Miracle-dz/UniFAR-FTUs huggingface dataset:
Management of Retinitis Pigmentosa via Platelet Rich Plasma or Combination with Electromagnetic Stimulation: Retrospective Analysis of One-year Results

Purpose To investigate whether the natural progression rate of retinitis pigmentosa can be decreased with subtenon autologous platelet rich plasma application alone or combination with retinal electromagnetic stimulation.Material and methods The study includes retrospective analysis of 60 retinitis pigmentosa patients. Patients constitute 3 groups with similar demographic characteristics. The combined management group consists of 20 retinitis pigmentosa patients (40 eyes) who received combined retinal electromagnetic stimulation and subtenon platelet rich plasma as Group1; The subtenon platelet rich plasma-only group consisted of 20 retinitis pigmentosa patients (40 eyes) as Group2; The natural course (control) group consists of 20 retinitis pigmentosa patients (40 eyes) who did not receive any treatment were classified as Group3. Horizontal and vertical ellipsoid zone width, fundus perimetry deviation index and best corrected visual acuity changes were compared within and between groups after one year follow up period.Results Horizontal ellipsoid zone percentage changes were detected +1 % in Group1, -2.85% in Group2, -9.36% in Group3 (Δp 1>2>3). Vertical ellipsoid zone percentage changes were detected +0.34 % in Group1, -3.05% in Group2, -9.09% in Group3 (Δp 1>2>3). Fundus perimetry deviation index percentage changes were detected +0.05% in Group1, -2.68% in Group2 and -8.78% in Group3 (Δp 1>2>3). Conclusion Platelet-rich plasma is a good source of growth factors, but its half-life is 4-6 months. Subtenon autologous platelet rich plasma might more effectively slow down photoreceptor loss when repeated as booster injections and combined with retinal electromagnetic stimulation.
Oxygen isotopes versus CLIMAP (18 ka) temperatures: A comparison from the tropical Atlantic

Glacial-interglacial differences in δ 18 O values of Globigerinoides sacculifer have been mapped in the western and central tropical Atlantic using data from surface and core sediments. Glacial-interglacial differences in the western tropical Atlantic, if attributed to temperature changes only, are at odds with temperature reconstructions performed by CLIMAP. In the Ceara Rise region delta δ 18 O values exceeding 2‰ are recorded. They can only be partly attributed to changes in the precipitation-evaporation regime, i.e., higher salinities and 18 O-enriched precipitation. An additional cooling of 1–2 °C as compared to CLIMAP9s results is required to balance the δ 18 O record. Thus, temperature changes from glacial to modern times amount to 2– 3 °C in the tropical Atlantic off the northern coast of Brazil.
Ocena głębokości znieczulenia podczas wziewnej i dożylnej indukcji znieczulenia ogólnego Assessment of the depth of anaesthesia during inhalational and intravenous induction of general anaesthesia

Background: Tracheal intubation is one of the strongest stimuli during general anaesthesia and may result in an insufficient depth of anaesthesia. The aim of the study was to compare the clinical evaluation of the depth of anaesthesia with an evaluation using entropy during inhalational and intravenous induction of general anaesthesia. Methods: This study involved 60 patients undergoing elective surgery under general anaesthesia. Patients were divided into two groups, group E (etomidate induction) and group S (sevoflurane induction). The systolic arterial pressure (SAP), heart rate (HR), response entropy (RE), and state entropy (SE) were determined at the following seven measurement points: before anaesthesia induction, at the loss of consciousness (LOC) point, before tracheal intubation, immediately after intubation, and 2 min., 4 min. and 6 min. after tracheal intubation. An increase in HR and/or SAP of more than 20% and/or the occurrence of lacrimation and/or perspiration in response to tracheal intubation was considered a marker of inadequate anaesthesia in the clinical evaluation. The depth of anaesthesia was considered insufficient according to entropy monitoring if the RE and SE were above 60. Results: In clinical evaluation, insufficient anaesthesia in response to tracheal intubation was observed in all the patients in group E and in more than half of the patients in group S. At the same time, the majority of patients in both groups had entropy values that did not exceed the recommended value as an appropriate level of anaesthesia. Conclusions: We found a discrepancy in the evaluation of the depth of anaesthesia based on clinical criteria compared with evaluations based on entropy values during both intravenous and inhalational induction of general anaesthesia.
The importance of vulnerable areas with potential tourismdevelopment: a case study of the Bohemian forest and SouthBohemia tourism regions

The significance of the vulnerability of nature-rich areas with high development potential for tourism was studied using three types of data: 1) spatial distribution of tourist attractions, 2) the appeal level of these attractions, and 3) the number of visitors. The Bohemian Forest and South Bohemia were chosen as study areas. Nine types of landscape spatial appeal were identified in the study area. Two most important types were defined based on their appeal where there are rare relic features in the natural environment dominated by the presence of peat bogs and natural habitats with scrub undergrowth or virgin forests. These types were also found in the areas with the greatest potential for tourism development. However, these areas are also the most important from the point of view of nature conservation and landscape protection in Central Europe.
A Multicenter Cohort Study of Histologic Findings and Long-Term Outcomes of Kidney Disease in Women Who Have Been Pregnant.

