Novel linkage approach to join community-acquired and national data.

BACKGROUND: Community optometrists in Scotland have performed regular free-at-point-of-care eye examinations for all, for over 15 years. Eye examinations include retinal imaging but image storage is fragmented and they are not used for research. The Scottish Collaborative Optometry-Ophthalmology Network e-research project aimed to collect these images and create a repository linked to routinely collected healthcare data, supporting the development of pre-symptomatic diagnostic tools. METHODS: As the image record was usually separate from the patient record and contained minimal patient information, we developed an efficient matching algorithm using a combination of deterministic and probabilistic steps which minimised the risk of false positives, to facilitate national health record linkage. We visited two practices and assessed the data contained in their image device and Practice Management Systems. Practice activities were explored to understand the context of data collection processes. Iteratively, we tested a series of matching rules which captured a high proportion of true positive records compared to manual matches. The approach was validated by testing manual matching against automated steps in three further practices. RESULTS: A sequence of deterministic rules successfully matched 95% of records in the three test practices compared to manual matching. Adding two probabilistic rules to the algorithm successfully matched 99% of records. CONCLUSIONS: The potential value of community-acquired retinal images can be harnessed only if they are linked to centrally-held healthcare care data. Despite the lack of interoperability between systems within optometry practices and inconsistent use of unique identifiers, data linkage is possible using robust, almost entirely automated processes.

Developments of Recent Applications for Early Diagnosis of Diseases Using Electronic-Nose and Other VOC-Detection Devices.

This Editorial provides summaries and an overview of research and review articles published in the Sensors journal, volumes 21 (2021), 22 (2022), and 23 (2023), within the biomedical Special Issue "Portable Electronic-Nose Devices for Noninvasive Early Disease Detection", which focused on recent sensors, biosensors, and clinical instruments developed for noninvasive early detection and diagnosis of human and animal diseases. The ten articles published in this Special Issue provide new information associated with recent electronic-nose (e-nose) and related volatile organic compound (VOC)-detection technologies developed to improve the effectiveness and efficiency of diagnostic methodologies for early disease detection prior to symptom development. For review purposes, the summarized articles were placed into three broad groupings or topic areas, including veterinary-wildlife pathology, human clinical pathology, and the detection of dietary effects on VOC emissions. These specified categories were used to define sectional headings devoted to related research studies with a commonality based on a particular disease being investigated or type of analytical instrument used in analyses.

Biomarker Metabolites Discriminate between Physiological States of Field, Cave and White-nose Syndrome Diseased Bats.

Analysis of volatile organic compound (VOC) emissions using electronic-nose (e-nose) devices has shown promise for early detection of white-nose syndrome (WNS) in bats. Tricolored bats, Perimyotis subflavus, from three separate sampling groups defined by environmental conditions, levels of physical activity, and WNS-disease status were captured temporarily for collection of VOC emissions to determine relationships between these combinations of factors and physiological states, Pseudogymnoascus destructans (Pd)-infection status, and metabolic conditions. Physiologically active (non-torpid) healthy individuals were captured outside of caves in Arkansas and Louisiana. In addition, healthy and WNS-diseased torpid bats were sampled within caves in Arkansas. Whole-body VOC emissions from bats were collected using portable air-collection and sampling-chamber devices in tandem. Electronic aroma-detection data using three-dimensional Principal Component Analysis provided strong evidence that the three groups of bats had significantly different e-nose aroma signatures, indicative of different VOC profiles. This was confirmed by differences in peak numbers, peak areas, and tentative chemical identities indicated by chromatograms from dual-column GC-analyses. The numbers and quantities of VOCs present in whole-body emissions from physiologically active healthy field bats were significantly greater than those of torpid healthy and diseased cave bats. Specific VOCs were identified as chemical biomarkers of healthy and diseased states, environmental conditions (outside and inside of caves), and levels of physiological activity. These results suggest that GC/E-nose dual-technologies based on VOC-detection and analyses of physiological states, provide noninvasive alternative means for early assessments of Pd-infection, WNS-disease status, and other physiological states.

Trident Nano-Indexing the Proteomics Table: Next-Version Clustering of Iron Carbide NPs and Protein Corona.

