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In liquid chromatography-mass spectrometry (LC-MS), parts of LC peaks are often corrupted by their co-eluting peptides, which results in increased quantification variance. In this paper, we propose to apply accurate LC peak boundary detection to remove the corrupted part of LC peaks. Accurate LC peak boundary detection is achieved by checking the consistency of intensity patterns within peptide elution time ranges. In addition, we remove peptides with erroneous mass assignment through model fitness check, which compares observed intensity patterns to theoretically constructed ones. The proposed algorithm can significantly improve the accuracy and precision of peptide ratio measurements. [ABSTRACT FROM AUTHOR]

Abstract: The ultrasound Doppler signal scattered from blood is heavily corrupted by the clutter signal reflected from slowly moving muscular tissue. The Doppler frequency shift of blood flow and clutter in different parts of human body greatly changes. Therefore, if a fixed wall filter is selected, the optimal filtering effect can not be attained. An adaptive clutter rejection method is proposed in this paper, which consists of a weak clutter rejector and a 2-order AR estimator. The clutter and blood power thresholds were preliminary defined in the weak clutter rejector. The echo power is compared with the two pre-defined thresholds, and the result was used to select an appropriate wall filter. The output of the weak clutter rejector is estimated by a 2-order AR estimator and two poles are acquired. The low frequency pole denotes the clutter signal and the high frequency pole denotes the blood signal. Before the AR estimation, a static signal is added to avoid producing split spectral peaks. It is illustrated in the simulation that the proposed method can detect the slower blood flow with smaller variance compared with the traditional wall filtering method. [Copyright &y& Elsevier]

Environmental pollution and energy deficiency are two key issues for the sustainable development of the modern society. Polyurethane foam is a typical commercial polymer with a large production quantity and its waste needs to be recycled. Lithium–sulfur battery is a promising energy-storage device with high energy density and low cost, but its demerits such as poor conductivity of the sulfur and severe capacity degradation due to the soluble lithium polysulfides are still a big challenge. This work reports a facile method to prepare nitrogen-doped porous carbon (NPC) from the polyurethane foam (PUF) waste and use it as a reservoir to impregnate sulfur for lithium–sulfur batteries. The obtained NPC has a unique interconnected sheet-like porous morphology with a large surface area of 1315 m 2 g −1 . The NPC-S composite delivers a large reversible capacity of 1118 mAh g −1 with good cycling performances and excellent high-rate capabilities. A large reversible capacity of 460 mAh g −1 can be retained at a large current of 5C (8.35 A g −1 ) after 100 cycles. [ABSTRACT FROM AUTHOR]

The Lamb-wave-based damage imaging via beamforming techniques, which can visualize the location of damage in the structure intuitively, is one of the most promising methods in the field of structural health monitoring (SHM). However, transducer array position errors are inevitable in practical application, which may lead to serious degradation in imaging performance. In this study, it is shown that the uncertainty of the steering vectors led by the imprecise position of transducers in an array can be suppressed by the doubly constrained robust Capon beamformer (DCRCB). After the unwanted side lobes are restrained by the DCRCB-based coherence factor (CF) weighting, an effective adaptive beamforming damage imaging method robust to transducer position errors is proposed. The numerical simulation and imaging experiment of damage on an aluminum plate are carried out to verify the effectiveness of the proposed algorithm. The results show that the proposed Lamb wave damage imaging method performs better than the reported beamforming ones in literature in terms of resolution, contrast, and robustness to transducer position errors. [ABSTRACT FROM AUTHOR]

Radioresistance of breast cancer is a major reason for therapeutic failure and limits further increases in the dose of radiation due to severe adverse effects. Recently, long noncoding RNAs (lncRNAs) have been shown to regulate cancer proliferation, chemoresistance, and radioresistance. Among these lncRNAs, lncRNA GAS5 expression was shown to be downregulated in breast cancer and related to trastuzumab resistance. However, its role in the radiation response is unclear. In this study, we demonstrated that lncRNA GAS5 expression was reduced in irradiated cells and that overexpression of GAS5 reduced cell viability and promoted cell apoptosis after irradiation. Moreover, overexpression of GAS5 resulted in increased G2/M arrest and unrepaired DNA damage, indicating a radiosensitizing role of GAS5 in breast cancer cells. Finally, we found that a GAS5-interacting miRNA, miR-21, reversed the radiosensitizing effects of GAS5 by inhibiting the apoptotic pathway. In conclusion, we found that lncRNA GAS5 sensitized breast cancer cells to ionizing radiation by inhibiting DNA repair and suppressing miR-21, identifying novel targets for breast cancer radiosensitization. [ABSTRACT FROM AUTHOR]

