Evaluation of Protein Identification and Quantification by the diaPASEF Method on timsTOF SCP.

Accurate and precise quantification is crucial in modern proteomics, particularly in the context of exploring low-amount samples. While the innovative 4D-data-independent acquisition (DIA) quantitative proteomics facilitated by timsTOF mass spectrometers gives enhanced sensitivity and selectivity for protein identification, the diaPASEF (parallel accumulation-serial fragmentation combined with data-independent acquisition) parameters have not been systematically optimized, and a comprehensive evaluation of the quantification is currently lacking. In this study, we conducted a thorough optimization of key parameters on a timsTOF SCP instrument, including sample loading amount (50 ng), ramp/accumulation time (140 ms), isolation window width (20 m/z), and gradient time (60 min). To further improve the identification of proteins in low-amount samples, we utilized different column settings and introduced 0.02% n-dodecyl-ß-d-maltoside (DDM) in the sample reconstitution solution, resulting in a remarkable 19-fold increase in protein identification at the single-cell-equivalent level. Moreover, a comprehensive comparison of protein quantification using a tandem mass tag reporter (TMT-reporter), complement TMT ions (TMTc), and diaPASEF revealed a strong correlation between these methods. Both diaPASEF and TMTc have effectively addressed the issue of ratio compression, highlighting the diaPASEF method's effectiveness in achieving accurate quantification data compared to TMT reporter quantification. Additionally, an in-depth analysis of in-group variation positioned diaPASEF between the TMT-reporter and TMTc methods. Therefore, diaPASEF quantification on the timsTOF SCP instrument emerges as a precise and accurate methodology for quantitative proteomics, especially for samples with small amounts.

Simple and scalable preparation of lignin based porous carbon coated nano-clay composites and their efficient removal for the diversified iodine.

Here, lignin and nano-clay were used to prepare novel composite adsorbents by one-step carbonization without adding activators for radioactive iodine capture. Specially, 1D nano-clay such as halloysite (Hal), palygorskite (Pal) and sepiolite (Sep) were selected as skeleton components, respectively, enzymatic hydrolysis lignin (EHL) as carbon source, lignin based porous carbon/nano-clay composites (ELC-X) were prepared through ultrasonic impregnation, freeze drying, and carbonization. Characterization results indicated lignin based porous carbon (ELC) well coated on the surface of nano-clay, and made its surface areas increase to 252 m2/g. These composites appeared the micro-mesoporous hierarchical structure, considerable N doping and good chemical stability. Results of adsorption experiments showed that the introduction of ELC could well promote iodine vapor uptake of nano-clay, and up to 435.0 mg/g. Meanwhile, the synergistic effect between lignin based carbon and nano-clay was very significant for the adsorption of iodine/n-hexane and iodine ions, their capacity were far exceed those of a single material, respectively. The relevant adsorption kinetic and thermodynamics, and mechanism of ELC-X composites were clarified. This work provided a class of low-cost and environmentally friendly adsorbents for radioactive iodine capture, and opened up ideas for the comprehensive utilization of waste lignin and natural clay minerals.

CSTS: Exploring Class-Specific and Task-Shared Embedding Representation for Few-Shot Learning.

Few-shot learning (FSL) is a challenging yet promising technique that aims to discriminate objects based on a few labeled examples. Learning a high-quality feature representation is key with few-shot data, and many existing models attempt to extract general information from the sample or task levels. However, the common sample-level means of feature representation limits the models generalizability to different tasks, while task-level representation may lose class characteristics due to excessive information aggregation. In this article, we synchronize the class-specific and task-shared information from the class and task levels to obtain a better representation. Structure-based contrastive learning is introduced to obtain class-specific representations by increasing the interclass distance. A hierarchical class structure is constructed by clustering semantically similar classes using the idea of granular computing. When guided by a class structure, it is more difficult to distinguish samples in different classes that have similar characteristics than those with large interclass differences. To this end, structure-guided contrastive learning is introduced to study class-specific information. A hierarchical graph neural network is established to transfer task-shared information from coarse to fine. It hierarchically infers the target sample based on all samples in the task and yields a more general representation for FSL classification. Experiments on four benchmark datasets demonstrate the advantages of our model over several state-of-the-art models.

The gonadal niche safeguards human fetal germline cell development following maternal SARS-CoV-2 infection.

