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In this paper, we propose a novel multiclass classifier for the open-set recognition scenario. This scenario is the one in which there are no a priori training samples for some classes that might appear during testing. Usually, many applications are inherently open set. Consequently, successful closed-set solutions in the literature are not always suitable for real-world recognition problems. The proposed open-set classifier extends upon the Nearest-Neighbor (NN) classifier. Nearest neighbors are simple, parameter independent, multiclass, and widely used for closed-set problems. The proposed Open-Set NN (OSNN) method incorporates the ability of recognizing samples belonging to classes that are unknown at training time, being suitable for open-set recognition. In addition, we explore evaluation measures for open-set problems, properly measuring the resilience of methods to unknown classes during testing. For validation, we consider large freely-available benchmarks with different open-set recognition regimes and demonstrate that the proposed OSNN significantly outperforms their counterparts in the literature.

Within a community, species may germinate at different times so as to mitigate competition and to take advantage of different aspects of the seasonal environment (temporal niche differentiation). We illustrated a hypothesis of the combined effects of abiotic and biotic competitive factors on germination timing and the subsequent upscale effects on community assembly. We estimated the germination timing (GT) for 476 angiosperm species of the eastern Tibetan Plateau grasslands under two light treatments in the field: high (i.e. natural) light and low light. We also measured the shift in germination timing (SGT) across treatments for all species. Furthermore, we used phylogenetic comparative methods to test if GT and SGT were associated with seed mass, an important factor in competitive interactions. We found a significant positive correlation between GT and seed mass in both light treatments. Additionally, small seeds (early germinating seeds) tended to germinate later and large seeds (late germinating seeds) tended to germinate earlier under low light vs high light conditions. Low light availability can reduce temporal niche differentiation by increasing the overlap in germination time between small and large seeds. In turn, reduced temporal niche differentiation may increase competition in the process of community assembly.

Patellofemoral Pain (PFP) is one of the main injuries in runners. Consistent evidence support strengthening programs to modulate symptoms, however, few studies investigated the effects of gait retraining programs. To investigate the effects of two different two-week partially supervised gait retraining programs on pain, function, and lower limb kinematics of runners with PFP. Randomized controlled trial. Thirty runners were allocated to gait retraining groups focusing on impact (n = 10) or cadence (n = 10), or to a control group (n = 10). Impact group received guidance to reduce tibial acceleration by 50%, while cadence group was asked to increase cadence by 7.5-10%. The control group did not receive any intervention. Usual and running pain, knee function, and lower limb kinematics (contralateral pelvic drop, hip adduction, knee flexion, ankle dorsiflexion, tibia inclination, and foot inclination) were evaluated before (T0), immediately after the intervention (T2), and six months after the protocol (T24). A significant group x time interaction was found for running pain (p = 0.010) and knee function (p = 0.019). Both programs had greater improvements in running pain compared to no intervention at T24 (Impact x Control-mean difference (MD) -3.2, 95% CI -5.1 to -1.3, p = 0.001; Cadence x Control-MD -2.9, 95% CI -4.8 to -1.0, p = 0.002). Participants of the impact group had greater improvements in knee function compared to no intervention at T2 (Impact x Control-MD 10.8, 95% CI 1.0 to 20.6, p = 0.027). No between-group differences in usual pain and lower limb kinematics were found (p>0.05). Compared to no intervention, both programs were more effective in improving running pain six months after the protocol. The program focused on impact was more effective in improving knee function immediately after the intervention. Clinical trial registry number: RBR-8yb47v.

The urgent need for drastic reduction in emissions due to global warming demands a radical energy transition in transportation. The role of biofuels is fundamental to bridging the current situation towards a clean and sustainable future. In passenger cars, the use of ethanol fuel reduces gas emissions (CO2 and other harmful gases), but can bring tribological challenges to the engine. This review addresses the current state-of-the-art on the effects of ethanol fuel on friction, lubrication, and wear in car engines, and identifies knowledge gaps and trends in lubricants for ethanol-fuelled engines. This review shows that ethanol affects friction and wear in many ways, for example, by reducing lubricant viscosity, which on the one hand can reduce shear losses under full film lubrication, but on the other can increase asperity contact under mixed lubrication. Therefore, ethanol can either reduce or increase engine friction depending on the driving conditions, engine temperature, amount of diluted ethanol in the lubricant, lubricant type, etc. Ethanol increases corrosion and affects tribocorrosion, with significant effects on engine wear. Moreover, ethanol strongly interacts with the lubricant’s additives, affecting friction and wear under boundary lubrication conditions. Regarding the anti-wear additive ZDDP, ethanol leads to thinner tribofilms with modified chemical structure, in particular shorter phosphates and increased amount of iron sulphides and oxides, thereby reducing their anti-wear protection. Tribofilms formed from Mo-DTC friction modifier are affected as well, compromising the formation of low-friction MoS2 tribofilms; however, ethanol is beneficial for the tribological behaviour of organic friction modifiers. Although the oil industry has implemented small changes in oil formulation to ensure the proper operation of ethanol-fuelled engines, there is a lack of research aiming to optimize lubricant formulation to maximize ethanol-fuelled engine performance. The findings of this review should shed light towards improved oil formulation as well as on the selection of materials and surface engineering techniques to mitigate the most pressing problems.

