Development of an application programming interface to automate downloading and processing of precision livestock data.

Advancements in technology have ushered in a new era of sensor-based measurement and management of livestock production systems. These sensor-based technologies have the ability to automatically monitor feeding, growth, and enteric emissions for individual animals across confined and extensive production systems. One challenge with sensor-based technologies is the large amount of data generated, which can be difficult to access, process, visualize, and monitor information in real time to ensure equipment is working properly and animals are utilizing it correctly. A solution to this problem is the development of application programming interfaces (APIs) to automate downloading, visualizing, and summarizing datasets generated from precision livestock technology (PLT). For this methods paper, we develop three APIs and accompanying processes for rapid data acquisition, visualization, systems tracking, and summary statistics for three technologies (SmartScale, SmartFeed, and GreenFeed) manufactured by C-Lock Inc (Rapid City, SD). Program R markdown documents and example datasets are provided to facilitate greater adoption of these techniques and to further advance PLT. The methodology presented successfully downloaded data from the cloud and generated a series of visualizations to conduct systems checks, animal usage rates, and calculate summary statistics. These tools will be essential for further adoption of precision technology. There is huge potential to further leverage APIs to incorporate a wide range of datasets such as weather data, animal locations, and sensor data to facilitate decision-making on time scales relevant to researchers and livestock managers.

Pumping up the Fight against Multiple Sclerosis: The Effects of High-Intensity Resistance Training on Functional Capacity, Muscle Mass, and Axonal Damage.

BACKGROUND: Resistance training (RT) has been recognized as a beneficial non-pharmacological intervention for multiple sclerosis (MS) patients, but its impact on neurodegeneration is not fully understood. This study aimed to investigate the effects of high-intensity RT on muscle mass, strength, functional capacity, and axonal damage in MS patients. METHODS: Eleven relapsing-remitting MS patients volunteered in this within-subject counterbalanced intervention study. Serum neurofilament light-chain (NfL) concentration, vastus lateralis thickness (VL), timed up-and-go test (TUG), sit-to-stand test (60STS), and maximal voluntary isometric contraction (MVIC) were measured before and after intervention. Participants performed 18 sessions of high-intensity RT (70-80% 1-RM) over 6 weeks. RESULTS: Significant (p < 0.05) differences were observed post-intervention for VL (ES = 2.15), TUG (ES = 1.98), 60STS (ES = 1.70), MVIC (ES = 1.78), and NfL (ES = 1.43). Although moderate correlations between changes in VL (R = 0.434), TUG (R = -0.536), and MVIC (R = 0.477) and changes in NfL were observed, only the correlation between VL and MVIC changes was significant (R = 0.684, p = 0.029). CONCLUSIONS: A 6-week RT program significantly increased muscle mass, functional capacity, and neuromuscular function while also decreasing serum NfL in MS patients. These results suggest the effectiveness of RT as a non-pharmacological approach to mitigate neurodegeneration while improving functional capacity in MS patients.

Influence of parallel pins on the angle deviation for placement of dental implants: an in vitro study.

The aim of the present study was to analyze and compare the angle deviation of two, four and six adjacent dental implants placed with and without straight parallel pins. MATERIALS AND METHODS: Two hundred and forty (240) dental implants were selected and randomly allocated into the following study groups: Two dental implants placed with straight parallel pins (Ref.: 144-100, BioHorizons, Birmingham, AL, USA) (n = 10) (2PP); Two dental implants placed without parallel pins (n = 10) (2withoutPP); Four dental implants placed with straight parallel pins hT(n = 10) (4PP); Four dental implants placed without parallel pins (n = 10) (4withoutPP); Six dental implants placed with straight parallel pins (n = 10) (6PP) and Six dental implants placed without parallel pins (n = 10) (6withoutPP). The dental implants randomly assigned to groups 2PP and 2withoutPP were placed into standardized polyurethane models of partially edentulous upper jaws in tooth positions 2.4 and 2.6, the dental implants randomly assigned to groups 4PP and 4withoutPP were placed into standardized polyurethane models of fully edentulous upper jaws in tooth positions 1.6, 1.4, 2.4 and 2.6, and the dental implants randomly assigned to groups 6PP and 6withoutPP were placed into standardized polyurethane models of fully edentulous upper jaws in tooth positions 1.6, 1.4, 1.2, 2.2, 2.4 and 2.6. Afterwards, postoperative CBCT scans and digital impressions were aligned in a 3D implant-planning software to compare the angle deviation (°) of two, four and six adjacent dental implants placed with and without straight parallel pins using the General Linear Model statistical analysis. RESULTS: Statistically significant differences were found between the angle deviation of 2 dental implants placed with straight parallel pins (p < 0.0001) and between the angle deviation of 4 dental implants placed with straight parallel pins (p = 0.0024); however, no statistically significant differences were found in the angle deviation of 6 dental implants placed with straight parallel pins (p = 0.9967). CONCLUSION: The use of a straight parallelization pin results in lower angle deviation between two and four adjacent dental implants; however, it is not effective for a larger number of dental implants.

