Health care professionals at risk of infection with Borna disease virus - evidence from a large hospital in China (Chongqing).

BACKGROUND: Human Borna disease virus (BDV) infections have recently been reported in China. BDV causes cognitive and behavioural disturbances in animals. The impact on human mental disorders is subject to debate, but previous studies worldwide have found neuropsychiatric patients more frequently infected than healthy controls. A few isolates were recovered from severely depressed patients, but contagiousness of BDV strain remains unknown. METHOD: We addressed the risk of infection in health care settings at the first affiliated hospital of Chongqing Medical University (CQMU), located in downtown Chongqing, a megacity in Southwest China. Between February 2012 and March 2013, we enrolled 1529 participants, of whom 534 were outpatients with major depressive disorder (MDD), 615 were hospital personnel, and 380 were healthy controls who underwent a health check. Infection was determined through BDV-specific circulating immune complexes (CIC), RNA, and selective antibodies (blood). RESULTS: One-fifth of the hospital staff (21.8%) were found to be infected (CIC positive), with the highest prevalence among psychiatry and oncology personnel, which is twice as many as were detected in the healthy control group (11.1%), and exceeds the prevalence detected in MDD patients (18.2%). CONCLUSION: BDV circulates unnoticed in hospital settings in China, putting medical staff at risk and warranting clarification of infection modes and introduction of prevention measures.

GC-MS-Based Metabonomic Profiling Displayed Differing Effects of Borna Disease Virus Natural Strain Hu-H1 and Laboratory Strain V Infection in Rat Cortical Neurons.

Borna disease virus (BDV) persists in the central nervous systems of a wide variety of vertebrates and causes behavioral disorders. Previous studies have revealed that metabolic perturbations are associated with BDV infection. However, the pathophysiological effects of different viral strains remain largely unknown. Rat cortical neurons infected with human strain BDV Hu-H1, laboratory BDV Strain V, and non-infected control (CON) cells were cultured in vitro. At day 12 post-infection, a gas chromatography coupled with mass spectrometry (GC-MS) metabonomic approach was used to differentiate the metabonomic profiles of 35 independent intracellular samples from Hu-H1-infected cells (n = 12), Strain V-infected cells (n = 12), and CON cells (n = 11). Partial least squares discriminant analysis (PLS-DA) was performed to demonstrate discrimination between the three groups. Further statistical testing determined which individual metabolites displayed significant differences between groups. PLS-DA demonstrated that the whole metabolic pattern enabled statistical discrimination between groups. We identified 31 differential metabolites in the Hu-H1 and CON groups (21 decreased and 10 increased in Hu-H1 relative to CON), 35 differential metabolites in the Strain V and CON groups (30 decreased and 5 increased in Strain V relative to CON), and 21 differential metabolites in the Hu-H1 and Strain V groups (8 decreased and 13 increased in Hu-H1 relative to Strain V). Comparative metabonomic profiling revealed divergent perturbations in key energy and amino acid metabolites between natural strain Hu-H1 and laboratory Strain V of BDV. The two BDV strains differentially alter metabolic pathways of rat cortical neurons in vitro. Their systematic classification provides a valuable template for improved BDV strain definition in future studies.

Real-time qPCR identifies suitable reference genes for Borna disease virus-infected rat cortical neurons.

Quantitative real-time reverse transcription polymerase chain reaction (RT-qPCR) is the most commonly-used technique to identify gene expression profiles. The selection of stably expressed reference genes is a prerequisite to properly evaluating gene expression. Here, the suitability of commonly-used reference genes in normalizing RT-qPCR assays of mRNA expression in cultured rat cortical neurons infected with Borna disease virus (BDV) was assessed. The expressions of eight commonly-used reference genes were comparatively analyzed in BDV-infected rat cortical neurons and non-infected control neurons mainly across 9 and 12 days post-infection. These reference genes were validated by RT-qPCR and separately ranked by four statistical algorithms: geNorm, NormFinder, BestKeeper and the comparative delta-Ct method. Then, the RankAggreg package was used to construct consensus rankings. ARBP was found to be the most stable internal control gene at Day 9, and ACTB at Day 12. As the assessment of the validity of the selected reference genes confirms the suitability of applying a combination of the two most stable references genes, combining the two most stable genes for normalization of RT-qPCR studies in BDV-infected rat cortical neurons is recommended at each time point. This study can contribute to improving BDV research by providing the means by which to obtain more reliable and accurate gene expression measurements.

