Miracle-dz/UniFAR-FTUs huggingface dataset:
Correlation of SOCS-1 gene with onset and prognosis of breast cancer

The aim of the present study is to study the expressions of suppressor of cytokine signaling (SOCS)-1 in the tumor tissues and adjacent normal tissues of patients with breast cancer. The study was also planned to investigate the association of SOCS-1 gene expression with patients' clinical pathology, molecular subtype and prognosis. A total of 60 cases of frozen and paraffin-embedded specimens of tumor tissues and corresponding adjacent normal tissues of patients with breast cancer were selected. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to detect the expression levels of SOCS-1 messenger RNA (mRNA) in the patients' tumor tissues and adjacent normal tissues. The immunohistochemical method was applied to detect the expressions of SOCS-1 proteins in the patients' breast cancer tissues and adjacent normal tissues. Moreover, the correlations of SOCS-1 protein expressions in breast cancer tissues with patients' pathological parameters, molecular subtypes and prognosis were analyzed in combination with the clinical data. The results of RT-qPCR showed that the SOCS-1 mRNA expression in breast cancer tissues was significantly lower than that in adjacent normal tissues (p<0.01). The immunohistochemical results indicated that the positive expression rate of the SOCS-1 proteins in breast cancer tissues (23.33%) was remarkably lower than that in adjacent normal tissues (88.33%) (p<0.01). The low expression of SOCS-1 in breast cancer tissues was related to lymph node metastasis and clinical staging. The positive expression rates of the luminal A SOCS-1 proteins were the highest (47.62%) (p<0.01). The 5-year overall survival rate of the breast cancer patients was 63.33% (38/60). The univariate survival analysis revealed that the patients with low expression of SOCS-1 had poorer prognosis. In conclusion, the low expression of SOCS-1 plays a key role in the pathogenesis of breast cancer; in particular, it is associated with the lymph node metastasis and clinical staging of the tumor; so, the SOCS-1 expression in breast cancer tissues can be regarded as an important reference for the prognostic estimation of breast cancer.
On the Learning of Deep Local Features for Robust Face Spoofing Detection

Biometrics emerged as a robust solution for security systems. However, given the dissemination of biometric applications, criminals are developing techniques to circumvent them by simulating physical or behavioral traits of legal users (spoofing attacks). Despite face being a promising characteristic due to its universality, acceptability and presence of cameras almost everywhere, face recognition systems are extremely vulnerable to such frauds since they can be easily fooled with common printed facial photographs. State-of-the-art approaches, based on Convolutional Neural Networks (CNNs), present good results in face spoofing detection. However, these methods do not consider the importance of learning deep local features from each facial region, even though it is known from face recognition that each facial region presents different visual aspects, which can also be exploited for face spoofing detection. In this work we propose a novel CNN architecture trained in two steps for such task. Initially, each part of the neural network learns features from a given facial region. Afterwards, the whole model is fine-tuned on the whole facial images. Results show that such pre-training step allows the CNN to learn different local spoofing cues, improving the performance and the convergence speed of the final model, outperforming the state-of-the-art approaches.
Use of Ozone-Based Eye Drops: A Series of Cases in Veterinary and Human Spontaneous Ocular Pathologies

Conjunctivitis, keratoconjunctivitis, and corneal ulcers are common eye disorders frequently diagnosed in both humans and animals, and are currently treated by topical administration of eye drops containing anti-inflammatory and antibacterial agents. The current molecules often lack efficacy because infections in hypoxic tissue contain methicillin-resistant Staphylococcus aureus and Pseudomonas aeruginosa; thus, new products for the treatment of ocular pain and inflammation are needed. The use of ozone, a molecule stabilized for topical use as an ozonide, could be providential due to its anti-inflammatory and bactericidal activity in certain anterior segment pathologies, in addition to promoting tissue repair properties. Ozonated oils have the same properties as gaseous ozone and are well tolerated by tissues. In the present study the repair and regeneration effect of ozonated oil in liposomes plus hypromellose (Ozodrop®, FB Vision, Ascoli Piceno, Italy) instilled 3–4 times a day in external ocular spontaneous pathologies both in animals and humans are reported.
Hide and Seek tracker: Real-time recovery from target loss

In this paper, we examine the real-time recovery of a video tracker from a target loss, using information that is already available from the original tracker and without a significant computational overhead. More specifically, before using the tracker output to update the target position we estimate the detection confidence. In the case of a low confidence, the position update is rejected and the tracker passes to a single-frame failure mode, during which the patch low-level visual content is used to swiftly update the object position, before recovering from the target loss in the next frame. Orthogonally to this improvement, we further enhance the running average method used for creating the query model in tracking-through-similarity. The experimental evidence provided by evaluation on standard tracking datasets (OTB-50, OTB-100 and OTB-2013) validate that target recovery can be successfully achieved without compromising the real-time update of the target position.
Void Space Surfaces to Convey Depth in Vessel Visualizations

