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Summary: Clear differentiation of high-grade and clinically insignificant prostate cancer (PCa) is critical for clinical decision-making. Here, we developed a proprietary urinary exosome isolation approach (EVLatch) and established a facile diagnostic workflow. We discovered that EEF1A1 levels, abundantly expressed on urinary exosomes, positively correlate to urinary exosome counts irrespective of source and collection time and demonstrated that EEF1A1 enables in-assay quantification of urinary exosomes. Importantly, a prostate cancer urinary EVLatch-based artificial intelligence diagnostics (PURE-AID) classification system utilizing PCA3, HOXC6, and DLX1 as targets with SPDEF for reference and EEF1A1 for quality checking, trained on 271 patients, achieved an area under the receiver operating characteristic curve (AUROC) of 0.76 in the test set of 351 patients. Combination of PURE-AID with prostate-specific antigen (PSA) and age increases AUROC to 0.80 and reduces 54.3% of unnecessary biopsies with 86.8% sensitivity. Our study provides a new classification system for differentiating high-grade PCa in a workflow- and patient-friendly manner.

Due to the emergence of root rot (RR), it can cause harm to the health of Zanthoxylum bungeanum (Z. bungeanum) plants. However, the reason for this is not clear, and further research is needed. We selected healthy and RR-affected 6-year-old Z. bungeanum trees (infected for 1 year), collected their rhizosphere and bulk soils, and analyzed soil microbial communities, enzyme activities, and physicochemical properties. When the plants were diseased, soil water content (SWC), available potassium (AK), organic matter (OM) contents, β-1,4-glucosidase (β-G) activity, and fungal Shannon index significantly decreased, while bacterial Shannon index significantly increased in the rhizosphere soil. Soil total nitrogen (TN), SWC, AK, OM contents, and β-G and N-acetyl-glucosamine (NAG) activity significantly reduced, while fungal Simpson index significantly increased in the bulk soil. There were significant differences in the bacterial communities of bulk and rhizosphere soil. However, there were no obvious differences in the fungal communities. Pearson's correlation analysis showed that changes in soil microbial communities were significantly correlated with changes in the contents of TN, AK, OM, and SWC, as well as the activities of β-G and NAG. In summary, changes in soil AK, OM, SWC, and TN resulted in changes in microbial community composition, as well as changes in β-G and NAG activities, and these alterations may lead to RR in Z. bungeanum.

Predicting student performance is crucial for improving students’ future academic achievements. Within student groups, common characteristics can reveal trends in overall student learning. Most studies tend to focus on the common characteristics of students but ignore their individual characteristics. However, individual characteristics are important in promoting student academic performance because they allow us to understand the unique learning performances of each student. To address this issue, this paper proposes a student performance prediction approach. First, addressing the problem of difficulty in effectively dividing the student samples under multi-dimensional discrete data, we propose a method that combines the relationship matrix-based bipartite network approach (RMBN) with Louvain clustering. Second, the hybrid neural network model based on a relationship matrix (RMHNN) is proposed to address the problem that discrete types of features are difficult to fit by algorithms. The results show that the implementation of the model on real student data can effectively predict student performance with an accuracy of 93.1% and an F1-score of 90.45%. With the model’s predicted student performance, educators can provide individualized support and assistance to each student.

In three studies, we investigated the impact of economic inequality on beliefs about meritocracy and potential mechanisms among the Chinese. Study 1 ('N' = 19,641) longitudinally substantiated that beliefs about meritocracy abate in tandem with the increasing inequality perception but not objective economic inequality (Gini coefficient). Studies 2a ('N' = 140) and 2b ('N' = 269) experimentally showed that inequality perception decreases belief in meritocracy. The lower classes were less willing to believe in meritocracy than the upper classes when exposed to inequality cues (Studies 1 and 2b). In Study 3 ('N' = 218), we again manipulated the level of economic inequality and found that laypeople construing distribution as unfair mediated the relationship between inequality perception and meritocratic belief. We highlighted that people’s interpretation of economic inequality might influence their beliefs about merit.

