Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk Factors for Surgical Site Infections Following Fasciotomy in Patients With Acute Compartment Syndrome: A Study on the February 2023 Kahramanmaraş Earthquake

Introduction: Surgical site infections (SSIs) developing after fasciotomy are difficult to treat, costly, and an important source of mortality and morbidity. This study aimed to determine the risk factors affecting the development of SSI in patients who underwent fasciotomy with the diagnosis of acute compartment syndrome (ACS) within 72 hours after two consecutive earthquakes of 7.7 and 7.6 magnitude that occurred in Kahramanmaraş on February 6, 2023. Method: A total of 116 patients were retrospectively analyzed. Patients were divided into two groups: those who developed SSI and those who did not. In this study, variables such as basic demographic characteristics, time of fasciotomy, center performing fasciotomy, type of wound closure, affected extremity, concomitant renal failure, hyperbaric oxygen (HBO) therapy, blood creatine kinase (CK) level were examined. Results: Of 116 patients, 58 (50%) had SSI. It was statistically observed that patients who underwent treatment with vacuum-assisted closure (VAC), those who underwent primary closure with the shoelace method, those who went into renal failure, and those whose fasciotomy was performed in an earthquake zone had a higher incidence of SSI (p<0.001). Blood CK level above 17.839 seemed to be a risk factor according to receiver operating characteristic (ROC) analysis (P<0.01). Age (p=0.193), gender (p=0.125), fasciotomy time (p=0.843), lower extremity (p=0.234), upper extremity (p=0.806), and HBO treatment (p=0.56) were not associated with SSI. Infection was found to be a significant risk factor for amputation (p<0.001). Conclusion: The use of VAC as a wound closure technique for SSI after fasciotomy in patients who developed ACS due to the earthquake, the presence of renal failure in the patients, and performing fasciotomy in the earthquake zone were independent risk factors. A blood CK level above 17.839 was also determined as a risk factor, but the confidence interval was found to be low.
GLOBAL OCEAN DATA ANALYSIS PROJECT, VERSION 2 (GLODAPv2)

Here we report the completion of a uniformly calibrated open ocean data product on inorganic carbon and carbon-relevant variables that we call Global Ocean Data Analysis Project version 2 or simply GLODAPv2. The product includes data from approximately one million individual seawater samples collected from over 700 cruises during the years 1972-2013. Extensive quality control and subsequent calibration were carried out for salinity, oxygen, nutrient, carbon dioxide, total alkalinity, pH and chlorofluorocarbon data. Following calibration, the data were used to produce global climatology maps for many of the parameters. In addition to the data products and the mapped distributions, all of the original data files without alteration other than formatting and unification of units are made freely available along with whatever metadata was collected. An on-line cruise summary table provides data access and additional information including references to publications that have used data from specific cruises. The GLODAPv2 database is available free of charge as a numeric data package (NDP-93) from the Carbon Dioxide Information Analysis Center (CDIAC) at http://cdiac.ornl. gov/oceans/GLODAPv2/. The NDP-93 consists of the original cruise data files, adjusted data products, mapped data product and this documentation, which describes the GLODAPv2 project.
Abrupt shifts in the late Cenozoic environment of north‐western China recorded in loess–palaeosol–red clay sequences

Aeolian deposits can provide a sensitive record of climatic change. Two abrupt changes in grain size and accumulation rate have been identified at c. 2.4–2.6 Ma and 1.1–1.2 Ma in the loess–palaeosol–red clay sequences from the Loess Plateau of China. These changes are apparent in two major stepwise increases in grain size and accumulation rate following two abrupt prominent change events of grain size, implying a two‐step reorganization that was probably connected with two abrupt environmental change events. The correlation among different sections suggests that regional rather than local environmental processes are responsible for these changes. These changes likely record abrupt fluctuations in winter monsoon and the provenance of sediments associated with renewed Plio‐Pleistocene uplift of the Tibetan plateau, and its role on climate and detrital sediment yield.
Land use/land cover change effects on temperature trends at U.S. Climate Normals stations

