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The design and implementation of a 16-bit Manchester carry chain adder (MCCA) with domino logic using FinFET technology are presented in this paper, focusing on both 8-bit and 16-bit configurations. The Manchester Carry Chain Adder, known for its efficient carry propagation, is a crucial component in modern processors’ arithmetic units. We conduct extensive simulations to compare the performance metrics of 8-bit and 16-bit MCCA designs implemented with FinFET technology. The evaluation includes key parameters such as power consumption, delay, and area utilization. Additionally, we explore the effects of varying operating conditions and supply voltages on the performance of these designs. [ABSTRACT FROM AUTHOR]

Brain network analysis has attracted the interest of neuroscience researchers in studying brain diseases. Magnetoencephalography (MEG) is especially proper for analyzing functional connectivity due to high temporal and spatial resolution. The application of graph theory for functional connectivity analysis has been studied widely, but research on network modeling for MEG still needs more. Temporal exponential random graph model (TERGM) considers temporal dependencies of networks. We performed the brain network analysis, including static/temporal network statistics, on two groups of epilepsy patients who removed the left (LT) or right (RT) part of the brain and healthy controls. We investigate network differences using Multiset canonical correlation analysis (MCCA) and TERGM between epilepsy patients and healthy controls (HC). The brain network of healthy controls had fewer temporal changes than patient groups. As a result of TERGM, on the simulation networks, LT and RT had less stable state than HC in the network connectivity structure. HC had a stable state of the brain network. [ABSTRACT FROM AUTHOR]

Joint blind source separation (JBSS) has wide applications in modeling latent structures across multiple related datasets. However, JBSS is computationally prohibitive with high-dimensional data, limiting the number of datasets that can be included in a tractable analysis. Furthermore, JBSS may not be effective if the data's true latent dimensionality is not adequately modeled, where severe overparameterization may lead to poor separation and time performance. In this paper, we propose a scalable JBSS method by modeling and separating the "shared" subspace from the data. The shared subspace is defined as the subset of latent sources that exists across all datasets, represented by groups of sources that collectively form a low-rank structure. Our method first provides the efficient initialization of the independent vector analysis (IVA) with a multivariate Gaussian source prior (IVA-G) specifically designed to estimate the shared sources. Estimated sources are then evaluated regarding whether they are shared, upon which further JBSS is applied separately to the shared and non-shared sources. This provides an effective means to reduce the dimensionality of the problem, improving analyses with larger numbers of datasets. We apply our method to resting-state fMRI datasets, demonstrating that our method can achieve an excellent estimation performance with significantly reduced computational costs. [ABSTRACT FROM AUTHOR]

Land use is a crucial factor affecting ecosystem service value (ESV), and forecasting future land use changes and ESV response can guide urban planning and sustainable development decisions. However, the traditional Cellular Automata (CA) model supposes that each cell has only one land use type at each time step, neglects the mixed structure and proportional distribution of land use units, does not take into account its quantitative continuous dynamic change, and lacks the exploration of land use quantity structure and spatial pattern optimization. This study employed a novel mixed-cell cellular automata (MCCA) approach, coupled with the system dynamics (SD) model to predict the spatiotemporal pattern of land use under the natural increase scenario (NIS), economic development scenario (EDS) and ecological protection scenario (EPS) in Xi’an, China, in 2030. The equivalent coefficient method was utilized to investigate the heterogeneity distribution and sensitivity of ESV. The results demonstrated that SD-MCCA exhibited remarkable prediction accuracy and robustness. The main changes in land use in 2000–2015 were due to urban expansion, the conversion of arable land into construction land, and the conversion between grassland and arable land. The total ESV increased from 19554.36×106 CNY in 2000 to 19618.39×106 CNY under the EPS in 2030, and the contribution of climate regulation and hydrological regulation to ESV was the highest. Spatial heterogeneity of ESV revealed a certain regularity, and the high value region was chiefly concentrated in woodland and grassland with favorable ecological conditions. Land use variations under NIS and EPS improved ESV, while the ESV had a negative response to land use transformations under the EDS. This research provides a new way to identify the relationship between future land utilization scenarios and ESV, which is of great significance for the management of land resources and formulation of ecological compensation standards.

