This paper proposes a numerical-integration perspective on the Gaussian filters. A Gaussian filter is approximation of the Bayesian inference with the Gaussian posterior probability density ...assumption being valid. There exists a variation of Gaussian filters in the literature that derived themselves from very different backgrounds. From the numerical-integration viewpoint, various versions of Gaussian filters are only distinctive from each other in their specific treatments of approximating the multiple statistical integrations. A common base is provided for the first time to analyze and compare Gaussian filters with respect to accuracy, efficiency and stability factor. This study is expected to facilitate the selection of appropriate Gaussian filters in practice and to help design more efficient filters by employing better numerical integration methods
Patients with Parkinson's disease undergo a loss of melanized neurons in substantia nigra pars compacta and locus coeruleus. Very few studies have assessed substantia nigra pars compacta and locus ...coeruleus pathology in Parkinson's disease simultaneously with magnetic resonance imaging (MRI). Neuromelanin-sensitive MRI measures of substantia nigra pars compacta and locus coeruleus volume based on explicit magnetization transfer contrast have been shown to have high scan-rescan reproducibility in controls, but no study has replicated detection of Parkinson's disease-associated volume loss in substantia nigra pars compacta and locus coeruleus in multiple cohorts with the same methodology. Two separate cohorts of Parkinson's disease patients and controls were recruited from the Emory Movement Disorders Clinic and scanned on two different MRI scanners. In cohort 1, imaging data from 19 controls and 22 Parkinson's disease patients were acquired with a Siemens Trio 3 Tesla scanner using a 2D gradient echo sequence with magnetization transfer preparation pulse. Cohort 2 consisted of 33 controls and 39 Parkinson's disease patients who were scanned on a Siemens Prisma 3 Tesla scanner with a similar imaging protocol. Locus coeruleus and substantia nigra pars compacta volumes were segmented in both cohorts. Substantia nigra pars compacta volume (Cohort 1: p = 0.0148; Cohort 2: p = 0.0011) and locus coeruleus volume (Cohort 1: p = 0.0412; Cohort 2: p = 0.0056) were significantly reduced in the Parkinson's disease group as compared to controls in both cohorts. This imaging approach robustly detects Parkinson's disease effects on these structures, indicating that it is a promising marker for neurodegenerative neuromelanin loss.
While skeletal muscle creatine levels can be enhanced by exogenous creatine supplementation, the elevation of brain creatine levels with oral creatine administration remains a challenge due to a lack ...of effective transportation of creatine through the blood-brain barrier. Intranasal administration can bypass the blood-brain barrier and deliver drugs directly to the brain. The purpose of this study was to assess the effect of intranasal administration of creatine on brain creatine level and cognitive performance. Rats were randomly assigned into three groups intranasal administration group, oral administration group, and control group. The intranasal group exhibited fewer errors and shorter primary latency compared to the control and oral groups, respectively, during the acquisition phase of the Barnes maze. The intranasal group spent a higher percentage of time in the target quadrant during the probe trial compared to the control group. Biochemical measurements showed that the concentration of creatine in the olfactory bulbs, medial prefrontal cortex, and hippocampus of the rats in the intranasal group was higher than in the oral, and control groups. These results indicate that intranasal administration of creatine hydrochloride increases the creatine level in the rat’s brain’s and improves their performance in the Barnes maze.
•Rats were administered creatine intranasally or orally and compared.•Intranasal creatine administration increased creatine levels in the brain.•Rats with intranasal creatine performed better in Barnes maze test.
Effect of minor chemical structures such as 1,2-diol content, ethylene content, tacticity, a degree of polymerization, and a degree of saponification of the main chain on biodegradability of ...polyvinyl alcohol (PVA) is summarized. Most PVA-degraders are Gram-negative bacteria belonging to the Pseudomonads and Sphingomonads, but Gram-positive bacteria also have PVA-degrading abilities. Several examples show symbiotic degradation of PVA by different mechanisms.
