Separate Dimensions Research Articles

Differentiation of oligosaccharide isomers by direct infusion multidimensional mass spectrometry.

Oligosaccharides demonstrate many bioactivities with applications in the pharmaceutical, cosmetic, and food industries. They also serve as biomarkers for various diseases including cancer and glycogen storage disorders. These make the structural characterization of oligosaccharides very important. Unfortunately, the structural diversity found in saccharides make their characterization challenging, necessitating the development of sophisticated instrumentation to enable isomer differentiation. Herein, we report the ability of halide (Cl- and Br-) adducts to enable direct differentiation of oligosaccharide isomers using conventional collision-induced dissociation (CID) tandem MS (MS/MS). The halide adducts were generated by direct infusion nano-electrospray ionization (nESI). For the first time, this traditional nESI CID MS/MS platform was used to differentiate stereoisomers of trisaccharides (cellotriose β(1 → 4) and maltotriose α(1 → 4), tetrasaccharides (cellotetraose and maltotetraose), and pentasaccharides (cellopentaose and maltopentaose)). In addition, the MS/MS of halide adducts enabled the differentiation of positional, structural, and linkage isomers from a total of 14 oligosaccharides. The isomer differentiation was realized by the generation of distinct diagnostic fragment ions in CID. We also performed principal component analysis using the entire range of MS/MS fragment ion profiles and found that negative-ion mode halide adduction provided more effective isomer differentiation compared with positive-ion mode sodium adduction. Finally, we demonstrated complex mixture analysis by spiking all 14 oligosaccharides into raw urine, of which we successfully distinguished species based on molecular weight (first dimension) and CID MS/MS fragmentation patterns as the second dimension separation. This work effectively showcases the potential to use direct infusion nESI-MS/MS to characterize synthetic oligosaccharide isomers in unpurified reaction mixture as well as from biofluids for diagnostic purposes.

Stress-Inducing Factors and Coping Scale for Children (SICoS–C)

The main goal of this study was to establish the psychometric characteristics of the Stress-Inducing Factors and Coping Scale for Children (SICoS–C). This work also identified stress-inducing situations and coping mechanisms within educational settings, as reported by professionals working with children aged 3–10 years. The study surveyed 215 professionals, including Preschool Teachers and Psychologists who completed the SICoS–C, a new scale composed by items related to stress-inducing situations and coping strategies. An Exploratory Factorial Analysis revealed that the SICoS–C encompasses three distinct dimensions/factors. Subsequent Confirmatory Factorial Analysis suggested a good adjustment for a two-dimensional model, which includes “Events” and “Fears”, as well as a separate one-dimensional dimension construct labelled "Directed Activities". Internal consistency revealed adequate values and demonstrated evidence of divergent and convergent validity. This exploratory study uncovers a scale with promising psychometric attributes that uniquely combines the identification of stress-inducing situations with coping strategies within a single instrument. This study should be replicated as a starting point for other cross-cultural studies. The implications of this exploratory study point to the usefulness of this instrument in the planning of educational practices and in contexts of psychological evaluation, but studies with larger samples are needed.

Fast and broad-coverage lipidomics enabled by ion mobility-mass spectrometry.

Aberrant lipid metabolism has been widely recognized as a hallmark of various diseases. However, the comprehensive analysis of distinct lipids is challenging due to the complexity of lipid molecular structures, wide concentration ranges, and numerous isobaric and isomeric lipids. Usually, liquid chromatography-mass spectrometry (LC-MS)-based lipidomics requires a long time for chromatographic separation to achieve optimal separation and selectivity. Ion mobility (IM) adds a new separation dimension to LC-MS, significantly enhancing the coverage, sensitivity, and resolving power. We took advantage of the rapid separation provided by ion mobility and optimized a fast and broad-coverage lipidomics method using the LC-IM-MS technology. The method required only 8 minutes for separation and detected over 1000 lipid molecules in a single analysis of common biological samples. The high reproducibility and accurate quantification of this high-throughput lipidomics method were systematically characterized. We then applied the method to comprehensively measure dysregulated lipid metabolism in patients with colorectal cancer (CRC). Our results revealed 115 significantly changed lipid species between preoperative and postoperative plasma of patients with CRC and also disclosed associated differences in lipid classes such as phosphatidylcholines (PC), sphingomyelins (SM), and triglycerides (TG) regarding carbon number and double bond. Together, a fast and broad-coverage lipidomics method was developed using ion mobility-mass spectrometry. This method is feasible for large-scale clinical lipidomic studies, as it effectively balances the requirements of high-throughput and broad-coverage in clinical studies.