A fast ceramic mixed OH-/H+ ionic conductor for low temperature fuel cells.

Low temperature ionic conducting materials such as OH- and H+ ionic conductors are important electrolytes for electrochemical devices. Here we show the discovery of mixed OH-/H+ conduction in ceramic materials. SrZr0.8Y0.2O3-δ exhibits a high ionic conductivity of approximately 0.01 S cm-1 at 90 °C in both water and wet air, which has been demonstrated by direct ammonia fuel cells. Neutron diffraction confirms the presence of OD bonds in the lattice of deuterated SrZr0.8Y0.2O3-δ. The OH- ionic conduction of CaZr0.8Y0.2O3-δ in water was demonstrated by electrolysis of both H218O and D2O. The ionic conductivity of CaZr0.8Y0.2O3-δ in 6 M KOH solution is around 0.1 S cm-1 at 90 °C, 100 times higher than that in pure water, indicating increased OH- ionic conductivity with a higher concentration of feed OH- ions. Density functional theory calculations suggest the diffusion of OH- ions relies on oxygen vacancies and temporarily formed hydrogen bonds. This opens a window to discovering new ceramic ionic conducting materials for near ambient temperature fuel cells, electrolysers and other electrochemical devices.

Real-time Spread Burst Detection in Data Streaming.

Data streaming has many applications in network monitoring, web services, e-commerce, stock trading, social networks, and distributed sensing. This paper introduces a new problem of real-time burst detection in flow spread, which differs from the traditional problem of burst detection in flow size. It is practically significant with potential applications in cybersecurity, network engineering, and trend identification on the Internet. It is a challenging problem because estimating flow spread requires us to remember all past data items and detecting bursts in real time requires us to minimize spread estimation overhead, which was not the priority in most prior work. This paper provides the first efficient, real-time solution for spread burst detection. It is designed based on a new real-time super spreader identifier, which outperforms the state of the art in terms of both accuracy and processing overhead. The super spreader identifier is in turn based on a new sketch design for real-time spread estimation, which outperforms the best existing sketches.

Effects of plant growth regulators and sucrose on proliferation and quality of embryogenic tissue in Picea pungens.

Embryogenic tissue (ET) is important for genetic modification and plant re-generation. The proliferation ability and vigor of ET are crucial for plant propagation via somatic embryogenesis. In this study, ET was induced from mature zygotic embryos in blue spruce (Picea pungens Engelm.). There were significant differences in ET induction between two provenances, i.e. 78.8 ± 12.5% and 62.50 ± 12.8% respectively. Effects of 2,4-Dichlorophenoxy acetic acid (2,4-D), 6-Benzyl amino-purine (6-BA) and/or sucrose on ET proliferation and somatic embryo (SE) maturation were further investigated with four cell lines. The highest ET proliferation rate reached 1473.7 ± 556.0% biweekly. Concentrations of 2,4-D or 6-BA applied at tissue proliferation stage impacted SE maturation among the cell lines, whereas sucrose showed less effects. The highest rate, 408 ± 230 mature SEs/g FW, was achieved in SE maturation cultures. This research demonstrated that the culture conditions, i.e. the specific concentrations of 2,4-D and BA, at ET proliferation stage affected not only ET growth, but also the quality of ET for SE maturation. This study revealed the necessity and benefit in developing both the general and the genotype-specific protocols for efficient production of mature SEs, or somatic plants in blue spruce.

A Hierarchical Spatial Transformer for Massive Point Samples in Continuous Space.

Transformers are widely used deep learning architectures. Existing transformers are mostly designed for sequences (texts or time series), images or videos, and graphs. This paper proposes a novel transformer model for massive (up to a million) point samples in continuous space. Such data are ubiquitous in environment sciences (e.g., sensor observations), numerical simulations (e.g., particle-laden flow, astrophysics), and location-based services (e.g., POIs and trajectories). However, designing a transformer for massive spatial points is non-trivial due to several challenges, including implicit long-range and multi-scale dependency on irregular points in continuous space, a non-uniform point distribution, the potential high computational costs of calculating all-pair attention across massive points, and the risks of over-confident predictions due to varying point density. To address these challenges, we propose a new hierarchical spatial transformer model, which includes multi-resolution representation learning within a quad-tree hierarchy and efficient spatial attention via coarse approximation. We also design an uncertainty quantification branch to estimate prediction confidence related to input feature noise and point sparsity. We provide a theoretical analysis of computational time complexity and memory costs. Extensive experiments on both real-world and synthetic datasets show that our method outperforms multiple baselines in prediction accuracy and our model can scale up to one million points on one NVIDIA A100 GPU. The code is available at https://github.com/spatialdatasciencegroup/HST.

