NeuralMatrix: Compute the Entire Neural Networks with Linear Matrix Operations for Efficient Inference

The inherent diversity of computation types within individual deep neural network (DNN) models necessitates a corresponding variety of computation units within hardware processors, leading to a significant constraint on computation efficiency during neural network execution. In this study, we introduce NeuralMatrix, a framework that transforms the computation of entire DNNs into linear matrix operations, effectively enabling their execution with one general-purpose matrix multiplication (GEMM) accelerator. By surmounting the constraints posed by the diverse computation types required by individual network models, this approach provides both generality, allowing a wide range of DNN models to be executed using a single GEMM accelerator and application-specific acceleration levels without extra special function units, which are validated through main stream DNNs and their variant models.Comment: 12 pages, 4figures, Submitted to 11th International Conference on Learning Representation

Applying cover crop residues as diverse mixtures increases initial microbial assimilation of crop residue-derived carbon

Increasing the diversity of the crops grown in arable soils delivers multiple ecological functions. Whether mixtures of residues from different crops grown in polyculture contribute to microbial assimilation of C to a greater extent than would be expected from applying individual residues is currently unknown. In this study, we used 13C isotope labelled cover crop residues (buckwheat, clover, radish, and sunflower) to track microbial assimilation of plant residue-derived C using phospholipid fatty acid (PLFA) analysis. We also quantified microbial assimilation of C derived from the soil organic matter (SOM) because fresh residue inputs also prime the decomposition of SOM. To consider the initial stages of residue decomposition, and preclude microbial turnover, we compared a quaternary mixture of residues with the average effect of their four components one day after incorporation. Our results show that the microbial biomass C (MBC) in the treatment receiving the mixed residue was significantly greater, by 132% (3.61 µg C g-1), than the mean plant residue-derived MBC in treatments receiving the four individual components of the mixture. However, there was no evidence that the mixture resulted in any additional assimilation of C derived from native SOM than the average observed in individual residue treatments. We surmise that, during the initial stages of crop residue decomposition, a greater biodiversity of residues increases microbial assimilation to a greater extent than would be expected from applying individual residues either due to faster decomposition or greater carbon use efficiency (CUE). This might be facilitated by functional complementarity in the soil microbiota permitted by a greater diversity of substrates, reducing competition for any single substrate. Therefore, growing and incorporating crop polycultures (e.g., cover crop mixtures) could be an effective method to increase microbial C assimilation in the early stages of cover crop decomposition

Cover crop residue diversity enhances microbial activity and biomass with additive effects on microbial structure

Cover crops have been widely used in agroecosystems to improve soil fertility and environmental sustainability. The decomposition of cover crop residues can have further effects on belowground communities and their activity, which is important for a series of soil functions (e.g., nutrient cycling and organic matter decomposition). We tested the effect of plant residues from a range of cover crop species on soil microbial activity and community assemblage. We predicted that cover crop residues would alter the soil microbial community and that a greater diversity of residues would enhance microbial decomposition. In an incubation study, we assessed the effect of crop residue diversity on microbial activity (soil respiration) and its consequent effects on microbial community composition (PLFA). We used either a biodiverse mixture of four cover crop residues (buckwheat, clover, sunflower, and radish) or an equal mass of the residues of each of the individual species. Cover crop residue incorporation significantly (P < 0.001) increased soil respiration during 84 days’ incubation and this universal response caused a significant change in microbial community composition by increasing the proportion of fungi and Gram-positive bacteria at the cost of decreasing Gram-negative bacteria. The diverse mixture of cover crop residues had a significantly (P < 0.05) greater soil respiration rate, by 57.61 µg C g-1 h-1, than the average of the four individual residues, but did not have a significantly different soil microbial biomass or microbial community structure. This finding could be attributed to a greater diversity of organic resources increasing the number biochemical niches, and hence activating dormant microbial communities to increase microbial activity without affecting microbial biomass or community composition. Greater respiration from similar microbial biomasses suggests that microbial activity might be more efficient after a more diverse substrate input. This study confirms the positive impact of cover crop residues on soil microbial biomass and activity and highlights that mixtures of cover crop residues may deliver enhanced soil functions beyond the sum of individual cover crop residues