During pregnancy, germline development is vital for maintaining the continuation of species. Recent studies have shown increased pregnancy risks in COVID-19 patients at the perinatal stage. However, the potential consequence of infection for reproductive quality in developing fetuses remains unclear. Here, we analyze the transcriptome and DNA methylome of the fetal germline following maternal severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We find that infection at early gestational age, a critical period of human primordial germ cell specification and epigenetic reprogramming, trivially affects fetal germ cell (FGC) development. Additionally, FGC-niche communications are not compromised by maternal infection. Strikingly, both general and SARS-CoV-2-specific immune pathways are greatly activated in gonadal niche cells to protect FGCs from maternal infection. Notably, there occurs an "in advance" development tendency in FGCs after maternal infection. Our study provides insights into the impacts of maternal SARS-CoV-2 infection on fetal germline development and serves as potential clinical guidance for future pandemics.

Sirolimus combined with glucocorticoids in the treatment of Kasabach-Merritt phenomenon in a neonate: A case report.

RATIONALE: Kaposiform hemangioendothelioma is an aggressive vascular tumor that is often associated with life-threatening coagulopathies and Kasabach-Merritt phenomenon. Pathologic biopsies can provide a good basis for diagnosis and treatment. Therapy with srolimus combined with glucocorticoids may offer patients a favorable prognosis. PATIENT CONCERNS: A large purplish-red mass on the knee of a child with extremely progressive thrombocytopenia and refractory coagulation abnormalities. Conventional doses of glucocorticoids alone failed to improve coagulation abnormalities and the child developed large cutaneous petechiae and scalp hematomas. DIAGNOSIS: Kaposiform hemangioendothelioma combined with Kasabach-Merritt phenomenon. INTERVENTIONS: The patient received prednisolone 2.0 mg/kg*d for 4 days. Blood products were transfused to ensure vital signs and to complete the pathologic biopsy. Sirolimus combined with prednisolone was given after clarifying the diagnosis of Kaposiform hemangioendothelioma. OUTCOMES: The tumor basically disappeared on examination and the ultrasound showed a subcutaneous hyperechoic mass with normal blood flow. LESSONS: Sirolimus combined with glucocorticoids is effective in controlling Kasabach-Merritt phenomenon and pathologic biopsy is important for definitive diagnosis.

Profiling Protein-Protein Interactions in the Human Brain by Refined Cofractionation Mass Spectrometry.

Proteins usually execute their biological functions through interactions with other proteins and by forming macromolecular complexes, but global profiling of protein complexes directly from human tissue samples has been limited. In this study, we utilized cofractionation mass spectrometry (CF-MS) to map protein complexes within the postmortem human brain with experimental replicates. First, we used concatenated anion and cation Ion Exchange Chromatography (IEX) to separate native protein complexes in 192 fractions and then proceeded with Data-Independent Acquisition (DIA) mass spectrometry to analyze the proteins in each fraction, quantifying a total of 4,804 proteins with 3,260 overlapping in both replicates. We improved the DIA's quantitative accuracy by implementing a constant amount of bovine serum albumin (BSA) in each fraction as an internal standard. Next, advanced computational pipelines, which integrate both a database-based complex analysis and an unbiased protein-protein interaction (PPI) search, were applied to identify protein complexes and construct protein-protein interaction networks in the human brain. Our study led to the identification of 486 protein complexes and 10054 binary protein-protein interactions, which represents the first global profiling of human brain PPIs using CF-MS. Overall, this study offers a resource and tool for a wide range of human brain research, including the identification of disease-specific protein complexes in the future.

Targeting DCAF5 suppresses SMARCB1-mutant cancer by stabilizing SWI/SNF.

