Shock chemistry is an excellent tool to shed light on the formation and destruction mechanisms of complex organic molecules (COMs). The L1157-mm outflow is the only low-mass protostellar outflow that has extensively been studied in this regard. Using the data taken as part of the PRODIGE (PROtostars & DIsks: Global Evolution) large program, we aim to map COM emission and derive the molecular composition of the protostellar outflow driven by the Class 0 protostar NGC1333 IRAS4B1 to introduce it as a new laboratory to study the impact of shocks on COM chemistry. In addition to typical outflow tracers such as SiO and CO, outflow emission is seen from H2CO, HNCO, and HC3N, as well as from the COMs CH3OH, CH3CN, and CH3CHO, and even from deuterated species such as DCN, D2CO, and CH2DOH. Maps of integrated intensity ratios between CH3OH and DCN, D2CO, and CH3CHO reveal gradients with distance from the protostar. Intensity ratio maps of HC3N and CH3CN with respect to CH3OH peak in the southern lobe where temperatures are highest. Rotational temperatures derived towards two positions, one in each lobe, are found in the range ~50-100 K. Abundances with respect to CH3OH are higher by factors of a few than for the L1157-B1. In conclusion, for the first time, we securely detected the COMs CH3CN, CH3CHO, and CH2DOH in the IRAS 4B1 outflow, serendipitously with limited sensitivity and bandwidth. Targeted observations will enable the discovery of new COMs and a more detailed analysis of their emission. Morphological differences between molecules in the IRAS 4B1 outflow lobes and their relative abundances provide first proof that this outflow is a promising new laboratory for shock chemistry, which will offer crucial information on COM formation and destruction as well as outflow structure and kinematics.
Oguzhan Durmaz, Cemil Can Eylem, Evren Ozcinar
et al.
Abstract While the organ-protective effects of hypothermia during cardiopulmonary bypass (CPB) are well known, the metabolomic impacts of different hypothermia levels remain unclear. This study aimed to evaluate the effects of mild (32–35 °C, n = 15) and moderate (26–31 °C, n = 14) hypothermia on plasma and urinary metabolomic profiles in adults undergoing CPB. Untargeted metabolomic analysis was conducted using gas chromatography–mass spectrometry (GC-MS) and liquid chromatography–quadrupole time-of-flight mass spectrometry (LC-qTOF-MS), which identified 137 plasma and 101 urinary metabolites. Statistical and multivariate analyses revealed significant differences in metabolic profiles between the two groups. In plasma, reduced levels of citric acid, proline, and inosine were observed in the mild hypothermia group, whereas myristic acid and glyceraldehyde levels were lower in the moderate group. In urine, the mild hypothermia group showed increased levels of creatinine and 5,6-dihydroxyeicosatrienoic acid (5,6-DHET), along with decreased levels of 2-hydroxybutyric acid, N-acetylserine, and oxaloacetic acid, indicating reduced renal stress. Conversely, elevated levels of C17-sphinganine and ceramides in the moderate group suggested altered lipid metabolism and greater cellular stress. Moderate hypothermia was associated with higher metabolic stress, whereas mild hypothermia was associated with relative metabolic stability. These findings present candidate biomarkers for optimizing hypothermia strategies during CPB.
Nerea Nogueira-Rio, Alicia del Carmen Mondragon Portocarrero, Alexandre Lamas Freire
et al.
The incidence of chronic kidney disease (CKD) has increased worldwide in recent years. Many factors can contribute to the progression of CKD, some of which are dietary patterns. Adequate control of protein, phosphorus, potassium, and sodium intake can significantly slow the progression of CKD. Most studies and nutritional guidelines addressing the care of people with CKD have focused primarily on dietary recommendations regarding macronutrient intake and the restriction of individual micronutrients. Traditionally, the consumption of fiber-rich fruits and vegetables has been restricted in patients with CKD to combat hyperkalemia. Among the reasons often given for this restriction are concerns about their high potassium and phosphorus contents. Limiting the intake of whole grains in CKD patients has also been recommended. However, findings indicate that phosphorus in plant foods is not fully absorbed in humans. Potassium contribution from vegetables can be reduced by culinary treatments, and when highly insoluble fiber is present in vegetables, it promotes potassium excretion through the intestine, which could help control the risk of hyperkalemia in CKD patients. Other recent findings have shown beneficial effects of vegetable bioactive compounds and resistant starch on CKD patients. The aim of the present review was to compile and discuss traditional recommendations for the use of plant-based foods for patients with CKD, as well as the mechanisms through which such foods may contribute to improving CKD progression.
