Microstructural alterations in white matter and related neurobiology based on the new clinical subtypes of Parkinson's disease

Background and objectivesThe advent of new clinical subtyping systems for Parkinson's disease (PD) has led to the classification of patients into distinct groups: mild motor predominant (PD-MMP), intermediate (PD-IM), and diffuse malignant (PD-DM). Our goal was to evaluate the efficacy of diffusion tensor imaging (DTI) in the early diagnosis, assessment of clinical progression, and prediction of prognosis of these PD subtypes. Additionally, we attempted to understand the pathological mechanisms behind white matter damage using single-photon emission computed tomography (SPECT) and cerebrospinal fluid (CSF) analyses.MethodsWe classified 135 de novo PD patients based on new clinical criteria and followed them up after 1 year, along with 45 healthy controls (HCs). We utilized tract-based spatial statistics to assess the microstructural changes of white matter at baseline and employed multiple linear regression to examine the associations between DTI metrics and clinical data at baseline and after follow-up.ResultsCompared to HCs, patients with the PD-DM subtype demonstrated reduced fractional anisotropy (FA), increased axial diffusivity (AD), and elevated radial diffusivity (RD) at baseline. The FA and RD values correlated with the severity of motor symptoms, with RD also linked to cognitive performance. Changes in FA over time were found to be in sync with changes in motor scores and global composite outcome measures. Furthermore, baseline AD values and their rate of change were related to alterations in semantic verbal fluency. We also discovered the relationship between FA values and the levels of α-synuclein and β-amyloid. Reduced dopamine transporter uptake in the left putamen correlated with RD values in superficial white matter, motor symptoms, and autonomic dysfunction at baseline as well as cognitive impairments after 1 year.ConclusionsThe PD-DM subtype is characterized by severe clinical symptoms and a faster progression when compared to the other subtypes. DTI, a well-established technique, facilitates the early identification of white matter damage, elucidates the pathophysiological mechanisms of disease progression, and predicts cognitively related outcomes. The results of SPECT and CSF analyses can be used to explain the specific pattern of white matter damage in patients with the PD-DM subtype

Preventive effect of Lactobacillus johnsonii YH1136 against uric acid accumulation and renal damages

BackgroundHyperuricemia (HUA) is a prevalent metabolic disorder whose development is associated with intestinal microbiota. Therefore, probiotics have emerged as a potential and safe approach for lowering uric acid (UA) levels. However, the underlying mechanisms of many effective probiotic strains remain unknown.Methods and resultsC57BL/6 mice were randomly divided into two groups: control and model groups. The model group received 12 weeks of potassium oxonate. Through 16s sequencing we found that HUA resulted in a significant decrease in the total diversity of all intestinal segments. When each intestinal segment was analyzed individually, the reduction in diversity was only significant in the cecum and colon sections. RDA analysis showed that lactobacilli in the rat colon exhibited a strong correlation with model group, suggesting that Lactobacillus may play an important role in HUA. Consequently, the preventive effects of Lactobacillus johnsonii YH1136 against HUA were investigated. C57BL/6 mice were randomly divided into three groups: control, model and YH1136 groups. The results showed that administering Lactobacillus johnsonii YH1136 effectively reduced serum UA levels in vivo by inhibiting hepatic xanthine oxidase (XOD) activity and promoting renal ABCG2 transporter expression. Moreover, supplementation with Lactobacillus johnsonii YH1136 significantly ameliorated pathological damage in the kidney and liver, thereby reducing UA accumulation.ConclusionHyperuricemia is accompanied by an altered composition of multiple gut bacteria, of which Lactobacillus is a key genus. Lactobacillus johnsonii YH1136 may ameliorate renal involvement in HUA via the gut-kidney axis

First Report of Integrative Conjugative Elements in Riemerella anatipestifer Isolates From Ducks in China

We report for the first time the occurrence of integrative conjugative elements (ICEs) in Riemerella anatipestifer (R.anatipestifer) isolated from diseased ducks in China. For this purpose, a total of 48 genome sequences were investigated, which comprised 30 publicly available R. anatipestifer genome sequences, and 18 clinical isolates genomes sequences. Two ICEs, named ICERanRCAD0133-1 and ICERanRCAD0179-1 following the classic nomenclature system, were identified in R. anatipestifer through the use of bioinformatics tools. Comparative analysis revealed that three ICEs in Ornithobacterium rhinotracheale showed a high degree of conservation with the core genes of ICERanRCAD0133-1, while 13 ICEs with high similarity to ICERanRCAD0179-1 were found in Bacteroidetes. Based on the definition of ICE family, ICERanRCAD0179-1 was grouped in CTnDOT/ERL family; however, ICERanRCAD0133-1, which had no significant similarity with known ICEs, might be classified into a novel ICE family. The sequences of ICERanRCAD0133-1 and ICERanRCAD0179-1 were 70890 bp and 49166 bp in length, had 33.14 and 50.34% GC content, and contained 77 CDSs and 51 CDSs, respectively. Cargo genes carried by these two ICEs were predicted to encode: R-M systems, IS elements, a putative TonB-dependent receptor, a bacteriocin/lantibiotic efflux ABC transporter, a tetracycline resistance gene and more. In addition, phylogenetic analyses revealed that ICERanRCAD0179-1 and related ICEs were derived from a common ancestor, which may have undergone divergence prior to integartation into the host bacterial chromosome, and that the core genes co-evolved via a related evolutionary process or experienced only a low degree of recombination events during spread from a common CTnDOT/ERL family ancestor. Collectively, this study is the first identification and characterization of ICEs in R. anatipestifer; and provides new insights into the genetic diversity, evolution, adaptation, antimicrobial resistance, and virulence of R. anatipestifer

COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

BACKGROUND: Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework. METHODS: In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. FINDINGS: Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1. INTERPRETATION: Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources. FUNDING: British Heart Foundation Data Science Centre, led by Health Data Research UK

Sharp threshold for blow-up and global existence in a semilinear parabolic equation with variable source

Abstract This paper deals with a semilinear parabolic equation with variable source under the case that the initial energy is less than the potential well depth. We deduce a sharp threshold for blow-up and global existence of solutions. Furthermore, we conclude that the global solution decays as the time goes to infinity

MultiSumm: Towards a Unified Model for Multi-Lingual Abstractive Summarization

Automatic text summarization aims at producing a shorter version of the input text that conveys the most important information. However, multi-lingual text summarization, where the goal is to process texts in multiple languages and output summaries in the corresponding languages with a single model, has been rarely studied. In this paper, we present MultiSumm, a novel multi-lingual model for abstractive summarization. The MultiSumm model uses the following training regime: (I) multi-lingual learning that contains language model training, auto-encoder training, translation and back-translation training, and (II) joint summary generation training. We conduct experiments on summarization datasets for five rich-resource languages: English, Chinese, French, Spanish, and German, as well as two low-resource languages: Bosnian and Croatian. Experimental results show that our proposed model significantly outperforms a multi-lingual baseline model. Specifically, our model achieves comparable or even better performance than models trained separately on each language. As an additional contribution, we construct the first summarization dataset for Bosnian and Croatian, containing 177,406 and 204,748 samples, respectively

A Fish-like Binocular Vision System for Underwater Perception of Robotic Fish

Biological fish exhibit a remarkably broad-spectrum visual perception capability. Inspired by the eye arrangement of biological fish, we design a fish-like binocular vision system, thereby endowing underwater bionic robots with an exceptionally broad visual perception capacity. Firstly, based on the design principles of binocular visual field overlap and tangency to streamlined shapes, a fish-like vision system is developed for underwater robots, enabling wide-field underwater perception without a waterproof cover. Secondly, addressing the significant distortion and parallax of the vision system, a visual field stitching algorithm is proposed to merge the binocular fields of view and obtain a complete perception image. Thirdly, an orientation alignment method is proposed that draws scales for yaw and pitch angles in the stitched images to provide a reference for the orientation of objects of interest within the field of view. Finally, underwater experiments evaluate the perception capabilities of the fish-like vision system, confirming the effectiveness of the visual field stitching algorithm and the orientation alignment method. The results show that the constructed vision system, when used underwater, achieves a horizontal field of view of 306.56°. The conducted work advances the visual perception capabilities of underwater robots and presents a novel approach to and insight for fish-inspired visual systems

Estimation of Forest Canopy Cover by Combining ICESat-2/ATLAS Data and Geostatistical Method/Co-Kriging

Accurately estimating forest canopy cover (FCC) is challenging by using traditional remote sensing images at the regional level due to the spectral saturation phenomenon. In this study, to improve the estimation accuracy, a new method of FCC wall-to-wall mapping was suggested based on ice, cloud, and land elevation satellite/advanced topographic laser altimeter system (ATLAS) data. Specifically, one dataset of FCC's observations was combined with preprocessed ATLAS data and topographic factors to build a random forest regression (RFR) model. Moreover, the Co-Kriging method was used to generate spatially explicit values that are required by the RFR from the point data of ATLAS parameters, and then the wall-to-wall mapping of the FCC was conducted. The results showed that the RFR model had an accuracy of relative root-mean-square error (rRMSE) = 0.09 with a coefficient of determination (R2) = 0.91. The best-fit semivariogram models between primary variables and covariates were asr and TR (Model: Gaussian model, R2 = 0.94, the residual sum of squares (RSS) = 1.73 × 10−6), landsat_perc and NDVI (Model: spherical model, R2 = 0.46, RSS = 1.58 × 10−4), and photon_rate_can and slope (Model: exponential model, R2 = 0.77, RSS = 6.45 × 10−4), respectively. FCC validation result showed that the FCC's wall-to-wall mapping was in great agreement with the dataset-2 (R2 = 0.79; rRMSE = 0.11)