Miracle-dz/UniFAR-FTUs huggingface dataset:
Rapid Communication First cosmogenic 10 Be constraints on LGM glaciation on New Zealand's North Island: Park Valley, Tararua Range

We report the first direct ages for late Quaternary glaciation on the North Island of New Zealand. Mt Ruapehu, the volcanic massif in the North Island's centre, is currently glaciated and probably sustained glaciers throughout the late Quaternary, yet no numeric ages have been reported for glacial advances anywhere on the North Island. Here, we describe cosmogenic 10 Be ages of the surface layers of a glacially transported boulder and glacially polished bedrock from the Tararua Range, part of the axial ranges of the North Island. Results indicate that a limited valley glaciation occurred, culminating in recession at the end of the last glacial coldest period (LGCP, ca. 18 ka). This provides an initial age for deglaciation on the North Island during the last glacial-interglacial transition (LGIT). It appears that glaciation occurred in response to an equilibrium-line altitude (ELA) lowering of � 1400 m below the present-day mean summer freezing level. Ages for glaciation in the Tararua Range correspond closely to exposure ages for the last glacial maximum (LGM) from the lateral moraines of Cascade Valley in the South Island, and in Cobb Valley, in northern South Island. The corollary is that glaciation in the Tararua Range coincided with the phase of maximum cooling during MIS 2, prior to the Antarctic Cold Reversal (ACR), during the LGCP. Copyright # 2008 John Wiley & Sons, Ltd.
An Operational Oil Drift Forecasting System for German Coastal Waters

Summary Today, the presented (oil-) drift and dispersion model is a well-established component of the German marine pollution response system. The oil drift model is part of a comprehensive operational ocean forecasting system applied at the Federal Maritime and Hydrographic Agency (BSH). Development of the oil drift model started already in the 1980’s, but it was considerably advanced in several directions over the years. The latest development is the operationalization of the SeatrackWeb system at BSH. A 3-dimensional regional ocean circulation model provides – in combination with numerical weather forecasts of the German Weather Service (DWD) – the forcing for the oil drift component. The region covered by the model system is the whole North and Baltic Sea with special focus on the German Bight and the western Baltic Sea. Based on the pre-calculated and archived forcing data the oil drift model can be run on demand at any time. The basic approach is a Lagrangian particle tracking method, i.e. the simulated oil spill is described by a large number of particles which carry characteristics of specific types of oil. By this approach not only the drift but also the so-called “weathering” of the oil can be calculated. All fundamental processes which alter the oil during the fate of an oil spill, e.g. spreading, dispersion, evaporation and emulsification, are included. The particle tracking and oil weathering components, which are at the core of the model are connected to a modern, interactive, graphical user interface (GUI), which provides the user, e.g., with the possibility to directly start simulations from satellite detections of oil spills. The GUI gives access to several layers of useful information, e.g. ocean currents, wind direction, the location of oil platforms or shipping routes. Besides that, it visualizes ship signals from the Automatic Identification System (AIS), which are important means when it comes to the identification the potential source of an oil spill. In this paper we first present the current BSH operational ocean forecasting system highlighting some recent developments. The core of the oil drift component will be described in some detail. The main part of the paper will show results of some real cases. Based on these results some scientific questions like, e.g., the influence of wave induced Stokes drift will be discussed.
Asymmetry of the Discrimination Function for Temporal Durations in Human Subjects

Ten human subjects were comparing durations of pairs of visual stimuli in a two-way forced-choice task. Mean durations of presented time intervals were ~3 s (short) or ~6 s (long); the duration ratio was varied at nine levels. The Weber fractions for the short and long durations were approximately equal, ~0.22. The ratio of subjective equality was almost exactly unity for the short durations, but it was significantly reduced (~0.76) for the long durations. This asymmetry of the discrimination function indicates time-dependent change of internal representations of past durations, and is well compatible with the dual klepsydra model. Model-based estimates of the internal time representation loss rate, derived from the present data, are in a good agreement with values obtained from earlier studies on duration reproduction.
Seasonality of the Observed Amplified Sahara Warming Trend and Implications for Sahel Rainfall

AbstractPrior results indicate an amplified annual mean warming trend over the Sahara, with temperature trends that are 2–4 times that of the tropical mean rate. Trend analysis is conducted using five atmospheric reanalyses and three observational datasets to better understand the seasonality and physical processes of this amplified warming and the implications for Sahel precipitation. The seasonality of the amplified warming is maximum during July–October with a minimum during June. Two processes related to the amplified warming are identified. A “dry process” supports amplified warming over the Sahara when there is limited latent heating and/or evaporation to cool the surface and distribute heat to the atmosphere. In this mechanism, the warming results from changes in the upward longwave and downward longwave fluxes that are tightly coupled to each other. The second, termed a “wet process,” occurs during the summer West African monsoon season. In this mechanism there are increases in the low- and midlev.
Artificial intelligence applied to analyzes during the pandemic: COVID-19 beds occupancy in the state of Rio Grande do Norte, Brazil

