PLoS ONE最新文献

The Yangtze River Delta (YRD) is an important engine of national economic development and a leading region in international competition. As economic exchanges and resource flows in the YRD region become closer, the inter-regional industrial linkages continue to grow, resulting in the formation of an industrial network structure characterized by a "complex network". The strength of the links between industrial sectors and the value and significance of the existence of industries in the network change over time, thus causing the overall evolution of the industrial network in the YRD region. Based on the input-output tables of the YRD region in 2012 and 2017, this paper uses the prediction index of network structure similarity to construct the prediction model of industrial network link between the YRD regions, and calculates the possibility of future links between industries in the Yangtze River Delta region through comparative analysis and selection of the RWR index of random walk similarity with the best effect, and concludes that: (1) the homogeneity of industries among provinces and cities in the Yangtze River Delta region is relatively high, resulting in homogeneous competition; (2) the overall nature of the industrial layout of the YRD is not prominent, and the depth and intensity of cross-regional industrial cooperation are lacking. On the basis of analysis and research, the countermeasures and suggestions for effectively realizing industrial integration are put forward from the macro level of the government and the meso level of the industry, so as to achieve a more complete industrial network in the YRD region and a more extensive length and width of the cross-regional industrial chain.

Ruminant livestock are major contributors to anthropogenic methane emissions in the United States and worldwide. Enteric methane is generated by methanogenic archaea residing in ruminant digestive tracts. Information on when methanogens colonize the gut and when they begin to interact with bacteria during the early phases of the ruminant life cycle is less explored. The objectives of this study were (i) to investigate the composition of the methanogenic archaeal community at birth and through the weaning transition and (ii) to determine if and when the methanogenic archaea begin to interact with bacteria in the lower gut of neonatal dairy calves. Ten female Holstein calves (approximately 45kg birth weight) were enrolled in the study. Fecal samples were collected every two weeks (Wk 2, 4, 6, 8, 10, and 12) between birth and weaning and analyzed for methanogenic archaeal diversity via 16S rRNA amplicon sequencing and quantitative real-time PCR (RT-qPCR). Estimates of alpha diversity (Observed species, and Shannon diversity index) and beta diversity (weighted and unweighted UniFrac distances) showed significant differences (P < 0.05) between archaeal communities across timepoints. Both 16S rRNA amplicon sequencing and RT-qPCR analyses revealed Methanobrevibacter was the most prevalent genus at Wk2, Wk4, and Wk6, whereas Methanosphaera gradually increased with time and was most abundant at Wk10 and Wk12. Correlation analysis revealed that Methanobrevibacter and Methanosphaera were inversely correlated with each other and formed distinct cohorts with specific bacterial lineages similar to those reported in the mature rumen, thus revealing that these associations are established during the preweaning period. Therefore, the preweaning period presents a window of opportunity to interfere with early-life methanogenic colonization with the ultimate goal of reducing enteric methane emissions without perturbing ruminal function later in the life of dairy cattle.

This study presents a novel Buyback Rail Freight Option (BRFO), leveraging Stackelberg game theory to enhance the strategic management of rail freight transactions. By integrating traditional buyback theory with a multi-phase trigeminal tree pricing model and parameter identification through a nonparametric Ito stochastic method, the research addresses key challenges of information asymmetry and market uncertainty. The proposed methodology emphasizes dynamic pricing strategies and market adaptation, constructing a Nash equilibrium framework within railway freight pricing. The findings suggest significant strategic benefits for railway enterprises, positioning BRFO as a crucial tool for improving competitiveness in the face of alternative transport options.

Aim: Utilizing a combination of micro-computed tomography (micro-CT) and anatomical techniques for the volumetric assessment of the eyeball and its constituents in Bama Miniature Pigs, New Zealand rabbits, and Sprague-Dawley(SD) rats.

Method: Six Bama Miniature pigs, New Zealand rabbits, and SD rats were enrolled in the study. Micro-CT and gross volumetric estimation of ocular volume were employed to acquire data on ocular volume, anterior chamber volume, lens volume, and vitreous cavity volume for each eye.

Results: The eyeball volume of pigs ranges from approximately 5.36 ± 0.27 to 5.55 ± 0.28 ml, the lens volume from approximately 0.33 ± 0.02 to 0.37 ± 0.06 ml, the anterior chamber volume from approximately 0.19 ± 0.05 to 0.28 ± 0.04 ml, and the vitreous volume is approximately 3.20 ± 0.18 ml. For rabbits, the eye volume, lens volume, anterior chamber volume, and vitreous volume range from approximately 3.02 ± 0.24 to 3.04 ± 0.24 ml, 0.41 ± 0.02 to 0.44 ± 0.02 ml, 0.23 ± 0.04 to 0.26 ± 0.05 ml, and 1.54 ± 0.14 ml, respectively. In SD rats, the volumes are 0.14 ± 0.02 to 0.15 ± 0.01 ml for the eyeball, 0.03 ± 0.00 to 0.03 ± 0.00 ml for the lens, 0.01 ± 0.00 to 0.01 ± 0.01 ml for the anterior chamber, and 0.04 ± 0.01 ml for the vitreous volume.