Structural connectome quantifies tumor invasion and predicts survival in glioblastoma patients

Glioblastoma widely affects brain structure and function, and remodels neural connectivity. Characterizing the neural connectivity in glioblastoma may provide a tool to understand tumor invasion. Here, using a structural connectome approach based on diffusion MRI, we quantify the global and regional connectome disruptions in individual glioblastoma patients and investigate the prognostic value of connectome disruptions and topological properties. We show that the disruptions in the normal-appearing brain beyond the lesion could mediate the topological alteration of the connectome (P &lt;0.001), associated with worse patient performance (P &lt;0.001), cognitive function (P &lt;0.001), and survival (overall survival: HR: 1.46, P = 0.049; progression-free survival: HR: 1.49, P = 0.019). Further, the preserved connectome in the normal-appearing brain demonstrates evidence of remodeling, where increased connectivity is associated with better overall survival (log-rank P = 0.005). Our approach reveals the glioblastoma invasion invisible on conventional MRI, promising to benefit patient stratification and precise treatment

Structural connectome quantifies tumor invasion and predicts survival in glioblastoma patients

Glioblastoma widely affects brain structure and function, and remodels neural connectivity. Characterizing the neural connectivity in glioblastoma may provide a tool to understand tumor invasion. Here, using a structural connectome approach based on diffusion MRI, we quantify the global and regional connectome disruptions in individual glioblastoma patients and investigate the prognostic value of connectome disruptions and topological properties. We show that the disruptions in the normal-appearing brain beyond the lesion could mediate the topological alteration of the connectome (P &lt;0.001), associated with worse patient performance (P &lt;0.001), cognitive function (P &lt;0.001), and survival (overall survival: HR: 1.46, P = 0.049; progression-free survival: HR: 1.49, P = 0.019). Further, the preserved connectome in the normal-appearing brain demonstrates evidence of remodeling, where increased connectivity is associated with better overall survival (log-rank P = 0.005). Our approach reveals the glioblastoma invasion invisible on conventional MRI, promising to benefit patient stratification and precise treatment

Structural connectome quantifies tumour invasion and predicts survival in glioblastoma patients

Glioblastoma is characterized by diffuse infiltration into the surrounding tissue along white matter tracts. Identifying the invisible tumour invasion beyond focal lesion promises more effective treatment, which remains a significant challenge. It is increasingly accepted that glioblastoma could widely affect brain structure and function, and further lead to reorganization of neural connectivity. Quantifying neural connectivity in glioblastoma may provide a valuable tool for identifying tumour invasion.Here we propose an approach to systematically identify tumour invasion by quantifying the structural connectome in glioblastoma patients. We first recruit two independent prospective glioblastoma cohorts: the discovery cohort with 117 patients and validation cohort with 42 patients. Next, we use diffusion MRI of healthy subjects to construct tractography templates indicating white matter connection pathways between brain regions. Next, we construct fractional anisotropy skeletons from diffusion MRI using an improved voxel projection approach based on the tract-based spatial statistics, where the strengths of white matter connection and brain regions are estimated. To quantify the disrupted connectome, we calculate the deviation of the connectome strengths of patients from that of the age-matched healthy controls. We then categorize the disruption into regional disruptions on the basis of the relative location of connectome to focal lesions. We also characterize the topological properties of the patient connectome based on the graph theory. Finally, we investigate the clinical, cognitive and prognostic significance of connectome metrics using Pearson correlation test, mediation test and survival models. Our results show that the connectome disruptions in glioblastoma patients are widespread in the normal-appearing brain beyond focal lesions, associated with lower preoperative performance (P &lt; 0.001), impaired cognitive function (P &lt; 0.001) and worse survival (overall survival: hazard ratio = 1.46, P = 0.049; progression-free survival: hazard ratio = 1.49, P = 0.019). Additionally, these distant disruptions mediate the effect on topological alterations of the connectome (mediation effect: clustering coefficient -0.017, P &lt; 0.001, characteristic path length 0.17, P = 0.008). Further, the preserved connectome in the normal-appearing brain demonstrates evidence of connectivity reorganization, where the increased neural connectivity is associated with better overall survival (log-rank P = 0.005). In conclusion, our connectome approach could reveal and quantify the glioblastoma invasion distant from the focal lesion and invisible on the conventional MRI. The structural disruptions in the normal-appearing brain were associated with the topological alteration of the brain and could indicate treatment target. Our approach promises to aid more accurate patient stratification and more precise treatment planning.</p

