Needle δ13C and mobile carbohydrates in Pinus koraiensis in relation to decreased temperature and increased moisture along an elevational gradient in NE China

A tree's crown interacts with atmospheric variables such as CO2, temperature, and humidity. Physioecology of leaves/needles (e.g. δ13C, mobile carbohydrates, and nitrogen) is, therefore, strongly affected by microclimate in and surrounding a tree crown. To understand the physiological responses of leaves to changes in air temperature and moisture, we measured δ13C, soluble sugars, starch, and total nitrogen (N) concentrations in current year and 1-yr-old needles of Pinus koraiensis trees, and compared the growing season air temperature and relative humidity within and outside P. koraiensis crowns along an elevational gradient from 760 to 1,420ma.s.l. on Changbai Mountain, NE China. Our results indicated that needle N and mobile carbohydrates concentrations, as well as needle δ13C values changed continuously with increasing elevation, corresponding to a continuous decrease in air temperature and an increase in relative humidity. Needle carbon and nitrogen status is highly significantly negatively correlated with temperature, but positively correlated with relative humidity. These results indicate that increases in air temperature in combination with decreases in relative humidity may result in lower levels of N and mobile carbohydrates in P. koraiensis trees, suggesting that future climate changes such as global warming and changes in precipitation patterns will directly influence the N and carbon physiology at P. koraiensis individual level, and indirectly affect the competitive ability, species composition, productivity and functioning at the stand and ecosystem level in NE China. Due to the relatively limited range of the transect (760-1,420m) studied, further research is needed to explain whether the present results are applicable to scales across large elevational gradient

Room-Temperature High-Performance H2S Sensor Based on Porous CuO Nanosheets Prepared by Hydrothermal Method

Porous CuO nanosheets were prepared on alumina tubes using a facile hydrothermal method, and their morphology, microstructure, and gas-sensing properties were investigated. The monoclinic CuO nanosheets had an average thickness of 62.5 nm and were embedded with numerous holes with diameters ranging from 5 to 17 nm. The porous CuO nanosheets were used to fabricate gas sensors to detect hydrogen sulfide (H2S) operating at room temperature. The sensor showed a good response sensitivity of 1.25 with respond/recovery times of 234 and 76 s, respectively, when tested with the H2S concentrations as low as 10 ppb. It also showed a remarkably high selectivity to the H2S, but only minor responses to other gases such as SO2, NO, NO2, H2, CO, and C2H5OH. The working principle of the porous CuO nanosheet based sensor to detect the H2S was identified to be the phase transition from semiconducting CuO to a metallic conducting CuS

SDSS-IV MaNGA:environmental dependence of stellar age and metallicity gradients in nearby galaxies

We present a study on the stellar age and metallicity distributions for 1105 galaxies using the STARLIGHT software on MaNGA integral field spectra. We derive age and metallicity gradients by fitting straight lines to the radial profiles, and explore their correlations with total stellar mass M*, NUV-r colour and environments, as identified by both the large scale structure (LSS) type and the local density. We find that the mean age and metallicity gradients are close to zero but slightly negative, which is consistent with the inside-out formation scenario. Within our sample, we find that both the age and metallicity gradients show weak or no correlation with either the LSS type or local density environment. In addition, we also study the environmental dependence of age and metallicity values at the effective radii. The age and metallicity values are highly correlated with M* and NUV-r and are also dependent on LSS type as well as local density. Low-mass galaxies tend to be younger and have lower metallicity in low-density environments while high-mass galaxies are less affected by environment.Comment: 18 pages, 24 figures, accepted for publication in MNRA

Statistical‐based approach for driving style recognition using Bayesian probability with kernel density estimation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166283/1/itr2bf00581.pd

