Med-Tuning: Exploring Parameter-Efficient Transfer Learning for Medical Volumetric Segmentation

Deep learning based medical volumetric segmentation methods either train the model from scratch or follow the standard "pre-training then finetuning" paradigm. Although finetuning a well pre-trained model on downstream tasks can harness its representation power, the standard full finetuning is costly in terms of computation and memory footprint. In this paper, we present the first study on parameter-efficient transfer learning for medical volumetric segmentation and propose a novel framework named Med-Tuning based on intra-stage feature enhancement and inter-stage feature interaction. Given a large-scale pre-trained model on 2D natural images, our method can exploit both the multi-scale spatial feature representations and temporal correlations along image slices, which are crucial for accurate medical volumetric segmentation. Extensive experiments on three benchmark datasets (including CT and MRI) show that our method can achieve better results than previous state-of-the-art parameter-efficient transfer learning methods and full finetuning for the segmentation task, with much less tuned parameter costs. Compared to full finetuning, our method reduces the finetuned model parameters by up to 4x, with even better segmentation performance

Evidence of spin density waves in La3_3Ni2_2O7−δ_{7-\delta}

The recently discovered superconductivity with critical temperature $T_{c}$ up to 80 K in the Ruddlesden-Popper phases La$_3$Ni$_2$O$_{7-\delta}$ under pressure has drawn great attention. Here we report the positive muon spin relaxation ($\mu^+$SR) study of polycrystalline La$_3$Ni$_2$O$_{7-\delta}$ under ambient pressure. The zero-field $\mu^+$SR experiments reveal the existence of static long range magnetic order in La$_3$Ni$_2$O$_{7-\delta}$, and the the muon spin depolarization spectra are consistent with the spin density wave internal field distribution. The weak transverse field $\mu^+$SR measurements suggest the bulk magnetic transition near $T_{\rm{N}}=148$ K. This is the first research which discovers the existence of the spin density wave in La$_3$Ni$_2$O$_{7-\delta}$ microscopically

Combining Dilution of Precision and Kalman Filtering for UWB Positioning in a Narrow Space

Affected by the spatial environment, the accuracy and stability of ultra-wideband (UWB) positioning in a narrow space are significantly lower than those in the general indoor environment, which limits navigation and positioning services in a complex scene. To improve the positioning accuracy and stability of a narrow space, this study proposed a positioning algorithm by combining Kalman filter (KF) and dilution of precision (DOP). Firstly, we calculated the DOP values of the target narrow space by changing the location of the test nodes throughout the space. Secondly, the initial coordinate values of the test nodes were calculated by the weighted least squares (WLS) positioning algorithm and were used as the observation values of KF. Finally, the DOP values were adaptively introduced into KF to update the coordinates of the nodes to be tested. The proposed algorithm was tested in two narrow scenes with different length–width ratios. The experimental results showed that the DOP values of the narrow space were much higher than that of the wide space. Furthermore, even if the ranging error was low, the positioning error was high in the narrow space. The proposed fusion positioning algorithm reported a higher positioning accuracy in the narrow space, and the higher DOP values of the scene, the greater the accuracy improvement of the algorithm. This study reveals that no matter how the base stations are configured, the DOP values of the narrow space are much higher than that of the wide space, thus causing larger positioning errors. The proposed positioning algorithm can effectively suppress the positioning error caused by the narrow spatial structure, so as to improve the positioning accuracy and stability

Predicting the State of Power of an Iron-Based Li-Ion Battery Pack Including the Constraint of Maximum Operating Temperature

To give full play to battery capability, the state of power (SoP) should be predicted in real time to inform the vehicle control unit (VCU) whether the upcoming driving scenarios of acceleration overtaking, ramp climbing, constant cruising and feedback braking can be sustained. In general, battery SoP conforms to prescribed constraints on voltage, current, and state of charge (SoC). Specifically, this paper takes the generally ignored operating temperature into consideration based on a differential temperature-changing model. Consequently, a SoP prediction method restricted by both electrical and thermal constraints was obtained. Experimental verifications on a Li-ion battery pack suggest that the proposed SoP prediction method can provide favorable reliability and rationality against diverse time durations, temperatures, and aging states in comparison with the instantaneous power obtained using the hybrid power pulse characteristic (HPPC) method

Combining Dilution of Precision and Kalman Filtering for UWB Positioning in a Narrow Space

