Species In Real Time Research Articles

Edge Artificial Intelligence (AI) for real-time automatic quantification of filariasis in mobile microscopy.

Filariasis, a neglected tropical disease caused by roundworms, is a significant public health concern in many tropical countries. Microscopic examination of blood samples can detect and differentiate parasite species, but it is time consuming and requires expert microscopists, a resource that is not always available. In this context, artificial intelligence (AI) can assist in the diagnosis of this disease by automatically detecting and differentiating microfilariae. In line with the target product profile for lymphatic filariasis as defined by the World Health Organization, we developed an edge AI system running on a smartphone whose camera is aligned with the ocular of an optical microscope that detects and differentiates filarias species in real time without the internet connection. Our object detection algorithm that uses the Single-Shot Detection (SSD) MobileNet V2 detection model was developed with 115 cases, 85 cases with 1903 fields of view and 3342 labels for model training, and 30 cases with 484 fields of view and 873 labels for model validation before clinical validation, is able to detect microfilariae at 10x magnification and distinguishes four species of them at 40x magnification: Loa loa, Mansonella perstans, Wuchereria bancrofti, and Brugia malayi. We validated our augmented microscopy system in the clinical environment by replicating the diagnostic workflow encompassed examinations at 10x and 40x with the assistance of the AI models analyzing 18 samples with the AI running on a middle range smartphone. It achieved an overall precision of 94.14%, recall of 91.90% and F1 score of 93.01% for the screening algorithm and 95.46%, 97.81% and 96.62% for the species differentiation algorithm respectively. This innovative solution has the potential to support filariasis diagnosis and monitoring, particularly in resource-limited settings where access to expert technicians and laboratory equipment is scarce.

Digital Classification of Chilean Pelagic Species in Fishing Landing Lines.

Fishing landings in Chile are inspected to control fisheries that are subject to catch quotas. The control process is not easy since the volumes extracted are large and the numbers of landings and artisan shipowners are high. Moreover, the number of inspectors is limited, and a non-automated method is utilized that normally requires months of training. In this work, we propose, design, and implement an automated fish landing control system. The system consists of a custom gate with a camera array and controlled illumination that performs automatic video acquisition once the fish landing starts. The imagery is sent to the cloud in real time and processed by a custom-designed detection algorithm based on deep convolutional networks. The detection algorithm identifies and classifies different pelagic species in real time, and it has been tuned to identify the specific species found in landings of two fishing industries in the Biobío region in Chile. A web-based industrial software was also developed to display a list of fish detections, record relevant statistical summaries, and create landing reports in a user interface. All the records are stored in the cloud for future analyses and possible Chilean government audits. The system can automatically, remotely, and continuously identify and classify the following species: anchovy, jack mackerel, jumbo squid, mackerel, sardine, and snoek, considerably outperforming the current manual procedure.

Estimating populations of dense animals using acoustics

Acoustic censusing methods offer an efficient and noninvasive method to monitor populations of dense aggregations of animals on a large scale. Large, dense colonies of bats are difficult to census, and current monitoring methods are often invasive and time consuming. Using acoustic recordings collected during emergence, we developed a linear regression model that uses multiple measures of acoustic energy to estimate the number of bats per second. The model was trained using data from audio recordings collected at seven cave locations and two species of bats, Brazilian free-tailed bats (Tadarida brasiliensis) and gray bats (Myotis grisenscens). Visual counts from synchronized thermal videos were used to ground truth the model. Environmental factors, such as distance from microphone to emergence, were integrated to create flexible model that is widely applicable across species and habitats. This method can be extended to other species that vocalize in large aggregations, such as sea birds and frogs. Ultimately, this model can be integrated into autonomous monitoring stations to monitor species in real time. It is imperative that efficient and noninvasive methods for monitoring bat populations are developed to increase population data available for informing crucial management decisions.

