Miracle-dz/UniFAR-FTUs huggingface dataset:
Underdiagnosed CKD in Geriatric Trauma Patients and Potent Prevention of Renal Impairment from Polypharmacy Risks through Individual Pharmacotherapy Management (IPM-III)

The aging global patient population with multimorbidity and concomitant polypharmacy is at increased risk for acute and chronic kidney disease, particularly with severe additional disease states or invasive surgical procedures. Because from the expertise of more than 58,600 self-reviewed medications, adverse drug reactions, drug interactions, inadequate dosing, and contraindications all proved to cause or exacerbate the worsening of renal function, we analyzed the association of an electronic patient record- and Summaries of Product Characteristics (SmPCs)-based comprehensive individual pharmacotherapy management (IPM) in the setting of 14 daily interdisciplinary patient visits with the outcome: further renal impairment with reduction of eGFR ≥ 20 mL/min (redGFR) in hospitalized trauma patients ≥ 70 years of age. The retrospective clinical study of 404 trauma patients comparing the historical control group (CG) before IPM with the IPM intervention group (IG) revealed a group-match in terms of potential confounders such as age, sex, BMI, arterial hypertension, diabetes mellitus, and injury patterns. Preexisting chronic kidney disease (CKD) > stage 2 diagnosed as eGFR < 60 mL/min/1.73 m2 on hospital admission was 42% in the CG versus 50% in the IG, although in each group only less than 50% of this was coded as an ICD diagnosis in the patients’ discharge letters (19% in CG and 21% in IG). IPM revealed an absolute risk reduction in redGFR of 5.5% (11 of 199 CG patients) to 0% in the IPM visit IG, a relative risk reduction of 100%, NNT 18, indicating high efficacy of IPM and benefit in improving outcomes. There even remained an additive superimposed significant association that included patients in the IPM group before/beyond the 14 daily IPM interventions, with a relative redGFR risk reduction of 0.55 (55%) to 2.5% (5 of 204 patients), OR 0.48 [95% CI 0.438–0.538] (p < 0.001). Bacteriuria, loop diuretics, allopurinol, eGFR ≥ 60 mL/min/1.73 m2, eGFR < 60 mL/min/1.73 m2, and CKD 3b were significantly associated with redGFR; of the latter, 10.5% developed redGFR. Further multivariable regression analysis adjusting for these and established risk factors revealed an additive, superimposed IPM effect on redGFR with an OR 0.238 [95% CI 0.06–0.91], relative risk reduction of 76.2%, regression coefficient −1.437 including patients not yet visited in the IPM period. As consequences of the IPM procedure, the IG differed from the CG by a significant reduction of NSAIDs (p < 0.001), HCT (p = 0.028) and Würzburger pain drip (p < 0.001), and significantly increased prescription rate of antibiotics (p = 0.004). In conclusion, (1) more than 50% of CKD in geriatric patients was not pre-recognized and underdiagnosed, and (2) the electronic patient records-based IPM interdisciplinary networking strategy was associated with effective prevention of further periinterventional renal impairment and requires obligatory implementation in all elderly patients to urgently improve patient and drug safety.
Short-Term Effects of Climate Variability on Childhood Diarrhoea in Bangladesh: Multi-Site Time-Series Regression Analysis

