Frequency Map Research Articles

High-Throughput Mapping of Chromosomal Conformations in E. coli Under Physiological Conditions Using Massively Multiplexed Mu Transposition.

Many approaches for measuring three-dimensional chromosomal conformations rely upon formaldehyde crosslinking followed by subsequent proximity ligation, a family of methods exemplified by 3C, Hi-C, etc. Here we provide an alternative crosslinking-free procedure for high-throughput identification of long-range contacts in the chromosomes of enterobacteria, making use of contact-dependent transposition of phage Mu to identify distant loci in close contact. The procedure described here will suffice to provide a comprehensive map of transposition frequencies between tens of thousands of loci in a bacterial genome, with the resolution limited by the diversity of the insertion site library used and the sequencing depth applied.

Inundation Mapping and Flood Frequency Analysis using HEC-RAS Hydraulic Model and EasyFit Software

Conducting a flood frequency analysis and mapping of the inundated area in rivers are important for river flow modeling. The main purpose of this research is to estimate the peak flow, model the inundated area using HEC-RAS, and conduct an analytical hierarchy process for the upper Baro Akobo basin in Ethiopia. The inundation area and river depth for 25, 50 and 100 years are considered while contemplating several factors which contribute to flooding. The downstream of the basin has experienced numerous floods that occurred in 2006, 2007, 2010, 2011, and 2012. Flood frequency analysis with stream flow data from 1990–2009 at the Baro-Gambella gauging station was carried out to estimate the expected peak floods of the watershed. The analysis was conducted using the Gumbel, Normal, and Log Pearson Type III distribution methods. The peak floods with return periods of 25, 50, and 100 years with a minimum statistical value calculated using the Normal distribution method resulted in 1739.586 m3/s, 1820.872 m3/s, and 1893.974 m3/s, respectively. The HEC-RAS model results indicated that the flood inundation areas under different land use changes for 25-, 50-, and 100-year return periods were 446.2 km2 (annual crop cover), 404.4 km2 (built area cover), 323.3 km2 (flooded vegetation), and 93.58 km2 (forest area), respectively, whereas the inundation depth ranged from 0–2.6 m, 0–2.9 m, and 0–3.2 m depth at the upstream and downstream of the river, respectively. The outcome of this study could be used to reduce temporal and permanent flood risk.

A new computational methodology for the characterization of complex molecular environments using IR spectroscopy: bridging the gap between experiments and computations.

The molecular interactions and dynamics of complex liquid solutions are now routinely measured using IR and 2DIR spectroscopy. In particular, the use of the latter allows the determination of the frequency fluctuation correlation function (FFCF), while the former provides us with the average frequency. In turn, the FFCF can be used to quantify the vibrational dynamics of a molecule in a solution, and the center frequency provides details about the chemical environment, solvatochromism, of the vibrational mode. In simple solutions, the IR methodology can be used to unambiguously assign the interactions and dynamics observed by a molecule in solution. However, in complex environments with molecular heterogeneities, this assignment is not simple. Therefore, a method that allows for such an assignment is essential. Here, a parametrization free method, called Instantaneous Frequencies of Molecules or IFM, is presented. The IFM method, when coupled to classical molecular simulations, can predict the FFCF of a molecule in solutions. Here, N-methylacetamide (NMA) in seven different chemical environments, both simple and complex, is used to test this new method. The results show good agreement with experiments for the NMA solvatochromism and FFCF dynamics, including characteristic times and amplitudes of fluctuations. In addition, the new method shows equivalent or improved results when compared to conventional frequency maps. Overall, the use of the new method in conjunction with molecular dynamics simulations allows unlocking the full potential of IR spectroscopy to generate molecular maps from vibrational observables, capable of describing the interaction landscape of complex molecular systems.

