Relevant Network Research Articles

Can Random Friends Seed More Buzz and Adoption? Leveraging the Friendship Paradox

A critical element of word of mouth (WOM) or buzz marketing is to identify seeds, often central actors with high degree in the social network. Seed identification typically requires data on the relevant network structure, which is often unavailable. We examine the impact of WOM seeding strategies motivated by the friendship paradox, which can obtain more central nodes without knowing network structure. Higher degree nodes may be less effective as seeds if these nodes communicate less with neighbors or are less persuasive when they communicate; therefore, whether friendship paradox–motivated seeding strategies increase or reduce WOM and adoption remains an empirical question. We develop and estimate a model of WOM and adoption using data on microfinance adoption across village social networks in India. Counterfactuals show that the proposed strategies with limited seeds are about 13%–30% more effective in increasing adoption relative to random seeding. These strategies are also on average 5%–11% more effective than the firm’s leader seeding strategy. We also find these strategies are relatively more effective when we have fewer seeds. This paper was accepted by Juanjuan Zhang, marketing. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.4991 .

Semantic fMRI neurofeedback of emotions: from basic principles to clinical applications.

During fMRI neurofeedback participants learn to self-regulate activity in relevant brain areas and networks based on ongoing feedback extracted from measured responses in those regions. This closed-loop approach has been successfully applied to reduce symptoms in mood disorders such as depression by showing participants a thermometer-like display indicating the strength of activity in emotion-related brain areas. The hitherto employed conventional neurofeedback is, however, 'blind' with respect to emotional content, i.e. patients instructed to engage in a specific positive emotion could drive the neurofeedback signal by engaging in a different (positive or negative) emotion. In this future perspective, we present a new form of neurofeedback that displays semantic information of emotions to the participant. Semantic information is extracted online using real-time representational similarity analysis of emotion-specific activity patterns. The extracted semantic information can be provided to participants in a two-dimensional semantic map depicting the current mental state as a point reflecting its distance to pre-measured emotional mental states (e.g. 'happy', 'content', 'sad', 'angry'). This new approach provides transparent feedback during self-regulation training, and it has the potential to enable more specific training effects for future therapeutic applications such as clinical interventions in mood disorders.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

Evolutionarily conserved fMRI network dynamics in the mouse, macaque, and human brain

Evolutionarily relevant networks have been previously described in several mammalian species using time-averaged analyses of fMRI time-series. However, fMRI network activity is highly dynamic and continually evolves over timescales of seconds. Whether the dynamic organization of resting-state fMRI network activity is conserved across mammalian species remains unclear. Using frame-wise clustering of fMRI time-series, we find that intrinsic fMRI network dynamics in awake male macaques and humans is characterized by recurrent transitions between a set of 4 dominant, neuroanatomically homologous fMRI coactivation modes (C-modes), three of which are also plausibly represented in the male rodent brain. Importantly, in all species C-modes exhibit species-invariant dynamic features, including preferred occurrence at specific phases of fMRI global signal fluctuations, and a state transition structure compatible with infraslow coupled oscillator dynamics. Moreover, dominant C-mode occurrence reconstitutes the static organization of the fMRI connectome in all species, and is predictive of ranking of corresponding fMRI connectivity gradients. These results reveal a set of species-invariant principles underlying the dynamic organization of fMRI networks in mammalian species, and offer novel opportunities to relate fMRI network findings across the phylogenetic tree.

Harmony in the brain: A narrative review on the shared neural substrates of emotion regulation and creativity.

