Miracle-dz/UniFAR-FTUs huggingface dataset:
Synaptopodin-2 plays an important role in the metastasis of breast cancer via PI3K/Akt/mTOR pathway

Introduction Synaptopodin 2 (SYNPO2) is a functioning protein. It has been detected in many malignancies. But the relation between SYNPO2 and breast cancer (BC) is unclear. Materials and methods In this study, we explored the expression and function of SYNPO2 in BC. We found that SYNPO2 gene in BC was downregulated at the transcriptional level in both validated and TCGA cohorts. Results The results revealed that age, lymph node metastasis, and clinical stage in the validated cohort were related to the expression of SYNPO2 negatively. Kaplan–Meier analysis showed that patients with lower SYNPO2 expression had a worse overall survival. Discussion We found that migration and invasion were promoted after knocking down SYNPO2 in MCF-7, MDA-MB-231, BT-549, and MDA-MB-468. Meanwhile, knockdown of SYNPO2 could enhance PI3K/AKT/mTOR signaling pathway, which may induce migration and invasion. Our findings reveal that SYNPO2 was associated with BC.
Estimating infection prevalence: Best practices and their theoretical underpinnings

Abstract Accurately estimating infection prevalence is fundamental to the study of population health, disease dynamics, and infection risk factors. Prevalence is estimated as the proportion of infected individuals (“individual‐based estimation”), but is also estimated as the proportion of samples in which evidence of infection is detected (“anonymous estimation”). The latter method is often used when researchers lack information on individual host identity, which can occur during noninvasive sampling of wild populations or when the individual that produced a fecal sample is unknown. The goal of this study was to investigate biases in individual‐based versus anonymous prevalence estimation theoretically and to test whether mathematically derived predictions are evident in a comparative dataset of gastrointestinal helminth infections in nonhuman primates. Using a mathematical model, we predict that anonymous estimates of prevalence will be lower than individual‐based estimates when (a) samples from infected individuals do not always contain evidence of infection and/or (b) when false negatives occur. The mathematical model further predicts that no difference in bias should exist between anonymous estimation and individual‐based estimation when one sample is collected from each individual. Using data on helminth parasites of primates, we find that anonymous estimates of prevalence are significantly and substantially (12.17%) lower than individual‐based estimates of prevalence. We also observed that individual‐based estimates of prevalence from studies employing single sampling are on average 6.4% higher than anonymous estimates, suggesting a bias toward sampling infected individuals. We recommend that researchers use individual‐based study designs with repeated sampling of individuals to obtain the most accurate estimate of infection prevalence. Moreover, to ensure accurate interpretation of their results and to allow for prevalence estimates to be compared among studies, it is essential that authors explicitly describe their sampling designs and prevalence calculations in publications.
The Radiologic Evaluation of Pediatric Acute Abdomen; Results of Tertiary Referral Center

Purpose: In this study we aimed to evaluate the radiological examinations of the pediatric patients who were operated with initial diagnosis of acute abdomen. Methods: We retrospectively reviewed the clinical records and imaging findings of 252 children. All patients were evaluated by plain abdominal radiographs (PAX) and ultrasonography (US). Only 10 patients were examined using computed tomography (CT). The findings of the PAX, US and CT of each patient were determined from their detailed archive records according to their clinical diagnosis. Results: The most frequent pathology was appendicitis in our study whereas the other pathologies were invagination, ovarian torsion, the complications of Meckel’s diverticulum, gastrointestinal obstruction and tuboovarian abscess in decreasing frequency. PAXs were valuable in diagnosis of the patients with ileus. It has been showed that US was the most useful for patients with appendicitis and invagination. CT was performed only in 4% of our cases as an advanced diagnostic method. Conclusion: The pediatric patients with acute abdomen have been evaluated radiologically by PAX and US routinely and frequently. CT was performed as an advanced diagnostic method very rarely. CT would be utilized to a lower extent as a more advanced method of imaging in unsolved patient group, as US and PAX solve the pediatric acute abdominal pathologies in high percentages.
Cinemotion, a Program of Cognitive Remediation to Improve the Recognition and Expression of Facial Emotions in Schizophrenia: A Pilot Study

