Validation of a novel AI-based automated multimodal image registration of CBCT and intraoral scan aiding presurgical implant planning.

OBJECTIVES: The objective of this study is to assess accuracy, time-efficiency and consistency of a novel artificial intelligence (AI)-driven automated tool for cone-beam computed tomography (CBCT) and intraoral scan (IOS) registration compared with manual and semi-automated approaches. MATERIALS AND METHODS: A dataset of 31 intraoral scans (IOSs) and CBCT scans was used to validate automated IOS-CBCT registration (AR) when compared with manual (MR) and semi-automated registration (SR). CBCT scans were conducted by placing cotton rolls between the cheeks and teeth to facilitate gingival delineation. The time taken to perform multimodal registration was recorded in seconds. A qualitative analysis was carried out to assess the correspondence between hard and soft tissue anatomy on IOS and CBCT. In addition, a quantitative analysis was conducted by measuring median surface deviation (MSD) and root mean square (RMS) differences between registered IOSs. RESULTS: AR was the most time-efficient, taking 51.4 ± 17.2 s, compared with MR (840 ± 168.9 s) and SR approaches (274.7 ± 100.7 s). Both AR and SR resulted in significantly higher qualitative scores, favoring perfect IOS-CBCT registration, compared with MR (p = .001). Additionally, AR demonstrated significantly superior quantitative performance compared with SR, as indicated by low MSD (0.04 ± 0.07 mm) and RMS (0.19 ± 0.31 mm). In contrast, MR exhibited a significantly higher discrepancy compared with both AR (MSD = 0.13 ± 0.20 mm; RMS = 0.32 ± 0.14 mm) and SR (MSD = 0.11 ± 0.15 mm; RMS = 0.40 ± 0.30 mm). CONCLUSIONS: The novel AI-driven method provided an accurate, time-efficient, and consistent multimodal IOS-CBCT registration, encompassing both soft and hard tissues. This approach stands as a valuable alternative to manual and semi-automated registration approaches in the presurgical implant planning workflow.

The dawn of climate securityThreat Multiplier: Climate, Military Leadership, and the Fight for Global Security Sherri Goodman Island Press, 2024. 272 pp.

A policy pioneer's memoir shows how climate science penetrated the defense sector.

Material conflictsThe War Below Ernest Scheyder One Signal Publishers/Atria, 2024. 384 pp.

A journalist probes tensions surrounding two minerals that are key to green technologies.

Deep learning driven segmentation of maxillary impacted canine on cone beam computed tomography images.

The process of creating virtual models of dentomaxillofacial structures through three-dimensional segmentation is a crucial component of most digital dental workflows. This process is typically performed using manual or semi-automated approaches, which can be time-consuming and subject to observer bias. The aim of this study was to train and assess the performance of a convolutional neural network (CNN)-based online cloud platform for automated segmentation of maxillary impacted canine on CBCT image. A total of 100 CBCT images with maxillary canine impactions were randomly allocated into two groups: a training set (n = 50) and a testing set (n = 50). The training set was used to train the CNN model and the testing set was employed to evaluate the model performance. Both tasks were performed on an online cloud-based platform, 'Virtual patient creator' (Relu, Leuven, Belgium). The performance was assessed using voxel- and surface-based comparison between automated and semi-automated ground truth segmentations. In addition, the time required for segmentation was also calculated. The automated tool showed high performance for segmenting impacted canines with a dice similarity coefficient of 0.99 ± 0.02. Moreover, it was 24 times faster than semi-automated approach. The proposed CNN model achieved fast, consistent, and precise segmentation of maxillary impacted canines.

Prevalence of Mupirocin and Methicillin-Resistant Staphylococcus aureus in Nasal Carriage Among Healthcare Workers in an Intensive Care Unit and Post-decolonization Screening Outcomes at a Tertiary Care Hospital: A Prospective Study.