Alterations in land use/land cover (LULC) in areas near meteorological observation stations can influence the measurement of climatological variables such as temperature. Urbanization near climate stations has been the focus of considerable research attention, however conversions between non‐urban LULC classes may also have an impact. In this study, trends of minimum, maximum, and average temperature at 366 U.S. Climate Normals stations are analyzed based on changes in LULC defined by the U.S. Land Cover Trends Project. Results indicate relatively few significant temperature trends before periods of greatest LULC change, and these are generally evenly divided between warming and cooling trends. In contrast, after the period of greatest LULC change was observed, 95% of the stations that exhibited significant trends (minimum, maximum, or mean temperature) displayed warming trends.
Effects of growing media on seedlings characteristics of patula pine (Pinus patula Schlecht. et Cham.) in Swaziland

Pinus patula Schlecht. et Cham. seedlings were grown in six growth media in Swaziland to assess the effects of growing media on seedling characteristics. The experiment was laid out in a randomized complete block design with four replications and six treatments (pine bark compost; Super medium, a commercial nursery medium; pine sawdust; topsoil; cattle manure; and bagasse). Pine-bark compost (seedling height, 18.7 cm), Super medium (height, 18.6 cm), and topsoil (height, 13.4 cm) produced significantly (p < 0.05) taller seedlings than other growth media. Owing to their superior performance, pine bark compost and Super medium are recommended as growing medium in the pine nurseries of Swaziland.
A CHAMP‐only gravity field model from kinematic orbits using the energy integral

In this paper we present results of a global gravity field recovery using half a year of CHAMP data. We use the energy integral of the motion of a satellite to transform satellite velocities into values of gravitational potential. The feasibility of this approach has already been demonstrated by several groups, using CHAMP reduced‐dynamic orbits. We show, that the potential recovered from this kind of orbits depends on the a priori gravity field used for orbit determination. Thus, it cannot be excluded that errors present in the prior field propagate into the new CHAMP gravity model. It is the intention of this paper to avoid this dependency through the use of kinematic orbits, which are free from prior information. The derived potential model, TUM‐1S, is validated by comparison to ground data and by satellite orbit residuals. It is shown to be comparable in quality to other state‐of‐the‐art gravity field models.
Remote Influence of the Tropical Atlantic on the Variability and Trend in North West Australia Summer Rainfall

AbstractRainfall in North West Australia (NWA) has been increasing over the past decades, occurring mainly in the austral summer season (December–March). A range of factors such as decreased land albedo in Australia and increasing anthropogenic aerosols in the Northern Hemisphere, identified using simulations from climate models, have been implicated in this wetting trend. However, the impact of land albedo and aerosols on Australian rainfall remains unclear. In addition, previous studies showed that dominant sea surface temperature (SST) signals in the Pacific–Indian Ocean including El Nino–Southern Oscillation (ENSO), ENSO Modoki, and the Indian Ocean dipole mode have no significant impact on the NWA rainfall trend. The present study proposes another viewpoint on the remote influence of tropical Atlantic atmospheric vertical motion on the observed rainfall variability and trend in NWA.It is found that, with the atmospheric ascent instigated by the warming of SST over the tropical Atlantic, a Rossby wave.
Establishing Flow-Catchment Interactions as Means of Regionalising Flow Characteristics of the Save Catchment in Zimbabwe