Pd3Pb1@Pt2 core–shell concave nanocubes to boost the ethanol oxidation reaction

The secret to raising catalyst selectivity and activity is to design nanocatalysts by regulating pore size and particle properties. Hence, it was effectively established that manipulating pore size and particle properties allowed for the manufacture of ternary Pd3Pb1@Pt2 concave nanocubes (Pd3Pb1@Pt2 CNCs). The synthesized Pd3Pb1@Pt2 CNCs/C shows superior electrocatalytic activity of 7500 mA mgPt+Pd−1 towards ethanol oxidation reaction (EOR) which is 7.08 times greater than the JM Pt/C (1060 mA mgPt−1). The catalyst has excellent stability, and the mass activity is still better than JM Pt/C after 3600 s stability test. The CO stripping measurements shows that the adsorption of CO is particularly weak, which proves that the catalyst has excellent resistance to medium toxicity. In situ attenuated total reflection infrared spectroscopy (ATR-IR) and external reflection-absorption infrared spectroscopy (IRRAS) have been employed to examine the molecular aspects of the EOR process. ATR-IR can monitor the production and removal of CO species in real time during EOR on Pd3Pb1@Pt2 CNCs/C. The band of CO2 is noticeably stronger on Pd3Pb1@Pt2 CNCs/C than it is on bare other catalysts, suggesting that the ethanol oxidation process may be promoted.

Nanostructures for In Situ SERS Analysis of High-Temperature Processes

Surface-enhanced Raman spectroscopy (SERS) is a highly sensitive analytical technique based on Raman scatter and utilizes the nanostructures of active metals, such as gold and silver, with roughened surfaces as a signal amplifier. With its enhancement effect and “fingerprint” ability, in situ SERS is able to capture the dynamics of microstructure evolution and trace surface species in real time, which provides direct information for the analysis of a reaction mechanism in various surface processes, including heterogeneous catalysis, electrochemical reactions, etc. To date, SERS has been widely used in operando analysis of surface processes under ordinary temperatures. For application in high-temperature processes, the harsh environment puts forward additional requirements in addition to high sensitivity for the SERS nanostructures, especially concerning thermal stability, chemical inertness, and surface universality. Therefore, it is necessary to develop specialized SERS nanostructures for in situ analysis of high-temperature processes. This paper reviews the research progress of the design and application of nanostructures for in situ SERS analysis of high-temperature processes, with special focus on how to solve the stability and sensitivity contradiction of the SERS nanostructures in the high-temperature complex environment through the design and regulation of the nanostructures. For the structure design, the strategies, preparation, and performance of the reported nanoarchitectures are compared. For the high-temperature application, the utilization of SERS nanostructures in in situ studies are summarized, including thermal crystallization, lattice dynamics, heterogeneous catalysis, and high-temperature electrode reactions.

Design Optimization of a Submersible Chemiluminescent Sensor (DISCO) for Improved Quantification of Reactive Oxygen Species (ROS) in Surface Waters.

Reactive oxygen species (ROS) are key drivers of biogeochemical cycling while also exhibiting both positive and negative effects on marine ecosystem health. However, quantification of the ROS superoxide (O2−) within environmental systems is hindered by its short half-life. Recently, the development of the diver-operated submersible chemiluminescent sensor (DISCO), a submersible, handheld instrument, enabled in situ superoxide measurements in real time within shallow coral reef ecosystems. Here, we present a redesigned and improved instrument, DISCO II. Similar to the previous DISCO, DISCO II is a self-contained, submersible sensor, deployable to 30 m depth and capable of measuring reactive intermediate species in real time. DISCO II is smaller, lighter, lower cost, and more robust than its predecessor. Laboratory validation of DISCO II demonstrated an average limit of detection in natural seawater of 133.1 pM and a percent variance of 0.7%, with stable photo multiplier tube (PMT) counts, internal temperature, and flow rates. DISCO II can also be optimized for diverse environmental conditions by adjustment of the PMT supply voltage and integration time. Field tests showed no drift in the data with a percent variance of 3.0%. Wand tip adaptations allow for in situ calibrations and decay rates of superoxide using a chemical source of superoxide (SOTS-1). Overall, DISCO II is a versatile, user-friendly sensor that enables measurements in diverse environments, thereby improving our understanding of the cycling of reactive intermediates, such as ROS, across various marine ecosystems.

