Miracle-dz/UniFAR-FTUs huggingface dataset:
In memory: Predicting preferences for memorializing lost loved ones

ABSTRACT People’s preferences for memorializing loved ones vary and may depend on their personal attitudes and experiences with death. Participants (N = 145) completed the memorializing preferences checklist and measures of personal attitudes and life experiences with death. Factor analysis identified four memorializing preferences. In terms of predicting preferences, greater acknowledgment of death, and having experienced more losses, predicted preference for memorializing through societal tradition, community legacy, and continuing intimacy. Greater death resistance and less closeness to the deceased predicted preference for memorializing through confronting loss. Findings are discussed in terms of the range of preferences for and motivations behind memorializing.
An Adaptive B-Spline Neural Network and Its Application in Terminal Sliding Mode Control for a Mobile Satcom Antenna Inertially Stabilized Platform

The mobile satcom antenna (MSA) enables a moving vehicle to communicate with a geostationary Earth orbit satellite. To realize continuous communication, the MSA should be aligned with the satellite in both sight and polarization all the time. Because of coupling effects, unknown disturbances, sensor noises and unmodeled dynamics existing in the system, the control system should have a strong adaptability. The significant features of terminal sliding mode control method are robustness and finite time convergence, but the robustness is related to the large switching control gain which is determined by uncertain issues and can lead to chattering phenomena. Neural networks can reduce the chattering and approximate nonlinear issues. In this work, a novel B-spline curve-based B-spline neural network (BSNN) is developed. The improved BSNN has the capability of shape changing and self-adaption. In addition, the output of the proposed BSNN is applied to approximate the nonlinear function in the system. The results of simulations and experiments are also compared with those of PID method, non-singularity fast terminal sliding mode (NFTSM) control and radial basis function (RBF) neural network-based NFTSM. It is shown that the proposed method has the best performance, with reliable control precision.
Applicability of non-specification-based approaches to logic testing for software

Testing is a crucial part of the development of highly dependable systems. In this paper, we consider the testing of an implementation that is intended to satisfy a Boolean formula. In the literature, specification-based testing has been suggested for this purpose. Typically, such methods first hypothesise a fault class and then generate tests. However, there is almost no research that justifies the fault classes proposed previously. Moreover, the specifications available for automatic test generation are not always available to testers in practice. Based on these observations, we examine the applicability of non-specification-based approaches, which need no specification in the form of a Boolean formula to create tests. We compare a specification-based approach to two non-specification-based approaches, namely random testing and combinatorial testing, which is an emerging technique based on combinatorial designs. The results of an experiment show that combinatorial testing is often comparative to specification-based testing and is always much superior to random testing.
Modeling of post-filling stage in vacuum infusion using compaction characterization

Two-dimensional finite-element method solution of the post-filling stage of vacuum infusion was studied based on mass conservation in an infinitesimal control volume. First, resin pressure distribution at the instant of mold filling was calculated and then used as the initial condition for the transient post-filling stage. Explicit time-marching algorithm was used for the evolution of resin pressure and part thickness, and its stability was ensured by selecting the time step adaptively. Finite-element method solution was verified analytically for one-dimensional case and numerically for two-dimensional cases using global mass conservation. The time that it took for the settlement of pressure and thickness was investigated to compare the effectiveness of different resin-bleeding scenarios where different number and locations of gates were used. It was shown that the settlement time increased exponentially as the dimensions of the mold increased, which proved that process simulation fed with correctly designed material characterization can replace tedious trial-and-error search of control actions to reduce the settlement time and variation in part thickness.
High Sensitivity Refractometer Based on Reflective Smf-Small Diameter No Core Fiber Structure

A high sensitivity refractive index sensor based on a single mode-small diameter no core fiber structure is proposed. In this structure, a small diameter no core fiber (SDNCF) used as a sensor probe, was fusion spliced to the end face of a traditional single mode fiber (SMF) and the end face of the SDNCF was coated with a thin film of gold to provide reflective light. The influence of SDNCF diameter and length on the refractive index sensitivity of the sensor has been investigated by both simulations and experiments, where results show that the diameter of SDNCF has significant influence. However, SDNCF length has limited influence on the sensitivity. Experimental results show that a sensitivity of 327 nm/RIU (refractive index unit) has been achieved for refractive indices ranging from 1.33 to 1.38, which agrees well with the simulated results with a sensitivity of 349.5 nm/RIU at refractive indices ranging from 1.33 to 1.38.
Utility of the Croatian translation of the community integration questionnaire-revised in a sample of adults with moderate to severe traumatic brain injury