The research was aimed at determining the areas of equal hydrology on the Save catchment for the purposes of estimating flow characteristics on ungauged catchments. Data quality control was first done on flow station data in order identify the best stations that could be use in the formulation the catchment flow characteristics. The flow characteristics created were mean annual runoff; coefficient of variation of runoff; base flow index; coefficient of variation of low flow; coefficient of variation of high flow and the coefficient of variation of monthly wet season flow. Literature was then used to select the catchment characteristics most likely to influence runoff. Redundancy analysis was done to correlate sub zone and runoff characteristics so that the nature and significance of relationships could be determined. Cluster analysis was then used to combine the selected catchment characteristics into natural groups of similar characteristics. Validation of the created clusters was done statistically using canonical variate analysis. The results showed that catchment characteristics significant are on the catchment rainfall (r=0.96); the proportion of each subzone under grasslands (r=0.56); the mean annual evaporation rates (r=-0.74), the coefficient of variation of mean annual rainfall (r=-0.55) and the catchment area (r=-0.53). The whole of the Save catchment had three different clusters and was found to be 60% similar. Validation showed that the catchment characteristics used in the study could explain only a total of 39% of the variation in flow characteristics in their clusters.
An Approach to Implementing High-Performance Computing for Problem Solving in Workflow-Based Energy Infrastructure Resilience Studies

Implementing high-performance computing (HPC) to solve problems in energy infrastructure resilience research in a heterogeneous environment based on an in-memory data grid (IMDG) presents a challenge to workflow management systems. Large-scale energy infrastructure research needs multi-variant planning and tools to allocate and dispatch distributed computing resources that pool together to let applications share data, taking into account the subject domain specificity, resource characteristics, and quotas for resource use. To that end, we propose an approach to implement HPC-based resilience analysis using our Orlando Tools (OT) framework. To dynamically scale computing resources, we provide their integration with the relevant software, identifying key application parameters that can have a significant impact on the amount of data processed and the amount of resources required. We automate the startup of the IMDG cluster to execute workflows. To demonstrate the advantage of our solution, we apply it to evaluate the resilience of the existing energy infrastructure model. Compared to similar approaches, our solution allows us to investigate large infrastructures by modeling multiple simultaneous failures of different types of elements down to the number of network elements. In terms of task and resource utilization efficiency, we achieve almost linear speedup as the number of nodes of each resource increases.
Optical dating of sand wedges and ice‐wedge casts from Qinghai Lake area on the northeastern Qinghai‐Tibetan Plateau and its palaeoenvironmental implications

Sand wedges and ice‐wedge casts in the Qinghai Lake area on the northeastern Qinghai‐Tibetan Plateau (QTP) occur within alluvial gravel or river terrace deposits. In this study, we report the results of quartz Optically Stimulated Luminescence (OSL) dating of the infill of five relict sand wedges and one ice‐wedge cast. Combining our dating results with previously published luminescence ages of permafrost wedges in the Qinghai Lake area, we show that sand/ice wedges formed at c. 62 ka, c. 45 ka and between 30 and 15 ka, and that the mean annual air temperature (MAAT) was depressed by at least ∼3°C relative to present during the sand/ice‐wedge formation periods. This new work is partially corroborated by post‐LGM proxy records from lakes and aeolian deposits reported from the northeastern QTP. It also significantly extends the palaeoenvironmental record in the region in the period before the LGM, when other proxy records are rare, allowing a better understanding of the palaeoenvironmental conditions on the northeastern QTP.
Protein Language Model-Powered 3D Ligand Binding Site Prediction from Protein Sequence

Prediction of ligand binding sites of proteins is a fundamental and important task for understanding the function of proteins and screening potential drugs. Most existing methods require experimentally determined protein holo-structures as input. However, such structures can be unavailable on novel or less-studied proteins. To tackle this limitation, we propose LaMPSite, which only takes protein sequences and ligand molecular graphs as input for ligand binding site predictions. The protein sequences are used to retrieve residue-level embeddings and contact maps from the pre-trained ESM-2 protein language model. The ligand molecular graphs are fed into a graph neural network to compute atom-level embeddings. Then we compute and update the protein-ligand interaction embedding based on the protein residue-level embeddings and ligand atom-level embeddings, and the geometric constraints in the inferred protein contact map and ligand distance map. A final pooling on protein-ligand interaction embedding would indicate which residues belong to the binding sites. Without any 3D coordinate information of proteins, our proposed model achieves competitive performance compared to baseline methods that require 3D protein structures when predicting binding sites. Given that less than 50% of proteins have reliable structure information in the current stage, LaMPSite will provide new opportunities for drug discovery.
Learning Multi-graph Structure for Temporal Knowledge Graph Reasoning