Penicillium
sp. is the only reported eukaryotic degrader. A vinyl alcohol oligomer-utilizing fungus,
Geotrichum fermentans
WF9101, has also been reported. Lignolytic fungi have displayed non-specific degradation of PVA. Extensive published studies have established a two-step process for the biodegradation of PVA. Some bacteria excrete extracellular PVA oxidase to yield oxidized PVA, which is partly under spontaneous depolymerization and is further metabolized by the second step enzyme (hydrolase). On the other hand, PVA (whole and depolymerized to some extent) must be taken up into the periplasmic space of some Gram-negative bacteria, where PVA is oxidized by PVA dehydrogenase, coupled to a respiratory chain. The complete
pva
operon was identified in
Sphingopyxis
sp. 113P3. Anaerobic biodegradability of PVA has also been suggested.
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•A yellow rust detection system was developed by learning from UAV imagery.•RVI, NDVI and OSAVI ranked top 3 among 18 SVIs for rust detection.•NIR and Red ranked top 2 among 5 VIS–NIR ...bands for rust detection.•An experiment was designed to verify the system with promising performance.
The use of a low-cost five-band multispectral camera (RedEdge, MicaSense, USA) and a low-altitude airborne platform is investigated for the detection of plant stress caused by yellow rust disease in winter wheat for sustainable agriculture. The research is mainly focused on: (i) determining whether or not healthy and yellow rust infected wheat plants can be discriminated; (ii) selecting spectral band and Spectral Vegetation Index (SVI) with a strong discriminating capability; (iii) developing a low-cost yellow rust monitoring system for use at farmland scales. An experiment was carefully designed by infecting winter wheat with different levels of yellow rust inoculum, where aerial multispectral images under different developmental stages of yellow rust were captured by an Unmanned Aerial Vehicle at an altitude of 16–24 m with a ground resolution of 1–1.5 cm/pixel. An automated yellow rust detection system is developed by learning (via random forest classifier) from labelled UAV aerial multispectral imagery. Experimental results indicate that: (i) good classification performance (with an average Precision, Recall and Accuracy of 89.2%, 89.4% and 89.3%) was achieved by the developed yellow rust monitoring at a diseased stage (45 days after inoculation); (ii) the top three SVIs for separating healthy and yellow rust infected wheat plants are RVI, NDVI and OSAVI; while the top two spectral bands are NIR and Red. The learnt system was also applied to the whole farmland of interest with a promising monitoring result. It is anticipated that this study by seamlessly integrating low-cost multispectral camera, low-altitude UAV platform and machine learning techniques paves the way for yellow rust monitoring at farmland scales.
Invasive fungal disease (IFD) poses a significant threat to immunocompromised patients and remains a global challenge due to limited treatment options, high mortality and morbidity rates, and the ...emergence of drug-resistant strains. Despite advancements in antifungal agents and diagnostic techniques, the lack of effective vaccines, standardized diagnostic tools, and efficient antifungal drugs contributes to the ongoing impact of invasive fungal infections (IFI). Recent studies have highlighted the presence of extracellular vesicles (EVs) released by fungi carrying various components such as enzymes, lipids, nucleic acids, and virulence proteins, which play roles in both physiological and pathological processes. These fungal EVs have been shown to interact with the host immune system during the development of fungal infections whereas their functional role and potential application in patients are not yet fully understood. This review summarizes the current understanding of the biologically relevant findings regarding EV in host-pathogen interaction, and aim to describe our knowledge of the roles of EV as diagnostic tools and vaccine vehicles, offering promising prospects for the treatment of IFI patients.
