Label-free measurements of reaction kinetics using a droplet-based optofluidic device.

Discovery of Mangifera indica-based natural inhibitors against TEM-1 β-lactamase from Escherichia coli using machine learning approaches.

The distribution and elimination of 0.9% saline given by intravenous infusion has not been compared between the conscious state and during inhalational anesthesia. Six adult sheep received an intravenous infusion of 25 ml/kg of 0.9% saline over 20 min in the conscious state and also during isoflurane anesthesia and mechanical ventilation. The distribution and elimination of infused fluid were studied by volume kinetics based on serial analysis of hemoglobin dilution in arterial blood and by mass balance that incorporated volume calculations derived from volume kinetic analysis and measurements of urinary volumes. The mass balance calculations indicated only minor differences in the time course of plasma volume expansion between the conscious and anesthetized states. However, isoflurane anesthesia markedly reduced urinary volume (median, 9 vs. 863 ml; P < 0.03). In conscious sheep, the central and peripheral volume expansion predicted by volume kinetics agreed well with the calculations based on mass balance. However, during isoflurane anesthesia and mechanical ventilation, calculation using volume kinetic analysis of the variable kr, an elimination factor that, in conscious humans and sheep, is closely related to urinary excretion, represented both urinary excretion and peripheral accumulation of fluid. This suggests that the previous assumption that kr approximates urinary excretion of infused fluid requires modification, i.e., kr simply reflects net fluid movement out of plasma. In both conscious and anesthetized, mechanically ventilated sheep, infusion of 0.9% saline resulted in minimal expansion of plasma volume over a 3-h interval. In conscious sheep, infused 0.9% saline was rapidly eliminated from the plasma volume by urinary excretion; in contrast, the combination of isoflurane anesthesia and mechanical ventilation reduced urinary excretion and promoted peripheral accumulation of fluid.

With the threshold for crop growth data collection having been markedly decreased by sensor miniaturization and cost reduction, unmanned aerial vehicle (UAV)-based low-altitude remote sensing has shown remarkable advantages in field phenotyping experiments. However, the requirement of interdisciplinary knowledge and the complexity of the workflow have seriously hindered researchers from extracting plot-level phenotypic data from multisource and multitemporal UAV images. To address these challenges, we developed the Integrated High-Throughput Universal Phenotyping (IHUP) software as a data producer and study accelerator that included 4 functional modules: preprocessing, data extraction, data management, and data analysis. Data extraction and analysis requiring complex and multidisciplinary knowledge were simplified through integrated and automated processing. Within a graphical user interface, users can compute image feature information, structural traits, and vegetation indices (VIs), which are indicators of morphological and biochemical traits, in an integrated and high-throughput manner. To fulfill data requirements for different crops, extraction methods such as VI calculation formulae can be customized. To demonstrate and test the composition and performance of the software, we conducted case-related rice drought phenotype monitoring experiments. In combination with a rice leaf rolling score predictive model, leaf rolling score, plant height, VIs, fresh weight, and drought weight were efficiently extracted from multiphase continuous monitoring data. Despite the significant impact of image processing during plot clipping on processing efficiency, the software can extract traits from approximately 500 plots/min in most application cases. The software offers a user-friendly graphical user interface and interfaces for customizing or integrating various feature extraction algorithms, thereby significantly reducing barriers for nonexperts. It holds the promise of significantly accelerating data production in UAV phenotyping experiments.

The Constrained Disorder Principle (CDP) defines systems by their inherent disorder, which is bounded by dynamic borders. This principle determines a system's functionality and efficiency based on its continuously changing boundaries. In this paper, we present the formulation of the principle using the equation B = F, where B represents the dynamic borders, and F denotes the system’s function. This equation suggests that the dynamic borders shape a system’s existence, functionality, and efficiency. However, these borders impose a limit beyond which the system cannot further enhance its performance. When disorder surpasses established limits, the system's efficiency begins to decline. Conversely, insufficient disorder may also be harmful in certain situations. The paper examines the causal relationship between disorder and function, illustrating how the equation reflects the system's adaptability, efficiency, learning capabilities, memory, energy consumption, aging, and eventual termination. We also discuss how this formula can be applied to correct malfunctions and enhance system functions. Furthermore, we introduce a second-generation artificial intelligence system based on the CDP formula that incorporates noise. In summary, the B = F equation provides a valuable framework for understanding complex systems and lays the groundwork for models designed to enhance system performance.