Experimental demonstration of the effect of frequency mapping on performance in an SOA-based C-RAN architecture based on the AIFoF technology for 5G communications

Experimental demonstration of the effect of frequency mapping on performance in an SOA-based C-RAN architecture based on the AIFoF technology for 5G communications

Hypobaric hypoxia drives selection of altitude-associated adaptative alleles in the Himalayan population

Genetic variants play a crucial role in shaping the adaptive phenotypes associated with high-altitude populations. Nevertheless, a comprehensive understanding of the specific impacts of different environments associated with increasing altitudes on the natural selection of these genetic variants remains undetermined. Hence, this study aimed to identify genetic markers responsible for high-altitude adaptation with specific reference to different altitudes, majorly focussing on an altitude elevation range of ∼1500 m and a corresponding decrease of ≥5 % in ambient oxygen availability. We conducted a comprehensive genome-wide investigation (n = 192) followed by a validation study (n = 514) in low-altitude and three high-altitude populations (>2400 m) of Nubra village (NU) (3048 m), Sakti village (SKT) (3812 m), and Tso Moriri village (TK) (4522 m). Extensive genetic analysis identified 86 SNPs that showed significant associations with high-altitude adaptation. Frequency mapping of these SNPs revealed 38 adaptive alleles and specific haplotypes that exhibited a strong linear correlation with increasing altitude. Notably, these SNPs spanned crucial genes, such as ADH6 and NAPG along with the vastly studied genes like EGLN1 and EPAS1, involved in oxygen sensing, metabolism, and vascular homeostasis. Correlation analyses between these adaptive alleles and relevant clinical and biochemical markers provided evidence of their functional relevance in physiological adaptation to hypobaric hypoxia. High-altitude population showed a significant increase in plasma 8-isoPGF2α levels as compared to low-altitude population. Similar observation showcased increased blood pressure in NU as compared to TK (P < 0.0001). In silico analyses further confirmed that these alleles regulate gene expression of EGLN1, EPAS1, COQ7, NAPG, ADH6, DUOXA1 etc. This study provides genetic insights into the effects of hypobaric-hypoxia on the clinico-physiological characteristics of natives living in increasing high-altitude regions. Overall, our findings highlight the synergistic relationship between environment and evolutionary processes, showcasing physiological implications of genetic variants in oxygen sensing and metabolic pathway genes in increasing high-altitude environments.

Diffusion model and vision transformer for intelligent fault diagnosis under small samples

The existing deep learning models can achieve a high level of fault diagnosis accuracy in the case of a large number of samples. However, in actual production, data is often limited due to the difficulty of data collection and labeling. For small sample fault diagnosis, a fault diagnosis method called diffusion model-overlapping-patch vision transformer (DM-OVT) is proposed in this paper. The method adds coordinate attention to the DM, so that it can consider both channel information and spatial information. In the patch embedding part of Vision Transformer, features are first extracted using convolutional layers, and then overlapping patch divisions are used to improve the correlation between each patch. To be specific, DM-OVT first uses short-time Fourier transform to convert the one-dimensional signals into the time–frequency maps. And then inputs them into the DM to generate different classes of fault data according to labels. Finally, OVT is used to classify the expanded data. The effectiveness of the proposed method was tested on data sets from laboratory multistage centrifugal fans and Case Western Reserve University, and the highest accuracy was achieved in the comparison experiments.

Interactions Between Slopes of Residual Hearing and Frequency Maps in Simulated Bimodal and Electric-Acoustic Stimulation Hearing.

The aim of this study was to determine the effects of residual hearing slopes and cochlear implant frequency map settings on bimodal and electric-acoustic stimulation (EAS) benefits in speech perception. Adults with normal hearing were recruited for simulated bimodal and EAS hearing. Sentence perception was measured unilaterally and bilaterally. For the acoustic stimulation, three slopes of high-frequency hearing loss were created using low-pass filters with a cutoff frequency of 500 Hz: steep (96 dB/octave), medium (48 dB/octave), and shallow (24 dB/octave). For the electric stimulation, an eight-channel sinewave vocoder was used with an output frequency range (1000-7938 Hz) with three input frequency ranges to create frequency maps, overlap (188-7938 Hz), meet (500-7938 Hz), and gap (750-7938 Hz), relative to the cutoff frequency in the acoustic stimulation. The largest bimodal/EAS benefit occurred with the shallow slope, and the smallest occurred with the steep slope. The effects of the slopes on bimodal/EAS benefit were greatest with the meet or gap map and the least with the overlap map. EAS benefit was greater than bimodal benefit at higher signal-to-noise ratios regardless of frequency map. The results indicate that correlation between bimodal/EAS benefit and residual hearing could potentially improve if slopes were considered. The optimal frequency map differed with different slopes, suggesting that the slopes of residual hearing should be carefully considered in fitting bimodal and EAS hearing. EAS hearing provided greater benefit over bimodal hearing, suggesting that spectrotemporal integration was better within one ear (i.e., EAS) than across ears (i.e., bimodal).