BACKGROUND AND OBJECTIVES For many women pregnancy is the first contact with health services, thus providing an opportunity to identify renal disease. This study compares causes and long-term renal outcomes of biopsy-proven renal disease identified during pregnancy or within 1 year postpartum, with nonpregnant women. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS Native renal biopsies (1997-2012), in women of childbearing age (16 to <50 years), from 21 hospitals were studied. The pregnancy-related diagnosis group included those women with abnormal urinalysis/raised creatinine identified during pregnancy or within 1 year postpartum. Pregnancy-related and control biopsies were matched for age and ethnicity (black versus nonblack). RESULTS One hundred and seventy-three pregnancy-related biopsies (19 antenatal, 154 postpregnancy) were identified and matched with 1000 controls. FSGS was more common in pregnancy-related biopsies (32.4%) than controls (9.7%) (P<0.001) but there were no differences in Columbia classification. Women with a pregnancy-related diagnosis were younger (32.1 versus 34.2 years; P=0.004) and more likely to be black (26.0% versus 13.3%; P<0.001) than controls, although there were no differences in ethnicities in women with FSGS. The pregnancy-related group (excluding antenatal biopsies) was more likely to have a decline in Chronic Kidney Disease Epidemiology Collaboration eGFR in the follow-up period than the control group (odds ratio, 1.67; 95% confidence interval, 1.03 to 2.71; P=0.04), and this decline appeared to be more rapid (-1.33 versus -0.56 ml/min per 1.73 m2 per year, respectively; P=0.045). However, there were no differences between groups in those who required RRT or who died. CONCLUSIONS Pregnancy is an opportunity to detect kidney disease. FSGS is more common in women who have been pregnant than in controls, and disease identified in pregnancy or within 1 year postpartum is more likely to show a subsequent decline in renal function. Further work is required to determine whether pregnancy initiates, exacerbates, or reveals renal disease.
Multi-scale 3D+t intracranial aneurysmal flow vortex detection

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Transition from continental to oceanic crust on the Wilkes‐Adelie margin of Antarctica

The Wilkes-Adelie margin of East Antarctica, a passive margin rifted in the Early Cretaceous, has an unusually reflective Moho which can be traced seismically across the continent-ocean transition. Velocity models and depth sections were constructed from a combined set of U.S. and French multichannel seismic reflection lines to investigate the transition from continental to oceanic crust. These data show that the boundary between oldest oceanic crust and transitional continental crust is marked by a minimum in subsediment crustal thickness and, in places, by a shoaling of Moho. The Moho reflection is continuous across the edge of oceanic crust, and gradually deepens landward under the continental edge
Effects of lithospheric strength on convection in the Earth's mantle

Convection in Earth's mantle is driven largely by horizontal density gradients that form when cold, dense, mantle lithosphere descends into the mantle interior, either through subduction for plate-scale flow, or as localized convective instability beneath lithospheric plates. The deformation associated with these processes is resisted by the extreme temperature-dependence of the lithosphere's strength. Ways in which lithosphere deformation affects convection in the mantle are examined here, by comparing both theory and the results of numerical experiments. Convective instability at the base of a cold thermal boundary layer with temperatureand strain-rate-dependent viscosity is investigated by defining a quantity, termed here the "available buoyancy," that takes into account the tradeoff between cold temperatures both promoting and resisting convective instability. This quantity can be used to determine approximately whether, and how fast, convective instability grows. Horizontal shortening is also included, which tends to increase gravitational instability, allowing up to 60% of the mantle lithosphere to be convectively removed. The subsequent influx of hot, buoyant, asthenosphere could cause rapid surface uplift. For plate-scale flow, subduction zone deformation may resist convection. This possibility is studied here using a regional finite element model of subduction. This model shows that for sufficiently strong lithosphere, convection is resisted more by the bending deformation of a subducting plate than by shearing of the underlying mantle. Such behavior can be explained by a variation of boundary layer theory that includes an analytic expression for the energy required to bend a viscous plate. For the mantle, the bending resistance should control plate velocities if the effective lithosphere viscosity is greater than about 1023 Pa s. This produces a reasonable distribution of plate velocities for Earth and may reconcile models for its thermal evolution with surface heat flow observations. These results are verified using a new method for implementing subduction that parameterizes plate bending within a small region of a mantle-scale convection model. This model also shows that small-scale convection, by removing the basal part of the oceanic lithosphere, can decrease the bending resistance and thus may be an essential aspect of plate tectonics on Earth. Thesis Co-Supervisor: Bradford H. Hager, Professor of Earth Sciences Thesis Co-Supervisor: Peter Molnar, Senior Research Associate
Another Drop in Water Vapor

