Dental Anomalies in Permanent Teeth after Trauma in Primary Dentition

OBJECTIVE: This retrospective study aims to evaluate the prevalence of dental anomalies in permanent teeth as a result of a trauma concerning the predecessor primary teeth. STUDY DESIGN: A total of 241 records of children (118 males and 123 females, mean age 3.62 ± 1.40) affected by trauma on primary teeth were analyzed. All patients were recalled to evaluate the status of the permanent successor teeth by clinical and radiographic investigations. RESULTS: Out of 241 patients, 106 patients (for a total of 179 traumatized primary teeth) presented at the recall. Dental anomalies on successor permanent teeth were detected in 21 patients (19.8%), for a total of 26 teeth (14.5%) and 28 anomalies. Anomalies of the eruptive process were the most observed disturbances (60.7%), followed by enamel hypoplasia (25%) and white spots (14.3%). A higher percentage of anomalies on permanent teeth was observed when trauma occurred at an age less than 36 months (38.5% of cases). Intrusive and extrusive luxation were related with the most cases of clinical disturbances in the successor permanent teeth. CONCLUSIONS: The results of this study highlight the risk of dental anomalies after a trauma in primary dentition, especially in early-aged children and in case of intrusive luxation

Semantic Image Collection Summarization with Frequent Subgraph Mining

Applications such as providing a preview of personal albums (e.g., Google Photos) or suggesting thematic collections based on user interests (e.g., Pinterest) require a semantically-enriched image representation, which should be more informative with respect to simple low-level visual features and image tags. To this aim, we propose an image collection summarization technique based on frequent subgraph mining. We represent images with a novel type of scene graphs including fine-grained relationship types between objects. These scene graphs are automatically derived by our method. The resulting summary consists of a set of frequent subgraphs describing the underlying patterns of the image dataset. Our results are interpretable and provide more powerful semantic information with respect to previous techniques, in which the summary is a subset of the collection in terms of images or image patches. The experimental evaluation shows that the proposed technique yields non-redundant summaries, with a high diversity of the discovered patterns

15 What is the future of (remote) work?

Among the individuals who worked continuously since the start of the COVID-19 pandemic, around 22% of men and 30% of women were working remotely in both waves of the SHARE Corona survey. Only 10% of the workers in our sample were initially working remotely, and then moved back to their usual workplace. Remote work adoption varied depending on the technical feasibility of performing a job remotel

Immigration and the utilization of preventive care in Europe: Results from retrospective data

We used retrospective information from the Survey on Health, Ageing and Retirement in Europe (SHARE) to analyze the utilization patterns of preventive care around the time of migration of a representative sample of migrants in Europe. We find heterogeneous behaviours across different types of preventive care. Migrants increase the utilization of dental care significantly as soon as they reach the host country compared to the years immediately before migration, while migrant women increase their use of blood pressure tests, gynaecological visits, and mammogram tests progressively after migration. Other types of care do not exhibit particular patterns in relation to the migration episode. We also observe relevant differences in preventive care use around migration by country of origin. Our results suggest that preventive care use by migrants cannot be given for granted and is intimately linked to the process of integration in the host countr

Immigration and the utilization of preventive care in Europe : Results from retrospective data

Preprin

GRACE: Online Gesture Recognition for Autonomous Camera-Motion Enhancement in Robot-Assisted Surgery

Camera navigation in minimally invasive surgery changed significantly since the introduction of robotic assistance. Robotic surgeons are subjected to a cognitive workload increase due to the asynchronous control over tools and camera, which also leads to interruptions in the workflow. Camera motion automation has been addressed as a possible solution, but still lacks situation awareness. We propose an online surgical Gesture Recognition for Autonomous Camera-motion Enhancement (GRACE) system to introduce situation awareness in autonomous camera navigation. A recurrent neural network is used in combination with a tool tracking system to offer gesture-specific camera motion during a robotic-assisted suturing task. GRACE was integrated with a research version of the da Vinci surgical system and a user study (involving 10 participants) was performed to evaluate the benefits introduced by situation awareness in camera motion, both with respect to a state of the art autonomous system (S) and current clinical approach (P). Results show GRACE improving completion time by a median reduction of 18.9s (8.1% ) with respect to S and 65.1s (21.1% ) with respect to P. Also, workload reduction was confirmed by statistical difference in the NASA Task Load Index with respect to S (p < 0.05). Reduction of motion sickness, a common issue related to continuous camera motion of autonomous systems, was assessed by a post-experiment survey ( p < 0.01 )

Improving Wildfire Severity Classification of Deep Learning U-Nets from Satellite Images

Uncontrolled wildfires are dangerous events capable of harming people safety. To contrast their increasing impact in recent years, a key task is an accurate detection of the affected areas and their damage assessment from satellite images. Current state-of-the-art solutions address such problem through a double convolutional neural network able to automatically detect wildfires in satellite acquisitions and associate a damage index from a defined scale. However, such deep-learning model performance is strongly dependent on many factors. In this work, we specifically focus on a key parameter, i.e., the loss function, exploited in the underlying neural networks. Besides the state-of-the-art solutions based on the Dice-MSE, among the many loss functions proposed in literature, we focus on the Binary Cross-Entropy (BCE) and the Intersection over Union (IoU), as two representatives of the distribution-based and region-based categories, respectively. Experiments show that the BCE loss function coupled with a double-step U-Net architecture provides better results than current state-of-the-art solutions on a public labeled dataset of European wildfires

Neurosteroids as Neuromodulators in the Treatment of Anxiety Disorders

Anxiety disorders are the most common psychiatric disorders. They are frequently treated with benzodiazepines, which are fast acting highly effective anxiolytic agents. However, their long-term use is impaired by tolerance development and abuse liability. In contrast, antidepressants such as selective serotonin reuptake inhibitors (SSRIs) are considered as first-line treatment but have a slow onset of action. Neurosteroids are powerful allosteric modulators of GABAA and glutamate receptors. However, they also modulate sigma receptors and they are modulated themselves by SSRIs. Both pre-clinical and clinical studies have shown that neurosteroid homeostasis is altered in depression and anxiety disorders and antidepressants may act in part through restoring neurosteroid disbalance. Moreover, novel drugs interfering with neurosteroidogenesis such as ligands of the translocator protein (18 kDa) may represent an attractive pharmacological option for novel anxiolytics which lack the unwarranted side effects of benzodiazepines. Thus, neurosteroids are important endogenous neuromodulators for the physiology and pathophysiology of anxiety and they may constitute a novel therapeutic approach in the treatment of these disorders