Development of a Conceptual Framework for Tuberculosis Management and Control; an Evidence Synthesis Using Text Mining Software: A Review.

The use of electronic systems supported by text-mining software applications that support the End TB strategy' needs to be explored. This study aimed to address this knowledge gap, and synthesis of evidence. The PubMed database was searched for structured review articles published in English since 2012 on interventions to control and manage TB. Nine hundred twenty-five articles met the inclusion criteria. The included articles were synthesized using the text and content analysis software Leximancer. The themes were chosen based on the hit words that emerged in the frequency and heat maps. After the themes were chosen, the concept built the themes based on likelihood. The framework resulting in the study focuses on early detection and treatment to minimize the chance of TB transmission in the population, especially for highly susceptable populations. The main area highlighted is the appropriate screening and treatment domains. The framework generated in this study is somewhat in line with the WHO Final TB Strategy. This study highlights the importance of improving TB prevention through a patient-centered approach and protecting susceptible populations. Our findings will be helpful in guiding TB practice, policy development and future research. Future research can elaborate the framework and elicit feedback from TB management stakeholdesr to assess its utility.

Dynamic Compensation Strategy for Scanning Error of Contact Probe Based on Damping Ratio and Frequency Mapping

Dynamic Compensation Strategy for Scanning Error of Contact Probe Based on Damping Ratio and Frequency Mapping

Influence of the Manipulator Configuration on Vibration Effects

Abstract Vibration analysis of industrial robots is one of the key issues in the context of robotisation of machining processes. Low-frequency vibrations result from flexibility in manipulator joints. Within the scope of the article, a model of a two-link robot manipulator was built. Dynamic equations of motion were formulated to study the influence of the robot arm configuration on vibration effects. Based on numerical simulations, the frequency spectrum of vibrations of the robot’s links was determined, and tests were carried out in a number of configurations, obtaining a map of resonant frequencies depending on the configuration of the manipulator. Experimental studies were then carried out, which confirmed the conclusions from the simulation studies. The results obtained confirm that the positioning of the manipulator’s links has a significant effect on vibration effects. Tests conducted using a vision system with a motion amplification application made it easier to interpret the results. The formulated mathematical model of the manipulator generates results that coincide with the results of experimental studies.

Using an ex vivo MRI atlas of the human medial temporal lobe to characterize the 3D distribution of neurofibrillary tangle burden across histologically defined subregions

AbstractBackgroundAccumulation of tau neurofibrillary tangles (NFT) in the medial temporal lobe (MTL) is an early pathological change associated with neurodegeneration in Alzheimer’s Disease (AD). Current understanding of the spread of NFTs within the MTL is based on 2‐D histological sections sampled at sparse locations. Here, using human MTL specimens from donors with diagnoses spanning the AD continuum, we examine 3‐D quantitative maps of NFT burden derived from serial histology leveraging a high‐resolution, ex vivo MRI atlas, thus enabling more granular analyses.MethodWe combined ex vivo MRI scans (0.2×0.2×0.2mm3, 9.4T) of 55 MTL specimens using a customized registration approach to construct a 3‐D atlas (Ravikumar et al., Acta Neuropathol Comm, 2021). MTL subregions in the atlas were labelled based on cytoarchitecture using serial Nissl histology (n = 17) (Fig.1). For 25 specimens with a primary diagnosis of AD or primary age‐related tauopathy (78.6±10.9 years; 16M/9F), quantitative NFT burden maps were derived from serial anti‐tau immunohistochemistry histology and registered to the atlas for group‐level analysis (Yushkevich et al., Brain, 2021). For quantitative comparisons, we computed the mean NFT burden and cortical thickness within each subregion.ResultFig.2A shows average and summary frequency maps of NFT burden, computed separately for specimens with a low Braak stage (Braak0‐II; n = 11) and high Braak stage (BraakIV‐VI), that reveal a clear anterior‐to‐posterior gradient in NFT burden. At low Braak stages, we observe high levels of NFT burden in cornu ammonis (CA)1, entorhinal cortex (EC), and Brodmann area 35 (BA35;≈transentorhinal region), particularly in the outer cortical layers. CA1 and EC also show higher NFT burden relative to BA35 (Fig.2C). At high Braak stages, overall NFT burden is visibly higher and more widespread, with significant increases found across almost all subregions (Fig.2B). Moreover, significant correlations between NFT burden and cortical thinning were found in the perforant pathway (PP; molecular layer of the subiculum), dentate gyrus, EC and Areas TE, TF and TH (Fig.3).ConclusionBuilding on earlier work, we provide a detailed characterization of the distribution of NFT burden in the MTL. Our findings of specific subregional involvement of early NFT pathology can help inform the development of improved in vivo neuroimaging biomarkers

