Miracle-dz/UniFAR-FTUs huggingface dataset:
Holistic view of heat acclimation alleviated intestinal lesion in mice with heat stroke based on microbiome‐metabolomics analysis

The severity of heat stroke (HS) is associated with intestinal injury, which is generally considered an essential issue for HS. Heat acclimation (HA) is considered the best strategy to protect against HS. In addition, HA has a protective effect on intestinal injuries caused by HS. Considering the essential role of gut microbes in intestinal structure and function, we decided to investigate the potential protective mechanism of HA in reducing intestinal injury caused by HS. HA model was established by male C57BL/6J mice (5–6 weeks old, 17–19 g) were exposed at (34 ± 0.7)°C for 4 weeks to establish an animal HA model. The protective effect of HA on intestinal barrier injury in HS was investigated by 16S rRNA gene sequencing and nontargeted liquid chromatography‐mass spectrometry (LC‐MS) metabolomics. According to the experimental results, HA can change the composition of the gut microbiota, which increases the proportion of lactobacilli, faecal bacteria, and urinobacteria but decreases the proportion of deoxycholic acid. Moreover, HA can reduce liver and kidney injury and systemic inflammation caused by HS and reduce intestinal injury by enhancing the integrity of the intestinal barrier. In addition, HA regulates inflammation by inhibiting NF‐κB signalling and increasing tight junction protein expression in HS mice. HA induces changes in the gut microbiota, which may enhance tight junction protein expression, thereby reducing intestinal inflammation, promoting bile acid metabolism, and ultimately maintaining the integrity of the intestinal barrier. In conclusion, HA induced changes in the gut microbiota. Among the gut microbiota, lactobacilli may play a key role in the potential protective mechanism of HA.
Postdischarge Opioid Prescription after Cesarean: A Quality Improvement Initiative

Abstract Objective This study aims to determine if adoption of a user-friendly algorithm for individualized opioid prescribing at discharge would decrease the number of opioids prescribed after cesarean delivery. Study Design As part of a quality initiative, we developed and implemented an algorithm for opioid prescribing at discharge for patients after cesarean delivery. The intervention group comprised patients delivering by cesarean in the 6 months following initiation of the intervention. The intervention group was divided into three groups based on inpatient opioid needs 24 to 48 hours after delivery. Oxycodone tablets were prescribed at discharge based on inpatient group. The control group comprised patients delivering at the same institution in the 6 months prior to initiation of the intervention. The primary outcome was number of oxycodone tablets prescribed at discharge. The secondary outcome was the proportion of patients with a pain-related encounter or additional oxycodone prescription up to 6 weeks after delivery. Results From July to December 2020, a total of 382 subjects met criteria for the intervention. The comparison group consisted of 391 subjects discharged from January to June 30, 2020, after cesarean. Baseline characteristics and inpatient opioid use 24 to 48 hours after delivery did not differ between the groups. Compared with the control group, subjects in the intervention group had fewer oxycodone tablets prescribed at discharge (11.1 vs. 15.8, p < 0.001). The number of pain-related encounters within 6 weeks of delivery did not differ between the intervention and comparison groups (10.5 vs. 10.3%, p = 0.82). There was no increase in the additional number of oxycodone prescriptions after discharge (4.7% in the intervention group vs. 4.3% in the control group, p = 0.81). Conclusion Discharge opioid prescribing based on inpatient use after cesarean reduces the number of opioids prescribed without increasing the number of pain-related encounters after discharge. A simple algorithm may ensure compliance from prescribers. Key Points Previous research has shown that most opioids prescribed after cesarean delivery are unused after discharge. We implemented an algorithm for opioid discharge prescribing after cesarean delivery based on inpatient opioid consumption. Implementing an individualized approach to opioid prescribing reduced the number of tablets prescribed at discharge after cesarean delivery. The decrease in discharge prescribing did not correspond to an increase in pain-related encounters or additional narcotic prescriptions in the 6 weeks following delivery. The overprescription of opioids at discharge after cesarean delivery is well-established, and previous authors have found success in prescribing opioids at discharge based on inpatient use. Our study proposes a simple, reproducible algorithm for opioid prescribing at discharge after cesarean.
The soil nitrogen dynamics in an inland salt marsh as affected by various experimental water levels

