Accelerating open modification spectral library searching on tensor core in high-dimensional space.

MOTIVATION: Driven by technological advances, the throughput and cost of mass spectrometry (MS) proteomics experiments have improved by orders of magnitude in recent decades. Spectral library searching is a common approach to annotating experimental mass spectra by matching them against large libraries of reference spectra corresponding to known peptides. An important disadvantage, however, is that only peptides included in the spectral library can be found, whereas novel peptides, such as those with unexpected post-translational modifications (PTMs), will remain unknown. Open modification searching (OMS) is an increasingly popular approach to annotate modified peptides based on partial matches against their unmodified counterparts. Unfortunately, this leads to very large search spaces and excessive runtimes, which is especially problematic considering the continuously increasing sizes of MS proteomics datasets. RESULTS: We propose an OMS algorithm, called HOMS-TC, that fully exploits parallelism in the entire pipeline of spectral library searching. We designed a new highly parallel encoding method based on the principle of hyperdimensional computing to encode mass spectral data to hypervectors while minimizing information loss. This process can be easily parallelized since each dimension is calculated independently. HOMS-TC processes two stages of existing cascade search in parallel and selects the most similar spectra while considering PTMs. We accelerate HOMS-TC on NVIDIA's tensor core units, which is emerging and readily available in the recent graphics processing unit (GPU). Our evaluation shows that HOMS-TC is 31× faster on average than alternative search engines and provides comparable accuracy to competing search tools. AVAILABILITY AND IMPLEMENTATION: HOMS-TC is freely available under the Apache 2.0 license as an open-source software project at https://github.com/tycheyoung/homs-tc.

HyperSpec: Ultrafast Mass Spectra Clustering in Hyperdimensional Space.

As current shotgun proteomics experiments can produce gigabytes of mass spectrometry data per hour, processing these massive data volumes has become progressively more challenging. Spectral clustering is an effective approach to speed up downstream data processing by merging highly similar spectra to minimize data redundancy. However, because state-of-the-art spectral clustering tools fail to achieve optimal runtimes, this simply moves the processing bottleneck. In this work, we present a fast spectral clustering tool, HyperSpec, based on hyperdimensional computing (HDC). HDC shows promising clustering capability while only requiring lightweight binary operations with high parallelism that can be optimized using low-level hardware architectures, making it possible to run HyperSpec on graphics processing units to achieve extremely efficient spectral clustering performance. Additionally, HyperSpec includes optimized data preprocessing modules to reduce the spectrum preprocessing time, which is a critical bottleneck during spectral clustering. Based on experiments using various mass spectrometry data sets, HyperSpec produces results with comparable clustering quality as state-of-the-art spectral clustering tools while achieving speedups by orders of magnitude, shortening the clustering runtime of over 21 million spectra from 4 h to only 24 min.

Classification of Chaotic Squeak and Rattle Vibrations by CNN Using Recurrence Pattern.

The chaotic squeak and rattle (S&R) vibrations in mechanical systems were classified by deep learning. The rattle, single-mode, and multi-mode squeak models were constructed to generate chaotic S&R signals. The repetition of nonlinear signals generated by them was visualized using an unthresholded recurrence plot and learned using a convolutional neural network (CNN). The results showed that even if the signal of the S&R model is chaos, it could be classified. The accuracy of the classification was verified by calculating the Lyapunov exponent of the vibration signal. The numerical experiment confirmed that the CNN classification using nonlinear vibration images as the proposed procedure has more than 90% accuracy. The chaotic status and each model can be classified into six classes.

Dialysis-specific factors and incident atrial fibrillation in hemodialysis patients.