Analyzing the relationship between rural settlements and rural population change under different policy scenarios is key in the sustainable development of China's urban and rural areas. We proposed a framework that comprised the mixed land use structure simulation (MCCA) model and the human–land coupling development model to assess the spatiotemporal dynamic changes in rural settlements and its' coupling relationship with the rural population in the economically developed region of Deqing, Zhejiang Province. The results showed that rural settlements and urban land increased by 14.36 and 29.07 km2, respectively, over the last 20 years. The expansion of some rural settlements and urban land occurred at the cost of cropland occupation. Rural settlements showed an expansion trend from 2000 to 2020, increasing from 42.69 km2 in 2000 to 57.05 km2 in 2020. In 2035, under the natural development scenario, the cropland protection scenario, and the rural development scenario, rural settlements are projected to show an expansion trend and Wukang and Leidian are the key regions with rural settlement expansion. The distance to Hangzhou, nighttime light data, distance to rivers, and precipitation are important factors influencing the expansion of rural settlements. The coupling relationship between rural settlements and the rural population developed in a coordinated manner from 2000 to 2020. For 2035, under different scenarios, the coupling relationship between rural settlements and the rural population showed different trends. In the rural development scenario, the highest number of towns with coordinated development between rural settlements and the rural population is in Deqing, predominantly with Type I coupling. Overall, an important recommendation from this study is that the sustainable development of regional land use can be promoted by controlling the occupation of cropland for urban and rural construction, balancing rural settlement expansion and rural population growth, and formulating land use policies that are more suitable for rural development. [ABSTRACT FROM AUTHOR]

To handle synthetic aperture radar (SAR) image target recognition problem, a multi-view method is proposed. For the multi-view SAR images be recognized, they are first clustered based on the correlation coefficients and divided into several view sets. Afterwards, for the view set containing two or more images, the multiset canonical correlation analysis (MCCA) is employed to fuse them as a single feature vector. For the view set with a single image, its corresponding feature vector is directly used. Finally, the joint sparse representation is used to characterize and classify the feature vectors from different view sets and determine the target label of the multi-view SAR images. Experiments and analysis on the moving and stationary target acquisition and recognition (MSTAR) dataset show that the proposed method can achieve an average recognition rate of 99.42% for 10 types of targets under the standard operating condition (SOC). Its performance is also better than several reference methods under the extended operating conditions (EOC) including noise interference and target occlusion. [ABSTRACT FROM AUTHOR]

• A coupling mixed pixel decomposition and mixed-cell simulation methods are proposed. • The subpixel-scale impact of land use structure dynamics on future UHIs is revealed. • How the multiscale land use composition mechanisms affecting UHIs are explored. • The potential influence of UHIs on future populations is evaluated in this study. Urban heat islands (UHIs) has been proven firmly related to the land use structure. Identifying interactions between UHIs and multiple land use components is a crucial step to obtain human heat welfare information. However, few studies have predicted sub cell scale land use structure dynamics on UHIs due to the lack of subpixel simulation methods. Herein, we present an integrated framework coupling subpixel unmixing and mixed-cell simulation methods to predict fine-scale land-use structural changes. A widely used XGBoost was used to determine the optimal scale for future UHIs prediction. This framework explores how multiscale changes in land use structure will affect future UHIs intensity, taking Wuhan, China as a study area. The results reveal that most influence comes from the scale below the 330-m grid, while the fine-scale land use components of a given position show limited impact on the UHI intensity. Impervious surfaces contribute more than 55% of the importance, while bare soil and water components within the 270-m grid also significantly affect UHIs. We also find that optimizing the structure of land use components can potentially release approximately 599,000 people from high-UHI regions in the study area. [Display omitted] [ABSTRACT FROM AUTHOR]

