Synergy level of urban resilience and urban land use efficiency in the Yellow River Basin: Spatial-temporal evolution characteristics and driving factors.

The complex global context, including globalization, rapid urbanization, and climate change, poses significant challenges to urban stability and development. Balancing urban land use efficiency and resilience is crucial for sustainable progress. Focusing on the vulnerable Yellow River Basin (YRB), this study examines the interplay between urban resilience and land use efficiency. Panel data from 2013 to 2020 for 54 cities in the YRB were used, it quantifies the Coupling Coordination Degree of Urban Resilience and Urban Land Use Efficiency (CCDUU), explores its spatiotemporal evolution and influencing factors. Key findings include: The CCDUU exhibits a sustained and discernible growth trend. Notably, CCDUU is higher in downstream areas in comparison to the middle reaches, reaching its lowest point in the upstream areas; however, the increase in CCDUU in the upstream areas surpasses that observed in other regions. Concurrently, regional disparities in CCDUU are diminishing. Despite the presence of a notable positive spatial correlation in CCDUU within the YRB, the strength of this spatial association is not sufficiently robust. Of paramount importance factor is the role of regional innovation, which significantly influences the enhancement of CCDUU. Following closely is the degree of openness, whereas the positive effects of government support and population density are concentrated predominantly in the upper YRB region. In contrast, urban-rural disparity exerts an adverse impact on CCDUU in most regions. Policy recommendations for enhancing CCDUU in YRB cities include strengthening government support and planning control, particularly in upstream regions, to achieve efficient resource utilization and environmental protection. Implementing population density management policies, encouraging rational movement, and promoting population migration to upstream areas can alleviate pressure in downstream cities. Enhancing openness, attracting foreign investment, and promoting innovation and industrial upgrading will drive economic structural upgrades and improve CCDUU.

Dissemination of Circulating Tumor Cells in Breast and Prostate Cancer: Implications for Early Detection.

Burgeoning evidence suggests that circulating tumor cells (CTCs) may disseminate into blood vessels at an early stage, seeding metastases in various cancers such as breast and prostate cancer. Simultaneously, the early-stage CTCs that settle in metastatic sites [termed disseminated tumor cells (DTCs)] can enter dormancy, marking a potential source of late recurrence and therapy resistance. Thus, the presence of these early CTCs poses risks to patients but also holds potential benefits for early detection and treatment and opportunities for possibly curative interventions. This review delves into the role of early DTCs in driving latent metastasis within breast and prostate cancer, emphasizing the importance of early CTC detection in these diseases. We further explore the correlation between early CTC detection and poor prognoses, which contribute significantly to increased cancer mortality. Consequently, the detection of CTCs at an early stage emerges as a critical imperative for enhancing clinical diagnostics and allowing for early interventions.

Particle generation and dispersion from high-speed dental drilling.

OBJECTIVE: To investigate the characteristics of particle generation and dispersion during dental procedure using digital inline holography (DIH) METHODS: Particles at two locations, near-field and far-field, which represent the field closer to the procedure location and within 0.5 m from the procedure location respectively, are studied using two different DIH systems. The effect of three parameters namely rotational speed, coolant flow rate, and bur angle on particle generation and dispersion are evaluated by using 10 different operating conditions. The particle characteristics at different operating conditions are estimated from the holograms using machine learning-based analysis. RESULTS: The particle concentration decreased by at least two orders of magnitude between the near-field and far-field locations across the 10 different operating conditions, indicating significant dispersion of the particles. High rotational speed is found to produce a larger number of smaller particles, while lower rotational speeds generate larger particles. Coolant flow rate is found to have a greater impact on particle transport to the far-field location. Irregular shape dental particles account for 29% of total particles at far-field location, with the majority of these irregular shape particles having diameters ranging from 12 to 18 µm. CONCLUSIONS: All three parameters have significant effects on particle generation and dispersion, with rotational speed having a more significant influence on particle generation at near-field and coolant flow rate playing a more important role on particle transport to the far-field. CLINICAL RELEVANCE: This study provides valuable insights on particle characteristics during high-speed drilling. It can help dental professionals minimize exposure risks for themselves and patients by optimizing clinical operating conditions.

In situ digital holographic microscopy for rapid detection and monitoring of the harmful dinoflagellate, Karenia brevis.