Digging into the intrinsic capacity concept: Can it be applied to Alzheimer's disease?

Historically, aging research has largely centered on disease pathology rather than promoting healthy aging. The World Health Organization's (WHO) policy framework (2015-2030) underscores the significance of fostering the contributions of older individuals to their families, communities, and economies. The WHO has introduced the concept of intrinsic capacity (IC) as a key metric for healthy aging, encompassing five primary domains: locomotion, vitality, sensory, cognitive, and psychological. Past AD research, constrained by methodological limitations, has focused on single outcome measures, sidelining the complexity of the disease. Our current scientific milieu, however, is primed to adopt the IC concept. This is due to three critical considerations: (I) the decline in IC is linked to neurocognitive disorders, including AD, (II) cognition, a key component of IC, is deeply affected in AD, and (III) the cognitive decline associated with AD involves multiple factors and pathophysiological pathways. Our study explores the application of the IC concept to AD patients, offering a comprehensive model that could revolutionize the disease's diagnosis and prognosis. There is a dearth of information on the biological characteristics of IC, which are a result of complex interactions within biological systems. Employing a systems biology approach, integrating omics technologies, could aid in unraveling these interactions and understanding IC from a holistic viewpoint. This comprehensive analysis of IC could be leveraged in clinical settings, equipping healthcare providers to assess AD patients' health status more effectively and devise personalized therapeutic interventions in accordance with the precision medicine paradigm. We aimed to determine whether the IC concept could be extended from older individuals to patients with AD, thereby presenting a model that could significantly enhance the diagnosis and prognosis of this disease.

Winners do what they fear: exercise and peripheral arterial disease-an umbrella review.

AIMS: This systematic review aims to evaluate and summarize findings from published meta-analyses on the effects of regular exercise in patients with peripheral arterial disease (PAD). The review will assess the impact of exercise on functional parameters, health-related quality of life, haemodynamic parameters, physical activity levels, adverse events, and mortality. METHODS AND RESULTS: A systematic search was performed in PubMed, Web of Science, Scopus, and Cochrane Library databases (up to May 2023) to identify meta-analyses including randomized controlled trials that examined the effects of regular exercise in patients with PAD. Sixteen studies, with a total of 198 meta-analyses, were identified. Results revealed with strong evidence that patients with PAD who exercised improved functional and health-related quality of life parameters. Specifically, supervised aerobic exercise (i.e. walking to moderate-maximum claudication pain) improves maximum walking distance [mean difference (MD): 177.94 m, 95% confidence interval (CI) 142.29-213.60; P < 0.00001; I2: 65%], pain-free walking distance (fixed MD: 68.78 m, 95% CI 54.35-83.21; P < 0.00001; I2: 67%), self-reported walking ability [i.e. distance score (MD: 9.22 points, 95% CI 5.74-12.70; P < 0.00001; I2: 0%), speed score (MD: 8.71 points, 95% CI 5.64-11.77; P < 0.00001, I2: 0%), stair-climbing score (MD: 8.02 points, 95% CI 4.84-11.21; P < 0.00001, I2: 0%), and combined score (MD: 8.76 points, 95% CI 2.78-14.74; P < 0.0001, I2: 0%)], aerobic capacity (fixed MD: 0.62 mL/kg/min, 95% CI 0.47-0.77, P < 0.00001, I2: 64%), and pain score (MD: 7.65, 95% CI 3.15-12.15; P = 0.0009; I2: 0%), while resistance exercise improves lower limb strength (standardized mean difference: 0.71, 95% CI 0.29-1.13, P = 0.0009; I2: 0%]. Regarding other outcomes, such as haemodynamic parameters, no significant evidence was found, while physical activity levels, adverse events, and mortality require further investigation. CONCLUSION: Synthesis of the currently available meta-analyses suggests that regular exercise may be beneficial for a broad range of functional tasks improving health-related quality of life in patients with PAD. Supervised aerobic exercise is the best type of exercise to improve walking-related outcomes and pain, while resistance exercise is more effective to improve lower limb strength.