Analyzing the relationship between rural settlements and rural population change under different policy scenarios is key in the sustainable development of China’s urban and rural areas. We proposed a framework that comprised the mixed land use structure simulation (MCCA) model and the human–land coupling development model to assess the spatiotemporal dynamic changes in rural settlements and its’ coupling relationship with the rural population in the economically developed region of Deqing, Zhejiang Province. The results showed that rural settlements and urban land increased by 14.36 and 29.07 km2, respectively, over the last 20 years. The expansion of some rural settlements and urban land occurred at the cost of cropland occupation. Rural settlements showed an expansion trend from 2000 to 2020, increasing from 42.69 km2 in 2000 to 57.05 km2 in 2020. In 2035, under the natural development scenario, the cropland protection scenario, and the rural development scenario, rural settlements are projected to show an expansion trend and Wukang and Leidian are the key regions with rural settlement expansion. The distance to Hangzhou, nighttime light data, distance to rivers, and precipitation are important factors influencing the expansion of rural settlements. The coupling relationship between rural settlements and the rural population developed in a coordinated manner from 2000 to 2020. For 2035, under different scenarios, the coupling relationship between rural settlements and the rural population showed different trends. In the rural development scenario, the highest number of towns with coordinated development between rural settlements and the rural population is in Deqing, predominantly with Type I coupling. Overall, an important recommendation from this study is that the sustainable development of regional land use can be promoted by controlling the occupation of cropland for urban and rural construction, balancing rural settlement expansion and rural population growth, and formulating land use policies that are more suitable for rural development.

