Regular Field Research Articles

Agronomic Practices for Management of Ginger Bacterial wilt Disease: A Review

Ginger is one of the most important spices, particularly for small-scale farmers in Ethiopia. However, bacterial wilt is a major constraint to ginger production, and it was reported for the first time in 2012. The disease is caused by the bacteria <i>Ralstonia</i> <i>solanacearum</i>. Common symptoms in infected plants include wilting, stunting, yellowing of foliage, and rhizome rot. The disease is now widespread across all ginger-growing regions worldwide, spreading through soil, water, infected rhizomes, and plant debris. A major challenge in managing bacterial wilt has been the lack of effective control methods. This review primarily focuses on recent advances in control measures, including agronomic and cultural practices such as soil amendment, rhizome treatment, and other cultural practices. Soil and rhizome solarization has proven to be a cost-effective method that is compatible with other pest management tactics. Furthermore, the use of organic matter such as crop residue and animal manure has been investigated as a means of inducing <i>R. solanacearum</i> suppression since it enhances the physical, chemical, and biological characteristics of soil. The application of certain plants and their essential oils as bio-fumigants has also been examined as an alternative approach to managing bacterial wilt as part of an integrated disease management system. Crop rotation, tillage, and field sanitation play vital roles in disease management. Furthermore, other farm practices, such as the use of healthy seeds, cultivation in disease-free areas, cover crops, bio-mulch, and regular field inspection, also contribute to the suppression of this pathogen. Overall, employing agronomic and cultural practices in combination with an integrated disease management strategy offers a promising approach for controlling bacterial wilt and ensuring sustainable ginger production.

Soft tissue scavenging patterns of mice on human remains.

Rodent scavenging of human remains is repeatedly documented in literature. However, most of this documentation is based on single-case examples. While this information is valuable, it does not provide an in-depth understanding of scavenger behavior in a single environment. Mouse scavenging data, in particular, lack larger sample sizes and data representing the full duration of scavenging activity. In documented cases, mice scavenge primarily the hands, the feet, and the head without significant progression into the rest of the body. At the Forensic Investigation Research Station in Whitewater, Colorado, deer mice (Peromyscus maniculatus group) scavenged tissue from 11 human donors. Motion activated game cameras and regular field photography documented scavenging progression and deer mouse behavior. Deer mice generally showed a preference for skin and fat, rarely consuming enough muscle to expose bone. The most scavenged sections of the body were the limbs and deer mice generally ignored the hands, the feet, and the head, unlike previous documentation. In a few cases, deer mice consumed tissue on the limbs in a distinct trail pattern. Most of the scavenging began in spring. This sample greatly expands on the existing documentation of mice overall and contributes much needed data on the progression of scavenging. Scavengers can have a significant impact on a death scene, so detailed documentation of different scavengers in a variety of environments can prove useful in the investigation process.

Visual field prediction based on temporal-spatial feature learning

Glaucoma stands as the leading irreversible cause of blindness worldwide. Regular visual field examinations play a crucial role in both diagnosing and treating glaucoma. Predicting future visual field changes can assist clinicians in making timely interventions to manage the progression of this disease. To integrate temporal and spatial features from past visual field examination results and enhance visual field prediction, a convolutional long short-term memory (ConvLSTM) network was employed to construct a predictive model. The predictive performance of the ConvLSTM model was validated and compared with other methods using a dataset of perimetry tests from the Humphrey field analyzer at the University of Washington (UWHVF). Compared to traditional methods, the ConvLSTM model demonstrated higher prediction accuracy. Additionally, the relationship between visual field series length and prediction performance was investigated. In predicting the visual field using the previous three visual field results of past 1.5~6.0 years, it was found that the ConvLSTM model performed better, achieving a mean absolute error of 2.255 dB, a root mean squared error of 3.457 dB, and a coefficient of determination of 0.960. The experimental results show that the proposed method effectively utilizes existing visual field examination results to achieve more accurate visual field prediction for the next 0.5~2.0 years. This approach holds promise in assisting clinicians in diagnosing and treating visual field progression in glaucoma patients.

