Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of the effects of different infant formulas on the growth and development and intestinal flora of infants

Abstract The purpose of this study was to compare the effects of different infant formulas on the growth and development, sleep, allergy symptoms, and intestinal flora of infants. A total of 428 infants participated in the study. Breastfeeding (BF) was used as the control, and the remaining subjects were randomly assigned to the full goat milk protein formula group (FGM), partial goat milk protein formula group (PGM), and cow milk formula group (M). During the 6‐month feeding experiment, data on the growth, sleep, allergy symptoms, and intestinal flora of infants were collected using questionnaires, anthropometric measurements, and biochemical examinations. In general, the basic information of the participants was consistent among the groups. There were no differences in infant weight, length, or head circumference among the groups (p > .05). The sleep time of infants in the formula‐fed groups was longer than that of the breastfeeding group at baseline (p < .05), but there were no differences at mid‐term or outcome (p > .05). The incidence of allergic symptoms continued to decrease, and the total scores of allergic symptoms did not differ among the groups (p > .05). The relative abundance of intestinal Bifidobacteriaceae in the PGM group was lower than that in the other groups (p < .05). There was no difference in the β‐diversity of intestinal flora between formula‐fed and breastfed infants (p > .05). There were strong correlations in the composition of the main intestinal flora at the family level between the formula and breastfeeding groups. This study showed that within 6 months of feeding, there were no significant differences in the growth and development, allergic symptoms, or intestinal flora of the infants among the groups.
Recent Advances in Eco-Friendly and Scaling-Up Bioproduction of Prodigiosin and Its Potential Applications in Agriculture

Prodigiosin is a red pigment produced by various microbial strains, of these, Serratia marcescens has been recorded as the major PG-producing strain. This microbial pigment has attracted much attention because it possesses potential applications in various fields. Thus, this active metabolite has been increasingly studied for bioproduction and investigated for its novel applications. Recently, several prodigiosin-related reviews were reported. These studies covered some aspects of the general physicochemical properties, pathway synthesis, production, and applications of prodigiosin in medicine. However, only a few works discussed the eco-friendly production of prodigiosin from organic wastes. Remarkably, the scaling-up of prodigiosin production and its potential applications in agriculture have rarely been reviewed or discussed. This review extensively presents and discusses the green biosynthesis, enhancement, and scaling-up of prodigiosin production from a wide range of organic byproducts/wastes using different methods of fermentation. Notably, this paper highlights the novel and promising applications of prodigiosin in agriculture via in vitro tests, greenhouse tests, and field studies. The action mechanisms related to some potential bioactivities and the toxicology studies of prodigiosin are also discussed. This review thus supplies scientific data for further research and the application of prodigiosin in the future.
Regularized sliced inverse regression for determination of the percentage of crystallinity in FCC catalysts

This paper proposes a new calibration model based on regularized sliced inverse regression (RSIR) for predicting the percentage of crystallinity of fluidized catalytic cracking catalysts (FCC) using Fourier transform mid‐infrared spectroscopy (FT‐MIR). RSIR is an effective dimension‐reduction tool that looks for a proper dimension‐reduction subspace without requiring a pre‐specified functional form for the relation between independent and dependent variables. Combinations of RSIR with linear and nonlinear learning algorithms like multiple linear regression (MLR) and Support vector regression (SVR) were applied to the preprocessed data set. A comparison of performance among the different approaches, including previous results reached using PLS, was done. RSIR–MLR achieved the highest prediction accuracy, leading to a simple calibration model. Copyright © 2010 John Wiley & Sons, Ltd.
Unique Physiological and Behavioural Adaptive Features of The One-Humped Camel (Camelus dromedarius) to Arid Environments

The dromedary camel is mainly found in the tropics of Africa, the Middle East, and the Indian sub-content, where it is of great importance to nomadic and rural communities to provide high-quality animal protein in the form of milk and meat and as a means of transportation. In the Arabian Gulf region, the camel gained popularity and importance recently as a racing animal. The camel is well adapted to harsh environments characterized by inadequate water and vegetation, high ambient temperature
Étude des états finals diphoton dans l’expérience ATLAS au LHC : Mesure de sections efficaces différentielles, découverte d’une nouvelle résonance dans la recherche du boson de Higgs et étude de ses propriétés.