Decoding Complexity in Synthetic Oligonucleotides: Unraveling Coeluting Isobaric Impurity Ions by High Resolution Mass Spectrometry.

Analyzing coeluting impurities with similar masses in synthetic oligonucleotides by liquid chromatography-mass spectrometry (LC-MS) poses challenges due to inadequate separation in either dimension. Herein, we present a direct method employing fully resolved isotopic envelopes, enabled by high resolution mass spectrometry (HRMS), to identify and quantify isobaric impurity ions resulting from the deletion or addition of a uracil (U) or cytosine (C) nucleotide from or to the full-length sequence. These impurities may each encompass multiple sequence variants arising from various deletion or addition sites. The method utilizes a full or targeted MS analysis to measure accurate isotopic distributions that are chemical formula dependent but nucleotide sequence independent. This characteristic enables the quantification of isobaric impurity ions involving sequence variants, a capability typically unavailable in sequence-dependent MS/MS methods. Notably, this approach does not rely on standard curves to determine isobaric impurity compositions in test samples; instead, it utilizes the individual isotopic distributions measured for each impurity standard. Moreover, in cases where specific impurity standards are unavailable, the measured isotopic distributions can be adequately replaced with the theoretical distributions (calculated based on chemical formulas of standards) adjusted using experiment-specific correction factors. In summary, this streamlined approach overcomes the limitations of LC-MS analysis for coeluting isobaric impurity ions, offering a promising solution for the in-depth profiling of complex impurity mixtures in synthetic oligonucleotide therapeutics.

Assessing Occupational Burnout Among a Cohort of New York City Sonographers: A Pilot Study

Objective: Occupational burnout has been documented among health care workers; however, there has been limited research investigating burnout among sonographers. The aim of this study was to measure burnout among a cohort of sonographers within New York City. Materials and Methods: 70 participants completed a questionnaire online. The questionnaire comprised 2 sections: demographic questions and the Oldenburg Burnout Inventory (OLBI). The OLBI was used to assess 2 core dimensions of burnout: exhaustion and disengagement. The data were analyzed descriptively with frequencies, percentages, mean values, and standard deviations. Results: The majority (84.3%) of the participants worked in a hospital setting. A moderate to high burnout score was found for 81.4% of the participants. In the separate dimensions of burnout, 84.3% scored moderate to high on exhaustion, and 76.6% scored moderate to high on disengagement. Conclusion: Occupational burnout in health care settings is a serious problem with detrimental consequences. Most of this cohort of 70 sonographers working in New York City were experiencing a moderate to high level of burnout. Development of educational programs and interventions can be considered a potential solution for addressing prevention of sonographer burnout.

Integrating Post-Ionization Separation via Differential Mobility Spectrometry into Direct Analysis in Real Time Mass Spectrometry for Toy Safety Screening.

