Classes Of Analytes Research Articles

Recent Developments in the Theory of Anderson Modules

Let K be a global function field over a finite field of characteristic p and let A be the ring of elements of K which are regular outside a fixed place of K. This report presents recent developments in the arithmetic of special L-values of Anderson A-modules. Provided that p does not divide the class number of K, we prove an analytic class number for Anderson A-modules with the help of a recent work of Debry. For tensor powers of the Carlitz module, we explain how to derive several log-algebraicity results from the class number formula for these Anderson modules.

The Chemistry of Paper in Paper Spray Ionization Mass Spectrometry

There are many rapidly evolving technologies that use simple papers as a surface for chemical reactions and detection of molecules. One example of this application is the use of paper as a surface for ionization and introduction of compounds into mass spectrometry. The current work was designed to investigate mechanisms of interaction between the paper and analytes using mass spectrometry as the detection instrument. Standard compounds with different sizes were used in this study. Factors investigated include the effects of the paper geometry (5 mm) and position (tp=90º) were found to be optimal. The role of atmospheric water and the effects of paper porosity were affected the signals of the tested compounds. Investigations of the interaction of standard solutions with the paper surface indicated that atmospheric water is required for ionization. Up to three water molecules were detected in association with the analytes indicating that the presence of some water is required. In addition, this study showed the potential of silicon dioxide nanoparticles for eluting the standards compound from the paper surface. The impact of trace elements in the commercial papers and the interactions between the paper cellulose with different classes of analytes was investigated.

Some connections between various classes of analytic functions associated with the power series distribution

The primary motivation of the paper is to investigate the power series distribution (Pascal model) for the analytic function classes TG, TGS and TGC. Furthermore, we give necessary and sufficient conditions for the Pascal distribution series belonging to these classes.

Tight bounds for private communication over bosonic Gaussian channels based on teleportation simulation with optimal finite resources

Upper bounds for private communication over quantum channels can be derived by adopting channel simulation, protocol stretching, and relative entropy of entanglement. All these ingredients have led to single-letter upper bounds to the secret key capacity which can be directly computed over suitable resource states. For bosonic Gaussian channels, the tightest upper bounds have been derived by employing teleportation simulation over asymptotic resource states, namely the asymptotic Choi matrices of these channels. In this work, we adopt a different approach. We show that teleporting over an analytical class of finite-energy resource states allows us to closely approximate the ultimate bounds for increasing energy, so as to provide increasingly tight upper bounds to the secret-key capacity of one-mode phase-insensitive Gaussian channels. We then show that an optimization over the same class of resource states can be used to bound the maximum secret key rates that are achievable in a finite number of channel uses.

A simple liquid chromatography coupled with tandem mass spectrometry approach for the simultaneous quantification of thirteen compounds in rats following oral administration of raw and processed Fructus Xanthii: Application in a comparative pharmacokinetic study

A simple and sensitive analysis using ultra high performance liquid chromatography with a tandem mass spectrometric system operated in selected reaction monitoring mode was developed for the determination of 11 phenolic acids, atractyloside, and carboxyatractyloside in rat plasma. The two classes of analytes were then separated on a Waters ACQUITY™ UPLC HSS T3 column (50mm × 2.1mm, 1.8 µm) using gradient elution with a mobile phase of 0.2% formic acid in water containing 10mM ammonium acetate and methanol. Detection was accomplished by selected reaction monitoring scanning via an electrospray source operating in negative ionization mode. The calibration curve was linear (R2 =0.990) over a concentration range of 1.20-3500ng/mL, while the validated lower limit of quantification was 1.20ng/mL. The precision varied from 0.84 to 4.62%, and the accuracy varied within ±5%. The method proved robust with sample freezing and thawing and with short- and long-term sample storage. The established method was used for simultaneous quantification and was successfully used for the first time for the pharmacokinetic evaluation of 13 compounds after the intragastric administration of raw and processed Fructus Xanthii in rats. The results indicated that processing affects the absorption and metabolism of Fructus Xanthii extract. Importantly, the results also indicated the importance of processing for the clinical application of traditional Chinese medicine.