To enhance depth perception and thus data comprehension, additional depth cues are often used in 3D visualizations of complex vascular structures. There is a variety of different approaches described in the literature, ranging from chromadepth color coding over depth of field to glyph-based encodings. Unfortunately, the majority of existing approaches suffers from the same problem: As these cues are directly applied to the geometry’s surface, the display of additional information on the vessel wall, such as other modalities or derived attributes, is impaired. To overcome this limitation we propose Void Space Surfaces which utilizes empty space in between vessel branches to communicate depth and their relative positioning. This allows us to enhance the depth perception of vascular structures without interfering with the spatial data and potentially superimposed parameter information. With this article, we introduce Void Space Surfaces, describe their technical realization, and show their application to various vessel trees. Moreover, we report the outcome of two user studies which we have conducted in order to evaluate the perceptual impact of Void Space Surfaces compared to existing vessel visualization techniques and discuss expert feedback.
Management and site effects on carbon balances of European mountain meadows and rangelands

We studied carbon balances and carbon stocks of mountain rangelands and meadows in a network of 8 eddy covariance sites and 14 sites with biomass data in Europe. Net ecosystem exchange of pastures and extensively managed semi-natural rangelands were usually close to zero, while meadows fixed carbon, with the exception of one meadow that was established on a drained peatland. When we accounted for off-site losses and inputs also the carbon budget of meadows approached zero. Soil carbon stocks in these ecosystems were high, comparable to those of forest ecosystems, while carbon stocks in plant biomass were smaller. Since soil carbon stocks of abandoned mountain grasslands are as high as in managed ecosystems, it is likely that the widespread abandonment of mountain rangelands used currently as pastures will not lead to an immediate carbon sink in those ecosystems.
Numerical inversion of Laplace transform using Haar wavelet operational matrices

In this paper, a unified derivation of the operational matrices of various orthogonal functions including the Haar wavelet is first given. Based on the derived operational matrix, this paper presents a new method for performing numerical inversion of the Laplace transform. Only matrix multiplications and ordinary algebraic operations are involved in the method. The proposed method is much simpler as compared with the dictionary-type method and the contour-integration method.
Randomized Trial Epidural Dexamethasone Influences Postoperative Analgesia after Major Abdominal Surgery

Methods: One hundred twenty ASA physical status I and II men, scheduled for gastrectomy, were enrolled. Patients were randomly assigned to receive one of 3 treatment regimens (n = 40 in each group): dexamethasone 5 mg (1 mL) with normal saline (1 mL) (group D) or dexamethasone 10 mg (2 mL) (group E) or 2 mL of normal saline (group C) mixed with 8 mL of 0.375% ropivacaine as a loading dose. After the surgery, 0.2% ropivacaine fentanyl 4 μg/mL was epidurally administered for analgesia. The infusion was set to deliver 4 mL/hr of the PCEA solution, with a bolus of 2 mL per demand and 15 minutes lockout time. The infused volume of PCEA, intensity of postoperative pain using visual analogue scale (VAS) during rest and coughing, incidence of postoperative nausea and vomiting (PONV), usage of rescue analgesia and rescue antiemetic, and side effects such as respiratory depression, urinary retention, and pruritus were recorded at 2, 6, 12, 24, and 48 hours after the end of surgery.
Parent–Child Relationships and Resilience Among Chinese Adolescents: The Mediating Role of Self-Esteem

The present study primarily aimed to examine whether self-esteem serves as a mediator in the associations between parent–child relationships, including parental support and parent–child conflict, and resilience among adolescents. Three hundred and four Chinese adolescents were surveyed with questionnaires and structural equation modeling was adopted to test the mediational hypothesis. The results indicated that the associations between parent–child relationships and adolescent resilience were primarily mediated by self-esteem and that parental support was more robustly linked with adolescent resilience than parent–adolescent conflict. The current study also tested a competitive mediational model in which resilience was the mediator and self-esteem was the outcome variable, and observed that this model was also well-established but inferior to the hypothesized mediational model. These findings extend our insight into the mechanisms underlying the associations among parent–child relationships, self-esteem, and resilience among adolescents and suggest that adolescent resilience promotion programs should focus on improving parental support in a family context and developing individual self-esteem.
Enhanced switching and familial susceptibility for psychosis