BACKGROUND: Atrial fibrillation (AF) is common in end-stage renal disease patients. Besides the traditional risk factors, we aimed to find dialysis-specific factors for developing incident AF. METHODS: From March 2017 to August 2018, we retrospectively reviewed all outpatient-based prevalent hemodialysis patients in our artificial kidney room, and they were followed up until August 2019. Dialysate calcium concentration (3 versus 2.5 mEq/L), time length (4 versus 3.5 h), frequency (thrice weekly versus twice weekly), dialyzer size (effective surface area of 1.4 m2 versus 1.8 m2), membrane permeability (high flux versus low flux), ultrafiltration rate (mL/kg/hour), and blood flow rate (mL/min) were evaluated. RESULTS: Among a total of 84 patients, 15 (17.9%) had newly detected AF with a follow-up period of 21 (13.3-24) months. By performing multivariate Cox regression analysis, blood flow rate (mL/min) and ultrafiltration rate (mL/kg/h) were considered significant factors for developing incident AF (adjusted hazard ratio [HR], 0.977; p = 0.011 and adjusted HR, 1.176; p = 0.013, respectively), while dialysis bath, time length, and frequency, dialyzer size, and membrane type were not considered significant factors. Ultrafiltration cutoff rate of 8.6 mL/kg/h was the best predictive factor for incident AF (area under the curve-receiver operating characteristic [AUC-ROC], 0.746; p < 0.005), while blood flow rate was not considered a significant factor for incident AF in ROC analysis (AUC-ROC, 0.623; p = 0.126). Ultrafiltration rate was largely dependent on interdialytic weight gain (p < 0.005, linear-by-linear association). CONCLUSION: Higher ultrafiltration rate was associated with incident AF in hemodialysis patients.

Change in alkaline phosphatase activity associated with intensive care unit and hospital length of stay in patients with septic acute kidney injury on continuous renal replacement therapy.

BACKGROUND: Evidence suggests that alkaline phosphatase attenuates inflammatory response in sepsis by lipopolysaccharide detoxification and adenosine triphosphate dephosphorylation. We sought to determine changes in alkaline phosphatase (AP) activity during septic acute kidney injury (AKI) and clinical parameters associated with AP activity. METHODS: In this retrospective study, we investigated baseline (when initiating CRRT) and follow-up AP activity on day 3, and associated outcomes in patients who underwent continuous renal replacement therapy (CRRT) due to septic AKI. RESULTS: We analyzed the baseline AP activity of 155 patients and day 3 AP activity in 123 patients. Baseline AP activity was not associated with renal or inflammatory biomarkers, or outcomes. It did not significantly differ between the 75 survivors and 80 non-survivors (p = 0.155). AP activity was higher on day 3 than at baseline (105 U/L [interquartile range, 79-156] vs 90 U/L [interquartile range, 59-133]). In particular, liver and bone isoforms increased significantly (p < 0.05), but intestine isoforms did not reach statistical significance (p = 0.367). In addition, day 3 AP activity showed a weak correlation with length of ICU stay (r = 0.213, p = 0.018) and length of hospital stay (r = 0.216, p = 0.017), but not with survival (r = - 0.035, p = 0.698). CONCLUSION: Endogenous AP activity significantly increased in patients with septic AKI. However, neither baseline nor follow-up AP activity was associated with survival.

Enhanced Message-Passing Based LEACH Protocol for Wireless Sensor Networks.

This paper proposes a distributed energy-efficient clustering protocol for wireless sensor networks (WSNs). Based on low-energy adaptive clustering hierarchy (LEACH) protocol, the proposed LEACH-eXtended Message-Passing (LEACH-XMP) substantially improves a cluster formation algorithm, which is critical for WSN operations. Unlike the previous approaches, a realistic non-linear energy consumption model is considered, which renders the clustering optimization highly nonlinear and challenging. To this end, a state-of-the-art message-passing approach is introduced to develop an efficient distributed algorithm. The main benefits of the proposed technique are its inherent nature of a distributed algorithm and the saving of computational load imposed for each node. Thus, it proves useful for a practical deployment. In addition, the proposed algorithm rapidly converges to a very accurate solution within a few iterations. Simulation results ensure that the proposed LEACH-XMP maximizes the network lifetime and outperforms existing techniques consistently.

Friedman's Gradient-Boosting Algorithm Predicts Lactate-Pyruvate Ratio Trends in Cases of Intracerebral Hemorrhages.

OBJECTIVE: The local effects of an intracerebral hemorrhage (ICH) on surrounding brain tissue can be detected bedside using multimodal brain monitoring techniques. The aim of this study is to design a gradient boosting regression model using the R package boostmtree with the ability to predict lactate-pyruvate ratio measurements in ICH. METHODS: We performed a retrospective analysis of 6 spontaneous ICH and 6 traumatic ICH patients who underwent surgical removal of the clot with microdialysis catheters placed in the perihematomal zone. Predictors of glucose, lactate, pyruvate, age, sex, diagnosis, and operation status were used to design our model. RESULTS: In a holdout analysis, the model forecasted lactate-pyruvate ratio trends in a representative in-sample testing set. We anticipate that boostmtree could be applied to designs of similar regression models to analyze trends in other multimodal monitoring features across other types of acute brain injury. CONCLUSIONS: The model successfully predicted hourly lactate-pyruvate ratios in spontaneous ICH and traumatic ICH cases after the hemorrhage evacuation and displayed significantly better performance than linear models. Our results suggest that boostmtree may be a powerful tool in developing more advanced mathematical models to assess other multimodal monitoring parameters for cases in which the perihematomal environment is monitored.