Evaluation of solar PV-based microgrids viability utilizing single and multi-criteria decision analysis

Feasibility analysis is crucial for solar projects to minimize risk and ensure goal achievement. Essential factors include the PV system's location, elevation, land slope, energy yield, soil texture, mean annual cloud frequency map, and distance from urban areas. This study examines the performance of a 3 MW PV system across five remote Egyptian regions characterized by desert weather conditions. The influence of available weather data sources and different yield estimation methods on the feasibility of the PV system is treated as a Single-Criteria Decision Making (SCDM) scheme. Meteorological data from Meteonorm 8.1, PVGIS, and NASA-SSE satellite data are utilized as input data for the designed PVsyst and ANSYS-based energy yield estimation models and for empirical formulas. Significant variations in estimated energy yield are observed, depending on the weather data source. The study also underscores the importance of diligently evaluating and comparing the accuracy and reliability of different weather data sources and estimation methods for simulating PV-based microgrid system performance. The yield estimation-based SCDM indicates that the New Valley PV system consistently outperformed the other systems. However, a Multi-Criteria Decision Making (MCDM) indicates that the Red Sea performs well in technical criteria evaluation, while alternative Aswan City excels in practical and feasibility criteria assessment. The study employs the MCDM approach to provide a more detailed evaluation. Specifically, the technical criteria focus on energy yield, system efficiency, and reliability.In contrast, a decision-maker prioritizing long-term sustainability might lean towards a system that excels in technical criteria, even if it is economically less attractive in the short term. Conversely, a decision-maker constrained by budget limitations might opt for a system that, while not the most technically efficient, offers the most cost-effective solution. This dual-criteria approach reveals that while one region may excel in technical performance, another is more financially viable. The choice between these two regions hinges on the specific priorities of the decision-maker.

Using acoustic emission for condition monitoring of the main shaft bearings in 4-point suspension wind turbine drivetrains

ABSTRACT The continuous growth of wind power technology makes condition monitoring of wind turbine components crucially important for their operational efficiency. The main shaft bearings in wind turbines have been identified as one of the most critical components in the system, especially with the ongoing increase in rotor size and weight. This increase made the 4-point suspension drivetrain more preferable. In this study, we present a novel approach for condition monitoring of the main shaft bearings in a 2 Megawatt wind turbine with 4-point suspension drivetrain using primarily acoustic emission (AE). The focus was on the analysis of time and frequency domains of the AE signal, where the dominant frequency of each AE hit was identified and plotted back in the time domain to create the so-called dominant frequency map in specific time intervals for each bearing. A comparison between the two dominant frequency maps of the two bearings gives valuable insights into the condition of the two bearings. The distinctive nature of the dominant frequency bands in the dominant frequency maps presented promising potential for this method. The presented method is straightforward and can be automated and then integrated into a planned predictive maintenance programme for this wind turbine.