Whereas oncogenes can potentially be inhibited with small molecules, the loss of tumour suppressors is more common and is problematic because the tumour-suppressor proteins are no longer present to be targeted. Notable examples include SMARCB1-mutant cancers, which are highly lethal malignancies driven by the inactivation of a subunit of SWI/SNF (also known as BAF) chromatin-remodelling complexes. Here, to generate mechanistic insights into the consequences of SMARCB1 mutation and to identify vulnerabilities, we contributed 14 SMARCB1-mutant cell lines to a near genome-wide CRISPR screen as part of the Cancer Dependency Map Project1-3. We report that the little-studied gene DDB1-CUL4-associated factor 5 (DCAF5) is required for the survival of SMARCB1-mutant cancers. We show that DCAF5 has a quality-control function for SWI/SNF complexes and promotes the degradation of incompletely assembled SWI/SNF complexes in the absence of SMARCB1. After depletion of DCAF5, SMARCB1-deficient SWI/SNF complexes reaccumulate, bind to target loci and restore SWI/SNF-mediated gene expression to levels that are sufficient to reverse the cancer state, including in vivo. Consequently, cancer results not from the loss of SMARCB1 function per se, but rather from DCAF5-mediated degradation of SWI/SNF complexes. These data indicate that therapeutic targeting of ubiquitin-mediated quality-control factors may effectively reverse the malignant state of some cancers driven by disruption of tumour suppressor complexes.

Application of hospital-community-home linkage management model in patients with type 2 diabetic nephropathy.

OBJECTIVE: To explore the effect of the hospital-community-home (HCH) linkage management mode in patients with type 2 diabetic nephropathy (DN). METHOD: A total of 80 patients with type 2 DN hospitalised in the Department of Nephrology of our hospital between July 2021 and June 2022 were recruited and subsequently divided into the observation group and the control group using the random number table method, with 40 patients in each group. The control group received routine health education and discharge guidance. The HCH linkage management model was implemented for the observation group based on routine care. The improvements in compliance behaviour, biochemical parameters of renal function, blood glucose level and self-management ability were compared before the intervention and at 3 and 6 months after the intervention. RESULTS: After the intervention, the scores for compliance behaviour of the observation group were better than those of the control group, with a statistically significant difference (P < 0.05). The biochemical indicators of renal function and blood glucose level were significantly lower in the observation group compared with in the control group, with a statistically significant difference (P < 0.05). After the intervention, the observation group showed a great improvement in self-management ability and cognition of the disease, with significant differences (P < 0.05). CONCLUSION: The HCH linkage management mode can improve the compliance behaviour of patients with type 2 DN, effectively improve the renal function and blood sugar level of patients, enhance the self-management ability and cognition of the disease and delay the development of the disease.

Encapsulated lignin-based slow-release manganese fertilizer with reduced cadmium accumulation in rice (Oryza sativa L.).

As an essential trace element for plant growth and development, manganese plays a crucial role in the uptake of the heavy metal cadmium by rice (Oryza sativa L.). In this study, we developed a novel slow-release manganese fertilizer named Mn@LNS-EL. Initially, lignin nanoparticles were derived from sodium lignosulfonate, and a one-step emulsification strategy was employed to prepare a water-in-oil-in-water (W/O/W) Pickering double emulsions. These double emulsions served as the template for interfacial polymerization of lignin nanoparticles and epichlorohydrin, resulting in the formation of microcapsule wall materials. Subsequently, manganese fertilizer (MnSO4) was successfully encapsulated within the microcapsules. Hydroponic experiments were conducted to investigate the effects of Mn@LNS-EL on rice growth and the cadmium and manganese contents in the roots and shoots of rice under cadmium stress conditions. The results revealed that the treatment with Mn@LNS-EL markedly alleviated the inhibitory effects of cadmium on rice growth, leading to notably lower cadmium levels in the rice roots and shoots compared to the specimens treated without manganese fertilizer. Specifically, there was a reduction of 37.9 % in the root cadmium content and a 17.1 % decrease in the shoot cadmium content. In conclusion, this study presents an innovative approach for the high-value utilization of lignin through effective encapsulation and slow-release mechanisms of trace-element fertilizers while offering a promising strategy for efficiently remediating cadmium pollution in rice.

Historical and contemporary management of infantile hemangiomas: a single-center experience.