Introduction: Cancer refers to a group of diseases characterized by the uncontrolled growth and spread of abnormal cells in the body. Due to its complexity, it has been hard to find an ideal medicine to treat all cancer types, although there is an urgent need for it. However, the cost of developing a new drug is high and time-consuming. In this sense, drug repurposing (DR) can hasten drug discovery by giving existing drugs new disease indications. Many computational methods have been applied to achieve DR, but just a few have succeeded. Therefore, this review aims to show in silico DR approaches and the gap between these strategies and their ultimate application in oncology. Methods: The scoping review was conducted according to the Arksey and O’Malley framework and the Joanna Briggs Institute recommendations. Relevant studies were identified through electronic searching of PubMed/MEDLINE, Embase, Scopus, and Web of Science databases, as well as the grey literature. We included peer-reviewed research articles involving in silico strategies applied to drug repurposing in oncology, published between 1 January 2003, and 31 December 2021. Results: We identified 238 studies for inclusion in the review. Most studies revealed that the United States, India, China, South Korea, and Italy are top publishers. Regarding cancer types, breast cancer, lymphomas and leukemias, lung, colorectal, and prostate cancer are the top investigated. Additionally, most studies solely used computational methods, and just a few assessed more complex scientific models. Lastly, molecular modeling, which includes molecular docking and molecular dynamics simulations, was the most frequently used method, followed by signature-, Machine Learning-, and network-based strategies. Discussion: DR is a trending opportunity but still demands extensive testing to ensure its safety and efficacy for the new indications. Finally, implementing DR can be challenging due to various factors, including lack of quality data, patient populations, cost, intellectual property issues, market considerations, and regulatory requirements. Despite all the hurdles, DR remains an exciting strategy for identifying new treatments for numerous diseases, including cancer types, and giving patients faster access to new medications.

This study leverages deep learning to analyze sex differences in brain MRI data, aiming to further advance fairness in medical imaging. We employed 3D T1-weighted Magnetic Resonance images from four diverse datasets: Calgary-Campinas-359, OASIS-3, Alzheimer's Disease Neuroimaging Initiative, and Cambridge Center for Aging and Neuroscience, ensuring a balanced representation of sexes and a broad demographic scope. Our methodology focused on minimal preprocessing to preserve the integrity of brain structures, utilizing a Convolutional Neural Network model for sex classification. The model achieved an accuracy of 87% on the test set without employing total intracranial volume (TIV) adjustment techniques. We observed that while the model exhibited biases at extreme brain sizes, it performed with less bias when the TIV distributions overlapped more. Saliency maps were used to identify brain regions significant in sex differentiation, revealing that certain supratentorial and infratentorial regions were important for predictions. Furthermore, our interdisciplinary team, comprising machine learning specialists and a radiologist, ensured diverse perspectives in validating the results. The detailed investigation of sex differences in brain MRI in this study, highlighted by the sex differences map, offers valuable insights into sex-specific aspects of medical imaging and could aid in developing sex-based bias mitigation strategies, contributing to the future development of fair AI algorithms. Awareness of the brain's differences between sexes enables more equitable AI predictions, promoting fairness in healthcare outcomes. Our code and saliency maps are available at https://github.com/mahsadibaji/sex-differences-brain-dl .

Graphene-based materials have great potential for tribological applications. Graphene’s unique properties such as low shear resistance, high stiffness, and thermal conductivity make it an attractive material for improving the properties of lubricants in a wide range of industrial applications, from vehicles to house refrigerators and industrial machinery such as gearboxes, large compressors, etc. The current review aims to give an engineering perspective, attributing more importance to commercially available graphene and fully formulated lubricants instead of laboratory-scaled produced graphene and base oils without additives. The use of lubricants with graphene-based additives has produced e.g., an increase in mechanical efficiency, consequently reducing energy consumption and CO2 emissions by up to 20% for domestic refrigerators and up to 6% for ICE vehicles. Potential effects, other than purely friction reduction, contributing to such benefits are also briefly covered and discussed.