В настоящее время исследования в области медицины направлены на разработку и внедрение новых стратегий профилактики, диагностики и лечения различного рода заболеваний, создания вакцин и т.д. Большинство исследований ориентировано на улучшение противораковой терапии. В последние десятилетия наблюдается повышенное внимание к разработке наносомальных форм для доставки лекарственных препаратов, в частности, для терапии онкологических заболеваний. Биоразлагаемые полимеры на основе лактида и его производных являются одними из перспективных материалов для получения наноразмерных носителей противораковых препаратов для адресной доставки. В обзоре приведены данные о некоторых коммерческих препаратах на основе данных полимеров.
Prabhleen Kaur, Niva Mahapatra, Nallamilli L. S. Roja
et al.
Introduction:
The rapid advancements in nanotechnology have opened new frontiers in dentistry, particularly with the introduction of nanorobotics. Despite the promising applications of nanorobots in dental treatments, such as precision surgery and drug delivery, there is limited knowledge among dental practitioners regarding their use.
Methods:
A cross-sectional survey was conducted among dental practitioners to evaluate their knowledge of nanorobotics. The questionnaire covered three key areas: awareness of nanorobotics, perceived benefits, and challenges to implementation. A total of 150 dental practitioners participated in the study.
Results:
The majority of respondents (60%) had heard of nanorobotics, but only 30% had a clear understanding of its applications. Among those aware, 40% believed nanorobots could improve precision in dental surgeries, while 35% felt that their cost could be a significant barrier. Statistical analysis using Chi-square tests revealed significant associations between years of practice and awareness levels (P < 0.05).
Conclusion:
The findings suggest a need for targeted educational programs to enhance knowledge of nanorobotics among dental practitioners. Further research should focus on addressing cost-related concerns to facilitate widespread adoption.
Samuele Piccinelli, Alberto Baiardi, Stefano Barison
et al.
Quantum algorithms based on classical processing of individual samples have recently emerged as the most effective and robust methods to approximate ground-state wave functions of many-body quantum systems on pre-fault-tolerant and early-fault-tolerant quantum devices. In these algorithms, the quantum computer acts as a sampling engine that generates the subspace in which the Hamiltonian is classically diagonalized. The recently proposed Sample-based Krylov Quantum Diagonalization (SKQD), uses quantum Krylov states as circuits from which samples are collected. Convergence guarantees can be derived for SKQD under similar assumptions to those of quantum phase estimation, provided that the ground-state wave function is well approximated by a polynomial subset of the full Hilbert space. However, implementations of SKQD for complex many-body Hamiltonians, such as quantum chemistry ones, are limited by the depths of time-evolution circuits needed to generate Krylov vectors. In this work, we introduce a method that combines SKQD with a qDRIFT randomized compilation of the Hamiltonian propagator. The resulting algorithm, termed SqDRIFT, enables quantum chemistry experiments on quantum processors, while preserving the convergence guarantees similar to the phase estimation algorithm. We demonstrate its viability by applying SqDRIFT to calculate the electronic ground-state energy of several polycyclic aromatic hydrocarbons, up to system sizes beyond the reach of exact diagonalization.
The Abraham's solvation parameter model is an important tool to model and predict distribution properties of compounds in numerous partitioning systems in chemistry, environmental chemistry, medicine, and biology. The evaluation of the distribution properties in extractables and leachables (E&L) studies in pharmaceutical and medical device industries is another major application of this model. This tutorial is aimed at illustrating the applications of the model in E&L studies. Specific examples of the application illustrated in this tutorial include: (a) establishment of equivalent or similar solvents; (b) determination of polarity of solvents, biological tissues, and materials; (c) development of drug product simulating solvents; (d) understanding solvent extraction power for a material; (e) selection of solvent and standards in pretreatment of extraction samples; and (f) chromatography retention prediction for E&L.
Rice is the most important staple crop for more than half of the world's population. As rice quality can deteriorate during storage, methods that can effectively classify rice according to its storage duration are essential. However, existing methods of assessing rice storage time are time-consuming, laborious, and incompatible with modern industrial processing technologies. Therefore, we investigated the ability of near-infrared spectroscopy combined with machine learning algorithms to distinguish rice storage duration. A total of 482 rice samples were analyzed, which included 74, 100, and 308 samples produced during 2015–2016, 2017–2018, and 2020–2021, respectively. Five pre-processing methods were initially applied to the spectra to enhance the accuracy of the discrimination model. Subsequently, two-dimensional correlation spectroscopy and competitive adaptive reweighted sampling (CARS) were used to extract the characteristic spectra associated with storage time. Finally, three pattern recognition methods (K-nearest neighbor analysis, linear discriminant analysis, and least squares support vector machine (LS-SVM)) were compared for their effectiveness in constructing classification models. The results indicated that the best model for identifying the storage duration of rice was established after spectral pre-processing with the standard normal variate and first derivative, using the CARS algorithm to select feature wavelengths, and applying the LS-SVM modeling method, which together yielded correct identification rates of 99.72 % and 91.67 % for the calibration and validation sets, respectively. Thus, we propose near-infrared spectroscopy coupled with machine learning algorithms as an effective approach for classifying rice according to storage duration, which can facilitate evaluations of rice freshness in the market.