The aim of this study was to estimate the effects of climate on childhood diarrhoea hospitalisations across six administrative divisions in Bangladesh and to provide scientific evidence for local health authorities for disease control and prevention. Fortnightly hospital admissions (August/2013–June/2017) for diarrhoea in children under five years of age, and fortnightly average maximum temperature, relative humidity and rainfall recordings for six administrative divisions were modelled using negative binomial regression with distributed lag linear terms. Flexible spline functions were used to adjust models for seasonality and long-term trends. During the study period, 25,385 diarrhoea cases were hospitalised. Overall, each 1 °C rise in maximum temperature increased diarrhoea hospitalisations by 4.6% (IRR = 1.046; 95% CI, 1.007–1.088) after adjusting for seasonality and long-term trends in the unlagged model. Using lagged effects of maximum temperature, and adjusting for relative humidity and rainfall for each of the six administrative divisions, the relationship between maximum temperature and diarrhoea hospitalisations varied between divisions, with positive and negative effect estimates. The temperature-diarrhoea association may be confounded by seasonality and long-term trends. Our findings are a reminder that the effects of climate change may be heterogeneous across regions, and that tailored diarrhoea prevention strategies need to consider region-specific recommendations rather than relying on generic guidelines.
Wavelet Residual Network for Low-Dose CT via Deep Convolutional Framelets

Model based iterative reconstruction (MBIR) algorithms for low-dose X-ray CT are computationally expensive. To address this problem, we recently proposed the world-first deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the texture were not fully recovered. To cope with this problem, here we propose a deep residual learning approach in directional wavelet domain. The proposed method is motivated by an observation that a deep convolutional neural network can be interpreted as a multilayer convolutional framelets expansion using non-local basis convolved with data-driven local basis. We further extend the idea to derive a deep convolutional framelet expansion by combining global redundant transforms and signal boosting from multiple signal representations. Extensive experimental results confirm that the proposed network has significantly improved performance and preserves the detail texture of the original images
Computational analysis of physical mechanisms at the onset of three-dimensionality in the wake of a square cylinder

A high-order spectral element method is used to perform direct numerical simulations of flow past a square cylinder at the transition from the two-dimensional von Kármán vortex street in the wake of the cylinder to three-dimensionality. A Reynolds number range between 100 and 300 has been considered and good agreement with previous numerical and experimental results is obtained. At transition, the spanwise perturbation observed in the cylinder wake occurs before the onset of the streamwise vortex separation, while the former mechanism is commonly shown, in the literature, to originate from the latter by an instability of the cores or interactions of shed vortices in the vicinity of the cylinder. It is shown that the first three-dimensional unstable mode, the mode $A$ , originates from an axial stretching of the upstream perturbed vorticity, existing on the braids, due to the strain field created by the spanwise vortices which evolve under a shear instability of the wake when viscous effects are small. On the other hand, the mode $B$ is observed to arise only after spanwise vortices are shed downstream of the cylinder.
Phase Transitions, Optimal Errors and Optimality of Message-Passing in Generalized Linear Models

We consider generalized linear models (GLMs) where an unknown $n$-dimensional signal vector is observed through the application of a random matrix and a non-linear (possibly probabilistic) componentwise output function. We consider the models in the high-dimensional limit, where the observation consists of m points, and $m/n\to\alpha$ where $\alpha$ stays finite in the limit $m,n\to\infty$. This situation is ubiquitous in applications ranging from supervised machine learning to signal processing. A substantial amount of theoretical work analyzed the model-case when the observation matrix has i.i. d. elements and the components of the ground-truth signal are taken independently from some known distribution. While statistical physics provided number of explicit conjectures for special cases of this model, results existing for non-linear output functions were so far non-rigorous. At the same time GLMs with non-linear output functions are used as a basic building block of powerful multilayer feedforward neural networks. Therefore rigorously establishing the formulas conjectured for the mutual information is a key open problem that we solve in this paper. We also provide an explicit asymptotic formula for the optimal generalization error, and confirm the prediction of phase transitions in GLMs. Analyzing the resulting formulas for several non-linear output functions, including the rectified linear unit or modulus functions, we obtain quantitative descriptions of information-theoretic limitations of high-dimensional inference. Our proof technique relies on a new version of the interpolation method with an adaptive interpolation path and is of independent interest. Furthermore we show that a polynomial-time algorithm referred to as generalized approximate message-passing reaches the optimal generalization error for a large set of parameters.
Biodecolorization of azo dye Acid Blue 92 (AB92) by Ceratophyllum de-mersum L.: process optimization using Taguchi method and toxicity as-sessment

