An Algorithm for the Generation of Segmented Parametric Software Estimation Models and Its Empirical Evaluation

Parametric software effort estimation techniques use mathematical cost-estimation relationships derived from historical project databases, usually obtained through standard curve regression techniques. Nonetheless, project databases -- especially in the case of consortium-created compilations like the ISBSG --, collect highly heterogeneous data, coming from projects that diverge in size, process and personnel skills, among other factors. This results in that a single parametric model is seldom able to capture the diversity of the sources, in turn resulting in poor overall quality. Segmented parametric estimation models use local regression to derive one model per each segment of data with similar characteristics, improving the overall predictive quality of parametrics. Further, the process of obtaining segmented models can be expressed in the form of a generic algorithm that can be used to produce candidate models in an automated process of calibration from the project database at hand. This paper describes the rationale for such algorithmic scheme along with the empirical evaluation of a concrete version that uses the EM clustering algorithm combined with the common parametric exponential model of size-effort, and standard quality-of-adjustment criteria. Results point out to the adequacy of the technique as an extension of existing single-relation models

LIPSNN: A Light Intrusion-Proving Siamese Neural Network Model for Facial Verification

Facial verification has experienced a breakthrough in recent years, not only due to the improvement in accuracy of the verification systems but also because of their increased use. One of the main reasons for this has been the appearance and use of new models of Deep Learning to address this problem. This extension in the use of facial verification has had a high impact due to the importance of its applications, especially on security, but the extension of its use could be significantly higher if the problem of the required complex calculations needed by the Deep Learning models, that usually need to be executed on machines with specialised hardware, were solved. That would allow the use of facial verification to be extended, making it possible to run this software on computers with low computing resources, such as Smartphones or tablets. To solve this problem, this paper presents the proposal of a new neural model, called Light Intrusion-Proving Siamese Neural Network, LIPSNN. This new light model, which is based on Siamese Neural Networks, is fully presented from the description of its two block architecture, going through its development, including its training with the well- known dataset Labeled Faces in the Wild, LFW; to its benchmarking with other traditional and deep learning models for facial verification in order to compare its performance for its use in low computing resources systems for facial recognition. For this comparison the attribute parameters, storage, accuracy and precision have been used, and from the results obtained it can be concluded that the LIPSNN can be an alternative to the existing models to solve the facet problem of running facial verification in low computing resource devices

Segmented software cost estimation models based on fuzzy clustering

Parametric software cost estimation models are based on mathematical relations, obtained from the study of historical software projects databases, that intend to be useful to estimate the effort and time required to develop a software product. Those databases often integrate data coming from projects of a heterogeneous nature. This entails that it is difficult to obtain a reasonably reliable single parametric model for the range of diverging project sizes and characteristics. A solution proposed elsewhere for that problem was the use of segmented models in which several models combined into a single one contribute to the estimates depending on the concrete characteristic of the inputs. However, a second problem arises with the use of segmented models, since the belonging of concrete projects to segments or clusters is subject to a degree of fuzziness, i.e. a given project can be considered to belong to several segments with different degrees. This paper reports the first exploration of a possible solution for both problems together, using a segmented model based on fuzzy clusters of the project space. The use of fuzzy clustering allows obtaining different mathematical models for each cluster and also allows the items of a project database to contribute to more than one cluster, while preserving constant time execution of the estimation process. The results of an evaluation of a concrete model using the ISBSG 8 project database are reported, yielding better figures of adjustment than its crisp counterpart.Ministerio de Ciencia y Tecnología TIN2004-06689-C0

Differential Role of Human Choline Kinase α and β Enzymes in Lipid Metabolism: Implications in Cancer Onset and Treatment