The COVID-19 pandemic is already considered one of the biggest global health crises. In Rio Grande do Norte, a Brazilian state, the RegulaRN platform was the health information system used to regulate beds for patients with COVID-19. This article explored machine learning and deep learning techniques with RegulaRN data in order to identify the best models and parameters to predict the outcome of a hospitalized patient. A total of 25,366 bed regulations for COVID-19 patients were analyzed. The data analyzed comes from the RegulaRN Platform database from April 2020 to August 2022. From these data, the nine most pertinent characteristics were selected from the twenty available, and blank or inconclusive data were excluded. This was followed by the following steps: data pre-processing, database balancing, training, and test. The results showed better performance in terms of accuracy (84.01%), precision (79.57%), and F1-score (81.00%) for the Multilayer Perceptron model with Stochastic Gradient Descent optimizer. The best results for recall (84.67%), specificity (84.67%), and ROC-AUC (91.6%) were achieved by Root Mean Squared Propagation. This study compared different computational methods of machine and deep learning whose objective was to classify bed regulation data for patients with COVID-19 from the RegulaRN Platform. The results have made it possible to identify the best model to help health professionals during the process of regulating beds for patients with COVID-19. The scientific findings of this article demonstrate that the computational methods used applied through a digital health solution, can assist in the decision-making of medical regulators and government institutions in situations of public health crisis.
Community Structure and Diversity of Rhizosphere Soil and Endophytic Bacteria during the Period of Growth of Moso Bamboo Shoots.

BACKGROUND The purpose of this study was to elucidate the community structure of rhizosphere soil bacteria and endophytic bacteria during the growth of moso bamboo (Phyllostachys edulis) shoots. METHODS This study collected the rhizospheric soil samples, tissue samples of rhizome roots, shoot buds, winter bamboo shoots, spring bamboo shoots, and samples of forest soil. Their metagenomic DNA was extracted, and the bacterial community structure and diversity characteristics were compared and analyzed using high-throughput sequencing technology. RESULTS These samples enabled the identification of 32 phyla, 52 classes, 121 orders, 251 families, and 593 genera of bacteria. The phyla primarily included Proteobacteria, Acidobacteria, and Cyanobacteria among others. Proteobacteria was the dominant phylum in the samples of bamboo shoots and rhizome roots, whereas Acidobacteria was dominant in the rhizosphere and forest soil samples. The predominant genera of the rhizome root samples were Acidothermus, Bradyrhizobium and Acidobacterium, and the predominant genera of the soil samples were Acidothermus and Acidobacterium. CONCLUSIONS This study preliminarily revealed the regularity between the growth and development of bamboo shoots and the changes in the community structure of rhizosphere soil and endophytic bacteria, which provides insights into the relationship between growth and the bacterial community structure in different stages of bamboo shoots.
Prediction of preference and effect of music on preference: a preliminary study on electroencephalography from young women

Neuromarketing is the application of the neuroscientific approaches to analyze and understand economically relevant behavior. In this study, the effect of loud and rhythmic music in a sample neuromarketing setup is investigated. The second aim was to develop an approach in the prediction of preference using only brain signals. In this work, 19- channel EEG signals were recorded and two experimental paradigms were implemented: no music/silence and rhythmic, loud music using a headphone, while viewing women shoes. For each 10-sec epoch, normalized power spectral density (PSD) of EEG data for six frequency bands was estimated using the Burg method. The effect of music was investigated by comparing the mean differences between music and no music groups using independent two-sample t-test. In the preference prediction part sequential forward selection, k-nearest neighbors (k-NN) and the support vector machines (SVM), and 5-fold cross-validation approaches were used. It is found that music did not affect like decision in any of the power bands, on the contrary, music affected dislike decisions for all bands with no exceptions. Furthermore, the accuracies obtained in preference prediction study were between 77.5 and 82.5% for k-NN and SVM techniques. The results of the study showed the feasibility of using EEG signals in the investigation of the music effect on purchasing behavior and the prediction of preference of an individual.
Relationship between the geometry of the transition section and the loads acting on the conveyor belt