An 87‐day incubation experiment was conducted to investigate the effects of various water levels on the soil nitrogen content in an inland salt marsh of Xianghai, China. Soil samples were collected from depths in the range of 0 to 30 cm at 10‐cm intervals after 15, 54 and 87 days of incubation. The total nitrogen (TN), available nitrogen, nitrate nitrogen (NO3−‐N) and ammonium nitrogen (NH4+‐N) contents and the nitrogen activation coefficient (NAC) were measured to investigate the soil nitrogen dynamics under three inundation treatments (−5, 0 and 5 cm). The results showed that TN was generally accumulated in all soil layers and exhibited a peak value in subsurface soils at the −5‐cm inundation level at the end of the incubation period, whereas no obvious changes were observed in the TN contents in the bottom soils at the three inundation levels. Compared with the initial contents in the soils, the NO3−‐N and NH4+‐N contents were significantly reduced in all soil layers at the 0‐cm and the 5‐cm inundation levels at the end of the incubation period. In general, the available nitrogen content was higher in the surface soils compared with the deeper soils at all inundation levels. The NAC values exhibited a pronounced change in the subsurface soils at all inundation levels, decreasing significantly from 0.17 to 0.04 at the 5‐cm inundation level; however, a higher mean NAC value (0.126) was found over the incubation period at the 0‐cm inundation level compared with the 5‐cm and the −5‐cm inundation levels. Nitrogen was released from plants into the soil under all inundation treatments over the incubation period. The soil organic matter exerted positive loadings on the TN and NO3−‐N contents and negative loadings on the NH4+‐N content at all inundation levels, and the water content generally exhibited a negative relationship with all forms of nitrogen. Additionally, the total phosphorus content exhibited a positive correlation with the TN and NO3−‐N contents. Copyright © 2013 John Wiley & Sons, Ltd.
Simulation of the Indian Summer Monsoon in the Superparameterized Climate Forecast System Version 2: Preliminary Results

AbstractAn analysis of a 5-yr (from 1 January 2009 to 31 December 2013) free run of the superparameterized (SP) Climate Forecast System (CFS) version 2 (CFSv2) (SP-CFS), implemented for the first time at a spectral triangular truncation at wavenumber 62 (T62) atmospheric horizontal resolution, is presented. The SP-CFS simulations are evaluated against observations and traditional convection parameterized CFSv2 simulations at T62 resolution as well as at some higher resolutions. The metrics for evaluating the model performance are chosen in order to mainly address the improvement in systematic biases observed in the CFSv2 documented in earlier studies. While the primary focus of this work is on evaluating the improvement of the simulation of the Indian summer monsoon (ISM) by the SP-CFS model, some results are also presented within the context of the global climate. The SP-CFS significantly reduces the dry bias of precipitation over the Indian subcontinent and better captures the monsoon intraseasonal osci.
Lithologie, hydrodynamisme et thermicité dans le système sédimentaire multicouche recoupé par les forages Andra de Montiers-sur-Saulx (Meuse)

Andra (Agence nationale pour la gestion des dechets radioactifs � French national radioactive waste management agency) has conducted a research program to determine the feasibility of a disposal facility for long-lived high level and medium level (LL-HLML) nuclear wastes in a deep geological environment. In 2005, following 15 years of research, Andra established the feasibility of this type of disposal in the Callovo-Oxfordian clay formations located in the eastern region of the Paris basin. Between 2006 and 2008, a geological survey campaign was carried out over 250 km2 at the boundary of the Meuse and Haute-Marne departments. Six drilling platforms were involved on the studied zone and a 2D seismic campaign was carried out using a 2 x 2 km to 3 x 3 km grid as regular as possible
Length of hospital stay for elective electrophysiological procedures: a survey from the European Heart Rhythm Association