Field investigation combined with modeling uncovers the ecological heterogeneity of Aedes albopictus habitats for strategically improving systematic management during urbanization.

BACKGROUND: Aedes albopictus is an invasive vector of serious Aedes-borne diseases of global concern. Habitat management remains a critical factor for establishing a cost-effective systematic strategy for sustainable vector control. However, the community-based characteristics of Ae. albopictus habitats in complex urbanization ecosystems are still not well understood. METHODS: A large-scale investigation of aquatic habitats, involving 12 sites selected as representative of four land use categories at three urbanization levels, was performed in Guangzhou, China during 2015-2017. The characteristics and dynamics of these Ae. albopictus habitats were assessed using habitat-type composition, habitat preference, diversity indexes and the Route index (RI), and the temporal patterns of these indexes were evaluated by locally weighted scatterplot smoothing models. The associations of RI with urbanization levels, land use categories and climatic variables were inferred using generalized additive mixed models. RESULTS: A total of 1994 potential habitats and 474 Ae. albopictus-positive habitats were inspected. The majority of these habitats were container-type habitats, with Ae. albopictus showing a particularly higher habitat preference for plastic containers, metal containers and ceramic vessels. Unexpectedly, some non-container-type habitats, especially ornamental ponds and surface water, were found to have fairly high Ae. albopictus positivity rates. Regarding habitats, the land use category residential and rural in Jiangpu (Conghua District, Guangzhou) had the highest number of Ae. albopictus habitats with the highest positive rates. The type diversity of total habitats (H-total) showed a quick increase from February to April and peaked in April, while the H-total of positive habitats (H-positive) and RIs peaked in May. RIs mainly increased with the monthly average daily mean temperature and monthly cumulative rainfall. We also observed the accumulation of diapause eggs in the winter and diapause termination in the following March. CONCLUSIONS: Ecological heterogeneity of habitat preferences of Ae. albopictus was demonstrated in four land use categories at three urbanization levels. The results reveal diversified habitat-type compositions and significant seasonal variations, indicating an ongoing adaptation of Ae. albopictus to the urbanization ecosystem. H-positivity and RIs were inferred as affected by climatic variables and diapause behavior of Ae. albopictus, suggesting that an effective control of overwintering diapause eggs is crucial. Our findings lay a foundation for establishing a stratified systematic management strategy of Ae. albopictus habitats in cities that is expected to complement and improve community-based interventions and sustainable vector management.

Training Medical-Diagnosis Neural Networks on the Cloud with Privacy-Sensitive Patient Data from Multiple Clients.

Artificial neural networks (ANNs) are changing the paradigm in medical diagnosis. However, it remains an open problem how to outsource the model training operations to the cloud while protecting the privacy of distributed patient data. Homomorphic encryption suffers from high overhead over data independently encrypted from numerous sources, differential privacy introduces a high level of noise which drastically increases the number of patient records needed to train a model, while federated learning requires all participants to perform synchronized local training that counters our goal of outsourcing all training operations to the cloud. This paper proposes to use matrix masking for outsourcing all model training operations to the cloud with privacy protection. After outsourcing their masked data to the cloud, the clients do not need to coordinate and perform any local training operations. The accuracy of the models trained by the cloud from the masked data is comparable to the accuracy of the optimal benchmark models that are trained directly from the original raw data. Our results are confirmed by experimental studies on privacy-preserving cloud training of medical-diagnosis neural network models based on real-world Alzheimer's disease data and Parkinson's disease data.

On Outsourcing Artificial Neural Network Learning of Privacy-Sensitive Medical Data to the Cloud.