Karenia brevis blooms, also known as red tide, are a recurring problem in the coastal Gulf of Mexico. These blooms have the capacity to inflict substantial damage to human and animal health as well as local economies. Thus, monitoring and detection of K. brevis blooms at all life stages and cell concentrations is essential for ensuring public safety. Current K. brevis monitoring methods have several limitations, including size resolution limits and concentration ranges, limited capacity for spatial and temporal profiling, and/or small sample volume processing. Here, a novel monitoring method wherein an autonomous digital holographic imaging microscope (AUTOHOLO), that overcomes these limitations and can characterize K. brevis concentrations in situ, is presented. Using the AUTOHOLO, in situ field measurements were conducted in the coastal Gulf of Mexico during an active K. brevis bloom over the 2020-21 winter season. Surface and sub-surface water samples collected during these field studies were also analyzed in the lab using benchtop holographic imaging and flow cytometry for validation. A convolutional neural network was trained for automated classification of K. brevis at all concentration ranges. The network was validated with manual counts and flow cytometry, yielding a 90% accuracy across diverse datasets with varying K. brevis concentrations. The usefulness of pairing the AUTOHOLO with a towing system was also demonstrated for characterizing particle abundance over large spatial distances, which could potentially facilitate characterization of spatial distributions of K. brevis during bloom events. Future applications of the AUTOHOLO can include integration into existing HAB monitoring networks to enhance detection capabilities for K. brevis in aquatic environments around the world.

In situ biological particle analyzer based on digital inline holography.

Obtaining in situ measurements of biological microparticles is crucial for both scientific research and numerous industrial applications (e.g., early detection of harmful algal blooms, monitoring yeast during fermentation). However, existing methods are limited to offer timely diagnostics of these particles with sufficient accuracy and information. Here, we introduce a novel method for real-time, in situ analysis using machine learning (ML)-assisted digital inline holography (DIH). Our ML model uses a customized YOLOv5 architecture specialized for the detection and classification of small biological particles. We demonstrate the effectiveness of our method in the analysis of 10 plankton species with equivalent high accuracy and significantly reduced processing time compared to previous methods. We also applied our method to differentiate yeast cells under four metabolic states and from two strains. Our results show that the proposed method can accurately detect and differentiate cellular and subcellular features related to metabolic states and strains. This study demonstrates the potential of ML-driven DIH approach as a sensitive and versatile diagnostic tool for real-time, in situ analysis of both biotic and abiotic particles. This method can be readily deployed in a distributive manner for scientific research and manufacturing on an industrial scale.

Reducing Virus Transmission from Heating, Ventilation, and Air Conditioning Systems of Urban Subways.

Aerosols carrying the virus inside enclosed spaces is an important mode of transmission for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as supported by growing evidence. Urban subways are one of the most frequented enclosed spaces. The subway is a utilitarian and low-cost transit system in modern society. However, studies are yet to demonstrate patterns of viral transmission in subway heating, ventilation, and air conditioning (HVAC) systems. To fill this gap, we performed a computational investigation of the airflow (and associated aerosol transmission) in an urban subway cabin equipped with an HVAC system. We employed a transport equation for aerosol concentration, which was added to the basic buoyant solver to resolve the aerosol transmission inside the subway cabin. This was achieved by considering the thermal, turbulent, and induced ventilation flow effects. Using the probability of encountering aerosols on sampling surfaces crossing the passenger breathing zones, we detected the highest infection risk zones inside the urban subway under different settings. We proposed a novel HVAC system that can impede aerosol spread, both vertically and horizontally, inside the cabin. In the conventional model, the maximum probability of encountering aerosols from the breathing of infected individuals near the fresh-air ducts was equal to 51.2%. This decreased to 3.5% in the proposed HVAC model. Overall, using the proposed HVAC system for urban subways led to a decrease in the mean value of the probability of encountering the aerosol by approximately 84% compared with that of the conventional system.

Characterization and mitigation of aerosols and spatters from ultrasonic scalers.