Survey of Occupational and Environmental Exposure Monitoring Solutions.

Service members are exposed to ambient airborne pollutants that have been linked to adverse health effects; however, capabilities to identify and characterize exposures across multi-domain operations are currently lacking. Occupational and environmental exposure monitoring is problematic because there is not a single simple solution, and current technological limitations suggest that simultaneous deployment of multiple devices may be the most effective near-term strategy. A broad industry scan of wearable, handheld, or portable occupational and environmental exposure monitoring devices was conducted, and subject matter experts were interviewed about the state of the field. This survey identified limitations including the inability to detect multiple analytes or analyte classes, size and weight, and detection limits, but multiple implementation strategies could be employed to meet a variety of combat needs. Device types could be layered, or specific device types could be deployed in acute toxic exposure environments such as dense urban population centers or subterranean spaces. Evolving technologies and data management strategies may advance personal exposure monitoring in the future. These new devices and methods will likely supplant current technologies, while still using the programmatic and data framework established with early implementation of current commercial off the shelf devices.

Concordance among four commercially available, validated programmed cell death ligand-1 assays in urothelial carcinoma

BackgroundAntibodies targeting the programmed cell death-1 (PD-1)/PD-ligand 1 (PD-1/PD-L1) checkpoint have shown promising clinical activity in patients with advanced urothelial carcinoma (UC). Expression of PD-L1 in UC tumors has been investigated using different antibody clones, staining protocols, and scoring algorithms. The aim was to establish the extent of concordance among PD-L1 immunohistochemistry (IHC) assays.MethodsTumor biopsy samples (N = 335) were assessed using four commercially available PD-L1 assays: VENTANA SP263, VENTANA SP142, PD-L1 IHC 28–8 pharmDx, and PD-L1 IHC 22C3 pharmDx. PD-L1 analytical staining and classification concordance, including agreement between clinically relevant scoring algorithms, were investigated using overall/positive/negative percentage agreement (OPA/PPA/NPA).ResultsGood analytical correlation was observed among the VENTANA SP263, PD-L1 IHC 22C3 pharmDx, and PD-L1 IHC 28–8 pharmDx assays for tumor cell (TC) and immune cell (IC) PD-L1 staining with Spearman rank coefficients of 0.92–0.93 for TCs and 0.88–0.91 for ICs. However, concordance (preset criterion: ≥85%) between patient PD-L1 status when applying the TC or ICICArea ≥ 25% (VENTANA SP263) cutoff was only achieved for PD-L1 IHC 22C3 pharmDx versus VENTANA SP263 (OPA 92.2%, PPA 86.4%, NPA 95.4%). Differences were observed between patient populations with UC tumors classified as PD-L1 high versus PD-L1 low/negative using combined positive score (CPS) ≥1, CPS ≥10, IC ≥5%, and TC/IC ≥25%.ConclusionsThe VENTANA SP263 and PD-L1 IHC 22C3 pharmDx assays are analytically similar in UC. When the different PD-L1 assays were combined with their specified clinical scoring algorithms, differences were seen in patient classification driven by substantial differences in scoring approaches.

VUV Photodissociation Induced by a Deuterium Lamp in an Ion Trap.

Tandem mass spectrometry represents an important analytical tool to unravel molecular structures and to study the gas-phase behavior of organic molecules. Besides commonly used methods like collision-induced dissociation and electron capture or transfer dissociation, new ultraviolet light-based techniques have the potential to synergistically add to the activation methods. Here, we present a new simple, yet robust, experimental design for polychromatic activation of trapped ions using the 115-160 nm output of a commercially available deuterium lamp. The resulting continuous dissociative excitation with photons of a wide energy range from 7.7 to 10.8 eV is studied for a comprehensive set of analyte classes in both positive and negative ion modes. While being simple, affordable, compact, and of low maintenance, the new setup initiates fragmentation of most precursor ions via their known dissociation pathways. Additionally, some new fragmentation patterns were discovered. Especially, electron loss and electron capture reactions with subsequent fragmentations were observed. For oligonucleotides, peptides, carbohydrates, and organic dyes, in comparison to collision-induced dissociation, a significantly wider fragment distribution was obtained, resulting in an information increase. Since the individual photons carry enough energy to post-ionize the nascent fragments, a permanent vacuum ultraviolet light exposure inside the ion trap potentially goes along with a general increase in detection capability.

