Miracle-dz/UniFAR-FTUs huggingface dataset:
Obesogenic Clusters Associated with Weight Status in Brazilian Adolescents of the Movimente School-Base Intervention

Background: the relationship between behavior clusters and weight status, mainly in low- and middle-income countries, remains unclear. This study aimed to examine the association between profiles of physical activity (PA), diet and sedentary behavior (SB) with weight status in adolescents from a southern Brazilian city, according to sex. Methods: data from the Movimente Intervention study were analyzed (n = 812 / mean age 13.0 years (sd 1.04). Data on SB hours per day, PA minutes per week and weekly consumption frequencies of fruits, vegetables, salty snacks, candies and soda were self-reported on the validated Movimente questionnaire. Classes of healthy and unhealthy behaviors were derived by latent class analysis. Logistic regression analysis was used to estimate the associations between adolescents’ weight status and classes. Results: two classes were identified for the whole sample and for boys and girls. All classes had high probabilities of engaging high time in SB. Male adolescents in the unhealthy class had low probabilities of being active and high probability of consuming a low-quality diet. In contrast, girls’ healthiest profile presented lower probabilities of being active compared to boys’ healthiest profiles. No association was found between weight status and classes. Conclusion: All classes had at least one unhealthy behavior, for both the whole sample, and for girls and boys. Girls’ profiles were unhealthier compared to boys’ profiles. Hence, it is recommended that intervention strategies to change behaviors need to be distinct according to sex, targeting more than one obesogenic behavior at the same time.
Poly ADP Ribose Polymerase Inhibitor Olaparib Targeting Microhomology End Joining in Retinoblastoma Protein Defective Cancer: Analysis of the Retinoblastoma Cell-Killing Effects by Olaparib after Inducing Double-Strand Breaks

Retinoblastoma is the most common intraocular cancer in childhood. Loss of function in both copies of the RB1 gene is the causal mutation of retinoblastoma. Current treatment for retinoblastoma includes the use of chemotherapeutic agents, such as the DNA damaging agent etoposide, which is a topoisomerase II poison that mainly generates DNA double-strand breaks (DSBs) and genome instability. Unfaithful repairing of DSBs could lead to secondary cancers and serious side effects. Previously, we found that RB knocked-down mammalian cells depend on a highly mutagenic pathway, the micro-homology mediated end joining (MMEJ) pathway, to repair DSBs. Poly ADP ribose polymerase 1 (PARP1) is a major protein in promoting the MMEJ pathway. In this study, we explored the effects of olaparib, a PARP inhibitor, in killing retinoblastoma cells. Retinoblastoma cell line Y79 and primary retinoblastoma cells expressed the cone-rod homeobox protein (CRX), a photoreceptor-specific marker. No detectable RB expression was found in these cells. The co-treatment of olaparib and etoposide led to enhanced cell death in both the Y79 cells and the primary retinoblastoma cells. Our results demonstrated the killing effects in retinoblastoma cells by PARP inhibitor olaparib after inducing DNA double-strand breaks. The use of olaparib in combination with etoposide could improve the cell-killing effects. Thus, lower dosages of etoposide can be used to treat retinoblastoma, which would potentially lead to a lower level of DSBs and a relatively more stable genome.
Current Management Options in Irvine–Gass Syndrome: A Systemized Review