Regular exercise is beneficial for a wide range of functional capacity-related outcomes that seem to improve health-related quality of life in patients with peripheral arterial disease (PAD). Supervised aerobic exercise (i.e. walking to moderate­maximum claudication pain) is the best type of exercise to improve walking-related outcomes and pain. Resistance exercise improves lower limb strength.

Improving Dry Matter Intake Estimates Using Precision Body Weight on Cattle Grazed on Extensive Rangelands.

An essential component required for calculating stocking rates for livestock grazing extensive rangeland is dry matter intake (DMI). Animal unit months are used to simplify this calculation for rangeland systems to determine the rate of forage consumption and the cattle grazing duration. However, there is an opportunity to leverage precision technology deployed on rangeland systems to account for the individual animal variation of DMI and subsequent impacts on herd-level decisions regarding stocking rate. Therefore, the objectives of this study were, first, to build a precision system model (PSM) to predict total DMI (kg) and required pasture area (ha) using precision body weight (BW), and second, to evaluate differences in PSM-predicted stocking rates compared to the traditional herd-level method using initial or estimated mid-season BW. A deterministic model was constructed in both Vensim (version 10.1.2) and Program R (version 4.2.3) to incorporate individual precision BW data into a commonly used rangeland equation using %BW to estimate individual DMI, daily herd DMI, and area (ha) required to meet animal DMI requirements throughout specific grazing periods. Using the PSM, differences in outputs were evaluated using three scenarios: (1) initial BW (business as usual); (2) average mid-season BW; and (3) individual precision BW using data from two precision rangeland experiments conducted at the South Dakota State University Cottonwood Field Station. The data from the two experiments were used to develop PSM case studies. The trial data were collected using precision weight data (SmartScale™) collected from replacement heifers (Case study 1, n = 60) and steers (Case study 2, n = 254) grazing native rangeland. In Case study 1 (heifers), Scenario 1 versus Scenario 3 resulted in an additional 73.41 ha required. Results from Case study 2 indicated an average additional 4.4 ha required per pasture when comparing Scenario 3 versus Scenario 1. Sensitivity analyses resulted in a difference between maximum and minimum simulated values of 27,995 and 4265 kg forage consumed, and 122 and 8.9 pasture ha required for Case studies 1 and 2, respectively. Thus, results from the scenarios indicate an opportunity to identify both under- and over-stocking situations using precision DMI estimates, which helps to identify high-leverage precision tools that have practical applications for enhancing animal and plant productivity and environmental sustainability on extensive rangelands.

ASAS-NANP symposium: mathematical modeling in animal nutrition-Making sense of big data and machine learning: how open-source code can advance training of animal scientists.