A Generic Liquid Chromatography-Tandem Mass Spectrometry Exposome Method for the Determination of Xenoestrogens in Biological Matrices.

We are constantly exposed to a variety of environmental contaminants and hormones, including those mimicking endogenous estrogens. These highly heterogeneous molecules are collectively referred to as xenoestrogens and hold the potential to affect and alter the delicate hormonal balance of the human body. To monitor exposure and investigate potential health implications, comprehensive analytical methods covering all major xenoestrogen classes are needed but not available to date. Herein, we describe a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for the simultaneous determination of multiple classes of endogenous as well as exogenous estrogens in human urine, serum, and breast milk to enable proper exposure and risk assessment. In total, 75 analytes were included, whereof a majority was successfully in-house validated in the three matrices. Extraction recoveries of validated analytes ranged from 71% to 110% and limits of quantification from 0.015 to 5 μg/L, 0.03 to 14 μg/L, and 0.03 to 4.6 μg/L in urine, serum, and breast milk, respectively. The applicability of the novel method was demonstrated in proof-of-principle experiments by analyzing urine from Austrian individuals and breast milk from Austrian and Nigerian individuals. Thereby, we proved the methods' feasibility to identify and quantify different classes of xenoestrogens simultaneously. The results illustrate the general importance of multiclass exposure assessment in the context of the exposome paradigm. Specifically, they highlight the need for estimating total estrogenic burden rather than single analyte or chemical class measurements and its potential impact in endocrine disruption and hormone related diseases including cancers.

Occupation and risk of female breast cancer: A case-control study in Morocco.

Breast cancer is the most frequent cancer among Moroccan women. Environmental and occupational factors may play a role in breast cancer etiology. This study aimed to investigate the association between occupation, industry, and breast cancer risk among Moroccan women. A total of 300 breast cancer cases and 300 controls (matched by age and area of residence) were included in this study. Full occupational history was collected, with a detailed description of each job held for at least 6 months. Occupations were coded according to the International Standard Classification of Occupations (ISCO 08) and the Moroccan Analytical Classification of Professions (2001). Industries were coded according to the Statistical Classification of Economic Activities in the European Community (2008). Odds ratios (ORs) and 95% confidence intervals (CIs), adjusted for potential confounders were estimated by using conditional logistic regression. An overall decreased risk of breast cancer was observed among women doing only household work (OR = 0.32; 95% CI = 0.18-0.55). An increased risk of breast cancer was observed among women in agricultural occupations, particularly those employed as agricultural laborers (ISCO 08 code: 921; OR = 2.91; 95% CI = 1.51-5.60) and the risk increased with duration of employment (P trend = .01). Analyses by industry corroborated these findings. Our findings suggest that occupational exposures may be associated with increased risk of breast cancer among female agricultural workers in this population. Further investigations, with advanced methods of occupational exposure assessment, are warranted to clarify the role of chemicals involved in this high-risk occupation and to suggest preventive actions and screening.

Private and public values of innovation

Emerging science and technology fields are increasingly expected to provide solutions to societal grand challenges. The promise of such solutions underwrites much public research funding. Universities, public research organizations and private enterprises draw on such research to secure patent property rights over potential applications. Patents represent a claim to economic returns from the novel outcomes of science and technology, justified by the social value promised through encouraging information sharing, further R&D investment, and useful applications. Despite the notion of the social utility of inventions as a patenting condition, and evidence of disconnection between societal needs and the goals of private actors, there is less analytical attention to public value interpretations. After considering legal, business, social and policy dimensions of patenting, we undertake a qualitative examination of patent content in synthetic biology, contributing a new analytical classification of private and public values. We probe the private and public value propositions framed in these patents in terms of potential private and public benefits of research and innovation. We shed light on questions of the values being nurtured in inventions in synthetic biology and discuss how further attention to public as well as private values opens up further promising avenues of research.