Working Memory and Task‐Switching are essential components of cognitive control, which underlies many symptoms evident across multiple neuropsychiatric disorders, including psychotic and mood disorders. Vulnerability to these disorders has a substantial genetic component, suggesting that clinically unaffected first‐degree relatives may carry some vulnerability‐related traits. Converging evidence from animal and human studies demonstrates that dopamine transmission, striatal and frontal brain regions, and attention and switching behaviors are essential components of a multilevel circuit involved in salience, and disruptions in that circuit may lead to features of psychosis. Yet, it is possible that unaffected relatives may also possess characteristics that protect against development of illness. We hypothesized that reduced switch cost in a cued task‐switching task, may be a behavioral expression of this “resilience” phenotype that will be observable in unaffected relatives.
How to Maximize the Spread of Social Influence: A Survey

This survey presents the main results achieved for the influence maximization problem in social networks. This problem is well studied in the literature and, thanks to its recent applications, some of which currently deployed on the field, it is receiving more and more attention in the scientific community. The problem can be formulated as follows: given a graph, with each node having a certain probability of influencing its neighbors, select a subset of vertices so that the number of nodes in the network that are influenced is maximized. Starting from this model, we introduce the main theoretical developments and computational results that have been achieved, taking into account different diffusion models describing how the information spreads throughout the network, various ways in which the sources of information could be placed, and how to tackle the problem in the presence of uncertainties affecting the network. Finally, we present one of the main application that has been developed and deployed exploiting tools and techniques previously discussed.
Neural Tangent Kernel: Convergence and Generalization in Neural Networks

At initialization, artificial neural networks (ANNs) are equivalent to Gaussian processes in the infinite-width limit, thus connecting them to kernel methods. We prove that the evolution of an ANN during training can also be described by a kernel: during gradient descent on the parameters of an ANN, the network function (which maps input vectors to output vectors) follows the so-called kernel gradient associated with a new object, which we call the Neural Tangent Kernel (NTK). This kernel is central to describe the generalization features of ANNs. While the NTK is random at initialization and varies during training, in the infinite-width limit it converges to an explicit limiting kernel and stays constant during training. This makes it possible to study the training of ANNs in function space instead of parameter space. Convergence of the training can then be related to the positive-definiteness of the limiting NTK. We then focus on the setting of least-squares regression and show that in the infinite-width limit, the network function follows a linear differential equation during training. The convergence is fastest along the largest kernel principal components of the input data with respect to the NTK, hence suggesting a theoretical motivation for early stopping. Finally we study the NTK numerically, observe its behavior for wide networks, and compare it to the infinite-width limit.
DEFRAG: Deep Euclidean Feature Representations through Adaptation on the Grassmann Manifold

We propose a novel technique for training deep networks with the objective of obtaining feature representations that exist in a Euclidean space and exhibit strong clustering behavior. Our desired features representations have three traits: they can be compared using a standard Euclidian distance metric, samples from the same class are tightly clustered, and samples from different classes are well separated. However, most deep networks do not enforce such feature representations. The DEFRAG training technique consists of two steps: first good feature clustering behavior is encouraged though an auxiliary loss function based on the Silhouette clustering metric. Then the feature space is retracted onto a Grassmann manifold to ensure that the L_2 Norm forms a similarity metric. The DEFRAG technique achieves state of the art results on standard classification datasets using a relatively small network architecture with significantly fewer parameters than many standard networks.
Fool's Mate: What Does Chess Tell Us About Individual Investor Trading Performance?

We investigate the shortâ€ term relation between individual investor trading and stock returns on the Australian Securities Exchange. Stocks heavily bought by individual investors underperform stocks heavily sold over the subsequent three days, with respective returns on to a longâ€“short portfolio of âˆ’93, âˆ’67 and âˆ’12 basis points on days one, two and three. Individuals underperform in small and midâ€ size stocks when they trade passively using limit orders waiting for the market price to move in their favour. Individuals underperform in large stocks when they trade aggressively using marketable orders. Foreign institutions gain from taking the opposite side of individual trades. We present an information asymmetryâ€ based explanation for the findings.
Amylase Production from Thermophilic Bacillus sp. BCC 021-50 Isolated from a Marine Environment

