Effects of attentional instructions on the behavioral and neural mechanisms of speech auditory feedback control.

The present study investigated how instructions for paying attention to auditory feedback may affect speech error detection and sensorimotor control. Electroencephalography (EEG) and speech signals were recorded from 21 neurologically intact adult subjects while they produced the speech vowel sound /a/ and received randomized ±100 cents pitch-shift alterations in their real-time auditory feedback. Subjects were instructed to pay attention to their auditory feedback and press a button to indicate whether they detected a pitch-shift stimulus during trials. Data for this group was compared with 22 matched subjects who completed the same speech task under altered auditory feedback condition without attentional instructions. Results revealed a significantly smaller magnitude of speech compensations in the attentional-instruction vs. no-instruction group and a positive linear association between the magnitude of compensations and P2 event-related potential (ERP) amplitudes. In addition, we found that the amplitude of P2 ERP component was significantly larger in the attentional-instruction vs. no-instruction group. Source localization analysis showed that this effect was accounted for by significantly stronger neural activities in the right hemisphere insula, precentral gyrus, postcentral gyrus, transverse temporal gyrus, and superior temporal gyrus in the attentional-instruction group. These findings suggest that attentional instructions may enhance speech auditory feedback error detection, and subsequently improve sensorimotor control via generating more stable speech outputs (i.e., smaller compensations) in response to pitch-shift alterations. Our data are informative for advancing theoretical models and motivating targeted interventions with a focus on the role of attentional instructions for improving treatment outcomes in patients with motor speech disorders.

Membrane Fusion-Mediated Loading of Therapeutic siRNA into Exosome for Tissue-Specific Application.

Tissue-specific delivery of oligonucleotide therapeutics beyond the liver remains a key challenge in nucleic acid drug development. To address this issue, exploiting exosomes as a novel carrier has emerged as a promising approach for efficient nucleic acid drug delivery. However, current exosome-based delivery systems still face multiple hurdles in their clinical applications. Herein, this work presents a strategy for constructing a hybrid exosome vehicle (HEV) through a DNA zipper-mediated membrane fusion approach for tissue-specific siRNA delivery. As a proof-of-concept, this work successfully fuses a liposome encapsulating anti-NFKBIZ siRNAs with corneal epithelium cell (CEC)-derived exosomes to form a HEV construct for the treatment of dry eye disease (DED). With homing characteristics inherited from exosomes, the siRNA-bearing HEV can target its parent cells and efficiently deliver the siRNA payloads to the cornea. Subsequently, the NFKBIZ gene silencing significantly reduces pro-inflammatory cytokine secretions from the ocular surface, reshapes its inflammatory microenvironment, and ultimately achieves an excellent therapeutic outcome in a DED mouse model. As a versatile platform, this hybrid exosome with targeting capability and designed therapeutic siRNAs may hold great potential in various disease treatments.

Cholesterol efflux from C1QB-expressing macrophages is associated with resistance to chimeric antigen receptor T cell therapy in primary refractory diffuse large B cell lymphoma.

Chimeric antigen receptor T (CAR-T) cell therapy has demonstrated promising efficacy in early trials for relapsed/refractory diffuse large B cell lymphoma (DLBCL). However, its efficacy in treating primary refractory DLBCL has not been comprehensively investigated, and the underlying resistance mechanisms remain unclear. Here, we report the outcomes of a phase I, open-label, single-arm clinical trial of relmacabtagene autoleucel (relma-cel), a CD19-targeted CAR-T cell product, with safety and efficacy as primary endpoints. Among the 12 enrolled patients, 8 experienced grade 4 hematologic toxicity of treatment-emergent adverse event. No grade ≥3 cytokine release syndrome or neurotoxicity occurred. Single-cell RNA sequencing revealed an increase proportion of C1QB-expressing macrophages in patients with progressive disease before CAR-T cell therapy. Cholesterol efflux from M2 macrophages was found to inhibit CAR-T cells cytotoxicity by inducing an immunosuppressive state in CD8+ T cells, leading to their exhaustion. Possible interactions between macrophages and CD8+ T cells, mediating lipid metabolism (AFR1-FAS), immune checkpoint activation, and T cell exhaustion (LGALS9-HAVCR2, CD86-CTLA4, and NECTIN2-TIGIT) were enhanced during disease progression. These findings suggest that cholesterol efflux from macrophages may trigger CD8+ T cell exhaustion, providing a rationale for metabolic reprogramming to counteract CAR-T treatment failure. Chinadrugtrials.org.cn identifier: CTR20200376.