This study evaluated the ability of the submerged aquatic plant Ceratophyllum demersum to remove the Acid Blue 92 (AB92) dye. The effect of some operational parameters such as the reaction time, initial dye concentration, initial plant biomass, and pH, on dye removal efficiency was studied. Based on Taguchi’s results, the optimized conditions for dye removal were time 7 days, dye concentration 20 mg l-1, initial plant biomass 4 g, and initial pH 5. Fourier-transform infrared spectroscopy (FTIR) results confirmed the interaction between dye molecules and plants. Based on the results of this study, C. demersum had a reusability to remove the dye, this fact confirming the mechanism of biodegradation in the dye removal process. Also, the effect of AB92 on the physiological responses of C. demersum was investigated. Minimum relative growth rate, tolerance index, chlorophyll a, chlorophyll b, total chlorophyll, and total carotenoids at a concentration of 20 mg l-1 of AB92 were observed. The concentration of cyanidin glycoside, lipid peroxidation, and antioxidant activity increased in both concentrations of 10 and 20 mg l-1. It can be concluded that both concentrations of AB92 induced antioxidant activity and the risk of oxidative stress for Ceratophyllum.
Silver Vapor Supersonic Jets: Expansion Dynamics, Cluster Formation, and Film Deposition

Supersonic jets of metal vapors with carrier gas are promising for producing nanostructured metal films at relatively low source temperatures and high deposition rates. However, the effects of the carrier gas on the jet composition and expansion dynamics, as well as on film properties, remain virtually unexplored. In this work, the free-jet expansion of a mixture of silver vapor with helium in a rarefied regime at an initial temperature of 1373 K is investigated through mass spectrometry and direct-simulation Monte Carlo methods. Introducing the carrier gas into the source is found to result in a transition from a collisionless to a collision-dominated expansion regime and dramatic changes in the Ag jet, which becomes denser, faster, and more forward-directed. The changes are shown to be favorable for the formation of small Ag clusters and film deposition. At a fairly high helium flow, silver Ag2 dimers are observed in the jet, both in the experiment and the simulations, with a mole fraction reaching 0.1%. The terminal velocities of silver atoms and dimers are nearly identical, indicating that the clusters are likely formed due to the condensation of silver vapor in the expanding jet. A high potential of supersonic Ag-He jets for the deposition of nanostructured silver films is demonstrated. The deposited jet Ag2 dimers appear to serve as nucleation centers and, thus, allow for controlling the size of the produced surface nanostructures.
Long-Term Effect of Guided Implant Surgery on Clinical Outcomes and Peri-Implantitis of Maxillary Implants—An Observational Cohort Study

(1) Although the accuracy of static computer-aided implant surgery (sCAIP) is well reported, information on its long-term effect on peri-implant health and complications is scarce. (2) Twenty-six patients initially treated were recalled. Implant survival, radiographic bone level, peri-implant health, and complications were registered. A multilevel regression model was applied to study the relationship between the research variables. (3) Sixteen patients participated in this study (average age 58.5 years; range 27.8–73.8). The mean follow-up time was 9.1 years (range 7.3–11.3). Two implants failed, resulting in a survival rate of 97.1%. The mean bone level change corresponded to a loss of 0.63 mm (SD 1.90) for the whole group, 0.17 mm (SD 1.46), and 0.91 mm (SD 2.09) for tooth- and mucosa-supported guides, respectively. The mean PPD for the total group was 4.24 mm (SD 1.25), and 3.79 mm (SD 0.97) and 4.51 mm (SD 1.33) for the tooth- and mucosa-supported guides, respectively. Four implants (6.3%) were diagnosed with peri-implantitis. Coronal deviation was slightly associated with having a negative impact on bone level at follow-up, but this was not statistically significant. Seven patients (43.8%) experienced technical complications. Biological complications were seen in 3/16 patients (18.75%). (4) SCAIP may contribute to more predictable implant placement; the long-term clinical outcome is similar to conventional nonguided surgery.
Evidence for Gain Reduction by a Precursor in an On-Frequency Forward Masking Paradigm