11 pages, 6 figures, 1 table.Background The Kennedy pathway generates phosphocoline and phosphoethanolamine through its two branches. Choline Kinase (ChoK) is the first enzyme of the Kennedy branch of synthesis of 1phosphocholine, the major component of the plasma membrane. ChoK family of proteins is composed by ChoKα and ChoKβ isoforms, the first one with two different variants of splicing. Recently ChoKα has been implicated in the carcinogenic process, since it is over-expressed in a variety of human cancers. However, no evidence for a role of ChoKβ in carcinogenesis has been reported. Methodology/Principal Findings Here we compare the in vitro and in vivo properties of ChoKα1 and ChoKβ in lipid metabolism, and their potential role in carcinogenesis. Both ChoKα1 and ChoKβ showed choline and ethanolamine kinase activities when assayed in cell extracts, though with different affinity for their substrates. However, they behave differentially when overexpressed in whole cells. Whereas ChoKβ display an ethanolamine kinase role, ChoKα1 present a dual choline/ethanolamine kinase role, suggesting the involvement of each ChoK isoform in distinct biochemical pathways under in vivo conditions. In addition, while overexpression of ChoKα1 is oncogenic when overexpressed in HEK293T or MDCK cells, ChoKβ overexpression is not sufficient to induce in vitro cell transformation nor in vivo tumor growth. Furthermore, a significant upregulation of ChoKα1 mRNA levels in a panel of breast and lung cancer cell lines was found, but no changes in ChoKβ mRNA levels were observed. Finally, MN58b, a previously described potent inhibitor of ChoK with in vivo antitumoral activity, shows more than 20-fold higher efficiency towards ChoKα1 than ChoKβ. Conclusion/Significance This study represents the first evidence of the distinct metabolic role of ChoKα and ChoKβ isoforms, suggesting different physiological roles and implications in human carcinogenesis. These findings constitute a step forward in the design of an antitumoral strategy based on ChoK inhibition.This work has been supported by grants to JCL from Comunidad de Madrid (GR-SAL-0821-2004), Ministerio de Ciencia e Innovación (SAF2008-03750, RD06/0020/0016), Fundación Mutua Madrileña, and by a grant to ARM from Fundación Mutua Madrileña.Peer reviewe

Key Factors Associated With Pulmonary Sequelae in the Follow-Up of Critically Ill COVID-19 Patients

Introduction: Critical COVID-19 survivors have a high risk of respiratory sequelae. Therefore, we aimed to identify key factors associated with altered lung function and CT scan abnormalities at a follow-up visit in a cohort of critical COVID-19 survivors. Methods: Multicenter ambispective observational study in 52 Spanish intensive care units. Up to 1327 PCR-confirmed critical COVID-19 patients had sociodemographic, anthropometric, comorbidity and lifestyle characteristics collected at hospital admission; clinical and biological parameters throughout hospital stay; and, lung function and CT scan at a follow-up visit. Results: The median [p25–p75] time from discharge to follow-up was 3.57 [2.77–4.92] months. Median age was 60 [53–67] years, 27.8% women. The mean (SD) percentage of predicted diffusing lung capacity for carbon monoxide (DLCO) at follow-up was 72.02 (18.33)% predicted, with 66% of patients having DLCO < 80% and 24% having DLCO < 60%. CT scan showed persistent pulmonary infiltrates, fibrotic lesions, and emphysema in 33%, 25% and 6% of patients, respectively. Key variables associated with DLCO < 60% were chronic lung disease (CLD) (OR: 1.86 (1.18–2.92)), duration of invasive mechanical ventilation (IMV) (OR: 1.56 (1.37–1.77)), age (OR [per-1-SD] (95%CI): 1.39 (1.18–1.63)), urea (OR: 1.16 (0.97–1.39)) and estimated glomerular filtration rate at ICU admission (OR: 0.88 (0.73–1.06)). Bacterial pneumonia (1.62 (1.11–2.35)) and duration of ventilation (NIMV (1.23 (1.06–1.42), IMV (1.21 (1.01–1.45)) and prone positioning (1.17 (0.98–1.39)) were associated with fibrotic lesions. Conclusion: Age and CLD, reflecting patients’ baseline vulnerability, and markers of COVID-19 severity, such as duration of IMV and renal failure, were key factors associated with impaired DLCO and CT abnormalities

Higher COVID-19 pneumonia risk associated with anti-IFN-α than with anti-IFN-ω auto-Abs in children