Machine learning and artificial neural networks (ANNs) have been at the forefront of medical research in the last few years. It is well known that ANNs benefit from big data and the collection of the data is often decentralized, meaning that it is stored in different computer systems. There is a practical need to bring the distributed data together with the purpose of training a more accurate ANN. However, the privacy concern prevents medical institutes from sharing patient data freely. Federated learning and multi-party computation have been proposed to address this concern. However, they require the medical data collectors to participate in the deep-learning computations of the data users, which is inconvenient or even infeasible in practice. In this paper, we propose to use matrix masking for privacy protection of patient data. It allows the data collectors to outsource privacy-sensitive medical data to the cloud in a masked form, and allows the data users to outsource deep learning to the cloud as well, where the ANN models can be trained directly from the masked data. Our experimental results on deep-learning models for diagnosis of Alzheimer's disease and Parkinson's disease show that the diagnosis accuracy of the models trained from the masked data is similar to that of the models from the original patient data.

N,N-Dimethylacetamide-Diluted Nitrate Electrolyte for Aqueous Zn//LiMn2O4 Hybrid Ion Batteries.

N,N-Dimethylacetamide (DMA) cooperated with LiNO3 salt has previously shown to be a promising electrolyte for a Li//O2 battery, showing good stability against both the O2 electrode reaction and Li stripping/plating. In this work, DMA is hybridized with a concentrated nitrate electrolyte [2.5 m Zn(NO3)2 + 13 m LiNO3 aqueous solution] for better electrochemical stability while using less dissolved salts. The widest electrochemical stability window for this DMA-diluted electrolyte is determined as 3.1 V, the negative critical stability potential of which is -1.6 V versus Ag/AgCl, indicating desirable stability against hydrogen evolution and Zn deposition. The findings can be attributed to the weakened Li+/Zn2+ solvation sheath caused by low permittivity of DMA, as revealed through Raman spectra characterization and molecular dynamics simulation. A Zn//Zn symmetrical cell and Zn//LiMn2O4 hybrid ion batteries are assembled in air directly, attributed to the stability of DMA toward O2. Zn stripping/plating with a dendrite-free morphology is delivered for 110 h and 200 charge/discharge cycles under 1 C rate, achieving 99.0% Coulombic efficiency. The maximum capacity of the battery is 121.0 mA h·g-1 under 0.2 C rate (based on the mass of LiMn2O4), delivering an energy density of 165.8 W h·kg-1 together with 2.0 V working voltage. This work demonstrates the feasibility and validity of utilizing a relatively dilute electrolyte dissolved in oxygen for a highly stable aqueous rechargeable battery.

An Efficient Symmetric Electrolyzer Based On Bifunctional Perovskite Catalyst for Ammonia Electrolysis.

Ammonia is a natural pollutant in wastewater and removal technique such as ammonia electro-oxidation is of paramount importance. The development of highly efficient and low-costing electrocatalysts for the ammonia oxidation reaction (AOR) and hydrogen evolution reaction (HER) associated with ammonia removal is subsequently crucial. In this study, for the first time, the authors demonstrate that a perovskite oxide LaNi0.5 Cu0.5 O3-δ after being annealed in Ar (LNCO55-Ar), is an excellent non-noble bifunctional catalyst towards both AOR and HER, making it suitable as a symmetric ammonia electrolyser (SAE) in alkaline medium. In contrast, the LNCO55 sample fired in air (LNCO55-Air) is inactive towards AOR and shows very poor HER activity. Through combined experimental results and theoretical calculations, it is found that the superior AOR and HER activities are attributed to the increased active sites, the introduction of oxygen vacancies, the synergistic effect of B-site cations and the different active sites in LNCO55-Ar. At 1.23 V, the assembled SAE demonstrates ≈100% removal efficiency in 2210 ppm ammonia solution and >70% in real landfill leachate. This work opens the door for developments towards bifunctional catalysts, and also takes a profound step towards the development of low-costing and simple device configuration for ammonia electrolysers.

Somatic embryogenesis in mature zygotic embryos of Picea pungens.

