FishMOT: A Simple and Effective Method for Fish Tracking Based on IoU Matching

The tracking of various fish species plays a profoundly significant role in understanding the behavior of individual fish and their groups. Present tracking methods suffer from issues of low accuracy or poor robustness. In order to address these concerns, this paper proposes a novel tracking approach, named FishMOT (Fish Multiple Object Tracking). This method combines object detection techniques with the IoU matching algorithm, thereby achieving efficient, precise, and robust fish detection and tracking. Diverging from other approaches, this method eliminates the need for multiple feature extractions and identity assignments for each individual, instead directly utilizing the output results of the detector for tracking, thereby significantly reducing computational time and storage space. Furthermore, this method imposes minimal requirements on factors such as video quality and variations in individual appearance. As long as the detector can accurately locate and identify fish, effective tracking can be achieved. This approach enhances robustness and generalizability. Moreover, the algorithm employed in this method addresses the issue of missed detections without relying on complex feature matching or graph optimization algorithms. This contributes to improved accuracy and reliability. Experimental trials were conducted in the open-source video dataset provided by idtracker.ai, and comparisons were made with state-of-the-art detector-based multi-object tracking methods. Additionally, comparisons were made with idtracker.ai and TRex, two tools that demonstrate exceptional performance in the field of animal tracking. The experimental results demonstrate that the proposed method outperforms other approaches in various evaluation metrics, exhibiting faster speed and lower memory requirements. The source codes and pre-trained models are available at: https://github.com/gakkistar/FishMO

Factors contributing to rapid decline of Arctic sea ice in autumn

Autumn Arctic sea ice has been declining since the beginning of the era of satellite sea ice observations. In this study, we examined the factors contributing to the decline of autumn sea ice concentration. From the Beaufort Sea to the Barents Sea, autumn sea ice concentration has decreased considerably between 1982 and 2020, and the rates of decline were the highest around the Beaufort Sea. We calculated the correlation coefficients between sea ice extent (SIE) anomalies and anomalies of sea surface temperature (SST), surface air temperature (SAT) and specific humidity (SH). Among these coefficients, the largest absolute value was found in the coefficient between SIE and SAT anomalies for August to October, which has a value of −0.9446. The second largest absolute value was found in the coefficient between SIE and SH anomalies for September to November, which has a value of −0.9436. Among the correlation coefficients between SIE and SST anomalies, the largest absolute value was found in the coefficient for August to October, which has a value of −0.9410. We conducted empirical orthogonal function (EOF) analyses of sea ice, SST, SAT, SH, sea level pressure (SLP) and the wind field for the months where the absolute values of the correlation coefficient were the largest. The first EOFs of SST, SAT and SH account for 39.07%, 63.54% and 47.60% of the total variances, respectively, and are mainly concentrated in the area between the Beaufort Sea and the East Siberian Sea. The corresponding principal component time series also indicate positive trends. The first EOF of SLP explains 41.57% of the total variance. It is mostly negative in the central Arctic. Over the Beaufort, Chukchi and East Siberian seas, the zonal wind weakened while the meridional wind strengthened. Results from the correlation and EOF analyses further verified the effects of the ice–temperature, ice–SH and ice–SLP feedback mechanisms in the Arctic. These mechanisms accelerate melting and decrease the rate of formation of sea ice. In addition, stronger meridional winds favor the flow of warm air from lower latitudes towards the polar region, further promoting Arctic sea ice decline

BaFe12O19 single-particle-chain nanofibers : preparation, characterization, formation principle, and magnetization reversal mechanism

BaFe12O19 single-particle-chain nanofibers have been successfully prepared by an electrospinning method and calcination process, and their morphology, chemistry, and crystal structure have been characterized at the nanoscale. It is found that individual BaFe12O19 nanofibers consist of single nanoparticles which are found to stack along the nanofiber axis. The chemical analysis shows that the atomic ratio of Ba/Fe is 1:12, suggesting a BaFe12O19 composition. The crystal structure of the BaFe12O19 single-particle-chain nanofibers is proved to be M-type hexagonal. The single crystallites on each BaFe12O19 single-particlechain nanofibers have random orientations. A formation mechanism is proposed based on thermogravimetry/differential thermal analysis (TG-DTA), X-ray diffraction (XRD), and transmission electron microscopy (TEM) at six temperatures, 250, 400, 500, 600, 650, and 800 �C. The magnetic measurement of the BaFe12O19 single-particle-chain nanofibers reveals that the coercivity reaches a maximum of 5943 Oe and the saturated magnetization is 71.5 emu/g at room temperature. Theoretical analysis at the micromagnetism level is adapted to describe the magnetic behavior of the BaFe12O19 single-particle-chain nanofibers

TaSnRK2.9, a Sucrose Non-fermenting 1-Related Protein Kinase Gene, Positively Regulates Plant Response to Drought and Salt Stress in Transgenic Tobacco