In this paper the geometrical characteristics and construction method of hex-tri reciprocal frame are studied. The naming rules of structural units, members and joints of hex-tri reciprocal frame are proposed. Based on the study of relationships between the structural geometrical parameters, the formulas for the joint coordinates of the structural unit are derived,and the calculating method for normalized direction vectors of the unit members is also developed. The geometry construction method of hex-tri reciprocal frame is established in this paper. By this method the whole structure is formed as an assembly of the units arranged in rings about the structural center. The influences of the diameter, the length and the binding length of the member on the rise of hex-tri reciprocal frame are analyzed by an example. If the rise of hex-tri reciprocal frame needs to be specified in the design, the values of the diameter, the length and the binding length of the structural member need to be coordinated to meet the design requirements.

With the development of science and technology, the field of 3D fashion design has been widely paid attention to and developed in recent years, and it has also made outstanding contributions to daily clothing and functional wearable clothing. 3D technology has changed traditional clothing production technology and it made clothing design faster and more comfortable. However, as 3D apparel design involves interdisciplinary research, scholars from various specialties have not defined the research field of 3D apparel design, and the research lacks a targeted direction. In order to gain a more in-depth understanding of the research status and research hotspots of 3D technology in the field of apparel design, this study adopts a quantitative research method for literature analysis. This study used CiteSpace software to visualize and analyze 1532 articles on "3D fashion design" in the core database of Web of Science (WoS) from 2000 to 2022 and analyzed and explored the future research trends according to the current research status. We also analyze and explore future research trends based on the current research status. In this paper, the visualization analysis is mainly for the annual number of articles in the database, author cooperation, institutional cooperation, national partnership, keyword clustering, and the evolution of the research trend of the literature review and cutting-edge trend analysis, for the related fields of researchers and research areas to represent the knowledge map of the literature and the visualization of the research information. [ABSTRACT FROM AUTHOR]

As the best integration point to integrate information technology and manufacturing technology, the digital factory is the basis for the intelligent transformation of manufacturing companies. This article will discuss how to build it and discuss the digital world. When companies develop, integrate, share, apply and update different knowledge, create more added value and benefits, and make significant contributions to the sustainable development of knowledge management companies, they pay more and more attention to improving their own competitiveness. Therefore, this article proposes a method to improve knowledge management based on the actual situation of the enterprise, and proposes a strategy for recent development. In the knowledge management applications of the digital factory, the high-speed transmission and processing of data becomes crucial. But the traditional network technology has been unable to meet the increasing demand for data. This paper explores the potential application and advantages of optical network transmission based on 5G network in the application of knowledge management in digital factory, studies the optical network transmission technology based on 5G network, and discusses its feasibility and advantages in the application of knowledge management in digital factory. The study found that the optical network transmission technology based on 5G network can greatly improve the data transmission speed and bandwidth, and meet the needs of digital factories for efficient data transmission. Optical network transmission technology also has the advantages of low latency, high reliability and low energy consumption. [ABSTRACT FROM AUTHOR]

A kind of lattice-type steel member is presented and a kind of prefabricated connector suitable for the connection between lattice-type steel members are proposed. The mechanical properties of the connectors are analyzed by using the finite element numerical simulation software ABAQUS. The connectors meet the design goals of the stiffness of connection stronger than members. Parameterized analysis is carried out on the prefabricated connector, and the flexural stiffness expression of the connector is obtained. The suggested values of each component of the prefabricated connectors are given based on the size of connected lattice-type steel members. [ABSTRACT FROM AUTHOR]

Agricultural cultural heritage is crucial in advancing comprehensive rural revitalization. The Yangtze River Basin is rich in biodiversity and abundant in fishery cultural resources. The cultural resources not only reflect the ecological wisdom of harmonious coexistence and the human–land relationship between humans and nature but also provide critical cultural support for rural revitalization and watershed sustainable development. This study investigates the spatial distribution, influencing factors, and historical evolution of fishery cultural resources in the Yangtze River Basin. The highest proportions of significant resources are found in fishery engineering and landscapes, culinary flavors, fishery customs, dances, fishery gear, and poetry. By analyzing 14 categories of fishery cultural resources and 157 national and provincial intangible cultural heritage items, this study reveals a significant geographical clustering of these resources in the mid-lower reaches, particularly around the middle reaches of the Yangtze River and Poyang Lake Basin, Wuhan and Dongting Lake, and the lower reaches of the Taihu Lake Basin. This study underscores the role of natural geographical conditions, aquatic biodiversity, socio-economic factors, and historical–cultural backgrounds in the distribution and evolution of fishery cultural resources, with these factors interacting dynamically. By advocating for a comprehensive approach to coordinating fishery culture preservation with rural revitalization, this study outlines a multidimensional strategy for the preservation and sustainable development of fishery cultural resources.