BACKGROUND: Dental procedures often produce aerosols and spatter, which have the potential to transmit pathogens such as severe acute respiratory syndrome coronavirus 2. The existing literature is limited. METHODS: Aerosols and spatter were generated from an ultrasonic scaling procedure on a dental manikin and characterized via 2 optical imaging methods: digital inline holography and laser sheet imaging. Capture efficiencies of various aerosol mitigation devices were evaluated and compared. RESULTS: The ultrasonic scaling procedure generated a wide size range of aerosols (up to a few hundred µm) and occasional large spatter, which emit at low velocity (mostly < 3 m/s). Use of a saliva ejector and high-volume evacuator (HVE) resulted in overall reductions of 63% and 88%, respectively, whereas an extraoral local extractor (ELE) resulted in a reduction of 96% at the nominal design flow setting. CONCLUSIONS: The study results showed that the use of ELE or HVE significantly reduced aerosol and spatter emission. The use of HVE generally requires an additional person to assist a dental hygienist, whereas an ELE can be operated hands free when a dental hygienist is performing ultrasonic scaling and other operations. PRACTICAL IMPLICATIONS: An ELE aids in the reduction of aerosols and spatters during ultrasonic scaling procedures, potentially reducing transmission of oral or respiratory pathogens like severe acute respiratory syndrome coronavirus 2. Position and airflow of the device are important to effective aerosol mitigation.

Airborne transmission of COVID-19 and mitigation using box fan air cleaners in a poorly ventilated classroom.

Many indoor places, including aged classrooms and offices, prisons, homeless shelters, etc., are poorly ventilated but resource-limited to afford expensive ventilation upgrades or commercial air purification systems, raising concerns on the safety of opening activities in these places in the era of the COVID-19 pandemic. To address this challenge, using computational fluid dynamics, we conducted a systematic investigation of airborne transmission in a classroom equipped with a single horizontal unit ventilator (HUV) and evaluate the performance of a low-cost box fan air cleaner for risk mitigation. Our study shows that placing box fan air cleaners in the classroom results in a substantial reduction of airborne transmission risk across the entire space. The air cleaner can achieve optimal performance when placed near the asymptomatic patient. However, without knowing the location of the patient, the performance of the cleaner is optimal near the HUV with the air flowing downwards. In addition, we find that it is more efficient in reducing aerosol concentration and spread in the classroom by adding air cleaners in comparison with raising the flow rate of HUV alone. The number and placement of air cleaners need to be adjusted to maintain their efficacy for larger classrooms and to account for the thermal gradient associated with a human thermal plume and hot ventilation air during cold seasons. Overall, our study shows that box fan air cleaners can serve as an effective low-cost alternative for mitigating airborne transmission risks in poorly ventilated spaces.

Droplet evaporation residue indicating SARS-COV-2 survivability on surfaces.

We conducted a systematic investigation of droplet evaporation on different surfaces. We found that droplets formed even with distilled water do not disappear with evaporation but instead shrink to a residue of a few micrometers lasting over 24 h. The residue formation process differs across surfaces and humidity levels. Specifically, under 40% relative humidity, 80% of droplets form residues on plastic and uncoated and coated glass, while less than 20% form on stainless steel and none on copper. The formation of residues and their variability are explained by modeling the evaporation process considering the presence of nonvolatile solutes on substrates and substrate thermal conductivity. Such variability is consistent with the survivability of SARS-CoV-2 measured on these surfaces. We hypothesize that these long-lasting microscale residues can potentially insulate the virus against environmental changes, allowing them to survive and remain infectious for extended durations.

Simulation-based study of COVID-19 outbreak associated with air-conditioning in a restaurant.

COVID-19 has shown a high potential of transmission via virus-carrying aerosols as supported by growing evidence. However, detailed investigations that draw direct links between aerosol transport and virus infection are still lacking. To fill in the gap, we conducted a systematic computational fluid dynamics (CFD)-based investigation of indoor airflow and the associated aerosol transport in a restaurant setting, where likely cases of airflow-induced infection of COVID-19 caused by asymptomatic individuals were widely reported by the media. We employed an advanced in-house large eddy simulation solver and other cutting-edge numerical methods to resolve complex indoor processes simultaneously, including turbulence, flow-aerosol interplay, thermal effect, and the filtration effect by air conditioners. Using the aerosol exposure index derived from the simulation, we are able to provide a spatial map of the airborne infection risk under different settings. Our results have shown a remarkable direct linkage between regions of high aerosol exposure index and the reported infection patterns in the restaurant, providing strong support to the airborne transmission occurring in this widely reported incident. Using flow structure analysis and reverse-time tracing of aerosol trajectories, we are able to further pinpoint the influence of environmental parameters on the infection risks and highlight the need for more effective preventive measures, e.g., placement of shielding according to the local flow patterns. Our research, thus, has demonstrated the capability and value of high-fidelity CFD tools for airborne infection risk assessment and the development of effective preventive measures.

Aerosol generation from different wind instruments.

