Miracle-dz/UniFAR-FTUs huggingface dataset:
Initial Orientation of Attention towards Emotional Faces in Children with Attention Deficit Hyperactivity Disorder

Objective Early recognition of negative emotions is considered to be of vital importance. It seems that children with attention deficit hyperactivity disorder have some difficulties recognizing facial emotional expressions, especially negative ones. This study investigated the preference of children with attention deficit hyperactivity disorder for negative (angry, sad) facial expressions compared to normal children. Method Participants were 35 drug naive boys with ADHD, aged between 6-11 years,and 31 matched healthy children. Visual orientation data were recorded while participants viewed face pairs (negative-neutral pairs) shown for 3000ms. The number of first fixations made to each expression was considered as an index of initial orientation. Results Group comparisons revealed no difference between attention deficit hyperactivity disorder group and their matched healthy counterparts in initial orientation of attention. A tendency towards negative emotions was found within the normal group, while no difference was observed between initial allocation of attention toward negative and neutral expressions in children with ADHD. Conclusion Children with attention deficit hyperactivity disorder do not have significant preference for negative facial expressions. In contrast, normal children have a significant preference for negative facial emotions rather than neutral faces.
An evaluation on robustness and brittleness of HOG features of human detection

Detecting humans in an image sequence is one of the most difficult problems in object recognition. It is necessary to define a robust descriptor which can extract human features from images, to improve the detecting performance. Histograms of Oriented Gradients(HOG) descriptor significantly outperforms compared with the others on human detection. The descriptor is known as a robustness descriptor for illumination changes and geometrical changes in local regions. To obtain the high detection performance using the descriptor effectively, it is necessary to know the robustness and brittleness of the descriptor. In this paper, we experiment the descriptor to verify its robustness to illumination changes and to scrutinize its brittleness. For the experiments, we use LogitBoost which can create a human-detector by learning human features.
iTUG, a Sensitive and Reliable Measure of Mobility

Timed Up and Go (TUG) test is a widely used clinical paradigm to evaluate balance and mobility. Although TUG includes several complex subcomponents, namely: sit-to-stand, gait, 180° turn, and turn-to-sit; the only outcome is the total time to perform the task. We have proposed an instrumented TUG, called iTUG, using portable inertial sensors to improve TUG in several ways: automatic detection and separation of subcomponents, detailed analysis of each one of them and a higher sensitivity than TUG. Twelve subjects in early stages of Parkinson's disease (PD) and 12 age matched control subjects were enrolled. Stopwatch measurements did not show a significant difference between the two groups. The iTUG, however, showed a significant difference in cadence between early PD and control subjects (111.1 ± 6.2 versus 120.4 ± 7.6 step/min, p <; 0.006) as well as in angular velocity of arm-swing (123 ± 32.0 versus 174.0 ± 50.4°/s, p <; 0.005), turning duration (2.18 ± 0.43 versus 1.79 ± 0.27 s, p <; 0.023), and time to perform turn-to-sits (2.96 ± 0.68 versus 2.40 ± 0.33 s, p <; 0.023). By repeating the tests for a second time, the test-retest reliability of iTUG was also evaluated. Among the subcomponents of iTUG, gait, turning, and turn-to-sit were the most reliable and sit-to-stand was the least reliable.
3D Model-based Reconstruction of the Proximal Femur from Low-dose Biplanar X-Ray Images