In 2000 a sudden severe drop in stratospheric water vapor levels interrupted the supposed long-term increase of this greenhouse gas, an important contributor to global warming and climate variability. Satellite sensors observed a recovery in the following years, hidden behind a large variability. More recently, during 2011 and 2012, measurements revealed another severe drop in stratospheric water vapor concentrations.
Bromine counts from XRF scanning as an estimate of the marine organic carbon content of sediment cores

XRF sediment core scanning technology is increasingly used to quantify sediment composition. The overall good correlation between biophilic halogen bromine (Br) and sedimentary total organic carbon (TOC) potentially allows the fast estimation of down core TOC profiles by XRF scanning. In order to test this approach we present data from the Arabian Sea and a Mediterranean brine basin, comparing XRF core scanning Br data with discrete sample TOC analyses. Overall, Br counts and TOC show a clear correlation, except when stable carbon isotope and C/N data indicate intervals characterized by enhanced input of terrestrial organic matter. Hence, solid phase Br is exclusively associated with marine organic matter (MOC) and can be used as a direct estimate of this parameter after a calibration is established. High pore water Br in the brine core steepens the Br/TOC correlation but after salt correction shows a nearly identical gradient to that of the Arabian Sea core.
Constant Approximation for Private Interdependent Valuations

The celebrated model of auctions with interdependent valuations, introduced by Milgrom and Weber in 1982, has been studied almost exclusively under private signals $s_{1}, \ldots, s_{n}$ of the n bidders and public valuation functions $v_{i}\left(s_{1}, \ldots, s_{n}\right)$. Recent work in TCS has shown that this setting admits a constant approximation to the optimal social welfare if the valuations satisfy a natural property called submodularity over signals (SOS). More recently, Eden et al. (2022) have extended the analysis of interdependent valuations to include settings with private signals and private valuations, and established $O\left(\log ^{2} n\right)$-approximation for SOS valuations. In this paper we show that this setting admits a constant factor approximation, settling the open question raised by Eden et al. (2022).
Object Manipulation Through Contact Configuration Regulation: Multiple and Intermittent Contacts

In this work, we build on our method for manipulating unknown objects via contact configuration regulation: the estimation and control of the location, geometry, and mode of all contacts between the robot, object, and environment. We further develop our estimator and controller to enable manipulation through more complex contact interactions, including intermittent contact between the robot/object, and multiple contacts between the object/environment. In addition, we support a larger set of contact geometries at each interface. This is accomplished through a factor graph based estimation framework that reasons about the complementary kinematic and wrench constraints of contact to predict the current contact configuration. We are aided by the incorporation of a limited amount of visual feedback; which when combined with the available F/T sensing and robot proprioception, allows us to differentiate contact modes that were previously indistinguishable. We implement this revamped framework on our manipulation platform, and demonstrate that it allows the robot to perform a wider set of manipulation tasks. This includes, using a wall as a support to re-orient an object, or regulating the contact geometry between the object and the ground. Finally, we conduct ablation studies to understand the contributions from visual and tactile feedback in our manipulation framework. Our code can be found at: https://github.com/mcubelab/pbal.
$\mu$2mech: a Software Package Combining Microstructure Modeling and Mechanical Property Prediction

We have developed a graphical user interface (GUI) based package $\mu$2mech to perform phase-field simulation for predicting microstructure evolution. The package can take inputs from ab initio calculations and CALPHAD (Calculation of Phase Diagrams) tools for quantitative microstructure prediction. The package also provides a seamless connection to transfer output from the mesoscale phase field method to the microscale finite element analysis for mechanical property prediction. Such a multiscale simulation package can facilitate microstructure-property correlation, one of the cornerstones in accelerated materials development within the integrated computational materials engineering (ICME) framework.
RT-GAN: Recurrent Temporal GAN for Adding Lightweight Temporal Consistency to Frame-Based Domain Translation Approaches

While developing new unsupervised domain translation methods for endoscopy videos, it is typical to start with approaches that initially work for individual frames without temporal consistency. Once an individual-frame model has been finalized, additional contiguous frames are added with a modified deep learning architecture to train a new model for temporal consistency. This transition to temporally-consistent deep learning models, however, requires significantly more computational and memory resources for training. In this paper, we present a lightweight solution with a tunable temporal parameter, RT-GAN (Recurrent Temporal GAN), for adding temporal consistency to individual frame-based approaches that reduces training requirements by a factor of 5. We demonstrate the effectiveness of our approach on two challenging use cases in colonoscopy: haustral fold segmentation (indicative of missed surface) and realistic colonoscopy simulator video generation. The datasets, accompanying code, and pretrained models will be made available at \url{https://github.com/nadeemlab/CEP}.
OpenMixup: A Comprehensive Mixup Benchmark for Visual Classification