Noura Alessa, Sourav Chandra Bidyasagar Bal, Fahanna Beegum
et al.
Objectives: To assess antibacterial effects of silver diamine fluoride (SDF) and fluoride varnish treatment against Streptococcus mutans and Lactobacillus acidophilus. Materials and Methods: The antibacterial effectiveness of SDF (group A) and fluoride varnish (group B) against S. mutans was investigated in an in vitro microbiological investigation, with distilled water (group C) serving as the positive and negative controls. After 24 hours of incubation, the antibacterial efficiency was assessed using the agar well diffusion technique, and the diameter of the zones of inhibition (ZOI) was quantified. Sumba mare's milk from MRS broth was extracted and then placed into a test tube. L. acidophilus was grown on Sumba mare's milk from MRS broth. On this media, each testing agent was poured and tested for the inhibitory zone. The obtained data was statistically analyzed. Results: SDF group had a higher mean zone of inhibition against S. mutans and Lactobacillus followed by fluoride varnish, and there was no ZOI in the case of distilled water. Intergroup comparison was significant. Conclusion: When compared to fluoride varnish, the SDF teeth remineralizing agent had greater antibacterial activity against S. mutans.
Rafael Martín-Doménech, Alexander DelFranco, Karin I. Öberg
et al.
Ice chemistry in the dense, cold interstellar medium (ISM) is probably responsible for the formation of interstellar complex organic molecules (COMs). Recent laboratory experiments performed at T=4 K have shown that irradiation of CO:N2 ice samples analog to the CO-rich interstellar ice layer can contribute to the formation of COMs when H2 molecules are present. We have tested this organic chemistry under a broader range of conditions relevant to the interior of dense clouds by irradiating CO:15N2:H2 ice samples with 2 keV electrons in the 4-15 K temperature range. The H2 ice abundance depended on both, the ice formation temperature and the thermal evolution of the samples. Formation of H-bearing organics such as formaldehyde (H2CO), ketene (C2H2O), and isocyanic acid (H15NCO) was observed upon irradiation of ice samples formed at temperatures up to 10 K, and also in ices formed at 6 K and subsequently warmed up and irradiated at temperatures up to 15 K. These results suggest that a fraction of the H2 molecules in dense cloud interiors might be entrapped in the CO-rich layer of interstellar ice mantles, and that energetic processing of this layer could entail an additional contribution to the formation of COMs in the coldest regions of the ISM.
Though weak surface interactions and adsorption can play an important role in plasma processing and materials science, they are not necessarily simple to model. A boron adatom adsorbed on a graphene sheet serves as a case study for how carefully one must select the correct technique from a toolbox of computational chemistry methods. Using a variety of molecular dynamics potentials and density functional theory functionals, we evaluate the adsorption energy, investigate barriers to adsorption and migration, calculate corresponding reaction rates, and show that a surprisingly high level of theory may be necessary to verify that the system is described correctly.
We propose a quantum algorithm to simulate the dynamics in quantum chemistry problems. It is based on adding fresh qubits at each Trotter step, which enables a simpler implementation of the dynamics in the extended system. After each step, the extra qubits are recycled, so that the whole process accurately approximates the correct unitary evolution. A key ingredient of the approach is an isometry that maps a simple, diagonal Hamiltonian in the extended system to the original one, and we give a procedure to compute this isometry. We estimate the error at each time step, as well as the number of gates, which scales as $O(N^2)$, where $N$ is the number of orbitals. We illustrate our results with three examples: the Hydrogen chain, small molecules, and the FeMoco molecule. In the Hydrogen chain and the Hydrogen molecule we observe that the error scales in the same way as the Trotter error. For FeMoco, we estimate the number of gates in a fault-tolerant setup.