Direct analysis in real time (DART) enables direct desorption and ionization of analytes, bypassing the time-consuming chromatographic separation traditionally required for mass spectrometry (MS) analysis. However, DART-MS suffers from matrix interference of complex samples, resulting in compromised detection sensitivity and quantitation accuracy. In this study, DART-MS was combined with differential mobility spectrometry (DMS) to provide an additional dimension of post-ionization ion mobility separation within a millisecond time scale, compensating for the lack of separation in DART-MS analysis. As proof-of-concept, primary aromatic amines (PAAs), a class of potentially hazardous chemicals, were analyzed in various toy products, including bubble solutions, finger paints, and plush toys. In addition to commercial Dip-it glass rod and metal mesh sampling tools, a customized rapid extractive evaporation device was designed for the accelerated extraction and sensitive analysis of solid toy samples. The incorporation of DMS in DART-MS analysis enabled the rapid separation and differentiation of isomeric analytes, leading to improved accuracy and reliability. The developed protocols were optimized and validated, achieving good linearity with correlation coefficients greater than 0.99 and acceptable repeatability with relative standard deviations less than 10%. Moreover, satisfactory sensitivity was realized with limits of detection and quantitation ranges of 0.2-5 and 1-20 μg/kg (μg/L) for the 11 PAA analytes. The established methodology was applied for the analysis of real toy samples (n = 18), which confirmed its appealing potential for toy safety screening and consumer health protection.

EXPLORATORY FACTOR ANALYSIS OF THE COMFORT ASSESSMENT IN DYING WITH DEMENTIA SCALE

Abstract The 14-item Comfort Assessment in Dying with Dementia (CAD-EOLD) scale is a widely used instrument measuring end-of-life care for people with dementia (PWD). The instrument has been used to evaluate symptom burden among PWD in nursing homes, but the measurement properties are less studied for symptoms reported by family and staff caregivers. We conducted an exploratory factor analysis to evaluate the psychometric properties of the scale using staff and family (N=476) responses to CAD-EOLD at the baseline of an NIH-funded clinical trial. Subjects were long-stay nursing home residents with moderate-to-severe cognitive impairment in Indiana and Maryland. Staff (n=368) and family members (n=108) completed the CAD-EOLD, describing participating residents. We performed separate exploratory factor analyses on family and staff responses. Family and staff data showed similar clustering patterns. Restlessness, anxiety, fear, crying, and moaning had high factor loadings in the first cluster, interpreted as “Physical and Psychological Distress” (loading range = 0.47–0.91). Choking, gurgling, and difficulty swallowing had high loadings in the second cluster that depicted “Dying Symptoms” (loading range = 0.62–1.15). Serenity, calm, and peace had high loadings in the third factor on “Well-Being” (loading range = 0.72–0.93). Three “Physical Distress” items (i.e., discomfort, pain, and shortness of breath) cross-loaded with “Dying Symptoms.” Accordingly, “Physical and Psychological Distress,” “Dying Symptoms,” and “Well-Being” represented important but separate dimensions for measuring end-of-life experiences of PWD. Findings demonstrated favorable construct validity of CAD-EOLD in PWD with moderate-to-severe cognitive impairment in nursing homes, as reported by staff and family caregivers.

Dimensional Reliabilism

The paper argues that (i) the notion of epistemic reliability, as it is standardly defined within mainstream epistemology, is a multidimensional concept, and that (ii) paying attention to reliability’s multidimensional nature can significantly expand reliabilism’s purview both in the theoretical and practical domain. Reliabilist theories of knowledge and justification agree that a process is reliable just in case it leads to a ‘sufficiently high preponderance of true over false beliefs.’ Given this straightforward definition, reliability appears to be, and so far has been implicitly treated by epistemologists as, a mono-dimensional concept. Nevertheless, the terminology and formulas that statisticians and computers scientists have developed for assessing the reliability of binary classification processes reveal that for any belief-forming process whose aim is to identify the presence or the absence of some quality ι, there are at least four different interpretations of the above definition. These interpretations correspond to four separate dimensions of reliability: Precision, Negative Predictive Value (NPV), Recall and Specificity. Though epistemologists do not explicitly specify how they interpret the above definition of reliability, due to their main interest in ‘know-that’, their mono-dimensional focus seems to have so far been mainly directed at Precision alone (or, at best, to both Precision and NPV—but without distinguishing between the two). As it transpires, however, the (entirely) neglected dimensions of Recall and Specificity can guide the satisfaction of another important epistemic goal, best captured by the locution ‘know-most/all.’ The upshot is that different epistemic goals call for high levels of reliability along different dimensions (and, often, their combinations too). This observation invites upgrading reliabilism into dimensional reliabilism, according to which assessing whether a process is knowledge-conducive depends on whether it manifests high reliability along the appropriate dimension(s), given the epistemic goal at hand.