Analysis of spatial and temporal changes and driving forces of arable land in the Weibei dry plateau region in China

Arable land is the lifeblood of food production, it is of great significance to promote the protection of arable land and ensure national food security by accurately understanding the change law of cultivated land and its driving mechanism. This study takes the Weibei dry plateau region of China as an example, explores its spatial and temporal change characteristics through the center of gravity shift and land use shift matrix, and couples the geographic probe model to reveal the driving mechanisms affecting arable land change. The results show that in the past 25 years, the total arable land area in the Weibei Dry Plateau Region of China has decreased by 5.58%, and the stability of arable land resources in the whole region has weakened. The center of gravity of arable land shifts to the northeast, and the standard deviation ellipse of arable land mainly undergoes the change process of "increase (1995–2015)-decrease (2015–2020)", and the spatial distribution of arable land tends to be dispersed. In the LISA frequency mapping, the proportion of stable constant and low-frequency areas is as high as 89.58%, and the spatial pattern of cultivated land is relatively stable. Medium and high frequency areas. The transformation mode is mainly "low-low" aggregation, "low–high" aggregation is not significant, and the decline of cultivated land in the study area is more obvious. In the past 25 years, a total of 1017.26 km2 of arable land was converted to construction land. The explanatory power of the influencing factors varies in each period (0.299 to 0.731), with total agricultural machinery power has the strongest explanatory power of 0.694, 0.592, and 0.731, respectively. The interaction between slope and annual average temperature and other factors being the highest, both greater than 0.8. Through the construction of LISA frequency mapping, combined with the center of gravity model and standard deviation ellipse, the spatial evolution trend of regional arable land is more comprehensively and dynamically grasped. By using the geodetector model, the driving mechanism of the changes of arable land is revealed comprehensively, which provides a theoretical basis for the scientific management and effective protection of arable land resources and a basis for decision-making.

Asphalt Pavement Transverse Cracking Detection Based on Vehicle Dynamic Response

Transverse cracking is thought of as the typical distress of asphalt pavements. A faster detection technique can provide pavement performance information for maintenance administrations. This paper proposes a novel vehicle-vibration-based method for transverse cracking detection. A theoretical model of a vehicle-cracked pavement vibration system was constructed using the d’Alembert principle. A testing system installed with a vibration sensor was put in and applied to a testing road. Then, parameter optimization of the Short-time Fourier transform (STFT) was conducted. Transverse cracking and normal sections were processed by the optimized STFT algorithm, generating two ideal indicators. The maximum power spectral density and the relative power spectral density, which were extracted from 3D time–frequency maps, performed well. It was found that the power spectral density caused by transverse cracks was above 100 dB/Hz. The power spectral density at normal sections was below 80 dB/Hz. The distribution of the power spectral density for the cracked sections is more discrete than for normal sections. The classification model based on the above two indicators had an accuracy, true positive rate, and false positive rate of 94.96%, 92.86%, and 4.80%, respectively. The proposed vehicle-vibration-based method is capable of accurately detecting pavement transverse cracking.

Modelling the spread of cystic fibrosis in Brittany using genealogical data over five centuries

BackgroundThe French region of Brittany presents one of the highest cystic fibrosis (CF)-causing allele frequency in Europe. Here, we tested two hypotheses: i) CF-causing allele carriers arrived by sea in the middle of the 1st millennium AD, and ii) a selective advantage for healthy carriers explains this high rate. MethodsFrom the census of cystic fibrosis patients, frequency maps of the most widespread alleles were established. A mathematical model was developed, based on birth date and place of the ancestors of these patients over 5 centuries, to determine the distribution of local migrations and their parameters for inter-generational intervals of 32 years. This model was applied to simulate the spread of CF-causing variant carriers, according to different scenarios that corresponded to the immigration of a given number of variant carriers at different times (year) and places, and to compare their results to current frequency maps. ResultsMigrants carrying a CFTR variant settled in several locations, around which they spread, notably in Central Brittany (F508del variant), Léon (G551D variant) and Cornouaille (1078delT variant). Until the end of the 18th century, the spreading of disease-causing allele carriers was relatively slow, and allele frequencies progressively increased. Then, the mean migration distances increased rapidly, leading to a decline in local frequencies. ConclusionsThe main CFTR variants could only have reached their current frequencies through a selective advantage for healthy carriers of the order of 4–6 % at each generation. For the most widespread variant (F508del), the model supports the hypothesis that it appeared around 190 generations ago.