This study developed somatic embryogenesis protocols for Picea pungens (Engelm), an important ornamental species, including initiation, proliferation, maturation, germination, and acclimation. Somatic embryogenic tissues were induced from mature zygotic embryos of five families, with a frequency of [Formula: see text] 22% for each. Embryogenic tissues (ET) from 13 clones of three families were proliferated for one week, achieving an average rate of 179.1%. The ET of 38 clones of three families were cultured in maturation medium for six weeks; 188 mature embryos on average were counted per gram ET cultured, of which [Formula: see text] 81.1% appeared normal, and each clone developed at least 28 normally matured embryos. A total of 69.9% or more of cotyledonary somatic embryos germinated normally and developed into normal emblings. The experiment of transplanting the emblings into a greenhouse had an average survival rate of 68.5%. Considerable variation among and within families during initiation and proliferation was observed, but this variation decreased in the maturation and germination. Changing the concentration of plant growth regulator of the initiation medium did not significantly change the initiation frequency. We recommend incorporating these protocols into the current Picea pungens practical programs, although further research is essential to increase efficiencies and reduce cost.

Threshold-Based Widespread Event Detection.

Widespread event detection is a fundamental network function that has many important applications in cybersecurity, traffic engineering, and distributed data mining. This paper introduces a new probabilistic threshold-based event detection problem, which is to find all events that appear in any w-out-of-a monitors with probabilistic guarantee on false positives, where a is the total number of monitors and the threshold w(≤ a) is a positive integer parameter that can be arbitrarily set, according to specific application requirements. We develop an efficient threshold filter solution and its improved versions, which combine Bloom filters, counting Bloom filter, threshold filter and compressed filters in a series of encoding and filtering steps, providing tradeoff between detection accuracy and communication overhead. We theoretically optimize the system parameters in the proposed solutions to minimize the communication overhead under the constraint of probabilistic detection guarantee. Extensive simulations demonstrate the practical viability of the proposed solutions in their ability of finding widespread events in a large network with few false positives and low communication overhead.

Investigation of Perovskite Oxide SrCo0.8 Cu0.1 Nb0.1 O3-δ as a Cathode Material for Room Temperature Direct Ammonia Fuel Cells.

Single-phase perovskite oxide SrCo0.8 Cu0.1 Nb0.1 O3-δ was synthesized using a Pechini method. X-ray diffraction (XRD) analysis indicated a cubic structure with a=3.8806(7) Å. The oxide material was combined with active carbon, forming a composite electrode to be used as the cathode in a room temperature ammonia fuel cell based on an anion membrane electrolyte and NiCu/C anode. An open circuit voltage (OCV) of 0.19 V was observed with dilute 0.02 m (340 ppm) ammonia solution as the fuel. The power density and OCV were improved upon the addition of 1 m NaOH to the fuel, suggesting that the addition of NaOH, which could be achieved through the introduction of alkaline waste to the fuel stream, could improve performance when wastewater is used as the fuel. It was found that the SrCo0.8 Cu0.1 Nb0.1 O3-δ cathode was converted from irregular shape into shuttle-shape during the fuel cell measurements. As the key catalysts for electrode materials for this fuel cell are all inexpensive, after further development, this could be a promising technology for removal of ammonia from wastewater.

Application of antibodies against Borna disease virus phosphoprotein and nucleoprotein on paraffin sections.

In order to study the application of antibodies against recombinant proteins for detecting Borna disease virus (BDV) phosphoprotein (p24) and nucleoprotein (p40) (BDV­p24/p40) on paraffin sections by immunohistochemistry. The purified fusion p24 and p40 proteins were used for the preparation of polyclonal and monoclonal anti­p24 and anti­40 antibodies, which were confirmed by ELISA and western blotting. Paraffin sections were made from BDV­infected Sprague­Dawley (SD) rats (n=20), PBS­injected SD rats (n=20), normal SD rats (n=20) and normal C57 mice (n=20). Immunohistochemical staining was performed according to the EnVision™ two­step protocol. Heat­mediated antigen retrieval was performed using the retrieval buffer sodium citrate (1 mM; pH 6.0). All the antibodies against recombinant proteins exhibited good sensitivity and specificity. There were significant differences between the BDV­infected group and the BDV­uninfected group for poly­ and monoclonal anti­p24 and ­p40 antibodies. These antibodies against recombinant proteins may be used effectively to detect BDV p24 and p40 in paraffin sections.

Different inhibitory effects on the proliferation and apoptosis of human and laboratory Borna disease virus­infected human neuroblastoma SH­SY5Y cells in vitro.

