for translational research.
Altered Levels of S-Adenosylmethionine and S-Adenosylhomocysteine in the Brains ofl-Isoaspartyl (d-Aspartyl)O-Methyltransferase-deficient Mice*

l-Isoaspartyl (d-aspartyl) O-methyltransferase (PCMT1) is a protein repair enzyme that initiates the conversion of abnormald-aspartyl and l-isoaspartyl residues to the normal l-aspartyl form. In the course of this reaction, PCMT1 converts the methyl donor S-adenosylmethionine (AdoMet) to S-adenosylhomocysteine (AdoHcy). Due to the high level of activity of this enzyme, particularly in the brain, it seemed of interest to investigate whether the lack of PCMT1 activity might alter the concentrations of these small molecules. AdoMet and AdoHcy were measured in mice lacking PCMT1 (Pcmt1−/−), as well as in their heterozygous (Pcmt1+/−) and wild type (Pcmt1+/+) littermates. Higher levels of AdoMet and lower levels of AdoHcy were found in the brains of Pcmt1−/−mice, and to a lesser extent in Pcmt1+/− mice, when compared withPcmt1+/+ mice. In addition, these levels appear to be most significantly altered in the hippocampus of the Pcmt1−/−mice. The changes in the AdoMet/AdoHcy ratio could not be attributed to increases in the activities of methionine adenosyltransferase II orS-adenosylhomocysteine hydrolase in the brain tissue of these mice. Because changes in the AdoMet/AdoHcy ratio could potentially alter the overall excitatory state of the brain, this effect may play a role in the progressive epilepsy seen in thePcmt1−/− mice.
Attaching physiological effects to motion-captured data

Today, using motion capture devices is the most common way to create realistic human motion data. In addition to that, various methods have been proposed to edit, morph and retarget such kind of motion. However, there are still few methods to add physiological effects to motion which are caused by fatigue, injuries, muscle training and muscle shrinking. This is because the innate structure of the human body, such as the musculoskeletal system, has been mostly neglected when handling human motion in computer graphics. In this paper, we propose a method to use the musculoskeletal system of the human body for editing and retargeting human motion which were captured using a motion-capture device. Using our method, not only physiological effects such as fatigue, or injuries but also physical effects caused by external force can be added to human motion. By changing the muscular parameters and size of the body, it is also possible to retarget the motion to different bodies such as a very trained muscular body, weak and narrow body, or a small childish body.
Tree Edit Distance Cannot be Computed in Strongly Subcubic Time (Unless APSP Can)

The edit distance between two rooted ordered trees with n nodes labeled from an alphabet Ʃ is the minimum cost of transforming one tree into the other by a sequence of elementary operations consisting of deleting and relabeling existing nodes, as well as inserting new nodes. Tree edit distance is a well-known generalization of string edit distance. The fastest known algorithm for tree edit distance runs in cubic O(n3) time and is based on a similar dynamic programming solution as string edit distance. In this article, we show that a truly subcubic O(n3-ε) time algorithm for tree edit distance is unlikely: For |Ʃ| = Ω (n), a truly subcubic algorithm for tree edit distance implies a truly subcubic algorithm for the all pairs shortest paths problem. For |Ʃ| = O(1), a truly subcubic algorithm for tree edit distance implies an O(nk-ε) algorithm for finding a maximum weight k-clique. Thus, while in terms of upper bounds string edit distance and tree edit distance are highly related, in terms of lower bounds string edit distance exhibits the hardness of the strong exponential time hypothesis (Backurs, Indyk STOC’15) whereas tree edit distance exhibits the hardness of all pairs shortest paths. Our result provides a matching conditional lower bound for one of the last remaining classic dynamic programming problems.
Constructing a CDS-Based Network Backbone for Energy Efficiency in Industrial Wireless Sensor Networks

In this work we design a virtual backbone to provide energy-efficient communication in Wireless Sensor Networks (WSNs). WSNs need virtual backbones to support in-network data transmission. In scenarios where there is no spatial redundancy (such as a sensor network in a petroleum installation), Connected Dominating Set (CDS)-based algorithms can be useful for hierarchical network management. We construct a CDSbased backbone to support the operation of an energy efficient network. We focus on three key ideas in our design: (a) a realistic weight matrix, (b) an asymmetric communication link between pairs of nodes, and (c) a role switching technique to prolong the lifetime of the CDS backbone. We simulate our proposed design using realistic scenarios for performance evaluation. We compare the proposed design with a traditional CDS algorithm. The simulation results prove the efficiency of the proposed algorithm in terms of network lifetime and packet loss.
Association of attention-deficit disorder and the dopamine transporter gene.