Numerical simulation of autoclaved aerated concrete masonry wall subjected to close-in explosion and the structural damage assessment.

In this study, we investigated the destructive effect of autoclaved aerated concrete (AAC) masonry walls subjected to close-in explosions. First, full-size refined finite-element models of the AAC masonry wall were established, and the accuracy of the models was verified by comparison with the test results. The destruction pattern and damage characteristics of the AAC wall were studied, and the effects of block size, wall thickness, mortar compressive strength, and explosion distance on the destruction degree of the AAC masonry walls were analyzed. The results showed that the destruction pattern of the AAC masonry wall subjected to close-in explosion manifested as punching damage in the middle of the wall. When the scaled distance remained unchanged, the punching damage area of the AAC masonry wall was positively correlated with the block size and negatively correlated with the wall thickness and mortar compressive strength. When the explosive equivalent remained unchanged and the explosion distance increased, the punching damage area first increased and then decreased. According to the damage mechanism of the AAC masonry wall, a calculation method for predicting the punching damage area of the AAC masonry wall was established, and the accuracy of this method was verified by comparing it with the numerical results. In addition, the damage criterion based on the punching damage area was established to determine the destruction levels of AAC masonry walls.

Analyzing Surgical Technique in Diverse Open Surgical Videos With Multitask Machine Learning.

Objective: To overcome limitations of open surgery artificial intelligence (AI) models by curating the largest collection of annotated videos and to leverage this AI-ready data set to develop a generalizable multitask AI model capable of real-time understanding of clinically significant surgical behaviors in prospectively collected real-world surgical videos. Design, Setting, and Participants: The study team programmatically queried open surgery procedures on YouTube and manually annotated selected videos to create the AI-ready data set used to train a multitask AI model for 2 proof-of-concept studies, one generating surgical signatures that define the patterns of a given procedure and the other identifying kinematics of hand motion that correlate with surgeon skill level and experience. The Annotated Videos of Open Surgery (AVOS) data set includes 1997 videos from 23 open-surgical procedure types uploaded to YouTube from 50 countries over the last 15 years. Prospectively recorded surgical videos were collected from a single tertiary care academic medical center. Deidentified videos were recorded of surgeons performing open surgical procedures and analyzed for correlation with surgical training. Exposures: The multitask AI model was trained on the AI-ready video data set and then retrospectively applied to the prospectively collected video data set. Main Outcomes and Measures: Analysis of open surgical videos in near real-time, performance on AI-ready and prospectively collected videos, and quantification of surgeon skill. Results: Using the AI-ready data set, the study team developed a multitask AI model capable of real-time understanding of surgical behaviors-the building blocks of procedural flow and surgeon skill-across space and time. Through principal component analysis, a single compound skill feature was identified, composed of a linear combination of kinematic hand attributes. This feature was a significant discriminator between experienced surgeons and surgical trainees across 101 prospectively collected surgical videos of 14 operators. For each unit increase in the compound feature value, the odds of the operator being an experienced surgeon were 3.6 times higher (95% CI, 1.67-7.62; P = .001). Conclusions and Relevance: In this observational study, the AVOS-trained model was applied to analyze prospectively collected open surgical videos and identify kinematic descriptors of surgical skill related to efficiency of hand motion. The ability to provide AI-deduced insights into surgical structure and skill is valuable in optimizing surgical skill acquisition and ultimately improving surgical care.

Predicting Anti-inflammatory Peptides by Ensemble Machine Learning and Deep Learning.