Temporal Knowledge Graph (TKG) reasoning that forecasts future events based on historical snapshots distributed over timestamps is denoted as extrapolation and has gained significant attention. Owing to its extreme versatility and variation in spatial and temporal correlations, TKG reasoning presents a challenging task, demanding efficient capture of concurrent structures and evolutional interactions among facts. While existing methods have made strides in this direction, they still fall short of harnessing the diverse forms of intrinsic expressive semantics of TKGs, which encompass entity correlations across multiple timestamps and periodicity of temporal information. This limitation constrains their ability to thoroughly reflect historical dependencies and future trends. In response to these drawbacks, this paper proposes an innovative reasoning approach that focuses on Learning Multi-graph Structure (LMS). Concretely, it comprises three distinct modules concentrating on multiple aspects of graph structure knowledge within TKGs, including concurrent and evolutional patterns along timestamps, query-specific correlations across timestamps, and semantic dependencies of timestamps, which capture TKG features from various perspectives. Besides, LMS incorporates an adaptive gate for merging entity representations both along and across timestamps effectively. Moreover, it integrates timestamp semantics into graph attention calculations and time-aware decoders, in order to impose temporal constraints on events and narrow down prediction scopes with historical statistics. Extensive experimental results on five event-based benchmark datasets demonstrate that LMS outperforms state-of-the-art extrapolation models, indicating the superiority of modeling a multi-graph perspective for TKG reasoning.
The fertilizing role of African dust in the Amazon rainforest: A first multiyear assessment based on data from Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations

The productivity of the Amazon rainforest is constrained by the availability of nutrients, in particular phosphorus (P). Deposition of long‐range transported African dust is recognized as a potentially important but poorly quantified source of phosphorus. This study provides a first multiyear satellite‐based estimate of dust deposition into the Amazon Basin using three‐dimensional (3‐D) aerosol measurements over 2007–2013 from the Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP). The 7 year average of dust deposition into the Amazon Basin is estimated to be 28 (8–48) Tg a−1 or 29 (8–50) kg ha−1 a−1. The dust deposition shows significant interannual variation that is negatively correlated with the prior‐year rainfall in the Sahel. The CALIOP‐based multiyear mean estimate of dust deposition matches better with estimates from in situ measurements and model simulations than a previous satellite‐based estimate does. The closer agreement benefits from a more realistic geographic definition of the Amazon Basin and inclusion of meridional dust transport calculation in addition to the 3‐D nature of CALIOP aerosol measurements. The imported dust could provide about 0.022 (0.006–0.037) Tg P of phosphorus per year, equivalent to 23 (7–39) g P ha−1 a−1 to fertilize the Amazon rainforest. This out‐of‐basin phosphorus input is comparable to the hydrological loss of phosphorus from the basin, suggesting an important role of African dust in preventing phosphorus depletion on timescales of decades to centuries.
A multimodal dense convolution network for blind image quality assessment

Technological advancements continue to expand the communications industry’s potential. Images, which are an important component in strengthening communication, are widely available. Therefore, image quality assessment (IQA) is critical in improving content delivered to end users. Convolutional neural networks (CNNs) used in IQA face two common challenges. One issue is that these methods fail to provide the best representation of the image. The other issue is that the models have a large number of parameters, which easily leads to overfitting. To address these issues, the dense convolution network (DSC-Net), a deep learning model with fewer parameters, is proposed for no-reference image quality assessment (NR-IQA). Moreover, it is obvious that the use of multimodal data for deep learning has improved the performance of applications. As a result, multimodal dense convolution network (MDSC-Net) fuses the texture features extracted using the gray-level co-occurrence matrix (GLCM) method and spatial features extracted using DSC-Net and predicts the image quality. The performance of the proposed framework on the benchmark synthetic datasets LIVE, TID2013, and KADID-10k demonstrates that the MDSC-Net approach achieves good performance over state-of-the-art methods for the NR-IQA task.
Shallow Seismic Refraction Interpretation with Visual Interactive Ray Trace (Virt) Modelling