Conclusion: The integration of micro-CT and gross volumetric estimation of ocular volume proves effective in determining the eyeball volume in Bama Miniature Pigs, New Zealand rabbits, and SD rats. Understanding the volume distinctions within the eyeballs and their components among these experimental animals can lay the groundwork for ophthalmology-related drug research.

Introduction: Emerging tick-borne viruses of medical and veterinary importance are increasingly being reported globally. This resurgence emphasizes the need for sustained surveillance to provide insights into tick-borne viral diversity and associated potential public health risks. We report on a virus tentatively designated Kinna virus (KIV) in the family Phenuiviridae and genus Bandavirus. The virus was isolated from a pool of Amblyomma gemma ticks from Kinna in Isiolo County, Kenya. High throughput sequencing of the virus isolate revealed close relatedness to the Guertu virus. The virus genome is consistent with the described genomes of other members of the genus Bandavirus, with nucleotides lengths of 6403, 3332 and 1752 in the Large (L), Medium (M) and Small (S) segments respectively. Phylogenetic analysis showed that the virus clustered with Guertu virus although it formed a distinct and well supported branch. The RdRp amino acid sequence had a 93.3% identity to that of Guertu virus, an indication that the virus is possibly novel. Neutralizing antibodies were detected in 125 (38.6%, 95% CI 33.3-44.1%) of the human sera from the communities in this region. In vivo experiments showed that the virus was lethal to mice with death occurring 6-9 days post-infection. The virus infected mammalian cells (Vero cells) but had reduced infectivity in the mosquito cell line (C636) tested.

Conclusion: Isolation of this novel virus with the potential to cause disease in human and animal populations necessitates the need to evaluate its public health significance and contribution to disease burden in the affected regions. This also points to the need for continuous monitoring of vector and human populations in high-risk ecosystems to update pathogen diversity.

Large language models such as ChatGPT have been shown to excel in solving complex math problems. However, they cannot solve basic arithmetic problems such as 758*639 = 484,362. This makes us ponder if LLMs have been trained to solve math and science problems in the right way. When a student learns math at school, she or he starts with arithmetic, then moves to word problems, polynomials, and calculus. Each skill she or he acquires will be used in the next stage to solve more advanced problems. In this paper we propose Scaffolding Learning for LLMs, which imitates how a student learns a subject in a step-by-step manner. For example, we first train an LLM to perform highly specific operations such as multiplication and division, and then apply such "skills" in a more generic task such as solving word problems. This is related to Curriculum Training, which trains a model on tasks following a specific order, such as training on easy tasks first and then gradually increases the difficulty. Our proposed approach goes from specific tasks to generic ones, which can be considered as a special case of Curriculum Training. Our empirical studies show that when an LLM has "mastered" a specific skill, only a small amount of training is required to teach it to apply the skill to a more generic application.

Background: In-hospital cardiac arrest (IHCA) still has a poor prognosis despite medical advancements in recent decades. Early and high-quality cardiopulmonary resuscitation (CPR), as well as good teamwork, are important prognostic factors. There are no clear guidelines regarding the composition of a dedicated hospital CPR team. We compared outcomes of IHCA treated by a dedicated hospital CPR team compared to ward medical staff with advanced cardiac life support (ACLS) training.

Methods: A single-center retrospective observational study based on the cardiopulmonary resuscitation database of Soroka University Medical Center from January 2016 until December 2019. We compared the results of resuscitations conducted by regular ward medical staff, certified in ACLS, versus those conducted by the dedicated hospital's CPR team.

Results: Of the 360 CPR events analyzed, 141 (39.1%) ended in return of spontaneous circulation, 70 (19.4%) patients were alive after 24 hours, 23 (6.4%) survived for 30 days, and 18 (5%) survived to discharge. Of those who survived to discharge, 11 (61.1%) had a cerebral performance category (CPC) score of 1-2, and 7 (38.9%) had a score of 3-4 (mean 2.09). Survival-to-discharge was significantly higher in the CPR-team group compared to the ward-team group (7.6% vs. 1.9%, p = 0.013). However, with propensity score analysis the difference in survival became insignificant (RR = 1.97, 95% CI: 0.40-9.63, p = 0.40).

Conclusion: We found no difference in survival between IHCA treated by a dedicated hospital CPR team compared to a standard ward team, both trained with biennial ACLS training. Nevertheless, crude survival-to-discharge was significantly higher in the CPR-team group.