The contribution of creativity in overall well-being through regulating emotions has sparkled research interest in employing artistic interventions recently for the improvement of mental health. Although the behavioural links between emotion regulation and creativity have been established, the neural networks reflecting these relations are yet to be investigated. In this mini review, we describe the neural underpinnings of all forms of creativity and of the emotion regulation strategies. Given the complexity of both of these constructs, we separate creativity in its various forms and report the regions and the neural networks involved. Similarly, we distinguish between the differential emotion regulation strategies and describe the relevant brain areas and networks. We then proceed to a critical exploration of common regions of interest and of neural pathways among these important functions. The studies included in this review point towards certain brain regions that are shared among creativity and affective control, such as the prefrontal cortex, the anterior cingulate cortex, the dorsolateral prefrontal cortex, the medial temporal lobe and the inferior parietal lobule. The common neural networks of emotion regulation and creativity mainly focus on the default mode, the executive control and the salience networks. We then suggest a shared neural mechanism that may underlie emotion regulation and creativity, involving both control and affective processing. Drawing on the limitations of the studies reviewed, directions for future research are suggested that could significantly inform the field.

Gauss Markov and Flow Balanced Vector Radial Learning network traffic classification on IoT with SDN.

Recent evolution in connected devices modelled a massive stipulation for network traffic resources and classification. Software-defined networking (SDN) enables ML techniques with the Internet of Things (IoT) to automate network traffic. This helps to reduce accuracy and improves latency. Problems by conventional techniques to categorize network traffic acquired from IoT and assign resources can be resolved through SDN solutions. This manuscript proposes a proposed network traffic classification technique on IoT with SDN called Gauss Markov and Flow-balanced Vector Radial Learning (GM-FVRL). With the network traffic features acquired from the IoT devices, SDN-enabled Gauss Markov Correlation-based IoT Network Traffic Feature Extraction is applied to extort relevant network aspects. Next, the flow-balanced radial-based ML model for network traffic categorization uses the relevant extracted network traffic features. With the aid of flow, the balanced radial basis function reduces the influence of noise due to distinct network flow. This helps to improve accuracy and minimize latency. Due to this, better precision and recall is ensured. Performance of our method has been evaluated utilizing a scheme using an SDN traffic dataset. The results show that our method classifies the network traffic with high classification accuracy and minimum latency, ensuring better precision and recall.

Capture of RNA-binding proteins across mouse tissues using HARD-AP

RNA-binding proteins (RBPs) modulate all aspects of RNA metabolism, but a comprehensive picture of RBP expression across tissues is lacking. Here, we describe our development of the method we call HARD-AP that robustly retrieves RBPs and tightly associated RNA regulatory complexes from cultured cells and fresh tissues. We successfully use HARD-AP to establish a comprehensive atlas of RBPs across mouse primary organs. We then systematically map RNA-binding sites of these RBPs using machine learning-based modeling. Notably, the modeling reveals that the LIM domain as an RNA-binding domain in many RBPs. We validate the LIM-domain-only protein Csrp1 as a tissue-dependent RNA binding protein. Taken together, HARD-AP is a powerful approach that can be used to identify RBPomes from any type of sample, allowing comprehensive and physiologically relevant networks of RNA-protein interactions.

Bladder Responses to Thoracolumbar Epidural Stimulation in Female Urethane-Anesthetized Rats with Graded Contusion Spinal Cord Injuries.

Spinal cord epidural stimulation (scES) is a therapeutic option that promotes functional improvements in sensory, motor, and autonomic functions following spinal cord injury (SCI). Previous scES mapping studies targeting the lower urinary tract (LUT) in rats demonstrated functional response variability based upon lumbosacral level, parameters used, extent of injury (spinally intact vs. chronic anatomically complete spinal transections), and sex. In the current study, female rats with clinically relevant graded incomplete T9 contusion injuries were mapped with scES at 60 days post-injury at three spinal levels (T13, L3, L6) with a novel miniature 15-electrode array designed to deliver optimal specificity. The results obtained during bladder fill and void cycles conducted under urethane anesthesia indicate frequency dependent sub-motor threshold effects on LUT function with a single row of electrodes positioned across the full medio-lateral extent of the dorsal cord. The findings of improved storage and emptying, represented by significantly longer inter-contractile intervals with T13 scES and L3 scES and by a significantly increased estimated void efficiency with L6 scES, respectively, are consistent with previous studies using intact and chronic complete transected male and female rats. The data support the efficacy of selective spinal network stimulation to drive functionally relevant networks for storage versus emptying phases of the urinary cycle. The current findings further demonstrate the translational promise of scES for SCI individuals with LUT dysfunctions, regardless of injury severity.