Protein corona composition and precise physiological understanding of differentially expressed proteins are key for identifying disease biomarkers. In this report, we presented a distinctive quantitative proteomics table of molecular cell signaling differentially expressed proteins of corona that formed on iron carbide nanoparticles (NPs). High-performance liquid chromatography/electrospray ionization coupled with ion trap mass analyzer (HPLC/ESI-Orbitrap) and MASCOT helped quantify 142 differentially expressed proteins. Among these proteins, 104 proteins showed upregulated behavior and 38 proteins were downregulated with respect to the control, whereas 48, 32 and 24 proteins were upregulated and 8, 9 and 21 were downregulated CW (control with unmodified NPs), CY (control with modified NPs) and WY (modified and unmodified NPs), respectively. These proteins were further categorized on behalf of their regularity, locality, molecular functionality and molecular masses using gene ontology (GO). A STRING analysis was used to target the specific range of proteins involved in metabolic pathways and molecular processing in different kinds of binding functionalities, such as RNA, DNA, ATP, ADP, GTP, GDP and calcium ion bindings. Thus, this study will help develop efficient protocols for the identification of latent biomarkers in early disease detection using protein fingerprints.

Physiological and behavioral responses as indicators for early disease detection in dairy calves.

This study investigated physiological and behavioral responses associated with the onset of neonatal calf diarrhea (NCD) in calves experimentally infected with rotavirus and assessed the suitability of these responses as early disease indicators. The suitability of infrared thermography (IRT) as a noninvasive, automated method for early disease detection was also assessed. Forty-three calves either (1) were experimentally infected with rotavirus (n = 20) or (2) acted as uninfected controls (n = 23). Health checks were conducted on a daily basis to identify when calves presented overt clinical signs of disease. In addition, fecal samples were collected to verify NCD as the cause of illness. Feeding behavior was recorded continuously as calves fed from an automated calf feeder, and IRT temperatures were recorded once per day across 5 anatomical locations using a hand-held IRT camera. Lying behavior was recorded continuously using accelerometers. Drinking behavior at the water trough was filmed continuously to determine the number and duration of visits. Respiration rate was recorded once per day by observing flank movements. The effectiveness of inoculating calves with rotavirus was limited because not all calves in the infected group contracted the virus; further, an unexpected outbreak of Salmonella during the trial led to all calves developing NCD, including those in the healthy control group. Therefore, treatment was ignored and instead each calf was analyzed as its own control, with data analyzed with respect to when each calf displayed clinical signs of disease regardless of the causative pathogen. Milk consumption decreased before clinical signs of disease appeared. The IRT temperatures were also found to change before clinical signs of disease appeared, with a decrease in shoulder temperature and an increase in side temperature. There were no changes in respiration rate or lying time before clinical signs of disease appeared. However, the number of lying bouts decreased and lying bout duration increased before and following clinical signs of disease. There was no change in the number of visits to the water trough, but visit duration increased before clinical signs of disease appeared. Results indicate that milk consumption, IRT temperatures of the side and shoulder, number and duration of lying bouts, and duration of time spent at the water trough show potential as suitable early indicators of disease.

Reviving post-mortem diagnostics as a tool to increase porcine herd health and strengthen early detection of pig diseases - the PathoPig project 2014-2016.

INTRODUCTION: Post-mortem diagnostics are an important tool for disease diagnosis and therefore early detection of (re-)emerging animal diseases and zoonoses as well as nationwide disease surveillance programs. To counteract the decline of porcine necropsies in Switzerland over the last ten years, the Federal Food Safety and Veterinary Office (FSVO) launched a national project in 2014 called PathoPig. Post-mortem examinations of pigs from herds with health problems were financially supported by the FSVO. During the first 3 years of the project, the number of pig necropsies increased by 195% (mean). An underlying cause of disease was identified in 74% of the cases. These findings resulted in specific recommendations by the attending veterinarians or by the Swiss Porcine Health Service. A follow-up survey revealed that herd health had improved in 90% of the farms implementing the recommendations.