The 3D modeling of the proximal femur is a valuable diagnostic tool for orthopedic surgery planning. The use of computed tomography is the most prominent modality to visualize bones both in terms of resolution as well as in term of bone/tissue separation. Towards reducing the impact of radiation to the patient, low-dose X-ray imaging systems have been introduced while still providing partial views with rather low signal-to-noise ratio. In this paper, we focus on automating the 3D proximal femur reconstruction from simultaneously acquired 2D views. A deformable model represented by triangulated mesh surfaces extends to a linear sub-space describing the variations across individuals. Segmentation consists of inferring a global deformation of 3D model followed by a local adaption based on the most prominent combination of the sub-space parameters. The ba-sis of which relies on the minimization of a cost function based on the biplanar projection of this model. To this end, we employ an active region model that aims at optimizing the 3D model parameters such that projection of surface is attracted from edge potentials, while creating an optimal partition between the bone class and the remaining structures. The global parameters of the model and the local ones are optimized through a gradient-free approach. Promising results demonstrate the potentials of our method compared to a supervised reconstruction technique.
Human aware UAS path planning in urban environments using nonstationary MDPs

A growing concern with deploying Unmanned Aerial Vehicles (UAVs) in urban environments is the potential violation of human privacy, and the backlash this could entail. Therefore, there is a need for UAV path planning algorithms that minimize the likelihood of invading human privacy. We formulate the problem of human-aware path planning as a nonstationary Markov Decision Process, and provide a novel model-based reinforcement learning solution that leverages Gaussian process clustering. Our algorithm is flexible enough to accommodate changes in human population densities by employing Bayesian nonparametrics, and is real-time computable. The approach is validated experimentally on a large-scale long duration experiment with both simulated and real UAVs.
Data-driven estimation of blood pressure using photoplethysmographic signals

Noninvasive measurement of blood pressure by optical methods receives considerable interest, but the complexity of the measurement and the difficulty of adjusting parameters restrict applications. We develop a method for estimating the systolic and diastolic blood pressure using a single-point optical recording of a photoplethysmographic (PPG) signal. The estimation is data-driven, we use automated machine learning algorithms instead of mathematical models. Combining supervised learning with a discrete wavelet transform, the method is insensitive to minor irregularities in the PPG waveform, hence both pulse oximeters and smartphone cameras can record the signal. We evaluate the accuracy of the estimation on 78 samples from 65 subjects (40 male, 25 female, age 29±7) with no history of cardiovascular disease. The estimate for systolic blood pressure has a mean error 4.9±4.9 mm Hg, and 4.3±3.7 mm Hg for diastolic blood pressure when using the oximeter-obtained PPG. The same values are 5.1±4.3 mm Hg and 4.6±4.3 mm Hg when using the phone-obtained PPG, comparing with A&D UA-767PBT result as gold standard. The simplicity of the method encourages ambulatory measurement, and given the ease of sharing the measured data, we expect a shift to data-oriented approaches deriving insight from ubiquitous mobile devices that will yield more accurate machine learning models in monitoring blood pressure.
Posttraumatic stress disorder in adolescents after Hurricane Andrew.

OBJECTIVE To examine rates and correlates of posttraumatic stress disorder (PTSD) in adolescents after Hurricane Andrew. METHOD A random-digit dialing sample of 158 Hispanic, 116 black, and 104 white adolescent-parent pairs were surveyed in high- and low-impact areas within Dade County, Florida, 6 months after Hurricane Andrew. Subjects completed a structured telephone interview focused on within-disaster experiences and emotional reaction, disaster-related losses, lifetime exposure to violent or traumatic events, recent stressful experiences, and psychiatric symptomatology. RESULTS Approximately 3% of males (95% confidence interval 0.4 to 5.3) and 9% of females (95% confidence interval 4.6 to 13.7) met the criteria for PTSD. Rates were highest among blacks (8.3%, 95% confidence interval 2.3 to 14.2) and Hispanics (6.1%, 95% confidence interval 2.2 to 9.9) and increased with age (odds ratio of 1.34, 95% confidence interval 1.04 to 1.72) and the number of undesirable events reported (odds ratio of 1.38, 95% confidence interval 1.21 to 1.57). CONCLUSIONS While only a relatively small percentage of adolescents reported symptoms consistent with a diagnosis of PTSD, most reported some posttraumatic symptoms. Postdisaster planning should recognize that common stressful events occurring after disasters may be more strongly associated with PTSD than magnitude of contact with the actual disaster.
Text clustering with important words using normalization