Equivalence of regular spinor fields

In the Lounesto classification, there are three types of regular spinors. They are classified by the condition that at least one of the scalar or pseudo scalar norms are non-vanishing. The Dirac spinors are regular spinors because their scalar and pseudo scalar norms are non-zero and zero respectively. We construct local and Lorentz-covariant fermionic fields from all three classes of regular spinors. By computing the invariants and bilinear covariants of the regular spinor fields, we show that they are physically equivalent to the Dirac fields in the sense that whatever interactions one writes down using the regular spinor fields, they can always be expressed in terms of the Dirac fields.

Occurrence Frequencies of White Dwarfs with Regular Magnetic Fields

Occurrence Frequencies of White Dwarfs with Regular Magnetic Fields

Geospatial Mapping and Health Status of Some Important Ornamental Fish Habitats of Upper Assam, India

To conserve and manage water resources, it is crucial to create a record of water bodies and their catchments, which can be accomplished through digitized maps. The research focused on five rivers - River Dibru, River Sessa, River Burhidihing, River Brahmaputra, as well as two wetlands of Upper Assam - Maguri Beel, and Mer Beel. These water bodies were chosen based on their abundant fishery resources. Each selected water body was divided into three segments, and one sampling station from each segment was selected for monitoring water quality and recording fish species. The distribution of the catchment area was analyzed in the form of raster maps and geomorphology maps. Based on fish landings, the water bodies were identified and classified, and fish samples were collected through regular field surveys. Over the period from January 2021 to December 2022, eleven physicochemical parameters of the selected water bodies were recorded seasonally to assess the health status of each water bodies. Similarly, the health status and growth coefficient of each fish specimen was done. Water parameters were mostly within the standard limits and demonstrated only a narrow range of seasonal variation. In the studied water bodies, 49 ornamental fish species representing 32 genera under 18 families and 8 orders were found. Among the water bodies, River Dibru had the highest number of ornamental fish species at 46. The order Cypriniformes was dominant in all the studied sites.

Numerical model of debris flow susceptibility using slope stability failure machine learning prediction with metaheuristic techniques trained with different algorithms

In this work, intelligent numerical models for the prediction of debris flow susceptibility using slope stability failure factor of safety (FOS) machine learning predictions have been developed. These machine learning techniques were trained using novel metaheuristic methods. The application of these training mechanisms was necessitated by the need to enhance the robustness and performance of the three main machine learning methods. It was necessary to develop intelligent models for the prediction of the FOS of debris flow down a slope with measured geometry due to the sophisticated equipment required for regular field studies on slopes prone to debris flow and the associated high project budgets and contingencies. With the development of smart models, the design and monitoring of the behavior of the slopes can be achieved at a reduced cost and time. Furthermore, multiple performance evaluation indices were utilized to ensure the model’s accuracy was maintained. The adaptive neuro-fuzzy inference system, combined with the particle swarm optimization algorithm, outperformed other techniques. It achieved an FOS of debris flow down a slope performance of over 85%, consistently surpassing other methods.

Canonical lifts in multisymplectic De Donder–Weyl Hamiltonian field theories

We define canonical lifts of vector fields to the multisymplectic multimomentum bundles of De Donder–Weyl Hamiltonian (first-order) field theories and to the appropriate premultisymplectic embedded constraint submanifolds on which singular field theories are studied. These new canonical lifts are used to study the so-called natural Noether symmetries present in both regular and singular Hamiltonian field theories along with their associated conserved quantities obtained from Noether’s theorem. The Klein–Gordon field, the Polyakov bosonic string, and Einstein–Cartan gravity in 3+1 dimensions are analyzed in depth as applications of these concepts; as a peripheral result obtained in the analysis of the bosonic string, we provide a new geometrical interpretation of the well-known Virasoro constraint.