Irvine–Gass syndrome (IGS) remains one of the most common complications following uneventful cataract surgery. In most cases, macular edema (ME) in IGS is benign, self-limiting, and resolves spontaneously without visual impairment; however, persistent edema and refractory cases may occur and potentially deteriorate visual function. Despite the relatively high prevalence of IGS, no solid management guidelines exist. We searched the PUBMED database for randomized clinical trials (RCT) or case series of at least 10 cases published since 2000 evaluating different treatment strategies in patients with cystoid macular edema (CME). The search revealed 28 papers that fulfilled the inclusion criteria with only seven RCTs. The scarceness of material makes it impossible to formulate strong recommendations for the treatment of IGS. Clinical practice and theoretical background support topical non-steroidal anti-inflammatory drugs (NSAIDs) as the first-line therapy. Invasive procedures, such as periocular steroids, intravitreal corticosteroids, and anti-vascular endothelial growth factor (anti-VEGF), are usually applied in prolonged or refractory cases. Results of novel applications of subthreshold micropulse laser (SML) are also promising and should be studied carefully in terms of the safety profile and cost effectiveness. Early initiation of invasive treatment for providing better functional results must be examined in further research.
Contributing to Understand the Crosstalk between Brain and Periphery in Methylmercury Intoxication: Neurotoxicity and Extracellular Vesicles

Human exposure to methylmercury (MeHg) is currently high in regions such as the Amazon. Understanding the molecular changes associated with MeHg-induced neurotoxicity and the crosstalk with the periphery is essential to support early diagnoses. This work aimed to evaluate cellular and molecular changes associated with behavioral alterations in MeHg acute exposure and the possible changes in extracellular vesicles (EVs) number and S100β content. Adults male Wistar rats were orally treated with 5 mg/kg for four days. Behavioral performance, molecular and histological changes in the cerebellum, and plasma EVs were assessed. MeHg-intoxicated animals performed significantly worse in behavioral tests. MeHg increased the number of GFAP+ cells and GFAP and S100β mRNA expression in the cerebellum but no change in NeuN+ or IBA-1+ cells number was detected. The number of exosomes isolated from plasma were decreased by the metal. S100B mRNA was detected in circulating plasma EVs cargo in MeHg exposure. Though preliminary, our results suggest astrocytic reactivity is displaying a protective role once there was no neuronal death. Interestingly, the reduction in exosomes number could be a new mechanism associated with MeHg-induced neurotoxicity and plasma EVs could represent a source of future biomarkers in MeHg intoxication.
Behavioral and Physiological Plasticity Provides Insights into Molecular Based Adaptation Mechanism to Strain Shift in Spodoptera frugiperda

How herbivorous insects adapt to host plants is a key question in ecological and evolutionary biology. The fall armyworm, (FAW) Spodoptera frugiperda (J.E. Smith), although polyphagous and a major pest on various crops, has been reported to have a rice and corn (maize) feeding strain in its native range in the Americas. The species is highly invasive and has recently established in China. We compared behavioral changes in larvae and adults of a corn population (Corn) when selected on rice (Rice) and the molecular basis of these adaptational changes in midgut and antennae based on a comparative transcriptome analysis. Larvae of S. frugiperda reared on rice plants continuously for 20 generations exhibited strong feeding preference for with higher larval performance and pupal weight on rice than on maize plants. Similarly, females from the rice selected population laid significantly more eggs on rice as compared to females from maize population. The most highly expressed DEGs were shown in the midgut of Rice vs. Corn. A total of 6430 DEGs were identified between the populations mostly in genes related to digestion and detoxification. These results suggest that potential adaptations for feeding on rice crops, may contribute to the current rapid spread of fall armyworm on rice crops in China and potentially elsewhere. Consistently, highly expressed DEGs were also shown in antennae; a total of 5125 differentially expressed genes (DEGs) s were identified related to the expansions of major chemosensory genes family in Rice compared to the Corn feeding population. These results not only provide valuable insight into the molecular mechanisms in host plants adaptation of S. frugiperda but may provide new gene targets for the management of this pest.
Hypoparathyroidism: State of the Art on Cell and Tissue Therapies

