Miracle-dz/UniFAR-FTUs huggingface dataset:
Differentiating amyloid beta spread in autosomal dominant and sporadic Alzheimer’s disease

Amyloid-beta (Aβ) deposition is one of the hallmark pathologies in both sporadic Alzheimer’s disease (sAD) and autosomal dominant Alzheimer’s disease (ADAD), the latter of which is caused by mutations in genes involved in Aβ processing. Despite Aβ deposition being a centerpiece to both sAD and ADAD, some differences between these AD subtypes have been observed with respect to the spatial pattern of Aβ. Previous work has shown that the spatial pattern of Aβ in individuals spanning the sAD spectrum can be reproduced with high accuracy using an epidemic spreading model (ESM), which simulates the diffusion of Aβ across neuronal connections and is constrained by individual rates of Aβ production and clearance. However, it has not been investigated whether Aβ deposition in the rarer ADAD can be modeled in the same way, and if so, how congruent the spreading patterns of Aβ across sAD and ADAD are. We leverage the ESM as a data-driven approach to probe individual-level variation in the spreading patterns of Aβ across three different large-scale imaging datasets (2 SAD, 1 ADAD). We applied the ESM separately to the Alzheimer’s Disease Neuroimaging initiative (N=737), the Open Access Series of Imaging Studies (N=510), and the Dominantly Inherited Alzheimer’s Network (N=249), the latter two of which were processed using an identical pipeline. We assessed inter- and intra-individual model performance in each dataset separately, and further identified the most likely epicenter of Aβ spread for each individual. Using epicenters defined in previous work in sAD, the ESM provided moderate prediction of the regional pattern of Aβ deposition across all three datasets. We further find that, while the most likely epicenter for most Aβ-positive subjects overlaps with the default mode network, 13% of ADAD individuals were best characterized by a striatal origin of Aβ spread. These subjects were also distinguished by being younger than ADAD subjects with a DMN Aβ origin, despite having a similar estimated age of symptom onset. Together, our results suggest that most ADAD patients express Aβ spreading patters similar to those of sAD, but that there may be a subset of ADAD patients with a separate, striatal phenotype.
Structurally Guided Channel Attention Networks: SGCA-Net

In this paper, we propose Structurally Guided Channel Attention Networks (SGCA-Net), a principled way to guide the channel attention of CNNs. Convolution operator constructs features maps by using both channel and spatial information within the receptive fields of its filters. Prior research has investigated the impact of strengthening the representational power of CNNs using channel attention modules. In this work, we guide the channel attention of networks using feature vectors that contain clinically relevant information. We do so by attaching guided attention modules into a state-of-the-art network architecture, and guiding these attention modules with domain knowledge using feature vectors. Experiments on a dataset of 5512 posterior retinal images, taken using a wide angle fundus camera, show that SGCA-Net achieves 0.983 and 0.948 AUC to predict plus and normal categories, respectively. SGCA-Net achieves higher performance than CNNs without attention modules and CNNs with unguided attention modules.
Prognostic Value of Right Ventricular 3D Speckle-Tracking Strain and Ejection Fraction in Patients With HFpEF

Background: Right ventricular longitudinal strain of free wall (RV FWLS) assessed by two-dimensional speckle-tracking echocardiography (2D-STE) is recognized as an independent predictor of poor prognosis in patients with heart failure with preserved ejection fraction (HFpEF). However, the prognostic implications of three-dimensional STE (3D-STE) parameters in patients with HFpEF have not been well-established. The purpose of our study was to determine whether 3D-STE parameters were the more powerful predictors of poor outcomes in HFpEF patients compared with 2D-STE indices. Methods: Eighty-one consecutive patients with HFpEF were studied by 2D-STE and 3D-STE. RV volumes, ejection fraction (EF) and 3D-RVFWLS were measured by 3D-STE. 2D-RVFWLS was determined by 2D-STE. Patients were followed for the primary end point of heart failure (HF)-related hospitalization and death for HF. Results: After a median follow-up period of 17 months, 39 (48%) patients reached the end point of cardiovascular events. Compared with HFpEF patients without end-point events, those with end-point events had lower RVEF and 3D-RVFWLS (P < 0.05). Separate multivariate Cox regression analyses revealed that 3D-RVFWLS (HR 5.73; 95% CI 2.77–11.85; P < 0.001), RVEF (HR 3.47; 95% CI 1.47–8.21; P = 0.005), and 2D-RVFWLS (HR 3.17; 95% CI 1.54–6.53; P = 0.002) were independent predictors of adverse outcomes. The models with 3D-RVFWLS (AIC = 246, C-index = 0.75) and RVEF (AIC = 247, C-index = 0.76) had similar predictive performance for future clinical events as with 2D-RVFWLS (AIC = 248, C-index = 0.74). Conclusions: 3D-STE parameters are powerful predictors of poor outcomes, providing a similar predictive value as 2D-STE indices in patients with HFpEF. These findings support the potential of RV 3D-STE to identify HFpEF patients at higher risk for adverse cardiac events.
Responses of Low-Cost Input Combinations on the Microbial Structure of the Maize Rhizosphere for Greenhouse Gas Mitigation and Plant Biomass Production