Cette these sera centree sur les analyses des evenements diphotons dans le detecteur ATLAS. L'activite evoluera au cours du temps et couvrira differents aspects: comprehension de la reponse du detecteur, participation a la prise de donnees, analyse physique et recherche de signaux de physique au-dela du modele standard.
Surface Functionalization Strategies of Polystyrene for the Development Peptide-Based Toxin Recognition

The development of a robust surface functionalization method is indispensable in controlling the efficiency, sensitivity, and stability of a detection system. Polystyrene (PS) has been used as a support material in various biomedical fields. Here, we report various strategies of polystyrene surface functionalization using siloxane derivative, divinyl sulfone, cyanogen bromide, and carbonyl diimidazole for the immobilization of biological recognition elements (peptide developed to detect ochratoxin A) for a binding assay with ochratoxin A (OTA). Our objective is to develop future detection systems that would use polystyrene cuvettes such as immobilization support of biological recognition elements. The goal of this article is to demonstrate the proof of concept of this immobilization support. The results obtained reveal the successful modification of polystyrene surfaces with the coupling agents. Furthermore, the immobilization of biological recognition elements, for the OTA binding assay with horseradish peroxidase conjugated to ochratoxin A (OTA-HRP) also confirms that the characteristics of the functionalized peptide immobilized on polystyrene retains its ability to bind to its ligand. The presented strategies on the functionalization of polystyrene surfaces will offer alternatives to the possibilities of immobilizing biomolecules with excellent order- forming monolayers, due to their robust surface chemistries and validate a proof of concept for the development of highly efficient, sensitive, and stable future biosensors for food or water pollution monitoring.
Milk-Compositional Study of Metabolites and Pathogens in the Milk of Bovine Animals Affected with Subclinical Mastitis

Bovine milk is an important food component in the human diet due to its nutrient-rich metabolites. However, bovine subclinical mastitis alters the composition and quality of milk. In present study, California mastitis testing, somatic cell count, pH, and electrical conductivity were used as confirmatory tests to detect subclinical mastitis. The primary goal was to study metabolome and identify major pathogens in cows with subclinical mastitis. In this study, 29 metabolites were detected in milk using gas chromatography–mass spectrometry. Volatile acidic compounds, such as hexanoic acid, hexadecanoic acid, lauric acid, octanoic acid, n-decanoic acid, tricosanoic acid, tetradecanoic acid, and hypogeic acid were found in milk samples, and these impart good flavor to the milk. Metaboanalyst tool was used for metabolic pathway analysis and principal component estimation. In this study, EC and pH values in milk were significantly increased (p < 0.0001), whereas fat (p < 0.04) and protein (p < 0.0002) significantly decreased in animals with subclinical mastitis in comparison to healthy animals. Staphylococcus aureus was the predominant pathogen found (n = 54), followed by Escherichia coli (n = 30). Furthermore, antibiotic sensitivity revealed that Staphylococcus aureus was more sensitive to gentamicin (79.6%), whereas Escherichia coli showed more sensitivity to doxycycline hydrochloride (80%).
Origin of the metallic to insulating transition of an epitaxial Bi(111) film grown on Si(111)

Transport characteristics of single crystal bismuth films on Si(111)-7 × 7 are found to be metallic or insulating at temperatures below or above TC, respectively. The transition temperature TC decreases as the film thickness increases. By combining thickness dependence of the films resistivity, we find the insulating behaviour results from the states inside film, while the metallic behaviour originates from the interface states. We show that quantum size effect in a Bi film, such as the semimetal-to-semiconductor transition, is only observable at a temperature higher than TC.
Hybrid-pumped, gain-switching operation of a Tm-doped fiber laser with linear-polarized output

We report an all-fiber laser with a linear-polarized, pulsed 2 μm output up to 4.6 W by applying a hybrid-pump gain-switched scheme. In this method, a 790 nm laser diode serves as the main supplier of inverse population while a 1 μm pulsed Yb-doped fiber laser triggers the 2 μm pulsed lasing, to realize the gain-switching operation. We construct a resonant cavity with a polarization-maintaining Tm-doped fiber and a fiber Bragg grating to achieve a polarized laser. The laser output is centered at 2021 nm and has a polarization extinction ratio of 13.7 dB; the maximum pulse energy is 230 μJ at a repetition rate of 20 kHz.
Influence of deep-pits on the device characteristics of metal-organic chemical vapor deposition grown AlGaN/GaN high-electron mobility transistors on silicon substrate