The potential airborne transmission of COVID-19 has raised significant concerns regarding the safety of musical activities involving wind instruments. However, currently, there is a lack of systematic study and quantitative information of the aerosol generation during these instruments, which is crucial for offering risk assessment and the corresponding mitigation strategies for the reopening of these activities. Collaborating with 15 musicians from the Minnesota Orchestra, we conduct a systematic study of the aerosol generation from a large variety of wind instruments under different music dynamic levels and articulation patterns. We find that the aerosol concentration from different brass and woodwinds exhibits two orders of magnitude variation. Accordingly, we categorize the instruments into low (tuba), intermediate (bassoon, piccolo, flute, bass clarinet, French horn, and clarinet) and high risk (trumpet, bass trombone, and oboe) levels based on a comparison of their aerosol generation with those from normal breathing and speaking. In addition, we observe that the aerosol generation can be affected by the changing dynamic level, articulation pattern, the normal respiratory behaviors of individuals, and even the usage of some special techniques during the instrument play. However, such effects vary substantially for different types of instrument, depending on specific breathing techniques as well as the tube structure and inlet design of the instrument. Overall, our findings can bring insights into the risk assessment of airborne decrease transmission and the corresponding mitigation strategies for various musical activities involving wind instrument plays, including orchestras, community and worship bands, music classes, etc.

Risk assessment of airborne transmission of COVID-19 by asymptomatic individuals under different practical settings.

The lack of quantitative risk assessment of airborne transmission of COVID-19 under practical settings leads to large uncertainties and inconsistencies in our preventive measures. Combining in situ measurements and computational fluid dynamics simulations, we quantify the exhaled particles from normal respiratory behaviors and their transport under elevator, small classroom, and supermarket settings to evaluate the risk of inhaling potentially virus-containing particles. Our results show that the design of ventilation is critical for reducing the risk of particle encounters. Inappropriate design can significantly limit the efficiency of particle removal, create local hot spots with orders of magnitude higher risks, and enhance particle deposition causing surface contamination. Additionally, our measurements reveal the presence of a substantial fraction of faceted particles from normal breathing and its strong correlation with breathing depth.

Risk assessment of airborne transmission of COVID-19 by asymptomatic individuals under different practical settings.

The lack of quantitative risk assessment of airborne transmission of COVID-19 under practical settings leads to large uncertainties and inconsistencies in our preventive measures. Combining in situ measurements and numerical simulations, we quantify the exhaled particles from normal respiratory behaviors and their transport under elevator, small classroom and supermarket settings to evaluate the risk of inhaling potentially virus-containing particles. Our results show that the design of ventilation is critical for reducing the risk of particle encounters. Inappropriate design can significantly limit the efficiency of particle removal, create local hot spots with orders of magnitude higher risks, and enhance particle deposition causing surface contamination. Additionally, our measurements reveal the presence of substantial fraction of crystalline particles from normal breathing and its strong correlation with breathing depth.

Machine learning holography for 3D particle field imaging.

We propose a new learning-based approach for 3D particle field imaging using holography. Our approach uses a U-net architecture incorporating residual connections, Swish activation, hologram preprocessing, and transfer learning to cope with challenges arising in particle holograms where accurate measurement of individual particles is crucial. Assessments on both synthetic and experimental holograms demonstrate a significant improvement in particle extraction rate, localization accuracy and speed compared to prior methods over a wide range of particle concentrations, including highly dense concentrations where other methods are unsuitable. Our approach can be potentially extended to other types of computational imaging tasks with similar features.

Microalgal swimming signatures and neutral lipids production across growth phases.

Microalgae have been shown as a potential bioresource for food, biofuel, and pharmaceutical products. During the growth phases with corresponding environmental conditions, microalgae accumulate different amounts of various metabolites. We quantified the neutral lipids accumulation and analyzed the swimming signatures (speed and trajectories) of the motile green alga, Dunaliella primolecta, during the lag-exponential-stationary growth cycle at different nutrient concentrations. We discovered significant changes in the neutral lipid content and swimming signatures of microalgae across growth phases. The timing of the maximum swimming speed coincided with the maximum neutral lipid content and both maxima occurred under nutrient stress at the stationary growth phase. Furthermore, the swimming trajectories suggested statistically significant changes in swimming modes at the stationary growth phase when the maximum intracellular neutral lipid content was observed. Our results provide the potential exploitation of microalgal swimming signatures as possible indicators of the cultivation conditions and the timing of microalgal harvest to maximize the lipid yield for biofuel production. The findings can also be implemented to explore the production of food and antibiotics from other microalgal metabolites with low energy costs.