Borna disease virus (BDV) is a neurotropic and non­cytolytic virus, which causes behavioral disorders in a wide range of warm­blooded species. It is well established that BDV induces neurodegeneration by impairing neurogenesis and interfering with neuronal functioning in the limbic system. In the present study, the potential role of BDV infection in SH­SY5Y cells was identified, and comparisons of two original BDV strains (the human Hu­H1 and the laboratory Strain V) were performed to further elucidate the phenotypes of BDV pathogenesis with strain differences. Cell Counting Kit­8 and flow cytometric analyses revealed that the two BDV strain­infected groups exhibited marked anti­proliferation and cell cycle arrest compared with the control group, and the Hu­H1 strain caused more evident effects. However, the Hu­H1 strain did not exert effects on the apoptosis of SH­SH5Y cells, while Strain V led to a marked increase in apoptosis upon initial infection. Western blot analysis confirmed the upregulation of apoptosis regulator BAX protein and the downregulation of apoptosis regulator Bcl­2 protein caused by the two BDV strains. The results of the present study provided evidence that infection with BDV suppressed SH­SY5Y cellular functioning and exhibited divergent antiproliferative and apoptotic roles in cells between the two strains. The present study provided an insight for future investigation of strain differences and underlying pathomechanisms.

Achieving Strong Privacy in Online Survey.

Thanks to the proliferation of Internet access and modern digital and mobile devices, online survey has been flourishing into data collection of marketing, social, financial and medical studies. However, traditional data collection methods in online survey suffer from serious privacy issues. Existing privacy protection techniques are not adequate for online survey for lack of strong privacy. In this paper, we propose a practical strong privacy online survey scheme SPS based on a novel data collection technique called dual matrix masking (DM2), which guarantees the correctness of the tallying results with low computation overhead, and achieves universal verifiability, robustness and strong privacy. We also propose a more robust scheme RSPS, which incorporates multiple distributed survey managers. The RSPS scheme preserves the nice properties of SPS, and further achieves robust strong privacy against joint collusion attack. Through extensive analyses, we demonstrate our proposed schemes can be efficiently applied to online survey with accuracy and strong privacy.

Collusion Resistant Multi-Matrix Masking for Privacy-Preserving Data Collection.

An integral part of any social or medical research is the availability of reliable data. For the integrity of participants' responses, a secure environment for collecting sensitive data is required. This paper introduces a novel privacy-preserving data collection method: collusion resistant multi-matrix masking (CRM3). The CRM3 method requires multiple masking service providers (MSP), each generating its own random masking matrices. The key step is that each participant's data is randomly decomposed into the sum of component vectors, and each component vector is sent to the MSPs for masking in a different order. The CRM3 method publicly releases two sets of masked data: one being right multiplied by random invertible matrices and the other being left multiplied by random orthogonal matrices. Both MSPs and the released data may be hosted on cloud platforms. Our data collection and release procedure is designed so that MSPs and the data collector are not able to derive the original participants' data hence providing strong privacy protection. However, statistical inference on parameters of interest will produce exactly the same results from the masked data as from the original data, under commonly used statistical methods such as general linear model, contingency table analysis, logistic regression, and Cox proportional hazard regression.

Inhibition of corticotropin-releasing hormone receptor 1 and activation of receptor 2 protect against colonic injury and promote epithelium repair.

Maternal separation (MS) in neonates can lead to intestinal injury. MS in neonatal mice disrupts mucosal morphology, induces colonic inflammation and increases trans-cellular permeability. Several studies indicate that intestinal epithelial stem cells are capable of initiating gut repair in a variety of injury models but have not been reported in MS. The pathophysiology of MS-induced gut injury and subsequent repair remains unclear, but communication between the brain and gut contribute to MS-induced colonic injury. Corticotropin-releasing hormone (CRH) is one of the mediators involved in the brain-gut axis response to MS-induced damage. We investigated the roles of the CRH receptors, CRHR1 and CRHR2, in MS-induced intestinal injury and subsequent repair. To distinguish their specific roles in mucosal injury, we selectively blocked CRHR1 and CRHR2 with pharmacological antagonists. Our results show that in response to MS, CRHR1 mediates gut injury by promoting intestinal inflammation, increasing gut permeability, altering intestinal morphology, and modulating the intestinal microbiota. In contrast, CRHR2 activates intestinal stem cells and is important for gut repair. Thus, selectively blocking CRHR1 and promoting CRHR2 activity could prevent the development of intestinal injuries and enhance repair in the neonatal period when there is increased risk of intestinal injury such as necrotizing enterocolitis.