A multi-layer refined network model for the identification of essential proteins

The identification of essential proteins in protein-protein interaction networks (PINs) can help to discover drug targets and prevent disease. In order to improve the accuracy of the identification of essential proteins, researchers attempted to obtain a refined PIN by combining multiple biological information to filter out some unreliable interactions in the PIN. Unfortunately, such approaches drastically reduce the number of nodes in the PIN after multiple refinements and result in a sparser PIN. It makes a considerable portion of essential proteins unidentifiable. In this paper, we propose a multi-layer refined network (MR-PIN) that addresses this problem. Firstly, four refined networks are constructed by respectively integrating different biological information into the static PIN to form a multi-layer heterogeneous network. Then scores of proteins in each network layer are calculated by the existing node ranking method, and the importance score of a protein in the MR-PIN is evaluated in terms of the geometric mean of its scores in all layers. Finally, all nodes are sorted by their importance scores to determine their essentiality. To evaluate the effectiveness of the multi-layer refined network model, we apply 16 node ranking methods on the MR-PIN, and compare the results with those on the SPIN, DPIN and RDPIN. Then the predictive performances of these ranking methods are validated in terms of the identification number of essential protein at top100 - top600, sensitivity, specificity, positive predictive value, negative predictive value, F-measure, accuracy, Jackknife, ROCAUC and PRAUC. The experimental results show that the MR-PIN is superior to the existing refined PINs in the identification accuracy of essential proteins

Safety and Effectiveness of Using Disposable Ultrasonic Shears to Coagulate 5–7 mm Blood Vessels: Protocol for a Prospective, Multicenter, Randomized, Parallel Controlled, Non-Inferiority Clinical Trial

BACKGROUND: The ultrasonic scalpel is widely used during surgery. It is safe and effective to close the pulmonary artery branch vessels of 7 mm or below with an ultrasonic energy device as reported. However, there have been no multicenter randomized clinical trial to assess the safety and effectiveness of using ultrasonic scalpel to coagulate 5-7 mm blood vessels in thoracic surgery. METHODS: This is a prospective, multicenter, randomized, parallel controlled, non-inferiority clinical trial. A total of 144 eligible patients planning to undergo lung or esophageal surgery will be randomly allocated to the experimental group and the control group. The investigational product (Disposable Ultrasonic Shears manufactured by Reach Surgical, Inc.) and the control product (Harmonic Ace + 7, 5 mm Diameter Shears with Advanced Hemostasis) will be used in each group. The primary endpoint is the success rate of coagulating target blood vessels during surgery. Secondary endpoints include postoperative rebleeding, intraoperative bleeding volume, drainage volume, surgical duration, etc. Postoperative follow-up before and after discharge will be performed. DISCUSSION: This clinical trial aims to evaluate the safety and effectiveness of using the investigational product (Disposable Ultrasonic Shears manufactured by Reach Surgical, Inc.) and that of the control product (Harmonic Ace + 7, 5 mm Diameter Shears with Advanced Hemostasis) to coagulate 5-7 mm blood vessels in thoracic surgery. TRIAL REGISTRATION: ClinicalTrials.gov: NCT06002737. The trial was prospectively registered on 16 August 2023, https://www. CLINICALTRIALS: gov/study/NCT06002737

EqCo: Equivalent Rules for Self-supervised Contrastive Learning

In this paper, we propose a method, named EqCo (Equivalent Rules for Contrastive Learning), to make self-supervised learning irrelevant to the number of negative samples in InfoNCE-based contrastive learning frameworks. Inspired by the InfoMax principle, we point that the margin term in contrastive loss needs to be adaptively scaled according to the number of negative pairs in order to keep steady mutual information bound and gradient magnitude. EqCo bridges the performance gap among a wide range of negative sample sizes, so that we can use only a few negative pairs (e.g. 16 per query) to perform self-supervised contrastive training on large-scale vision datasets like ImageNet, while with almost no accuracy drop. This is quite a contrast to the widely used large batch training or memory bank mechanism in current practices. Equipped with EqCo, our simplified MoCo (SiMo) achieves comparable accuracy with MoCo v2 on ImageNet (linear evaluation protocol) while only involves 4 negative pairs per query instead of 65536, suggesting that large quantities of negative samples might not be a critical factor in InfoNCE loss