Sucrose non-fermenting 1-related protein kinase 2 (SnRK2) family members play crucial roles in plant abiotic stress response. However, the precise mechanism underlying the function of SnRKs has not been thoroughly elucidated in plants. In this research, a novel SnRK2 gene, TaSnRK2.9 was cloned and characterized from common wheat. The expression of TaSnRK2.9 was upregulated by polyethylene glycol (PEG), NaCl, H2O2, abscisic acid (ABA), methyl jasmonate (MeJA), and ethrel treatments. TaSnRK2.9 was mainly expressed in wheat young root, stamen, pistil, and lemma. Overexpressing TaSnRK2.9 in transgenic tobacco enhanced plants’ tolerance to drought and salt stresses both in young seedlings and mature plants with improved survival rate, seed germination rate, and root length. Physiological analyses suggest that TaSnRK2.9 improved antioxidant system such as superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), and glutathione (GSH) to reduce the H2O2 content under drought or salt stress. Additionally, TaSnRK2.9 overexpression plants had elevated ABA content, implying that the function of TaSnRK2.9 may be ABA-dependent. Moreover, TaSnRK2.9 increased the expression of some ROS-related, ABA-related, and stress-response genes under osmotic or salt treatment. TaSnRK2.9 could interact with NtABF2 in yeast two-hybrid assay, and increased the expression of NtABF2 under mannitol or NaCl treatment in transgenic tobacco plants. In conclusion, overexpression of TaSnRK2.9 in tobacco conferred plants tolerance to drought and salt stresses through enhanced ROS scavenging ability, ABA-dependent signal transduction, and specific SnRK-ABF interaction

Degradation of HK2 by chaperone-mediated autophagy promotes metabolic catastrophe and cell death

Hexokinase II (HK2), a key enzyme involved in glucose metabolism, is regulated by growth factor signaling and is required for initiation and maintenance of tumors. Here we show that metabolic stress triggered by perturbation of receptor tyrosine kinase FLT3 in non–acute myeloid leukemia cells sensitizes cancer cells to autophagy inhibition and leads to excessive activation of chaperone-mediated autophagy (CMA). Our data demonstrate that FLT3 is an important sensor of cellular nutritional state and elucidate the role and molecular mechanism of CMA in metabolic regulation and mediating cancer cell death. Importantly, our proteome analysis revealed that HK2 is a CMA substrate and that its degradation by CMA is regulated by glucose availability. We reveal a new mechanism by which excessive activation of CMA may be exploited pharmacologically to eliminate cancer cells by inhibiting both FLT3 and autophagy. Our study delineates a novel pharmacological strategy to promote the degradation of HK2 in cancer cells

Observations of water transparency in China’s lakes from space

Water transparency, usually denoted by Secchi disk depth (SSD), represents the first-order description of water quality and has important implications for the diversity and productivity of aquatic life. In China, lakes supply freshwater and ecosystem services to nearly a billion people. Therefore, real time monitoring of lake transparency is of great significance. Moreover, understanding how and why transparency varies in space and time in response to different driving forces is needed to understand, manage, and predict lake water quality. Based on the time-saving and low-cost Google Earth Engine cloud platform, this study developed a new algorithm for quickly mapping SDDs in Chinese lakes. SDDs were retrieved for 412 Chinese lakes (> 20 km2) for the period 2000–2018. Results demonstrated that lake water depth spatially differentiated transparency. Deep lakes usually had high transparency and water depth explained 88.81 % of the spatial variations. With increasing catchment vegetation coverage and lake water depth, 70.15 % of lakes witnessed increasing transparency during 2000–2018. Of these 42.72 % were significant (p < 0.05). Transparency of deep lakes was generally determined by phytoplankton density not sediment resuspension. Minimum transparency occurred in summer. Future increases in lake water levels in response to factors such as climate change may contribute to further improvements in transparency. Management should focus on controlling eutrophication and increasing vegetation cover in catchments

Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial

Background: Previous cluster-randomized controlled trials evaluating the impact of implementing evidence-based guidelines for nutrition therapy in critical illness do not consistently demonstrate patient benefits. A large-scale, sufficiently powered study is therefore warranted to ascertain the effects of guideline implementation on patient-centered outcomes. Methods: We conducted a multicenter, cluster-randomized, parallel-controlled trial in intensive care units (ICUs) across China. We developed an evidence-based feeding guideline. ICUs randomly allocated to the guideline group formed a local "intervention team", which actively implemented the guideline using standardized educational materials, a graphical feeding protocol, and live online education outreach meetings conducted by members of the study management committee. ICUs assigned to the control group remained unaware of the guideline content. All ICUs enrolled patients who were expected to stay in the ICU longer than seven days. The primary outcome was all-cause mortality within 28 days of enrollment. Results: Forty-eight ICUs were randomized to the guideline group and 49 to the control group. From March 2018 to July 2019, the guideline ICUs enrolled 1399 patients, and the control ICUs enrolled 1373 patients. Implementation of the guideline resulted in significantly earlier EN initiation (1.20 vs. 1.55 mean days to initiation of EN; difference − 0.40 [95% CI − 0.71 to − 0.09]; P = 0.01) and delayed PN initiation (1.29 vs. 0.80 mean days to start of PN; difference 1.06 [95% CI 0.44 to 1.67]; P = 0.001). There was no significant difference in 28-day mortality (14.2% vs. 15.2%; difference − 1.6% [95% CI − 4.3% to 1.2%]; P = 0.42) between groups. Conclusions: In this large-scale, multicenter trial, active implementation of an evidence-based feeding guideline reduced the time to commencement of EN and overall PN use but did not translate to a reduction in mortality from critical illness. Trial registration: ISRCTN, ISRCTN12233792. Registered November 20th, 2017