Rana chensinensis ovum oil (RCOO) is an emerging source of unsaturated fatty acids (UFAs), but it is lacking in green and efficient extraction methods. In this work, using the response surface strategy, we developed a green and efficient CO2 supercritical fluid extraction (CO2-SFE) technology for RCOO. The response surface methodology (RSM), based on the Box–Behnken Design (BBD), was used to investigate the influence of four independent factors (pressure, flow, temperature, and time) on the yield of RCOO in the CO2-SFE process, and UPLC-ESI-Q-TOP-MS and HPLC were used to identify and analyze the principal UFA components of RCOO. According to the BBD response surface model, the optimal CO2-SFE condition of RCOO was pressure 29 MPa, flow 82 L/h, temperature 50 °C, and time 132 min, and the corresponding predicted optimal yield was 13.61%. The actual optimal yield obtained from the model verification was 13.29 ± 0.37%, and the average error with the predicted value was 0.38 ± 0.27%. The six principal UFAs identified in RCOO included eicosapentaenoic acid (EPA), α-linolenic acid (ALA), docosahexaenoic acid (DHA), arachidonic acid (ARA), linoleic acid (LA), and oleic acid (OA), which were important biologically active ingredients in RCOO. Pearson correlation analysis showed that the yield of these UFAs was closely related to the yield of RCOO (the correlation coefficients were greater than 0.9). Therefore, under optimal conditions, the yield of RCOO and principal UFAs always reached the optimal value at the same time. Based on the above results, this work realized the optimization of CO2-SFE green extraction process and the confirmation of principal bioactive ingredients of the extract, which laid a foundation for the green production of RCOO. [ABSTRACT FROM AUTHOR]

• A novel ultrasonic guided wave tomography method, incorporating recurrent neural network into full waveform inversion, is proposed. • The wave equation of an acoustic model is solved by a forward model with the cyclic calculation units of an RNN. • The gradient of the objective function can be obtained by automatic differentiation while the parameters of the waveform velocity model are updated by the adaptive momentum estimation algorithm. • The U-Net deep image prior (DIP) is used as the velocity model regularization in each iteration. Corrosion quantitative detection of plate or plate-like structure materials is crucial in industrial Non-Destructive Testing (NDT) for determining their remaining life. For doing that, a novel ultrasonic guided wave tomography method, incorporating recurrent neural network (RNN) into full waveform inversion (FWI) called as RNN-FWI, is proposed in this paper. When the wave equation of an acoustic model is solved by a forward model with the cyclic calculation units of an RNN, it is shown that the inversion of the forward model can be obtained iteratively by minimizing a waveform misfit function of quadratic Wasserstein distance between the modeled and measured data. It is also demonstrated that the gradient of the objective function can be obtained by automatic differentiation while the parameters of the waveform velocity model are updated by the adaptive momentum estimation algorithm (Adam). The U-Net deep image prior (DIP) is used as the velocity model regularization in each iteration. The final thickness maps of the plate or plate-like structure materials shown can be archived by the dispersion characteristics of guided waves. Both the numerical simulation and experimental results show that the proposed RNN-FWI tomography method performs better than the conventional time-domain FWI in terms of convergence rate, initial model requirement, and robustness. [ABSTRACT FROM AUTHOR]

Abstract: The wide use of nylon 6 (PA 6) resulting in the resource recovery of waste PA 6 is urgent and its hydrolysis in sub-critical water to produce ɛ-caprolactam is a very important method. Hydrothermal diamond anvil cell (HDAC) was used to in situ observe transformation of PA 6 in water up to 513K. The result shows that PA 6 melts at 488K, and then hydrolyzes to produce low molecular dissoluble compounds such as 6-aminocaproic acid and ɛ-caprolactam from cleavage of acyl–amido bond. Different from the microreactor (about 0.075μl) of HDAC, degradation of PA 6 in hydrothermal vessel of 18ml is far slow, so the higher temperatures of 553, 573, 583, and 603K are tried. As for hydrothermal vessel runs, liquid products after degradation are mainly composed of ɛ-caprolactam. Phosphotungstic heteropoly acid (HPA) as catalyst can improve hydrolysis rate and yield of ɛ-caprolactam, and 3% HPA can attain preferable effect. The optimum hydrolysis conditions for ɛ-caprolactam are as follows: HPA amount is 3%, reaction temperature is 573K and reaction time is 85min, and the yield of ɛ-caprolactam is 77.96%. In the temperature range of 553–603K, the non-catalytic and catalytic degradation kinetics are studied as pseudo-first-order reaction and apparent activation energy is 86.64 and 77.38kJ/mol for no catalyst and 3% HPA, respectively. [Copyright &y& Elsevier]

As non-receptor tyrosine kinases, Janus kinases (JAKs) have become an attractive target for the treatment of autoimmune diseases and cancers. JAKs play a pivotal role in innate immunity, inflammation, and hematopoiesis by mediating the signaling of numerous cytokines, growth factors, and interferons (IFNs). Selective inhibitors of a variety of JAK members are expected to inhibit pro-inflammatory cytokine-mediated inflammation and immune responses, while preventing targeting other subtypes of JAKs. In this work, poorly selective compounds based on 4- or 6-phenyl-pyrimidine derivatives have been improved to highly potent and selective compounds by designing a covalent binding tether, which attaches to the unique cysteine (Cys909) residue in JAK3. Compound 12 exhibited potent JAK3 inhibitory activity (IC 50 = 1.7 nM) with an excellent selectivity profile when compared to the other JAK isoforms (>588-fold). In a cellular assay, compound 12 strongly inhibited JAK3-dependent signaling and T cell proliferation. Moreover, in vivo data revealed that compound 12 significantly suppressed oxazolone (OXZ)-induced delayed hypersensitivity responses in Balb/c mice. Compound 12 also displayed decent pharmacokinetic properties and was suitable for in vivo use. Taken together, these results indicated that compound 12 may be a promising tool compound as a selective JAK3 inhibitor for treating autoimmune diseases. Image 1 • Compound 12 is a potent JAK3 inhibitor (IC 50 = 1.7 nM), highly selective vs JAK1 (x 777) and JAK2 (x 588). • Compound 12 obviously inhibited the JAK3-dependent signaling and T cell proliferation. • Compound 12 suppressed the oxazolone-induced delayed type hypersensitivity responses in mice. • Compound 12 displayed moderate pharmacokinetic properties and is suitable for in vivo use. [ABSTRACT FROM AUTHOR]