Inflammation is a biological response to harmful stimuli, aiding in the maintenance of tissue homeostasis. However, excessive or persistent inflammation can precipitate a myriad of pathological conditions. Although current treatments such as NSAIDs, corticosteroids, and immunosuppressants are effective, they can have side effects and resistance issues. In this backdrop, anti-inflammatory peptides (AIPs) have emerged as a promising therapeutic approach against inflammation. Leveraging machine learning methods, we have the opportunity to accelerate the discovery and investigation of these AIPs more effectively. In this study, we proposed an advanced framework by ensemble machine learning and deep learning for AIP prediction. Initially, we constructed three individual models with extremely randomized trees (ET), gated recurrent unit (GRU), and convolutional neural networks (CNNs) with attention mechanism and then used stacking architecture to build the final predictor. By utilizing various sequence encodings and combining the strengths of different algorithms, our predictor demonstrated exemplary performance. On our independent test set, our model achieved an accuracy, MCC, and F1-score of 0.757, 0.500, and 0.707, respectively, clearly outperforming other contemporary AIP prediction methods. Additionally, our model offers profound insights into the feature interpretation of AIPs, establishing a valuable knowledge foundation for the design and development of future anti-inflammatory strategies.

Mechanism of dioscin ameliorating renal fibrosis through NF­κB signaling pathway­mediated inflammatory response.

Dioscin (DIS) is a natural compound derived from Chinese herbal medicine. In recent years, multiple studies have reported that DIS has immunoregulation, anti­fibrosis, anti­inflammation, anti­viral and anti­tumor effects. However, the mechanism by which DIS ameliorates renal fibrosis and inflammation remains to be elucidated. The aim of the present study was to investigate the role of DIS in renal fibrosis and inflammation and to explore its underlying mechanism. It used network pharmacology to predict the targets of DIS for the treatment of renal interstitial fibrosis. The present study was performed using unilateral ureteral obstruction mice and HK­2 cells in vivo and in vitro. The mice were treated with different doses of DIS. Kidney tissues were collected for histopathology staining, western blotting, immunohistochemistry staining and reverse transcription­quantitative (RT­q) PCR. TGF­ß1 (2 ng/ml) was used to induce renal fibrosis in the cells. Then, cells were respectively treated with DIS (3.125, 6.25, 12.5 µM) and Bay11­7082 (an inhibitor of NF­κB p65 nuclear transcription, 1 µM) for another 24 h. The expressions of inflammatory factors and NF­κB pathway proteins were detected by immunofluorescence, ELISA, western blotting and RT­qPCR. DIS alleviated renal injury in the UUO mice. Mechanistically, DIS not only decreased the expressions of inflammatory factors including IL­1ß, NOD­like receptor thermal protein domain associated protein 3, monocyte chemotactic protein 1, IL­6, TNF­α and IL­18 but also reduced the level of phosphorylation of NF­κB p65 in vivo and in vitro, which was similar to the impact of Bay11­7082. DIS ameliorated renal fibrosis by inhibiting the NF­κB signaling pathway­mediated inflammatory response, which may be a therapeutic pathway for delaying chronic kidney disease.

Co-exposure to nanoplastics and acetaminophen causes skeletal dysplasia and behavioral abnormalities in zebrafish.

Nanoplastics (NPs) and acetaminophen (APAP) are thought to be common contaminants and are invariably detected in the environment. Despite the increasing awareness of their toxicity to humans and animals, the embryonic toxicity, skeletal development toxicity, and mechanism of action of their combined exposure have not been clarified. This study was performed to investigate whether combined exposure to NPs and APAP induces abnormal embryonic and skeletal development in zebrafish and to explore the potential toxicological mechanisms. All zebrafish juveniles in the high-concentration compound exposure group showed some abnormal phenomena such as pericardial edema, spinal curvature, cartilage developmental abnormality and melanin inhibition together with a significant downward trend in body length. Behavioral data also implicated that the exposure of APAP alone, as well as the co-exposure of NPs and APAP, caused a depression in the total distance, swimming speed and the maximum acceleration. Furthermore, real-time polymerase chain reaction analysis showed that compared with exposure alone, the expression level of genes related to osteogenesis, runx2a, runx2b, Sp7, bmp2b and shh was significantly reduced with compound exposure. These results suggest that the compound exposure of NPs and APAP has adverse impacts on zebrafish embryonic development and skeletal growth.