Quantile regression is an alternate type of regression analysis that has been shown to have numerous advantages over standard linear regression. Unlike linear regression, which uses the mean to fit a linear model, quantile regression uses a data set's quantiles (or percentiles), which leads to a more comprehensive analysis of the data. However, while relatively common in other scientific fields such as economic and environmental modeling, it is infrequently used to understand biological and microbiological systems. We analyzed a set of bacterial growth rates using quantile regression analysis to better understand the effects of antibiotics on bacterial fitness. Using a bacterial model system containing 16 variant genotypes of the TEM β-lactamase enzyme, we compared our quantile regression analysis to a previously published study that uses the Tukey's range test, or Tukey honestly significantly difference (HSD) test. We find that trends in the distribution of bacterial growth rate data, as viewed through the lens of quantile regression, can distinguish between novel genotypes and ones that have been clinically isolated from patients. Quantile regression also identified certain combinations of genotypes and antibiotics that resulted in bacterial populations growing faster as the antibiotic concentration increased-the opposite of what was expected. These analyses can provide new insights into the relationships between enzymatic efficacy and antibiotic concentration. Quantile regression analysis enhances our understanding of the impacts of sublethal antibiotic concentrations on enzymatic (TEM β-lactamase) efficacy and bacterial fitness. We illustrate that quantile regression analysis can link patterns in growth rates with clinically relevant mutations and provides an understanding of how increasing sub-lethal antibiotic concentrations, like those found in our modern environment, can affect bacterial growth rates, and provide insight into the genetic basis for varied resistance.

The effectiveness of municipal social governance is directly related to the harmony and stability of the municipal society, serving as a key part of the modernization of the national governance system and governance capabilities. The use of emerging information technologies such as big data, artificial intelligence, and blockchain to promote technological empowerment and improve the intelligence and refinement of social governance is one of the important focal points for promoting the modernization of Municipal social governance. This paper takes the construction of a centralized management platform for group rental housing in Wujin District of Changzhou City as an example to systematically study data aggregation, data sharing, application scenario innovation, and platform integration, and propose countermeasures and suggestions for Municipal social governance in the era of big data.

We report on sensitive tunable laser absorption spectroscopy using a multipass gas cell and a solid-state photoacoustic optical power detector. Unlike photoacoustic spectroscopy (PAS), this method readily allows a low gas pressure for high spectral selectivity and a free gas flow for continuous measurements. Our photoacoustic optical power detector has a large linear dynamic range and can be used at almost any optical wavelength, including the middle infrared and THz regions that are challenging to cover with traditional optical detectors. Furthermore, our approach allows for compensation of laser power drifts with a single detector. As a proof of concept, we have measured very weak CO2 absorption lines at 9.2 µm wavelength and achieved a normalized noise equivalent absorption (NNEA) of 2.35·10−9 Wcm−1Hz−1/2 with a low-power quantum cascade laser. The absolute value of the gas absorption coefficient is obtained directly from the Beer-Lambert law, making the technique calibration-free.

Due to the increasing software complexity, how to integrate the legacy data resources and systems is a big challenge for the enterprise. This paper makes full use of agent technologies to establish an agent grid intelligent platform for enterprise heterogeneous system integration. The agent grid intelligent platform adopts a hierarchical integration model and middleware to provide efficient integration capability for different application systems. Finally, this paper introduces the grid based city emergency interaction system to illustrate the AGrIP practical system integration.

Ever enhancing computational capability of digital system along with upgraded tactics, technology and procedure (TTPs) enforced by the cybercriminals, does not match to the conventional security mechanism for detection of intrusion and prevention of threat in current cyber security landscape. Integration of artificial intelligence, machine learning and cyber threat intelligence platform with the signature-based threat detection models like intrusion detection system (IDS), SNORT, security information and event management (SIEM) which are being primarily implemented in the network for continuous analysis of the indicator of compromise (IoC) becomes inevitable, for prompt identification of true events and subsequent mitigation of the threat. In this paper, author illustrated the approach to integrate artificial intelligence and machine learning with the cyber threat intelligence for the collection of actionable threat intelligence from various sources like dark web, hacker’s forum, hacker’s assets, honeypot, etc. Furthermore, the application of threat intelligence in the aspect of cyber security has been discussed in this paper. Finally, a model has been proposed for generating actionable threat intelligence implementing a supervised machine learning approach employing Naive Bayes classifier.

Optical manipulation and detection of biological particulates are crucial procedures in biophotonics. Optical sectioning (OS) opens the avenue to three-dimensional (3D) microscopy, but nonoptical approaches, including sample adhesion and mechanical scanning, have always been required in this technique, rendering it impossible to image suspended cells. Here, we develop optical tweeze-sectioning microscopy by coupling structured illumination microscopy (SIM) with holographic optical tweezers (HOTs). By sculpting light in HOTs, we demonstrate that the position fluctuations of suspended yeast cells can be optically squeezed to tens of nanometers, which is sufficient for the implementation of OS with SIM. Sample scanning is achieved through optical delivery of the cells, instead of translation stages as in the conventional way. Our work presents an all-optical solution for OS, broadening its application to nonadherent, suspended cells. It further furnishes the original technique that enables both SIM-based 3D imaging and optical manipulation.