Information and Confrontation in Legislative Oversight

Committees can use oversight hearings to collect and communicate the information Congress needs to oversee the bureaucracy, but many worry that members instead focus on scoring political points by lambasting witnesses. We leverage the collective judgment of congressional staff to measure how exchanges between legislators and witnesses vary on two separable dimensions: information and confrontation. Contrary to the conventional wisdom that confrontation crowds out information, we show that members of the president's party engage in less confrontational oversight and reveal no more or less information than their peers.

Multi-Dimensional Liquid Chromatography of Pulse Triacylglycerols with Triple Parallel Mass Spectrometry

We analyzed ten pulses (the dried seeds of legumes), i.e., baby lima beans, black beans, black-eyed peas, butter beans, cranberry beans, garbanzo beans, green split peas, lentils, navy beans, and pinto beans, using three-dimensional liquid chromatography (3D-LC) with parallel second dimensions, LC × (LC + LC). We combined non-aqueous reversed-phase (NARP) chromatography as the first dimension separation, 1D, with argentation UHPLC for separation based on degree and location of unsaturation in the first second dimension, 2D(1), and multi-cycle NARP-UHPLC in the second second dimension, 2D(2). Pulses contained 1.9% to 2.7% lipids, except garbanzo beans, which contained 6.2% lipids. High-resolution, accurate-mass (HRAM) orbitrap mass spectrometry (MS) was used to perform lipidomic analysis of the 2D(2) and percent relative quantification, showing that the most abundant average triacylglycerol (TAG) molecular species across all pulses were PLL at 10.67% and PLLn at 10.45%. Common beans (Phaseolus vulgaris) were clustered together using principal component analysis (PCA), showing the highest levels of linolenic acid, C18:3, in molecular species such as PLnLn, LLnLn, and OLLn, with palmitic (P), C16:0, linoleic (L), 18:2, linolenic (Ln), 18:3, and oleic (O), 18:1, FAs. Calibration curves derived from interweaved sets of regioisomer standards allowed the absolute quantification of 1,2- and 1,3-regioisomers for a subset of TAGs.

Dimensional interaction is driven by feature integration: A binding theory of garner interference.

For nearly half a century now, Garner interference has been serving as the gold standard measure of dimensional interaction and selective attention. But the mechanisms that generate Garner interference are still not well understood. The current study proposes a novel theory that ascribes the interference (and dimensional interaction in general) to episodic feature integration processes at the micro (trial-to-trial) level. The novel account builds on earlier well-established notions of "feature integration" and "object files," and is augmented by formal derivations. The sequential binding account predicts that the magnitude of Garner interference is related to the strength of feature integration along consecutive trials. Three experiments were set to test this novel binding theory. Experiment 1 and Experiment 2 tested performance with integral dimensions (chroma and value, and width and height of rectangles); whereas Experiment 3 examined performance with a pair of separable dimensions (circle's size and angle of a diameter). In addition, the time lag ensuing between consecutive trials was manipulated. The results strongly supported the predictions of the sequential binding account: (a) with integral dimensions, substantial amounts of Garner interference were correlated with large partial repetition costs (e.g., consensual markers of feature integration), but this pattern was not observed with separable dimensions, and (b) the magnitude of both Garner interference and partial repetition costs diminished as a function of the ensuing time lag between consecutive trials, pointing to a common time-dependent memory mechanism. These results adduce strong support in the predictions of the feature binding theory of Garner interference, giving currency to the idea that dimensional interaction is driven by feature integration. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The dynamic interaction between symptoms and pharmacological treatment in patients with major depressive disorder: the role of network intervention analysis