INTRODUCTION: Nasal carriage of Staphylococcus species plays an important role in the epidemiology and pathogenesis of both community and healthcare-associated infections. Coinciding the emergence of methicillin-resistant Staphylococcus aureus (MRSA) is a challenge for clinicians to prevent their spread. Mupirocin is a topical antimicrobial agent approved for eradicating nasal carriage of staphylococcal species in adult patients and healthcare workers (HCWs). The increasing prevalence of mupirocin resistance among Staphylococcus aureus and coagulase-negative staphylococci species could be an important threat to the future use of mupirocin against MRSA. OBJECTIVE: The aim of this study is to determine the prevalence of MRSA from nasal swabs of HCWs in intensive care units and its level of resistance pattern of mupirocin in all isolates of Staphylococcus species by disk diffusion and epsilometer test (E-test) and to determine post decolonization screening. MATERIALS AND METHODS:  A total of 67 HCWs (doctors, nursing staff, technicians, and housekeeping staff) in the medical and surgical intensive care units were included in the study. Nasal swabs were collected from the subjects and cultured onto nutrient and blood agar, which were then incubated at 37ºC for 18 to 24 hours. Staphylococcus aureus and coagulase-negativeStaphylococcus species (CoNS) were identified by standard biochemical techniques. Methicillin resistance was detected by the disk diffusion method using a 30 µg cefoxitin disk as per the Clinical and Laboratory Standards Institute (CLSI) guidelines, and mupirocin resistance was detected using a 5 µg mupirocin disk. The resistance strains were further subjected to E-strip testing to determine the level of mupirocin resistance. RESULTS:  A total of 72 isolates were grown from the 67 subjects used in this study. Nine strains (12.5%) grew S. aureus, and 52 strains (72.2%) grew CoNS. Methicillin resistance was seen in five isolates (6.9%) of S. aureus and 45 isolates (62.5%) of CoNS. Mupirocin resistance was seen in 11 isolates of methicillin-resistant coagulase-negative Staphylococcus species (MRCoNS), where three isolates (4.1%) showed low-level mupirocin resistance MuL and eight isolates (11.11%) showed high-level mupirocin resistance MuH. None of the isolates of MRSA, methicillin-sensitive Staphylococcus aureus (MSSA), and methicillin-sensitive coagulase-negative Staphylococcusspecies (MSCoNS) were resistant to mupirocin. Seven out of nine HCWs (77.8%) showed clearance of the organism after decolonization therapy. CONCLUSION: The prevalence of emerging resistance to mupirocin in MRSA and MRCoNS is of great concern, especially in the nasal carrier state of HCWs. Hence, methicillin and mupirocin resistance in S. aureus and CoNS must be detected in HCWs as a routine protocol, and decolonization measures should be undertaken to prevent healthcare-associated infections.

Natural resources modulate the nexus between environmental shocks and human mobility.

In the context of natural resource degradation, migration can act as means of adaptation both for those leaving and those supported by remittances. Migration can also result from an inability to adapt in-situ, with people forced to move, sometimes to situations of worse or of the same exposure to environmental threats. The deleterious impacts of resource degradation have been proposed in some situations to limit the ability to move. In this contribution, we use remote sensed information coupled with population density data for continental Africa to assess quantitatively the prevalence of migration and immobility in the context of one cause of resource degradation: drought. We find that the effect of drought on mobility is amplified with the frequency at which droughts are experienced and that higher income households appear more resilient to climatic shocks and are less likely to resort to mobility as an adaptation response.

Indirect Response of the Temperature, Humidity, and Rainfall on the Spread of COVID-19 over the Indian Monsoon Region.

This article examines the role of the meteorological variable in the spread of the ongoing pandemic coronavirus disease 2019 (COVID-19) across India. COVID-19 has created an unprecedented situation for public health and brought the world to a standstill. COVID-19 had caused more than 1,523,242 deaths out of 66,183,029 confirmed cases worldwide till the first week of December 2020. We have examined the surface temperature, relative humidity, and rainfall over five cities: Delhi, Mumbai, Kolkata, Bengaluru, and Chennai, which were severely affected by COVID-19. It is found that the prevailing southwest (SW) monsoon during the pandemic has acted as a natural sanitizer in limiting the spread of the virus. The mean rainfall is ~ 20-40 mm over the selected cities, resulting in an average decrease in COVID cases by ~ 18-26% for the next 3 days after the rainfall. The day-to-day variations of the meteorological parameters and COVID-19 cases clearly demonstrate that both surface temperature and relative humidity play a vital role in the indirect transport of the virus. Our analysis reveals that most COVID-19 cases fall within the surface temperature range from 24 to 30 °C and relative humidity range from 50% to 80%. At a given temperature, COVID-19 cases show a large dependency on the relative humidity; therefore, the coastal environments were more prone to infections. Wavelet transforms coherence analysis of the daily COVID-19 cases with temperature and relative humidity reveals a significant coherence within 8 days.