Concurrent remission of lymphoma and Sjögren's disease following anti-CD19 chimeric antigen receptor-T cell therapy for diffuse large B-cell lymphoma: a case report.

Anti-CD19 chimeric antigen receptor (CAR)-T cells not only target CD19-positive malignant lymphoma cells but also normal B cells. The utility of CAR-T cell therapy has been reported in rheumatoid arthritis and systemic lupus erythematosus; however, its use in Sjögren's disease (SjD) remains unknown. In this study, we describe the case of a 76-year-old woman with active SjD for 10 years who was diagnosed with diffuse large B-cell lymphoma. After receiving anti-CD19 CAR-T cell therapy, she achieved complete remission (CR) on day 28. Since the onset of her 10-year history with SjD, she was negative for antinuclear antibodies and anti-Ro-52 for the first time on day 90 after CAR-T cell therapy. Six months after CAR-T cell therapy, the CR status was maintained, serum cytokine levels returned to their normal levels, and dry mouth symptoms improved. The EULAR Sjögren's Syndrome Disease Activity Index score decreased from 5 to 2, indicating a partial remission of SjD activity compared with that before CAR-T cell treatment. In the early stage of treatment, she presented with grade 2 cytokine release syndrome and grade 1 neurotoxicity, which were completely controlled after an active intervention. This case highlights the potential application of CAR-T cells in treating autoimmune diseases, such as SjD.

Boosting Delirium Identification Accuracy With Sentiment-Based Natural Language Processing: Mixed Methods Study.

BACKGROUND: Delirium is an acute neurocognitive disorder that affects up to half of older hospitalized medical patients and can lead to dementia, longer hospital stays, increased health costs, and death. Although delirium can be prevented and treated, it is difficult to identify and predict. OBJECTIVE: This study aimed to improve machine learning models that retrospectively identify the presence of delirium during hospital stays (eg, to measure the effectiveness of delirium prevention interventions) by using the natural language processing (NLP) technique of sentiment analysis (in this case a feature that identifies sentiment toward, or away from, a delirium diagnosis). METHODS: Using data from the General Medicine Inpatient Initiative, a Canadian hospital data and analytics network, a detailed manual review of medical records was conducted from nearly 4000 admissions at 6 Toronto area hospitals. Furthermore, 25.74% (994/3862) of the eligible hospital admissions were labeled as having delirium. Using the data set collected from this study, we developed machine learning models with, and without, the benefit of NLP methods applied to diagnostic imaging reports, and we asked the question "can NLP improve machine learning identification of delirium?" RESULTS: Among the eligible 3862 hospital admissions, 994 (25.74%) admissions were labeled as having delirium. Identification and calibration of the models were satisfactory. The accuracy and area under the receiver operating characteristic curve of the main model with NLP in the independent testing data set were 0.807 and 0.930, respectively. The accuracy and area under the receiver operating characteristic curve of the main model without NLP in the independent testing data set were 0.811 and 0.869, respectively. Model performance was also found to be stable over the 5-year period used in the experiment, with identification for a likely future holdout test set being no worse than identification for retrospective holdout test sets. CONCLUSIONS: Our machine learning model that included NLP (ie, sentiment analysis in medical image description text mining) produced valid identification of delirium with the sentiment analysis, providing significant additional benefit over the model without NLP.

Physician Experience Design (PXD): More Usable Machine Learning Prediction for Clinical Decision Making.

Delirium is an acute neurocognitive disorder, which is difficult to identify and predict. Using GEMINI, Canada's largest hospital data and analytics study, we had a labeled sample of around 4,000 cases with approximately 25% of cases being labeled as having delirium. Based on this labeled data, we developed machine learning (ML) models and interacted with physicians to interpret the ML models and their predictions. We developed a preliminary Explainable Artificial Intelligence (XAI) framework for physician experience design (PXD) to improve the uptake of ML models by improving the transparency of model results, thereby increasing physician trust in models as well as the uptake of model results for clinical decision making. We developed our PXD approach first with Conceptual Investigation to collect and extract physicians' feedback on ML models and their evaluation requirements. We carried out a case study, working closely with the physicians in a participatory design process to develop a dashboard that presents ML delirium identification results interactively based on physician selections and inputs. In this approach a physician-preferred ML model for clinical decision making is selected through PXD evaluation.