Abstract Aims Electrophysiological (EP) operations that have traditionally involved long hospital lengths of stay (LOS) are now being undertaken as day case procedures. The coronavirus disease-19 pandemic served as an impetus for many centres to shorten LOS for EP procedures. This survey explores LOS for elective EP procedures in the modern era. Methods and results An online survey consisting of 27 multiple-choice questions was completed by 245 respondents from 35 countries. With respect to de novo cardiac implantable electronic device (CIED) implantations, day case procedures were reported for 79.5% of implantable loop recorders, 13.3% of pacemakers (PMs), 10.4% of implantable cardioverter defibrillators (ICDs), and 10.2% of cardiac resynchronization therapy (CRT) devices. With respect to CIED generator replacements, day case procedures were reported for 61.7% of PMs, 49.2% of ICDs, and 48.2% of CRT devices. With regard to ablations, day case procedures were reported for 5.7% of atrial fibrillation (AF) ablations, 10.7% of left-sided ablations, and 17.5% of right-sided ablations. A LOS ≥ 2 days for CIED implantation was reported for 47.7% of PM, 54.5% of ICDs, and 56.9% of CRT devices and for 54.5% of AF ablations, 42.2% of right-sided ablations, and 46.1% of left-sided ablations. Reimbursement (43–56%) and bed availability (20–47%) were reported to have no consistent impact on the organization of elective procedures. Conclusion There is a wide variation in the LOS for elective EP procedures. The LOS for some procedures appears disproportionate to their complexity. Neither reimbursement nor bed availability consistently influenced LOS.
On Memorization in Diffusion Models

Due to their capacity to generate novel and high-quality samples, diffusion models have attracted significant research interest in recent years. Notably, the typical training objective of diffusion models, i.e., denoising score matching, has a closed-form optimal solution that can only generate training data replicating samples. This indicates that a memorization behavior is theoretically expected, which contradicts the common generalization ability of state-of-the-art diffusion models, and thus calls for a deeper understanding. Looking into this, we first observe that memorization behaviors tend to occur on smaller-sized datasets, which motivates our definition of effective model memorization (EMM), a metric measuring the maximum size of training data at which a learned diffusion model approximates its theoretical optimum. Then, we quantify the impact of the influential factors on these memorization behaviors in terms of EMM, focusing primarily on data distribution, model configuration, and training procedure. Besides comprehensive empirical results identifying the influential factors, we surprisingly find that conditioning training data on uninformative random labels can significantly trigger the memorization in diffusion models. Our study holds practical significance for diffusion model users and offers clues to theoretical research in deep generative models. Code is available at https://github.com/sail-sg/DiffMemorize.
Single-cell RNA seq analysis of erythroid cells reveals a specific sub-population of stress erythroid progenitors

ABSTRACT Background : Erythroid cells play important roles in hemostasis and disease. However, there is still significant knowledge gap regarding stress erythropoiesis. Methods : Two single-cell RNAseq datasets of erythroid cells on GEO with accession numbers GSE149938 and GSE184916 were obtained. The datasets from two sources, bone marrow and peripheral blood were analyzed using Seurat v4.1. 1, and other tools in R. QC metrics were performed, data were normalized and scaled. Principal components that capture the variation of the data were determined. In clustering the cells, KNN graph was constructed and Louvain algorithm was applied to optimize the standard modularity function. Clusters were defined via differential expression of features. Results We identified 9 different cell types, with a particular cluster representing the stress erythroids. The clusters showed differentially expressed genes as observed from the gene signature plot. The stress erythroid cluster differentially expressed some genes including ALAS2, HEMGN, and GUK1. Conclusion The erythroid population was found to be heterogeneous, with a distinct sub-cell type constituting the stress erythroids; this may have important implications for our knowledge of steady-state and stress erythropoiesis, and the markers found in this cluster may prove useful for future research into the dynamics of stress erythroid progenitor cell differentiation.
Time-Series Classification in Smart Manufacturing Systems: An Experimental Evaluation of State-of-the-Art Machine Learning Algorithms