Objective: This study explores the 22-year evolution of Infantile Hemangiomas (IHs) treatment strategies at a single-center hospital, aiming to establish an individualized IHs management protocol. Methods: Retrospective review of IHs infants 2000-2022 at the Department of Plastic Surgery, Jiangxi Provincial Children's Hospital. Results: In our study of 27,513 IHs cases, 72.2% were female, with the median age at first hospital visit being 25 days. The majority of cases had localized and superficial lesions primarily on the head, face, and neck (67.5%). Ulceration rates fell from 21.1% to 12.6% with the introduction of propranolol. Management strategies have shifted over time, with the proportion of cases undergoing expectant management dropping from 32.9% to 12.4%. Since 2008, 26.1% of patients were treated with oral propranolol, largely replacing corticosteroids. Topical ß-blockers have been used in 12.1% of cases, leading to a reduction in local injection therapy from 20.8% to 13.2%. Laser therapy, introduced in 2016, has been used in 13.8% of cases, while surgical excision has dropped from 25.0% to 8.5% due to alternative treatment options. Combination therapy was used in 8.8% of cases post-2015, indicating a rising trend. Drawing from the evolution of IHs management strategies, an individualized protocol for the management of IHs was successfully established. Conclusion: Treatment for IHs has evolved over recent decades, with less invasive medical interventions increasingly replacing more invasive methods. Furthermore, a personalized treatment protocol established in this study could boost the cure rate of IHs while minimizing potential side effects and complications.

Intrauterine adhesions treated with hysteroscopic adhesiolysis and subsequent obstetric outcome: A retrospective matched cohort study.

OBJECTIVE: To examine whether a history of hysteroscopic adhesiolysis (HA)-treated intrauterine adhesions (IUAs) was associated with an increased risk of adverse obstetrical outcomes in subsequent pregnancies. DESIGN: Retrospective cohort study. SETTING: A tertiary-care hospital in Shanghai, China. POPULATION: A cohort of 114 142 pregnant women who were issued an antenatal card and received routine antenatal care in Shanghai First Maternity and Infant Hospital, between January 2016 and October 2021. METHODS: From the cohort of 114 142 pregnant women, each woman with a history of HA-treated IUA prior to the current pregnancy (n = 780) was matched with four women without a history of IUAs (n = 3010) using propensity score matching. The matching variables were maternal age and parity, mode of conception, pre-pregnancy body mass index and prior history of abortion. MAIN OUTCOME MEASURES: Pregnancy complications, placental abnormalities, postpartum haemorrhage and adverse birth outcomes. RESULTS: Compared with women with no history of IUAs, women with a history of HA-treated IUAs were at higher risk of pre-eclampsia (RR 1.69, 95% CI 1.23-2.33), placenta accreta spectrum (RR 4.72, 95% CI 3.9-5.73), placenta praevia (RR 4.23, 95% CI 2.85-6.30), postpartum haemorrhage (RR 2.86, 95% CI 1.94-4.23), preterm premature rupture of membranes (RR 3.02, 95% CI 1.97-4.64) and iatrogenic preterm birth (RR 2.86, 95% CI 2.14-3.81). Those women were also more likely to receive cervical cerclage (RR 5.63, 95% CI 3.95-8.02) during pregnancy and haemostatic therapies after delivery (RR 2.17, 95% CI 1.75-2.69). Moreover, we observed that the RRs of those adverse obstetrical outcomes increased with the increasing number of hysteroscopic surgeries. CONCLUSIONS: This study found that a history of HA-treated IUAs, especially a history of repeated HAs, was associated with an increased risk of adverse obstetrical outcomes.

Targeting autophagy drug discovery: Targets, indications and development trends.

Autophagy plays a vital role in sustaining cellular homeostasis and its alterations have been implicated in the etiology of many diseases. Drugs development targeting autophagy began decades ago and hundreds of agents were developed, some of which are licensed for the clinical usage. However, no existing intervention specifically aimed at modulating autophagy is available. The obstacles that prevent drug developments come from the complexity of the actual impact of autophagy regulators in disease scenarios. With the development and application of new technologies, several promising categories of compounds for autophagy-based therapy have emerged in recent years. In this paper, the autophagy-targeted drugs based on their targets at various hierarchical sites of the autophagic signaling network, e.g., the upstream and downstream of the autophagosome and the autophagic components with enzyme activities are reviewed and analyzed respectively, with special attention paid to those at preclinical or clinical trials. The drugs tailored to specific autophagy alone and combination with drugs/adjuvant therapies widely used in clinical for various diseases treatments are also emphasized. The emerging drug design and development targeting selective autophagy receptors (SARs) and their related proteins, which would be expected to arrest or reverse the progression of disease in various cancers, inflammation, neurodegeneration, and metabolic disorders, are critically reviewed. And the challenges and perspective in clinically developing autophagy-targeted drugs and possible combinations with other medicine are considered in the review.