Structural connectome quantifies tumour invasion and predicts survival in glioblastoma patients

Glioblastoma is characterized by diffuse infiltration into the surrounding tissue along white matter tracts. Identifying the invisible tumour invasion beyond focal lesion promises more effective treatment, which remains a significant challenge. It is increasingly accepted that glioblastoma could widely affect brain structure and function, and further lead to reorganization of neural connectivity. Quantifying neural connectivity in glioblastoma may provide a valuable tool for identifying tumour invasion.Here we propose an approach to systematically identify tumour invasion by quantifying the structural connectome in glioblastoma patients. We first recruit two independent prospective glioblastoma cohorts: the discovery cohort with 117 patients and validation cohort with 42 patients. Next, we use diffusion MRI of healthy subjects to construct tractography templates indicating white matter connection pathways between brain regions. Next, we construct fractional anisotropy skeletons from diffusion MRI using an improved voxel projection approach based on the tract-based spatial statistics, where the strengths of white matter connection and brain regions are estimated. To quantify the disrupted connectome, we calculate the deviation of the connectome strengths of patients from that of the age-matched healthy controls. We then categorize the disruption into regional disruptions on the basis of the relative location of connectome to focal lesions. We also characterize the topological properties of the patient connectome based on the graph theory. Finally, we investigate the clinical, cognitive and prognostic significance of connectome metrics using Pearson correlation test, mediation test and survival models. Our results show that the connectome disruptions in glioblastoma patients are widespread in the normal-appearing brain beyond focal lesions, associated with lower preoperative performance (P &lt; 0.001), impaired cognitive function (P &lt; 0.001) and worse survival (overall survival: hazard ratio = 1.46, P = 0.049; progression-free survival: hazard ratio = 1.49, P = 0.019). Additionally, these distant disruptions mediate the effect on topological alterations of the connectome (mediation effect: clustering coefficient -0.017, P &lt; 0.001, characteristic path length 0.17, P = 0.008). Further, the preserved connectome in the normal-appearing brain demonstrates evidence of connectivity reorganization, where the increased neural connectivity is associated with better overall survival (log-rank P = 0.005). In conclusion, our connectome approach could reveal and quantify the glioblastoma invasion distant from the focal lesion and invisible on the conventional MRI. The structural disruptions in the normal-appearing brain were associated with the topological alteration of the brain and could indicate treatment target. Our approach promises to aid more accurate patient stratification and more precise treatment planning.</p

Decoding the spermatogonial stem cell niche under physiological and recovery conditions in adult mice and humans

The intricate interaction between spermatogonial stem cell (SSC) and testicular niche is essential for maintaining SSC homeostasis; however, this interaction remains largely uncharacterized. In this study, to characterize the underlying signaling pathways and related paracrine factors, we delineated the intercellular interactions between SSC and niche cell in both adult mice and humans under physiological conditions and dissected the niche-derived regulation of SSC maintenance under recovery conditions, thus uncovering the essential role of C-C motif chemokine ligand 24 and insulin-like growth factor binding protein 7 in SSC maintenance. We also established the clinical relevance of specific paracrine factors in human fertility. Collectively, our work on decoding the adult SSC niche serves as a valuable reference for future studies on the aetiology, diagnosis, and treatment of male infertility.</p