Methods of computational quantum chemistry provide accurate approximations of molecular properties crucial for computer-aided drug discovery and other areas of chemical science. However, high computational complexity limits the scalability of their applications. Neural network potentials (NNPs) are a promising alternative to quantum chemistry methods, but they require large and diverse datasets for training. This work presents a new dataset and benchmark called $\nabla^2$DFT that is based on the nablaDFT. It contains twice as much molecular structures, three times more conformations, new data types and tasks, and state-of-the-art models. The dataset includes energies, forces, 17 molecular properties, Hamiltonian and overlap matrices, and a wavefunction object. All calculations were performed at the DFT level ($ω$B97X-D/def2-SVP) for each conformation. Moreover, $\nabla^2$DFT is the first dataset that contains relaxation trajectories for a substantial number of drug-like molecules. We also introduce a novel benchmark for evaluating NNPs in molecular property prediction, Hamiltonian prediction, and conformational optimization tasks. Finally, we propose an extendable framework for training NNPs and implement 10 models within it.
Melaku Getahun, Rahel Belete Abebe, Ashenafi Kibret Sendekie
et al.
Veterinary drugs are pharmacologically and biologically active chemical agents. At present, veterinary drugs are extensively used to prevent and treat animal diseases, to promote animal growth, and to improve the conversion rate of feed. However, the use of veterinary drugs in food-producing animals may leave residues of the parent compounds and/or their metabolites in food products resulting in harmful effects on humans. To ensure food safety, sensitive and effective analytical methods have been developing rapidly. This review describes sample extraction and cleanup methods, and different analytical techniques are used for the determination of veterinary drug residues in milk and meat. Sample extraction methods, such as solvent extraction, liquid-liquid extraction, and cleanup methods such as dispersive solid-phase extraction and immunoaffinity chromatography, were summarized. Different types of analytical methods such as microbial, immunological, biosensor, thin layer chromatography, high-performance liquid chromatography, and liquid chromatography–tandem mass spectrometry were discussed for the analysis of veterinary drug residues in animal-derived foods. Liquid chromatography–tandem mass spectrometry is the most widely used analytical technique for the determination of antibiotic drug residues. This is due to the powerful separation of LC and accurate identification of MS, and LC-MS/MS is more popular in the analysis of veterinary drug residues.
The atmospheres of brown dwarfs have been long observed to exhibit a multitude of non-equilibrium chemical signatures and spectral variability across the L, T and Y spectral types. We aim to investigate the link between the large-scale 3D atmospheric dynamics and time-dependent chemistry in the brown dwarf regime, and to assess its impact on spectral variability. We couple the miniature kinetic chemistry module `mini-chem' to the Exo-FMS general circulation model (GCM). We then perform a series of idealised brown dwarf regime atmospheric models to investigate the dynamical 3D chemical structures produced by our simulations. The GCM output is post-processed using a 3D radiative-transfer model to investigate hemisphere-dependent spectral signatures and rotational variability. Our results show the expected strong non-equilibrium chemical behaviour brought on by vertical mixing as well as global spacial variations due to zonal flows. Chemical species are generally globally homogenised, showing variations of $\pm$10\% or less, dependent on pressure level, and follow the dynamical structures present in the atmosphere. However, we find localised storm regions and eddies can show higher contrasts, up to $\pm$100\%, in mixing ratio compared to the background global mean. This initial study represents another step in understanding the connection between three-dimensional atmospheric flows in brown dwarfs and their rich chemical inventories.
Daniel Burgarth, Paolo Facchi, Alexander Hahn
et al.
Efficient error estimates for the Trotter product formula are central in quantum computing, mathematical physics, and numerical simulations. However, the Trotter error's dependency on the input state and its application to unbounded operators remains unclear. Here, we present a general theory for error estimation, including higher-order product formulas, with explicit input state dependency. Our approach overcomes two limitations of the existing operator-norm estimates in the literature. First, previous bounds are too pessimistic as they quantify the worst-case scenario. Second, previous bounds become trivial for unbounded operators and cannot be applied to a wide class of Trotter scenarios, including atomic and molecular Hamiltonians. Our method enables analytical treatment of Trotter errors in chemistry simulations, illustrated through a case study on the hydrogen atom. Our findings reveal: (i) for states with fat-tailed energy distribution, such as low-angular-momentum states of the hydrogen atom, the Trotter error scales worse than expected (sublinearly) in the number of Trotter steps; (ii) certain states do not admit an advantage in the scaling from higher-order Trotterization, and thus, the higher-order Trotter hierarchy breaks down for these states, including the hydrogen atom's ground state; (iii) the scaling of higher-order Trotter bounds might depend on the order of the Hamiltonians in the Trotter product for states with fat-tailed energy distribution. Physically, the enlarged Trotter error is caused by the atom's ionization due to the Trotter dynamics. Mathematically, we find that certain domain conditions are not satisfied by some states so higher moments of the potential and kinetic energies diverge. Our analytical error analysis agrees with numerical simulations, indicating that we can estimate the state-dependent Trotter error scaling genuinely.