We have investigated the regions around deep-pits and their limitations on the performance of metal-organic chemical vapor deposition grown AlGaN/GaN high-electron-mobility transistors (HEMTs) on Si. The device characteristics such as maximum drain current density (IDSmax), threshold voltage (Vth), and three terminal-OFF breakdown (BVOFF) of a HEMT were affected based on the distance of deep-pits from a device active region. The deterioration of HEMT characteristics are significant if the active region is present within 50 μm radius of a deep-pit and becomes insignificant for distance beyond 50 μm. These device characteristics were compared with optical measurements and it was found the region around 50 μm radius of a pit have large dislocations and defects as confirmed by cathodoluminescence. Further, the E2 Raman shift measurements reveal additional stress in the 50 μm region around a pit.
Surface Plasmon Excitation at Topmost Surface in Reflection High-Energy Positron Diffraction

To investigate the excitation process of surface plasmon by fast positrons, we measured the positron energy loss spectra from Si(111)-7×7, Al(111)-1×1, and Bi(001)-1×1 surfaces under the total reflection condition using an energy-filtered reflection high-energy positron diffraction. We observed the multiple surface plasmon excitations, which can be represented by the Poisson distribution with the mean excitation number of 2.4-2.8. The energy loss spectrum is nearly independent of the glancing angle of the incident positron beam. We also measured the electron energy loss spectra at similar glancing angles and determined the mean excitation number to be 1.4-1.8. We found that due to the total reflection positrons are able to excite more surface plasmons as compared to electrons. [DOI: 10.1380/ejssnt.2010. 190]
Fusion of Multitemporal Multisensor Velocities Using Temporal Closure of Fractions of Displacements

Numerous glacier velocity observations, derived from spaceborne imagery, are available online, but it remains difficult to analyze them because they are measured with different temporal baselines, by various sensors. In this study, we propose a novel formulation of the temporal closure to fuse multitemporal multisensor velocity observations without prior information on the displacement behavior and the data uncertainty. We establish a system of linear equations between combinations of displacement observations and fractions of estimated displacements. The proposed approach provides a velocity time-series with a regular and optimal temporal sampling, the latter representing a compromise between the temporal resolution and the signal-to-noise ratio. The proposed approach is first evaluated on synthetic datasets and second on Sentinel-2 and Venus velocity observations over the Fox glacier in New Zealand. The results show the intra-annual variability of Fox glacier surface velocity with a reduced uncertainty and complete temporal coverage.
The irrigation suitability potential of an effluent treated by a pseudo‐natural wetland

The performance of a pseudo‐natural wetland to purify wastewater for irrigation potential was explored in this study. To study the system's performance and the hazard level of the raw wastewater and treated effluent, several water quality parameters were chosen, including electrical conductivity (EC), total dissolved solids (TDS), sodium (Na+), calcium (Ca2+) and magnesium (Mg2+). Four different sampling points (inlet, two intermediate points, and the outlet) were investigated in the study. According to the electrical conductivity, wastewater flowing to the wetland was classified as category four (C4), ‘extremely high salinity’ and thus unfit for irrigation. Both intermediate sampling points were in C3, whereas the outlet sampling point was in C1. According to SAR, 20% of the samples collected at the inlet were classified as ‘very high sodium hazard’ (S4), 45% as ‘high sodium hazard’ (S3) and 35% as ‘medium sodium hazard’ (S2). The findings of this study also demonstrated that the pseudo‐natural wetland can be significantly effective in reducing pollution and providing suitable water for irrigation. Furthermore, the findings raise greater awareness of the importance of protecting and conserving these valuable resources, particularly in urban areas.
Revisión de técnicas de Deep Learning y Machine Learning para la detección y localización de micro aneurismas, exudados y hemorragias en imágenes de fondo de ojo