Leveraging gene correlations in single cell transcriptomic data

BackgroundMany approaches have been developed to overcome technical noise in single cell RNA-sequencing (scRNAseq). As researchers dig deeper into data—looking for rare cell types, subtleties of cell states, and details of gene regulatory networks—there is a growing need for algorithms with controllable accuracy and fewer ad hoc parameters and thresholds. Impeding this goal is the fact that an appropriate null distribution for scRNAseq cannot simply be extracted from data in which ground truth about biological variation is unknown (i.e., usually).ResultsWe approach this problem analytically, assuming that scRNAseq data reflect only cell heterogeneity (what we seek to characterize), transcriptional noise (temporal fluctuations randomly distributed across cells), and sampling error (i.e., Poisson noise). We analyze scRNAseq data without normalization—a step that skews distributions, particularly for sparse data—and calculate p values associated with key statistics. We develop an improved method for selecting features for cell clustering and identifying gene–gene correlations, both positive and negative. Using simulated data, we show that this method, which we call BigSur (Basic Informatics and Gene Statistics from Unnormalized Reads), captures even weak yet significant correlation structures in scRNAseq data. Applying BigSur to data from a clonal human melanoma cell line, we identify thousands of correlations that, when clustered without supervision into gene communities, align with known cellular components and biological processes, and highlight potentially novel cell biological relationships.ConclusionsNew insights into functionally relevant gene regulatory networks can be obtained using a statistically grounded approach to the identification of gene–gene correlations.

GluN3A and Excitatory Glycine Receptors in the Adult Hippocampus.

The GluN3A subunit of N-methyl-D-aspartate receptors (NMDARs) plays an established role in synapse development, but its contribution to neural circuits in the adult brain is less clear. Recent work has demonstrated that in select cell populations, GluN3A assembles with GluN1 to form GluN1/GluN3A receptors that are insensitive to glutamate and instead serve as functional excitatory glycine receptors (eGlyRs). Our understanding of these eGlyRs, and how they contribute to intrinsic excitability and synaptic communication within relevant networks of the developing and the mature brain, is only beginning to be uncovered. Here, using male and female mice, we demonstrate that GluN3A subunits are enriched in the adult ventral hippocampus (VH), where they localize to synaptic and extrasynaptic sites and can assemble as functional eGlyRs on CA1 pyramidal cells. GluN3A expression was barely detectable in the adult dorsal hippocampus (DH). We also observed a high GluN2B content in the adult VH, characterized by slow NMDAR current decay kinetics and a high sensitivity to the GluN2B-containing NMDAR antagonist ifenprodil. Interestingly, the GluN2B enrichment in the adult VH was dependent on GluN3A as GluN3A deletion accelerated NMDAR decay and reduced ifenprodil sensitivity in the VH, suggesting that GluN3A expression can regulate the balance of conventional NMDAR subunit composition at synaptic sites. Lastly, we found that GluN3A knock-out also enhanced both NMDAR-dependent calcium influx and NMDAR-dependent long-term potentiation in the VH. Together, these data reveal a novel role for GluN3A and eGlyRs in the control of ventral hippocampal circuits in the mature brain.

Physical activity and frontoparietal network connectivity in traumatic brain injury.

