ClusterFix: A Cluster-Based Debiasing Approach without Protected-Group Supervision

The failures of Deep Networks can sometimes be ascribed to biases in the data or algorithmic choices. Existing debiasing approaches exploit prior knowledge to avoid unintended solutions; we acknowledge that, in real-world settings, it could be unfeasible to gather enough prior information to characterize the bias, or it could even raise ethical considerations. We hence propose a novel debiasing approach, termed ClusterFix, which does not require any external hint about the nature of biases. Such an approach alters the standard empirical risk minimization and introduces a per-example weight, encoding how critical and far from the majority an example is. Notably, the weights consider how difficult it is for the model to infer the correct pseudo-label, which is obtained in a self-supervised manner by dividing examples into multiple clusters. Extensive experiments show that the misclassification error incurred in identifying the correct cluster allows for identifying examples prone to bias-related issues. As a result, our approach outperforms existing methods on standard benchmarks for bias removal and fairness

Input Perturbation Reduces Exposure Bias in Diffusion Models

Denoising Diffusion Probabilistic Models have shown an impressive generation quality although their long sampling chain leads to high computational costs. In this paper, we observe that a long sampling chain also leads to an error accumulation phenomenon, which is similar to the exposure bias problem in autoregressive text generation. Specifically, we note that there is a discrepancy between training and testing, since the former is conditioned on the ground truth samples, while the latter is conditioned on the previously generated results. To alleviate this problem, we propose a very simple but effective training regularization, consisting in perturbing the ground truth samples to simulate the inference time prediction errors. We empirically show that, without affecting the recall and precision, the proposed input perturbation leads to a significant improvement in the sample quality while reducing both the training and the inference times. For instance, on CelebA 64×64, we achieve a new state-of-the-art FID score of 1.27, while saving 37.5% of the training time. The code is available at https://github.com/forever208/DDPM-IP

Y RNA: an overview of their role as potential biomarkers and molecular targets in human cancers

Y RNA are a class of small non-coding RNA that are largely conserved. Although their discovery was almost 40 years ago, their function is still under investigation. This is evident in cancer biology, where their role was first studied just a dozen years ago. Since then, only a few contributions were published, mostly scattered across different tumor types and, in some cases, also suffering from methodological limitations. Nonetheless, these sparse data may be used to make some estimations and suggest routes to better understand the role of Y RNA in cancer formation and characterization. Here we summarize the current knowledge about Y RNA in multiple types of cancer, also including a paragraph about tumors that might be included in this list in the future, if more evidence becomes available. The picture arising indicates that Y RNA might be useful in tumor characterization, also relying on non-invasive methods, such as the analysis of the content of extracellular vesicles (EV) that are retrieved from blood plasma and other bodily fluids. Due to the established role of Y RNA in DNA replication, it is possible to hypothesize their therapeutic targeting to inhibit cell proliferation in oncological patients

Scoring pleurisy in slaughtered pigs using convolutional neural networks

Diseases of the respiratory system are known to negatively impact the profitability of the pig industry, worldwide. Considering the relatively short lifespan of pigs, lesions can be still evident at slaughter, where they can be usefully recorded and scored. Therefore, the slaughterhouse represents a key check-point to assess the health status of pigs, providing unique and valuable feedback to the farm, as well as an important source of data for epidemiological studies. Although relevant, scoring lesions in slaughtered pigs represents a very time-consuming and costly activity, thus making difficult their systematic recording. The present study has been carried out to train a convolutional neural network-based system to automatically score pleurisy in slaughtered pigs. The automation of such a process would be extremely helpful to enable a systematic examination of all slaughtered livestock. Overall, our data indicate that the proposed system is well able to differentiate half carcasses affected with pleurisy from healthy ones, with an overall accuracy of 85.5%. The system was better able to recognize severely affected half carcasses as compared with those showing less severe lesions. The training of convolutional neural networks to identify and score pneumonia, on the one hand, and the achievement of trials in large capacity slaughterhouses, on the other, represent the natural pursuance of the present study. As a result, convolutional neural network-based technologies could provide a fast and cheap tool to systematically record lesions in slaughtered pigs, thus supplying an enormous amount of useful data to all stakeholders in the pig industry

Swallowing disorders after thyroidectomy: What we know and where we are. A systematic review