Abstract Purpose: To examine the utility of the Community Integration Questionnaire-Revised, translated into Croatian, in a sample of adults with moderate to severe traumatic brain injury. Method: The Community Integration Questionnaire-Revised was administered to a sample of 88 adults with traumatic brain injury and to a control sample matched by gender, age and education. Participants with traumatic brain injury were divided into four subgroups according to injury severity. Results: The internal consistency of the Community Integration Questionnaire-Revised was satisfactory. The differences between the group with traumatic brain injury and the control group were statistically significant for the overall Community Integration Questionnaire-Revised score, as well as for all the subscales apart from the Home Integration subscale. The community Integration Questionnaire-Revised score varied significantly for subgroups with different severity of traumatic brain injury. Conclusions: The results show that the Croatian translation of the Community Integration Questionnaire-Revised is useful in assessing participation in adults with traumatic brain injury and confirm previous findings that severity of injury predicts community integration. Results of the new Electronic Social Networking scale indicate that persons who are more active on electronic social networks report better results for other domains of community integration, especially social activities. Implications for rehabilitation The Croatian translation of the Community Integration Questionnaire-Revised is a valid tool for long-term assessment of participation in various domains in persons with moderate to severe traumatic brain injury Persons with traumatic brain injury who are more active in the use of electronic social networking are also more integrated into social and productivity domains. Targeted training in the use of new technologies could enhance participation after traumatic brain injury.
Ablation of human telomerase reverse transcriptase (hTERT) induces cellular senescence in gastric cancer through a galectin-3 dependent mechanism

The human Telomerase Reverse Transcriptase (hTERT) gene encodes a rate-limiting catalytic subunit of telomerase that maintains genomic integrity. Suppression of hTERT expression could induce cellular senescence and is considered a potent approach for gastric cancer therapy. However, control of hTERT expression and function remains poorly understood in gastric cancer. In this study, we demonstrated that high expression levels of hTERT in malignant tissues are correlated with poor survival probability in gastric cancer patients. Knockdown of hTERT expression retarded cell proliferation and cellular senescence, which was confirmed by increased protein expression levels of p21cip1 and p27kip1, and decreased phosphorylation of Rb. In contrast, overexpression of hTERT increased cell proliferation and decreased cellular senescence. Remarkably, the down-regulation of hTERT expression was detected in lgals3−/− mouse embryo fibroblasts (MEFs). Knockdown of galectin-3 decreased the expression of hTERT in gastric cancer cells. Galectin-3 ablation-induced cellular senescence was rescued by concomitant overexpression of hTERT. hTERT ablation-induced cellular senescence and p21cip1 and p27kip1 expression was rescued by concomitant overexpression of galectin-3. The size of tumor burdens was increased in hTERT-overexpressed gastric cancer cells xenografted mice, whereas it was repressed by concomitant depletion of galectin-3. Additionally, we determined that the N-terminal domain of galectin-3 directly interacted with hTERT. The telomeric activity of hTERT was also decreased by galectin-3 ablation. Taken together, ablation of hTERT induces cellular senescence and inhibits the growth of gastric cancer cells, suggesting that it could be a potent target in gastric cancer therapy. We also propose that galectin-3 is an important regulator of hTERT expression and telomeric activity in gastric tumorigenesis.
Psychotropic Polypharmacy in Patients with Dementia: Prevalence and Predictors.

BACKGROUND Antipsychotics and other psychotropics are frequently used to treat neuropsychiatric symptoms in patients with dementia, even though the evidence for effect is limited. Concerns have been raised about the safety of antipsychotics, but concomitant use of multiple psychotropic drug classes (psychotropic polypharmacy) may also pose a risk for patients. OBJECTIVE To investigate the prevalence and predictors associated with use of psychotropic polypharmacy in patients with dementia. METHODS A population-based study using nationwide registers. Patients with dementia were identified among all Danish residents ≥65 years on January 1, 2012. Data on prescriptions and comorbidity was included in the analysis. Overlapping prescriptions for different psychotropic drug classes were used to determine psychotropic polypharmacy. A multivariable logistic regression analysis was conducted to evaluate factors independently associated with the prescription of other psychotropic drug classes among patients already using antipsychotics. RESULTS Among all patients registered with dementia (34,553), 25.3% (8,728) used ≥2 psychotropic drugs. Among patients treated with antipsychotics 75.8% (5,403) used at least one other psychotropic drug during the antipsychotic treatment period. Nursing home residency, number of non-psychotropic medications used in 2011, and prior psychiatric diagnosis were associated with psychotropic polypharmacy among antipsychotic drug users. The most frequent combination of psychotropic drugs was antipsychotics and antidepressants. CONCLUSION Concomitant use of psychotropic drugs was frequent in dementia patients. Patients living in nursing homes had the highest risk of receiving a combination of antipsychotics and other psychotropic drugs. Concomitant use of psychotropics may cause adverse events, and potential consequences for patients' safety call for further investigation.
Evaluating Surface Impedance Models for Terahertz Frequencies at Room Temperature