Real-time identification of marine mammal calls based on convolutional neural networks

Animal vocalization is one of the most important ways to identify the existence and occurrence of marine mammals. Accurately detecting and identifying marine mammal species in real time is significant in the field of marine mammal research. In this study, we developed a novel method based on convolutional neural networks (CNNs) to precisely identify the calls of bottlenose dolphins (Tursiops), dugongs (Dugong dugong), and the Indo-Pacific humpback dolphin (Sousa chinensis). Compared with traditional detection and classification techniques, the proposed method is a one-step algorithm that combines detection and classification. It can automatically learn the features of different marine mammal calls and position the target calls with bounding boxes in a spectrogram. Furthermore, the whole process can be conducted in real time. With this method, the raw data are cut into fixed lengths and spectrograms are created. Next, the annotated sound data are sent to the CNNs to train an identification model. Experimental results showed that the proposed method achieved >91% precision and >84% recall of test data with real-time speed. In the future, this method can be applied to passive acoustic observation equipment for quick, accurate pre-detection of marine mammal calls.

Diseño y desarrollo de aplicación móvil para la clasificación de flora nativa chilena utilizando redes neuronales convolucionales

Introduction : Mobile apps, through artificial vision, are capable of recognizing vegetable species in real time. However, the existing species recognition apps do not take in consideration the wide variety of endemic and native (Chilean) species, which leads to wrong species predictions. This study introduces the development of a chilean species dataset and an optimized classification model implemented to a mobile app. Method : the data set was built by putting together pictures of several species captured on the field and by selecting some pictures available from other datasets available online. Convolutional neural networks were used in order to develop the images prediction models. The networks were trained by performing a sensitivity analysis, validating with k-fold cross validation and performing tests with different hyper-parameters, optimizers, convolutional layers, and learning rates in order to identify and choose the best models and then put them together in one classification model. Results : The final data set was compounded by 46 species, including native species, endemic and exotic from Chile, with 6120 training pictures and 655 testing pictures. The best models were implemented on a mobile app, obtaining a 95% correct prediction rate with respect to the set of tests. C onclusion : The app developed in this study is capable of classifying species with a high level of accuracy, depending on the state of the art of the artificial vision and it can also show relevant information related to the classified species.

Air-Laser-Based Standoff Coherent Raman Spectrometer

Among currently available optical spectroscopic methods, Raman spectroscopy has versatile application to investigation of dynamical processes of molecules leading to chemical changes in the gas and liquid phases. However, it is still a challenge to realize an ideal standoff coherent Raman spectrometer with which both high temporal resolution and high-frequency resolution can be achieved, so that one can remotely probe chemical species in real time with high temporal resolution while monitoring the populations in their respective rovibronic levels in the frequency domain with sufficiently high spectral resolution. In the present study, we construct an air-laser-based Raman spectrometer, in which near-infrared femtosecond (fs) laser pulses at 800 nm and cavity-free picosecond N 2 + air-laser pulses at 391 nm generated by the filamentation induced by the fs laser pulses are simultaneously used, enabling us to generate a hybrid ps/fs laser source at a desired standoff position for standoff surveillance of chemical and biochemical species. With this prototype Raman spectrometer, we demonstrate that the temporal evolution of the electronic, vibrational, and rotational states of N 2 + and the coupling processes of the rovibrational wave packet of N 2 molecules can be probed.

Back Cover: Advanced Real‐Time Process Analytics for Multistep Synthesis in Continuous Flow (Angew. Chem. Int. Ed. 15/2021)

Advanced process analytics are becoming an increasingly important part of continuous flow chemistry. In their Research Article on page 8139, Jason D. Williams, C. Oliver Kappe and co-workers describe the use of four different process analytical technologies, controlled by one central system, in a multistep continuous flow process. Advanced data processing methods, including artificial intelligence, are used to accurately quantify up to nine process species in real time, toward self-optimization and model predictive control applications.