Metal-Organic-Framework-based Gas Chromatographic Separation.

Metal-organic frameworks (MOFs) have been applied in various fields because of their fascinating structures and excellent properties. MOFs can serve as stationary phases in gas chromatography (GC), which has led to exceptional improvements of performance. Here, we summarize the application of MOFs in GC based on the classification of analytes. The advantages and separation mechanism of MOFs as stationary phases in GC are discussed in combination with the characteristics and structures of MOFs. The limitations are also summarized in this review, which can provide prospects on further research for the applications of MOFs.

Virus-like particle size and molecular weight/mass determination applying gas-phase electrophoresis (native nES GEMMA)

(Bio-)nanoparticle analysis employing a nano-electrospray gas-phase electrophoretic mobility molecular analyzer (native nES GEMMA) also known as nES differential mobility analyzer (nES DMA) is based on surface-dry analyte separation at ambient pressure. Based on electrophoretic principles, single-charged nanoparticles are separated according to their electrophoretic mobility diameter (EMD) corresponding to the particle size for spherical analytes. Subsequently, it is possible to correlate the (bio-)nanoparticle EMDs to their molecular weight (MW) yielding a corresponding fitted curve for an investigated analyte class. Based on such a correlation, (bio-)nanoparticle MW determination via its EMD within one analyte class is possible. Turning our attention to icosahedral, non-enveloped virus-like particles (VLPs), proteinaceous shells, we set up an EMD/MW correlation. We employed native electrospray ionization mass spectrometry (native ESI MS) to obtain MW values of investigated analytes, where possible, after extensive purification. We experienced difficulties in native ESI MS with time-of-flight (ToF) detection to determine MW due to sample inherent characteristics, which was not the case for charge detection (CDMS). nES GEMMA exceeds CDMS in speed of analysis and is likewise less dependent on sample purity and homogeneity. Hence, gas-phase electrophoresis yields calculated MW values in good approximation even when charge resolution was not obtained in native ESI ToF MS. Therefore, both methods-native nES GEMMA-based MW determination via an analyte class inherent EMD/MW correlation and native ESI MS-in the end relate (bio-)nanoparticle MW values. However, they differ significantly in, e.g., ease of instrument operation, sample and analyte handling, or costs of instrumentation.Graphical abstract

Investigating the performance characteristics of the barrier discharge ionization detector and comparison to the flame ionization detector for the gas chromatographic analysis of volatile and semivolatile organic compounds

The response behavior and performance characteristics of the recently introduced barrier discharge ionization detector (BID) for gas chromatography (GC-BID) were investigated by analyzing different classes of organic compounds such as alcohols, alkanes, cycloaliphatic compounds, polycyclic aromatic hydrocarbons (PAHs), and others. The results obtained by GC-BID were compared with those of gas chromatography with flame ionization detection (GC-FID), aiming to demonstrate the particular merits of the new BID detector over the well-established FID. The response of the BID not only was found to be strongly dependent on the detector settings, but also shows a high dependence on the analyte class and the individual analyte. The sensitivity of the BID detector compared to the FID was higher by a factor of ca. 4 on average when considering all compounds analyzed. The relative standard deviation (RSD) was better than 5% for the majority of the cases. The BID detector showed better precision (lower RSD) in comparison with the FID for the investigated compounds. Linear calibrations were obtained for the analytes over more than four orders of magnitude with coefficients of determination typically higher than 0.999 and the limits of detection varied from 0.04 to 1.48 ng/s for the GC-BID.