: This article presents a universal theoretical model of the belt in the transition section of a troughed conveyor, for a steel-cord belt which is divided into cables and layers of rubber, and for a textile belt which is divided into narrow strips. It describes geometrical forces in the transition section of the belt and offers an illustrative analysis of loads acting on the belt. The analysis addresses the influence of the belt type on the non-uniform character of loads in the transition section of the conveyor. It focuses on how the non-uniformity of loads acting on the belt is affected by the length of the transition section, the trough angle, the belt width, and the height difference between the contour of the pulley coat and the coat of the middle idler in the trough. It presents optimal values of the pulley height with respect to the middle idler in the trough, which mitigate this non-uniformity. The use of a belt type different than the original type has been found to affect the belt load non-uniformity in the transition section of the conveyor.
A Deforestation Detection Network Using Deep Learning-Based Semantic Segmentation

Semantic segmentation is an important task in which the class label of each pixel is predicted. Thus, it is quite tough compared with classification and classical segmentation. Recently, deforestation has been a serious environmental issue, as it causes numerous environmental concerns: climate change and ecological loss. Hence, it is essential to recognize deforestation to save the environment. In this work, an efficient convolution neural network (CNN) model is proposed to identify deforestation in the Amazon Rainforest more precisely. At first, two modified SegNet methods are presented to make the semantic segmentation more effective. More importantly, an efficient semantic segmentation framework is proposed by integrating the merits of ResNet18-based modified SegNet, ShuffleNet-based modified SegNet, and UNet to yield more effective segmentation. Moreover, the employment of computationally faster ResNet18 or ShuffleNet in modified SegNet leads to improvement of computational efficiency and semantic segmentation performance. Thus, the proposed framework also retains advantages such as residual learning, skip connection, pointwise group convolution, and channel shuffling, which are responsible for making the optimization easier and the network efficient and faster. In addition, a Laplacian-of-Gaussian-based modified high boosting filter (LoGMHF) is employed for deblurring, edge enhancement, and denoising. The experimental analysis also shows that the proposed framework outperforms others.
Advanced Understanding of the Tropopause Inversion Layer: High-Resolution GPS-RO Observations and Reanalyses

The upper troposphere and lower stratosphere (UTLS) is a region of the atmosphere that acts as a 'transition' between the two layers and consequently has properties from both. Within this region, a fine-scale feature is located: the Tropopause Inversion Layer (TIL), which consists of a sharp temperature inversion at the tropopause and the corresponding high static stability values aloft. The latter theoretically affects the dispersion relations of atmospheric waves like Rossby or Inertia-Gravity waves and hampers stratosphere-troposphere exchange (STE), which is why the TIL is established as an important feature of the UTLS. The present thesis aims to improve the observational knowledge about the TIL by analyzing high-resolution GPS radio-occultation (GPS-RO) data globally. The focus is on day-to-day and synoptic-scale TIL variability, a novel approach to build upon the climatological point of view from earlier TIL studies. Also, a dynamical mechanism for TIL enhancement is studied and quantified: the transient tropopause modulation by equatorial and extratropical waves, and the resulting net TIL enhancement. The role of this transient wave modulation mechanism has not been investigated in TIL literature, and its quantification from GPS-RO observations puts it among the most important TIL enhancing processes. Lastly, the paradigm that data assimilation worsens the representation of the TIL in reanalyses, valid a decade ago, has been tested in modern systems: the ERA-Interim reanalysis and the ECMWF forecasts. Both systems show TIL improvement by data assimilation increments, updating the earlier status quo. As a whole, this thesis significantly improves our knowledge about observed properties of the TIL and the mechanisms responsible for its formation and maintenance, and shows that reanalyses are a valuable tool for TIL research.
ANÁLISIS MULTIDIMENSIONAL DE UNA BASE DE DATOS DE MUJERES PIMA/ MULTIDIMENSIONAL ANALYSIS FROM A DATABASE OF PIMA WOMEN

Se presenta una tipologia multidimensional de mujeres estadounidenses Pima con datos de la Pima Indians Diabetes Database, analizada mediante Componentes Principales y posterior clasificacion. Fueron construidos tres clusters individuales con: (1) 64% de positividad para diabetes, glucemia acorde con tolerancia alterada a la glucosa, hiperinsulinemia, obesidad segun indice de masa corporal, grosor del pliegue de la piel del triceps superior al promedio general y presion diastolica cercana a prehipertension; (2) 50% de positividad para diabetes, glucemia cercana al limite inferior de tolerancia alterada a la glucosa, indice de masa corporal correspondiente a obesidad y grosor del pliegue de la piel del triceps, presion diastolica, edad y numero de embarazos mayores a la media general; (3) 16% de positividad para diabetes, indice de masa corporal indicando sobrepeso y numero de embarazos, funcion de pedigri de diabetes, edad, glucemia, insulinemia y grosor del pliegue de la piel del triceps menores al promedio general. Resulta destacable el mayor grado de antecedentes familiares en el grupo de riesgo y la relevancia del sobrepeso asi como la configuracion espacial de los individuos revelando posibles fases en el desarrollo diabetico de estas mujeres. Finalmente, se subraya la utilidad de la clusterizacion en problemas biologicos. ABSTRACTA multidimensional typology of U.S. Pima women is presented with data obtained from Pima Indians Diabetes Database, and analyzed employing Principal Components and subsequent classification. Three individual clusters were obtained: (1) 64% of positivity for diabetes, glycemia compatible with abnormal glucose tolerance, hyperinsulinemia, body mass index corresponding to obesity, triceps skin fold thickness above the general average and diastolic pressure near to prehypertension; (2) 50% of positivity for diabetes, glycemia near the lower level of abnormal glucose tolerance, body mass index corresponding to obesity and skin fold thickness, diastolic pressure, age and number of pregnancies above the overall average; (3) 16% of positivity for diabetes, body mass index corresponding to overweight and number of pregnancies, diabetes pedigree function, age, glycemia, insulinemia and skin fold thickness below the overall average. Greater family history in risk groups, relevance of overweight and the spatial configuration of subjects revealing possible developmental phases towards diabetes in these women have to be outlined. The usefulness of clusterizacion in biological disturbances is finally remarked.
Addition of immune checkpoint inhibitors to chemotherapy versus chemotherapy alone in patients with triple‐negative breast cancer: A systematic review and meta‐analysis