Prolonged changes to functional network connectivity as a result of a traumatic brain injury (TBI) may relate to long-term cognitive complaints reported by TBI survivors. No interventions have proven to be effective at treating long-term cognitive complaints after TBI but physical activity has been shown to promote cognitive function and modulate functional network connectivity in non-injured adults. Therefore, the objective of this study was to test if physical activity engagement was associated with functional connectivity of the cognitively relevant frontoparietal control network (FPCN) in adults with a TBI history. In a case-control study design, resting state function magnetic resonance imaging and physical activity data from a subset of participants (18-81 years old) from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study was analyzed. Fifty-seven participants reported a prior head injury with loss of consciousness and 57 age and sex matched controls were selected. Seed-based functional connectivity analyses were performed using seeds in the dorsolateral prefrontal cortex and the inferior parietal lobule, to test for differences in functional connectivity between groups, associations between physical activity and functional connectivity within TBI as well as differential associations between physical activity and functional connectivity between TBI and controls. Seed-based connectivity analyses from the dorsolateral prefrontal cortex showed that those with a history of TBI had decreased positive connectivity between dorsolateral prefrontal cortex and intracalcarine cortex, lingual gyrus, and cerebellum, and increased positive connectivity between dorsolateral prefrontal cortex and cingulate gyrus and frontal pole in the TBI group. Results showed that higher physical activity was positively associated with increased connectivity between the dorsolateral prefrontal cortex and inferior temporal gyrus. Differential associations were observed between groups whereby the strength of the physical activity-functional connectivity association was different between the inferior parietal lobule and inferior temporal gyrus in TBI compared to controls. Individuals with a history of TBI show functional connectivity alterations of the FPCN. Moreover, engagement in physical activity is associated with functional network connectivity of the FPCN in those with a TBI. These findings are consistent with the evidence that physical activity affects FPCN connectivity in non-injured adults; however, this effect presents differently in those with a history of TBI.

Approximation for probability of coverage in softly inhibitive cellular networks

AbstractThe Strauss point process is a very popular model for describing the random cellular networks, yet several key statistical properties such as intensity, empty space function, and probability generating functional have remained elusive. This article addresses these issues by first leveraging the Poisson saddle point method to approximate the distance‐conditioned intensity for Strauss point processes. Subsequently, the author derives an analytically tractable expression for the distribution of empty space distance based on a conditional thinning mechanism. Additionally, the author establishes an upper bound for the probability generating functional in Strauss point processes, which is crucial for evaluating the Laplace transform of cumulative interference in relevant cellular networks. These findings facilitate the systematic derivation of spatially averaged probability of coverage, and the accuracy of analytic results is validated through simulations.

PSInet: a new global water potential network.

Given the pressing challenges posed by climate change, it is crucial to develop a deeper understanding of the impacts of escalating drought and heat stress on terrestrial ecosystems and the vital services they offer. Soil and plant water potential play a pivotal role in governing the dynamics of water within ecosystems and exert direct control over plant function and mortality risk during periods of ecological stress. However, existing observations of water potential suffer from significant limitations, including their sporadic and discontinuous nature, inconsistent representation of relevant spatio-temporal scales and numerous methodological challenges. These limitations hinder the comprehensive and synthetic research needed to enhance our conceptual understanding and predictive models of plant function and survival under limited moisture availability. In this article, we present PSInet (PSI-for the Greek letter Ψ used to denote water potential), a novel collaborative network of researchers and data, designed to bridge the current critical information gap in water potential data. The primary objectives of PSInet are as follows. (i) Establishing the first openly accessible global database for time series of plant and soil water potential measurements, while providing important linkages with other relevant observation networks. (ii) Fostering an inclusive and diverse collaborative environment for all scientists studying water potential in various stages of their careers. (iii) Standardizing methodologies, processing and interpretation of water potential data through the engagement of a global community of scientists, facilitated by the dissemination of standardized protocols, best practices and early career training opportunities. (iv) Facilitating the use of the PSInet database for synthesizing knowledge and addressing prominent gaps in our understanding of plants' physiological responses to various environmental stressors. The PSInet initiative is integral to meeting the fundamental research challenge of discerning which plant species will thrive and which will be vulnerable in a world undergoing rapid warming and increasing aridification.