In this paper, we design an adjustable connector of reciprocal frame, and a three-dimensional solid model of this connector with Circular Hollow Section has been created in the FEM software Abaqus CAE to study its mechanical properties. When the plastic hinge is formed at the end of the Circular Hollow Section, the connector is still in an elastic state. It is concluded that the adjustable connector of reciprocal frame has high strength and rigidity, realizing the goal for designing higher connector strength over Circular Hollow Section strength. Then parametric analysis is used to analyse the influence of the connector about each part on the mechanical properties, and the flexural rigidity of the connector has been derived. A three-dimensional wire model of reciprocal frames has been created in the FEM software Abaqus CAE, and a full-scale test model of the structure is designed. The numerical simulation results agree well with the test results. It is verified that the reliability of the modeling method and the accuracy of the connector mechanical model. [ABSTRACT FROM AUTHOR]

Nowadays, China is developing new energy industries to reduce carbon emissions to meet the challenge of world climate change, so investors can consider to invest in stocks of Chinese new energy companies to gain income. In order to study how to forecast stock closing prices of new energy companies in China, we have chosen 12 representative companies, and first used autoregressive univariate time series models to predict the trends of the stock closing prices in the next month. The results show that Seasonal Autoregressive Integrated Moving Average model has the best out-of-sample trend prediction effect. Second, we use multivariate time series forecasting models to predict the stock closing prices of each day through external variables. The results show that Temporal Convolutional Attention Neural Networks has the best effect of out-of-sample prediction. We recommend that investors who are interested in investing in new energy companies in China first use the Seasonal Autoregressive Integrated Moving Average model to predict the short-term stock closing price trend in the future, and then use the Temporal Convolutional Attention Neural Networks model to predict the stock closing price on the next day to decide whether to invest.

Histone demethylases, enzymes responsible for removing methyl groups from histone proteins, have emerged as critical players in regulating gene expression and chromatin dynamics, thereby influencing various cellular processes. LSD2 and LSD1 have attracted considerable interest among these demethylases because of their associations with cancer. However, while LSD1 has received significant attention, LSD2 has not been recognized to the same extent. In this study, we conduct a comprehensive comparison between LSD2 and LSD1, with a focus on exploring LSD2’s implications. While both share structural similarities, LSD2 possesses unique features as well. Functionally, LSD2 shows diverse roles, particularly in cancer, with tissue-dependent roles. Additionally, LSD2 extends beyond histone demethylation, impacting DNA methylation, cancer cell reprogramming, E3 ubiquitin ligase activity and DNA damage repair pathways. This study underscores the distinct roles of LSD2, providing insights into their contributions to cancer and other cellular processes.

Diffusion magnetic resonance imaging (dMRI) tractography has yielded intriguing insights into brain circuits and their relationship to behavior in response to gene mutations or neurological diseases across a number of species. Still, existing tractography approaches suffer from limited sensitivity and specificity, leading to uncertain interpretation of the reconstructed connections. Hence, in this study, we aimed to optimize the imaging and computational pipeline to achieve the best possible spatial overlaps between the tractography and tracer-based axonal projection maps within the mouse brain corticothalamic network. We developed a dMRI-based atlas of the mouse forebrain with structural labels imported from the Allen Mouse Brain Atlas (AMBA). Using the atlas and dMRI tractography, we first reconstructed detailed node-to-node mouse brain corticothalamic structural connectivity matrices using different imaging and tractography parameters. We then investigated the effects of each condition for accurate reconstruction of the corticothalamic projections by quantifying the similarities between the tractography and the tracer data from the Allen Mouse Brain Connectivity Atlas (AMBCA). Our results suggest that these parameters significantly affect tractography outcomes and our atlas can be used to investigate macroscopic structural connectivity in the mouse brain. Furthermore, tractography in mouse brain gray matter still face challenges and need improved imaging and tractography methods.