Triple‐negative breast cancer (TNBC) is a relatively common malignant tumor with high mortality rates. There are limited treatment options and current therapy regimens often fall short of providing positive outcomes. The development of immune checkpoint inhibitors (ICIs) have provided a vital treatment option although efficacy has varied. Here, we review patient response to current TNBC treatment with and without the addition of ICIs.
Facile synthesis of β-Ga2O3 based high-performance electronic devices via direct oxidation of solution-processed transition metal dichalcogenides

Gallium oxide (Ga2O3) is a promising wide bandgap semiconductor that is viewed as a contender for the next generation of high-power electronics due to its high theoretical breakdown electric field and large Baliga’s figure of merit. Here, we report a facile route of synthesizing β-Ga2O3 via direct oxidation conversion using solution-processed two-dimensional (2D) GaS semiconducting nanomaterial. Higher order of crystallinity in x-ray diffraction patterns and full surface coverage formation in scanning electron microscopy images after annealing were achieved. A direct and wide bandgap of 5 eV was calculated, and the synthesized β-Ga2O3 was fabricated as thin film transistors (TFT). The β-Ga2O3 TFT fabricated exhibits remarkable electron mobility (1.28 cm2 Vs−1) and a good current ratio (I on/I off) of 2.06 × 105. To further boost the electrical performance and solve the structural imperfections resulting from the exfoliation process of the 2D nanoflakes, we also introduced and doped graphene in β-Ga2O3 TFT devices, increasing the electrical device mobility by ∼8-fold and thereby promoting percolation pathways for the charge transport. We found that electron mobility and conductivity increase directly with the graphene doping concentration. From these results, it can be proved that the β-Ga2O3 networks have excellent carrier transport properties. The facile and convenient synthesis method successfully developed in this paper makes an outstanding contribution to applying 2D oxide materials in different and emerging optoelectronic applications.
Metaproteomic assessment of gut microbial and host functional perturbations in Helicobacter pylori-infected patients subjected to an antimicrobial protocol

ABSTRACT The impact of therapeutic interventions on the human gut microbiota (GM) is a clinical issue of paramount interest given the strong interconnection between microbial dynamics and human health. Orally administered antibiotics are known to reduce GM biomass and modify GM taxonomic profile. However, the impact of antimicrobial therapies on GM functions and biochemical pathways has scarcely been studied. Here, we characterized the fecal metaproteome of 10 Helicobacter pylori-infected patients before (T0) and after 10 days (T1) of a successful quadruple therapy (bismuth, tetracycline, metronidazole, and rabeprazole) and 30 days after therapy cessation (T2), to investigate how GM and host functions change during the eradication and healing processes. At T1, the abundance ratio between microbial and host proteins was reversed compared with that at T0 and T2. Several pathobionts (including Klebsiella, Proteus, Enterococcus, Muribaculum, and Enterocloster) were increased at T1. Therapy reshaped the relative contributions of the functions required to produce acetate, propionate, and butyrate. Proteins related to the uptake and processing of complex glycans were increased. Microbial cross-feeding with sialic acid, fucose, and rhamnose was enhanced, whereas hydrogen sulfide production was reduced. Finally, microbial proteins involved in antibiotic resistance and inflammation were more abundant after therapy. Moreover, a reduction in host proteins with known roles in inflammation and H. pylori-mediated carcinogenesis was observed. In conclusion, our results support the use of metaproteomics to monitor drug-induced remodeling of GM and host functions, opening the way for investigating new antimicrobial therapies aimed at preserving gut environmental homeostasis.