Objective: Patients with schizophrenia exhibit impaired social cognition, especially in the recognition and expression of facial emotions, aspects of communication profoundly interlinked in an embodied approach of cognition. Nevertheless, many training programs have been developed that focus on either of these deficits but not both. We therefore designed a training program, Cinemotion, intended to remedy the 2 deficits and investigated its feasibility and effects in patients with schizophrenia. Design: Thirty-one patients undergoing treatment for schizophrenia and presenting deficit in emotion recognition were randomized to a group of 16 to undergo Cinemotion training, delivered in weekly group sessions, and to a control treatment group of 15. At the conclusion of training or after 10 weeks in controls, we reassessed and compared original and final results to determine improvement. Methods: Facial emotions recognition (TREF), empathy (Questionnaire of Cognitive and Affective Empathy, QCAE), and attributional style (Ambiguous Intentions Hostility Questionnaire, AIHQ) were assessed before (T0) and after (T1) the program. External evaluators also assessed ability and accuracy of Cinemotion participants to self-generate facial emotion expression in response to verbal instruction. Results: Between T0 and T1, Cinemotion participants significantly improved total TREF, sadness, disgust, and anger scores, compared to findings in control treatment group. They also improved their ability and accuracy to self-generate facial expressions, especially sadness and fear, with no significant improvement in other components of social recognition. Conclusions: Our findings show the apparent efficacy of training using the Cinemotion program to improve the recognition and expression of facial emotions in schizophrenia.
Physical Adversarial Examples for Object Detectors

Deep neural networks (DNNs) are vulnerable to adversarial examples-maliciously crafted inputs that cause DNNs to make incorrect predictions. Recent work has shown that these attacks generalize to the physical domain, to create perturbations on physical objects that fool image classifiers under a variety of real-world conditions. Such attacks pose a risk to deep learning models used in safety-critical cyber-physical systems. In this work, we extend physical attacks to more challenging object detection models, a broader class of deep learning algorithms widely used to detect and label multiple objects within a scene. Improving upon a previous physical attack on image classifiers, we create perturbed physical objects that are either ignored or mislabeled by object detection models. We implement a Disappearance Attack, in which we cause a Stop sign to "disappear" according to the detector-either by covering thesign with an adversarial Stop sign poster, or by adding adversarial stickers onto the sign. In a video recorded in a controlled lab environment, the state-of-the-art YOLOv2 detector failed to recognize these adversarial Stop signs in over 85% of the video frames. In an outdoor experiment, YOLO was fooled by the poster and sticker attacks in 72.5% and 63.5% of the video frames respectively. We also use Faster R-CNN, a different object detection model, to demonstrate the transferability of our adversarial perturbations. The created poster perturbation is able to fool Faster R-CNN in 85.9% of the video frames in a controlled lab environment, and 40.2% of the video frames in an outdoor environment. Finally, we present preliminary results with a new Creation Attack, where in innocuous physical stickers fool a model into detecting nonexistent objects.
Fully Convolutional Pixel Adaptive Image Denoiser

We propose a new image denoising algorithm, dubbed as Fully Convolutional Adaptive Image DEnoiser (FC-AIDE), that can learn from an offline supervised training set with a fully convolutional neural network as well as adaptively fine-tune the supervised model for each given noisy image. We significantly extend the framework of the recently proposed Neural AIDE, which formulates the denoiser to be context-based pixelwise mappings and utilizes the unbiased estimator of MSE for such denoisers. The two main contributions we make are; 1) implementing a novel fully convolutional architecture that boosts the base supervised model, and 2) introducing regularization methods for the adaptive fine-tuning such that a stronger and more robust adaptivity can be attained. As a result, FC-AIDE is shown to possess many desirable features; it outperforms the recent CNN-based state-of-the-art denoisers on all of the benchmark datasets we tested, and gets particularly strong for various challenging scenarios, e.g., with mismatched image/noise characteristics or with scarce supervised training data. The source code our algorithm is available at \href{https://github.com/csm9493/FC-AIDE-Keras}{\textcolor{magenta}{https://github.com/csm9493/FC-AIDE-Keras}}.
3D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single Image