Wastewater and surface monitoring to detect COVID-19 in elementary school settings: The Safer at School Early Alert project.

Background: Schools are high-risk settings for SARS-CoV-2 transmission, but necessary for children's educational and social-emotional wellbeing. Previous research suggests that wastewater monitoring can detect SARS-CoV-2 infections in controlled residential settings with high levels of accuracy. However, its effective accuracy, cost, and feasibility in non-residential community settings is unknown. Methods: The objective of this study was to determine the effectiveness and accuracy of community-based passive wastewater and surface (environmental) surveillance to detect SARS-CoV-2 infection in neighborhood schools compared to weekly diagnostic (PCR) testing. We implemented an environmental surveillance system in nine elementary schools with 1700 regularly present staff and students in southern California. The system was validated from November 2020 - March 2021. Findings: In 447 data collection days across the nine sites 89 individuals tested positive for COVID-19, and SARS-CoV-2 was detected in 374 surface samples and 133 wastewater samples. Ninety-three percent of identified cases were associated with an environmental sample (95% CI: 88% - 98%); 67% were associated with a positive wastewater sample (95% CI: 57% - 77%), and 40% were associated with a positive surface sample (95% CI: 29% - 52%). The techniques we utilized allowed for near-complete genomic sequencing of wastewater and surface samples. Interpretation: Passive environmental surveillance can detect the presence of COVID-19 cases in non-residential community school settings with a high degree of accuracy. Funding: County of San Diego, Health and Human Services Agency, National Institutes of Health, National Science Foundation, Centers for Disease Control.

Safer at school early alert: an observational study of wastewater and surface monitoring to detect COVID-19 in elementary schools.

Background: Schools are high-risk settings for SARS-CoV-2 transmission, but necessary for children's educational and social-emotional wellbeing. Previous research suggests that wastewater monitoring can detect SARS-CoV-2 infections in controlled residential settings with high levels of accuracy. However, its effective accuracy, cost, and feasibility in non-residential community settings is unknown. Methods: The objective of this study was to determine the effectiveness and accuracy of community-based passive wastewater and surface (environmental) surveillance to detect SARS-CoV-2 infection in neighborhood schools compared to weekly diagnostic (PCR) testing. We implemented an environmental surveillance system in nine elementary schools with 1700 regularly present staff and students in southern California. The system was validated from November 2020 to March 2021. Findings: In 447 data collection days across the nine sites 89 individuals tested positive for COVID-19, and SARS-CoV-2 was detected in 374 surface samples and 133 wastewater samples. Ninety-three percent of identified cases were associated with an environmental sample (95% CI: 88%-98%); 67% were associated with a positive wastewater sample (95% CI: 57%-77%), and 40% were associated with a positive surface sample (95% CI: 29%-52%). The techniques we utilized allowed for near-complete genomic sequencing of wastewater and surface samples. Interpretation: Passive environmental surveillance can detect the presence of COVID-19 cases in non-residential community school settings with a high degree of accuracy. Funding: County of San Diego, Health and Human Services Agency, National Institutes of Health, National Science Foundation, Centers for Disease Control.

Swapping Metagenomics Preprocessing Pipeline Components Offers Speed and Sensitivity Increases.