A promising approach for scalable Gaussian processes (GPs) is the Karhunen-Loève (KL) decomposition, in which the GP kernel is represented by a set of basis functions which are the eigenfunctions of the kernel operator. Such decomposed kernels have the potential to be very fast, and do not depend on the selection of a reduced set of inducing points. However KL decompositions lead to high dimensionality, and variable selection thus becomes paramount. This paper reports a new method of forward variable selection, enabled by the ordered nature of the basis functions in the KL expansion of the Bayesian Smoothing Spline ANOVA kernel (BSS-ANOVA), coupled with fast Gibbs sampling in a fully Bayesian approach. It quickly and effectively limits the number of terms, yielding a method with competitive accuracies, training and inference times for tabular datasets of low feature set dimensionality. Theoretical computational complexities are [Formula: see text] in training and [Formula: see text] per point in inference, where N is the number of instances and P the number of expansion terms. The inference speed and accuracy makes the method especially useful for dynamic systems identification, by modeling the dynamics in the tangent space as a static problem, then integrating the learned dynamics using a high-order scheme. The methods are demonstrated on two dynamic datasets: a 'Susceptible, Infected, Recovered' (SIR) toy problem, along with the experimental 'Cascaded Tanks' benchmark dataset. Comparisons on the static prediction of time derivatives are made with a random forest (RF), a residual neural network (ResNet), and the Orthogonal Additive Kernel (OAK) inducing points scalable GP, while for the timeseries prediction comparisons are made with LSTM and GRU recurrent neural networks (RNNs) along with the SINDy package.

The prevalence and predictors of mortality following an ischemic stroke or intracerebral hemorrhage have not been well established among patients in Vietnam. 2885 consecutive diagnosed patients with ischemic stroke and intracerebral hemorrhage at ten stroke centres across Vietnam were involved in this prospective study. Posthoc analyses were performed in 2209 subjects (age was 65.4 ± 13.7 years, with 61.4% being male) to explore the clinical characteristics and prognostic factors associated with 90-day mortality following treatment. An explainable machine learning model using extreme gradient boosting and SHapley Additive exPlanations revealed the correlation between original clinical research and advanced machine learning methods in stroke care. In the 90 days following treatment, the mortality rate for ischemic stroke was 8.2%, while for intracerebral hemorrhage, it was higher at 20.5%. Atrial fibrillation was an elevated risk of 90-day mortality in the ischemic stroke patient (OR 3.09; 95% CI 1.90-5.02, p<0.001). Among patients with intracerebral hemorrhage, there was no statistical significance in those with hypertension compared to their counterparts without hypertension (OR 0.65, 95% CI 0.41-1.03, p > 0.05). The baseline NIHSS score was a significant predictor of 90-day mortality in both patient groups. The machine learning model can predict a 0.91 accuracy prediction of death rate after 90 days. Age and NIHSS score were in the top high risks with other features, such as consciousness, heart rate, and white blood cells. Stroke severity, as measured by the NIHSS, was identified as a predictor of mortality at discharge and the 90-day mark in both patient groups.

Objective: The objective of this proposed scoping review is to identify and map the available evidence on interventions that aim to help informal caregivers identify and/or manage delirium in an older person at home.

Introduction: Delirium is a neurocognitive condition characterized by acute confusion and is associated with increased risk of morbidity and mortality. Research estimates delirium to be present in 17% of community-dwellers aged 85 and older, increasing proportionally with age to 45% in those aged 90 and older. Delirium often occurs at the onset of an older person's acute illness or exacerbation of a chronic illness (sometimes while at home) and, because of its protracted nature, usually continues after a hospital stay. Even when an older person's delirium resolves during hospitalization, they remain at risk of its recurrence after discharge home. Consequently, knowing how to detect and manage delirium is critical for informal caregivers of older people at home. However, there are no reviews focused exclusively on this topic in this setting.

Inclusion criteria: The population of interest includes informal caregivers of a person aged 65+. Concepts of interest include delirium detection and/or management interventions. The context of interest is any setting where informal care is delivered, including the transition from hospital to home, in any geographical area.

Materials and methods: The review will be conducted according to the JBI guidelines for scoping reviews. A three-step search strategy will be used to locate both published and unpublished papers in MEDLINE, Embase, CINAHL, PsycINFO, Web of Science Core Collection, ProQuest Nursing & Allied Health, SCOPUS, LILACS, and SciELO, PQD&T, NDLTD, Google Scholar and Google. No language restrictions will be placed on the review. Papers will be screened for eligibility at the title, abstract, and full text level by two independent reviewers. Data will be extracted by two independent reviewers and managed in Covidence. Any disagreements in screening or data extraction will be resolved by consensus or a third reviewer. Results will be summarised in narrative and tabular formats.