Physicochemical and functional characteristics of glycated collagen protein from giant salamander skin induced by ultrasound Maillard reaction.

Chinese giant salamander skin collagen (CGSSC) was successfully conjugated with glucose (Glu)/xylose (Xy) by ultrasound Maillard reaction (MR) in nature deep eutectic solvents (NADES). The effects of ultrasound and reducing sugar types on the degree graft (DG) of MR products (MRPs), as well as the influence of DG on the structure and functional properties of MRPs were investigated. The results indicated that the ultrasound assisted could markedly enhance the MR of CGSSC, and low molecular weight reducing sugars were more reactive in MR. The ultrasound MR significantly changed the microstructure, secondary and tertiary structures of CGSSC. Moreover, the free sulfhydryl content of MRPs were increased, thus enhancing the surface hydrophobicity, emulsifying properties and antioxidant activity, which were positively correlated with DG. These findings provided theoretical insights into the effects of ultrasound assisted and different sugar types on the functional properties of collagen induced by MR.

Identifying Sex-Specific Serum Patterns of Alzheimer's Mice through Deep TMT Profiling and a Concentration-Dependent Concatenation Strategy.

Alzheimer's disease (AD) is the most prevalent form of dementia, disproportionately affecting women in disease prevalence and progression. Comprehensive analysis of the serum proteome in a common AD mouse model offers potential in identifying possible AD pathology- and gender-associated biomarkers. Here, we introduce a multiplexed, nondepleted mouse serum proteome profiling via tandem mass-tag (TMTpro) labeling. The labeled sample was separated into 475 fractions using basic reversed-phase liquid chromatography (RPLC), which were categorized into low-, medium-, and high-concentration fractions for concatenation. This concentration-dependent concatenation strategy resulted in 128 fractions for acidic RPLC-tandem mass spectrometry (MS/MS) analysis, collecting ∼5 million MS/MS scans and identifying 3972 unique proteins (3413 genes) that cover a dynamic range spanning at least 6 orders of magnitude. The differential expression analysis between wild type and the commonly used AD model (5xFAD) mice exhibited minimal significant protein alterations. However, we detected 60 statistically significant (FDR < 0.05), sex-specific proteins, including complement components, serpins, carboxylesterases, major urinary proteins, cysteine-rich secretory protein 1, pregnancy-associated murine protein 1, prolactin, amyloid P component, epidermal growth factor receptor, fibrinogen-like protein 1, and hepcidin. The results suggest that our platform possesses the sensitivity and reproducibility required to detect sex-specific differentially expressed proteins in mouse serum samples.

Transfer learning for clustering single-cell RNA-seq data crossing-species and batch, case on uterine fibroids.

Due to the high dimensionality and sparsity of the gene expression matrix in single-cell RNA-sequencing (scRNA-seq) data, coupled with significant noise generated by shallow sequencing, it poses a great challenge for cell clustering methods. While numerous computational methods have been proposed, the majority of existing approaches center on processing the target dataset itself. This approach disregards the wealth of knowledge present within other species and batches of scRNA-seq data. In light of this, our paper proposes a novel method named graph-based deep embedding clustering (GDEC) that leverages transfer learning across species and batches. GDEC integrates graph convolutional networks, effectively overcoming the challenges posed by sparse gene expression matrices. Additionally, the incorporation of DEC in GDEC enables the partitioning of cell clusters within a lower-dimensional space, thereby mitigating the adverse effects of noise on clustering outcomes. GDEC constructs a model based on existing scRNA-seq datasets and then applying transfer learning techniques to fine-tune the model using a limited amount of prior knowledge gleaned from the target dataset. This empowers GDEC to adeptly cluster scRNA-seq data cross different species and batches. Through cross-species and cross-batch clustering experiments, we conducted a comparative analysis between GDEC and conventional packages. Furthermore, we implemented GDEC on the scRNA-seq data of uterine fibroids. Compared results obtained from the Seurat package, GDEC unveiled a novel cell type (epithelial cells) and identified a notable number of new pathways among various cell types, thus underscoring the enhanced analytical capabilities of GDEC. Availability and implementation: https://github.com/YuzhiSun/GDEC/tree/main.

Comprehensive characterization of human brain-derived extracellular vesicles using multiple isolation methods: Implications for diagnostic and therapeutic applications.