Important words, which usually exist in part of Title, Subject and Keywords, can briefly reflect the main topic of a document. In recent years, it is a common practice to exploit the semantic topic of documents and utilize important words to achieve document clustering, especially for short texts such as news articles. This paper proposes a novel method to extract important words from Subject and Keywords of articles, and then partition documents only with those important words. Considering the fact that frequencies of important words are usually low and the scale matrix dataset for important words is small, a normalization method is then proposed to normalize the scale dataset so that more accurate results can be achieved by sufficiently exploiting the limited information. The experiments validate the effectiveness of our method.
Additional role of O-acetylserine as a sulfur status-independent regulator during plant growth.

O-acetylserine (OAS) is one of the most prominent metabolites whose levels are altered upon sulfur starvation. However, its putative role as a signaling molecule in higher plants is controversial. This paper provides further evidence that OAS is a signaling molecule, based on computational analysis of time-series experiments and on studies of transgenic plants conditionally displaying increased OAS levels. Transcripts whose levels correlated with the transient and specific increase in OAS levels observed in leaves of Arabidopsis thaliana plants 5-10 min after transfer to darkness and with diurnal oscillation of the OAS content, showing a characteristic peak during the night, were identified. Induction of a serine-O-acetyltransferase gene (SERAT) in transgenic A. thaliana plants expressing the genes under the control of an inducible promoter resulted in a specific time-dependent increase in OAS levels. Monitoring the transcriptome response at time points at which no changes in sulfur-related metabolites except OAS were observed and correlating this with the light/dark transition and diurnal experiments resulted in identification of six genes whose expression was highly correlated with that of OAS (adenosine-5'-phosphosulfate reductase 3, sulfur-deficiency-induced 1, sulfur-deficiency-induced 2, low-sulfur-induced 1, serine hydroxymethyltransferase 7 and ChaC-like protein). These data suggest that OAS displays a signalling function leading to changes in transcript levels of a specific gene set irrespective of the sulfur status of the plant. Additionally, a role for OAS in a specific part of the sulfate response can be deduced.
Microinjected antibodies against the cytoplasmic domain of vesicular stomatitis virus glycoprotein block its transport to the cell surface.

Polyclonal and monoclonal antibodies were raised against a synthetic peptide containing the 15 carboxy‐terminal amino acids (497‐511) of vesicular stomatitis virus glycoprotein (VSV‐G). The polyclonal antibodies (alpha P4) reacted with epitopes distributed along the 15‐residue peptide, whereas the monoclonal antibody (P5D4) reacted with one epitope containing the five carboxy‐terminal amino acids. Both types of antibodies recognized the cytoplasmic domain of VSV‐G synthesized by tissue culture cells infected with the temperature‐sensitive 045‐VSV mutant (ts045‐VSV). They recognized immature forms of VSV‐G in the rough endoplasmic reticulum (RER) and Golgi complex, as well as mature VSV‐G at the cell surface and in budding virus. The effect of these antibodies on intracellular transport and maturation of VSV‐G was studied by microinjection. Both divalent antibodies (alpha P4 and P5D4) blocked transport of VSV‐G to the cell surface. Monovalent Fab' fragments of alpha P4 (alpha P4‐Fabs) also interfered with the appearance of VSV‐G at the cell surface; Fab fragments of P5D4 (P5D4‐Fabs), however, had no inhibitory effect. These results suggest that accessibility of a cytoplasmic domain, located within the sequence of amino acids 497‐506 of the carboxy‐terminal tail, is essential for transport of VSV‐G to the cell surface.
Nuclei Detection Using Deep Learning