A comparison of first-arrival times obtained by both finite-difference and ray trace modelling demonstrates the applicability of ray tracing to shallow refraction interpretation. Most shallow seismic refraction interpretations are still achieved with conventional time-term methods and those, such as the GRM, that are derived from these methods. In complex conditions, these methods are inaccurate, as many first-arrivals originate from diffracted waves and waves that have laterally penetrated irregular refractors. These processes also affect first-arrival amplitudes, as is demonstrated with an irregular finite-difference model; nevertheless, first-arrival times are still primarily used in shallow refraction interpretation. An interactive ray-trace modelling method, based on two-point ray tracing through a discrete, two-dimensional model with constant velocity compartments and incorporating buried diffraction sources, is used to compute accurate first-arrival-times. This method is designed for checking and refining interpretations in pre-selected areas, rather than as a production tool for routine processing of large refraction data sets. Implementation of the method on a PC using screen displays of first-arrival ray paths, and field and synthetic arrival time data, allows inappropriate arrivals caused by model deficiencies to be recognised and eliminated. Model adjustments are made directly on the screen and are guided by their effect on computed times and the agreement with field times. The improved algorithms and interactive steps of the visual interactive ray trace (VIRT) modelling eliminate instabilities that occur with many automated, ray-based inversion methods and inaccuracies arising from use of oversimplified or inaccurate initial seismic models. This interpretation method is a powerful teaching tool that readily highlights model and data deficiencies and can generate robust models for automated inversion. The VIRT method is applied to re-interpret shallow refraction data from the London-Victoria shallow gold deposit occurring in a major fault zone. The final model obtained with VIRT agreed closely with known geology and showed that the gold mineralisation occurs in a higher-velocity region within the weathered rock above a lower-velocity region in the underlying bedrock.
First records of the 'bathroom mothmidge' Clogmia albipunctata, a conspicuous element of the Belgian fauna that went unnoticed (Diptera: Psychodidae)

The 'bathroom fly' Clogmia albipunctata (Williston, 1893) (Diptera: Psychodidae) is a cosmopolitan species that is commonly found in bathrooms, kitchens, sewage treatment plants and compost heaps. Of circumtropical origin, the species probably spread to synanthropic habitats in northern and central Europe during the past decades. The first documented findings in Belgium are discussed, together with general information on the biology and recognition of the species. Samenvatting. Eerste vermelding van de motmug Clogmia albipunctata, een opvallende, maar onopgemerkte soort van de Belgische fauna (Diptera: Psychodidae) De motmug Clogmia albipunctata (Williston, 1893) (Diptera: Psychodidae) is een kosmopolitische soort die vaak wordt aangetroffen in toiletten en badkamers, en daarom in het Engels wel bekend staat als bathroom fly. De soort is ook in Belgie waarschijnlijk algemeen. Dit artikel bespreekt de eerste gedocumenteerde Belgische vondsten en geeft algemene informatie over de verspreiding, herkenning en biologie. Resume. Premiere mention de Clogmia albipunctata, une espece commune mais passee inapercue de la faune belge (Diptera: Psychodidae) La 'mouche de salle de bain' Clogmia albipunctata (Williston, 1893) est une espece cosmopolite frequemment rencontree dans les salles de bain, les cuisines, les conduites d'eau usee et les tas de compost. D'origine circumtropicale, cette espece s'est probablement etendue a des habitats synanthropiques dans le nord et le centre de l'Europe au cours des dernieres decennies. La decouverte de l'espece en Belgique est examinee, et des informations generales sur la biologie et la reconnaissance de l'espece sont donnees.