The microbial composition of the rhizosphere and greenhouse gas (GHG) emissions under the most common input combinations in maize (Zea mays L.) cultivated in Brazil have not been characterized yet. In this study, we evaluated the influence of maize stover coverage (S), urea-topdressing fertilization (F), and the microbial inoculant Azospirillum brasilense (I) on soil GHG emissions and rhizosphere microbial communities during maize development. We conducted a greenhouse experiment and measured methane (CH4), carbon dioxide (CO2), and nitrous oxide (N2O) fluxes from soil cultivated with maize plants under factorial combinations of the inputs and a control treatment (F, I, S, FI, FS, IS, FIS, and control). Plant biomass was evaluated, and rhizosphere soil samples were collected at V5 and V15 stages and DNA was extracted. The abundance of functional genes (mcrA, pmoA, nifH, and nosZ) was determined by quantitative PCR (qPCR) and the structure of the microbial community was assessed through 16S rRNA amplicon sequencing. Our results corroborate with previous studies which used fewer input combinations and revealed different responses for the following three inputs: F increased N2O emissions around 1 week after application; I tended to reduce CH4 and CO2 emissions, acting as a plant growth stimulator through phytohormones; S showed an increment for CO2 emissions by increasing carbon-use efficiency. IS and FIS treatments presented significant gains in biomass that could be related to Actinobacteria (19.0%) and Bacilli (10.0%) in IS, and Bacilli (9.7%) in FIS, which are the microbial taxa commonly associated with lignocellulose degradation. Comparing all factors, the IS (inoculant + maize stover) treatment was considered the best option for plant biomass production and GHG mitigation since FIS provides small gains toward the management effort of F application.
Insight Into the Role of PGPR in Sustainable Agriculture and Environment

A multitude of roles is played by microbes in food and agriculture that include nutrient cycling and management, organic matter decomposition and fermentation. Plant growth promoting rhizobacteria (PGPR), representing microbial groups and with ability of colonizing plant roots, influence plant growth through various indirect and direct modes in order to promote its growth and/or protect it from diseases or damage due to insect attack. Thus, PGPR research has received renewed interest worldwide. Increasing number of crop-specific PGPR are being commercialized these days. Approaches like seed-inoculation and soil application either alone or in combination with bacterial culture/product for increased nutrient availability through phosphate solubilisation, potassium solubilisation, sulfur oxidation, nitrogen fixation, iron, and copper chelation are gaining popularity. Arbuscular mycorrhizal fungi (AMF) are root fungal symbiont that improve management of abiotic stress such as phosphorus deficiency. PGPR involves roles like production of indole acetic acid (IAA), ammonia (NH3), hydrogen cyanide (HCN), catalase, etc. PGPR also improve nutrient uptake by altering the level of plant hormone that enhances root surface area by increasing its girth and shape, thereby helping in absorbing more nutrients. PGPR facilitate seed germination, seedling growth and crop yield. An array of microbes including Pseudomonas, Azospirillum, Azotobacter, Klebsiella, Enterobacter, Alcaligenes, Arthrobacter, Burkholderia, Bacillus, and Serratia enhance plant growth. Various Pseudomonas sp. have demonstrated significant increase in germination, seedling growth and yield in different agricultural crops, including wheat. Hence, developing a successful crop-specific PGPR formulation, the candidate should possess characteristics like high rhizosphere competence, extensive competitive saprophytic ability, growth enhancing ability, ease of mass production, broad-spectrum action, safety toward the environment and compatibility with other partnering organisms.
Identification and Validation of Immune-Related Methylation Clusters for Predicting Immune Activity and Prognosis in Breast Cancer