Vacuum Ultraviolet Laser Induced Breakdown Spectroscopy (VUV-LIBS) with machine learning for pharmaceutical analysis

Vacuum ultraviolet laser induced breakdown spectroscopy (VUV-LIBS) experiments were carried out on pharmaceutical samples and machine learning techniques were applied to analyze the samples. The motivation for the application of these machine learning techniques is the classification of analytes, allowing us to distinguish pharmaceuticals from one another based on their spectra. Three machine learning techniques have been compared, self-organizing maps (SOM), support vector machines (SVM) and convolutional neural networks (CNN). For multiclass and 1vs1 testing CNNs appeared to perform the best of the three machine learning techniques on the relatively small number of pharmaceutical LIBS spectra used in this study.

A Modified Drift Tube Ion Mobility-Mass Spectrometer for Charge-Multiplexed Collision-Induced Unfolding.

Collision-induced unfolding (CIU) of protein ions and their noncovalent complexes offers relatively rapid access to a rich portfolio of biophysical information, without the need to tag or purify proteins prior to analysis. Such assays have been characterized extensively for a range of therapeutic proteins, proving exquisitely sensitive to alterations in protein sequence, structure, and post-translational modification state. Despite advantages over traditional probes of protein stability, improving the throughput and information content of gas-phase protein unfolding assays remains a challenge for current instrument platforms. In this report, we describe modifications to an Agilent 6560 drift tube ion mobility-mass spectrometer in order to perform robust, simultaneous CIU across all precursor ions detected. This approach dramatically increases the speed associated with typical CIU assays, which typically involve mass selection of narrow m/ z regions prior to collisional activation, and thus their development requires a comprehensive assessment of charge-stripping reactions that can unintentionally pollute CIU data with chemical noise when more than one precursor ion is allowed to undergo simultaneous activation. By studying the unfolding and dissociation of intact antibody ions, a key analyte class associated with biotherapeutics, we reveal a predictive relationship between the precursor charge state, the amount of buffer components bound to the ions of interest, and the amount of charge stripping detected. We then utilize our knowledge of antibody charge stripping to rapidly capture CIU data for a range of antibody subclasses and subtypes across all charge states simultaneously, demonstrating a strong charge state dependence on the information content of CIU. Finally, we demonstrate that CIU data collection times can be further reduced by scanning fewer voltage steps, enabling us to optimize the throughput of our improved CIU methods and confidently differentiate antibody variant ions using ∼20% of the data typically collected during CIU. Taken together, our results characterize a new instrument platform for biotherapeutic stability measurements with dramatically improved throughput and information content.

Development of statistical classifications to study the content and structure of the primary real estate market

Statistical study of the primary real estate market is always associated with the problem that the object is in a constant, and very intense change, both in quantitative terms and in its internal content. Therefore, the issues of constant adaptation of statistical methodology in relation to the primary real estate market are relevant continuously. Among the tasks that are constantly in the field of statistical science and practice in relation to the primary real estate market are the following. First, a critical analysis of the content of the modern primary real estate market, which should allow to identify its fundamental features as an object of statistical research, to systematize the economic relations of the subjects of this market segment for a comprehensive statistical analysis of its functioning and development in any region of Russia. Secondly, it is the construction of analytical classifications of the primary real estate market, systematization of criteria for their construction, which should allow for a qualitative analysis of the structure of this market segment in the modern market economy. Third, the continuous improvement of the system of statistical indicators of the primary real estate market, which should be constantly supplemented by various blocks of indicators, the use of which will link the development of the primary real estate market with indicators of the standard of living of the population and socio-economic development of a particular region on the basis of the formation and ordering of its various subsystems, which will increase their consistency with indicators of living standards and socio-economic development. To Develop the theory and statistical methodology of the complex study of the primary real estate market in terms of the development of criteria for the division of the object into homogeneous groups by typological characteristics and the construction on their basis of statistical classifications necessary for the structural analysis of the primary real estate market. To obtain scientific results in this article, General scientific methods of cognition, such as scientific abstraction, analysis and synthesis, are used, since it is a question of dividing a single whole into typologically homogeneous complexes, the organic relationship between which ensures the integrity and unity of the studied object – the primary real estate market. In addition, the use of the statistical grouping method is considered throughout the work, as it is an applied application of typological criteria. The main classification features of economic assets traded in the primary real estate market of the region are Substantiated and formulated. The development of criteria for the typological division of the object allowed to build statistical classifications necessary for a comprehensive analysis of the structure and structural changes in the primary real estate market. In the scientific article deals with the problematic aspects of a statistical study of the primary real estate market in parts of its etymology on the basis of clear criteria against which to understand the quality and properties of the traded on its economic assets, to build a statistical classification. All this is a step in the first stage of the statistical study in the sequence that is classically considered by the General theory of statistics. Statistical classifications and groupings by typological features precede the science-intensive substantiation and application of complex mathematical and static methods for factor analysis and forecasting of primary real estate market indicators.