IntroductionThe resolution of magnetic resonance imaging is often limited at the millimeter level due to its inherent signal-to-noise disadvantage compared to other imaging modalities. Super-resolution (SR) of MRI data aims to enhance its resolution and diagnostic value. While deep learning-based SR has shown potential, its applications in MRI remain limited, especially for preclinical MRI, where large high-resolution MRI datasets for training are often lacking.MethodsIn this study, we first used high-resolution mouse brain auto-fluorescence (AF) data acquired using serial two-photon tomography (STPT) to examine the performance of deep learning-based SR for mouse brain images.ResultsWe found that the best SR performance was obtained when the resolutions of training and target data were matched. We then applied the network trained using AF data to MRI data of the mouse brain, and found that the performance of the SR network depended on the tissue contrast presented in the MRI data. Using transfer learning and a limited set of high-resolution mouse brain MRI data, we were able to fine-tune the initial network trained using AF to enhance the resolution of MRI data.DiscussionOur results suggest that deep learning SR networks trained using high-resolution data of a different modality can be applied to MRI data after transfer learning.

Diffusion MRI (dMRI) tractography is the only tool for non-invasive mapping of macroscopic structural connectivity over the entire brain. Although it has been successfully used to reconstruct large white matter tracts in the human and animal brains, the sensitivity and specificity of dMRI tractography remained limited. In particular, the fiber orientation distributions (FODs) estimated from dMRI signals, key to tractography, may deviate from histologically measured fiber orientation in crossing fibers and gray matter regions. In this study, we demonstrated that a deep learning network, trained using mesoscopic tract-tracing data from the Allen Mouse Brain Connectivity Atlas, was able to improve the estimation of FODs from mouse brain dMRI data. Tractography results based on the network generated FODs showed improved specificity while maintaining sensitivity comparable to results based on FOD estimated using a conventional spherical deconvolution method. Our result is a proof-of-concept of how mesoscale tract-tracing data can guide dMRI tractography and enhance our ability to characterize brain connectivity.

Abstract Background In mammals, maternal gene products decay and zygotic genome activation (ZGA) during maternal to zygotic transition (MZT) is critical for the early embryogenesis. Y-box binding protein YBX1 plays vital roles in RNA stabilization and transcriptional regulation, but its roles remain to be elucidated during pre-implantation development. Methods In the present study, we re-analyzed transcriptional level of YBX1 in mice, human, bovine, and goat embryos using public RNA-seq datasets. We further performed siRNA microinjection to knock down the expression of YBX1, and RNA sequencing of the 8-cell stage embryos in the control and YBX1 knockdown group. To reveal the regulation mechanisms of YBX1, we conducted differentially expression analysis, alternative splicing (AS) analysis, enrichment analysis, and 5-EU staining using DESeq2, rMATs, clusterProfiler, and immunofluorescence technique, respectively. Results The expression of YBX1 was increased during MZT in goat, bovine, human, and mice, but significantly decreased in YBX1 knockdown embryos compared with the controls, suggesting successfully knockdown of YBX1. The percentage of blastocyst was decreased, while embryos blocked at the 2- and 4-cell stage were increased in YBX1 knockdown embryos compared to the controls. Using RNA-seq, we identified 1623 up-regulated and 3531 down-regulated genes in the 8-cell stage YBX1 knockdown embryos. Of note, the down-regulated genes were enriched in regulation of RNA/mRNA stability and spliceosome, suggesting that YBX1 might medicate RNA stability and AS. To this end, we identified 3284 differential AS events and 1322 differentially expressed maternal mRNAs at the 8-cell stage YBX1 knockdown embryos. Meanwhile, the splicing factors and mRNA decay-related genes showed aberrant expression, and the transcriptional activity during ZGA in goat and mice was compromised when YBX1 was knocked down. Conclusion YBX1 serves an important role in maternal mRNA decay, alternative splicing, and the transcriptional activity required for early embryogenesis, which will broaden the current understanding of YBX1 functions during the stochastic reprogramming events.