INTRODUCTION: Les diagnostics post-mortem constituent un outil important pour le diagnostic des maladies et, partant, la détection précoce des maladies animales et des zoonoses (ré)-émergentes ainsi que pour les programmes nationaux de surveillance des maladies. Pour contrer le déclin des nécropsies porcines en Suisse au cours des dix dernières années, l'Office fédéral de la sécurité alimentaire et vétérinaire (OSAV) a lancé en 2014 un projet national baptisé PathoPig. Les examens post-mortem des porcs provenant d'exploitations avec des problèmes de santé ont été soutenus financièrement par l'OSAV. Au cours des trois premières années du projet, le nombre de nécropsies porcines a augmenté de 195% (moyenne). Une cause sous-jacente de maladie a été identifiée dans 74% des cas. Ces constatations ont abouti à des recommandations spécifiques des vétérinaires participants ou du Service sanitaire porcin suisse. Une enquête de suivi a révélé que la santé des troupeaux s'était améliorée dans 90% des exploitations appliquant les recommandations.

Biophotonics in Dentistry-An Overview.

Biophotonics, an interdisciplinary field merging biology with photonics, has transformed dentistry by offering innovative techniques and tools for diagnosis, treatment, and research. This overview explores the applications and benefits of biophotonics in dentistry, including early disease detection, precision in procedures, restorative dentistry assessment, real-time monitoring, and teeth whitening. We discuss how biophotonics improves patient care and the potential for future developments in personalized treatment, targeted therapy, enhanced imaging, and pain management. Biophotonics promises to continue revolutionizing oral healthcare, leading to better patient outcomes worldwide.

Comparative sensitivity of early cystic fibrosis lung disease detection tools in school aged children.

BACKGROUND: Effective detection of early lung disease in cystic fibrosis (CF) is critical to understanding early pathogenesis and evaluating early intervention strategies. We aimed to compare ability of several proposed sensitive functional tools to detect early CF lung disease as defined by CT structural disease in school aged children. METHODS: 50 CF subjects (mean±SD 11.2 ± 3.5y, range 5-18y) with early lung disease (FEV1≥70 % predicted: 95.7 ± 11.8 %) performed spirometry, Multiple breath washout (MBW, including trapped gas assessment), oscillometry, cardiopulmonary exercise testing (CPET) and simultaneous spirometer-directed low-dose CT imaging. CT data were analysed using well-evaluated fully quantitative software for bronchiectasis and air trapping (AT). RESULTS: CT bronchiectasis and AT occurred in 24 % and 58 % of patients, respectively. Of the functional tools, MBW detected the highest rates of abnormality: Scond 82 %, MBWTG RV 78 %, LCI 74 %, MBWTG IC 68 % and Sacin 51 %. CPET VO2peak detected slightly higher rates of abnormality (9 %) than spirometry-based FEV1 (2 %). For oscillometry AX (14 %) performed better than Rrs (2 %) whereas Xrs and R5-19 failed to detect any abnormality. LCI and Scond correlated with bronchiectasis (r = 0.55-0.64, p < 0.001) and AT (r = 0.73-0.74, p < 0.001). MBW-assessed trapped gas was detectable in 92 % of subjects and concordant with CT-assessed AT in 74 %. CONCLUSIONS: Significant structural and functional deficits occur in early CF lung disease, as detected by CT and MBW. For MBW, additional utility, beyond that offered by LCI, was suggested for Scond and MBW-assessed gas trapping. Our study reinforces the complementary nature of these tools and the limited utility of conventional oscillometry and CPET in this setting.

A clinically actionable and explainable real-time risk assessment framework for stroke-associated pneumonia.

The current medical practice is more responsive rather than proactive, despite the widely recognized value of early disease detection, including improving the quality of care and reducing medical costs. One of the cornerstones of early disease detection is clinically actionable predictions, where predictions are expected to be accurate, stable, real-time and interpretable. As an example, we used stroke-associated pneumonia (SAP), setting up a transformer-encoder-based model that analyzes highly heterogeneous electronic health records in real-time. The model was proven accurate and stable on an independent test set. In addition, it issued at least one warning for 98.6 % of SAP patients, and on average, its alerts were ahead of physician diagnoses by 2.71 days. We applied Integrated Gradient to glean the model's reasoning process. Supplementing the risk scores, the model highlighted critical historical events on patients' trajectories, which were shown to have high clinical relevance.

A CNN model for early detection of pepper Phytophthora blight using multispectral imaging, integrating spectral and textural information.