The role of DNA methylation of breast cancer-infiltrating immune cells has not been fully explored. We conducted a cohort-based retrospective study analyzing the genome-wide immune-related DNA methylation of 1057 breast cancer patients from the TCGA cohort and GSE72308 cohort. Based on patients’ overall survival (OS), a prognostic risk score system using 18 immune-related methylation genes (IRMGs) was established and further validated in an independent cohort. Kaplan–Meier analysis showed a clear separation of OS between the low- and high-risk groups. Patients in the low-risk group had a higher immune score and stromal score compared with the high-risk group. Moreover, the characteristics based on 18-IRMGs signature were related to the tumor immune microenvironment and affected the abundance of tumor-infiltrating immune cells. Consistently, the 18-IRMGs signatures showed similar influences on immune modulation and survival in another external validation cohort (GSE72308). In conclusion, the proposed 18-IRMGs signature could be a potential marker for breast cancer prognostication.
A Time-Resolved Diffusion Technique for Detection of the Conformational Changes and Molecular Assembly/Disassembly Processes of Biomolecules

Biological liquid–liquid phase separation (LLPS) is driven by dynamic and multivalent interactions, which involves conformational changes and intermolecular assembly/disassembly processes of various biomolecules. To understand the molecular mechanisms of LLPS, kinetic measurements of the intra- and intermolecular reactions are essential. In this review, a time-resolved diffusion technique which has a potential to detect molecular events associated with LLPS is presented. This technique can detect changes in protein conformation and intermolecular interaction (oligomer formation, protein-DNA interaction, and protein-lipid interaction) in time domain, which are difficult to obtain by other methods. After the principle and methods for signal analyses are described in detail, studies on photoreactive molecules (intermolecular interaction between light sensor proteins and its target DNA) and a non-photoreactive molecule (binding and folding reaction of α-synuclein upon mixing with SDS micelle) are presented as typical examples of applications of this unique technique.
High-Frequency Deep Brain Stimulation of the Substantia Nigra Pars Reticulata Facilitates Extinction and Prevents Reinstatement of Methamphetamine-Induced Conditioned Place Preference

Persistent and stable drug memories lead to a high rate of relapse among addicts. A number of studies have found that intervention in addiction-related memories can effectively prevent relapse. Deep brain stimulation (DBS) exhibits distinct therapeutic effects and advantages in the treatment of neurological and psychiatric disorders. In addition, recent studies have also found that the substantia nigra pars reticulata (SNr) could serve as a promising target in the treatment of addiction. Therefore, the present study aimed to investigate the effect of DBS of the SNr on the reinstatement of drug-seeking behaviors. Electrodes were bilaterally implanted into the SNr of rats before training of methamphetamine-induced conditioned place preference (CPP). High-frequency (HF) or low-frequency (LF) DBS was then applied to the SNr during the drug-free extinction sessions. We found that HF DBS, during the extinction sessions, facilitated extinction of methamphetamine-induced CPP and prevented drug-primed reinstatement, while LF DBS impaired the extinction. Both HF and LF DBS did not affect locomotor activity or induce anxiety-like behaviors of rats. Finally, HF DBS had no effect on the formation of methamphetamine-induced CPP. In conclusion, our results suggest that HF DBS of the SNr could promote extinction and prevent reinstatement of methamphetamine-induced CPP, and the SNr may serve as a potential therapeutic target in the treatment of drug addiction.
LC-MS-Based Untargeted Metabolomics Reveals Early Biomarkers in STZ-Induced Diabetic Rats With Cognitive Impairment

