Miracle-dz/UniFAR-FTUs huggingface dataset:
pH-Dependent Behavior of Novel 5-FU Delivery System in Environmental Conditions Comparable to the Gastro-Intestinal Tract

A biogenic carrier for 5-fluorouracil (5-FU) loading and subsequent tableting as a new drug formulation for slow release has been proposed using the biomineral from blue crab carapace. Due to its highly ordered 3D porous nanoarchitecture, the biogenic carbonate carrier could achieve increased effectiveness in colorectal cancer cure provided that the formulation would successfully pass through the gastric acid conditions. Following the recently proven viability of the concept by demonstrating the slow release of the drug from the carrier using the highly sensitive SERS technique, here we investigated the 5-FU release from the composite tablet drug in pH conditions replicating the gastric environment. The released drug from the tablet was studied in solutions with three relevant pH values, pH 2, pH 3, and pH 4. The 5-FU SERS spectral signature for each pH value was used to build calibration curves for quantitative SERS analysis. The results suggested a similarly slow-releasing pattern in acid pH environments to that in neutral conditions. Although biogenic calcite dissolution was expected in acid conditions, the X-ray diffraction and Raman spectroscopy showed preservation of calcite mineral along with the monohydrocalcite during acid solution exposure for two hours. The total released amount in a time course of seven hours, however, was lower in acidic pH solutions, with a maximum fraction of ~40% of the total amount of loaded drug, for pH 2, as opposed to ~80% for neutral values. Nonetheless, these results clearly prove that the novel composite drug retains its slow-releasing character in environmental conditions compatible with the gastrointestinal pH and that it is a viable and biocompatible alternative for oral delivery of anticancer drug to reach the lower gastro-intestinal tract.
Safety and efficacy of Levorag emulgel in the treatment of anal fissures using a validated scoring system

Introduction Anal fissure is one of the most common anal disease characterized by intense anal pain, and deterioration of patients quality of life. Treatment is mainly based on the topical administration of calcium antagonist or nitric oxide ointments, and in cases refractory to medical treatment patients can undergo surgery. This study aims to assess the efficacy and safety of Levorag emulgel in the treatment of acute and chronic fissures using of a validated scoring system. Material and Methods A prospective observational study was carried out on patients with anal fissures between February and May 2022. The efficacy of the treatment was evaluated using the REALISE score, a new validated scoring system that rates VAS for pain, NSAID use, pain duration, bleeding, and quality of life (QoL), recorded after 10, 20 and 30 days from the beginning of treatment. Results Forty patients (median age 46 years, IQR 29–57, 70% women) with acute (22, 55%) or chronic (18, 45%) anal fissures entered the study. The median anal pain score according to the VAS scale decreased significantly from 7 (IQR 4.7–8) at baseline to 1 (IQR 0–3.2, p = 0.05) after 20 days. At the 30-day proctological examination, 22 patients (61%) were pain free (median VAS of 0, IQR 0–1.2, p < 0.05). Pain duration after defecation measured according to the REALISE score, showed a significant decrease after 10 days, from a median value of 2 (IQR 1–4) to 1 (IQR 1–1.2) (p < 0.005). The median value of the REALISE score decreased significantly, from 15 (IQR 11–19.25) at first proctological evaluation to 4 (IQR 4–6, p = 0.139) after 30 days of treatment. At day 30, complete fissure healing was achieved in 30 patients (80%). The healing rate was 82% and 78% in patients with acute and chronic anal fissures, respectively. Conclusion The use of Levorag® Emulgel may represent a safe and effective non-invasive first line treatment in patients affected by acute or chronic anal fissure.
A minimally invasive ultrasound probe using non-coax cabling