Advancements in precision livestock technology have resulted in an unprecedented amount of data being collected on individual animals. Throughout the data analysis chain, many bottlenecks occur, including processing raw sensor data, integrating multiple streams of information, incorporating data into animal growth and nutrition models, developing decision support tools for producers, and training animal science students as data scientists. To realize the promise of precision livestock management technologies, open-source tools and tutorials must be developed to reduce these bottlenecks, which are a direct result of the tremendous time and effort required to create data pipelines from scratch. Open-source programming languages (e.g., R or Python) can provide users with tools to automate many data processing steps for cleaning, aggregating, and integrating data. However, the steps from data collection to training artificial intelligence models and integrating predictions into mathematical models can be tedious for those new to statistical programming, with few examples pertaining to animal science. To address this issue, we outline how open-source code can help overcome many of the bottlenecks that occur in the era of big data and precision livestock technology, with an emphasis on how routine use and publication of open-source code can help facilitate training the next generation of animal scientists. In addition, two case studies are presented with publicly available data and code to demonstrate how open-source tutorials can be utilized to streamline data processing, train machine learning models, integrate with animal nutrition models, and facilitate learning. The National Animal Nutrition Program focuses on providing research-based data on animal performance and feeding strategies. Open-source data and code repositories with examples specific to animal science can help create a reinforcing mechanism aimed at advancing animal science research.

Livestock production is undergoing a new revolution of incorporating advanced technology to inform animal management. As more and more technologies come to market, new challenges arise with developing a workforce trained to handle big datasets generated from these technologies and turning datasets into insight for livestock producers. This can be especially challenging as multiple data streams ranging from climate and weather information to real-time metrics on animal performance need to be efficiently processed and incorporated into animal production models. Open-source code is one possible solution to these challenges because it is designed to be made publicly available so any user can view, alter, and improve upon existing code. This paper aims to highlight how open-source code can help address many of the challenges of precision livestock technology, including efficient data processing, data integration, development of decision tools, and training of future animal scientists. In addition, the need for open-source tutorials and datasets specific to animal science are included to help facilitate greater adoption of open science.

Comparative analysis of two navigation techniques based on augmented reality technology for the orthodontic mini-implants placement.

To analyze and compare the accuracy and root contact prevalence, comparing a conventional freehand technique and two navigation techniques based on augmented reality technology for the orthodontic self-drilling mini-implants placement. Methods Two hundred and seven orthodontic self-drilling mini-implants were placed using either a conventional freehand technique (FHT) and two navigation techniques based on augmented reality technology (AR TOOTH and AR SCREWS). Accuracy across different dental sectors was also analyzed. CBCT and intraoral scans were taken both prior to and following orthodontic self-drilling mini-implants placement. The deviation angle and horizontal were then analyzed; these measurements were taken at the coronal entry point and apical endpoint between the planned and performed orthodontic self-drilling mini-implants. In addition, any complications resulting from mini-implant placement, such as spot perforations, were also analyzed across all dental sectors.Results The statistical analysis showed significant differences between study groups with regard to the coronal entry-point (p < 0.001), apical end-point(p < 0.001) and angular deviations (p < 0.001). Furthermore, statistically significant differences were shown between the orthodontic self-drilling mini-implants placement site at the coronal entry-point (p < 0.0001) and apical end-point (p < 0.001). Additionally, eight root perforations were observed in the FHT group, while there were no root perforations in the two navigation techniques based on augmented reality technology.Conclusions The navigation techniques based on augmented reality technology has an effect on the accuracy of orthodontic self-drilling mini-implants placement and results in fewer intraoperative complications, comparing to the conventional free-hand technique. The AR TOOTH augmented reality technique showed more accurate results between planned and placed orthodontic self-drilling mini-implants, comparing to the AR SCREWS and conventional free-hand techniques. The navigation techniques based on augmented reality technology showed fewer intraoperative complications, comparing to the conventional free-hand technique.

Integrative metabolomics science in Alzheimer's disease: Relevance and future perspectives.