Hypoparathyroidism is an endocrine disorder characterized by low serum calcium levels, high serum phosphorus levels, and by inappropriate or absent secretion of the parathyroid hormone (PTH). The most common therapeutic strategy to treat this condition is hormone replacement therapy with calcium and vitamin D but, unfortunately, in the long term this treatment may not be sufficient to compensate for the loss of endocrine function. Glandular autotransplantation is considered the most effective technique in place of replacement therapy. Although it leads to excellent results in most cases, autotransplantation is not always possible. Allograft is a good way to treat patients who have not been able to undergo autograft, but this technique has limited success due to side effects related to tissue rejection. This therapy is supported by systemic immunosuppression, which leads to the onset of serious side effects in patients, with a risk of endocrine toxicity. Today, research on endocrine disorders is focused on discovering alternative graft therapies that can allow optimal results with the fewest possible side effects. In this review, we will make an update on the current state of the art about the cell and tissue therapy as treatment for hypoparathyroidism, to identify which type of therapeutic strategy could be valid for a future clinical use.
What Happens after SGD Reaches Zero Loss? -A Mathematical Framework

Understanding the implicit bias of Stochastic Gradient Descent (SGD) is one of the key challenges in deep learning, especially for overparametrized models, where the local minimizers of the loss function $L$ can form a manifold. Intuitively, with a sufficiently small learning rate $\eta$, SGD tracks Gradient Descent (GD) until it gets close to such manifold, where the gradient noise prevents further convergence. In such a regime, Blanc et al. (2020) proved that SGD with label noise locally decreases a regularizer-like term, the sharpness of loss, $\mathrm{tr}[\nabla^2 L]$. The current paper gives a general framework for such analysis by adapting ideas from Katzenberger (1991). It allows in principle a complete characterization for the regularization effect of SGD around such manifold -- i.e., the"implicit bias"-- using a stochastic differential equation (SDE) describing the limiting dynamics of the parameters, which is determined jointly by the loss function and the noise covariance. This yields some new results: (1) a global analysis of the implicit bias valid for $\eta^{-2}$ steps, in contrast to the local analysis of Blanc et al. (2020) that is only valid for $\eta^{-1.6}$ steps and (2) allowing arbitrary noise covariance. As an application, we show with arbitrary large initialization, label noise SGD can always escape the kernel regime and only requires $O(\kappa\ln d)$ samples for learning an $\kappa$-sparse overparametrized linear model in $\mathbb{R}^d$ (Woodworth et al., 2020), while GD initialized in the kernel regime requires $\Omega(d)$ samples. This upper bound is minimax optimal and improves the previous $\tilde{O}(\kappa^2)$ upper bound (HaoChen et al., 2020).
Benchmarking the Robustness of Spatial-Temporal Models Against Corruptions

The state-of-the-art deep neural networks are vulnerable to common corruptions (e.g., input data degradations, distortions, and disturbances caused by weather changes, system error, and processing). While much progress has been made in analyzing and improving the robustness of models in image understanding, the robustness in video understanding is largely unexplored. In this paper, we establish a corruption robustness benchmark, Mini Kinetics-C and Mini SSV2-C, which considers temporal corruptions beyond spatial corruptions in images. We make the first attempt to conduct an exhaustive study on the corruption robustness of established CNN-based and Transformer-based spatial-temporal models. The study provides some guidance on robust model design and training: Transformer-based model performs better than CNN-based models on corruption robustness; the generalization ability of spatial-temporal models implies robustness against temporal corruptions; model corruption robustness (especially robustness in the temporal domain) enhances with computational cost and model capacity, which may contradict the current trend of improving the computational efficiency of models. Moreover, we find the robustness intervention for image-related tasks (e.g., training models with noise) may not work for spatial-temporal models.
Architecture of a Junkyard Datacenter