The electrical and acoustical performance of ultrasonic transducers is critically dependent on the characteristics of the cable to which they are attached. The traditional solution for medical ultrasound probes is to use coaxial cables. However, as the number of elements increases and as size and weight requirements become of more concern, the pressure for cabling solutions that are smaller, lighter, and less expensive also continues to increase. One solution is to use non-coaxial ribbon cables that have been designed to minimize crosstalk and provide higher signal density and better ergonomics. Minimally-invasive ultrasound probes are a natural place for early adoption of this kind of nontraditional cable since laparoscopic, endoscopic, and catheter probes have exceptionally difficult space constraints. This paper describes the use of IMAGIN/sup TM/ probe cable and MICROFLAT/sup TM/ ribbon cable, products developed by the W.L. Gore company, for a wiring harness of an ultrasound laparoscope containing a 128 element array. Small coaxial cables physically fit but are very difficult to terminate.
On the use of common random numbers in activity-based travel demand modeling for scenario comparison

ABSTRACT Activity-based travel demand models provide a high level of detail when modeling complex travel behavior. Since stochastic simulation is used, however, this high level may induce large random fluctuations in the output, necessitating many model reruns to produce reliable output. This may become prohibitive in terms of computation time when comparing travel behavior between multiple scenarios, in which case each scenario requires its own simulation. To alleviate this issue, we study the use of common random numbers, which is a technique that reuses the same random numbers for choices made by travelers between scenarios. This ensures that any observed difference in output across scenarios cannot be attributed to mutual differences in drawn random numbers, eliminating an important source of random fluctuation. We demonstrate by a numerical study that common random numbers can greatly reduce the number of runs needed, and thus also the required computation time, to obtain reliable output.
Towards Optimization of Privacy-Utility Trade-Off Using Similarity and Diversity Based Clustering

Most data owners publish personal data for information consumers, which is used for hidden knowledge discovery. But data publishing in its original form may be subjected to unwanted disclosure of subjects’ identities and their associated sensitive information, and therefore, data is usually anonymized before publication. Many anonymization techniques have been proposed, but most of them often sacrifice utility for privacy, or vice versa, and explicitly disclose sensitive information when original data have skewed distributions. To address these technical problems, we propose a novel anonymization method using similarity and diversity-based clustering that effectively preserves both the subjects’ privacy and anonymous-data utility. We identify influential attributes from the original data using a machine learning algorithm that assists in preserving a subject's privacy in imbalanced clusters, and that remained unexplored in previous research. The objective function of the clustering process considers both similarity and diversity in the attributes while assigning records to clusters, whereas most of the existing clustering-based anonymity techniques consider either similarity or diversity, thereby sacrificing either privacy or utility. Attribute values in each cluster set are minimally generalized to effectively achieve both competing goals. Extensive experiments were conducted on four real-world benchmark datasets to prove the feasibility of proposed method. The experimental results showed that the common and AI-based privacy risks were reduced by 13.01% and 24.3% respectively in contrast to existing methods. Data utility was augmented by 11.25% and 20.21% on two distinct metrics compared to its counterparts. The complications (e.g., # of iterations) of the clustering process were 2.25× lower than the state-of-the-art methods.
Model-Based Deep Reinforcement Learning with Traffic Inference for Traffic Signal Control

In the modern world, the extremely rapid growth of traffic demand has become a major problem for urban traffic development. Continuous optimization of signal control systems is an important way to relieve traffic pressure in cities. In recent years, with the impressive development of deep reinforcement learning (DRL), some DRL approaches have started to be applied to traffic signal control. Unlike traditional signal control methods, agents trained using DRL approaches continuously receive feedback from the environment to continuously improve the policy. Since current research in the field is more focused on the performance of the agent, data efficiency during training is ignored to some extent. However, in traffic signal control tasks, the cost of trial and error is very expensive. In this paper, we propose a DRL approach based on a traffic inference model. The proposed traffic inference model is based on the future information given based on upstream intersections and data from the environment to continuously learn the changing patterns of the traffic environment in order to make inferences about changes in the traffic environment. In the proposed algorithm, the inference model interacts with the agent instead of the environment. Through comprehensive experiments based on realistic datasets, we demonstrate that our proposed algorithm is superior to other algorithms in terms of its data efficiency and stronger performance.
Low humoral and cellular immune responses early after breakthrough infection may contribute to severe COVID-19