Diabetes in the elderly increases cognitive impairment, but the underlying mechanisms are still far from fully understood. A non-targeted metabolomics approach based on liquid chromatography-mass spectrometry (LC-MS) was performed to screen out the serum biomarkers of diabetic mild cognitive impairment (DMMCI) in rats. Total 48 SD rats were divided into three groups, Normal control (NC) group, high-fat diet (HFD) fed group and type 2 diabetes mellitus (T2DM) group. The T2DM rat model was induced by intraperitoneal administration of streptozotocin (STZ, 35 mg/kg) after 6 weeks of high-fat diet (HFD) feeding. Then each group was further divided into 4-week and 8-week subgroups, which were calculated from the time point of T2DM rat model establishment. The novel object recognition test (NORT) and the Morris water maze (MWM) method were used to evaluate the cognitive deficits in all groups. Compared to the NC-8w and HFD-8w groups, both NOR and MWM tests indicated significant cognitive dysfunction in the T2DM-8w group, which could be used as an animal model of DMMCI. Serum was ultimately collected from the inferior vena cava after laparotomy. Metabolic profiling analysis was conducted using ultra high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) technology. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to verify the stability of the model. According to variable importance in the project (VIP > 1) and the p-value of t-test (P < 0.05) obtained by the OPLS-DA model, the metabolites with significant differences were screened out as potential biomarkers. In total, we identified 94 differentially expressed (44 up-regulated and 50 down-regulated) endogenous metabolites. The 10 top up-regulated and 10 top down-regulated potential biomarkers were screened according to the FDR significance. These biomarkers by pathway topology analysis were primarily involved in the metabolism of sphingolipid (SP) metabolism, tryptophan (Trp) metabolism, Glycerophospholipid (GP) metabolism, etc. Besides, SP metabolism, Trp metabolism and GP metabolism mainly belonging to the lipid metabolism showed marked perturbations over DMMCI and may contribute to the development of disease. Taken collectively, our results revealed that T2DM could cause cognitive impairment by affecting a variety of metabolic pathways especially lipid metabolism. Besides, serum PE, PC, L-Trp, and S1P may be used as the most critical biomarkers for the early diagnosis of DMMCI.
Monitoring and Prognostic Analysis of Severe Cerebrovascular Diseases Based on Multi-Scale Dynamic Brain Imaging

Severe cerebrovascular disease is an acute cerebrovascular event that causes severe neurological damage in patients, and is often accompanied by severe dysfunction of multiple systems such as breathing and circulation. Patients with severe cerebrovascular disease are in critical condition, have many complications, and are prone to deterioration of neurological function. Therefore, they need closer monitoring and treatment. The treatment strategy in the acute phase directly determines the prognosis of the patient. The case of this article selected 90 patients with severe cerebrovascular disease who were hospitalized in four wards of the Department of Neurology and the Department of Critical Care Medicine in a university hospital. The included cases were in accordance with the guidelines for the prevention and treatment of cerebrovascular diseases. Patients with cerebral infarction are given routine treatments such as improving cerebral circulation, protecting nutrient brain cells, dehydration, and anti-platelet; patients with cerebral hemorrhage are treated within the corresponding safe time window. We use Statistical Product and Service Solutions (SPSS) Statistics21 software to perform statistical analysis on the results. Based on the study of the feature extraction process of convolutional neural network, according to the hierarchical principle of convolutional neural network, a backbone neural network MF (Multi-Features)—Dense Net that can realize the fusion, and extraction of multi-scale features is designed. The network combines the characteristics of densely connected network and feature pyramid network structure, and combines strong feature extraction ability, high robustness and relatively small parameter amount. An end-to-end monitoring algorithm for severe cerebrovascular diseases based on MF-Dense Net is proposed. In the experiment, the algorithm showed high monitoring accuracy, and at the same time reached the speed of real-time monitoring on the experimental platform. An improved spatial pyramid pooling structure is designed to strengthen the network’s ability to merge and extract local features at the same level and at multiple scales, which can further improve the accuracy of algorithm monitoring by paying a small amount of additional computational cost. At the same time, a method is designed to strengthen the use of low-level features by improving the network structure, which improves the algorithm’s monitoring performance on small-scale severe cerebrovascular diseases. For patients with severe cerebrovascular disease in general, APACHEII1, APACHEII2, APACHEII3 and the trend of APACHEII score change are divided into high-risk group and low-risk group. The overall severe cerebrovascular disease, severe cerebral hemorrhage and severe cerebral infarction are analyzed, respectively. The differences are statistically significant.
Toxocara canis Infection Alters lncRNA and mRNA Expression Profiles of Dog Bone Marrow