3D reconstruction from single view images is an ill-posed problem. Inferring the hidden regions from self-occluded images is both challenging and ambiguous. We propose a two-pronged approach to address these issues. To better incorporate the data prior and generate meaningful reconstructions, we propose 3D-LMNet, a latent embedding matching approach for 3D reconstruction. We first train a 3D point cloud auto-encoder and then learn a mapping from the 2D image to the corresponding learnt embedding. To tackle the issue of uncertainty in the reconstruction, we predict multiple reconstructions that are consistent with the input view. This is achieved by learning a probablistic latent space with a novel view-specific diversity loss. Thorough quantitative and qualitative analysis is performed to highlight the significance of the proposed approach. We outperform state-of-the-art approaches on the task of single-view 3D reconstruction on both real and synthetic datasets while generating multiple plausible reconstructions, demonstrating the generalizability and utility of our approach.
Squeezing out the last egg—annual fish increase reproductive efforts in response to a predation threat

Abstract Both constitutive and inducible antipredator strategies are ubiquitous in nature and serve to maximize fitness under a predation threat. Inducible strategies may be favored over constitutive defenses depending on their relative cost and benefit and temporal variability in predator presence. In African temporary ponds, annual killifish of the genus Nothobranchius are variably exposed to predators, depending on whether larger fish invade their habitat from nearby rivers during floods. Nonetheless, potential plastic responses to predation risk are poorly known. Here, we studied whether Nothobranchius furzeri individuals adjust their life history in response to a predation threat. For this, we monitored key life history traits in response to cues that signal the presence of predatory pumpkinseed sunfish (Lepomis gibbosus). While growth rate, adult body size, age at maturation, and initial fecundity were not affected, peak and total fecundity were higher in the predation risk treatment. This contrasts with known life history strategies of killifish from permanent waters, which tend to reduce reproduction in the presence of predators. Although our results show that N. furzeri individuals are able to detect predators and respond to their presence by modulating their reproductive output, these responses only become evident after a few clutches have been deposited. Overall our findings suggest that, in the presence of a predation risk, it can be beneficial to increase the production of life stages that can persist until the predation risk has faded.
Limitation of Maximal Heart Rate in Hypoxia: Mechanisms and Clinical Importance

The use of exercise intervention in hypoxia has grown in popularity amongst patients, with encouraging results compared to similar intervention in normoxia. The prescription of exercise for patients largely rely on heart rate recordings (percentage of maximal heart rate (HRmax) or heart rate reserve). It is known that HRmax decreases with high altitude and the duration of the stay (acclimatization). At an altitude typically chosen for training (2,000-3,500 m) conflicting results have been found. Whether or not this decrease exists or not is of importance since the results of previous studies assessing hypoxic training based on HR may be biased due to improper intensity. By pooling the results of 86 studies, this literature review emphasizes that HRmax decreases progressively with increasing hypoxia. The dose–response is roughly linear and starts at a low altitude, but with large inter-study variabilities. Sex or age does not seem to be a major contributor in the HRmax decline with altitude. Rather, it seems that the greater the reduction in arterial oxygen saturation, the greater the reduction in HRmax, due to an over activity of the parasympathetic nervous system. Only a few studies reported HRmax at sea/low level and altitude with patients. Altogether, due to very different experimental design, it is difficult to draw firm conclusions in these different clinical categories of people. Hence, forthcoming studies in specific groups of patients are required to properly evaluate (1) the HRmax change during acute hypoxia and the contributing factors, and (2) the physiological and clinical effects of exercise training in hypoxia with adequate prescription of exercise training intensity if based on heart rate.
Relationship between bridging and dimensions of sella turcica with classification of craniofacial skeleton