Background Little is known about the immune determinants for severe coronavirus disease 2019 (COVID-19) in individuals vaccinated against severe acute respiratory syndrome coronavirus 2. We therefore attempted to identify differences in humoral and cellular immune responses between patients with non-severe and severe breakthrough COVID-19. Methods We prospectively enrolled hospitalized patients with breakthrough COVID-19 (severe and non-severe groups) and uninfected individuals who were vaccinated at a similar time (control group). Severe cases were defined as those who required oxygen therapy while hospitalized. Enzyme-linked immunosorbent assays and flow cytometry were used to evaluate humoral and cellular immune responses, respectively. Results Anti-S1 IgG titers were significantly lower in the severe group than in the non-severe group within 1 week of symptom onset and higher in the non-severe group than in the control group. Compared with the control group, the cellular immune response tended to be diminished in breakthrough cases, particularly in the severe group. In multivariate analysis, advanced age and low anti-S1 IgG titer were associated with severe breakthrough COVID-19. Conclusions Severe breakthrough COVID-19 might be attributed by low humoral and cellular immune responses early after infection. In the vaccinated population, delayed humoral and cellular immune responses may contribute to severe breakthrough COVID-19.
Threshold-current analysis of InGaAs-InGaAsP multiquantum well separate-confinement lasers

An analysis of the threshold behavior of long-wavelength ( lambda =1.55 mu m) multiquantum well separate-confinement lasers with InGaAs wells and quaternary ( lambda /sub g/=1.3 mu m) barriers is presented. Using the effective mass approximation and Fermi statistics for carriers, an approximately logarithmic dependence of optical gain on carrier density for quantum well lasers with one confined electron state is predicted theoretically. This prediction is verified by measured threshold currents of broad-area lasers of various cavity lengths and different numbers of quantum wells. Moreover, the characteristic parameters, such as transparency current density, gain constant, and absorption outside the active region, are determined. >
Carbon-Based Stimuli-Responsive Nanomaterials: Classification and Application

Carbon-based nanomaterials, including carbon nanotubes, carbon nanospheres, and carbon nanofibers, are becoming a research hotspot due to their unique structure and good mechanical, thermal, electrical, optical, and chemical properties. With the development of material synthesis technology, they can be functionalized and used in various fields such as energy, environment, and biomedicine. In particular, stimuli-responsive carbon-based nanomaterials have stood out in recent years because of their smart behavior. Researchers have applied carbon-based nanomaterials to different disease treatments based on their stimulus-response properties. In this paper, based on stimuli-responsive carbon-based nanomaterials’ morphology, we categorize them into carbon nanotubes, carbon nanospheres, and carbon nanofibers according to their morphology. Then, their applications in probes, bioimaging, tumor therapy, and other fields are discussed. Finally, we address the advantages and disadvantages of carbon-based stimuli-responsive nanomaterials and discuss their future perspective.
Estimate Method for Parameters of Asynchronous Motor Based on Inverse Heat Conduction Problem Analysis

The conduction coefficient of insulative layer between ring and stator of asynchronous motor and the convection coefficient within the gap between stator teeth and rotor were computed by L-M method. The 2D thermal fieild of stator was rebuilt by finite volume method. The inverse heat conduction problem analysis can estimate the parameter and boundary conditions of motor accurately. When measurements are precision, the calculation results are accurate. With little errors of measurements, the calculation results can satisfy engineering need. For plus errors, the anti-jamming of identification of the two parameters is much better. When measurement error reach 0.2℃, the computing error of conduction coefficient is only 3.36% and that of the covection coefficient is near 10%. When estimate parameteres of asynchronous motor based on inverse heat conduction problem analysis minus errors should be avoided.
Heme Scavenging and Delivery: The Role of Human Serum Albumin