Plant-based drinks have garnered significant attention as viable substitutes for traditional dairy milk, providing options for individuals who are lactose intolerant or allergic to dairy proteins, and those who adhere to vegan or vegetarian diets. In recent years, demand for plant-based drinks has expanded rapidly. Each variety has unique characteristics in terms of flavor, texture, and nutritional composition, offering consumers a diverse range of choices tailored to meet individual preferences and dietary needs. In this review, we aimed to provide a comprehensive overview of the various types of plant-based drinks and explore potential considerations including their nutritional compositions, health benefits, and processing technologies, as well as the challenges facing the plant-based drink processing industry. We delve into scientific evidence supporting the consumption of plant-based drinks, discuss their potential roles in meeting dietary requirements, and address current limitations and concerns regarding their use. We hope to illuminate the growing significance of plant-based drinks as sustainable and nutritious alternatives to dairy milk, and assist individuals in making informed choices regarding their dietary habits, expanding potential applications for plant-based drinks, and providing necessary theoretical and technical support for the development of a plant-based drink processing industry.

Long non-coding RNAs (lncRNAs) are involved in shaping chromosome conformation and regulation of preimplantation development. However, the role of lncRNA during somatic cell nuclear transfer (SCNT) reprogramming remains largely unknown. In the present study, we identified 114 upregulated lncRNAs in the 8-cell SCNT embryos as candidate key molecules involved in nuclear reprogramming in goat. We found that H3K4me3 was an epigenetic barrier in goat nuclear reprogramming that and injection of Kdm5b mRNA greatly improved SCNT embryos development through removal of H3K4me3. We further reported that knockdown of lnc_3712 increased the expression of Kdm5b, which led to H3K4me3 demethylation. Of note, the development of goat SCNT embryos was improved when lnc_3712 was knocked down, whereas the blastocyst rate showed no difference in lnc_3712 and Kdm5b double knockdown SCNT embryos compared with the negative control SCNT embryos. Specifically, in lnc_3712 knockdown SCNT embryos, partial of the transcriptional activity and the expression of critical embryonic genes (Wee1, Ctsb, and Ybx1) were similar with that of in vitro fertilization embryos. Therefore, our results elucidate the critical role of lnc_3712 in regulating the development of goat SCNT embryos via repressing Kdm5b, which advances our current understanding of the role of lncRNAs during nuclear reprogramming.

Feature loss in IoT scenarios is a common problem. This situation poses a greater challenge in terms of real-time and accuracy for the security of intelligent edge computing systems, which also includes network security intrusion detection systems (NIDS). Losing some packet information can easily confuse NIDS and cause an oversight of security systems. We propose a novel network intrusion detection framework based on an improved neural network. The new framework uses 23 subframes and a mixer for multi-classification work, which improves the parallelism of NIDS and is more adaptable to edge networks. We also incorporate the K-Nearest Neighbors (KNN) algorithm and Genetic Algorithm (GA) for feature selection, reducing parameters, communication, and memory overhead. We named the above system as Combinatorial Multi-Classification-NIDS (CM-NIDS). Experiments demonstrate that our framework can be more flexible in terms of the parameters of binary classification, has a fairly high accuracy in multi-classification, and is less affected by feature loss.

1H MRI maps brain structure and function non-invasively through versatile contrasts that exploit inhomogeneity in tissue micro-environments. Inferring histopathological information from magnetic resonance imaging (MRI) findings, however, remains challenging due to absence of direct links between MRI signals and cellular structures. Here, we show that deep convolutional neural networks, developed using co-registered multi-contrast MRI and histological data of the mouse brain, can estimate histological staining intensity directly from MRI signals at each voxel. The results provide three-dimensional maps of axons and myelin with tissue contrasts that closely mimic target histology and enhanced sensitivity and specificity compared to conventional MRI markers. Furthermore, the relative contribution of each MRI contrast within the networks can be used to optimize multi-contrast MRI acquisition. We anticipate our method to be a starting point for translation of MRI results into easy-to-understand virtual histology for neurobiologists and provide resources for validating novel MRI techniques.