Increasing data volumes on high-throughput sequencing instruments such as the NovaSeq 6000 leads to long computational bottlenecks for common metagenomics data preprocessing tasks such as adaptor and primer trimming and host removal. Here, we test whether faster recently developed computational tools (Fastp and Minimap2) can replace widely used choices (Atropos and Bowtie2), obtaining dramatic accelerations with additional sensitivity and minimal loss of specificity for these tasks. Furthermore, the taxonomic tables resulting from downstream processing provide biologically comparable results. However, we demonstrate that for taxonomic assignment, Bowtie2's specificity is still required. We suggest that periodic reevaluation of pipeline components, together with improvements to standardized APIs to chain them together, will greatly enhance the efficiency of common bioinformatics tasks while also facilitating incorporation of further optimized steps running on GPUs, FPGAs, or other architectures. We also note that a detailed exploration of available algorithms and pipeline components is an important step that should be taken before optimization of less efficient algorithms on advanced or nonstandard hardware. IMPORTANCE In shotgun metagenomics studies that seek to relate changes in microbial DNA across samples, processing the data on a computer often takes longer than obtaining the data from the sequencing instrument. Recently developed software packages that perform individual steps in the pipeline of data processing in principle offer speed advantages, but in practice they may contain pitfalls that prevent their use, for example, they may make approximations that introduce unacceptable errors in the data. Here, we show that differences in choices of these components can speed up overall data processing by 5-fold or more on the same hardware while maintaining a high degree of correctness, greatly reducing the time taken to interpret results. This is an important step for using the data in clinical settings, where the time taken to obtain the results may be critical for guiding treatment.

Blood temperature monitoring-guided vascular access intervention improved dialysis adequacy.

OBJECTIVES: The aim of this study was to investigate whether blood temperature monitoring-guided vascular access intervention could improve dialysis adequacy. METHODS: We retrospectively evaluated all patients who received outpatient-based prevalent hemodialysis patients (n = 84) in our artificial kidney room between January 2019 and October 2019. Through blood temperature monitoring, access blood flow was calculated every month and Kt/V was calculated every 3 months. The reference point was set at the time of vascular intervention in the patients (n = 27) who underwent intervention or at the middle of the study period in patients (n = 57) who did not undergo intervention. The mean blood temperature monitoring-estimated access flow and Kt/V before and after the reference point were calculated and compared. RESULTS: Among 84 patients, 30 (35.7%) showed access flow rates of <500 mL/min, calculated by blood temperature monitoring during the study period. Twenty-seven patients (32.1%) underwent vascular intervention, of whom 24 (28.6%) showed access flow rates of <500 mL/min, 2 (2.4%) showed weak bruit or thrill incapable of needling, and 1 (1.2%) presented acute occlusion. Six patients (7.1%) whose access flow rates were <500 mL/min refused to undergo intervention. All angiographies in the patients whose access flow rates were <500 mL/min who underwent intervention showed a significant stenosis. The mean change in blood temperature monitoring-estimated access flow and Kt/V before and after vascular intervention was 483.3 ± 490.6 and 0.19 ± 0.21, respectively, which showed significant differences (all p < 0.05). A weak positive correlation between the mean change in blood temperature monitoring-estimated access flow and Kt/V was shown in all study patients by Pearson's correlation analysis (r = 0.234, p = 0.033). CONCLUSION: Access flow estimation by blood temperature monitoring might identify candidates who require vascular intervention. Blood temperature monitoring-guided vascular intervention significantly improved access flow and dialysis adequacy.

Nutritional Adequacy and Latent Tuberculosis Infection in End-Stage Renal Disease Patients.

BACKGROUND: Latent tuberculosis infection (LTBI) is prevalent in end-stage renal disease (ESRD) patients. The risk of tuberculosis activation is also high. The appropriate LTBI screening and treatment is required in this population. Meanwhile, whether hemodialysis adequacy is associated with LTBI in the ESRD population is unclear. In this study, we aimed to investigate the association between hemodialysis adequacy and LTBI in ESRD patients. METHODS: In the present cross-sectional study, we reviewed all outpatient-based ESRD patients in our artificial kidney room. Interferon gamma release assay (IGRA) was used for the diagnosis of LTBI. Clinical variables including nutritional adequacy (i.e., normalized protein catabolic rate, nPCR) and dialysis adequacy (i.e., Kt/V) were compared between IGRA-positive and IGRA-negative patients. RESULTS: A total of 90 patients were enrolled, of which 20 (22.2%) had positive IGRA results using the QuantiFERON-TB method. Old fibrotic changes and nPCR (g/kg/day) were significantly different between IGRA-positive and IGRA-negative patients (both p < 0.005), while serum albumin and Kt/V were comparable (p = 0.429 and p = 0.590, respectively). Normalized PCR remained to be significant in a multivariate logistic regression analysis (adjusted hazard ratio, 0.911 (0.861-0.963); p = 0.001). The cutoff nPCR value less than 0.87 g/kg/day had an adjusted hazard ratio of 7.74 (1.77-33.74) for predicting LTBI. Patients with nPCR value less than 0.87 g/kg/day were older and had lower serum hemoglobin, albumin, calcium concentration, and Kt/V levels than those with nPCR value greater than 0.87 g/kg/day. CONCLUSIONS: Nutritional adequacy, especially when assessing nPCR value, was associated with LTBI, while dialysis adequacy was not associated with LTBI.