Urine protein in patients with type I hypersensitivity is indicative of reversible renal tube injury.

AIMS: In our clinical work, some patients with type I hypersensitivity could be detected protein in their urine. This study focused on the early renal injury in patients with type I hypersensitivity. MAIN METHODS: From 43 type I hypersensitivity patients with proteinuria, 10 patients were randomly selected for mass spectrometry analysis of 24-h urine together with 5 healthy volunteers. Mice were vaccinated with Dermatophagoides farina (Der f) and ovalbumin (OVA) were used as antigen to establish the type I hypersensitivity animal models. KEY FINDINGS: The urine protein of hypersensitivity patients was significantly increased in the alpha-1-microglobulin/ bikunin precursor (Protein AMBP) (t = 3.140, P = 0.008), retinol binding protein 4 (RBP4) (t = 2.426, P = 0.031), kininogen-1 (t = 2.501, P = 0.027), and transferrin appeared only in patients' urine. After immunizing mice with antigens, significant increases of the total serum immunoglobulin E (IgE) were observed in both Der f (86.92 ± 36.01 U/mL, t = 5.231, P = 0.0004) and OVA group (34.65 ± 24.72 U/mL, t = 2.891, P = 0.0161) compared with the negative control group (2.68 ± 0.47 U/mL). Meanwhile, definite eosinophil infiltration around the impaired renal tubules as well as the bronchus in Der f mice were observed, and urine protein appeared. After stopping the allergen stimulation, proteinuria disappeared. Instead, when the mice were treated with the antigen again, proteinuria reappeared. SIGNIFICANCE: Our findings suggest that renal tubular damage in patients with type I hypersensitivity is reversible, and proteinuria disappears with allergy symptoms remission.

The Majority of Patients Aged 40 and Older Having Allograft Anterior Cruciate Ligament Reconstruction Achieve a Patient Acceptable Symptomatic State.

PURPOSE: To evaluate patient satisfaction, retear rates, and patient-reported outcomes (PROs) in patients aged 40 and older undergoing allograft anterior cruciate ligament reconstruction (ACLR). The secondary goal was to compare these parameters between groups of patients with intact versus failed grafts, and to evaluate these in relation to a historically reported International Knee Documentation Committee (IKDC) patient-acceptable symptoms state (PASS) score. METHODS: Records of patients aged 40 and older who underwent ACLR between 2005 and 2016 at a single institution with a minimum 2-year follow-up were retrospectively reviewed. Patient-reported satisfaction, outcome scores, and failure rates were analyzed. The rate of achieving a previously defined IKDC PASS score based on younger cohorts was reported, and an updated PASS threshold for older patients was calculated. RESULTS: 201 patients were included with a mean age of 48.6 years (range: 40-68) and mean follow-up of 6.2 years (range: 2.8-11.2). 182 (90.5%) patients reported satisfaction following surgery. 16 (8.0%) patients experienced failure of their ACLR, 10 of which underwent revision ACLR. The median IKDC score in the intact ACLR group was 86.2, compared to 66.7 in the failure group (P < .001). In total, 134 (72.4%) patients in the intact group achieved the historical PASS score of 75.9 on IKDC compared to only 4 (25%) in the failure group (χ2 = 15.396, P < .001). An updated IKDC PASS threshold for older cohorts was calculated to be 66.7. CONCLUSION: Patients aged 40 and older who underwent allograft ACLR had an 8.0% failure rate at a mean follow-up of 6 years. Graft failure in patients aged 40 and older was associated with worse PROs. The majority of patients achieved the historically reported IKDC PASS threshold. Additionally, an updated age-appropriate IKDC PASS score of 66.7 was calculated to aid in future ACLR studies assessing older patients. STUDY DESIGN: Level IV.