Extracellular vesicles (EVs) have emerged as critical mediators of intercellular communication and promising biomarkers and therapeutics in the central nervous system (CNS). Human brain-derived EVs (BDEVs) provide a comprehensive snapshot of physiological changes in the brain's environment, however, the isolation of BDEVs and the comparison of different methods for this purpose have not been fully investigated. In this study, we compared the yield, morphology, subtypes and protein cargo composition of EVs isolated from the temporal cortex of aged human brains using three established separation methods: size-exclusion chromatography (SEC), phosphatidylserine affinity capture (MagE) and sucrose gradient ultracentrifugation (SG-UC). Our results showed that SG-UC method provided the highest yield and collected larger EVs compared to SEC and MagE methods as assessed by transmission electron microscopy and nanoparticle tracking analysis (NTA). Quantitative tandem mass-tag (TMT) mass spectrometry analysis of EV samples from three different isolation methods identified a total of 1158 proteins, with SG-UC showing the best enrichment of common EV proteins with less contamination of non-EV proteins. In addition, SG-UC samples were enriched in proteins associated with ATP activity and CNS maintenance, and were abundant in neuronal and oligodendrocytic molecules. In contrast, MagE samples were more enriched in molecules related to lipoproteins, cell-substrate junction and microglia, whereas SEC samples were highly enriched in molecules related to extracellular matrix, Alzheimer's disease and astrocytes. Finally, we validated the proteomic results by performing single-particle analysis using the super-resolution microscopy and flow cytometry. Overall, our findings demonstrate the differences in yield, size, enrichment of EV cargo molecules and single EV assay by different isolation methods, suggesting that the choice of isolation method will have significant impact on the downstream analysis and protein discovery.

Heterogeneous graph framework for predicting the association between lncRNA and disease and case on uterine fibroid.

Long non-coding RNAs (lncRNAs) play crucial regulatory roles in various cellular processes, including gene expression, chromatin remodeling, and protein localization. Dysregulation of lncRNAs has been linked to several diseases, making it essential to understand their functions in disease mechanisms and therapeutic strategies. However, traditional experimental methods for studying lncRNA function are time-consuming, expensive, and offer limited insights. In recent years, computational methods have emerged as valuable tools for predicting lncRNA functions and their associations with diseases. However, many existing methods focus on constructing separate networks for lncRNA and disease similarity, resulting in information loss and insufficient processing capacity for isolated nodes. To address this, we developed 'RGLD' by combining Random Walk with restarting (RWR), Graph Neural Network (GNN), and Graph Attention Networks (GAT) to predict lncRNA-disease associations in a heterogeneous network. RGLD achieved an impressive AUC of 0.88, outperforming other methods. It can also predict novel associations between lncRNAs and diseases. RGLD identified HOTAIR, MEG3, and PVT1 as lncRNAs associated with uterine fibroids. Biological experiments directly or indirectly verified the involvement of these three lncRNAs in uterine fibroids, validating the accuracy of RGLD's predictions. Furthermore, we extensively discussed the functions of the target genes regulated by these lncRNAs in uterine fibroids, providing evidence for their role in the development and progression of the disease.

Differentiated Fractionation of Various Biomass Resources by p-Toluenesulfonic Acid at Mild Conditions.

Biomass is the ideal substitute for petrochemical resources because of its renewable and abundant sources. p-Toluenesulfonic acid (p-TsOH) can effectively separate lignin from biomass under mild conditions, so it is highly expected in biomass fractionation to improve the utilization efficiency. In this study, we investigated the effect of p-TsOH differentiated fractionation of poplar sawdust, eucalyptus sawdust, and rice straw below 100 °C. According to the experimental results, upon pretreatment by p-TsOH of the three kinds of raw biomass, most of the lignin and hemicellulose of poplar sawdust and eucalyptus sawdust were removed, whereas the cellulose was retained, but most of the hemicellulose and cellulose of rice straw were kept, whereas the lignin was removed at similar conditions. The structures and compositions of pretreatment residues, lignin, and hemicellulose extracted from raw biomass were characterized by XRD, FTIR, HSQC-NMR, XPS, and SEM. The differentiated fractionation mechanism of biomass was analyzed. A better recognition and understanding of the factors affecting biomatrix opening and fractionation will allow for the identification of new pretreatment strategies that improve biomass utilization and permit the rational enzymatic hydrolysis of cellulose.