Atomic probe of dark matter differential interactions with subatomic particles

Searching for physics beyond the Standard Model is one of the main tasks of experimental physics. Candidates for dark matter include axionlike ultralight bosonic particles. Comagnetometers form ultrahigh sensitivity probes for such particles and any exotic field that interacts with the spin of an atom. Here, we propose a multiatom-species probe that enables one not only to discover such fields and measure their spectrum but also to determine the ratios of their coupling strengths to the subatomic particles—electrons, neutrons, and protons. We further show that the multifaceted capabilities of this probe can be demonstrated with synthetic exotic fields generated by a combination of regular magnetic fields and light-induced fictitious magnetic fields in alkali atoms. These synthetic fields also enable the accurate calibration of any magnetometer or comagnetometer probe for exotic physics. Published by the American Physical Society 2024

Shape-Scale Co-Awareness Network for 3D Brain Tumor Segmentation.

The accurate segmentation of brain tumor is significant in clinical practice. Convolutional Neural Network (CNN)-based methods have made great progress in brain tumor segmentation due to powerful local modeling ability. However, brain tumors are frequently pattern-agnostic, i.e. variable in shape, size and location, which can not be effectively matched by traditional CNN-based methods with local and regular receptive fields. To address the above issues, we propose a shape-scale co-awareness network (S2CA-Net) for brain tumor segmentation, which can efficiently learn shape-aware and scale-aware features simultaneously to enhance pattern-agnostic representations. Primarily, three key components are proposed to accomplish the co-awareness of shape and scale. The Local-Global Scale Mixer (LGSM) decouples the extraction of local and global context by adopting the CNN-Former parallel structure, which contributes to obtaining finer hierarchical features. The Multi-level Context Aggregator (MCA) enriches the scale diversity of input patches by modeling global features across multiple receptive fields. The Multi-Scale Attentive Deformable Convolution (MS-ADC) learns the target deformation based on the multiscale inputs, which motivates the network to enforce feature constraints both in terms of scale and shape for optimal feature matching. Overall, LGSM and MCA focus on enhancing the scale-awareness of the network to cope with the size and location variations, while MS-ADC focuses on capturing deformation information for optimal shape matching. Finally, their effective integration prompts the network to perceive variations in shape and scale simultaneously, which can robustly tackle the variations in patterns of brain tumors. The experimental results on BraTS 2019, BraTS 2020, MSD BTS Task and BraTS2023-MEN show that S2CA-Net has superior overall performance in accuracy and efficiency compared to other state-of-the-art methods. Code: https://github.com/jiangyu945/S2CA-Net.

Wind direction and speed calculation model with deep learning

Abstract In view of the problems of complex process and weak representation in regular atmospheric wind field detection, this paper adopts deep learning method, uses global high-altitude meteorological detection data, and establishes a deep learning model for calculating wind direction and speed with different altitudes and temperatures by using keras software package. The model is verified by using third-party independent sounding data from a meteorological observatory in Shanghai. The calculation accuracy of the model above 2000 m is 0.9830 and the value of loss function is 0.0482. The accuracy under 2000 m is 0.9164 and the value of loss function is 0.0377. There are significant differences in the performance of the model between under 2000 meters and above 2000 meters due to surface friction. The model shows that wind direction and speed of different height layers can be calculated by using only height and temperature at the same height. This model can also be used to check whether the quality of regular wind detection work is good or not with big old data.

Closed inguinal castration technique in horses compared with field castrations using post‐operative serum amyloid A analysis

SummaryThe inflammatory response to a truly closed castration procedure in 51 horses using an inguinal approach was quantified by serum amyloid A (SAA) measurements to be on average 94 mg/L on day three after surgery. The average SAA value after field castrations, using a scrotal approach, was reported by four different investigations to be between 480 and 708 mg/L on day three post‐operatively. The discrepancy in SAA levels between these different approaches proved to be significant (p < 0.001), quantifying a substantially lower inflammatory response of the closed inguinal technique over regular field castrations using a scrotal approach.