IntroductionThe Major Depressive Disorder (MDD) is a mental health disorder that affects millions of people worldwide. It is characterized by persistent feelings of sadness, hopelessness, and a loss of interest in activities that were once enjoyable. MDD is a major public health concern and is the leading cause of disability, morbidity, institutionalization, and excess mortality, conferring high suicide risk. Pharmacological treatment with Selective Serotonin Reuptake Inhibitors (SSRIs) and Serotonin Noradrenaline Reuptake Inhibitors (SNRIs) is often the first choice for their efficacy and tolerability profile. However, a significant percentage of depressive individuals do not achieve remission even after an adequate trial of pharmacotherapy, a condition known as treatment-resistant depression (TRD).MethodsTo better understand the complexity of clinical phenotypes in MDD we propose Network Intervention Analysis (NIA) that can help health psychology in the detection of risky behaviors, in the primary and/or secondary prevention, as well as to monitor the treatment and verify its effectiveness. The paper aims to identify the interaction and changes in network nodes and connections of 14 continuous variables with nodes identified as "Treatment" in a cohort of MDD patients recruited for their recent history of partial response to antidepressant drugs. The study analyzed the network of MDD patients at baseline and after 12 weeks of drug treatment.ResultsAt baseline, the network showed separate dimensions for cognitive and psychosocial-affective symptoms, with cognitive symptoms strongly affecting psychosocial functioning. The MoCA tool was identified as a potential psychometric tool for evaluating cognitive deficits and monitoring treatment response. After drug treatment, the network showed less interconnection between nodes, indicating greater stability, with antidepressants taking a central role in driving the network. Affective symptoms improved at follow-up, with the highest predictability for HDRS and BDI-II nodes being connected to the Antidepressants node.ConclusionNIA allows us to understand not only what symptoms enhance after pharmacological treatment, but especially the role it plays within the network and with which nodes it has stronger connections.

On-tissue dataset-dependent MALDI-TIMS-MS2 bioimaging

Trapped ion mobility spectrometry (TIMS) adds an additional separation dimension to mass spectrometry (MS) imaging, however, the lack of fragmentation spectra (MS2) impedes confident compound annotation in spatial metabolomics. Here, we describe spatial ion mobility-scheduled exhaustive fragmentation (SIMSEF), a dataset-dependent acquisition strategy that augments TIMS-MS imaging datasets with MS2 spectra. The fragmentation experiments are systematically distributed across the sample and scheduled for multiple collision energies per precursor ion. Extendable data processing and evaluation workflows are implemented into the open source software MZmine. The workflow and annotation capabilities are demonstrated on rat brain tissue thin sections, measured by matrix-assisted laser desorption/ionisation (MALDI)-TIMS-MS, where SIMSEF enables on-tissue compound annotation through spectral library matching and rule-based lipid annotation within MZmine and maps the (un)known chemical space by molecular networking. The SIMSEF algorithm and data analysis pipelines are open source and modular to provide a community resource.

Data-driven robust optimization for a multi-trip truck-drone routing problem

Using drones along with conventional vehicles, such as trucks, can potentially improve the cost-effectiveness and speed of delivery operations. This research considers a multi-trip truck-drone routing problem under uncertainty. Each truck is allowed to take multiple trips from the depot,while each drone is allowed to perform a single trip from the launch locations. Based on customer preferences, deliveries are grouped into deliveries by trucks to distribution centers and deliveries by drones from distribution centers to customers. A mixed-integer linear programming model is formulated to minimize the sum of the waiting times of all customers. To deal with uncertainty, a new two-stage clustering algorithm that uses multiple-kernel learning-based and single-kernel learning-based methods is introduced to construct uncertainty sets. In the case of column-wised uncertainty, a two-stage clustering with a dimensional separation algorithm is developedto avoid over-conservatism.A two-step solution method is provided for solving the proposed robust model to reduce the CPU time. The performance of the proposed algorithms is evaluated on several test problems. The results indicate that the developed algorithms can effectively avoid over-conservatism, reduce variables and constraints of the data-driven robust counterpart model, and reduce the CPU time required to solve the problem.