Alzheimer's disease (AD) is determined by various pathophysiological mechanisms starting 10-25 years before the onset of clinical symptoms. As multiple functionally interconnected molecular/cellular pathways appear disrupted in AD, the exploitation of high-throughput unbiased omics sciences is critical to elucidating the precise pathogenesis of AD. Among different omics, metabolomics is a fast-growing discipline allowing for the simultaneous detection and quantification of hundreds/thousands of perturbed metabolites in tissues or biofluids, reproducing the fluctuations of multiple networks affected by a disease. Here, we seek to critically depict the main metabolomics methodologies with the aim of identifying new potential AD biomarkers and further elucidating AD pathophysiological mechanisms. From a systems biology perspective, as metabolic alterations can occur before the development of clinical signs, metabolomics - coupled with existing accessible biomarkers used for AD screening and diagnosis - can support early disease diagnosis and help develop individualized treatment plans. Presently, the majority of metabolomic analyses emphasized that lipid metabolism is the most consistently altered pathway in AD pathogenesis. The possibility that metabolomics may reveal crucial steps in AD pathogenesis is undermined by the difficulty in discriminating between the causal or epiphenomenal or compensatory nature of metabolic findings.

Healthcare Professionals' Knowledge about Pediatric Chronic Pain: A Systematic Review.

Pediatric chronic pain is a common public health problem with a high prevalence among children and adolescents. The aim of this study was to review the current knowledge of health professionals on pediatric chronic pain between 15-30% among children and adolescents. However, since this is an underdiagnosed condition, it is inadequately treated by health professionals. To this aim, a systematic review was carried out based on a search of the electronic literature databases (PubMed and Web of Science), resulting in 14 articles that met the inclusion criteria. The analysis of these articles seems to show a certain degree of heterogeneity in the surveyed professionals about the awareness of this concept, especially regarding its etiology, assessment, and management. In addition, the extent of knowledge of the health professionals seems to be insufficient regarding these aspects of pediatric chronic pain. Hence, the knowledge of the health professionals is unrelated to recent research that identifies central hyperexcitability as the primary factor affecting the onset, persistence, and management of pediatric chronic pain.

Effect of the computer-aided static navigation technique on the accuracy of bicortical mini-implants placement site for maxillary skeletal expansion appliances: an in vitro study.

The objective of the present study was to evaluate and compare the effect of the computer-aided static navigation technique on the accuracy of the maxillary skeletal expansion (MSE) appliances. MATERIAL AND METHODS: Forty orthodontic self-drilling mini-implants were placed in ten anatomically based standardized polyurethane models of a completely edentulous upper maxilla, manufactured using a 3D impression procedure. The four orthodontic self-drilling mini-implants for anchoring the MSE appliance were digitally planned on 3D planning software, based on preoperative cone-beam computed tomography (CBCT) scan and a 3D extraoral surface scan. Afterwards, the surgical templates were virtually planned and manufactured using stereolithography. Subsequently, the orthodontic self-drilling mini-implants were placed an postoperative CBCT scans were performed. Finally, coronal entry-point, apical end-point and angular deviations were calculated using a t-test for independent samples or a non-parametric Signed Rank test. RESULTS: Statistically significant differences were not shown at coronal entry-point (p = 0.13), apical end-point (p = 0.41) and angular deviations (p = 0.27) between the planned and performed orthodontic self-drilling mini-implants. CONCLUSIONS: Computer-aided static navigation technique enables accurate orthodontic mini-implant placement for the MSE appliances.

ASAS-NANP Symposium: Mathematical Modeling in Animal Nutrition: Opportunities and challenges of confined and extensive precision livestock production.

Modern animal scientists, industry, and managers have never faced a more complex world. Precision livestock technologies have altered management in confined operations to meet production, environmental, and consumer goals. Applications of precision technologies have been limited in extensive systems such as rangelands due to lack of infrastructure, electrical power, communication, and durability. However, advancements in technology have helped to overcome many of these challenges. Investment in precision technologies is growing within the livestock sector, requiring the need to assess opportunities and challenges associated with implementation to enhance livestock production systems. In this review, precision livestock farming and digital livestock farming are explained in the context of a logical and iterative five-step process to successfully integrate precision livestock measurement and management tools, emphasizing the need for precision system models (PSMs). This five-step process acts as a guide to realize anticipated benefits from precision technologies and avoid unintended consequences. Consequently, the synthesis of precision livestock and modeling examples and key case studies help highlight past challenges and current opportunities within confined and extensive systems. Successfully developing PSM requires appropriate model(s) selection that aligns with desired management goals and precision technology capabilities. Therefore, it is imperative to consider the entire system to ensure that precision technology integration achieves desired goals while remaining economically and managerially sustainable. Achieving long-term success using precision technology requires the next generation of animal scientists to obtain additional skills to keep up with the rapid pace of technology innovation. Building workforce capacity and synergistic relationships between research, industry, and managers will be critical. As the process of precision technology adoption continues in more challenging and harsh, extensive systems, it is likely that confined operations will benefit from required advances in precision technology and PSMs, ultimately strengthening the benefits from precision technology to achieve short- and long-term goals.