A random forest-based analysis of cassava mosaic disease-related factors affecting the on-farm livelihoods of cassava farmers

This study aimed to identify key CMD-related factors affecting Cameroon cassava farmers’ incomes originating from both the sale of cassava cuttings (V215) and the sale of cassava roots (V216). To achieve this, nine CMD-related variables were used to independently train two Random Forest models. These models were later employed for regression-based prediction of both financial targets V215 and V216. The Random Forest (RF)-based mean absolute percentage error for targets V215 and V216 were 0.19 and 1.25 respectively. The RF-based mean Gaussian deviance for targets V215 and V216 were 0.07 and 0.51 respectively. Based on RF feature importance scores (RFFI), the top 3 factors affecting income originating from the sale of cassava cuttings were found to be: late appearance of symptoms as a difficulty associated with regular field monitoring (RFFI of 0.2594), removal of infected plants as a method of controlling frequent occurrence of viral diseases in respondents’ cassava fields (RFFI of 0.1633) and lack of healthy planting material due to frequent occurrence of viral diseases in respondents’ cassava fields (RFFI of 0.1495). Also, the top 3 factors affecting income originating from the sale of cassava roots were found to be: the replacement of infected plants with healthy cuttings as a method of controlling the frequent occurrence of viral diseases in respondents’ cassava fields (RFFI of 0.1974), decrease in yield due to frequent occurrence of viral diseases in respondents’ cassava fields (RFFI of 0.1530) and poor plant growth due to frequent occurrence of viral diseases in respondents’ cassava fields (RFFI of 0.1388).

Derivation, characterization, and application of complete orthonormal sequences for representing general three-dimensional states of residual stress

Residual stresses are self-equilibrated stresses on unloaded bodies. Owing to their complex origins, it is useful to develop functions that can be linearly combined to represent any sufficiently regular residual stress field. In this work, we develop orthonormal sequences that span the set of all square-integrable residual stress fields on a given three-dimensional region. These sequences are obtained by extremizing the most general quadratic, positive-definite functional of the stress gradient on the set of all sufficiently regular residual stress fields subject to a prescribed normalization condition; each such functional yields a sequence. For the special case where the sixth-order coefficient tensor in the functional is homogeneous and isotropic and the fourth-order coefficient tensor in the normalization condition is proportional to the identity tensor, we obtain a three-parameter subfamily of sequences. Upon a suitable parameter normalization, we find that the viable parameter space corresponds to a semi-infinite strip. For a further specialized spherically symmetric case, we obtain analytical expressions for the sequences and the associated Lagrange multipliers. Remarkably, these sequences change little across the entire parameter strip. To illustrate the applicability of our theoretical findings, we employ three such spherically symmetric sequences to accurately approximate two standard residual stress fields. Our work opens avenues for future exploration into the implications of different sequences, achieved by altering both the spatial distribution and the material symmetry class of the coefficient tensors, toward specific objectives.

Stochastic CFD numerical model, algorithm, and simulation on regular wave fields

This paper invites higher-order derivative numerical technology to research the stochastic characteristics in regular wave fields. The randomness of the parameters vector was incorporated during the stochastic space establishment, by which, the stochastic CFD numerical model and algorithm were developed in the study. Meantime, Taylor expansion coupled with the perturbation was also utilized to realize the investigation on the random behaviours of the regular wave fields where the perturbative values of the parameters vector included 1%, 2%, and 3%. The results indicated the merit of the stochastic CFD numerical model and algorithm in wave field research.

Garmin Fénix 7® Underestimates Performance at the Lactate Threshold in Comparison to Standardized Blood Lactate Field Test.

