Selective deletion of the soluble Colony-Stimulating Factor 1 isoform in vivo prevents estrogen-deficiency bone loss in mice

Neutralizing CSF1 in vivo completely prevents ovariectomy （OVX）-induced bone loss in mice. There are two isoforms of CSF1, soluble （sCSF1）, and membrane-bound （mCSF1）, but their individual biological functions are unclear. It had been previously reported that mCSF1 knockout （K/O） and wild type （Wt） female mice experience the same degree of bone loss following OVX. In Wt mice the expression of sCSF1 was elevated fourfold in skeletal tissue following OVX while expression of mCSF1 was unchanged. To examine the role of sCSF1 in OVX-induced bone loss, mice were engineered in which sCSF1 was not expressed but expression of mCSF1 was unaffected （sCSF1 K/O）. Isoform-specific reverse transcription PCR confirmed the absence of transcripts for sCSF1 in bone tissue isolated from these animals and no circulating CSF1 was detected by ELISA. Surprisingly, there were no significant differences in bone mineral density （BMD） between sCSF1 K/O mice and Wt controls as assessed by dual-energy X-ray absorptiometry and micro-CT. However, one month after OVX, femoral, spinal and total BMD had declined by 11.2%, 8.9%, and 8.7% respectively in OVX-Wt animals as compared to Sham-OVX. In contrast OVX sCSF1 K/O mice showed changes of ＋0.1%, - 2.4%, and ＋2.3% at the same 3 sites compared to Sham-OVX sCSF1 K/O mice. These data indicate important non-redundant functions for the two isoforms of CSF1 and suggest that sCSF1, but not mCSF1, plays a key role in estrogen-deficiency bone loss.

Effect of montmorillonite on hydrate-based methane separation from mine gas

Three types of mine gas samples were used in the solutions of tetrahydrofuran(THF),sodium dodecyl sulfate(SDS)and THF-SDS with/without MMT respectively to investigate the effect of montmorillonite(MMT)on separation characteristics of methane recovered from mine gas based on hydrate method.The partition coefficient,separation factor and recovery rate were used to evaluate the effects of MMT,and the selection factor was primarily proposed to define the selectivity of mine gas hydrate in the relative target gases.The experimental results indicate that MMT could improve the following factors including hydration separation factor,the selection factor,the partition coefficient,and the recovery rate.Furthermore,the effect of SDS on the function of MMT is analyzed in the process of hydration separation.Finally,due to the results of the experiment,it is concluded that MMT hydration mechanism explores the effect of MMT enrichment methane from mine gas.

Numerical studies of atomic three-step photoionization processes with non-monochromatic laser fields

The atomic selective multi-step photoionization process is a critical step in laser isotope separation.In this work,we study three-step photoionization processes with non-monochromatic laser fields theoretically based on the semi-classical theory.Firstly,three bandwidth models,including the chaotic field model,de-correlation model,and phase diffusion model,are introduced into the density matrix equations.The numerical results are compared with each other comprehensively.The phase diffusion model is selected for further simulations in terms of the correspondence degree to physical practice.Subsequently,numerical calculations are carried out to identify the influences of systematic parameters,including laser parameters(Rabi frequency,bandwidth,relative time delay,frequency detuning)and atomic Doppler broadening,on photoionization processes.In order to determine the optimal match among different systematic parameters,the ionization yield of resonant isotope,and selectivity factor are adopted as evaluation indexes to guide the design and optimization process.The results in this work can provide a rewarding reference for laser isotope separation.

Multi-Domain Collaborative Recommendation with Feature Selection

Collaborative f iltering, as one of the most popular techniques, plays an important role in recommendation systems. However,when the user-item rating matrix is sparse,its performance will be degenerate. Recently,domain-specific recommendation approaches have been developed to address this problem.The basic idea is to partition the users and items into overlapping domains, and then perform recommendation in each domain independently. Here, a domain means a group of users having similar preference to a group of products. However, these domain-specific methods consisting of two sequential steps ignore the mutual benefi t of domain segmentation and recommendation. Hence, a unified framework is presented to simultaneously realize recommendation and make use of the domain information underlying the rating matrix in this paper. Based on matrix factorization,the proposed model learns both user preferences of multiple domains and preference selection vectors to select relevant features for each group of products. Besides, local context information is utilized from the user-item rating matrix to enhance the new framework.Experimental results on two widely used datasets, e.g., Ciao and Epinions, demonstrate the effectiveness of our proposed model.

Nest Site Selection of Loggerhead and Leatherback Sea Turtles at the Eastern Coast of Inhaca Island, Mozambique

Nest site selection represents an important reproductive strategy for sea turtles as it can strongly affect the development and survival of the offsprings and the reproductive fitness of the adults. In this study we analyzed the nest site selection of loggerhead and leatherback turtles and the factors correlated with that selection at Inhaca Island. The spatial position of the nests was recorded during sea turtles nests monitoring and six nesting seasons were used. Satellite images of Inhaca were used to characterize some of the beach features that might be correlated with nest site selection. Nest distribution analysis revealed that loggerhead turtles tend to spread their nests along the entire beach, but a higher aggregation of nests was evident at the northern most section of the eastern coast. Leatherback turtles tend to nest in a restricted area, with approximately 40% nests at the central sections of the coast. Beach height was the physical variable significantly correlated to loggerhead＇s nest density （r = 0.309, N = 125, P 〈 0.01） while fin grain particles （specifically 0.125 mm grain size particles） （r = 0.399, N = 125, P 〈 0.01） and organic content （r = 0.218, N = 125, P 〈 0.05） were the variables significantly correlated to leatherback＇s nest density. No other factors investigated affected nest site selection of either species.

Experimental study of population density using an optimized random forest model

Random forest model is the mainstream research method used to accurately describe the distribution law and impact mechanism of regional population.We took Shijiazhuang as the research area,with comprehensive zoning based on endowments as the modeling unit,conducted stratified sampling on a hectare grid cell,and systematically carried out incremental selection experiments of population density impact factors,optimizing the population density random forest model throughout the process(zonal modeling,stratified sampling,factor selection,weighted output).The results are as follows:(1)Zonal modeling addresses the issue of confusion in population distribution laws caused by a single model.Sampling on a grid cell not only ensures the quality of training data by avoiding the modifiable areal unit problem(MAUP)but also attempts to mitigate the adverse effects of the ecological fallacy.Stratified sampling ensures the stability of population density label values(target variable)in the training sample.(2)Zonal selection experiments on population density impact factors help identify suitable combinations of factors,leading to a significant improvement in the goodness of fit(R^(2))of the zonal models.(3)Weighted combination output of the population density prediction dataset substantially enhances the model's robustness.(4)The population density dataset exhibits multi-scale superposition characteristics.On a large scale,the population density in plains is higher than that in mountainous areas,while on a small scale,urban areas have higher density compared to rural areas.The optimization scheme for the population density random forest model that we propose offers a unified technical framework for uncovering local population distribution law and the impact mechanisms.