Interest and investment in precision technologies are growing within the livestock sector. Though these technologies offer many promises of increased efficiency and reduced inputs, there is a need to assess the opportunities and challenges associated with precision technology implementation in livestock production systems. In this review, precision livestock measurement and management tools are explained in the context of a logical and iterative five-step process that highlights the need for systems computer modeling to realize anticipated benefits from these technologies and avoid unintended consequences. This review includes key case studies to highlight past challenges and current opportunities within operations that house animals in a central area or building with sufficient infrastructure (confined livestock production systems) and other operation settings that utilize large grasslands that contain far less infrastructure (extensive livestock production systems). The key to precision livestock management success is training the next generation of animal scientists in computer modeling, precision technologies, computer programming, and data science while still being grounded in traditional animal science principles.

ASAS-NANP symposium: mathematical modeling in animal nutrition: limitations and potential next steps for modeling and modelers in the animal sciences.

The field of animal science, and especially animal nutrition, relies heavily on modeling to accomplish its day-to-day objectives. New data streams ("big data") and the exponential increase in computing power have allowed the appearance of "new" modeling methodologies, under the umbrella of artificial intelligence (AI). However, many of these modeling methodologies have been around for decades. According to Gartner, technological innovation follows five distinct phases: technology trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, and plateau of productivity. The appearance of AI certainly elicited much hype within agriculture leading to overpromised plug-and-play solutions in a field heavily dependent on custom solutions. The threat of failure can become real when advertising a disruptive innovation as sustainable. This does not mean that we need to abandon AI models. What is most necessary is to demystify the field and place a lesser emphasis on the technology and more on business application. As AI becomes increasingly more powerful and applications start to diverge, new research fields are introduced, and opportunities arise to combine "old" and "new" modeling technologies into hybrids. However, sustainable application is still many years away, and companies and universities alike do well to remain at the forefront. This requires investment in hardware, software, and analytical talent. It also requires a strong connection to the outside world to test, that which does, and does not work in practice and a close view of when the field of agriculture is ready to take its next big steps. Other research fields, such as engineering and automotive, have shown that the application power of AI can be far reaching but only if a realistic view of models as whole is maintained. In this review, we share our view on the current and future limitations of modeling and potential next steps for modelers in the animal sciences. First, we discuss the inherent dependencies and limitations of modeling as a human process. Then, we highlight how models, fueled by AI, can play an enhanced sustainable role in the animal sciences ecosystem. Lastly, we provide recommendations for future animal scientists on how to support themselves, the farmers, and their field, considering the opportunities and challenges the technological innovation brings.

Modeling in the animal sciences has received a boost by large-scale adoption of sensor technology, increased computing power, and the further development of artificial intelligence (AI) in the form of machine learning (ML) and deep learning (DL) models. Together with open-source programming languages, extensive modeling libraries, and heavy marketing, modeling reached a larger audience via AI. However, like most technological innovations, AI overpromised. By adopting an almost singular model-centric view to solving business needs, models failed to integrate with existing business processes. Models, especially AI, need data and both need humans. Together, they need room to learn and fail and by offering them as the end-solution to a problem, they are unable to act as sparring partners for all relevant stakeholders. In this article, we highlight fundamental model limitations exemplified via AI, and we offer solutions toward a more sustainable adoption of AI as a catalyst for modeling. This means sharing data and code and placing a more realistic view on models. Universities and industry both play a fundamental role in offering technological prowess and business experience to the future modeler. People, not technology, are the key to a more successful adoption of models.

Effect of cycling exercise on lumbopelvic control performance in elite female cyclists.