Manufacturing is gathering extensive amounts of diverse data, thanks to the growing number of sensors and rapid advances in sensing technologies. Among the various data types available in SMS settings, time-series data plays a pivotal role. Hence, TSC emerges is crucial in this domain. The objective of this study is to fill this gap by providing a rigorous experimental evaluation of the SoTA ML and DL algorithms for TSC tasks in manufacturing and industrial settings. We first explored and compiled a comprehensive list of more than 92 SoTA algorithms from both TSC and manufacturing literature. Following, we selected the 36 most representative algorithms from this list. To evaluate their performance across various manufacturing classification tasks, we curated a set of 22 manufacturing datasets, representative of different characteristics that cover diverse manufacturing problems. Subsequently, we implemented and evaluated the algorithms on the manufacturing benchmark datasets, and analyzed the results for each dataset. Based on the results, ResNet, DrCIF, InceptionTime, and ARSENAL are the top-performing algorithms, boasting an average accuracy of over 96.6% across all 22 manufacturing TSC datasets. These findings underscore the robustness, efficiency, scalability, and effectiveness of convolutional kernels in capturing temporal features in time-series data, as three out of the top four performing algorithms leverage these kernels for feature extraction. Additionally, LSTM, BiLSTM, and TS-LSTM algorithms deserve recognition for their effectiveness in capturing features within time-series data using RNN-based structures.
Decadal Trends of Soil Loss and Runoff in the Koga Catchment, Northwestern Ethiopia

This study used the physically based distributed Annualized Agricultural Non‐Point Source model to simulate decadal trends in soil loss and runoff with changes in land use/cover in the 98·4 km2 upper part of the Koga catchment. The study indicated that soil loss in the study area has increased from 17 Mg ha−1 y−1 in 1957 to 25 Mg ha−1 y−1 in 2010 because of a decrease in the amount of woody vegetation cover. We found that over the past 50 years, high risk erosion areas with soil loss greater than 35 Mg ha−1 y−1 covered, on average, 41% of the study area, while these same areas contributed, on average, 78% of the soil loss from the study area. Twenty one percent of the study area has been in the high risk erosion category throughout all the years since 1957. The total amount of soil loss from the study area varied with changes in the amount of woody vegetation cover. Other factors such as landforms and soil type also affected the amount of soil erosion. Whereas most of the high erosion areas were found in the upper part of the catchment, most of the high runoff areas were found in the lower part of the catchment, indicating that the amount of soil erosion is not directly related to runoff volume in the study area. Crop fields contributed to most of the soil loss in the study area. Copyright © 2015 John Wiley & Sons, Ltd.
Grabbing “Green”: Markets, Environmental Governance and the Materialization of Natural Capital