It requires signiﬁcant energy to manufacture and deploy computational devices. Traditional discussions of the energy-efﬁciency of compute measure operational energy, i.e. how many FLOPS in a 50 MW datacenter. However, if we consider the true lifetime energy use of modern devices, the majority actually comes not from runtime use but from manufacture and deployment. In this paper, then, we suggest that perhaps the most climate-impactful action we can take is to extend the service lifetime of existing compute. We design two new metrics to measure how to balance continued service of older devices with the superlinear runtime improvements of newer machines. The ﬁrst looks at carbon per raw compute, amortized across the operation and manufacture of devices. The second considers use of components beyond compute, such as batteries or radios in smartphone platforms. We use these metrics to redeﬁne device service lifetime in terms of carbon efﬁciency. We then realize a real-world “junkyard datacenter” made up of Nexus 4 and Nexus 5 phones, which are nearly a decade past their ofﬁcial end-of-life dates. This new-old datacenter is able to nearly match and occasionally exceed modern cloud compute offerings.
Well-classified Examples are Underestimated in Classification with Deep Neural Networks

The conventional wisdom behind learning deep classification models is to focus on bad-classified examples and ignore well-classified examples that are far from the decision boundary. For instance, when training with cross-entropy loss, examples with higher likelihoods (i.e., well-classified examples) contribute smaller gradients in back-propagation. However, we theoretically show that this common practice hinders representation learning, energy optimization, and margin growth. To counteract this deficiency, we propose to reward well-classified examples with additive bonuses to revive their contribution to the learning process. This counterexample theoretically addresses these three issues. We empirically support this claim by directly verifying the theoretical results or significant performance improvement with our counterexample on diverse tasks, including image classification, graph classification, and machine translation. Furthermore, this paper shows that we can deal with complex scenarios, such as imbalanced classification, OOD detection, and applications under adversarial attacks because our idea can solve these three issues. Code is available at https://github.com/lancopku/well-classified-examples-are-underestimated.
On Convergence of Training Loss Without Reaching Stationary Points

It is a well-known fact that nonconvex optimization is computationally intractable in the worst case. As a result, theoretical analysis of optimization algorithms such as gradient descent often focuses on local convergence to stationary points where the gradient norm is zero or negligible. In this work, we examine the disconnect between the existing theoretical analysis of gradient-based algorithms and actual practice. Speciﬁcally, we provide numerical evidence that in large-scale neural network training, such as in ImageNet, ResNet, and WT103 + TransformerXL models, the Neural Network weight variables do not converge to stationary points where the gradient of the loss function vanishes. Remarkably, however, we observe that while weights do not converge to stationary points, the value of the loss function converges. Inspired by this observation, we propose a new perspective based on ergodic theory of dynamical systems. We prove convergence of the distribution of weight values to an approximate invariant measure (without smoothness and assumptions) that explains how the training loss can stabilize without weights necessarily converging to stationary points. We further discuss how this perspective can better align the theory with empirical observations.
Random separation property for stochastic Allen-Cahn-type equations

We study a large class of stochastic $p$-Laplace Allen-Cahn equations with singular potential. Under suitable assumptions on the (multiplicative-type) noise we first prove existence, uniqueness, and regularity of variational solutions. Then, we show that a random separation property holds, i.e. almost every trajectory is strictly separated in space and time from the potential barriers. The threshold of separation is random, and we further provide exponential estimates on the probability of separation from the barriers. Eventually, we exhibit a convergence-in-probability result for the random separation threshold towards the deterministic one, as the noise vanishes, and we obtain an estimate of the convergence rate.
MedNet: Pre-trained Convolutional Neural Network Model for the Medical Imaging Tasks