OBJECTIVES: The purpose of the present study is to assess the effects of an intense cycling training session on the stability of the lumbopelvic-hip complex through two dynamic exercise tests - the single-leg-deadlift (SLD) and a variation of the bird-modified dog (BD), via the OCTOcore application. METHODS: Thirty-one elite female road cyclists were self-evaluated with their own smartphones, before and immediately after finishing their training sessions. Right, left and composite were measured for each exercise test. RESULTS: There was a significant time effect on performance for both the SLB and BD tests (p<0.05; η2=0.137), and the SLD and BD tests were increased with respect to the pre-test at 15% and 17%, respectively. CONCLUSION: An intense cycling training session produced significant alterations in lumbopelvic behavior in the elite female cyclists. The OCTOcore application demonstrated that it was a sensitive tool in detecting these changes and it could easily be used by the cyclists themselves.

A Replicable and Reproducible Digital Method for Quantifying Maxillary Sinus Airway Changes after Sinus Lifts Using the Lateral Window Approach Technique-A Retrospective Study.

In the present retrospective study, we aimed to assess the replicability and reproducibility of a novel digital measurement technique for analyzing the volumes of the left and right maxillary sinuses and the nasal and maxillary sinus airway complex after a sinus lift procedure using the lateral window approach, to provide an accurate measurement technique for easily applying in clinical practice and to allow pre-operative assessment of maxillary sinus lift surgery, avoiding complications and making surgery more predictable. MATERIAL AND METHODS: Thirty patients with partially edentulous posterior maxilla were selected and submitted to bilateral sinus lift using the lateral window approach technique, with grafting materials selected and submitted to cone beam computed tomography (CBCT) scans, both pre- and postoperatively. Then, datasets were uploaded to therapeutic digital planning software to measure the volume of the right and left maxillary sinuses and the nasal and maxillary sinus airway complex. Gage R&R statistical analysis was performed to assess the replicability and reproducibility of the digital measurement technique. RESULTS: The variability attributable to the novel digital measurement technique was 3.4% for replicability and 3.4% for reproducibility of the total variability of the samples. CONCLUSION: The novel digital method proposed is a replicable and reproducible technique for analyzing the volume of the right and left maxillary sinuses and the nasal and maxillary sinus airway complex after a sinus lift using the lateral window approach technique, allowing an accurate pre-operative assessment of maxillary sinus lift surgery, avoiding complications and making surgery more predictable.

Survival Rate and Prosthetic and Sinus Complications of Zygomatic Dental Implants for the Rehabilitation of the Atrophic Edentulous Maxilla: A Systematic Review and Meta-Analysis.

The aim of this systematic review and meta-analysis was to analyze and compare the survival rate and prosthetic and sinus complications of zygomatic dental implants for the rehabilitation of the atrophic edentulous maxilla. MATERIALS AND METHODS: We conducted a systematic literature review and meta-analysis, based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations, of clinical studies that evaluated the survival rate and prosthetic and sinus complications of zygomatic dental implants for the rehabilitation of the atrophic edentulous maxilla. Four databases were consulted during the literature search: Pubmed-Medline, Scopus, Embase, and Web of Science. After eliminating duplicate articles and applying the inclusion criteria, 46 articles were selected for the qualitative analysis and 32 for the quantitative analysis. RESULTS: Four randomized controlled trials, 19 prospective clinical studies, 20 retrospective studies, and 3 case series were included in the meta-analysis. Conventional dental implants failure (n = 3549) were seen in 2.89% (IC-95% 1.83-3.96%), while zygomatic dental implants failure (n = 1895) were seen in 0.69% (IC-95% 0.21-1.16%). The measure of the effect size used was the Odds Ratio, which was estimated at 2.05 with a confidence interval of 95% between 1.22 and 3.44 (z test = 2.73; p-value = 0.006). The failure risk of conventional dental implants is 2.1 times higher than that of zygomatic dental implants. Slight heterogeneity was determined in the meta-analysis between 23 combined studies (Q test = 32.4; p-value = 0.070; I2 = 32.1%). Prosthetic complications were recorded in 4.9% (IC-95% 2.7-7.3%) and mild heterogeneity was observed in a meta-analysis of 28 combined studies (Q test = 88.2; p-value = 0.001; I2 = 69.4%). Sinus complications were seen in 4.7% (IC-95% 2.8-6.5%) and mild heterogeneity was observed in a meta-analysis of 32 combined studies (Q test = 75.3; p-value = 0.001; I2 = 58.8%). CONCLUSIONS: The high survival rate and low prosthetic and sinus complications related to zygomatic dental implants suggest the use of zygomatic dental implants for the rehabilitation of the atrophic edentulous maxilla.