We found that 19 (10.4%) of 183 unvaccinated children hospitalized for COVID-19 pneumonia had autoantibodies (auto-Abs) neutralizing type I IFNs (IFN-alpha 2 in 10 patients: IFN-alpha 2 only in three, IFN-alpha 2 plus IFN-omega in five, and IFN-alpha 2, IFN-omega plus IFN-beta in two; IFN-omega only in nine patients). Seven children (3.8%) had Abs neutralizing at least 10 ng/ml of one IFN, whereas the other 12 (6.6%) had Abs neutralizing only 100 pg/ml. The auto-Abs neutralized both unglycosylated and glycosylated IFNs. We also detected auto-Abs neutralizing 100 pg/ml IFN-alpha 2 in 4 of 2,267 uninfected children (0.2%) and auto-Abs neutralizing IFN-omega in 45 children (2%). The odds ratios (ORs) for life-threatening COVID-19 pneumonia were, therefore, higher for auto-Abs neutralizing IFN-alpha 2 only (OR [95% CI] = 67.6 [5.7-9,196.6]) than for auto-Abs neutralizing IFN-. only (OR [95% CI] = 2.6 [1.2-5.3]). ORs were also higher for auto-Abs neutralizing high concentrations (OR [95% CI] = 12.9 [4.6-35.9]) than for those neutralizing low concentrations (OR [95% CI] = 5.5 [3.1-9.6]) of IFN-omega and/or IFN-alpha 2

Adaptación de las Métricas de Reusabilidad de la Ingeniería del Software a los Learning Objects

La reusabilidad se considera una característica esencial de los learning objects, los cuales constituyen la idea central de las aproximaciones actuales a la estandarización del diseño de contenidos de aprendizaje. En consecuencia, los instrumentos de medida de la reusabilidad de los learning objects deberían ser adoptados en función de su validez como evaluadores de la calidad de los mismos, de tal forma que permitiesen escoger entre varios diseños alternativos posibles. En este artículo se analiza la aplicabilidad que diferentes métricas desarrolladas en el campo de la ingeniería del software, pudieran tener para resolver el problema de la medición de la reusabilidad de los learning objects

Adapting software engineering reusability metrics to Learning object

La reusabilidad se considera una característica esencial de los learning objects, los cuales constituyen la idea central de las aproximaciones actuales a la estandarización del diseño de contenidos de aprendizaje. En consecuencia, los instrumentos de medida de la reusabilidad de los learning objects deberían ser adoptados en función de su validez como evaluadores de la calidad de los mismos, de tal forma que permitiesen escoger entre varios diseños alternativos posibles. En este artículo se analiza la aplicabilidad que diferentes métricas desarrolladas en el campo de la ingeniería del software, pudieran tener para resolver el problema de la medición de la reusabilidad de los learning objectsReusability is considered to be an essential characteristic of learning objects, which are the central notion of current approaches to standardized learning content design. In consequence, measurement instruments for learning object reusability should be developed for the sake of quality assessment and also to serve as criteria to choose between alternate designs. In this paper, the applicability of metrics borrowed from the field of Software Engineering is analyzed, providing analogies for several metrics that can be given an interpretation in terms of learning object

ADAPTACIÓN DE LAS MÉTRICAS DE REUSABILIDAD DE LA INGENIERÍA DEL SOFTWARE A LOS LEARNING OBJECTS

Reusability is considered to be an essential characteristic of learning objects, which are the central notion of current approaches to standardized learning content design. In consequence, measurement instruments for learning object reusability should be developed for the sake of quality assessment and also to serve as criteria to choose between alternate designs. In this paper, the applicability of metrics borrowed from the field of Software Engineering is analyzed, providing analogies for several metrics that can be given an interpretation in terms of learning objects.La reusabilidad se considera una característica esencial de los learning objects, los cuales constituyen la idea central de las aproximaciones actuales a la estandarización del diseño de contenidos de aprendizaje. En consecuencia, los instrumentos de medida de la reusabilidad de los learning objects deberían ser adoptados en función de su validez como evaluadores de la calidad de los mismos, de tal forma que permitiesen escoger entre varios diseños alternativos posibles. En este artículo se analiza la aplicabilidad que diferentes métricas desarrolladas en el campo de la ingeniería del software, pudieran tener para resolver el problema de la medición de la reusabilidad de los learning objects