The Identification of Fritillaria Species Using Hyperspectral Imaging with Enhanced One-Dimensional Convolutional Neural Networks via Attention Mechanism.

Combining deep learning and hyperspectral imaging (HSI) has proven to be an effective approach in the quality control of medicinal and edible plants. Nonetheless, hyperspectral data contains redundant information and highly correlated characteristic bands, which can adversely impact sample identification. To address this issue, we proposed an enhanced one-dimensional convolutional neural network (1DCNN) with an attention mechanism. Given an intermediate feature map, two attention modules are constructed along two separate dimensions, channel and spectral, and then combined to enhance relevant features and to suppress irrelevant ones. Validated by Fritillaria datasets, the results demonstrate that an attention-enhanced 1DCNN model outperforms several machine learning algorithms and shows consistent improvements over a vanilla 1DCNN. Notably under VNIR and SWIR lenses, the model obtained 98.97% and 99.35% for binary classification between Fritillariae Cirrhosae Bulbus (FCB) and other non-FCB species, respectively. Additionally, it still achieved an extraordinary accuracy of 97.64% and 98.39% for eight-category classification among Fritillaria species. This study demonstrated the application of HSI with artificial intelligence can serve as a reliable, efficient, and non-destructive quality control method for authenticating Fritillaria species. Moreover, our findings also illustrated the great potential of the attention mechanism in enhancing the performance of the vanilla 1DCNN method, providing reference for other HSI-related quality controls of plants with medicinal and edible uses.

The fourth dimension: Time between crimes and impact on behavioral change in violent serial crime

While the time dimension is part of the definition of serial crime, little attention has been given to understanding offending timelines and the impact of patterns of shorter or longer delays between crimes on the evolution of series on the ability to link crimes. Given the scarcity of research focusing on the timelines of serial offenders, the current study aimed to (1) determine whether series can be classified based on their timeline trajectories (e.g., progressively shorter intervals or progressively longer), and (2) determine whether these timeline trajectories correlate with trajectories of behavioral consistency and change across series. Data included 43 homicide series encompassing 216 crime scenes. Trajectories were determined based on whether intervals increased, decreased, or remained consistent across series, and whether timeline trajectories and behavioral trajectories align. Results revealed that distinct timeline trajectories can reliably distinguish between series. Results also suggested a complex interaction between time between crimes and how this relates to behavioral consistency and change trajectories, thus, suggesting that time is a useful, but potentially separate dimension in the linkage process.

Higher-order Singular Value Decomposition Filter for Contrast Echocardiography.

Assessing the coronary circulation with contrast-enhanced echocardiography has high clinical relevance. However, it is not being routinely performed in clinical practice because the current clinical tools generally could not provide adequate image quality. The contrast agent's visibility in the myocardium is generally poor, impaired by motion and non-linear propagation artifacts. The established multi-pulse contrast schemes (MPCS) and the more experimental singular value decomposition (SVD) filter also fall short to solve these issues. Here, we propose a scheme to process AM/AMPI echoes with higher-order singular value decomposition (HOSVD) instead of conventionally summing the complementary pulses. The echoes from the complementary pulses form a separate dimension in the HOSVD algorithm. Then, removing the ranks in that dimension with dominant coherent signals coming from tissue scattering would provide the contrast detection. We performed both in vitro and in vivo experiments to assess the performance of our proposed method in comparison with the current standard methods. A flow phantom study shows that HOSVD on AM pulsing exceeds the contrast-to-background ratio (CBR) of conventional AM and an SVD filter by 10dB and 14dB, respectively. In vivo porcine heart results also demonstrate that, compared to AM, HOSVD improves CBR in open-chest acquisition (up to 19dB) and contrast ratio in closed-chest acquisition (3dB).