Getting Ahead of the Arms Race: Hothousing the Coevolution of VirusTotal with a Packer.

Malware detection is in a coevolutionary arms race where the attackers and defenders are constantly seeking advantage. This arms race is asymmetric: detection is harder and more expensive than evasion. White hats must be conservative to avoid false positives when searching for malicious behaviour. We seek to redress this imbalance. Most of the time, black hats need only make incremental changes to evade them. On occasion, white hats make a disruptive move and find a new technique that forces black hats to work harder. Examples include system calls, signatures and machine learning. We present a method, called Hothouse, that combines simulation and search to accelerate the white hat's ability to counter the black hat's incremental moves, thereby forcing black hats to perform disruptive moves more often. To realise Hothouse, we evolve EEE, an entropy-based polymorphic packer for Windows executables. Playing the role of a black hat, EEE uses evolutionary computation to disrupt the creation of malware signatures. We enter EEE into the detection arms race with VirusTotal, the most prominent cloud service for running anti-virus tools on software. During our 6 month study, we continually improved EEE in response to VirusTotal, eventually learning a packer that produces packed malware whose evasiveness goes from an initial 51.8% median to 19.6%. We report both how well VirusTotal learns to detect EEE-packed binaries and how well VirusTotal forgets in order to reduce false positives. VirusTotal's tools learn and forget fast, actually in about 3 days. We also show where VirusTotal focuses its detection efforts, by analysing EEE's variants.

Detecting Malware with Information Complexity.

Malware concealment is the predominant strategy for malware propagation. Black hats create variants of malware based on polymorphism and metamorphism. Malware variants, by definition, share some information. Although the concealment strategy alters this information, there are still patterns on the software. Given a zoo of labelled malware and benign-ware, we ask whether a suspect program is more similar to our malware or to our benign-ware. Normalized Compression Distance (NCD) is a generic metric that measures the shared information content of two strings. This measure opens a new front in the malware arms race, one where the countermeasures promise to be more costly for malware writers, who must now obfuscate patterns as strings qua strings, without reference to execution, in their variants. Our approach classifies disk-resident malware with 97.4% accuracy and a false positive rate of 3%. We demonstrate that its accuracy can be improved by combining NCD with the compressibility rates of executables using decision forests, paving the way for future improvements. We demonstrate that malware reported within a narrow time frame of a few days is more homogeneous than malware reported over two years, but that our method still classifies the latter with 95.2% accuracy and a 5% false positive rate. Due to its use of compression, the time and computation cost of our method is nontrivial. We show that simple approximation techniques can improve its running time by up to 63%. We compare our results to the results of applying the 59 anti-malware programs used on the VirusTotal website to our malware. Our approach outperforms each one used alone and matches that of all of them used collectively.

Mimicking Anti-Viruses with Machine Learning and Entropy Profiles.

The quality of anti-virus software relies on simple patterns extracted from binary files. Although these patterns have proven to work on detecting the specifics of software, they are extremely sensitive to concealment strategies, such as polymorphism or metamorphism. These limitations also make anti-virus software predictable, creating a security breach. Any black hat with enough information about the anti-virus behaviour can make its own copy of the software, without any access to the original implementation or database. In this work, we show how this is indeed possible by combining entropy patterns with classification algorithms. Our results, applied to 57 different anti-virus engines, show that we can mimic their behaviour with an accuracy close to 98% in the best case and 75% in the worst, applied on Windows' disk resident malware.