The high cost of fermentation media is one of the technical barriers in amylase production from microbial sources. Amylase is used in several industrial processes or industries, for example, in the food industry, the saccharification of starchy materials, and in the detergent and textile industry. In this study, marine microorganisms were isolated to identify unique amylase-producing microbes in starch agar medium. More than 50 bacterial strains with positive amylase activity, isolated from marine water and soil, were screened for amylase production in starch agar medium. Bacillus sp. BCC 021-50 was found to be the best amylase-producing strain in starch agar medium and under submerged fermentation conditions. Next, fermentation conditions were optimized for bacterial growth and enzyme production. The highest amylase concentration of 5211 U/mL was obtained after 36 h of incubation at 50 °C, pH 8.0, using 20 g/L molasses as an energy source and 10 g/L peptone as a nitrogen source. From an application perspective, crude amylase was characterized in terms of temperature and pH. Maximum amylase activity was noted at 70 °C and pH 7.50. However, our results show clear advantages for enzyme stability in alkaline pH, high-temperature, and stability in the presence of surfactant, oxidizing, and bleaching agents. This research contributes towards the development of an economical amylase production process using agro-industrial residues.
Aging related cognitive changes associated with Alzheimer's disease in Down syndrome

Objective Individuals with Down syndrome (DS) have an extremely high genetic risk for Alzheimer’s disease (AD) however the course of cognitive decline associated with progression to dementia is ill-defined. Data-driven methods can estimate long-term trends from cross-sectional data while adjusting for variability in baseline ability, which complicates dementia assessment in those with DS. Methods We applied an event-based model to cognitive test data and informant-rated questionnaire data from 283 adults with DS (the largest study of cognitive functioning in DS to date) to estimate the sequence of cognitive decline and individuals’ disease stage. Results Decline in tests of memory, sustained attention / motor coordination, and verbal fluency occurred early, demonstrating that AD in DS follows a similar pattern of change to other forms of AD. Later decline was found for informant measures. Using the resulting staging model, we showed that adults with a clinical diagnosis of dementia and those with APOE 3:4 or 4:4 genotype were significantly more likely to be staged later, suggesting the model is valid. Interpretation Our results identify tests of memory and sustained attention may be particularly useful measures to track decline in the preclinical/prodromal stages of AD in DS whereas informant-measures may be useful in later stages (i.e. during conversion to dementia, or post-diagnosis). These results have implications for the selection of outcome measures of treatment trials to delay or prevent cognitive decline due to AD in DS. As clinical diagnoses are generally made late into AD progression, early assessment is essential.
Identifying stylometric correlates of social power

This thesis takes a stylometric approach to the measurement of social power, particularly hierarchical power in an organisational setting. Following the social constructionist view of identity, we infer that construction of identity is an ongoing process incorporating the full scope of human behaviour, including linguistic behaviour. We test the primary hypothesis that stylistic choice in language is indicative of power relations, and that a stylometric signal can be extracted from natural language to enable prediction of relationship status. Additionally, we consider the effect of individual variation versus interpersonal variation, and the effects of aggregating predictions to boost the predictive strength of the model. Three different datasets are used to validate the proposed approach across three different genres: email, spoken conversation, and online chat. We also present a vector space approach to modelling linguistic style accommodation, and undertake a preliminary examination of the correlation between linguistic accommodation and social power.
Moderators of the disapproval of peer punishment

Recent studies have found disapproval of peer punishment of norm violations. This seems puzzling, given the potential benefits peer punishers contribute to the group. We suggest part of the answer is that peer punishers tend to come across as aggressive and as such may be viewed as more problematic than beneficial to have around. We used simple computer animations of geometric shapes to enact 15 precise variations of social sanctions against a norm violator. More than 1,800 subjects were recruited to watch an animation and judge the behavior and character of the animated agents. They also completed a trait aggression measure. Across the variations peer punishment was typically disapproved of, especially when severe or openly aggressive, and especially by subjects low on trait aggression. We conclude that there seems to be a social norm against peer punishment and that dislike of aggressiveness seems to be part of the reason why.
NARCISSISTIC CHIEF EXECUTIVE OFFICERS AND CORPORATE ACQUISITIONS An Upper Echelons Perspective

This study investigates how chief executive officer narcissism impacts the organizations strategic decisions and outcomes. Rooted in the idea that the organization is a reflection of its top managers, I theorize that narcissistic chief executive officers may influence the decision process leading to corporate acquisitions. Furthermore, taking into account the Dotcom bubble and subprime-crash, research on merger waves is incorporated into the research framework as an outside of the organization effect impacting organizational outcomes. Using a sample of 116 acquisitions by 60 chief executive officers at public listed U.S. firms this is tested from 1994-2017. The results show little to no connection between chief executive officer narcissism and corporate acquisitions in total, international acquisitions or through truly large acquisitions. Though there is a positive connection between observable characteristics such as education and age influencing the decision to acquire firms. When comparing the sample of 116 acquisitions to a general market sample of 4379 acquisitions, a connection to merger wave theory is identified.