Louise Welsh, Ryan Cooke, Michele Fumagalli
et al.
We present new Keck/HIRES data of the most metal-poor damped Lyman-alpha (DLA) system currently known. By targeting the strongest accessible Fe II features, we have improved the upper limit of the [Fe/H] abundance determination by ~1 dex, finding [Fe/H]<-3.66 (2 sigma). We also provide the first upper limit on the relative abundance of an odd-atomic number element for this system [Al/H]<-3.82 (2 sigma). Our analysis thus confirms that this z_abs=3.07 DLA is not only the most metal-poor DLA but also the most iron-poor DLA currently known. We use the chemistry of this DLA, combined with a stochastic chemical enrichment model, to probe its enrichment history. We find that this DLA is best modelled by the yields of an individual Population III progenitor rather than multiple Population III stars. We then draw comparisons with other relic environments and, particularly, the stars within nearby ultra-faint dwarf galaxies. We identify a star within Bootes I, with a similar chemistry to that of the DLA presented here, suggesting that it may have been born in a gas cloud that had similar properties. The extremely metal-poor DLA at redshift z_abs=3.07 (i.e. ~2 Gyrs after the Big Bang) may reside in one of the least polluted environments in the early Universe.
Ashish S. Newale, Pushan Sharma, Stephen B. Pope
et al.
LES/PDF approaches can be used for simulating challenging turbulent combustion configurations with strong turbulence chemistry interactions. Transported PDF methods are computationally expensive compared to flamelet-like turbulent combustion models. The pre-partitioned adaptive chemistry (PPAC) methodology was developed to address this cost differential. PPAC entails an offline preprocessing stage, where a set of reduced models are generated starting from an initial database of representative compositions. At runtime, this set of reduced models are dynamically utilized during the reaction fractional step leading to computational savings. We have recently combined PPAC with in-situ adaptive tabulation (ISAT) to further reduce the computational cost. We have shown that the combined method reduced the average wall-clock time per time step of large-scale LES/particle PDF simulations of turbulent combustion by 39\%. A key assumption in PPAC is that the initial database used in the offline stage is representative of the compositions encountered at runtime. In our previous study this assumption was trivially satisfied as the initial database consisted of compositions extracted from the turbulent combustion simulation itself. Consequently, a key open question remains as to whether such databases can be generated without having access to the turbulent combustion simulation. Towards answering this question, in the current work, we explore whether the compositions for forming such a database can be extracted from computationally-efficient low-dimensional simulations such as 1D counterflow flames and partially stirred reactors. We show that a database generated using compositions extracted from a partially stirred reactor configuration leads to performance comparable to the optimal case, wherein the database is comprised of compositions extracted directly from the LES/PDF simulation itself.
Maria Guadalupe Yañez-Cruz, Maricela Villanueva-Ibáñez, Fabiola Méndez-Arriaga
et al.
Abstract Background Photocatalyst oxides added with silicon improve their photocatalytic properties. In this research, nanostructured β-Bi2O3/SiO2 and β-Bi2O3/Bi2O2.75/SiO2 were obtained by means of a green method mediated by the using the aqueous extract of J. regia shell as the source of reducing biomolecules and as a natural source of plant silicon. Method The β-Bi2O3/SiO2 and β-Bi2O3/Bi2O2.75/SiO2 nanostructures were characterized by Fourier transform infrared spectroscopy (FT-IR), Raman spectroscopy, X-ray diffraction, high-resolution transmission electron microscopy (HR-TEM), field emission scanning electron microscopy, X-ray photoelectron spectroscopy (XPS), ultraviolet–visible diffuse reflectance spectroscopy (UV–Vis DRS), and photoluminescence spectroscopy. The photocatalytic activity was measured by the degradation of Reactive Black 5 dye (RB-5). Results FT-IR and XPS demonstrated the presence of plant silicon in the bismuth oxide photocatalysts. HR-TEM showed that the crystal size of the as-synthesized materials is ~ 25 nm and revealed that the β-Bi2O3 synthesized with ground shell extract and heat-treated at 300 °C contains the Bi2O2.75 phase. Good photocatalytic activity was found in all the studied materials; particularly, the heat-treated nanostructures showed excellent properties resulting in 92% degradation of RB-5 under UV–Vis light after 15 min of exposure, and 98% after 180 min. Conclusions The findings of this research suggest that the metabolites coating the Bi2O3, which generate a large amount of hydroxyl radicals, the plant silicon content, and the crystalline defects conferred by the synthesis medium, all contribute to the improved degradation of the azo dye, providing the nanostructures with better photocatalytic activity.