Attention-deficit hyperactivity disorder (ADHD) has been shown to be familial and heritable, in previous studies. As with most psychiatric disorders, examination of pedigrees has not revealed a consistent Mendelian mode of transmission. The response of ADHD patients to medications that inhibit the dopamine transporter, including methylphenidate, amphetamine, pemoline, and bupropion, led us to consider the dopamine transporter as a primary candidate gene for ADHD. To avoid effects of population stratification and to avoid the problem of classification of relatives with other psychiatric disorders as affected or unaffected, we used the haplotype-based haplotype relative risk (HHRR) method to test for association between a VNTR polymorphism at the dopamine transporter locus (DAT1) and DSM-III-R-diagnosed ADHD (N = 49) and undifferentiated attention-deficit disorder (UADD) (N = 8) in trios composed of father, mother, and affected offspring. HHRR analysis revealed significant association between ADHD/UADD and the 480-bp DAT1 allele (chi 2 7.51, 1 df, P = .006). When cases of UADD were dropped from the analysis, similar results were found (Chi 2 7.29, 1 df, P = .007). If these findings are replicated, molecular analysis of the dopamine transporter gene may identify mutations that increase susceptibility to ADHD/UADD. Biochemical analysis of such mutations may lead to development of more effective therapeutic interventions.
A MAP-like weighting scheme for MLLR speaker adaptation

This paper presents an approach for fast, unsupervised, on-line MLLR speaker adaptation using two MAP-like weighting schemes, a static and a dynamic one. While for the standard MLLR approach several sentences are necessary before a reli-able estimation of the transformations is possible, the weighted approach shows good results even if adaptation is conducted after only a few short utterances. Experimental results show that using the static approach can improve the word error rate by ap-prox. 27% if adaptation is conducted after every 4 utterances (single words or short phrases). Using the dynamic approach, results can be improved by 28%. The most important advantage of the dynamic weight is that it is rather insensitive with respect to the initial weight whereas for the static approach it is very crit-ical which initial weight to chose. Moreover, useful values for the weights in the static case depend very much on the corpus. If the standard MLLR approach is used, even a drastic increase in sentence error rate can be observed for these small amounts of adaptation data.
Distress from voluntary refusal of food and fluids to hasten death: what is the role of continuous deep sedation?

In assisted dying, the end-of-life trajectory is shortened to relieve unbearable suffering. Unbearable suffering is defined broadly enough to include cognitive (early dementia), psychosocial or existential distress. It can include old-age afflictions that are neither life-threatening nor fatal in the “vulnerable elderly”. The voluntary refusal of food and fluids (VRFF) combined with continuous deep sedation (CDS) for assisted dying is legal. Scientific understanding of awareness of internal and external nociceptive stimuli under CDS is rudimentary. CDS may blunt the wakefulness component of human consciousness without eradicating internal affective awareness of thirst and hunger. Patients may suffer because of the slow dying process following dehydration and starvation. The difficulty to adequately control distress, without bringing the dying process to a rapid conclusion by lethal pharmacological interventions, can cause feelings of guilt among hospice and medical staff. Furthermore, the double-effect principle is not applicable in these situations because the primary objective of VRFF is to hasten death. Legal and societal debate should focus on sharpening the boundaries between assisted dying and palliative care. This separation is necessary to: 1) uphold trust in the patient-phyician relationship, and 2) preserve integrity and ethics of the medical profession.
Understanding and evolving the ml module system

The ML module system stands as a high-water mark of programming language support for data abstraction. Nevertheless, it is not in a fully evolved state. One prominent weakness is that module interdependencies in ML are restricted to be acyclic, which means that mutually recursive functions and data types must be written in the same module even if they belong conceptually in different modules. Existing efforts to remedy this limitation either involve drastic changes to the notion of what a module is, or fail to allow mutually recursive modules to hide type information from one another. Another issue is that there are several dialects of ML, and the module systems of these dialects differ in subtle yet semantically significant ways that have been difficult to account for in any rigorous way. It is important to come to a clear assessment of the existing design space and consolidate what is meant by “the ML module system” before embarking on such a major extension as recursive modules. In this dissertation I contribute to the understanding and evolution of the ML module system by: (1) developing a unifying account of the ML module system in which existing variants may be understood as subsystems that pick and choose different features, (2) exploring how to extend ML with recursive modules in a way that does not inhibit data abstraction, and (3) incorporating the understanding gained from (1) and (2) into the design of a new, evolved dialect of ML. I formalize the language of part (3) using the framework of Harper and Stone, in which the meanings of “external” ML programs are interpreted by translation into an “internal” type system. In my exploration of the recursive module problem, I also propose a type system for statically detecting whether or not recursive module definitions are “safe”—that is, whether they can be evaluated without referring to one another prematurely—thus enabling more efficient compilation of recursive modules. Future work remains, however, with regard to type inference and type system complexity, before my proposal can be feasibly incorporated into ML.
A multi-objective dynamic programming-based metaheuristic to solve a bi-objective unit commitment problem using a multi-objective decoder