Flexible and context-dependent behaviors require animals, including humans, to identify their current contextual state for proper rules to apply, especially when information that defines these states is partially observable. Depending on behavioral needs, contextual states usually persist for prolonged periods and across other events, including sensory stimuli, actions, and rewards, highlighting prominent challenges of holding a reliable state representation. The orbitofrontal cortex (OFC) is crucial in behaviors requiring the identification of the current context (e.g., reversal learning); however, how single units in the OFC accomplish this function has not been assessed. Do they maintain such information persistently, in separate populations from those responding phasically to events within a task, or is contextual information dynamic and embedded in these phasic responses? Here, we investigated this question by recording single units from OFC in rats performing a task that required them to identify the current contextual state related to estimated proximity to future reward with distracting olfactory cues. We found that while some OFC neurons encode contextual states, most change their selectivity upon the transition of task events. Nevertheless, despite dynamic activities in single neurons, the neural populations maintain persistent representations regarding current contextual states within particular neural subspaces. • Rats track the current contextual state related to distance to future reward • OFC neurons encode contextual states dissociable from distracting odor stimuli • Neural coding for contextual states dynamically interacts with ongoing events • Persistent neural codes for contextual states exist in certain neural subspaces Ma et al. find that individual orbitofrontal neurons encode contextual states, dissociable from distracting sensory stimuli but dynamically changing selectivity upon the transition of task events. However, persistent codes for the current contextual state are found within particular neural subspaces. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) and vegetation optical depth (VOD) estimates using passive microwave remote sensing at L-band (1.4 GHz) are essential for attaining a better understanding of water exchanges at the land-atmosphere interface. However, current retrieval algorithms often ignore the polarization dependence of vegetation effects. This study proposed a parameter self-calibrating framework for the multi-channel collaborative algorithm (MCCA) and presented a new SM and the first polarization-dependent VOD product based on the dual-polarized L-band observations at a fixed angle (40°) from the NASA Soil Moisture Active Passive (SMAP) mission. The parameter self-calibrating framework utilizes an information theory-based approach to obtain surface roughness and effective scattering albedo globally. Furthermore, the MCCA does not require auxiliary data for vegetation or soil moisture to constrain the retrieval process. Comparison with other SM and VOD products, such as MT-DCA version 5, DCA, SCA-H, SCA-V from SMAP Level-3 products version 8, and SMAP-IB, demonstrate analogous spatial patterns. The MCCA-derived SM exhibits the lowest unbiased root mean square deviation (ubRMSD, about 0.055 m3/m3), followed by SMAP-IB and DCA (0.061 m3/m3), with an overall Pearson's correlation coefficient of 0.744 (SMAP-IB performed best with R = 0.764) when evaluated against in-situ observations from 18 dense soil moisture networks. The MCCA generates VOD values for both vertical and horizontal polarization, demonstrating a slight polarization difference of vegetation effect at the satellite scale. Both VODs exhibit a strong linear relationship with above-ground biomass and canopy height. The polarization difference of VODs is primarily observed in densely vegetated and arid areas. • No ancillary data are used to calibrate parameters or constrain the cost function. • MCCA SM shows lower uncertainty and similar correlation compared to SMAP products. • The first SMAP VOD product that takes account of its polarization dependence. • VOD at H-polarization is greater than VOD at V-polarization over dense vegetation. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the effect and mechanism of methylcrotonyl-CoA carboxylase subunit 1 (MCCA) on multidrug resistance in multiple myeloma (MM). The apoptosis kit and CCK-8 reagent were used to detect drug-induced cell apoptosis and viability. Immunoprecipitation, immunofluorescence staining, and protein structural simulation were used to detect the interaction between MCCA and Bad. Immunodeficient mice were injected with ARD cells and treated with bortezomib. Changes in tumor burden were recorded by bioluminescence imaging, and κ light chain content in the blood of mice was detected by enzyme-linked immunoassay. Patients with high MCCA expression from a primary MM dataset had superior overall survival. After treatment with different anti-MM drugs, MCCA knockdown MM (MCCA-KD) cells had higher survival rates than control knockdown (CTR-KD) cells (p < 0.05). Mechanistic studies have revealed that MCCA-KD cells had dysfunctional mitochondria with decreased Bax and Bad levels and increased Bcl-xl and Mcl-1 levels. Furthermore, that MCCA and Bad demonstrated protein–protein interactions. The half-life of Bad in MCCA-KD cells is significantly shorter than that in CTR-KD cells (7.34 vs. 2.42 h, p < 0.05). In a human MM xenograft mouse model, we confirmed that MCCA-KD tumors had a poor response to anti-MM drugs in vivo. Finally, we showed that MCCA might contribute to multidrug resistance in different human cancers, particularly in solid tumors. Our findings demonstrated a novel function of MCCA in multidrug resistance. The lack of MCCA expression promoted antiapoptotic cell signaling in MM cells. [ABSTRACT FROM AUTHOR]

Studies featuring multimodal neuroimaging data fusion for understanding brain function and structure, or disease characterization, leverage the partial information available in each of the modalities to reveal data variations not exhibited through the independent analyses. Similar to other complex syndromes, the characteristic brain abnormalities in schizophrenia may be better understood with the help of the additional information conveyed by leveraging an advanced modeling method involving multiple modalities. In this study, we propose a novel framework to fuse feature spaces corresponding to functional magnetic resonance imaging (functional) and gray matter (structural) data from 151 schizophrenia patients and 163 healthy controls. In particular, the features for the functional and structural modalities include dynamic (i.e., time-varying) functional network connectivity (dFNC) maps and the intensities of the gray matter (GM) maps, respectively. The dFNC maps are estimated from group independent component analysis (ICA) network time-courses by first computing windowed functional correlations using a sliding window approach, and then estimating subject specific states from this windowed data using temporal ICA followed by spatio-temporal regression. For each subject, the functional data features are horizontally concatenated with the corresponding GM features to form a combined feature space that is subsequently decomposed through a symmetric multimodal fusion approach involving a combination of multiset canonical correlation analysis (mCCA) and joint ICA (jICA). Our novel combined analyses successfully linked changes in the two modalities and revealed significantly disrupted links between GM volumes and time-varying functional connectivity in schizophrenia. Consistent with prior research, we found significant group differences in GM comprising regions in the superior parietal lobule, precuneus, postcentral gyrus, medial/superior frontal gyrus, superior/middle temporal gyrus, insula and fusiform gyrus, and several significant aberrations in the inter-regional functional connectivity strength as well. Importantly, structural and dFNC measures have independently shown changes associated with schizophrenia, and in this work we begin the process of evaluating the links between the two, which could shed light on the illness beyond what we can learn from a single imaging modality. In future work, we plan to evaluate replication of the inferred structure-function relationships in independent partitions of larger multi-modal schizophrenia datasets.