An important objective in the analysis of Pap smear images is automatic detection of a cell’s nucleus. Various methods that automatically detect the nuclei of cervical cells have been proposed to improve the analysis of screening test images. In this paper, we propose a Convolutional Neural Networksbased method that automatically detects the nuclei of cervical cells. Following training using a public dataset provided by the Overlapping Cervical Cytology Image Segmentation Challenge ISBI 2014, the network’s fully connected layers are converted to convolutional layers to enable processing of images of any size. Our results were then compared with those achieved by other participants who successfully submitted their work to ISBI 2014 and other studies that used the same dataset. Our experimental results indicate that the methodology provides fast nuclei detection with precision and recall that are comparable with the state-of-the-art methods used to detect the nuclei of cervical cells. Keywords— Nucleus detection, Convolutional Neural Networks.
A Galaxy of Correlations Detecting Linear Correlated Clusters through k-Tuples Sampling using Parameter Space Transform

In different research domains conducted experiments aim for the detection of (hyper)linear correlations among multiple features within a given data set. For this purpose methods exist where one among them is highly robust against noise and detects linear correlated clusters regardless of any locality assumption. This method is based on parameter space transformation. The currently available parameter transform based algorithms detect the clusters scanning explicitly for intersections of functions in parameter space. This approach comes with drawbacks. It is difficult to analyze aspects going beyond the sole intersection of functions, such as e.g. the area around the intersections and further it is computationally expensive. The work in progress method we provide here overcomes the mentioned drawbacks by sampling d-dimensional tuples in data space, generating a (hyper)plane and representing this plane as a single point in parameter space. By this approach we no longer scan for intersection points of functions in parameter space but for dense regions of such parameter vectors. By this approach in future work well established clustering algorithms can be applied in parameter space to detect e.g. dense regions, modes or hierarchies of linear correlations in parameter space.
Complexity : Measurements , Metrics , and Implications

Over the years, the web has evolved from simple text content from one server to a complex ecosystem with different types of content from servers spread across several administrative domains. There is anecdotal evidence of users being frustrated with high page load times or when obscure scripts cause their browser windows to freeze. Because page load times are known to directly impact user satisfaction, providers would like to understand if and how the complexity of their websites affects the user experience. While there is an extensive literature on measuring web graphs, website popularity, and the nature of web traffic, there has been little work in understanding how complex individual websites are, and how this complexity impacts the clients’ experience. This paper is a first step to address this gap. To this end, we identify a set of metrics to characterize the complexity of websites both at a content-level (e.g., number and size of images) and service-level (e.g., number of servers/origins). We find that the distributions of these metrics are largely independent of a website’s popularity rank. However, some categories (e.g., News) are more complex than others. More than 60% of websites have content from at least 5 non-origin sources and these contribute more than 35% of the bytes downloaded. In addition, we analyze which metrics are most critical for predicting page render and load times and find that the number of objects requested is the most important factor. With respect to variability in load times, however, we find that the number of servers is the best indicator.
Prostate Cancer Detection and Prognosis: From Prostate Specific Antigen (PSA) to Exosomal Biomarkers

Prostate specific antigen (PSA) remains the most used biomarker in the management of early prostate cancer (PCa), in spite of the problems related to false positive results and overdiagnosis. New biomarkers have been proposed in recent years with the aim of increasing specificity and distinguishing aggressive from non-aggressive PCa. The emerging role of the prostate health index and the 4Kscore is reviewed in this article. Both are blood-based tests related to the aggressiveness of the tumor, which provide the risk of suffering PCa and avoiding negative biopsies. Furthermore, the use of urine has emerged as a non-invasive way to identify new biomarkers in recent years, including the PCA3 and TMPRSS2:ERG fusion gene. Available results about the PCA3 score showed its usefulness to decide the repetition of biopsy in patients with a previous negative result, although its relationship with the aggressiveness of the tumor is controversial. More recently, aberrant microRNA expression in PCa has been reported by different authors. Preliminary results suggest the utility of circulating and urinary microRNAs in the detection and prognosis of PCa. Although several of these new biomarkers have been recommended by different guidelines, large prospective and comparative studies are necessary to establish their value in PCa detection and prognosis.
Polymers for DNA Delivery