Regularized inverse holographic volume reconstruction for 3D particle tracking.

The key limitations of digital inline holography (DIH) for particle tracking applications are poor longitudinal resolution, particle concentration limits, and case-specific processing. We utilize an inverse problem method with fused lasso regularization to perform full volumetric reconstructions of particle fields. By exploiting data sparsity in the solution and utilizing GPU processing, we dramatically reduce the computational cost usually associated with inverse reconstruction approaches. We demonstrate the accuracy of the proposed method using synthetic and experimental holograms. Finally, we present two practical applications (high concentration microorganism swimming and microfiber rotation) to extend the capabilities of DIH beyond what was possible using prior methods.

Digital Fresnel reflection holography for high-resolution 3D near-wall flow measurement.

We propose a novel backscatter holographic imaging system, as a compact and effective tool for 3D near-wall flow diagnostics at high resolutions, utilizing light reflected at the solid-liquid interface as a reference beam. The technique is fully calibrated, and is demonstrated in a densely seeded channel to achieve a spatial resolution of near-wall flows equivalent to or exceeding prior digital inline holographic measurements using local tracer seeding technique. Additionally, we examined the effects of seeding concentration and laser coherence on the measurement resolution and sample volume resolved, demonstrating the potential to manipulate sample domain by tuning the laser coherence profile.

Graphene plasmonics for surface enhancement near-infrared absorptivity.

Monolayer graphene has poor absorption in the near-infrared region. Its layer is only as thick as a single atom so it cannot have a high absorptivity. In this paper, in order to form a hybrid system, the absorption characteristics of monolayer graphene covering a metal/dielectric/metal substrate has been theoretically analyzed. The magnetic polaritons in the metal/dielectric couple with the plasmonic resonance in the graphene to dramatically enhance the graphene absorptivity. This study analyzes the factors that enhance the absorptivity, including the geometric parameters and the relative positions of the graphene. The local electromagnetic field and the power dissipation density are illustrated to explain the underlying mechanisms further. These numerical results can provide potential application in the field of optical detection and optoelectronic devices.

3D Holographic Observatory for Long-term Monitoring of Complex Behaviors in Drosophila.

Drosophila is an excellent model organism towards understanding the cognitive function, aging and neurodegeneration in humans. The effects of aging and other long-term dynamics on the behavior serve as important biomarkers in identifying such changes to the brain. In this regard, we are presenting a new imaging technique for lifetime monitoring of Drosophila in 3D at spatial and temporal resolutions capable of resolving the motion of limbs and wings using holographic principles. The developed system is capable of monitoring and extracting various behavioral parameters, such as ethograms and spatial distributions, from a group of flies simultaneously. This technique can image complicated leg and wing motions of flies at a resolution, which allows capturing specific landing responses from the same data set. Overall, this system provides a unique opportunity for high throughput screenings of behavioral changes in 3D over a long term in Drosophila.

High fidelity digital inline holographic method for 3D flow measurements.

Among all the 3D optical flow diagnostic techniques, digital inline holographic particle tracking velocimetry (DIH-PTV) provides the highest spatial resolution with low cost, simple and compact optical setups. Despite these advantages, DIH-PTV suffers from major limitations including poor longitudinal resolution, human intervention (i.e. requirement for manually determined tuning parameters during tracer field reconstruction and extraction), limited tracer concentration, and expensive computations. These limitations prevent this technique from being widely used for high resolution 3D flow measurements. In this study, we present a novel holographic particle extraction method with the goal of overcoming all the major limitations of DIH-PTV. The proposed method consists of multiple steps involving 3D deconvolution, automatic signal-to-noise ratio enhancement and thresholding, and inverse iterative particle extraction. The entire method is implemented using GPU-based algorithm to increase the computational speed significantly. Validated with synthetic particle holograms, the proposed method can achieve particle extraction rate above 95% with fake particles less than 3% and maximum position error below 1.6 particle diameter for holograms with particle concentration above 3000 particles/mm3. The applicability of the proposed method for DIH-PTV has been further validated using the experiment of laminar flow in a microchannel and the synthetic tracer flow fields generated using a DNS turbulent channel flow database. Such improvements will substantially enhance the implementation of DIH-PTV for 3D flow measurements and enable the potential commercialization of this technique.