Lactate threshold (LT) is a critical performance measure traditionally obtained using costly laboratory-based tests. Wearables offer a practical and noninvasive alternative for LT assessment in recreational and professional athletes. However, the comparability of these estimates with the regular field tests requires further evaluation. In our sample of 26 participants (nf=7 and nm=19), we compared the estimated running pace and heart rate (HR) at LT with two subsequent tests. First, participants performed the Fenix 7® threshold running test after a calibration phase. Subsequently, they were tested in a standardized, graded blood lactate field test. Age was 25.97 (± 6.26) years, and body mass index (BMI) was 24.58 (± 2.8) kg/m2. Pace at LT calculated by Fenix 7® (M=11.87 km/h±1.26 km/h) was 11.96% lower compared to the field test (M=13.28 km/h±1.72 km/h), which was significant (p <0.001, d=-1.19). HR estimated by the Fenix 7® at LT was 1.71% lower (p>0.05). LT data obtained in the field test showed greater overall variance. Our results suggest sufficient accuracy of Fenix 7® LT estimates for recreational athletes. It can be assumed that for professional athletes, it would fail to provide the nuanced data needed for high-quality training management.

Time-gated single-pixel imaging of Cherenkov emission from a medical linear accelerator.

Cherenkov imaging is an ideal tool for real-time in vivo verification of a radiation therapy dose. Given that radiation is pulsed from a medical linear accelerator (LINAC) together with weak Cherenkov emissions, time-gated high-sensitivity imaging is required for robust measurements. Instead of using an expensive camera system with limited efficiency of detection in each pixel, a single-pixel imaging (SPI) approach that maintains promising sensitivity over the entire spectral band could be used to provide a low-cost and viable alternative. A prototype SPI system was developed and demonstrated here in Cherenkov imaging of LINAC dose delivery to a water tank. Validation experiments were performed using four regular fields and an intensity-modulated radiotherapy (IMRT) delivery plan. The Cherenkov image-based projection percent depth dose curves (pPDDs) were compared to pPDDs simulated by the treatment planning system (TPS), with an overall average error of 0.48, 0.42, 0.65, and 1.08% for the 3, 5, 7, and 9 cm square beams, respectively. The composite image of the IMRT plan achieved a 85.9% pass rate using 3%/3 mm gamma index criteria, in comparing Cherenkov intensity and TPS dose. This study validates the feasibility of applying SPI to the Cherenkov imaging of radiotherapy dose for the first time to our knowledge.

Novel records of parasitoids targeting fall armyworm, &lt;i&gt;Spodoptera frugiperda&lt;/i&gt; (J. E. Smith) (Lepidoptera: Noctuidae), in rice-corn-based cropping systems in the Philippines

In the Philippines, the Fall Armyworm (FAW) was initially documented infesting corn in June 2019 and rice in May 2021. Relying on commercially available insecticides as the primary, if not exclusive, management strategy raises concerns about potential issues arising from FAW developing resistance to these chemicals, and the associated risks these insecticides pose to non-target organisms and the environment. To address these challenges, it is imperative to explore potential naturally occurring indigenous biological control agents and entomopathogens of the FAW. Regular field samplings and monitoring of FAW and its natural enemies were systematically conducted in selected municipalities of Central and Northern Luzon, as well as the Cagayan Valley regions of the Philippines. FAW larvae suspected of parasitization were collected from the field and subsequently reared in the laboratory for parasitoid emergence and other natural enemies. The parasitoids that emerged were sent to the Centre for Agriculture and Biosciences International United Kingdom (CABI UK) Diagnostic and Advisory Service for identification. Two hymenopteran parasitoids, Brachymeria lasus Walker (Family: Chalcididae) and Copidosoma floridanum (Ashmead) (Family: Encyrtidae), were identified from the lab-reared FAW larval samples. These two FAW parasitoids are new records in the Philippines and elsewhere.