Summary A forward masking technique was used to measure cochlear gain reduction which might be consistent with the medial olivocochlear reflex (MOCR). A 4-kHz signal was set at 20 dB SL, and an on-frequency forward masker adjusted to just mask the signal. Adding a pink noise precursor before the signal and masker increased the level of the masker needed to mask the signal, in contrast to what would be expected from theories such as additivity of masking. The magnitude and pattern of this increase was similar to the increase in signal threshold seen with an off-frequency masker following a precursor.
Seed priming influences on yield and protein content of wheat sown at different time

The aim of this field experiment conducted at the Agronomy Field Laboratory, Bangladesh Agricultural University, Mymensingh, was to investigate the impact of different seed priming techniques and sowing dates on the yield and quality of wheat. The study aimed to identify the most effective seed priming technique and optimal sowing date to enhance wheat productivity and minimize yield reduction. The experiment employed a split plot design with two factors: seed priming techniques (no priming, hydropriming with distilled water, osmopriming with PEG, and halopriming with CaCl2) and sowing dates (November 20, December 05, and December 20). The trial was conducted from November 2019 to April 2020 at the research field. The study consisted of three replications for each treatment combination. Osmopriming exhibited the most favorable results among all priming techniques, showing significantly higher values for effective tillers hill-1 (3.91), number of grains spike-1 (43.82), number of spikelets spike-1 (16.16), grain yield (3.87 tons hectare-1), biological yield (6.02 t ha-1), and harvest index (39.03%). No priming condition resulted in the highest protein content (12.11%), while osmopriming had the lowest protein content (11.77%). The sowing conducted on November 20 yielded the highest number of effective tillers hill-1 (3.57), number of grains spike-1 (42.49), number of spikelets spike-1 (15.75), grain yield (3.71 t ha-1), biological yield (9.70 t ha-1), and lowest protein content (11.74%). Sowing on December 20 resulted in the highest protein content (12.20%). Based on the study's findings, it can be concluded that the osmopriming technique, combined with sowing on November 20, offers the most promising approach to mitigate the yield reduction of wheat. This combination demonstrated the highest grain yield (4.23 t ha-1) compared to other treatments. Therefore, farmers and agricultural practitioners are recommended to adopt the osmopriming technique with a sowing date of November 20 for optimizing wheat production and enhancing overall crop quality.
Contributions to the theory of maximum entropy estimation for ill-posed models

Statistical techniques are essential in most areas of science being linear regression one of the most widely used. It is well-known that under fairly conditions linear regression is a powerful statistical tool. Unfortunately, some of these conditions are usually not satisfied in practice and the regression models become ill-posed, which means that the application of traditional estimation methods may lead to non-unique or highly unstable solutions. This work is mainly focused on the maximum entropy estimation of ill-posed models, in particular the estimation of regression models with small samples sizes affected by collinearity and outliers. The research is developed in three directions, namely the estimation of technical efficiency with state-contingent production frontiers, the estimation of the ridge parameter in ridge regression, and some developments in maximum entropy estimation. In the estimation of technical efficiency with state-contingent production frontiers, this work reveals that the maximum entropy estimators outperform the maximum likelihood estimator in most of the cases analyzed, namely in models with few observations in some states of nature and models with a large number of states of nature, which usually represent models affected by collinearity. The maximum entropy estimators are expected to make an important contribution to the increase of empirical work with state-contingent production frontiers. The main challenge in ridge regression is the selection of the ridge parameter. There is a huge number of methods to estimate the ridge parameter and no single method emerges in the literature as the best overall. In this work, a new method to select the ridge parameter in ridge regression is presented. The simulation study reveals that, in the case of regression models with small samples sizes affected by collinearity, the new estimator is probably one of the best ridge parameter estimators available in the literature on ridge regression. Founded on the Shannon entropy, the ordinary least squares estimator and some concepts from quantum electrodynamics, the maximum entropy Leuven estimator overcomes the main weakness of the generalized maximum entropy estimator, avoiding exogenous information that is usually not available. Based on the maximum entropy Leuven estimator, information theory and robust regression, new developments on the theory of maximum entropy estimation are provided in this work. The simulation studies and the empirical applications reveal that the new estimators are a good choice in the estimation of linear regression models with small samples sizes affected by collinearity and outliers. Finally, a contribution to the increase of computational resources on the maximum entropy estimation is also accomplished in this work.
A scale-invariant keypoint detector in log-polar space