Proof-of-concept analytical instrument for label-free optical deconvolution of protein species in a mixture

The adoption of process analytical technologies by the biopharmaceutical industry can reduce the cost of therapeutic drugs and facilitate investigation of new bioprocesses. Control of critical process parameters to retain critical product quality attributes within strict bounds is important for ensuring a consistently high product quality, but developing the sophisticated analytical technologies required has proven to be a major challenge. Here, we demonstrate a new optical technique for continuous monitoring of protein species as they are eluted from a chromatographic column, even when they fully co-elute with other protein species, without making any assumption about or peak-fitting to the elution profile. To achieve this, we designed and constructed a time-resolved intrinsic fluorescence lifetime chromatograph, and established an analytical framework for deconvolving and quantifying distinct but co-eluting protein species in real time. This proof-of-concept technology has potentially useful applications as a process analytical technology and more generally as an analytical technique for label-free quantification of proteins in mixtures.

Nanodiamond Relaxometry-Based Detection of Free-Radical Species When Produced in Chemical Reactions in Biologically Relevant Conditions.

Diamond magnetometry is a quantum sensing method involving detection of magnetic resonances with nanoscale resolution. For instance, T1 relaxation measurements, inspired by equivalent concepts in magnetic resonance imaging (MRI), provide a signal that is equivalent to T1 in conventional MRI but in a nanoscale environment. We use nanodiamonds (between 40 and 120 nm) containing ensembles of specific defects called nitrogen vacancy (NV) centers. To perform a T1 relaxation measurement, we pump the NV center in the ground state (using a laser at 532 nm) and observe how long the NV center can remain in this state. Here, we use this method to provide real-time measurements of free radicals when they are generated in a chemical reaction. Specifically, we focus on the photolysis of H2O2 as well as the so-called Haber–Weiss reaction. Both of these processes are important reactions in biological environments. Unlike other fluorescent probes, diamonds are able to determine spin noise from different species in real time. We also investigate different diamond probes and their ability to sense gadolinium spin labels. Although this study was performed in a clean environment, we take into account the effects of salts and proteins that are present in a biological environment. We conduct our experiments with nanodiamonds, which are compatible with intracellular measurements. We perform measurements between 0 and 108 nM, and we are able to reach detection limits down to the nanomolar range and typically find T1 times of a few 100 μs. This is an important step toward label-free nano-MRI signal quantification in biological environments.

Fast cropping method for proper input size of convolutional neural networks in underwater photography

AbstractThe convolutional neural network (CNN) is widely used in object detection and classification and shows promising results. However, CNN has the limitation of fixed input size. If the input image size of the CNN is different from the image size of the system to which the CNN is applied, additional processes, such as cropping, warping, or padding, are necessary. They take additional time to process these processes, and fast cutting methods are required for systems that require real‐time processing. The purpose of our system to which the CNN model will be applied is to classify fish species in real time, using cameras installed in a shallow stream. Therefore, in this paper, we propose a straightforward real‐time image cropping method for fast cutting to the proper input size of CNN. In the experiments, we evaluate the proposed method using CNNs (AlexNet, Vgg 16, Vgg 9, and GoogLeNet).

Optoplasmonic characterisation of reversible disulfide interactions at single thiol sites in the attomolar regime

Probing individual chemical reactions is key to mapping reaction pathways. Trace analysis of sub-kDa reactants and products is obfuscated by labels, however, as reaction kinetics are inevitably perturbed. The thiol-disulfide exchange reaction is of specific interest as it has many applications in nanotechnology and in nature. Redox cycling of single thiols and disulfides has been unresolvable due to a number of technological limitations, such as an inability to discriminate the leaving group. Here, we demonstrate detection of single-molecule thiol-disulfide exchange using a label-free optoplasmonic sensor. We quantify repeated reactions between sub-kDa thiolated species in real time and at concentrations down to 100’s of attomolar. A unique sensing modality is featured in our measurements, enabling the observation of single disulfide reaction kinetics and pathways on a plasmonic nanoparticle surface. Our technique paves the way towards characterising molecules in terms of their charge, oxidation state, and chirality via optoplasmonics.