Bus Operation Safety Business Intelligence Solution: Applying Analytics for Key Performance Indicator, Investigation, and Targeted Actions Analyses with a Centralized Data Warehouse

A business intelligence (BI) system was designed to create a proactive, reactive, and precursor decision tool to improve safety of bus operations at the Massachusetts Bay Transportation Authority (MBTA). The system’s development was motivated in part by recommendations of a safety inspection by the Federal Transportation Administration, and the realization that the MBTA has access to vest amounts of data that are not being used to create information effectively. The developers reduced the complexity of the BI system by incorporating analytics to determine the areas of focus where safety has the most impact. Challenges were posed by the need to integrate internal and external databases with various formats and structures, and the desire to create a self-service BI solution for multiple MBTA users. The resulting BI solution includes applications that support key performance indicator analyses, root cause investigations, and targeted improvement actions. These applications represent an enhanced approach that combines data for a more holistic analysis and eliminates the need to transfer datasets or data analyses by email. The system includes various forms of visualizations to help users navigate the myriad of information, including geospatial maps, interactive pie, line, and bar charts, and word frequency and relationship mapping. The paper details the system’s development and how analytical approaches were used to expose important information that is hidden in the data. Examples of applications are shown using screenshots and a general workflow is presented that could be applicable to agencies with fewer resources.

Cortical surface plasticity promotes map remodeling and alleviates tinnitus in adult mice

Tinnitus induced by hearing loss is caused primarily by irreversible damage to the peripheral auditory system, which results in abnormal neural responses and frequency map disruption in the central auditory system. It remains unclear whether and how electrical rehabilitation of the auditory cortex can alleviate tinnitus. We hypothesize that stimulation of the cortical surface can alleviate tinnitus by enhancing neural responses and promoting frequency map reorganization. To test this hypothesis, we assessed and activated cortical maps using our newly designed graphene-based electrode array with a noise-induced tinnitus animal model. We found that cortical surface stimulation increased cortical activity, reshaped sensory maps, and alleviated hearing loss-induced tinnitus behavior in adult mice. These effects were likely due to retained long-term synaptic potentiation capabilities, as shown in cortical slices from the mice model. These findings suggest that cortical surface activation can be used to facilitate practical functional recovery from phantom percepts induced by sensory deprivation. They also provide a working principle for various treatment methods that involve electrical rehabilitation of the cortex.

Energetic and structural insights behind calcium induced conformational transition in calmodulin.

Calmodulin (CaM) is a key signaling protein that triggers several cellular and physiological processes inside the cell. Upon binding with calcium ion, CaM undergoes large scale conformational transition from a closed state to an open state that facilitates its interaction with various target protein and regulates their activity. This work explores the origin of the energetic and structural variation of the wild type and mutated CaM and explores the molecular origin for the structural differences between them. We first calculated the sequential calcium binding energy to CaM using the PDLD/S-LRA/β approach. This study shows a very good correlation with experimental calcium binding energies. Next we calculated the calcium binding energies to the wild type CaM and several mutated CaM systems which were reported experimentally. On the structural aspect, it has been reported experimentally that certain mutation (Q41L-K75I) in calcium bound CaM leads to complete conformational transition from an open to a closed state. By using equilibrium molecular dynamics simulation, free energy calculation and contact frequency map analysis, we have shown that the formation of a cluster of long-range hydrophobic contacts, initiated by the Q41L-K75I CaM variant is the driving force behind its closing motion. This study unravels the energetics and structural aspects behind calcium ion induced conformational changes in wild type CaM and its variant.

Depth Evaluation of Tiny Defects on or near Surface Based on Convolutional Neural Network

This paper proposes a method for the detection and depth assessment of tiny defects in or near surfaces by combining laser ultrasonics with convolutional neural networks (CNNs). The innovation in this study lies in several key aspects. Firstly, a comprehensive analysis of changes in ultrasonic signal characteristics caused by variations in defect depth is conducted in both the time and frequency domains, based on discrete frequency spectra and original A—scan signals. Continuous wavelet transform (CWT) is employed to obtain wavelet time–frequency maps, demonstrating the consistent characteristics of this image with crack depth variations. A crucial innovation in this research involves the targeted design and optimization of the model based on the characteristics of ultrasonic signals and dataset size. This includes aspects such as data preparation, CNN architecture construction, and hyperparameter selection. The model is tested using a random validation set, which effectively demonstrates the CNN model’s validity and high precision. The proposed method enables the recognition and depth assessment of tiny defects on or near surfaces.