Heme is the reactive center of several metal-based proteins that are involved in multiple biological processes. However, free heme, defined as the labile heme pool, has toxic properties that are derived from its hydrophobic nature and the Fe-atom. Therefore, the heme concentration must be tightly controlled to maintain cellular homeostasis and to avoid pathological conditions. Therefore, different systems have been developed to scavenge either Hb (i.e., haptoglobin (Hp)) or the free heme (i.e., high-density lipoproteins (HDL), low-density lipoproteins (LDL), hemopexin (Hx), and human serum albumin (HSA)). In the first seconds after heme appearance in the plasma, more than 80% of the heme binds to HDL and LDL, and only the remaining 20% binds to Hx and HSA. Then, HSA slowly removes most of the heme from HDL and LDL, and finally, heme transits to Hx, which releases it into hepatic parenchymal cells. The Hx:heme or HSA:heme complexes are internalized via endocytosis mediated by the CD91 and CD71 receptors, respectively. As heme constitutes a major iron source for pathogens, bacteria have evolved hemophores that can extract and uptake heme from host proteins, including HSA:heme. Here, the molecular mechanisms underlying heme scavenging and delivery from HSA are reviewed. Moreover, the relevance of HSA in disease states associated with increased heme plasma concentrations are discussed.
Novel two-stage piezoelectric-based ocean wave energy harvesters for moored or unmoored buoys

Harvesting mechanical energy from ocean wave oscillations for conversion to electrical energy has long been pursued as an alternative or self-contained power source. The attraction to harvesting energy from ocean waves stems from the sheer power of the wave motion, which can easily exceed 50 kW per meter of wave front. The principal barrier to harvesting this power is the very low and varying frequency of ocean waves, which generally vary from 0.1Hz to 0.5Hz. In this paper the application of a novel class of two-stage electrical energy generators to buoyant structures is presented. The generators use the buoy's interaction with the ocean waves as a low-speed input to a primary system, which, in turn, successively excites an array of vibratory elements (secondary system) into resonance - like a musician strumming a guitar. The key advantage of the present system is that by having two decoupled systems, the low frequency and highly varying buoy motion is converted into constant and much higher frequency mechanical vibrations. Electrical energy may then be harvested from the vibrating elements of the secondary system with high efficiency using piezoelectric elements. The operating principles of the novel two-stage technique are presented, including analytical formulations describing the transfer of energy between the two systems. Also, prototypical design examples are offered, as well as an in-depth computer simulation of a prototypical heaving-based wave energy harvester which generates electrical energy from the up-and-down motion of a buoy riding on the ocean's surface.
Switching nonparametric regression models

We propose a methodology to analyse data arising from a curve that, over its domain, switches among J states. We consider a sequence of response variables, where each response y depends on a covariate x according to an unobserved state z. The states form a stochastic process and their possible values are j=1, … , J. If z equals j the expected response of y is one of J unknown smooth functions evaluated at x. We call this model a switching nonparametric regression model. We develop an Expectation–Maximisation algorithm to estimate the parameters of the latent state process and the functions corresponding to the J states. We also obtain standard errors for the parameter estimates of the state process. We conduct simulation studies to analyse the frequentist properties of our estimates. We also apply the proposed methodology to the well-known motorcycle dataset treating the data as coming from more than one simulated accident run with unobserved run labels.
Evaluation of Computational Complexity for Distribution Systems State Estimation