Studies featuring multimodal neuroimaging data fusion for understanding brain function and structure, or disease characterization, leverage the partial information available in each of the modalities to reveal data variations not exhibited through the independent analyses. Similar to other complex syndromes, the characteristic brain abnormalities in schizophrenia may be better understood with the help of the additional information conveyed by leveraging an advanced modeling method involving multiple modalities. In this study, we propose a novel framework to fuse feature spaces corresponding to functional magnetic resonance imaging (functional) and gray matter (structural) data from 151 schizophrenia patients and 163 healthy controls. In particular, the features for the functional and structural modalities include dynamic (i.e., time-varying) functional network connectivity (dFNC) maps and the intensities of the gray matter (GM) maps, respectively. The dFNC maps are estimated from group independent component analysis (ICA) network time-courses by first computing windowed functional correlations using a sliding window approach, and then estimating subject specific states from this windowed data using temporal ICA followed by spatio-temporal regression. For each subject, the functional data features are horizontally concatenated with the corresponding GM features to form a combined feature space that is subsequently decomposed through a symmetric multimodal fusion approach involving a combination ofmultiset canonical correlation analysis (mCCA) and joint ICA (jICA). Our novel combined analyses successfully linked changes in the two modalities and revealed significantly disrupted links between GM volumes and time-varying functional connectivity in schizophrenia. Consistent with prior research, we found significant group differences in GM comprising regions in the superior parietal lobule, precuneus, postcentral gyrus, medial/superior frontal gyrus, superior/middle temporal gyrus, insula and fusiform gyrus, and several significant aberrations in the inter-regional functional connectivity strength as well. Importantly, structural and dFNC measures have independently shown changes associated with schizophrenia, and in this work we begin the process of evaluating the links between the two, which could shed light on the illness beyond what we can learn from a single imaging modality. In future work, we plan to evaluate replication of the inferred structure-function relationships in independent partitions of larger multi-modal schizophrenia datasets. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) and vegetation optical depth (VOD) are essential variables in the terrestrial ecosystem. The multi-frequency radiometers AMSR-E and AMSR2 provide >20 years of data records, enabling the development of long-term SM and VOD products. Most of the current retrieval algorithms either only focus on SM or VOD, and generally ignore the polarization or simplify the frequency dependence of vegetation effects for reducing the unknowns and facilitating the retrieval process, limiting the synergic applicability of VOD and SM products in the soil-plant-atmosphere continuum. In this study, a new global SM and frequency- and polarization-dependent VOD product from 2002 to 2021 was developed using the multi-channel collaborative algorithm (MCCA), based on the inter-calibrated AMSR-E/2 multi-frequency passive microwave measurements. The MCCA algorithm comprehensively considers the physical relationship between multiple microwave channels and could retrieve frequency- and polarization-dependent VOD while considering the accuracy of the SM retrievals. In the overall comparison with other SM products (AMSR-ANN, CCI-passive v07.1, LPRM-C/X, JAXA) over 25 dense SM networks, MCCA achieved the best scores in terms of root mean square error (RMSE = 0.074 m3/m3), unbiased root mean square error (ubRMSE = 0.073 m3/m3) and bias (0.007 m3/m3), and presented slightly lower value of Pearson's correlation coefficient (R = 0.709) than LPRM-X (R = 0.735). For the indirect evaluation of VOD with aboveground biomass (AGB) and MODIS NDVI, the MCCA product showed the performance comparable to other products (LPRM-C/X, VODCA-C/X/Ku). MCCA-derived VODs, especially for the H-polarized VODs, exhibited smooth non-linear density distribution with AGB and high temporal correlations with MODIS NDVI over most regions of the globe. In particular, MCCA-derived VODs can physically present reasonable variations across the microwave spectrum (values of VOD increase with microwave frequency), which is superior to the LPRM and VODCA products. It is expected that the MCCA algorithm can be extended to the observations of the ongoing AMSR2 or other similar satellite missions with multi-frequency capability, such as FY-3B/C/D/F/G or the upcoming AMSR3 and CMIR missions. • A physically consistent SM product over two decades is developed using the MCCA. • MCCA-derived SM shows the best accuracy among the six AMSR SM products. • MCCA-derived VODs present reasonable variations across the microwave spectrum. • H-polarized VOD is more sensitive to vegetation in terms of biomass and greenness than V-polarized VOD. [ABSTRACT FROM AUTHOR]