Abstract 6185: Generative AI for virtual HE-staining of whole slide images of unstained breast cancer tissue

Abstract Introduction Hematoxylin and eosin (HE) is the standard stain used in histology to make tissue visible to the human eye by highlighting certain cellular and tissue structures, and it is a technique that is widely used in the diagnosis of cancer and other pathologies. Chemical staining, however, is irreversible, making the tissue unusable for subsequent measurements, such as spatial transcriptomics. Here we utilize generative AI method based on pix2pix image-to-image translation to generate virtual HE-staining for whole slide images (WSIs) acquired of unstained tissue with brightfield microscopy and perform a thorough histological evaluation for the feasibility in breast cancer diagnostics. Materials and Methods We optimized sample preparation and imaging setup for virtual staining purposes, and developed a custom generative adversarial network architecture for learning the virtual staining from paires samples of unstained and H&amp;E stained tissue. Here, we focus on the utility of virtual staining in breast cancer diagnostics, using a set of breast cancer samples for acquiring whole slide images from unstained tissue before H&amp;E staining and from reference H&amp;E stained tissue after chemical staining. Hold-out set of sample pairs are left for validation, allowing us to evaluate the virtual staining performance for a vast array of tissue components and to examine the potential shortcomings in staining reproduction. We use a comprehensive set of quantitative metrics both on pixel and object level to evaluate virtual staining quality. In addition, we perform thorough visual evaluation of histological feasibility by histology experts to examine the computational staining. Results We demonstrate that by careful optimization of both sample preparation and imaging workflow, as well as the computational methods, generative adversarial networks can be used for virtual staining of whole slide images of breast cancer tissue acquired from unstained tissue using regular bright field microscopy. We analyzed the virtual staining performance quantitatively and visually, and highlight the potential of the method through successful cases and demonstrate the applicability in breast cancer diagnostics, as well as discuss the challenges and shortcomings of virtual staining for clinical samples. Conclusions We demonstrate the potential of virtual HE staining for clinical histopathology of breast cancer tissue. Notably, our virtual staining based on generative AI shows promise towards more sustainable and streamlined sample processing and staining process in digital pathology. Citation Format: Leena Latonen, Umair Khan, Sonja Koivukoski, Johan Hartman, Pekka Ruusuvuori. Generative AI for virtual HE-staining of whole slide images of unstained breast cancer tissue [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6185.

The impact of multiple abiotic stresses on ns-LTP2.8 gene transcript and ns-LTP2.8 protein accumulation in germinating barley (Hordeum vulgare L.) embryos.

Abiotic stresses occur more often in combination than alone under regular field conditions limiting in more severe way crop production. Stress recognition in plants primarily occurs in the plasma membrane, modification of which is necessary to maintain homeostasis in response to it. It is known that lipid transport proteins (ns-LTPs) participate in modification of the lipidome of cell membranes. Representative of this group, ns-LTP2.8, may be involved in the reaction to abiotic stress of germinating barley plants by mediating the intracellular transport of hydrophobic particles, such as lipids, helping to maintain homeostasis. The ns-LTP2.8 protein was selected for analysis due to its ability to transport not only linear hydrophobic molecules but also compounds with a more complex spatial structure. Moreover, ns-LTP2.8 has been qualified as a member of pathogenesis-related proteins, which makes it particularly important in relation to its high allergenic potential. This paper demonstrates for the first time the influence of various abiotic stresses acting separately as well as in their combinations on the change in the ns-LTP2.8 transcript, ns-LTP2.8 protein and total soluble protein content in the embryonal axes of germinating spring barley genotypes with different ns-LTP2.8 allelic forms and stress tolerance. Tissue localization of ns-LTP2.8 transcript as well as ns-LTP2.8 protein were also examined. Although the impact of abiotic stresses on the regulation of gene transcription and translation processes remains not fully recognized, in this work we managed to demonstrate different impact on applied stresses on the fundamental cellular processes in very little studied tissue of the embryonal axis of barley.