Blended Learning Instruction at Tertiary Level: Experiences of Students in One Tertiary Institution in Eswatini

This study explores the experiences of university students on the use of Blended Learning instruction at tertiary level in one tertiary institution in Eswatini. The study adopted the qualitative research approach where the case study research design was used. The study targeted students in one campus of the tertiary institution. Data were collected from a total of 15 (fifteen) students of this campus who were selected using the Simple Random Sampling technique. The data were collected using one on one interviews and were analysed using Content Analysis. The findings of the study revealed that students have varied experiences about Blended Learning. While some have pleasant experiences of this mode of learning others have unpleasant experiences. The study found that some students view Blended Learning as a good mode of instruction that offers them the opportunity to access education anywhere anytime. The findings also revealed that the Blended mode of instruction benefits students in terms of its flexibility in learning times, vast sources of information and covering a lot of content in a short period of time. This mode, however, has numerous challenges which inhibit its successful implementation. These are limited data bundles and poor internet connection which inhibit learners from accessing lessons on line. The study therefore recommends that institutions must work with the relevant network providers (Eswatini MTN and Eswatini Mobile) to provide unrestricted data and network services to students.

Lithology identification of coal-bearing strata based on data-driven dual-channel relevance networks in coal mine roadway drilling process

The accurate prediction of coal seam thickness and the identification of the lithology of coal-bearing strata represent a crucial aspect of the intelligent perception of the stratigraphic environment. These technologies represent a fundamental aspect of intelligent mining operations with respect to coal. Accordingly, this paper concentrates on the investigation of lithology identification in coal-bearing strata through the utilization of data-driven dual-channel relevance networks in the context of coal mine roadway drilling operations. In consideration of the impact of coal seam thickness fluctuations on lithology identification, the region in question is initially delineated into two distinct categories: those comprising relatively stable coal seams and those comprising unstable coal seams. This is achieved through the application of coal seam thickness prediction and stability evaluation techniques. Subsequently, in consideration of the varying challenges posed by the region's coal seams, which can be either relatively stable or unstable, distinct lithology identification schemes are employed in different regions. In regions where the coal seam is relatively stable, there is a significant demand for classification of soft and hard coal seams. Additionally, the non-coal seam samples belong to a limited number of classes, necessitating the use of the SMOTE-GBDT algorithm to classify the coal-bearing strata into soft coal seam, hard coal seam, and non-coal seam. In the case of an unstable coal seam, the ability to distinguish between different seams is of paramount importance for lithology identification purposes. To this end, a double-layer binary classification RVM approach is employed to categorize coal-bearing strata into weak interlayer, coal seam and mudstone layer. The proposed methodology entails the utilization of diverse techniques in accordance with the specific characteristics of varying coal seam stability regions, with the objective of meticulously delineating the targets of lithology identification. This approach facilitates the generation of a comprehensive understanding of the coal-bearing strata environment, which can subsequently inform the implementation of intelligent adaptive control strategies.

Prognostic significance of HS2ST1 expression in patients with hepatocellular carcinoma

BackgroundHeparan sulfate 2-O-sulfotransferase 1 (HS2ST1) catalyzes the sulfation of glucuronic acid residues in heparan sulfate proteoglycans, enabling these proteoglycans to interact with numerous ligands within tumor microenvironments. However, the prognostic role of HS2ST1 expression in cancer remains unclear.ObjectiveThis investigated HS2ST1 expression levels and their prognostic significance in various cancer types, demonstrated the prognostic value of HS2ST1 expression in hepatocellular carcinoma (HCC) patients, and identified molecular signatures associated with HS2ST1 expression.MethodsHS2ST1 expression and patient survival data from The Cancer Genome Atlas (TCGA) datasets were analyzed using the Gene Expression Profiling Interactive Analysis (GEPIA) portal. We obtained gene expression and clinicopathological information on HCC patients from the TCGA and the Japan and France International Cancer Genome Consortium (ICGC) databases and performed survival analyses. We also examined relevant protein networks, differentially expressed genes, gene set enrichments, and tumor immune microenvironment features associated with HS2ST1 expression.ResultsHS2ST1 exhibited higher expression in eight tumor types compared with normal tissues and was associated with poor prognoses in five tumors, including HCC. HS2ST1 status correlated with poor prognosis in two ICGC HCC cohorts. Elevated HS2ST1 expression in HCC tumors was associated with signaling pathways involved in cell cycle progression, protein secretion, and mTORC1 signaling. Moreover, HS2ST1 expression levels were inversely correlated with immune cell infiltration in the tumor microenvironment.ConclusionOur study elucidates the prognostic significance of HS2ST1 expression in HCC patients and provides insights into the potential roles of HS2ST1 in signaling pathways and the tumor microenvironment.