BACKGROUND: Pepper Phytophthora blight is a devastating disease during the growth process of peppers, significantly affecting their yield and quality. Accurate, rapid, and non-destructive early detection of pepper Phytophthora blight is of great importance for pepper production management. This study investigated the possibility of using multispectral imaging combined with machine learning to detect Phytophthora blight in peppers. Peppers were divided into two groups: one group was inoculated with Phytophthora blight, and the other was left untreated as a control. Multispectral images were collected at 0-h samples before inoculation and at 48, 60, 72, and 84 h after inoculation. The supporting software of the multispectral imaging system was used to extract spectral features from 19 wavelengths, and textural features were extracted using a gray-level co-occurrence matrix (GLCM) and a local binary pattern (LBP). The principal component analysis (PCA), successive projection algorithm (SPA), and genetic algorithm (GA) were used for feature selection from the extracted spectral and textural features. Two classification models were established based on effective single spectral features and significant spectral textural fusion features: a partial least squares discriminant analysis (PLS_DA) and one-dimensional convolutional neural network (1D-CNN). A two-dimensional convolutional neural network (2D-CNN) was constructed based on five principal component (PC) coefficients extracted from the spectral data using PCA, weighted, and summed with 19-channel multispectral images to create new PC images. RESULTS: The results indicated that the models using PCA for feature selection exhibit relatively stable classification performance. The accuracy of PLS-DA and 1D-CNN based on single spectral features is 82.6% and 83.3%, respectively, at the 48h mark. In contrast, the accuracy of PLS-DA and 1D-CNN based on spectral texture fusion reached 85.9% and 91.3%, respectively, at the same 48h mark. The accuracy of the 2D-CNN based on 5 PC images is 82%. CONCLUSIONS: The research indicates that Phytophthora blight infection can be detected 48 h after inoculation (36 h before visible symptoms). This study provides an effective method for the early detection of Phytophthora blight in peppers.

The potential of virtual triage AI to improve early detection, care acuity alignment, and emergent care referral of life-threatening conditions.

Objective: To evaluate the extent to which patient-users reporting symptoms of five severe/acute conditions requiring emergency care to an AI-based virtual triage (VT) engine had no intention to get such care, and whose acuity perception was misaligned or decoupled from actual risk of life-threatening symptoms. Methods: A dataset of 3,022,882 VT interviews conducted over 16 months was evaluated to quantify and describe patient-users reporting symptoms of five potentially life-threatening conditions whose pre-triage healthcare intention was other than seeking urgent care, including myocardial infarction, stroke, asthma exacerbation, pneumonia, and pulmonary embolism. Results: Healthcare intent data was obtained for 12,101 VT patient-user interviews. Across all five conditions a weighted mean of 38.5% of individuals whose VT indicated a condition requiring emergency care had no pre-triage intent to consult a physician. Furthermore, 61.5% intending to possibly consult a physician had no intent to seek emergency medical care. After adjustment for 13% VT safety over-triage/referral to ED, a weighted mean of 33.5% of patient-users had no intent to seek professional care, and 53.5% had no intent to seek emergency care. Conclusion: AI-based VT may offer a vehicle for early detection and care acuity alignment of severe evolving pathology by engaging patients who believe their symptoms are not serious, and for accelerating care referral and delivery for life-threatening conditions where patient misunderstanding of risk, or indecision, causes care delay. A next step will be clinical confirmation that when decoupling of patient care intent from emergent care need occurs, VT can influence patient behavior to accelerate care engagement and/or emergency care dispatch and treatment to improve clinical outcomes.

A labeled spectral dataset with cassava disease occurrences using virus titre determination protocol.

In this work, we present a novel dataset composed of spectral data and images of cassava crops with and without diseases. Together with the description of the dataset, we describe the protocol to collect such data in a controlled environment and in an open field where pests are not controlled. Crop disease diagnosis has been done in the past through the analysis of plant images taken with a smartphone camera. However, in some cases, disease symptoms are not visible. Furthermore, for some cassava diseases, once symptoms have manifested on the aerial part of the plant, the root which is the edible part of the plant has been totally destroyed. The goal of collecting this multimodality of the crop disease is early intervention, following the hypothesis that diseased crops without visible symptoms can be detected using spectral information. We collected visible and near-infrared spectra captured from leaves infected with two common cassava diseases namely; Cassava Brown Streak Disease and Cassava Mosaic Disease, as well as from healthy plants. Together, we also captured leaf imagery data that corresponds to the spectral information. In our experiments, biochemical data is collected and taken as the ground truth. Finally, agricultural experts provided a disease score per plant leaf from 1 to 5, 1 representing healthy and 5 severely diseased. The process of disease monitoring and data collection took 19 and 15 consecutive weeks for screenhouse and open field, respectively, until disease symptoms were visibly seen by the human eye.