Ab.ai - A Novel Automated AI Tool for Reporting Antibiograms.

Anti-Microbial Resistance is one of the greatest threats that mankind faces right now due to the inappropriate use of antibiotics. Institution of appropriate antibiotics in right dose for the right patient at right time is the "gamechanger" in fighting AMR. Antibiotic Sensitivity Testing (AST) or antibiogram is done to ascertain the sensitivity profile of the organism. The most widely used method in laboratory practice in India is the Kirby-Bauer's disk diffusion test. There are few shortcomings in the manual interpretation of antibiograms in the form of high inter-operator variability, mandatory requirement of trained microbiologists - which is difficult in low-resource settings and high degree of interpersonal bias due to various factors like stress, workload, and visual acuity. We propose the Ab.ai tool for automating the AST procedures in laboratory. The Ab.ai tool comprises of 3 phases: first for data collection, second for data processing and the third for generation of antibiotic sensitivity reports. Various software packages like OpenCV and EasyOCR are used for the development of the Ab.ai tool. A total of 50 antibiograms of both GPC and GNB are interpreted both by manual and automated method. The manual method is considered the "gold-standard" and the performance of Ab.ai tool was compared against the manual method. The Ab.ai tool achieved an agreement of 98.4% on susceptibility categorization of GPC antibiotics and 97.6% on that of GNB antibiotics against the gold standard manual method. The proposed Ab.ai tool serves as a perfect candidate for automating AST procedures and would prove to be a "game-changer" in battling AMR.

Closing the Infrastructure Gap for Decarbonization: The Case for an Integrated Mineral Supply Agreement.

Significant amounts of feedstock metals will be required to build the infrastructure for the green energy transition. It is currently estimated, however, that the world may be facing an "infrastructure gap" that could prevent us from meeting United Nations Sustainable Development Goal targets. Prior investigations have focused on the extractive aspects of the mining industry to meet these targets and on looming bottlenecks and regional challenges in these upstream market segments. Scant attention has been paid to the downstream processing segments of the raw materials value chain, which also has a high degree of market concentration. Growing international tensions and geopolitical events have resulted in a shift toward "reshoring" and "near-shoring" of mining processing capabilities as regional powers attempt to make metal supply chains more secure. While increasing resilience, these shifts can also dilute the overall effectiveness of the global mining supply network and subsequently hamper the world's ability to close the green energy infrastructure gap. We argue that broadening the remit of the International Renewable Energy Agency (IRENA) to include coordinating these mission-critical metal processing functions can mitigate these issues. The G20 is one potential forum for enabling an integrated mineral processing agreement under the auspices of IRENA.

The allure of an ordered UniverseTen Patterns That Explain the Universe Brian Clegg MIT Press, 2021. 224 pp.

A science writer's ode to patterns packs a visual punch.

Energy implications of the 21st century agrarian transition.

The ongoing agrarian transition from small-holder farming to large-scale commercial agriculture is reshaping systems of production and human well-being in many regions. A fundamental part of this global transition is manifested in large-scale land acquisitions (LSLAs) by agribusinesses. Its energy implications, however, remain poorly understood. Here, we assess the multi-dimensional changes in fossil-fuel-based energy demand resulting from this agrarian transition. We focus on LSLAs by comparing two scenarios of low-input and high-input agricultural practices, exemplifying systems of production in place before and after the agrarian transition. A shift to high-input crop production requires industrial fertilizer application, mechanization of farming practices and irrigation, which increases by ~5 times fossil-fuel-based energy consumption compared to low-input agriculture. Given the high energy and carbon footprints of LSLAs and concerns over local energy access, our analysis highlights the need for an approach that prioritizes local resource access and incorporates energy-intensity analyses in land use governance.

Estimation of waste outflows for multiple product types in China from 2010-2050.