Over the past two decades, the incorporation of market logics into environment and conservation policy has led to a reconceptualization of “nature. ” Resulting constructs like ecosystem services and biodiversity derivatives, as well as finance mechanisms like Reducing Emissions from Deforestation and Forest Degradation, species banking, and carbon trading, offer new avenues for accumulation and set the context for new enclosures. As these practices have become more apparent, geographers have been at the forefront of interdisciplinary research that has highlighted the effects of “green grabs”—in which “green credentials” are used to justify expropriation of land and resources—in specific locales. While case studies have begun to reveal the social and ecological marginalization associated with green grabs and the implementation of market mechanisms in particular sites, less attention has been paid to the systemic dimensions and “logics” mobilizing these projects. Yet, the emergence of these constructs reflects a larger transformation in international environmental governance—one in which the discourse of global ecology has accommodated an ontology of natural capital, culminating in the production of what is taking shape as “The Green Economy. ” The Green Economy is not a natural or coincidental development, but is contingent upon, and coordinated by, actors drawn together around familiar and emergent institutions of environmental governance. Indeed, the terrain for green grabbing is increasingly cultivated through relationships among international environmental policy institutions, organizations, activists, academics, and transnational capitalist and managerial classes. This special issue of Human Geography brings together papers that draw on a range of theoretical perspectives to investigate the systemic dimensions and logics mobilizing green grabs and the creation of new market mechanisms. In inverting the title – “grabbing green” instead of the more conventional green grabs – we explore how “the environment” is being used instrumentally by various actors to extend the potential for capital accumulation under the auspices of “being green. ” Using a diversity of empirical material that spans local to global scales, the papers reveal the formation of the social relations and metrics that markets require to function. They identify the “frictions” that inhibit the production of these social relations, and they link particular cases to the scalar configurations of power that mobilize and give them shape.
Large Language Models Can Be Good Privacy Protection Learners

The proliferation of Large Language Models (LLMs) has driven considerable interest in fine-tuning them with domain-specific data to create specialized language models. Nevertheless, such domain-specific fine-tuning data often contains sensitive personally identifiable information (PII). Direct fine-tuning LLMs on this data without privacy protection poses a risk of leakage. To address this challenge, we introduce Privacy Protection Language Models (PPLM), a novel paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding data privacy. Our work offers a theoretical analysis for model design and delves into various techniques such as corpus curation, penalty-based unlikelihood in training loss, and instruction-based tuning, etc. Extensive experiments across diverse datasets and scenarios demonstrate the effectiveness of our approaches. In particular, instruction tuning with both positive and negative examples, stands out as a promising method, effectively protecting private data while enhancing the model's knowledge. Our work underscores the potential for Large Language Models as robust privacy protection learners.
Mamonia Complex, southwest Cyprus: Evolution and emplacement of a Mesozoic continental margin

The Mamonia Complex in southwest Cyprus comprises an assemblage of Upper Triassic to Lower Cretaceous sedimentary rocks, Upper Triassic mafic igneous rocks, and subordinate metamorphic rocks, which together lie adjacent to the southwest margin of the Troodos ophiolitic complex and its Upper Cretaceous sedimentary cover. The sedimentary facies of the Mamonia Complex record the progressive development of a Mesozoic passive continental margin. Initial crustal subsidence associated with deltaic and turbiditic terrigenous clastic sedimentation was followed by continental rifting and then the genesis of marginal oceanic crust in Late Triassic time. Subsequent pelagic and hemipelagic deposition during Jurassic and Early Cretaceous time reflects passive continental margin subsidence. Shallow-water calcareous material derived from an adjacent carbonate platform, probably now located in southern Turkey, was deposited on the margin by gravity flows. After initial rifting, coarse terrigenous clastic input was minimal until a major influx of terrigenous material in Early Cretaceous time, possibly contemporaneous with an episode of renewed ocean-floor spreading in the area. Allowing for the 90° Tertiary rotation of Cyprus, the Mamonia rocks formed part of the northern margin of a small ocean basin during Jurassic and Cretaceous time. The Troodos Complex is likely to represent a younger surviving fragment of the same basin. By Late Cretaceous time, ocean-floor spreading had ended, followed in the Maastrichtian by disruption, folding, and emplacement of the Mamonia sedimentary sequences onto the Troodos ophiolitic rocks. The Mamonia sedimentary rocks are now arranged in subhorizontal sheets which, on the basis of fold vergence and facing directions, were emplaced toward the present northeast – that is, prior to paleorotation, toward the original southeast. The displaced sheets probably represent down-margin gravity slides. The evidence is compatible with both the Mamonia and Troodos Complexes having a local (“external”) origin in contrast to the currently favored “internalist” hypothesis involving long-distance transport.