Introduction Dysphagia and hoarseness are possible complications that can be observed in patients undergoing thyroidectomy or other neck surgery procedures. These complaints are usually related to superior and inferior laryngeal nerves dysfunction, but these can appear even after uncomplicated surgical procedure. Methods We reviewed the current literature available on MEDLINE database, concerning the swallowing disorders appearing after the thyroidectomy. The articles included in the review reported pathophysiology and diagnostic concerns. Results Twenty articles were selected for inclusion in the review. Depends on the possible causes of the difficulty swallowing (related to nerve damage or appearing after uncomplicated thyroidectomy), different types of diagnostic procedures could be used to study patient discomfort, as well as intraoperative nerve monitoring, fiber optic laryngoscopy, endoscopy, pH monitoring, esophageal manometry and videofluorography. Among all these procedures, videofluorography is considered the gold standard to evaluate the entire swallowing process, since that allows a real-time study of all the three phases of swallowing: oral phase, pharyngeal phase and esophageal phase. Conclusion The diagnostic procedures described can help to identify the mechanisms involved in swallowing disorders, with the aim to choose the best therapeutic option. More studies are needed for understanding the causes of the dysphagia appearing after thyroidectomy

JAB1 deletion in oligodendrocytes causes senescence-induced inflammation and neurodegeneration in mice

Oligodendrocytes are the primary target of demyelinating disorders, and progressive neurodegenerative changes may evolve in the CNS. DNA damage and oxidative stress are considered key pathogenic events, but the underlying molecular mechanisms remain unclear. Moreover, animal models do not fully recapitulate human diseases, complicating the path to effective treatments. Here we report that mice with cell-autonomous deletion of the nuclear COP9 signalosome component CSN5 (JAB1) in oligodendrocytes develop DNA damage and defective DNA repair in myelinating glial cells. Interestingly, oligodendrocytes lacking JAB1 expression underwent a senescence-like phenotype that fostered chronic inflammation and oxidative stress. These mutants developed progressive CNS demyelination, microglia inflammation, and neurodegeneration, with severe motor deficits and premature death. Notably, blocking microglia inflammation did not prevent neurodegeneration, whereas the deletion of p21CIP1 but not p16INK4a pathway ameliorated the disease. We suggest that senescence is key to sustaining neurodegeneration in demyelinating disorders and may be considered a potential therapeutic target

The Comparative Oncology Trials Consortium: Using Spontaneously Occurring Cancers in Dogs to Inform the Cancer Drug Development Pathway

Chand Khanna and colleagues describe the work of the Comparative Oncology Trials Consortium (COTC), which provides infrastructure and resources to integrate naturally occurring dog cancer models into the development of new human cancer drugs, devices, and imaging techniques

Myocardial Autophagy after Severe Burn in Rats

Autophagy plays a major role in myocardial ischemia and hypoxia injury. The present study investigated the effects of autophagy on cardiac dysfunction in rats after severe burn.Protein expression of the autophagy markers LC3 and Beclin 1 were determined at 0, 1, 3, 6, and 12 h post-burn in Sprague Dawley rats subjected to 30% total body surface area 3rd degree burns. Autophagic, apoptotic, and oncotic cell death were evaluated in the myocardium at each time point by immunofluorescence. Changes of cardiac function were measured in a Langendorff model of isolated heart at 6 h post-burn, and the autophagic response was measured following activation by Rapamycin and inhibition by 3-methyladenine (3-MA). The angiotensin converting enzyme inhibitor enalaprilat, the angiotensin receptor I blocker losartan, and the reactive oxygen species inhibitor diphenylene iodonium (DPI) were also applied to the ex vivo heart model to examine the roles of these factors in post-burn cardiac function.Autophagic cell death was first observed in the myocardium at 3 h post-burn, occurring in 0.008 ± 0.001% of total cardiomyocytes, and continued to increase to a level of 0.022 ± 0.005% by 12 h post-burn. No autophagic cell death was observed in control hearts. Compared with apoptosis, autophagic cell death occurred earlier and in larger quantities. Rapamycin enhanced autophagy and decreased cardiac function in isolated hearts 6 h post-burn, while 3-MA exerted the opposite response. Enalaprilat, losartan, and DPI all inhibited autophagy and enhanced heart function.Myocardial autophagy is enhanced in severe burns and autophagic cell death occurred early at 3 h post-burn, which may contribute to post-burn cardiac dysfunction. Angiotensin II and reactive oxygen species may play important roles in this process by regulating cell signaling transduction