Exploration of resting-state brain functional connectivity as preclinical markers for arousal prediction in prolonged disorders of consciousness: A pilot study based on functional near-infrared spectroscopy.

There is no diagnostic assessment procedure with moderate or strong evidence of use, and evidence for current means of treating prolonged disorders of consciousness (pDOC) is sparse. This may be related to the fact that the mechanisms of pDOC have not been studied deeply enough and are not clear enough. Therefore, the aim of this study was to explore the mechanism of pDOC using functional near-infrared spectroscopy (fNIRS) to provide a basis for the treatment of pDOC, as well as to explore preclinical markers for determining the arousal of pDOC patients. Five minutes resting-state data were collected from 10 pDOC patients and 13healthy adults using fNIRS. Based on the concentrations of oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) in the time series, the resting-state cortical brain functional connectivity strengths of the two groups were calculated, and the functional connectivity strengths of homologous and heterologous brain networks were compared at the sensorimotor network (SEN), dorsal attention network (DAN), ventral attention network (VAN), default mode network (DMN), frontoparietal network (FPN), and visual network (VIS) levels. Univariate binary logistic regression analyses were performed on brain networks with statistically significant differences to identify brain networks associated with arousal in pDOC patients. The receiver operating characteristic (ROC) curves were further analyzed to determine the cut-off value of the relevant brain networks to provide clinical biomarkers for the prediction of arousal in pDOC patients. The results showed that the functional connectivity strengths of oxyhemoglobin (HbO)-based SEN∼SEN, VIS∼VIS, DAN∼DAN, DMN∼DMN, SEN∼VIS, SEN∼FPN, SEN∼DAN, SEN∼DMN, VIS∼FPN, VIS∼DAN, VIS∼DMN, HbR-based SEN∼SEN, and SEN∼DAN were significantly reduced in the pDOC group and were factors that could reflect the participants' state of consciousness. The cut-off value of resting-state functional connectivity strength calculated by ROC curve analysis can be used as a potential preclinical marker for predicting the arousal state of subjects. Resting-state functional connectivity strength of cortical networks is significantly reduced in pDOC patients. The cut-off values of resting-state functional connectivity strength are potential preclinical markers for predicting arousal in pDOC patients.

Management of Context-Specific Knowledge Creation and Application through Collective Social Responsibility in School?Community Partnerships

In knowledge-based institutions such as schools, productivity is associated with effective creation and application of operational knowledge. Using collective fingers theory, this qualitative case study explored how a considerably effective partnership between a South African township secondary school and a network of community organizations/structures, facilitated the creation and application of context-specific and socially responsive knowledge that led to improved internal school conditions, problem solving strategies, poverty alleviation, and extension of social support to vulnerable learners and their families. Based on the thematic analysis drawn from utterances of eight purposefully sampled participants, the study showed that the school’s promotion of common purpose, mutual respect, collective decision making, care for others, collective social responsibility and democratic leadership practices contributed to the success of its mutually beneficial partnership with relevant community networks. It also emerged that by going an extra mile to lobby for multisectoral social support, the school richly benefited from the benevolence of a wide network of government departments; private-corporate sector and local businesses; faith-based formations; and non-governmental organizations some of which offered similar or overlapping services while others offered a diverse range of services to the school. The study proposed that the school must minimize the frequency of late arrival and unwarranted early recusals in knowledge production meetings. It further called for increased cultural capital and inclusion of parents from lower socioeconomic households in decision-making processes; and elimination of procurement policy red tape that occasionally delayed the provision of much-needed social support to schools, families and learners.