The scale-invariant feature transform (SIFT) algorithm is devised to detect keypoints via the difference of Gaussian (DoG) images. However, the DoG data lacks the high-frequency information, which can lead to a performance drop of the algorithm. To address this issue, this paper proposes a novel log-polar feature detector (LPFD) to detect scale-invariant blubs (keypoints) in log-polar space, which, in contrast, can retain all the image information. The algorithm consists of three components, viz. keypoint detection, descriptor extraction and descriptor matching. Besides, the algorithm is evaluated in detecting keypoints from the INRIA dataset by comparing with the SIFT algorithm and one of its fast versions, the speed up robust features (SURF) algorithm in terms of three performance measures, viz. correspondences, repeatability, correct matches and matching score.
A New Model for Tensor Completion: Smooth Convolutional Tensor Factorization

Tensor completion is the problem of filling-in missing parts of multidimensional data using the values of the reference elements. Recently, Multiway Delay-embedding Transform (MDT), which considers a low-dimensional space in a delay-embedded space with high expressive capability, has attracted attention as a tensor completion method. Although MDT has a high complementary performance, its computational cost is considerably high. Therefore, we propose a new model called smooth convolutional tensor factorization (SCTF) for tensor completion based on a delay-embedded space. The proposed method is small in computational complexity because of its concise model of rank-1 decomposition in the delay-embedded space, and because it does not directly perform optimization in the delay-embedded space. In addition, a smooth constraint term is assigned to the factor tensors as a prior data structure in the optimization to improve the completion accuracy further. In our experiments, we completed clipped and random missing image data, and confirmed that the proposed method achieved high completion accuracy without high computational cost.
DeHi: A Decoupled Hierarchical Architecture for Unaligned Ground-to-Aerial Geo-Localization

Ground-to-aerial (G2A) geo-localization remains extremely challenging due to the drastic appearance and geometry differences between ground and aerial views, especially when their relative orientation is unknown. In this paper, we focus on the challenging problem of unaligned G2A geo-localization, where the query ground-level image is not perfectly orientation-aligned with respect to reference aerial imagery. We cast this problem as a metric embedding task and propose a decoupled hierarchical (DeHi) architecture to progressively learn meaningful multi-grained features. Specifically, DeHi first leverages CNN to extract high-level semantic features, and then introduces a novel orthogonally factorized transformer model consisting of part-level and global transformer encoders to learn part-level and global feature descriptors sequentially. For the purpose of enhancing representation power, cross-level connections are introduced to enrich part-level and global descriptors by CNN features, and the pooled part-level descriptor is combined with the global descriptor to construct the final query representation. Furthermore, such a decoupled hierarchical architecture allows for incorporating multi-level deep supervision. We introduce two part-level losses combined with one cross-level loss to complement the widely used global retrieval loss. Extensive experiments on standard benchmark datasets show significant boosting in recall rates compared with the previous state-of-the-art. Remarkably, DeHi improves the recall rate @top-1 from 78.59% to 82.38% (+3.79%) and from 72.91% to 77.94% (+5.03%) on CVUSA and CVACT datasets, respectively, under random orientation misalignments. Besides, DeHi maintains competitive inference efficiency with less parameters compared to existing transformer-based methods.