A risk scoring model to predict renal progression associated with postcontrast acute kidney injury in chronic kidney disease patients.

Postcontrast acute kidney injury (AKI) occurs more frequently in patients with lower estimated glomerular filtration rate. We hypothesized that postcontrast AKI in chronic kidney disease (CKD) patients with distinct risk factors might be associated with accelerated renal progression.We undertook this retrospective cohort study to develop and validate a risk scoring model for predicting renal progression. In a development dataset, 18,278 contrast-enhanced CT scans were performed in 9097 patients with CKD (estimated glomerular filtration rate [eGFR] < 60 mL/min/1.73 m) who were not undergoing dialysis. Postcontrast AKI was observed in 5.8% (1051/18,278) of all contrast-enhanced CTs with 7.6% (689/9097) of the total CKD patients. We investigated the 1-year renal outcome in 224 eligible patients. A risk scoring model was developed with multivariate regression analysis and was assessed in external validation (independent 154 patients).Among 224 patients, 70 (31.3%) patients had progression of renal dysfunction at 1 year (defined as reduction in estimated GFR ≥25% at 1 year). A risk score of 4, 4, 6, 6, 7, or 6 was assigned to diabetes, baseline estimated GFR < 45 mL/min/1.73 m, hypertension, repeated contrast exposure, congestive heart failure, and persistent renal injury (defined as an elevation of serum creatinine ≥25% at 3 months), respectively. An increasing risk score was associated with renal progression. Of note, persistent renal injury was more prevalent in the progression group than in the non-progression group. The AUROC of the model in the development population was 0.765. In the validation dataset, however, the discriminative power decreased (AUROC = 0.653).Our suggested model provided the risk of renal progression, aiding in predicting prognosis, counseling, and improving outcomes in CKD patients complicated by postcontrast AKI.

Predictive Factors of Duration of Continuous Renal Replacement Therapy in Acute Kidney Injury Survivors.

The factors influencing continuous renal replacement therapy (CRRT) duration for critically ill patients with acute kidney injury (AKI) are unclear. Therefore, we investigated the clinical factors that could influence the duration of CRRT for AKI survivors. In this retrospective observational study, the medical records of all hospital survivors who required CRRT for AKI in intensive care units were analyzed. The CRRT duration (median, 6 days) was categorized as short-duration CRRT (≤ 6 days, n = 65) and long-duration CRRT (> 6 days, n = 59), according to the median CRRT duration. A urine output of less than 0.5 mL/kg/h (adjusted odds ratio [OR], 3.4; P = 0.010), mechanical ventilation use (adjusted OR, 7.9; P = 0.001), and extracorporeal membrane oxygenation (ECMO) use (adjusted OR, 6.5; P = 0.010) were independent predictors of long-duration CRRT, whereas serum creatinine and neutrophil gelatinase-associated lipocalin were not significant predictors. A clinical model demonstrated a good discriminatory ability to predict long-duration CRRT (area under the curve, 0.84; 95% confidence interval, 0.76-0.90). The urine output immediately before CRRT initiation and factors associated with disease severity significantly affected the duration of CRRT. Simultaneously considering the urine output, mechanical ventilation use, and ECMO use predicted CRRT duration in AKI survivors.

Rhabdomyolysis following severe hypokalemia caused by familial hypokalemic periodic paralysis.

Rhabdomyolysis continues to appear with increasing frequency and represents a medical emergency requiring rapid appropriate treatment. One of the unusual causes of nontraumatic rhabdomyolysis is hypokalemic periodic paralysis without secondary causes. Primary hypokalemic periodic paralysis is a rare genetic disease characterized by episodic attacks of muscle weakness due to decreases in serum potassium. A 30-year-old woman who had 3 episodic attacks of hypokalemic periodic paralysis was admitted in emergency room with sudden onset symmetrical muscle weakness. After several hours, she started to complain myalgia and severe ache in both calves without any changes. Laboratory test showed markedly elevated creatine phosphokinase, lactic dehydrogenase levels with hypokalemia, rhabdomyolysis resulting from hypokalemia was diagnosed. Here, we report an unusual case of rhabdomyolysis caused by severe hypokalemia, which was suggested a result of familial hypokalemic periodic paralysis.