Data mixing, or mixup, is a data-dependent augmentation technique that has greatly enhanced the generalizability of modern deep neural networks. However, a full grasp of mixup methodology necessitates a top-down hierarchical understanding from systematic impartial evaluations and empirical analysis, both of which are currently lacking within the community. In this paper, we present OpenMixup, the first comprehensive mixup benchmarking study for supervised visual classification. OpenMixup offers a unified mixup-based model design and training framework, encompassing a wide collection of data mixing algorithms, a diverse range of widely-used backbones and modules, and a set of model analysis toolkits. To ensure fair and complete comparisons, large-scale standard evaluations of various mixup baselines are conducted across 12 diversified image datasets with meticulous confounders and tweaking powered by our modular and extensible codebase framework. Interesting observations and insights are derived through detailed empirical analysis of how mixup policies, network architectures, and dataset properties affect the mixup visual classification performance. We hope that OpenMixup can bolster the reproducibility of previously gained insights and facilitate a better understanding of mixup properties, thereby giving the community a kick-start for the development and evaluation of new mixup methods. The source code and user documents are available at \url{https://github.com/Westlake-AI/openmixup}.
Improved Crop and Weed Detection with Diverse Data Ensemble Learning in Agriculture

Modern agriculture heavily relies on Site-Specific Farm Management practices, necessitating accurate detection, localization, and quantification of crops and weeds in the field, which can be achieved using deep learning techniques. In this regard, crop and weed-specific binary segmentation models have shown promise. However, uncontrolled field conditions limit their performance from one field to the other. To improve semantic model generalization, existing methods augment and synthesize agricultural data to account for uncontrolled field conditions. However, given highly varied field conditions, these methods have limitations. To overcome the challenges of model deterioration in such conditions, we propose utilizing data specific to other crops and weeds for our specific target problem. To achieve this, we propose a novel ensemble framework. Our approach involves utilizing different crop and weed models trained on diverse datasets and employing a teacher-student configuration. By using homogeneous stacking of base models and a trainable meta-architecture to combine their outputs, we achieve significant improvements for Canola crops and Kochia weeds on unseen test data, surpassing the performance of single semantic segmentation models. We identify the UNET meta-architecture as the most effective in this context. Finally, through ablation studies, we demonstrate and validate the effectiveness of our proposed model. We observe that including base models trained on other target crops and weeds can help generalize the model to capture varied field conditions. Lastly, we propose two novel datasets with varied conditions for comparisons.
uSee: Unified Speech Enhancement and Editing with Conditional Diffusion Models

Speech enhancement aims to improve the quality of speech signals in terms of quality and intelligibility, and speech editing refers to the process of editing the speech according to specific user needs. In this paper, we propose a Unified Speech Enhancement and Editing (uSee) model with conditional diffusion models to handle various tasks at the same time in a generative manner. Specifically, by providing multiple types of conditions including self-supervised learning embeddings and proper text prompts to the score-based diffusion model, we can enable controllable generation of the unified speech enhancement and editing model to perform corresponding actions on the source speech. Our experiments show that our proposed uSee model can achieve superior performance in both speech denoising and dereverberation compared to other related generative speech enhancement models, and can perform speech editing given desired environmental sound text description, signal-to-noise ratios (SNR), and room impulse responses (RIR). Demos of the generated speech are available at https://muqiaoy.github. io/usee.
Advancements in Optimization: Adaptive Differential Evolution with Diversification Strategy

This study presents a population-based evolutionary optimization algorithm (Adaptive Differential Evolution with Diversification Strategies or ADEDS). The algorithm developed using the sinusoidal objective function and subsequently evaluated with a wide-ranging set of 22 benchmark functions, including Rosenbrock, Rastrigin, Ackley, and DeVilliersGlasser02, among others. The study employs single-objective optimization in a two-dimensional space and runs ADEDS on each of the benchmark functions with multiple iterations. In terms of convergence speed and solution quality, ADEDS consistently outperforms standard DE for a variety of optimization challenges, including functions with numerous local optima, plate-shaped, valley-shaped, stretched-shaped, and noisy functions. This effectiveness holds great promise for optimizing supply chain operations, driving cost reductions, and ultimately enhancing overall performance. The findings imply the importance of effective optimization strategy for improving supply chain efficiency, reducing costs, and enhancing overall performance.