This paper aimed to describe health insurance coverage in Brazil. Data from the 2013 and 2019 editions of the National Health Survey (PNS) were analyzed. The medical or dental health insurance coverage was analyzed according to demographic and socioeconomic characteristics, work status, urban/rural area, and Federation Unit. Coverage of medical or dental health insurance was 27.9% (95% CI: 27.1-28.8) for 2013 and 28.5% (95% CI: 27.8-29.2) for 2019. The results show coverage is still concentrated in large urban centers, in the Southeast and South, among those with better socioeconomic status and some formal employment. In 2019, only 30.7% of formal workers reported the monthly payment is made directly to the providers, while 72.7% of informal workers reported this information. About 92% of medical health insurance covers hospitalization, and almost 20% of women with health insurance are not covered for labor. Only 11.7% of women aged between 15 and 44 are covered for childbirth by health insurance. The results show the health insurance coverage is still quite unequal, reinforcing the Unified Health System (SUS) importance for the Brazilian population.

Postmenopausal osteoporosis and poor dietary habits can lead to overweightness and obesity. Bisphosphonates are the first-line treatment for osteoporosis. However, some studies show that they may increase the risk of osteonecrosis of the jaw. Considering the antimicrobial, angiogenic and vasodilatory potential of nitric oxide, this study aims to evaluate the local activity of this substance during the placement of surface-treated implants. Seventy-two Wistar rats were divided into three groups: SHAM (SHAM surgery), OVX + HD (ovariectomy + cafeteria diet), and OVX + HD + RIS (ovariectomy + cafeteria diet + sodium risedronate treatment), which were further subdivided according to the surface treatment of the future implant: CONV (conventional), TE10, or TE100 (TERPY at 10 or 100 μM concentration); n = 8 per subgroup. The animals underwent surgery for implant installation in the proximal tibia metaphysis and were euthanized after 28 days. Data obtained from removal torque and RT-PCR (OPG, RANKL, ALP, IBSP and VEGF expression) were subjected to statistical analysis at 5% significance level. For biomechanical analysis, TE10 produced better results in the OVX + HD group (7.4 N/cm, SD = 0.6819). Molecular analysis showed: (1) significant increase in OPG gene expression in OVX groups with TE10; (2) decreased RANKL expression in OVX + HD + RIS compared to OVX + HD; (3) significantly increased expressions of IBSP and VEGF for OVX + HD + RIS TE10. At its lowest concentration, TERPY has the potential to improve peri-implant conditions.

Background The demographic shift and epidemiologic transition in Brazil have drawn attention to ways of measuring population health that complement studies of mortality. In this paper, we investigate regional differences in healthy life expectancy based on information from the National Health Survey (PNS), 2013. Methods In the survey, a three-stage cluster sampling (census tracts, households and individuals) with stratification of the primary sampling units and random selection in all stages was used to select 60,202 Brazilian adults (18 years and over). Healthy life expectancies (HLE) were estimated by Sullivan’s method according to sex, age and geographic region, using poor self-rated health for defining unhealthy status. Logistic regression models were used to investigate socioeconomic and regional inequalities in poor self-rated health, after controlling by sex and age. Results Wide disparities by geographic region were found with the worst indicators in the North and Northeast regions, whether considering educational attainment, material deprivation, or health care utilization. Life expectancy at birth for women and men living in the richest regions was 5 years longer than for those living in the less wealthy regions. Modeling the variation across regions for poor self-rated health, statistically significant effects ( p < 0.001) were found for the North and Northeast when compared to the Southeast, even after controlling for age, sex, diagnosis of at least one non-communicable chronic disease, and schooling or socioeconomic class. Marked regional inequalities in HLE were found, with the loss of healthy life much higher among residents of the poorest regions, especially among the elderly. Conclusions By combining data on self-rated health status and mortality in a single indicator, Healthy Life Expectancy, this study demonstrated the excess burden of poor health experienced by populations in the less wealthy regions of Brazil. To mitigate the effects of social exclusion, the development of strategies at the regional level is essential to provide health care to all persons in need, reduce risk exposures, support prevention policies for adoption of healthy behaviors. Such strategies should prioritize population groups that will experience the greatest impact from such interventions.