Physical reserve and its underpinning functional neural networks moderate the relationship between white matter hyperintensity and postural balance in older adults with subcortical ischemic vascular cognitive impairment

White matter hyperintensities (WMH) are markers of subcortical ischemic vascular cognitive impairment (SIVCI) associated with impaired postural balance. Physical reserve (PR) is a recently established construct that reflects one’s capacity to maintain physical function despite brain pathology. This cross-sectional study aims to map functional networks associated with PR, and examining the relationship between PR, WMH, and postural balance. PR was defined in 22 community-dwelling older adults with SIVCI. Functional networks of PR were computed using general linear model. Subsequent analyses examined whether PR and relevant networks moderated the relationship between WMH and postural balance under two conditions—eyes open while standing on foam (EOF) or on floor (EONF). We found that PR and the relevant networks—frontoparietal network (FPN) and default mode network (DMN)—significantly moderated the association between WMH and postural balance. For individuals with high PR, postural balance remained stable regardless of the extent of WMH load; whereas for those with low PR, postural balance worsened as WMH load increased. These results suggest the attenuated effects of WMH on postural stability due to PR may be underpinned by functional neural network reorganization in the FPN and DMN as a part of compensatory processes.

Straw Logistics Network Optimization Considering Cost Importance and Carbon Emission under the Concept of Sustainable Development

As biomass power generation projects progress, the supply chain of biomass resources has become a key link for the green development of biomass power facilities. The role of biomass recycling logistics in the sustainable development of biomass resources and power generation endeavors has grown increasingly significant. To realize environmentally friendly and economical straw logistics, it is imperative to establish an efficient, economic and sustainable straw recycling logistics network. Therefore, based on the weighting theory and top-down method, this study proposes a planning model aiming at minimum total cost and minimum carbon emission to locate the logistics network. The immune algorithm is applied to solve and analyze the proposed examples. It is concluded that under the condition that the total cost is reasonable, reducing transportation cost. The verification model can enhance the environmental and economic performance of straw logistics networks. From the perspective of theory and practice, we provide ideas for the optimization of relevant biomass logistics networks and promote the realization of low-carbon sustainable development of logistics networks.

Gene co-expression network analysis for porcine intramuscular fatty acid composition

In pigs, meat quality depends markedly on the fatty acid (FA) content and composition of the intramuscular fat, which is partly determined by the gene expression in this tissue. The aim of this work was to identify the link between muscle gene expression and its FA composition. In an (Iberian × Duroc) × Duroc backcrossed pig population, we identified modules of co-expressed genes, and correlation analyses were performed for each of them versus the phenotypes, finding four relevant modules. Two of the modules were positively correlated with saturated FAs (SFAs) and monounsaturated FAs (MUFAs), while negatively correlated with polyunsaturated FAs (PUFAs) and the omega-6/omega-3 ratio. The gene-enrichment analysis showed that these modules had over-representation of pathways related with the biosynthesis of unsaturated FAs, the Peroxisome proliferator-activated receptor signalling pathway and FA elongation. The two other relevant modules were positively correlated with PUFA and the n-6/n-3 ratio, but negatively correlated with SFA and MUFA. In this case, they had an over-representation of pathways related with fatty and amino acid degradation, and with oxidative phosphorylation. Using a graphical Gaussian model, we inferred a network of connections between the genes within each module. The first module had 52 genes with 87 connections, and the most connected genes were ADIPOQ, which is related with FA oxidation, and ELOVL6 and FABP4, both involved in FA metabolism. The second module showed 196 genes connected by 263 edges, being FN1 and MAP3K11 the most connected genes. On the other hand, the third module had 161 genes connected by 251 edges and ATG13 was the top neighbouring gene, while the fourth module had 224 genes and 655 connections, and its most connected genes were related with mitochondrial pathways. Overall, this work successfully identified relevant muscle gene networks and modules linked with FA composition, providing further insights on how the physiology of the pigs influences FA composition.