The design of strapdown inertial navigation system (INS) algorithms based on dual quaternions is addressed. Dual quaternion is a most concise and efficient mathematical tool to represent rotation and ...translation simultaneously, i.e., the general displacement of a rigid body. The principle of strapdown inertial navigation is represented using the tool of dual quaternion. It is shown that the principle can be expressed by three continuous kinematic equations in dual quaternion. These equations take the same form as the attitude quaternion rate equation. Subsequently, one new numerical integration algorithm is structured to solve the three kinematic equations, utilizing the traditional two-speed approach originally developed in attitude integration. The duality between the coning and sculling corrections, raised in the recent literature, can be essentially explained by splitting the new algorithm into the corresponding rotational and translational parts. The superiority of the new algorithm over conventional ones in accuracy is analytically derived. A variety of simulations are carried out to support the analytic results. The numerical results agree well with the analyses. The new algorithm turns out to be a better choice than any conventional algorithm for high-precision navigation systems and high-maneuver applications. Several guidelines in choosing a suitable navigation algorithm are also provided.
There have been successful applications of deep learning to functional magnetic resonance imaging (fMRI), where fMRI data were mostly considered to be structured grids, and spatial features from ...Euclidean neighbors were usually extracted by the convolutional neural networks (CNNs) in the computer vision field. Recently, CNN has been extended to graph data and demonstrated superior performance. Here, we define graphs based on functional connectivity and present a connectivity-based graph convolutional network (cGCN) architecture for fMRI analysis. Such an approach allows us to extract spatial features from connectomic neighborhoods rather than from Euclidean ones, consistent with the functional organization of the brain. To evaluate the performance of cGCN, we applied it to two scenarios with resting-state fMRI data. One is individual identification of healthy participants and the other is classification of autistic patients from normal controls. Our results indicate that cGCN can effectively capture functional connectivity features in fMRI analysis for relevant applications.
Most neuroimaging studies of resting state networks have concentrated on functional connectivity (FC) based on instantaneous correlation in a single network. In this study we investigated both FC and ...effective connectivity (EC) based on Granger causality of four important networks at resting state derived from functional magnetic resonance imaging data — default mode network (DMN), hippocampal cortical memory network (HCMN), dorsal attention network (DAN) and fronto-parietal control network (FPCN).
A method called correlation-purged Granger causality analysis was used, not only enabling the simultaneous evaluation of FC and EC of all networks using a single multivariate model, but also accounting for the interaction between them resulting from the smoothing of neuronal activity by hemodynamics. FC was visualized using a force-directed layout upon which causal interactions were overlaid. FC results revealed that DAN is very tightly coupled compared to the other networks while the DMN forms the backbone around which the other networks amalgamate. The pattern of bidirectional causal interactions indicates that posterior cingulate and posterior inferior parietal lobule of DMN act as major hubs. The pattern of unidirectional causal paths revealed that hippocampus and anterior prefrontal cortex (aPFC) receive major inputs, likely reflecting memory encoding/retrieval and cognitive integration, respectively. Major outputs emanating from anterior insula and middle temporal area, which are directed at aPFC, may carry information about interoceptive awareness and external environment, respectively, into aPFC for integration, supporting the hypothesis that aPFC-seeded FPCN acts as a control network.
Our findings indicate the following. First, regions whose activities are not synchronized interact via time-delayed causal influences. Second, the causal interactions are organized such that cingulo-parietal regions act as hubs. Finally, segregation of different resting state networks is not clear cut but only by soft boundaries.
►In resting state, regions whose activities are not instantaneously synchronized, interact via time-delayed causal influences ►The bi-directional causal interactions at rest are organized such that cingulo-parietal regions act as hubs ►Unidirectional causal paths revealed that hippocampus and anterior prefrontal cortex (aPFC) receive major inputs, likely reflecting memory encoding/retrieval and cognitive integration, respectively ►Major outputs emanating from anterior insula and middle temporal area, which are directed at aPFC, may carry information about interoceptive awareness and external environment, respectively, into aPFC for integration, supporting the hypothesis that aPFC-seeded FPCN acts as a control network ►Segregation of different resting state networks is not clear cut but only by soft boundaries
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