Affected by the spatial environment, the accuracy and stability of ultra-wideband (UWB) positioning in a narrow space are significantly lower than those in the general indoor environment, which limits navigation and positioning services in a complex scene. To improve the positioning accuracy and stability of a narrow space, this study proposed a positioning algorithm by combining Kalman filter (KF) and dilution of precision (DOP). Firstly, we calculated the DOP values of the target narrow space by changing the location of the test nodes throughout the space. Secondly, the initial coordinate values of the test nodes were calculated by the weighted least squares (WLS) positioning algorithm and were used as the observation values of KF. Finally, the DOP values were adaptively introduced into KF to update the coordinates of the nodes to be tested. The proposed algorithm was tested in two narrow scenes with different length–width ratios. The experimental results showed that the DOP values of the narrow space were much higher than that of the wide space. Furthermore, even if the ranging error was low, the positioning error was high in the narrow space. The proposed fusion positioning algorithm reported a higher positioning accuracy in the narrow space, and the higher DOP values of the scene, the greater the accuracy improvement of the algorithm. This study reveals that no matter how the base stations are configured, the DOP values of the narrow space are much higher than that of the wide space, thus causing larger positioning errors. The proposed positioning algorithm can effectively suppress the positioning error caused by the narrow spatial structure, so as to improve the positioning accuracy and stability

Endocrine Disrupting Effects of Triclosan on the Placenta in Pregnant Rats.

Triclosan (TCS) is a broad-spectrum antimicrobial agent that is frequently used in pharmaceuticals and personal care products. Reports have shown that TCS is a potential endocrine disruptor; however, the potential effects of TCS on placental endocrine function are unclear. The aim of this study was to investigate the endocrine disrupting effects of TCS on the placenta in pregnant rats. Pregnant rats from gestational day (GD) 6 to GD 20 were treated with 0, 30, 100, 300 and 600 mg/kg/d TCS followed by analysis of various biochemical parameters. Of the seven tissues examined, the greatest bioaccumulation of TCS was observed in the placenta. Reduction of gravid uterine weight and the occurrence of abortion were observed in the 600 mg/kg/d TCS-exposed group. Moreover, hormone detection demonstrated that the serum levels of progesterone (P), estradiol (E2), testosterone (T), human chorionic gonadotropin (hCG) and prolactin (PRL) were decreased in groups exposed to higher doses of TCS. Real-time quantitative reverse transcriptase-polymerase chain reaction (Q-RT-PCR) analysis revealed a significant increase in mRNA levels for placental steroid metabolism enzymes, including UDP-glucuronosyltransferase 1A1 (UGT1A1), estrogen sulfotransferase 1E1 (SULT1E1), steroid 5α-reductase 1 (SRD5A1) and steroid 5α-reductase 2 (SRD5A2). Furthermore, the transcriptional expression levels of progesterone receptor (PR), estrogen receptor (ERα) and androgen receptor (AR) were up-regulated. Taken together, these data demonstrated that the placenta was a target tissue of TCS and that TCS induced inhibition of circulating steroid hormone production might be related to the altered expression of hormone metabolism enzyme genes in the placenta. This hormone disruption might subsequently affect fetal development and growth

Reconstructing the evolution history of networked complex systems

Abstract The evolution processes of complex systems carry key information in the systems’ functional properties. Applying machine learning algorithms, we demonstrate that the historical formation process of various networked complex systems can be extracted, including protein-protein interaction, ecology, and social network systems. The recovered evolution process has demonstrations of immense scientific values, such as interpreting the evolution of protein-protein interaction network, facilitating structure prediction, and particularly revealing the key co-evolution features of network structures such as preferential attachment, community structure, local clustering, degree-degree correlation that could not be explained collectively by previous theories. Intriguingly, we discover that for large networks, if the performance of the machine learning model is slightly better than a random guess on the pairwise order of links, reliable restoration of the overall network formation process can be achieved. This suggests that evolution history restoration is generally highly feasible on empirical networks

Soil bacterial succession with different land uses along a millennial chronosequence derived from the Yangtze River flood plain

Wetlands reclamation has been a traditional and effective practice for obtaining new land to alleviate the pressure induced by population growth. However, the evolution of soil-dwelling microorganisms along with reclamation and the potential influence of land-use patterns on them remain unclear. In this study, a soil chronosequence derived from Yangtze River sediments was established, comprising of circa 0, 60, 160, 280, 2000, and 3000 years, to examine the succession of soil bacterial communities across different land uses. Our analysis revealed obvious development in soil properties and orderly bacterial succession along reclamation gradients. Over time, reclaimed land suffered from varying degrees of abundance loss and biodiversity simplification, with dryland being the most sensitive to reclamation duration changes, whereas woodland and paddies showed slight reductions. Bacterial communities tended to shift from oligotrophs (K-strategist) to copiotrophs (rstrategist) at the phylum level as reclamation proceeded for all land use types. The relative abundance of certain bacterial functional groups associated with the carbon (C) and nitrogen (N) cycles were significantly increased, including those involved in Aerobic chemoheterotrophy, Chitinolysis, Nitrate reduction, Nitrate respiration, and Ureolysis, while other groups, such as those related to Fermentation, Methylotrophy, Nitrification, and Hydrocarbon degradation, exhibited decreased expression. Notably, prolonged reclamation can also trigger ecological issues in soil, including a continuous increase of predatory/exoparasitic bacteria in dryland and woodland, as well as a significant increase in pathogenic bacteria during the later stages in paddy fields. Overall, our study identified the impact of long-term reclamation on soil bacterial communities and functional groups, providing insight into the development of land-use-oriented ecological protection strategies