La pérdida de la visión ha sido uno de los casos más desafortunados de pérdida de los sentidos. Es sabido que la perdida de la visión disminuye considerablemente la calidad de vida de una persona, sin importar la edad. De acuerdo con la OMS, el deterioro de la visión impacta negativamente a niños pequeños en etapa escolar, dado que afecta en gran medida su etapa de desarrollo; en cuanto a los adultos, el deterioro de la visión se ve reflejado a partir de las tasas de participación en el mercado laboral y de productividad de esta población, no obstante, suele ser más baja. Una de las causas de la pérdida de la visión es la retinopatía diabética, que consiste en una enfermedad ocular producto de la diabetes mellitus por un prolongado periodo de tiempo que causa la pérdida parcial o total de la visión. En ese orden de ideas, el objetivo de la presente investigación consiste en recopilar y analizar los diferentes estudios sobre el tema en cuestión; tal información fue tomada de diversas bases de datos especializadas, teniendo en cuenta una clasificación de los cuartiles Q1 y Q2, y cuyo factor de impacto estuvo por encima 2,5 para su selección. Estos en torno a la detección y localización de las tres más importantes anomalías que hacen presencia en personas que sufren de RD, estas son micro aneurismas, exudados y hemorragias. Para ello se necesitaron imágenes de fondo de ojo de pacientes que padecen o están propensos a padecer retinopatía diabética, haciendo uso de algoritmos con técnicas de deep learning y machine. Dichos algoritmos estuvieron guiados por métricas tales como exactitud, sensibilidad y especificad con el fin de medir la eficiencia de algoritmo desarrollado.
Capacitive effective thickness of a few nanometers by atomic layer deposition and device performance in Ge gate-all-around fin field effect transistors

Abstract. Ge gate-all-around fin field-effect transistors (Ge FinFETs) with a capacitive effective thickness of a few nanometers have been successfully achieved via atomic-layer-deposited (ALD) high-dielectric Al2O3 on GeO2/Ge and by adopting low-cost thermo ALD equipment. The MOS interface properties of the ZrO2 or Al2O3/GeO2/Ge structures have been studied systematically. It has been found that a GeO2 interfacial layer that is greater than approximately 2.5 nm results in a significant degradation of the MOS interfaces, while an equivalent oxide thickness of <3 nm is still possible while maintaining good GeO2/Ge interface quality. The Ge FinFET’s value has been demonstrated with the Al2O3/GeO2/Ge gate stack prepared using a thermal ALD layer of Al2O3. The experimental results indicate that the MOS interface quality obtained with the technique developed for high-permittivity/Ge gate stacks is also extremely useful for the fabrication of triangle-fin complementary metal oxide semiconductor devices. An Ion/Ioff ratio of 3.2×104 and a subthreshold swing of 103 mV/dec were obtained for the triangular n-type Ge gate-all-around FET with (111) sidewalls. The drain current at VGS−VT=VDS=−1.5 V is 88 mA/mm.
Comparative study on the effects of micro- and nano-sized zinc oxide supplementation on zinc-deficient mice

Background Zinc (Zn) is an essential cofactor for physiological homeostasis in the body. Zn oxide (ZnO), an inorganic compound that supplies Zn, exists in various sizes, and its bioavailability may vary depending on the size in vivo. However, comparative studies on the nutritional effects of micro-sized ZnO (M-ZnO) and nano-sized ZnO (N-ZnO) supplementation on Zn deficiency (ZnD) animal models have not been reported. Objectives This study investigated the nutritional bioavailability of N-ZnO and M-ZnO particles in dietary-induced ZnD mice. Methods Animals were divided into six experimental groups: normal group, ZnD control group, and four ZnO treatment groups (Nano-Low, Nano-High, Micro-Low, and Micro-High). After ZnD induction, N-ZnO or M-ZnO was administered orally every day for 4 weeks. Results ZnD-associated clinical signs almost disappeared 7 days after N-ZnO or M-ZnO administration. Serum Zn concentrations were higher in the Nano-High group than in the ZnD and M-ZnO groups on day 7 of ZnO treatment. In the liver and testis, Nano-Low and Nano-High groups showed significantly higher Zn concentrations than the other groups after 14-day treatment. ZnO supplementation increased Mt-1 mRNA expression in the liver and testis and Mt-2 mRNA expression in the liver. Based on hematoxylin-and-eosin staining results, N-ZnO supplementation alleviated histological damage induced by ZnD in the testis and liver. Conclusions This study suggested that N-ZnO can be utilized faster than M-ZnO for nutritional restoration at the early stage of ZnD condition and presented Mt-1 as an indicator of Zn status in the serum, liver, and testis.
Approximation Algorithms for Survivable Network Design