Maternal mRNA clearance is critical for the early embryo development, which is under the tight control of RNA N6-methyladenosine (m6A). However, little information is known regarding the maternal mRNA clearance and mechanisms behind it in farm animals. In the present study, 3362 differentially expressed genes (DEGs) were found during the maternal-to-zygotic transition (MZT) and determined as maternal mRNAs in goat. Of which, 1961 was decreased at the 4-cell stage embryos, while 1401 was trigged down-regulation at the 8-cell stage embryos, which were termed as maternally encoded mRNA decay genes and zygotic genome activation (ZGA)-dependent maternal mRNAs, respectively. The expression of m6A reader YTHDF2 was increased during goat ZGA, and knockdown of YTHDF2 resulted in decreased blastocyst rate. In the 8-cell stage YTHDF2 knockdown embryos, the M-decay and Z-decay maternal mRNA clearance were impaired. Specifically, the expression of deadenylase (CNOT1 and CNOT11) and decapping enzymes (DCP1A and DCP2) was decreased. In conclusion, we ascertained maternal mRNAs and inferred that maternal mRNA clearance is also ZGA-dependent in goat. We reported that YTHDF2 is vital for goat early embryogenesis as it advances maternal mRNA clearance, which might through the recruitment of deadenylases and mRNA decapping enzymes. This work will be of great value for understanding the stochastic reprogramming events during MZT and achieving better development of goat embryos in vitro.

The uterus, as part of the female reproductive tract, is essential for embryo survival and in the maintenance of multiple pregnancies in domestic animals. This study was conducted to investigate the effects of WNT6 on Hu sheep endometrial epithelial cells (EECs) and uterine glands (UGs) in Hu sheep, with high prolificacy rates. In the present study, Hu sheep with different fecundity, over three consecutive pregnancies, were divided into two groups: high prolificacy rate group (HP, litter size = 3) and low prolificacy rate group (LP, litter size = 1). A comparative analysis of the endometrial morphology was performed by immunofluorescence. RNA-seq was used to analyze the gene’s expression in endometrium of HP and LP Hu sheep, providing a candidate gene, which was investigated in EECs and organoid culture. Firstly, higher density of UGs was found in the HP Hu sheep groups (p < 0.05). The RNA-seq data revealed the importance of the WNT signaling pathway and WNT6 gene in Hu sheep endometrium. Functionally, WNT6 could promote the cell cycle progression of EECs via WNT/β-catenin signal and enhance UGs organogenesis. Taken together, WNT6 is a crucial regulator for sheep endometrial development; this finding may offer a new insight into understanding the regulatory mechanism of sheep prolificacy.

It has been reported that hypoxic environments were more suitable for the in vitro development of mammalian embryos, but the underlying mechanisms were still unclear. In the present study, RNA-seq was performed to compare 8-cell-stage and blastocyst-stage goat embryos under hypoxic and normoxic conditions; zygotes were checked at 72 and 168 h to 8-cell stage (L8C) and blastocyst stage (LM) in hypoxic conditions and 8-cell stage (H8C) and blastocyst stage (HM) in normoxic conditions. In the H8C and L8C groups, 399 DEGs were identified, including 348 up- and 51 down-regulated DEGs. In the HM and LM groups, 1710 DEGs were identified, including 1516 up- and 194 down-regulated DEGs. The expression levels of zygotic genes, transcription factors, and maternal genes, such as WEE2, GDF9, HSP70.1, BTG4, and UBE2S showed significant changes. Functional enrichment analysis indicated that these DEGs were mainly related to biological processes and function regulation. In addition, combined with the pathway–gene interaction network and protein–protein interaction network, twenty-two of the hub genes were identified and they are mainly involved in energy metabolism, immune stress response, cell cycle, receptor binding, and signal transduction pathways. The present study provides comprehensive insights into the effects of oxidative stress on early embryo development in goats.