Real-time monitoring of population dynamics and physical interactions in a synthetic yeast ecosystem by use of multicolour flow cytometry.

Ecological interactions between different species of yeasts have been observed and described extensively, but the mechanisms of interaction remain poorly understood. A hindrance to the characterization of multispecies yeast ecosystems is the lack of accurate methods for rapid real-time analysis of population dynamics in synthetic multispecies consortia. Here, we sought to accelerate and improve the sensitivity of ecological modelling and characterization of a synthetic yeast ecosystem by developing a flow cytometry-based method that tracks and sorts fluorescently tagged individual yeast species in real time during growth in model multispecies consortia. A protocol for integrative genetic modification of non-conventional yeasts was developed. The application of the method was demonstrated in a model four-species synthetic wine-yeast ecosystem that consisted of species commonly isolated from natural wine fermentations. The data show that this method allows for rapid generation of meaningful ecological data that contributes to our understanding of multispecies synthetic yeast ecosystems. Furthermore, interspecies interactions have been shown to impact the evolution of yeasts in natural ecosystems, and this platform will provide an ideal tool to better evaluate the impact of biotic selection pressures.Key Points• Fluorescent labelling of yeast species in a consortium for multicolour flow cytometry• Method developed to track population dynamics of multispecies yeast consortia• Enables real-time visualization, manipulation and response analyses of population dynamics• Produces accurate, reproducible data with powerful visual analyses potential at a rapid rate.

The Real-Time Mobile Application for Classifying of Endangered Parrot Species Using the CNN Models Based on Transfer Learning

Among the many deep learning methods, the convolutional neural network (CNN) model has an excellent performance in image recognition. Research on identifying and classifying image datasets using CNN is ongoing. Animal species recognition and classification with CNN is expected to be helpful for various applications. However, sophisticated feature recognition is essential to classify quasi-species with similar features, such as the quasi-species of parrots that have a high color similarity. The purpose of this study is to develop a vision-based mobile application to classify endangered parrot species using an advanced CNN model based on transfer learning (some parrots have quite similar colors and shapes). We acquired the images in two ways: collecting them directly from the Seoul Grand Park Zoo and crawling them using the Google search. Subsequently, we have built advanced CNN models with transfer learning and trained them using the data. Next, we converted one of the fully trained models into a file for execution on mobile devices and created the Android package files. The accuracy was measured for each of the eight CNN models. The overall accuracy for the camera of the mobile device was 94.125%. For certain species, the accuracy of recognition was 100%, with the required time of only 455 ms. Our approach helps to recognize the species in real time using the camera of the mobile device. Applications will be helpful for the prevention of smuggling of endangered species in the customs clearance area.

Visualising and Characterising Zinc Ion Transport for Zinc Ion Batteries By Fluorescence Microscopy

Zinc has been regarded as a promising anode material for aqueous batteries in view of its advantages including high specific capacity, abundance and intrinsic safety. Aqueous zinc ion batteries have attracted growing attention as a potential alternative to lithium ion batteries, especially for medium and large-scale energy storage. Aqueous zinc ion batteries consist of a zinc anode and a zinc intercalating cathode in a zinc-salt-containing electrolyte and use zinc ions as charge carriers. The electrolyte transporting zinc ions between the anode and the cathode plays an essential role in determining battery performance and life. Visualising ion transport in the electrolytes of zinc ion batteries can give insights into electrolyte dynamics as well as battery processes. Yet, this is limited by the spatial-temporal resolution of the existing techniques. Moreover, most of the existing in-situ visualisation techniques are very expensive and not easily accessible. Fluorescence microscopy provides a powerful tool for probing tiny structures and tracking species in real time. It relies on fluorescence which occurs when molecules absorb light with a certain wavelength followed by the re-emission of light with a longer wavelength. These excitation and emission wavelengths are usually unique fingerprints of certain substances. Fluorescent sensors, with high sensitivity of fluorescence assays, have been widely exploited as a useful tool for species detection and mechanistic studies in biology and chemistry. In this study, a novel microfluidics-based fluorescence microscopy platform is developed for visualising and characterising zinc ion transport in the electrolytes of zinc ion batteries. The platform is calibrated by comparing the measurement results with the literature data. Key transport properties of zinc ions are quantified under different electrolyte conditions. The developed platform is demonstrated to be a simple and versatile tool for electrolyte characterisation, which provides key information guiding future electrolyte development and battery performance improvement.