Nucleic acid delivery has many applications in basic science, biotechnology, agriculture, and medicine. One of the main applications is DNA or RNA delivery for gene therapy purposes. Gene therapy, an approach for treatment or prevention of diseases associated with defective gene expression, involves the insertion of a therapeutic gene into cells, followed by expression and production of the required proteins. This approach enables replacement of damaged genes or expression inhibition of undesired genes. Following two decades of research, there are two major methods for delivery of genes. The first method, considered the dominant approach, utilizes viral vectors and is generally an efficient tool of transfection. Attempts, however, to resolve drawbacks related with viral vectors (e.g., high risk of mutagenicity, immunogenicity, low production yield, limited gene size, etc.), led to the development of an alternative method, which makes use of non-viral vectors. This review describes non-viral gene delivery vectors, termed "self-assembled" systems, and are based on cationic molecules, which form spontaneous complexes with negatively charged nucleic acids. It introduces the most important cationic polymers used for gene delivery. A transition from in vitro to in vivo gene delivery is also presented, with an emphasis on the obstacles to achieve successful transfection in vivo.
Microgravity modifies protein kinase C isoform translocation in the human monocytic cell line U937 and human peripheral blood T‐cells

Individual protein kinase C (PKC) isoforms fulfil distinct roles in the regulation of the commitment to differentiation, cell cycle arrest, and apoptosis in both monocytes and T‐cells. The human monocyte like cell line U937 and T‐cells were exposed to microgravity, during spaceflight and the translocation (a critical step in PKC signaling) of individual isoforms to cell particulate fraction examined. PKC activating phorbol esters induced a rapid translocation of several PKC isoforms to the particulate fraction of U937 monocytes under terrestrial gravity (1g) conditions in the laboratory. In microgravity, the translocation of PKC βII, δ, and ε in response to phorbol esters was reduced in microgravity compared to 1g, but was enhanced in weak hypergravity (1.4g). All isoforms showed a net increase in particulate PKC following phorbol ester stimulation, except PKC δ which showed a net decrease in microgravity. In T‐cells, phorbol ester induced translocation of PKC δ was reduced in microgravity, compared to 1g, while PKC βII translocation was not significantly different at the two g‐levels. These data show that microgravity differentially alters the translocation of individual PKC isoforms in monocytes and T‐cells, thus providing a partial explanation for the modifications previously observed in the activation of these cell types under microgravity. J. Cell. Biochem. 87: 39–50, 2002. © 2002 Wiley‐Liss, Inc.
Distance-Based Sampling of Software Configuration Spaces

Configurable software systems provide a multitude of configuration options to adjust and optimize their functional and non-functional properties. For instance, to find the fastest configuration for a given setting, a brute-force strategy measures the performance of all configurations, which is typically intractable. Addressing this challenge, state-of-the-art strategies rely on machine learning, analyzing only a few configurations (i.e., a sample set) to predict the performance of other configurations. However, to obtain accurate performance predictions, a representative sample set of configurations is required. Addressing this task, different sampling strategies have been proposed, which come with different advantages (e.g., covering the configuration space systematically) and disadvantages (e.g., the need to enumerate all configurations). In our experiments, we found that most sampling strategies do not achieve a good coverage of the configuration space with respect to covering relevant performance values. That is, they miss important configurations with distinct performance behavior. Based on this observation, we devise a new sampling strategy, called distance-based sampling, that is based on a distance metric and a probability distribution to spread the configurations of the sample set according to a given probability distribution across the configuration space. This way, we cover different kinds of interactions among configuration options in the sample set. To demonstrate the merits of distance-based sampling, we compare it to state-of-the-art sampling strategies, such as t-wise sampling, on $10$ real-world configurable software systems. Our results show that distance-based sampling leads to more accurate performance models for medium to large sample sets.