Finite dimensional invariant KAM tori for tame vector fields

We discuss a Nash-Moser/KAM algorithm for the construction of invariant tori for tame vector fields. Similar algorithms have been studied widely both in finite and infinite dimensional contexts: we are particularly interested in the second case where tameness properties of the vector fields become very important. We focus on the formal aspects of the algorithm and particularly on the minimal hypotheses needed for convergence. We discuss various applications where we show how our algorithm allows one to reduce to solving only linear forced equations. We remark that our algorithm works at the same time in analytic and Sobolev classes.

Ordered-Assembly Conductive Nanowires Array with Tunable Polymeric Structure for Specific Organic Vapor Detection.

An artificial organic vapor sensor based on a finite number of 1D nanowires arrays can provide a strategy to allow classification and identification of different analytes with high efficiency, but fabricating a 1D nanowires array is challenging. Here, a coaxial Ag/polymer nanowires array is prepared as an organic vapor sensor with specific recognition, using a strategy combining superwettability-based nanofabrication and polymeric swelling-induced resistance change. Such organic vapor sensor containing commercial polymers can successfully classify and identify various organic vapors with good separation efficiency. An Ag/polymer nanowires array with synthetic polyethersulfone polymers is also fabricated, through molecular structure modification of the polymers, to distinguish the similar organic vapors of methanol and ethanol. Theoretical simulation results demonstrate introduction of specific molecular interaction between the designed polymers and organic vapors can improve the specific recognition performance of the sensors.

Recent Applications of Magnetic Solid-phase Extraction for Sample Preparation

This minireview is dedicated to the discussion of analytical methods based on magnetic solid-phase extraction for the investigation of different analyte classes in complex matrices. Magnetic solid-phase extraction represents one of the most exploited approaches for sample preparation, which benefits from the development of new materials and from the coupling with other purification and clean-up strategies. New materials are continuously described for the isolation and enrichment of a variety of compounds, from small molecules to biologic macromolecules. Such magnetic materials developed for magnetic solid-phase extraction are discussed in this minireview, spanning across different types of materials, from the more traditional magnetic nanoparticles functionalized with polymers, to molecularly imprinted polymers, but also graphene, carbon nanotubes, graphitized carbon black, metal organic frameworks, covalent organic frameworks, composite materials, biopolymers (polydopamine, chitosan), materials from wastes and natural products and the newly introduced knitting aromatic polymers. The magnetic solid-phase extraction methods are collected from the recent literature and organized in sections based on the target analyte classes, which include drugs, endocrine-disrupting compounds, pesticides, polycyclic aromatic hydrocarbons, metals, toxins, peptides, proteins, metabolites and a final chapter dedicated to applications to other common pollutants, contaminants and multiresidue methods. A selection of recent applications and variations of the traditional magnetic solid-phase extraction protocols is discussed for food, environmental and biologic matrices. Finally, the compliance of magnetic solid-phase extraction with the principles of green analytical chemistry is also briefly discussed, with recent examples, indicating the use of waste or sustainable materials, development of green material preparations and reduction of organic solvents as the main strategies for future development of environmentally friendly magnetic solid-phase extraction methods.