Purpose In orthodontics, it is essential to determine the craniofacial skeleton pattern (class I, II, III) for planning treatment. Sella turcica bridging that is seen on lateral cephalometric radiographs is considered as a normal finding. This study aimed to compare sella turcica bridging and its dimensions in patients with various craniofacial patterns. Material and methods A total of 105 lateral cephalometric radiographs (53 men and 52 women), aged 14–26 years, were randomly and equally assigned to three groups of class I, II, and III, respectively. The length, diameter, and depth of the sella turcica as well as sella turcica bridging were determined on radiographs. The chi-squared test was used for assessing the relationship between sella turcica bridging and craniofacial skeleton classification. ANOVA was used for assessing the relationship between the dimensions of the sella turcica and craniofacial skeleton classification. The Pearson’s correlation coefficient was used for assessing the relationship between age and the dimensions of the sella turcica. Results The sella turcica had a normal shape in 64.76% of patients, whereas 35.33% of patients had sella turcica bridging. In total, 11.42% of patients belonged to class I, 34.28% to class II, and 66.62% to class III. The diameter of the sella turcica had a significant relationship with age; the diameter of the sella turcica increased with age (p < 0.001). Conclusions There is a significant relationship between craniofacial skeleton patterns and sella turcica bridging, i.e., the incidence of sella turcica bridging is higher in class III patients. The sella turcica had a greater diameter in older patients.
Cultured Human Thymic-Derived Cells Display Medullary Thymic Epithelial Cell Phenotype and Functionality

Thymic epithelial cells are one of the main components of the thymic microenvironment required for T-cell development. In this work, we describe an efficient method free of enzymatic and Facs-sorted methods to culture human medullary thymic epithelial cells without affecting the cell phenotypic, physiologic and functional features. Human medulla thymic epithelial cells (mTECs) are obtained by culturing thymic biopsies explants. After 7 days of primo-culture, mTECs keep their ability to express key molecules involved in immune tolerance processes such as autoimmune regulator, tissue-specific antigens, chemokines, and cytokines. In addition, the cells sensor their cultured environment and consequently adjust their gene expression network. Therefore, we describe and provide a human mTEC model that may be used to test the effect of various molecules on thymic epithelial cell homeostasis and physiology. This method should allow the investigations of the specificities and the knowledge of human mTECs in normal or pathological conditions and therefore discontinue the extrapolations done on the murine models.
MicroRNA-384 inhibits proliferation migration and invasion of glioma by targeting at CDC42

Background Accumulative evidence indicated that microRNAs (miRNAs) play a critical role in carcinogenesis and biological behaviors of glioma. Further bio-molecular mechanisms of miRNAs in glioma cells remain largely unknown, which can contribute to novel therapeutic strategy. Methods In the present study, we detected the expression level of miR-384 by RT-PCR and Western blot. Meanwhile, Gain and loss function assay of miR-384 by transfection of miR-384 mimics and inhibitor. Moreover, wild and mutant psiCHECK-2-CDC42-3′-UTR luciferase reporter vectors were constructed and transfected into glioma cells with miR-384 mimics or miR-NC. Results miR-384 was dramatically down-regulated in human glioma tissues. It was also demonstrated that miR-384 significantly inhibited proliferation, migration and invasion of glioma cells. Cell division cycle 42 (Cdc42) was a direct target of miR-384 according to results of RT-PCR and Western blotting. Conclusion Our research demonstrated that miR-384 exerted an inhibitory effect on proliferation, migration and invasion of glioma via suppressing the expression of CDC42, meaning that miR-384 may be regarded as a potential target in the treatment of glioma.
An Efficient End-to-End Neural Model for Handwritten Text Recognition