Deep Learning (DL) requires a large amount of training data to provide quality outcomes. However, the field of medical imaging suffers from the lack of sufficient data for properly training DL models because medical images require manual labelling carried out by clinical experts thus the process is time-consuming, expensive, and error-prone. Recently, transfer learning (TL) was introduced to reduce the need for the annotation procedure by means of transferring the knowledge performed by a previous task and then fine-tuning the result using a relatively small dataset. Nowadays, multiple classification methods from medical imaging make use of TL from general-purpose pre-trained models, e.g., ImageNet, which has been proven to be ineffective due to the mismatch between the features learned from natural images (ImageNet) and those more specific from medical images especially medical gray images such as X-rays. ImageNet does not have grayscale images such as MRI, CT, and X-ray. In this paper, we propose a novel DL model to be used for addressing classification tasks of medical imaging, called MedNet. To do so, we aim to issue two versions of MedNet. The first one is Gray-MedNet which will be trained on 3M publicly available gray-scale medical images including MRI, CT, X-ray, ultrasound, and PET. The second version is Color-MedNet which will be trained on 3M publicly available color medical images including histopathology, taken images, and many others. To validate the effectiveness MedNet, both versions will be fine-tuned to train on the target tasks of a more reduced set of medical images. MedNet performs as the pre-trained model to tackle any real-world application from medical imaging and achieve the level of generalization needed for dealing with medical imaging tasks, e.g. classification. MedNet would serve the research community as a baseline for future research.
Mixed local-nonlocal operators: maximum principles, eigenvalue problems and their applications

In this article we consider a class of non-degenerate elliptic operators obtained by superpositioning the Laplacian and a general nonlocal operator. We study the existence-uniqueness results for Dirichlet boundary value problems, maximum principles and generalized eigenvalue problems. As applications to these results, we obtain Faber-Krahn inequality and a one-dimensional symmetry result related to the Gibbons' conjecture. The latter results substantially extend the recent results of Biagi et. \ al. [7,9] who consider the operators of the form $-\Delta + (-\Delta)^s$ with $s\in (0, 1)$.
i-QER: An Intelligent Approach Towards Quantum Error Reduction

Quantum computing has become a promising computing approach because of its capability to solve certain problems, exponentially faster than classical computers. A n-qubit quantum system is capable of providing 2n computational space to a quantum algorithm. However, quantum computers are prone to errors. Quantum circuits that can reliably run on today’s Noisy Intermediate-Scale Quantum (NISQ) devices are not only limited by their qubit counts but also by their noisy gate operations. In this article, we have introduced i-QER, a scalable machine learning-based approach to evaluate errors in a quantum circuit and reduce these without using any additional quantum resources. The i-QER predicts possible errors in a given quantum circuit using supervised learning models. If the predicted error is above a pre-specified threshold, it cuts the large quantum circuit into two smaller sub-circuits using an error-influenced fragmentation strategy for the first time to the best of our knowledge. The proposed fragmentation process is iterated until the predicted error reaches below the threshold for each sub-circuit. The sub-circuits are then executed on a quantum device. Classical reconstruction of the outputs obtained from the sub-circuits can generate the output of the complete circuit. Thus, i-QER also provides classical control over a scalable hybrid computing approach, which is a combination of quantum and classical computers. The i-QER tool is available at https://github.com/SaikatBasu90/i-QER.
Object-Region Video Transformers

Recently, video transformers have shown great success in video understanding, exceeding CNN performance; yet existing video transformer models do not explicitly model objects, although objects can be essential for recognizing actions. In this work, we present Object-Region Video Transformers (ORViT), an object-centric approach that extends video transformer layers with a block that directly incorporates object representations. The key idea is to fuse object-centric representations starting from early layers and propagate them into the transformer-layers, thus affecting the spatio-temporal representations throughout the network. Our ORViT block consists of two object-level streams: appearance and dynamics. In the appearance stream, an “Object-Region Attention” module applies self-attention over the patches and object regions. In this way, visual object regions interact with uniform patch tokens and enrich them with contextualized object information. We further model object dynamics via a separate “Object-Dynamics Module”, which captures trajectory interactions, and show how to integrate the two streams. We evaluate our model on four tasks and five datasets: compositional and few-shot action recognition on SomethingElse, spatio-temporal action detection on AVA, and standard action recognition on Something-Something V2, Diving48 and Epic-Kitchen100. We show strong performance improvement across all tasks and datasets considered, demonstrating the value of a model that incorporates object representations into a transformer architecture. For code and pretrained models, visit the project page at https://roeiherz.github. io/ORViT/