Towards risk-based surveillance of African Swine Fever in Switzerland.

African Swine Fever (ASF) has emerged as a disease of great concern to swine producers and government disease control agencies because of its severe consequences to animal health and the pig industry. Early detection of an ASF introduction is considered essential for reducing the impact of the disease. Risk-based surveillance approaches have been used as enhancements to early disease epidemic detection systems in livestock populations. Such approaches may consider the role wildlife plays in hosting and transmitting a disease. In this study, a method is presented to estimate and map the risk of introducing ASF into the domestic pig population through wild boar intermediate hosts. It makes use of data about hunted wild boar, rest areas along motorways connecting ASF affected countries to Switzerland, outdoor piggeries, and forest cover. These data were used to compute relative wild boar abundance as well as to estimate the risk of both disease introduction into the wild boar population and disease transmission to domestic pigs. The way relative wild boar abundance was calculated adds to the current state of the art by considering the effect of beech mast on hunting success and the probability of wild boar occurrence when distributing relative abundance values among individual grid cells. The risk of ASF introduction into the domestic pig population by wild boar was highest near the borders of France, Germany, and Italy. On the north side of the Alps, areas of high risk were located on the unshielded side of the main motorway crossing the Central Plateau, which acts as a barrier for wild boar. Estimating the risk of disease introduction into the domestic pig population without the intermediary of wild boar suggested that dispersing wild boar may play a key role in spreading the risk to areas remote from motorways. The results of this study can be used to focus surveillance efforts for early disease detection on high risk areas. The developed method may also inform policies to control other diseases that are transmitted by a direct contact from wild boar to domestic pigs.

Characteristics of thermal images of the mammary gland and of performance in sows differing in health status and parity.

Precision livestock farming can combine sensors and complex data to provide a simple score of meaningful productivity, pig welfare, and farm sustainability, which are the main drivers of modern pig production. Examples include using infrared thermography to monitor the temperature of sows to detect the early stages of the disease. To take account of these drivers, we assigned 697 hybrid (BHZP db. Viktoria) sows to four parity groups. In addition, by pooling clinical findings from every sow and their piglets, sows were classified into three groups for the annotation: healthy, clinically suspicious, and diseased. Besides, the udder was thermographed, and performance data were documented. Results showed that the piglets of diseased sows with eighth or higher parity had the lowest daily weight gain [healthy; 192 g ± 31.2, clinically suspicious; 191 g ± 31.3, diseased; 148 g ± 50.3 (p < 0.05)] and the highest number of stillborn piglets (healthy; 2.2 ± 2.39, clinically suspicious; 2.0 ± 1.62, diseased; 3.91 ± 4.93). Moreover, all diseased sows showed higher maximal skin temperatures by infrared thermography of the udder (p < 0.05). Thus, thermography coupled with Artificial Intelligence (AI) systems can help identify and orient the diagnosis of symptomatic animals to prompt adequate reaction at the earliest time.

Multiplexing surface anchored functionalized iron carbide nanoparticle: A low molecular weight proteome responsive nano-tracer.

Harvesting the low molecular weight (LMW) proteins from the cellular exudates is a big challenge for early disease detection. Here, we introduce a unique probe composed of surface-functionalized Fe2C NPs with different functional groups to harvest, identify and profile differentially expressed biomarker proteins. Three different functionalization of Fe2C NPs with Fe2C@NH2, Fe2C@COOH and Fe2C@PEG enabled to harvest 119 differentially expressed proteins from HeLa cell exudates. Among these proteins, 57 were LMW which 82.46 % were up-regulated and 17.54 % were down-regulated. The Fe2C@NH2 were able to separate 60S ribosomal proteins L7a, and L11, and leucine-rich repeat-containing protein 59. These proteins play a vital role in the maturation of large subunit ribosomal ribonucleic acid, mRNA splicing via spliceosome and cancer cell inhibitor, respectively. While, Fe2C@COOH identifies the 60S ribosomal protein types L7, 40S ribosomal protein S11, and 60S ribosomal protein L24. These proteins were important for large ribosomal subunit biogenesis, translational initiation, and assembly of large subunit precursor of pre-ribosome. Finally, the Fe2C@PEG extracted 40S ribosomal protein S2, splicing factor, arginine/serine-rich and 40S ribosomal protein S4, X isoform which were responsible for nonsense-mediated decay, oligodendrocyte differentiation and multicellular organism development. Thus, these results help us in defining oncogenic biomarkers for early disease detection.