Commercial electromagnetic modeling software employs overly-simplifled models for the terahertz simulation of metal structures. For the flrst time, this paper gives a unique review of various modeling strategies (classical, semiclassical and quantum mechanical based) for normal metals and discusses their limitations with frequency at room temperature. High frequency CAD software packages employ overly-simplifled models for the electromagnetic simulation of metal structures; using either classical skin-efiect or classical relaxation-efiect models. At room temperatures, these models are accurate beyond the upper edges of the microwave and sub-millimeter-wave parts of the frequency spectrum, respectively. However, semiclassical models are needed to extend modeling well into the terahertz region or at signiflcantly lower temperatures. Here, issues relating to the specular or difiuse nature of electron re∞ections at the air-metal interface become apparent at very low temperatures. Within the near-infrared, visible and ultra-violet parts of the frequency spectrum, Commer- cial CAD software packages again may employ overly-simplifled empirically-fltted relaxation-efiect models, which only work over relatively narrow spectral bandwidths. However, to be accurate, an analytical model must be adopted that employs a quantum mechanical treatment, as this takes into account both energy dispersion and electron wavefunctions. The author has investigated modeling strategies for normal metals. In one study, experimental measurements that suggested the possibility of anomalous room-temperature conduction losses were examined between DC and 12.5THz (1). It was found that the classical relaxation-efiect model was still valid up to these frequencies. In another study, an elaborate semiclassical model to describe anomalous excess conduction losses at room temperature was found to be completely erroneous (2). In order to create accurate analytical models, it is important to develop semiclassical modeling strategies (3) or develop quantum mechanical treatments. To this end, and for the flrst time, this paper will review various approaches to the modeling of normal metals at room temperature. More importantly, their limitations will be discussed in detail. It will be shown that a number of well-know approaches have severe limitations to general applications. 2. CLASSICAL TREATMENT Drude's model of intraband transitions describes an ideal system of free electrons having a spherical Fermi surface. The classical relaxation-efiect model takes into account electron-phonon collisions, represented by the following expression for surface impedance, ZSR, in terms of Drude's model for intrinsic bulk conductivity, aeR:
Word length and the location of primary word stress in Dutch, German, and English

Abstract This study addresses the extent to which the location of primary stress in Dutch, German, and English monomorphemic words is affected by the syllables preceding the three final syllables. We present analyses of the monomorphemic words in the CELEX lexical database, which showed that penultimate primary stress is less frequent in Dutch and English trisyllabic than quadrisyllabic words. In addition, we discuss paper-and-pencil experiments in which native speakers assigned primary stress to pseudowords. These experiments provided evidence that in all three languages penultimate stress is more likely in quadrisyllabic than in trisyllabic words. We explain this length effect with the preferences in these languages for word-initial stress and for alternating patterns of stressed and unstressed syllables. The experimental data also showed important intra- and interspeaker variation, and they thus form a challenging test case for theories of language variation.
Astrocyte-produced miR-146a as a mediator of motor neuron loss in spinal muscular atrophy

Spinal muscular atrophy (SMA), the leading genetic cause of infant mortality, is caused by the loss of the survival motor neuron-1 (SMN1) gene, which leads to motor neuron loss, muscle atrophy, respiratory distress, and death. Motor neurons exhibit the most profound loss, but the mechanisms underlying disease pathogenesis are not fully understood. Recent evidence suggests that motor neuron extrinsic influences, such as those arising from astrocytes, contribute to motor neuron malfunction and loss. Here we investigated both loss-of-function and toxic gain-of-function astrocyte mechanisms that could play a role in SMA pathology. We had previously found that glial derived neurotrophic factor (GDNF) is reduced in SMA astrocytes. However, reduced GDNF expression does not play a major role in SMA pathology as viral-mediated GDNF re-expression did not improve astrocyte function or motor neuron loss. In contrast, we found that SMA astrocytes increased microRNA (miR) production and secretion compared to control astrocytes, suggesting potential toxic gain-of-function properties. Specifically, we found that miR-146a was significantly upregulated in SMA induced pluripotent stem cell (iPSC)-derived astrocytes and SMNΔ7 mouse spinal cord. Moreover, increased miR-146a was sufficient to induce motor neuron loss in vitro, whereas miR-146a inhibition prevented SMA astrocyte-induced motor neuron loss. Together, these data indicate that altered astrocyte production of miR-146a may be a contributing factor in astrocyte-mediated SMA pathology.
Locally Adaptive Learning Loss for Semantic Image Segmentation