Equivalence of volume, enthalpy and viscosity relaxation kinetics in glass-forming silicate liquids

Gold(I) complexes such as auranofin or aurothiomalate have been used as therapeutic agents for the treatment of rheumatoid arthritis for several decades. Several gold(I) and gold(III) complexes have also shown in vitro anticancer properties against human cancer cell lines, including cell lines resistant to cisplatin. Because of the thiophilicity of gold, cysteine-containing proteins appear as likely targets for gold complexes. Among them, zinc finger proteins have attracted attention and, recently, gold(I) and gold(III) complexes have been shown to inhibit poly(adenosine diphosphate ribose)polymerase-1 (PARP-1), which is an essential protein involved in DNA repair and in cancer resistance to chemotherapies. In this Article, we characterize the reactivity of the gold(III) complex [Au(III)(terpy)Cl]Cl2 (Auterpy) with a model of Zn(Cys)4 "zinc ribbon" zinc finger by a combination of absorption spectroscopy, circular dichroism, mass spectrometry, high-performance liquid chromatography analysis, and X-ray absorption spectroscopy. We show that the Zn(Cys)4 site of Zn·LZR is rapidly oxidized by Auterpy to form a disulfide bond. The Zn(2+) ion is released, and the two remaining cysteines coordinate the Au(+) ion that is produced during the redox reaction. Subsequent oxidation of these cysteines can take place in conditions of excess gold(III) complex. In the presence of excess free thiols mimicking the presence of glutathione in cells, mixing of the zinc finger model and gold(III) complex yields a different product: complex (Au(I))2·LZR with two Au(+) ions bound to cysteines is formed. Thus, on the basis of detailed speciation and kinetic measurements, we demonstrate herein that the destruction of Zn(Cys)4 zinc fingers by gold(III) complexes to achieve the formation of "gold fingers" is worth consideration, either directly or mediated by reducing agents.

A research on open CNC system based on architecture/component software reuse technology

Peroxy radicals (HO2, RO2) are important intermediates in Earth's atmosphere. They are intermediates in the oxidation of alkanes and CO in combustion and atmospheric chemical processes. In earth's atmosphere, the rates of their self and cross reactions are often the dominant loss processes when NOx concentrations fall below tens of pptv. These reactions have proven difficult to study in laboratory experiments, due to complex secondary chemistry and ambiguities in radical detection. This thesis describes a new laser-photolysis apparatus to measure the rates of peroxy radical reactions under atmospheric conditions that employs simultaneous UV direct absorption and IR wavelength-modulation spectroscopy to detect the peroxy radicals. Prior kinetic measurements of gas-phase peroxy radical reactions have typically employed flash-photolysis methods coupled with detection of the radicals only by UV absorption spectroscopy. However, uncertainties can arise because several different species often contribute to the absorption signal. The IR channel provides an independent means of monitoring HO2 radicals by detection of specific rovibrational transitions. With this apparatus, the rates of the reactions HO2 + NO2, HO2 + CH3O2, CH3O2 + CH3O2, and HO2 + HO2 were studied at temperatures from 219 K to 300 K. Our measurements have, in some cases, led to significant revision of previously accepted rate constants, mechanisms, or product yields, especially at conditions relevant to the upper atmosphere. The new rate coefficients for the HO2 + HO2 reaction are shown to account for a long-standing discrepancy in modeled vs. observed hydrogen peroxide in the stratosphere. A key finding has been the observation that many previous measurements of HO2 reactions at low temperatures have suffered from problems due to complexation between HO2 and methanol, a precursor used to generate HO2. Direct kinetic evidence is presented for the formation of the HO2?CH3OH complex; the rate coefficients, equilibrium constant, and enthalpy of reaction for HO2 + CH3OH HO2?CH3OH were measured. These results are the first direct study of the chaperone effect proposed to explain the enhancement of the observed rates of the HO2 self-reaction by hydrogen-bonding species. The effects of methanol enhancement on the HO2 + NO2, HO2 + CH3O2, CH3O2 + CH3O2, and HO2 + HO2 reaction rates were measured. For the HO2 + NO2 reaction, overlapping, time-dependent signals in the UV due to the equilibrium between NO2 and N2O4 were observed that may not have been properly accounted for in previous measurements. Other studies of NO2 reactions conducted at temperatures below 250 K may be subject to similar errors. In the CH3O2 + CH3O2 reaction, detection of HO2 products has raised questions concerning the product yields and reaction mechanisms.

During recent years, greater emphasis has been placed on the control of particulate emissions from painting operations. This has gained more importance as more is learned about the potential release of toxic metals to the atmosphere from painting operations. This has led to queries about the efficiency of various painting arrestor systems to reduce particulate discharges to the atmosphere. Even more important is the capability of the arrestor systems to control PM10 emissions. In 1995, the U.S. Environmental Protection Agency initiated a study to evaluate various dry paint overspray arrestor systems. This study was designed to evaluate not only the total emissions control capability of the arrestor but also the PM10 control capability of the various system designs. Paint overspray arrestor systems using five different filtration concepts or materials were selected. They include systems constructed of fiberglass, paper, Styrofoam, and cardboard materials. These systems used filtration techniques incorporating the following filtration phenomena and designs: cyclone, baffle, bag systems, and mesh systems. The testing used an optical particle counting procedure to determine the concentration of particles of a given size fraction to penetrate a test arrestor system. The results of the testing indicated that there are significant differences in the efficiency of the tested system designs to capture and retain PM10. This paper summarizes the results of the research conducted to determine the capability of the arrestor systems to capture particulate of sizes down to approximately 1 μm in surface diameter.