Material flow has been accelerated from underground natural minerals and is accumulating as aboveground waste stock. China is not only the largest producer and consumer of material-driven products, but also the largest generator of product waste. No official annual product waste data are released for China, which creates challenges especially in light of China's emerging waste management policies. Previous studies have presented only estimations of waste streams for single products. In this study, we considered three product types and 33 technological products and collected all the available data. A Kuznets curve and Bass diffusion model were employed to forecast their future consumption. Based on urban consumption metabolism, we created one systematic estimation model of product waste generation related to material flow and social regulation. Typical technological product waste outflows were estimated from 2010 to 2050, which can assist further material flow and environmental impact research, as well as waste management policy-making and technology development. The created model can be potentially extended to other types of product waste estimation.

Effects of Intraligamentary Injection of Osteogenic-Induced Gingival Fibroblasts on Cementum Thickness in the Dog Model of Tooth Root Resorption.

Tooth root resorption is an unwanted result of orthodontic tooth movement, and it can be expressed by a reduction in cementum thickness. The aim of this experimental study was to evaluate the effect of intraligamentary injection of osteogenic-induced gingival fibroblasts (OIGF) on cellular and acellular tooth root cementum thickness in modeled orthodontic tooth movement. Six beagle dogs were used in the study. All the upper and lower third and fourth premolars were subjected to mechanical loading for 4 weeks, which induced orthodontic tooth movement. Fifteen premolars were assigned to the OIGF group, which received a single OIGF injection through the periodontal ligament near the root apex (n = 7 teeth), and to the control group, which received a single injection of Dulbecco's modified eagle's medium in the periapical area (n = 8 teeth). The evaluation of histomorphometry was performed to assess the thicknesses of cellular and acellular cementum at the root apex and four bilateral sites distal to the apex. We found no statistically significant enhancing effects of gingival fibroblasts on either cellular or acellular cementum thicknesses when compared with the control group. We conclude that a single intraligamentary injection of OIGF does not stimulate the formation of tooth root cementum in the dog model of orthodontic tooth movement. Thus, OIGF is unlikely to prevent orthodontic-induced tooth root resorption.

Mapping anthropogenic mineral generation in China and its implications for a circular economy.

Anthropogenic mineral is absorbing wide concern in the context of circular economy, but its generation mechanism and quantity from product to waste remain unclear. Here we consider three product groups, 30 products, and use the revised Weibull lifespan model to map the generation of anthropogenic mineral and 23 types of the capsulated materials by targeting their evolution from 2010 to 2050. Total weight of anthropogenic mineral on average in China reached 39 Mt in 2010, but it will double in 2022 and quadruple in 2045. Stocks of precious metals and rare earths will increase faster than most base materials. The total economic potential in yearly-generated anthropogenic mineral is anticipated to grow markedly from 100 billion US$ in 2020 to 400 billion US$ in 2050. Furthermore, anthropogenic mineral of around 20 materials will be capable to meet projected consumption of three product groups by 2050.

Mining, land restoration and sustainable development in isolated islands: An industrial ecology perspective on extractive transitions on Nauru.

In this empirically grounded perspective, we explore how, if managed correctly, mining might go beyond a straight conversion of finite natural capital to financial resources. We suggest a process where mineral extraction could act as a catalyst for more diversified growth and even serve as a basis to restore forms of 'natural capital' it had previously diminished. The case in point-small in scale but significant in consequence-is the particularly challenging instance of the small-island state of Nauru, which has a very negative history of socio-ecological impacts of phosphate mining. Yet, the degraded landscape requires capital investment which could be reaped from restoration of the land using revenues generated from exporting the waste rock pinnacles as branded household counter-tops and pavement stone products with an "island provenance premium". Furthermore, we use an industrial ecology method to show that Nauru's secondary phosphate can be shown to be less environmentally impactful than comparable phosphate from other sources. This has potential for further "green branding" of the Island's products. We contend that implementing such a restoration approach that harnesses the remaining mineral capital with care has the potential, to diversify the island's economy from one dependent on extractive industries and donors to agroforestry, fishing and tourism. A holistic approach is offered that considers prudent use of Nauru's remaining mineral resources towards an agenda of ecological restoration and economic diversification that will allow the island to prosper after the phosphates it has traditionally relied upon are depleted.