We propose a novel locally adaptive learning estimator for enhancing the inter- and intra- discriminative capabilities of Deep Neural Networks, which can be used as improved loss layer for semantic image segmentation tasks. Most loss layers compute pixel-wise cost between feature maps and ground truths, ignoring spatial layouts and interactions between neighboring pixels with same object category, and thus networks cannot be effectively sensitive to intra-class connections. Stride by stride, our method firstly conducts adaptive pooling filter operating over predicted feature maps, aiming to merge predicted distributions over a small group of neighboring pixels with same category, and then it computes cost between the merged distribution vector and their category label. Such design can make groups of neighboring predictions from same category involved into estimations on predicting correctness with respect to their category, and hence train networks to be more sensitive to regional connections between adjacent pixels based on their categories. In the experiments on Pascal VOC 2012 segmentation datasets, the consistently improved results show that our proposed approach achieves better segmentation masks against previous counterparts.
How You Behave in School Predicts Life Success Above and Beyond Family Background, Broad Traits, and Cognitive Ability

In this study, we investigated the role of student characteristics and behaviors in a longitudinal study over a 50-year timespan (using a large U.S. representative sample of high school students). We addressed the question of whether behaviors in school have any long-lasting effects for one‘s later life. Specifically, we investigated the role of being a responsible student, interest in school, writing skills, and reading skills in predicting educational attainment, occupational prestige, and income 11 years (N = 81,912) and 50 years (N = 1,952) after high school. We controlled for parental socioeconomic status, IQ, and broad personality traits in all analyses. We found that student characteristics and behaviors in adolescence predicted later educational and occupational success above and beyond parental socioeconomic status, IQ, and broad personality traits. Having higher interest in school was related to higher educational attainment at years 11 and 50, higher occupational prestige at year 11, and higher income at year 50. Higher levels of being a responsible student were related to higher educational attainment and higher occupational prestige at years 11 and 50. This was the first longitudinal study to test the role of student characteristics and behaviors over and above broad personality traits. It highlights the potential importance of what students do in school and how they react to their experiences during that time. It also highlights the possibility that things that happen in specific periods of one’s life may play out in ways far more significant than we expect.
Evolutionary history of metastatic breast cancer reveals minimal seeding from axillary lymph nodes

Metastatic breast cancers are still incurable. Characterizing the evolutionary landscape of these cancers, including the role of metastatic axillary lymph nodes (ALNs) in seeding distant organ metastasis, can provide a rational basis for effective treatments. Here, we have described the genomic analyses of the primary tumors and metastatic lesions from 99 samples obtained from 20 patients with breast cancer. Our evolutionary analyses revealed diverse spreading and seeding patterns that govern tumor progression. Although linear evolution to successive metastatic sites was common, parallel evolution from the primary tumor to multiple distant sites was also evident. Metastatic spreading was frequently coupled with polyclonal seeding, in which multiple metastatic subclones originated from the primary tumor and/or other distant metastases. Synchronous ALN metastasis, a well-established prognosticator of breast cancer, was not involved in seeding the distant metastasis, suggesting a hematogenous route for cancer dissemination. Clonal evolution coincided frequently with emerging driver alterations and evolving mutational processes, notably an increase in apolipoprotein B mRNA–editing enzyme, catalytic polypeptide-like–associated (APOBEC-associated) mutagenesis. Our data provide genomic evidence for a role of ALN metastasis in seeding distant organ metastasis and elucidate the evolving mutational landscape during cancer progression.
Data-Based Adaptive Critic Designs for Nonlinear Robust Optimal Control With Uncertain Dynamics

In this paper, the infinite-horizon robust optimal control problem for a class of continuous-time uncertain nonlinear systems is investigated by using data-based adaptive critic designs. The neural network identification scheme is combined with the traditional adaptive critic technique, in order to design the nonlinear robust optimal control under uncertain environment. First, the robust optimal controller of the original uncertain system with a specified cost function is established by adding a feedback gain to the optimal controller of the nominal system. Then, a neural network identifier is employed to reconstruct the unknown dynamics of the nominal system with stability analysis. Hence, the data-based adaptive critic designs can be developed to solve the Hamilton-Jacobi-Bellman equation corresponding to the transformed optimal control problem. The uniform ultimate boundedness of the closed-loop system is also proved by using the Lyapunov approach. Finally, two simulation examples are presented to illustrate the effectiveness of the developed control strategy.
On the local convergence of secant-type methods

In this article, we carry out a local convergence study for Secant-type methods. Our goal is to enlarge the radius of convergence, without increasing the necessary hypothesis. Finally, some numerical tests and comparisons with early results are analyzed. E-mail: sonia.busquier@upct.es E-mail: sergio.amat@upct.es