Bone marrow is the main hematopoietic organ that produces red blood cells, granulocytes, monocyte/macrophages, megakaryocytes, lymphocytes, and myeloid dendritic cells. Many of these cells play roles in the pathogenesis of Toxocara canis infection, and understanding how infection alters the dynamics of transcription regulation in bone marrow is therefore critical for deciphering the global changes in the dog transcriptional signatures during T. canis infection. In this study, long non-coding RNA (lncRNA) and messenger RNA (mRNA) expression profiles in the bone marrow of Beagle dogs infected with T. canis were determined at 12 h post-infection (hpi), 24 hpi, 96 hpi, and 36 days post-infection (dpi). RNA-sequencing and bioinformatics analysis identified 1,098, 984, 1,120, and 1,305 differentially expressed lncRNAs (DElncRNAs), and 196, 253, 223, and 328 differentially expressed mRNAs (DEmRNAs) at 12 h, 24 h, 96 h, and 36 days after infection, respectively. We also identified 29, 36, 38, and 68 DEmRNAs potentially cis-regulated by 44, 44, 51, and 80 DElncRNAs at 12 hpi, 24 hpi, 96 hpi, and 36 dpi, respectively. To validate the sequencing findings, qRT-PCR was performed on 10 randomly selected transcripts. Many altered genes were involved in the differentiation of bone marrow cells. GO of DElncRNAs and GO and KEGG pathway analyses of DEmRNAs revealed alterations in several signaling pathways, including pathways involved in energy metabolism, amino acid biosynthesis and metabolism, Wnt signaling pathway, Huntington's disease, HIF-1 signaling pathway, cGMP–PKG signaling pathway, dilated cardiomyopathy, and adrenergic signaling in cardiomyocytes. These findings revealed that bone marrow of T. canis-infected dogs exhibits distinct lncRNA and mRNA expression patterns compared to healthy control dogs. Our data provide novel insights into T. canis interaction with the definitive host and shed light on the significance of the non-coding portion of the dog genome in the pathogenesis of toxocariasis.
Comparison of Resampling Algorithms to Address Class Imbalance when Developing Machine Learning Models to Predict Foodborne Pathogen Presence in Agricultural Water

Recent studies have shown that predictive models can supplement or provide alternatives to E. coli-testing for assessing the potential presence of food safety hazards in water used for produce production. However, these studies used balanced training data and focused on enteric pathogens. As such, research is needed to determine 1) if predictive models can be used to assess Listeria contamination of agricultural water, and 2) how resampling (to deal with imbalanced data) affects performance of these models. To address these knowledge gaps, this study developed models that predict nonpathogenic Listeria spp. (excluding L. monocytogenes) and L. monocytogenes presence in agricultural water using various combinations of learner (e.g., random forest, regression), feature type, and resampling method (none, oversampling, SMOTE). Four feature types were used in model training: microbial, physicochemical, spatial, and weather. “Full models” were trained using all four feature types, while “nested models” used between one and three types. In total, 45 full (15 learners*3 resampling approaches) and 108 nested (5 learners*9 feature sets*3 resampling approaches) models were trained per outcome. Model performance was compared against baseline models where E. coli concentration was the sole predictor. Overall, the machine learning models outperformed the baseline E. coli models, with random forests outperforming models built using other learners (e.g., rule-based learners). Resampling produced more accurate models than not resampling, with SMOTE models outperforming, on average, oversampling models. Regardless of resampling method, spatial and physicochemical water quality features drove accurate predictions for the nonpathogenic Listeria spp. and L. monocytogenes models, respectively. Overall, these findings 1) illustrate the need for alternatives to existing E. coli-based monitoring programs for assessing agricultural water for the presence of potential food safety hazards, and 2) suggest that predictive models may be one such alternative. Moreover, these findings provide a conceptual framework for how such models can be developed in the future with the ultimate aim of developing models that can be integrated into on-farm risk management programs. For example, future studies should consider using random forest learners, SMOTE resampling, and spatial features to develop models to predict the presence of foodborne pathogens, such as L. monocytogenes, in agricultural water when the training data is imbalanced.