Carcinoma of unknown primary (CUP) is a rare metastatic disease in which a primary tumor site cannot be identified. CUP is a diagnosis of exclusion requiring prior workup to identify a primary site. We present a case of a 64-year-old male with vague abdominal pain, a history of gastroesophageal reflux disease (GERD), gastritis, esophagitis, hepatitis C, alcoholic pancreatitis, liver hemangioma, and Warthin tumor, and family history of cancer that was found to have CUP. The diagnosis was made after an extensive workup was done including serum tumor markers, computed tomography (CT) and ultrasound (US) imaging, flow cytometry, and an array of immunohistochemistry stains positive for only cytokeratin 7., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2023, Abou-Ghaida et al.)

In order to improve the utilization of channel resources, modern technologies of self-organizing wireless system increasingly use methods of deterministic access, in which the stations preliminary reserve time intervals for packet transmission. To reduce the overhead caused by the distributed reservation of time intervals, these intervals have equal duration and are strictly periodic, i.e., equidistant from each other. Typically, the reservation periodicity is chosen according to the properties of a particular data stream. In the present work, we show that this approach is inefficient and propose a new method in which the periodicity is a multiple of a number called the basic periodicity. Using analytical modeling and simulation we show that the proposed method significantly increases the network capacity. [ABSTRACT FROM AUTHOR]

With the end of the Cold War, the creation of a South American economic space has become an important priority of regional powers (Brazil, Argentina, Chile), and the great powers after the war, the U.S. and the European Union (the current name). This integration process has had particular features derived from characteristics of Latin American countries. Multitude of organizations integrative role once again demonstrates the specificity of this process in Latin America to other areas of the world: Africa, Asia, Europe, etc. Contradictory developments phenomenon / Latin American integration process gives substance its characteristic and I will make, probably deeply and future. [ABSTRACT FROM AUTHOR]

Computer content analysis provides another approach to measuring differential aspects of social structure (positions and perspectives), as evident in language. Using verbatim transcripts of interviews with occupants of positions in nursing homes talking about their organizational situation, Minnesota Contextual Content Analysis (MCCA-PC) analyzes the meaning in these texts and computes a language-based measurement of social distance as a function of differences between perspectives, facilitating an examination of social distance with other organizational and personal outcomes. Correlates of distance between roles across nursing homes suggest consequences for organizational structure and meanings residents express about their experience. Content scores permit identification of each respondent with a particular nursing home, a measurable aspect of organizational culture. This methodology is compared to techniques in information retrieval for characterizing documents by semantic vectors. Semantic analysis of MCCA categories using WordNet reveals semantic domains, whose refinement may better characterize identified differences. [ABSTRACT FROM PUBLISHER]