We explored the performance of structure-based computational analysis in four neurodegenerative conditions [Ataxia (AT, n = 16), Huntington's Disease (HD, n = 52), Alzheimer's Disease (AD, n = 66), and Primary Progressive Aphasia (PPA, n = 50)], all characterized by brain atrophy. The independent variables were the volumes of 283 anatomical areas, derived from automated segmentation of T1-high resolution brain MRIs. The segmentation based volumetric quantification reduces image dimensionality from the voxel level [on the order of O(106)] to anatomical structures [O(102)] for subsequent statistical analysis. We evaluated the effectiveness of this approach on extracting anatomical features, already described by human experience and a priori biological knowledge, in specific scenarios: (1) when pathologies were relatively homogeneous, with evident image alterations (e.g., AT); (2) when the time course was highly correlated with the anatomical changes (e.g., HD), an analogy for prediction; (3) when the pathology embraced heterogeneous phenotypes (e.g., AD) so the classification was less efficient but, in compensation, anatomical and clinical information were less redundant; and (4) when the entity was composed of multiple subgroups that had some degree of anatomical representation (e.g., PPA), showing the potential of this method for the clustering of more homogeneous phenotypes that can be of clinical importance. Using the structure-based quantification and simple linear classifiers (partial least square), we achieve 87.5 and 73% of accuracy on differentiating AT and pre-symptomatic HD patents from controls, respectively. More importantly, the anatomical features automatically revealed by the classifiers agreed with the patterns previously described on these pathologies. The accuracy was lower (68%) on differentiating AD from controls, as AD does not display a clear anatomical phenotype. On the other hand, the method identified PPA clinical phenotypes and their respective anatomical signatures. Although most of the data are presented here as proof of concept in simulated clinical scenarios, structure-based analysis was potentially effective in characterizing phenotypes, retrieving relevant anatomical features, predicting prognosis, and aiding diagnosis, with the advantage of being easily translatable to clinics and understandable biologically.

Brain parcellation tools based on multiple-atlas algorithms have recently emerged as a promising method with which to accurately define brain structures. When dealing with data from various sources, it is crucial that these tools are robust for many different imaging protocols. In this study, we tested the robustness of a multiple-atlas, likelihood fusion algorithm using Alzheimer's Disease Neuroimaging Initiative (ADNI) data with six different protocols, comprising three manufacturers and two magnetic field strengths. The entire brain was parceled into five different levels of granularity. In each level, which defines a set of brain structures, ranging from eight to 286 regions, we evaluated the variability of brain volumes related to the protocol, age, and diagnosis (healthy or Alzheimer's disease). Our results indicated that, with proper pre-processing steps, the impact of different protocols is minor compared to biological effects, such as age and pathology. A precise knowledge of the sources of data variation enables sufficient statistical power and ensures the reliability of an anatomical analysis when using this automated brain parcellation tool on datasets from various imaging protocols, such as clinical databases.

Time delay of economic policy and memory property in a real economy system is omnipresent and inevitable. In this paper, a business cycle model with fractional-order time delay which describes the delay and memory property of economic control is investigated. Stochastic averaging method is applied to obtain the approximate analytical solution. Numerical simulations are done to verify the method. The effects of the fractional order, time delay, economic control and random excitation on the amplitude of the economy system are investigated. The results show that time delay, fractional order and intensity of random excitation can all magnify the amplitude and increase the volatility of the economy system.

The objective of this study was to conduct a systematic review of published literature on ammonia (NH3) and enteric methane (CH4) emissions from beef and dairy cattle operations to obtain statistically representative emission factors based on dietary intakes of nutrients or energy, and to identify major causes of emission variations. NH3 emissions from lagoon or other manure storage facilities were not included in this review. The NH3 and CH4 emission rates, expressed as a percentage losses of dietary nutrients or energy, demonstrated much less variation compared with emission rates expressed in g/animal/day. Air temperature and dietary crude protein (CP) content were identified as two major factors that can affect NH3 emission rates in addition to farm type. Feed digestibility and energy intake were identified as two major factors that can affect CH4 emission rates expressed as a percentage losses of dietary energy. Generally, increasing productivity and feed efficiency represented the greatest opportunity for mitigating NH3 or CH4 emissions per unit of livestock product. Expressing CH4 loss on a digestible energy basis rather than a gross energy intake basis can better represent the large variation among diets and the effects of varying dietary emission mitigation strategies.