The Application of Cameras in Precision Pig Farming: An Overview for Swine-Keeping Professionals.

Pork is the meat with the second-largest overall consumption, and chicken, pork, and beef together account for 92% of global meat production. Therefore, it is necessary to adopt more progressive methodologies such as precision livestock farming (PLF) rather than conventional methods to improve production. In recent years, image-based studies have become an efficient solution in various fields such as navigation for unmanned vehicles, human-machine-based systems, agricultural surveying, livestock, etc. So far, several studies have been conducted to identify, track, and classify the behaviors of pigs and achieve early detection of disease, using 2D/3D cameras. This review describes the state of the art in 3D imaging systems (i.e., depth sensors and time-of-flight cameras), along with 2D cameras, for effectively identifying pig behaviors and presents automated approaches for the monitoring and investigation of pigs' feeding, drinking, lying, locomotion, aggressive, and reproductive behaviors.

A Systematic Review of Automatic Health Monitoring in Calves: Glimpsing the Future From Current Practice.

Infectious diseases, particularly bovine respiratory disease (BRD) and neonatal calf diarrhea (NCD), are prevalent in calves. Efficient health-monitoring tools to identify such diseases on time are lacking. Common practice (i.e., health checks) often identifies sick calves at a late stage of disease or not at all. Sensor technology enables the automatic and continuous monitoring of calf physiology or behavior, potentially offering timely and precise detection of sick calves. A systematic overview of automated disease detection in calves is still lacking. The objectives of this literature review were hence: to investigate previously applied sensor validation methods used in the context of calf health, to identify sensors used on calves, the parameters these sensors monitor, and the statistical tools applied to identify diseases, to explore potential research gaps and to point to future research opportunities. To achieve these objectives, systematic literature searches were conducted. We defined four stages in the development of health-monitoring systems: (1) sensor technique, (2) data interpretation, (3) information integration, and (4) decision support. Fifty-four articles were included (stage one: 26; stage two: 19; stage three: 9; and stage four: 0). Common parameters that assess the performance of these systems are sensitivity, specificity, accuracy, precision, and negative predictive value. Gold standards that typically assess these parameters include manual measurement and manual health-assessment protocols. At stage one, automatic feeding stations, accelerometers, infrared thermography cameras, microphones, and 3-D cameras are accurate in screening behavior and physiology in calves. At stage two, changes in feeding behaviors, lying, activity, or body temperature corresponded to changes in health status, and point to health issues earlier than manual health checks. At stage three, accelerometers, thermometers, and automatic feeding stations have been integrated into one system that was shown to be able to successfully detect diseases in calves, including BRD and NCD. We discuss these findings, look into potentials at stage four, and touch upon the topic of resilience, whereby health-monitoring system might be used to detect low resilience (i.e., prone to disease but clinically healthy calves), promoting further improvements in calf health and welfare.

Soybean Cyst Nematodes Influence Aboveground Plant Volatile Signals Prior to Symptom Development.

Soybean cyst nematode (SCN), Heterodera glycines, is one of the most destructive soybean pests worldwide. Unlike many diseases, SCN doesn't show above ground evidence of disease until several weeks after infestation. Knowledge of Volatile Organic Compounds (VOCs) related to pests and pathogens of foliar tissue is extensive, however, information related to above ground VOCs in response to root damage is lacking. In temporal studies, gas chromatography-mass spectrometry analysis of VOCs from the foliar tissues of SCN infested plants yielded 107 VOCs, referred to as Common Plant Volatiles (CPVs), 33 with confirmed identities. Plants showed no significant stunting until 10 days after infestation. Total CPVs increased over time and were significantly higher from SCN infested plants compared to mock infested plants post 7 days after infestation (DAI). Hierarchical clustering analysis of expression ratios (SCN: Mock) across all time points revealed 5 groups, with the largest group containing VOCs elevated in response to SCN infestation. Linear projection of Principal Component Analysis clearly separated SCN infested from mock infested plants at time points 5, 7, 10 and 14 DAI. Elevated Styrene (CPV11), D-Limonene (CPV32), Tetradecane (CPV65), 2,6-Di-T-butyl-4-methylene-2,5-cyclohexadiene-1-one (CPV74), Butylated Hydroxytoluene (CPV76) and suppressed Ethylhexyl benzoate (CPV87) levels, were associated with SCN infestation prior to stunting. Our findings demonstrate that SCN infestation elevates the release of certain VOCs from foliage and that some are evident prior to symptom development. VOCs associated with SCN infestations prior to symptom development may be valuable for innovative diagnostic approaches.