Overview of Rapid Detection Methods for Salmonella in Foods: Progress and Challenges.

Salmonella contamination in food production and processing is a serious threat to consumer health. More and more rapid detection methods have been proposed to compensate for the inefficiency of traditional bacterial cultures to suppress the high prevalence of Salmonella more efficiently. The contamination of Salmonella in foods can be identified by recognition elements and screened using rapid detection methods with different measurable signals (optical, electrical, etc.). Therefore, the different signal transduction mechanisms and Salmonella recognition elements are the key of the sensitivity, accuracy and specificity for the rapid detection methods. In this review, the bioreceptors for Salmonella were firstly summarized and described, then the current promising Salmonella rapid detection methods in foodstuffs with different signal transduction were objectively summarized and evaluated. Moreover, the challenges faced by these methods in practical monitoring and the development prospect were also emphasized to shed light on a new perspective for the Salmonella rapid detection methods applications.

Dual-color meta-image display with a silver nanopolarizer based metasurface.

Plasmonic metallic nanostructures with anisotropic design have unusual polarization-selective characteristic which can be utilized to build nanopolarizers at the nanoscale. Herein, we propose a dual-color image display platform by reconfiguring two types of silver nanoblocks in a single-celled metasurface. Governed by Malus's law, the two types of silver nanoblocks both acting as nanopolarizers with different orientations can continuously modulate the intensity of incident linearly polarized red and green light pixel-by-pixel, respectively. As a result, an ultra-compact, high-resolution, and continuous-greyscale dual-color image can be recorded right at the surface of the meta-device. We demonstrate the dual-color Malus metasurface by successfully encoding and decoding a red-green continuously-grayscale image into a metasurface sample. The experimentally captured meta-image with high-fidelity and resolution as high as 63500 dots per inch (dpi) has verified our proposal. With the advantages such as continuous grayscale modulation, ultrathin, high stability and high density, the proposed dual-color encoded metasurfaces can be readily used in ultra-compact image displays, high-end anti-counterfeiting, high-density optical information storage and information encryption, etc.

Evaluation of the Effects of Different Dietary Patterns on Breast Cancer: Monitoring Circulating Tumor Cells.

The occurrence and development of breast cancer are closely related to dietary factors, especially dietary patterns. This study was to investigate the effects of dietary patterns on the process of tumor metastasis by in vivo circulating tumor cell (CTC) capture strategy and monitoring changes of CTC numbers in breast tumor mice model. Meanwhile, the effects of different dietary patterns on the development of lung metastases of breast cancer and the volume and weight of carcinoma in situ were investigated. In this study, the increase in the number of CTCs was significantly promoted by dietary patterns such as high-salt diet, high-sugar diet, and high-fat diet, while it was delayed by ketogenic diet, low-fat diet, low-protein diet, diet restriction, and Mediterranean diet. These results indicated that the in vivo capture and detection of CTCs provides a convenient method for real-time cancer metastasis monitoring, and through in-depth study of the effects of different dietary patterns on tumor growth and metastasis, it can expand a new horizon in future cancer treatments.

Genetic polymorphism of both 29 Y-STRs and 213 Y-SNPs in Han populations from Shandong Province, China.

Genetic markers on the Y chromosome, including short tandem repeats (Y-STRs) and single nucleotide polymorphisms (Y-SNPs), are used widely in forensic genetics. Both Y-STR-based haplotypes and Y-SNP-based haplogroups provide information on a population's genetic structure, which is useful for the identification of individuals. However, there are few studies on these two types of genetic markers in the various Chinese populations. In this study, 284 Han individuals from four prefecture-level cities in Shandong Province (Binzhou, Dezhou, Heze, and Weihai) were genotyped by 29 Y-STRs (from our previous study) and 213 Y-SNPs (self-designed for the Haplogroup O2 Y-SNP panel). Haplogroup O was the most predominant among the four cities. The highest haplogroup diversity (0.9745) was observed in the Heze population, with a discrimination capacity (DC) value of 0.5625. The haplotype diversity and DC values of the Binzhou and Heze populations were 1.0000. Furthermore, genetic differences were observed between the coastal and inland cities; the results of their statistical analysis are presented herein.