The Criteria for Reliability

We conducted a large sample representative survey to get a deeper understanding of the Hungarian population's opinion on what makes them think a product or service is trustworthy. The main objective of the research is to design the Reliable Product Rewards Programme around a set of research-acceptable metrics that are valid and acceptable to all in 2021 and beyond. The University of Óbuda will participate in the development of the Reliable Product reward scheme in the future. When assessing the reliability criteria, 6 out of 10 factors were included. Based on the results of the research, it is proposed that the following 6 factors should be included in the Reliable Product reward system: 1. product value for money 2. product quality 3. recommendation by others (acquaintances, friends, family) 4. satisfaction with the product, after previous tasting/use 5. trial, during previous tasting/use. In our further research, familiarity will be considered as a separate dimension 0 as a filtering criterion in the reward system.

The 5th Dimension and its Implications for the String Theory, Conservation of Energy and Heisenberg Uncertainty Principle

In 1919, the mathematician Theodor Kaluza began to play around with Albert Einstein’s formulas for gravity. Out of curiosity, he reworked the equations to see how they‘d look in five, rather than four, dimensions. The exercise yielded an extra set of equations, which were the same as James Clerk Maxwell’s equations for the electromagnetic field. His idea of a fifth dimension had produced a mathematical unification of gravity and electromagnetism. Since Kaluza’s calculations yielded an extra set of equations with Einstein’s formulas for gravity, it’s logical to assume that extending the number of known dimensions to five might also extend E = mc2. This article also visits conservation of mass and energy / mass-energy equivalence, Electromagnetism, Dark Matter, Dark Energy, the Matrix, Antigravitons, Incompleteness Theorems, New Irrationals, Calculus, Celluloid Motion, and Vector-Tensor-Scalar Geometry. And as the text accompanying Figure 5 says, “The counterclockwise rotation does not have to encompass 360 degrees. It can be divided into single degrees – and even arcseconds which are 3,600 times smaller than a degree. Each arcsecond (or tiny part of it) could correspond to a separate dimension and the total dimensions might make up a temporal multiverse that’s reminiscent of string theory, the cosmological framework that says particles are composed of one-dimensional objects called strings (a group of binary digits is called a single-dimensional bit string).” Finally, Heisenberg’s Principle of Indeterminacy - in which the position and the velocity of a particle cannot both be measured exactly, at the same time, even in theory - is revised. Quantum uncertainty is changed into quantum certainty by software containing a new type of irrational number – actually, irrational equations - that’s based on mathematics’ Matrix and topology’s Mobius strip, as well as being interdimensional. Ludwig Wittgenstein, an Austrian-British philosopher, once stated, "The limits of my language mean the limits of my world." Wittgenstein proposed that our understanding of the world is shaped by the language we use to describe it. This concept extends to mathematics, which can be seen as a specialized language. So if a person has no doubt that 1 + 1 = 2, he or she can never arrive at a truly Unified Theory in which only one thing exists. The maths accepted in 2023 is indispensable in enabling advancements in technology and science that have transformed our lives but is incapable of fully describing Unification. Scientific history has shown that it can go partway. Scientists consequently assume it will eventually go all the way.

Use of online sequential segmented heart cutting modulation and orthogonality approach for determination of isomeric impurity profile of a drug molecule using parallel 2-dimensional mass spectroscopy

Determination of impurity profile including chiral and achiral impurities is challenging as well as time consuming while analysis of the active moiety. With the recent evolution of 2-Dimensional separation modes, significant improvement can be made for simultaneous determination of both achiral and chiral impurities in a single set-up. Herein, the present work represents example of how both chiral and achiral impurities can be determined simultaneously. To represent the work, galantamine HBr was selected as the active moiety since it has achiral as well as many chiral impurities. Further, a 1D UPLC method with shorter runtime was developed to determine the achiral impurities and simultaneous heart cutting approach was used to cut the impurities and active compound and transfer to 2D chiral setup to separate out chiral impurities and perform its quantification. Both methods were proven to be orthogonal to each other with orthogonality factor of 0.241.