Characterization of nanosensitive multifractality in submicron scale tissue morphology and its alteration in tumor progression.

SIGNIFICANCE: Assessment of disease using optical coherence tomography is an actively investigated problem, owing to many unresolved challenges in early disease detection, diagnosis, and treatment response monitoring. The early manifestation of disease or precancer is typically associated with subtle alterations in the tissue dielectric and ultrastructural morphology. In addition, biological tissue is known to have ultrastructural multifractality. AIM: Detection and characterization of nanosensitive structural morphology and multifractality in the tissue submicron structure. Quantification of nanosensitive multifractality and its alteration in progression of tumor. APPROACH: We have developed a label free nanosensitive multifractal detrended fluctuation analysis(nsMFDFA) technique in combination with multifractal analysis and nanosensitive optical coherence tomography (nsOCT). The proposed method deployed for extraction and quantification of nanosensitive multifractal parameters in mammary fat pad (MFP). RESULTS: Initially, the nsOCT approach is numerically validated on synthetic submicron axial structures. The nsOCT technique was applied to pathologically characterized MFP of murine breast tissue to extract depth-resolved nanosensitive submicron structures. Subsequently, two-dimensional MFDFA were deployed on submicron structural en face images to extract nanosensitive tissue multifractality. We found that nanosensitive multifractality increases in transition from healthy to tumor. CONCLUSIONS: This method for extraction of nanosensitive tissue multifractality promises to provide a noninvasive diagnostic tool for early disease detection and monitoring treatment response. The novel ability to delineate the dominant submicron scale nanosensitive multifractal properties may also prove useful for characterizing a wide variety of complex scattering media of non-biological origin.

UAV-Based Thermal, RGB Imaging and Gene Expression Analysis Allowed Detection of Fusarium Head Blight and Gave New Insights Into the Physiological Responses to the Disease in Durum Wheat.

Wheat is one of the world's most economically important cereal crop, grown on 220 million hectares. Fusarium head blight (FHB) disease is considered a major threat to durum (Triticum turgidum subsp. durum (Desfontaines) Husnache) and bread wheat (T. aestivum L.) cultivars and is mainly managed by the application of fungicides at anthesis. However, fungicides are applied when FHB symptoms are clearly visible and the spikes are almost entirely bleached (% of diseased spikelets > 80%), by when it is too late to control FHB disease. For this reason, farmers often react by performing repeated fungicide treatments that, however, due to the advanced state of the infection, cause a waste of money and pose significant risks to the environment and non-target organisms. In the present study, we used unmanned aerial vehicle (UAV)-based thermal infrared (TIR) and red-green-blue (RGB) imaging for FHB detection in T. turgidum (cv. Marco Aurelio) under natural field conditions. TIR and RGB data coupled with ground-based measurements such as spike's temperature, photosynthetic efficiency and molecular identification of FHB pathogens, detected FHB at anthesis half-way (Zadoks stage 65, ZS 65), when the percentage (%) of diseased spikelets ranged between 20% and 60%. Moreover, in greenhouse experiments the transcripts of the key genes involved in stomatal closure were mostly up-regulated in F. graminearum-inoculated plants, demonstrating that the physiological mechanism behind the spike's temperature increase and photosynthetic efficiency decrease could be attributed to the closure of the guard cells in response to F. graminearum. In addition, preliminary analysis revealed that there is differential regulation of genes between drought-stressed and F. graminearum-inoculated plants, suggesting that there might be a possibility to discriminate between water stress and FHB infection. This study shows the potential of UAV-based TIR and RGB imaging for field phenotyping of wheat and other cereal crop species in response to environmental stresses. This is anticipated to have enormous promise for the detection of FHB disease and tremendous implications for optimizing the application of fungicides, since global food crop demand is to be met with minimal environmental impacts.