Distribution system state estimation (DSSE) is a key monitoring function for system operators to automate, control, and operate the active distribution grids. The computational complexity of the DSSE algorithm is a design factor for implementing a real-time monitoring system. As a common criterion, the elapsed time has been used in many works to determine whether a developed DSSE solver, regardless of data acquisition and bad data detection steps, can satisfy the requirement of real-time operation. This measure, however, is machine-dependent and thus may not be a good indicator to show the computational burden of a state estimation method in run-time. This article proposes instead to use floating point operation (FLOP) which is a machine-independent computational complexity measure. The number of FLOPs is a suitable relative performance indicator in determining how fast a method could be executed. In this article, the computational cost of different DSSE algorithms is mathematically derived in terms of FLOPs. On this basis, this article shows that FLOP is a better measure than the more commonly used elapsed time for making design decisions about the choice of the DSSE method based on computational complexity.
Surface wave scattering by submerged cylinders of arbitrary cross–section

The reflection of surface waves normally incident upon an infinite uniform horizontal cylinder of arbitrary cross–section totally immersed beneath the free surface of a fluid of either finite or infinite depth is considered under the assumptions of linearized theory. The problem is formulated in terms of a first–kind integral equation for an unknown function related to the tangential velocity of the fluid around the cylinder. The kernel of the integral operator is symmetric and only weakly singular, a property that is particularly advantageous when considering thin submerged plates. Numerical results are obtained by approximating the solution using Galerkin's method, and it is shown how only a few terms in the expansion are needed to gain high accuracy in the reflection and transmission coefficients. Similar accuracy is observed for thin plates, where special test functions are introduced to correctly model singular behaviour at the ends of the plate. Results for both infinite and finite depth scattering are compared with existing results and new results are also presented showing zeros of transmission for families of submerged obstacles.
How Accurate Is the Diagnosis of “Chronic Obstructive Pulmonary Disease” in Patients Hospitalized with an Acute Exacerbation?

Rationale: COPD diagnosis requires relevant symptoms and an FEV1/FVC ratio of <0.7 post-bronchodilator on spirometry. Patients are frequently labeled as COPD based on clinical presentation and admitted to the hospital with this diagnosis even though spirometry is either not available or has never been performed. The aim of this study was to evaluate the accuracy of COPD diagnosis based on post-bronchodilator spirometry, following hospital admission for COPD exacerbation. Methods: This is a retrospective study with a cross-sectional analysis of a subgroup of patients. Demographic and clinical data and pre-admission spirometry were collected from electronic records of patients hospitalized with a primary diagnosis of COPD. Patients without available spirometry were contacted for a pulmonary consultation and spirometry. Three groups were compared: patients with a confirmed COPD diagnosis (FEV1/FVC < 0.7), without COPD (FEV1/FVC > 0.7), and those who have never performed spirometry. Results: A total of 1138 patients with a recorded diagnosis of COPD were identified of which 233 patients were included in the analysis. Only 44.6% of patients had confirmed COPD according to GOLD criteria. In total, 32.6% of the patients had never undergone spirometry but were treated as COPD, and 22.7% had performed spirometry without evidence of COPD. Recurrent admission due to COPD was a strong predictor of a confirmed COPD diagnosis. Conclusions: Among the patients admitted to the hospital with a COPD diagnosis, a high proportion were not confirmed by the current GOLD report or had never performed spirometry. Stricter implementation of the diagnostic criteria of COPD in admitted patients is necessary to improve diagnosis and the treatment outcomes in these patients.
Approximation by k-sparse sums of eigenfunctions of linear operators In der Signalverarbeitung besitzt man oft ein a priori-Wissendie Struktur der zu untersuchenden Signale. Die Aufgabe besteht nun darin, aus gegebenen Messdaten auf die speziellen Parameter dieser Struktur zu schlieen.

with complex parameters cj and Tj , j = 1, . , k, and assuming that −π < ImT1 < . < ImTk < π, one aims to reconstruct cj and Tj from a given small amount of (possibly noisy) measurement values f(`). Using Prony’s method or one of its stabilized variants, one is able to reconstruct f with only 2k function values f(`), ` = 0, . , 2k − 1. The solution of this problem involves the determination of a so-called “Prony polynomial”