This article presents a computer content analysis approach to the problem of assessing the validity of Likert scales used in survey research. The wording of questions that make up a scale communicates meaning to the respondent in order to acquire relevant responses that, when combined, serve to measure some concept. Traditionally, beyond informal judgments of face validity, investigators examine responses of a sample of subjects to scale questions in order to determine if expected relationships and structure are found. The content analysis procedure proposed here directly examines the wording of scale questions to see if expected meanings, relationships, and structure are found in order to assess the validity of a scale. Possibilities of this approach are illustrated using two previously analyzed sets of scales: alienation and perception of police and crime. [ABSTRACT FROM PUBLISHER]

The current paper is the one of the few known reports that depict the influence of a distinctive morphology control chemical additive (MCCA) namely, ammonium fluoride on crystal structure and photoluminescence of Eu2+-doped BaMgAl 10 O 17 (BAM:Eu2+) nanophosphor better suited for many active displays and lighting devices. [Display omitted] • MCCAs are highly desired for tuning of morphology in BAM:Eu2+ nanophosphor. • Better crystallinity in nanophosphor could be achieved using MCCA. • BAM:Eu2+ nanophosphor exhibits intense emission compared to its bulk. In this letter, experimental results on structure and photoluminescence (PL) properties of Eu2+-doped BaMgAl 10 O 17 (BAM:Eu2+) nanophosphors modified by a distinctive morphology control chemical additive (MCCA) namely, ammonium fluoride are reported. X-ray diffraction confirmed hexagonal phase with p6 3 /mmc space group whereas, scanning electron microscopy images revealed layered rod-like morphologies for the MCCA modified BAM:Eu2+ nanophosphors. Upon excitation at 300 nm, the room-temperature PL showed enhanced emission peak at ~467 nm attributed to 4f65d1 → 4f7 transition of Eu2+ ions with (0.22, 0.17) as color coordinates. The obtained results suggest that MCCA modified BAM:Eu2+ nanophosphors would be suitable for active displays and lighting devices. [ABSTRACT FROM AUTHOR]

The preservation of near-typical function in distributed brain networks is associated with less severe deficits in chronic stroke patients. However, it remains unclear how task-evoked responses in networks that support complex cognitive functions such as semantic processing relate to the post-stroke brain anatomy. Here, we used recently developed methods for the analysis of multimodal MRI data to investigate the relationship between regional tissue concentration and functional MRI activation evoked during auditory semantic decisions in a sample of 43 chronic left hemispheric stroke patients and 43 age, handedness, and sex-matched controls. Our analyses revealed that closer-to-normal levels of tissue concentration in left temporo-parietal cortex and the underlying white matter correlated with the level of task-evoked activation in distributed regions associated with the semantic network. This association was not attributable to the effects of left hemispheric lesion or brain volumes, and similar results were obtained when using explicit lesion data. Left temporo-parietal tissue concentration and the associated task-evoked activations predicted patient performance on the in-scanner task, and also predicted patient performance on out-of-scanner naming and verbal fluency tasks. Exploratory analyses using the average HCP-842 tractography dataset revealed the presence of fronto-temporal, fronto-parietal, and temporo-parietal semantic network connections in the locations where tissue concentration was found to correlate with task-evoked activation in the semantic network. In summary, our results link the preservation of left posterior temporo-parietal structures with the preservation of task-evoked semantic network function in chronic left hemispheric stroke patients. Speculatively, this relationship may reflect the status of posterior temporo-parietal areas as cortical and white matter convergence zones that support coordinated processing in the distributed semantic network. Damage to these regions may contribute to atypical task-evoked responses during semantic processing in chronic stroke patients., (Copyright © 2017 Elsevier Ltd. All rights reserved.)