Development and validation of a custom panel including 183 Y-SNPs for Chinese Y-chromosomal haplogroups dissection using a MALDI-TOF MS system.

Y-chromosome SNP haplogroups exhibit geographic structuring in many populations around the world. Therefore, Y-chromosome haplogroups have been widely used to infer paternal biogeographical ancestry and high-resolution paternal lineage classification. In the present study, we designed a customized panel containing 183 Y-SNPs based on previous studies and evaluated the genotyping performance and repeatability, concordance, sensitivity, and ability of analysing case-type samples using a MALDI-TOF MS platform. The average call rate for duplicate typing of any one SNP in the panel was 97.0%. In the concordance and accuracy study, the results of haplogroup designation obtained from MALDI-TOF MS platform were fully consistent with those obtained from the next-generation sequencing (NGS) platform. The optimal amount of template DNA in the PCR seemed to be 10 ng. However, if less DNA (≥156.25 pg) was available, it was still possible to obtain meaningful haplogroup information. For the application part, this panel could be applied for the detection of blood, semen, and buccal swabs samples. Particularly, blood stain on FTA card samples could be dissected by direct PCR amplification on the MALDI-TOF MS platform. Besides, 371 unrelated male individuals from four Chinese ethnic groups (Han, Hui, Mongolian, and Kazak) were detected using this panel. Total 78 terminal haplogroups were found and the haplogroup diversity was 0.933576. The results demonstrate that this panel could be an accurate, fast, and cost-effective method for database construction where the amount of sample material is less of a concern and when the cost of the assay is taken into consideration.

Integrated Survival Analysis of mRNA and microRNA Signature of Patients with Breast Cancer Based on Cox Model.

Precision medicine has become a trend in the field of biomedical research. Analysis of mRNA and microRNA (miRNA) is a basis of it. Although the analyses of mRNA and miRNA in different cancers have been studied widely, only a few integrated studies concerning their interactions on large patient cohorts have been done. In this article, we study the integrated significance of mRNA, miRNA, and their interactions in the survival analysis of breast cancer (BRCA). We detect differential expression genes, then we analyze significant genes of BRCA by the Cox model. The model described in this article has better performance than the model without predictors of interactions of miRNA and mRNA. The average C index considering miRNA-target interactions is 0.7, which achieves 84% improvement compared with the model without interactions, which is 0.38.

Can bioenergy carbon capture and storage aggravate global water crisis?

Bioenergy carbon capture and storage (BECCS) is an effective option for mitigation of greenhouse gas (GHG) emissions. Nevertheless, there is barely serious debate about whether its implementation can possibly jeopardize the global water resources security. Here, we provided an assessment of biomass-based Substitute Natural Gas (BioSNG) production combined with CCS, a promising BECCS technology, in terms of global water resources security, with a focus on the growth of two typical second-generation bioenergy crops: switchgrass and miscanthus. A bottom-up analysis approach was applied in this paper to calculating water consumption for BECCS and estimating water quality deterioration caused by increasing fertilizer and pesticide application. The results indicated that water usage of BECCS was equal to adding 12.86%-16.64% (switchgrass) and 17.59%-26.06% (miscanthus) additional water stress on global available water resources at 2100. Additional N fertilizer application in 2100 would be equal to over 84%, 55% and 42% for both switchgrass and miscanthus under three CCS capture efficiency scenarios, respectively, comparing to such global scale in 2012. Additional phosphate fertilizer adding to global annual available water at 2100 were 0.004-0.008 mg L-1 (switchgrass) and 0.003-0.006 mg L-1 (miscanthus), respectively. The secondary environmental hazards, such as N2O emission, would offset GHG emission mitigation by BECCS. Meanwhile, the enrichment and leaching of pesticide residues increased the risk of groundwater contamination. This study revealed water consumption and contamination issues caused by BECCS cannot be neglected. Thus, additional studies of accurate land-use models in global scale and advanced technology for biofuel extraction are needed in the future.