Many relevant discrete optimization problems are believed to be hard to solve e ciently (i.e. they cannot be solved in polynomial time unless P=NP). An approximation algorithm is one of the ways to tackle these hard optimization problems. These algorithms have polynomial running time and compute a feasible solution whose value is within a proven factor (approximation factor) of the optimal solution value. The field of approximation algorithms has grown quickly over the last few decades, leading to the development of several algorithmic and analytical techniques. In this doctoral dissertation, we focus on Survivable Network Design problems, where the goal is to construct low-cost networks that are resilient to a few edge/node faults. More specifically, we consider a basic problem in this area, that is the Connectivity Augmentation problem (CAP). In this problem, we are given a k-edge-connected graph (namely, a graph in which removing any k ≠ 1 edges preserves the connectivity of the graph) and a collection of extra edges (links). Our goal is to identify a minimum cardinality subset of links whose addition to the graph makes it (k + 1)-edge connected. This problem is NP-hard and has many interesting real-world applications; For this reason it has been studied through the lens of approximation algorithms in the past. Despite the e orts of several researchers, no progress was made on this problem after the 2-approximation algorithm by Frederickson and JáJá [1981]. We remark that a 2 approximation is known even for wide generalizations of CAP. The main contribution of this thesis is breaching the 2 approximation barrier for CAP by presenting a 1.91 approximation algorithm. Our result is based on a non-trivial reduction to another fundamental problem, Steiner Tree. Along the way to this main achievement, we studied a special case of CAP, the Cycle Augmentation problem (CycAP) for which 2 was the bestknown approximation factor. Here we are given a cycle plus additional links, and the goal is to find a subset of links with minimum size whose addition to G makes it 3-edge-connected. We show that CycAP is APX-hard, in particular it does not admit an approximation factor arbitrarily close to 1 (even the NP-
Multi-fidelity Bayesian experimental design using power posteriors

As experimental tools in the physical and life sciences become increasingly sophisticated and costly, there is a need to optimize the choice of experimental parameters to maximize the informativeness of the data and minimize cost. When designing an experiment, there is often a choice among a suite of data collection modalities or instruments with varying fidelities that trade off between accuracy and cost. For example, biologists have access to a range of tools to study living tissues, from state-of-theart yet expensive technologies for mapping the molecular makeup of individual cells to more dated yet cheap tools to measure the aggregate molecular composition of the tissue’s cells. Analyzing the tradeoff between high-fidelity, high-cost measurements and low-fidelity, low-cost measurements is often difficult due to complex data collection procedures and budget constraints.
Entering H ∞ -Optimal Control Robustness into a Macroeconomic LQ-Tracking Model

This analysis explores robust designs for an applied macroeconomic discrete-time LQ tracking model with perfect state measurements. We develop a procedure that reframes the tracking problem as a regulator problem that is then used to simulate the deterministic, stochastic LQG, H-inﬁnity, multiple-parameter minimax, and mixed stochastic/H-inﬁnity control, for quarterly ﬁscal policy. We compare the results of the ﬁve different design structures within a closed-economy accelerator model using data for the United States for the period 1947–2012. When the consumption and investment tracking errors are more heavily emphasized, the H-inﬁnity design renders the most aggressive ﬁscal policy, followed by the multiple-parameter minimax, mixed, LQG, and deterministic versions. When the control tracking errors are heavily weighted, the resulting ﬁscal policy is initially more aggressive under the multi-parameter speciﬁcation than under the H-inﬁnity and mixed designs. The results from both weighting schemes show that ﬁscal policy remains more aggressive under the robust designs than the deterministic model. The simulations show that the multi-parameter minimax and mixed designs provide a balancing compromise between the stochastic and robust methods when the worst-case concerns can be primarily limited to a subset of the state-space.