Front Cover: Dynamic observation and quantification of type I/II collagen in chondrogenesis of mesenchymal stem cells by second‐order susceptibility microscopy (J. Biophotonics 2/2019)

Top row is time‐lapse, multiphoton imaging of induced chondrogenesis from stem cells. The second row shows the time‐lapse second harmonic generation imaging of generated collagen. However, only the third row of second order susceptibility imaging allows the differentiation of the content of the two types of collagen (I and II) that were produced over time. The method allows the noninvasive and label‐free discrimination of different collagen species in real time and can be used for quality control in tissue engineering.Further details can be found in the article by Chiu‐Mei Hsueh, Hung‐Ming Lin, Te‐Yu Tseng, et al. (e201800097). image

Mass Spectrometry Measurement of Single Suspended Cells Using a Combined Cell Manipulation System and a Single-Probe Device.

Existing single cell mass spectrometry (SCMS) sampling platforms are largely designed to work only with immobilized cells and not the suspended cells isolated from patient samples. Here, we present a novel method that integrates a commercially available cell manipulation system commonly used for in vitro fertilization with the Single-probe SCMS sampling technology. The combined Single-probe SCMS/cell manipulating platform is capable of rapidly analyzing intracellular species in real time from a suspension leukemia cell line. A broad range of molecular species was detected, and species of interest were verified using tandem MS (MS/MS). Experimental results were analyzed utilizing statistical analyses such as principle component analysis (PCA) and t-tests. The developed SCMS/cell manipulation system is a versatile tool to provide rapid single cell analysis of broad types of patient cell samples.

Effect of Pretreatment Method on the Nanostructure and Performance of Supported Co Catalysts in Fischer–Tropsch Synthesis

Understanding precursor transformation to active catalysts is crucial to heterogeneous Fischer–Tropsch (FT) catalysis directed toward production of hydrocarbons for transportation fuels. Despite considerable literature on FT catalysis, the effect of pretreatment of supported cobalt catalysts on cobalt dispersion, dynamic atomic structure, and the activity of the catalysts is not well understood. Here we present systematic studies into the formation of active catalyst phases in supported Co catalyst precursors in FT catalysis using in situ environmental (scanning) transmission electron microscopy (E(S)TEM) with single-atom resolution under controlled reaction environments for in situ visualization, imaging, and analysis of reacting atomic species in real time, EXAFS, XAS, DRIFTS analyses, and catalytic activity measurements. We have synthesized and analyzed dried reduced (D) and dried calcined reduced (DC) Co real-world (practical) catalysts on reducible and nonreducible supports, such as SiO2, Al2O3, TiO2, and ZrO2. Comparisons of dynamic in situ atomic structural observations of reacting single atoms, atomic clusters, and nanoparticles of Co and DRIFTS, XAS, EXAFS, and catalytic activity data of the D and DC samples reveal in most cases better dispersion in the D samples, leading to a larger number of low-coordination Co0 sites and a higher number of active sites for CO adsorption. The experimental findings on the degree of reduction of D and DC catalysts on reducible and nonreducible supports and correlations between hexagonal (hcp) Co sites and the activity of the catalysts generate structural insights into the catalyst dynamics, important to the development of efficient FT catalysts.