The unit commitment problem UCP is a heavily constrained scheduling problem, where the on/off scheduling and production amounts of heterogeneous power production units have to be determined for a discrete time horizon. Due to environmental concerns, the traditional UCP to solely minimise the production cost is no longer adequate, and a second objective, to minimise the gas emissions has to be added to properly model reality. In this paper, we propose an efficient metaheuristic to solve this multi-objective version of the UCP. The proposed method, MO-DYNAMOP, is a generalisation of DYNAMOP DYNAmic programming-based Metaheuristic for Optimisation Problems, a state-of-the-art hybrid optimiser which was successfully applied to the single-objective unit commitment problem. The main difficulty in extending DYNAMOP to the multi-objective UCP is that it uses an indirect representation of solution that gives the on/off scheduling of each unit. The real production amounts are computed by an exact sub-optimiser which minimises the production cost assuming that the on/off scheduling is fixed. Since the sub-optimiser now has to solve a multi-objective problem, each on/off scheduling induces an entire Pareto optimal set of solutions. We handle this complication by assigning an approximation of the corresponding set to each on/off scheduling solution. A comparison study with methods previously proposed in the literature indicates that MO-DYNAMOP performs considerably better on many benchmark instances.
Targeted Radionuclide Therapy with A 177Lu-labeled Anti-HER2 Nanobody

RIT has become an attractive strategy in cancer treatment, but still faces important drawbacks due to poor tumor penetration and undesirable pharmacokinetics of the targeting vehicles. Smaller radiolabeled antibody fragments and peptides feature highly specific target accumulation, resulting in low accumulation in healthy tissue, except for the kidneys. Nanobodies are the smallest (MW < 15 kDa) functional antigen-binding fragments that are derived from heavy chain-only camelid antibodies. Here, we show that the extend of kidney retention of nanobodies is predominantly dictated by the number of polar residues in the C-terminal amino acid tag. Three nanobodies were produced with different C-terminal amino-acid tag sequences (Myc-His-tagged, His-tagged, and untagged). Dynamic planar imaging of Wistar rats with 111In-DTPA-nanobodies revealed that untagged nanobodies showed a 70 % drop in kidney accumulation compared to Myc-His-tagged nanobodies at 50 min p.i. In addition, coinfusion of untagged nanobodies with the plasma expander Gelofusin led to a final reduction of 90 %. Similar findings were obtained with different 177Lu-DTPA-2Rs15d nanobody constructs in HER2pos tumor xenografted mice at 1 h p.i. Kidney accumulation decreased 88 % when comparing Myc-His-tagged to untagged 2Rs15d nanobody, and 95 % with a coinfusion of Gelofusin, without affecting the tumor targeting capacity. Consequently, we identified a generic method to reduce kidney retention of radiolabeled nanobodies. Dosimetry calculations of Gelofusin-coinfused, untagged 177Lu-DTPA-2Rs15d revealed a dose of 0.90 Gy/MBq that was delivered to both tumor and kidneys and extremely low doses to healthy tissues. In a comparative study, 177Lu-DTPA-Trastuzumab supplied 6 times more radiation to the tumor than untagged 177Lu-DTPA-2Rs15d, but concomitantly also a 155, 34, 80, 26 and 4180 fold higher radioactivity burden to lung, liver, spleen, bone and blood. Most importantly, nanobody-based targeted radionuclide therapy in mice bearing small estiblashed HER2pos tumors led to an almost complete blockade of tumor growth and a significant difference in event-free survival between the treated and the control groups (P < 0.0001). Based on histology analyses, no evidence of renal inflammation, apoptosis or necrosis was obtained. In conclusion, these data highlight the importance of the amino acid composition of the nanobody's C-terminus, as it has a predominant effect on kidney retention. Moreover, we show successful nanobody-based targeted radionuclide therapy in a xenograft model and highlight the potential of radiolabeled nanobodies as a valuable adjuvant therapy candidate for treatment of minimal residual and metastatic disease.
Influence of prion variant and yeast strain variation on prion-molecular chaperone requirements

Prions of budding yeast serve as a tractable model of amyloid behavior. Here we address the issue of the effect of yeast strain variation on prion stability, focusing also on the effect of amyloid conformation and the involvement of the co-chaperone Sis1, an essential J-protein partner of Hsp70. We found, despite an initial report to the contrary, that yeast strain background has little effect on the requirement for particular Sis1 domains for stable propagation of the prion [RNQ+], if the level of Sis1 expression is controlled. On the other hand, some variation in prion behavior was observed between yeast strains, in particular, the stability of certain [PSI+] variants. Future examination of such yeast strain-specific phenomena may provide useful insights into the basis of prion/chaperone dynamics.