Offline handwritten text recognition from images is an important problem for enterprises attempting to digitize large volumes of handmarked scanned documents/reports. Deep recurrent models such as Multi-dimensional LSTMs have been shown to yield superior performance over traditional Hidden Markov Model based approaches that suffer from the Markov assumption and therefore lack the representational power of RNNs. In this paper we introduce a novel approach that combines a deep convolutional network with a recurrent Encoder-Decoder network to map an image to a sequence of characters corresponding to the text present in the image. The entire model is trained end-to-end using Focal Loss, an improvement over the standard Cross-Entropy loss that addresses the class imbalance problem, inherent to text recognition. To enhance the decoding capacity of the model, Beam Search algorithm is employed which searches for the best sequence out of a set of hypotheses based on a joint distribution of individual characters. Our model takes as input a downsampled version of the original image thereby making it both computationally and memory efficient. The experimental results were benchmarked against two publicly available datasets, IAM and RIMES. We surpass the state-of-the-art word level accuracy on the evaluation set of both datasets by 3.5% & 1.1%, respectively.
Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors

We study the problem of generating adversarial examples in a black-box setting in which only loss-oracle access to a model is available. We introduce a framework that conceptually unifies much of the existing work on black-box attacks, and we demonstrate that the current state-of-the-art methods are optimal in a natural sense. Despite this optimality, we show how to improve black-box attacks by bringing a new element into the problem: gradient priors. We give a bandit optimization-based algorithm that allows us to seamlessly integrate any such priors, and we explicitly identify and incorporate two examples. The resulting methods use two to four times fewer queries and fail two to five times less often than the current state-of-the-art.
Multi-criteria Evolution of Neural Network Topologies: Balancing Experience and Performance in Autonomous Systems

Majority of Artificial Neural Network (ANN) implementations in autonomous systems use a fixed/user-prescribed network topology, leading to sub-optimal performance and low portability. The existing neuro-evolution of augmenting topology or NEAT paradigm offers a powerful alternative by allowing the network topology and the connection weights to be simultaneously optimized through an evolutionary process. However, most NEAT implementations allow the consideration of only a single objective. There also persists the question of how to tractably introduce topological diversification that mitigates overfitting to training scenarios. To address these gaps, this paper develops a multi-objective neuro-evolution algorithm. While adopting the basic elements of NEAT, important modifications are made to the selection, speciation, and mutation processes. With the backdrop of small-robot path-planning applications, an experience-gain criterion is derived to encapsulate the amount of diverse local environment encountered by the system. This criterion facilitates the evolution of genes that support exploration, thereby seeking to generalize from a smaller set of mission scenarios than possible with performance maximization alone. The effectiveness of the single-objective (optimizing performance) and the multi-objective (optimizing performance and experience-gain) neuro-evolution approaches are evaluated on two different small-robot cases, with ANNs obtained by the multi-objective optimization observed to provide superior performance in unseen scenarios.
Multiple adaptive and non-adaptive processes determine responsiveness to heterospecific alarm calls in African savannah herbivores

Heterospecific alarm calls may provide crucial survival benefits shaping animal behaviour. Multispecies studies can disentangle the relative importance of the various processes determining these benefits, but previous studies have included too few species for alternative hypotheses to be tested quantitatively in a comprehensive analysis. In a community-wide study of African savannah herbivores, we here, for the first time to our knowledge, partition alarm responses according to distinct aspects of the signaller–receiver relationship and thereby uncover the impact of several concurrent adaptive and non-adaptive processes. Stronger responses were found to callers who were vulnerable to similar predators and who were more consistent in denoting the presence of predators of the receiver. Moreover, alarm calls resembling those of conspecifics elicited stronger responses, pointing to sensory constraints, and increased responsiveness to more abundant callers indicated a role of learning. Finally, responses were stronger in risky environments. Our findings suggest that mammals can respond adaptively to variation in the information provided by heterospecific callers but within the constraints imposed by a sensory bias towards conspecific calls and reduced learning of less familiar calls. The study thereby provides new insights central to understanding the ecological consequences of interspecific communication networks in natural communities.