In Lamb wave imaging based on a phased array, higher frequencies narrowband excitation pulses enable more precise damage detection and localization. However, due to the size constraints of individual transducer elements, the spacing between array elements may exceed half the wavelength of the excitation signal. This can lead to a grating lobe effect. To overcome this limitation, a Lamb wave imaging method via dual-frequency fusion for grating lobe effect compensation is proposed in this study. Analyses indicate that the grating lobe effect may introduce artifacts or distortions in the imaging results. This method utilizes two frequencies of narrowband excitation pulses for imaging and subsequently fuses the results. By doing so, the imaging artifacts caused by the grating lobes produced by high-frequency narrowband excitation pulses are effectively compensated. The proposed method is validated through simulations and experiments on an aluminum plate, showing superior accuracy, contrast, and imaging quality., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

HDAC8 can mediate signals by using its enzymatic or nonenzymatic functions, which are expected to be critical for various types of cancer. Herein, we employed proteolysis targeting chimera (PROTAC) technology to target the enzymatic as well as the nonenzymatic functions of HDAC8. A potent and selective HDAC8 PROTAC Z16 (CZH-726) with low nanomolar DC 50 values in various cell lines was identified. Interestingly, Z16 induced structural maintenance of chromosomes protein 3 (SMC3) hyperacetylation at low concentrations and histone hyperacetylation at high concentrations, which can be explained by HDAC8 degradation and off-target HDAC inhibition, respectively. Notably, Z16 potently inhibited proliferation of various cancer cell lines and the antiproliferative mechanisms proved to be cell-type-dependent, which, to a large extent, is due to off-target HDAC inhibition. In conclusion, we report a hydrazide-based HDAC8 PROTAC Z16 , which can be used as a probe to investigate the biological functions of HDAC8.

Ferroptosis is a form of regulated cell death that can be modulated by small molecules and has the potential for the development of therapeutics for oncology. Although excessive lipid peroxidation is the defining hallmark of ferroptosis, DNA damage may also play a significant role. In this study, a potential mechanistic role for MIF in homologous recombination (HR) DNA repair is identified. The inhibition or genetic depletion of MIF or other HR proteins, such as breast cancer type 1 susceptibility protein (BRCA1), is demonstrated to significantly enhance the sensitivity of cells to ferroptosis. The interference with HR results in the translocation of the tumor suppressor protein p53 to the mitochondria, which in turn stimulates the production of reactive oxygen species. Taken together, the findings demonstrate that MIF-directed small molecules enhance ferroptosis via a putative MIF-BRCA1-RAD51 axis in HR, which causes resistance to ferroptosis. This suggests a potential novel druggable route to enhance ferroptosis by targeted anticancer therapeutics in the future., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

Recurrent AKI has been found common among hospitalized patients after discharge, and early prediction may allow timely intervention and optimized post-discharge treatment [1]. There are significant gaps in the literature regarding the risk prediction on the post-AKI population, and most current works only included a limited number of pre-selected variables [2]. In this study, we built and compared machine learning models using both knowledge-based and data-driven features in predicting the risk of recurrent AKI within 1-year of discharge. Our results showed that the additional use of data-driven features statistically improved the model performances, with best AUC=0.766 by using logistic regression.

Macrophage polarization plays a crucial role in inflammatory processes. The histone deacetylase 3 (HDAC3) has a deacetylase-independent function that can activate pro-inflammatory gene expression in lipopolysaccharide-stimulated M1-like macrophages and cannot be blocked by traditional small-molecule HDAC3 inhibitors. Here we employed the proteolysis targeting chimera (PROTAC) technology to target the deacetylase-independent function of HDAC3. We developed a potent and selective HDAC3-directed PROTAC, P7, which induces nearly complete HDAC3 degradation at low micromolar concentrations in both THP-1 cells and human primary macrophages. P7 increases the anti-inflammatory cytokine secretion in THP-1-derived M1-like macrophages. Importantly, P7 decreases the secretion of pro-inflammatory cytokines in M1-like macrophages derived from human primary macrophages. This can be explained by the observed inhibition of macrophage polarization from M0-like into M1-like macrophage. In conclusion, we demonstrate that the HDAC3-directed PROTAC P7 has anti-inflammatory activity and blocks macrophage polarization, demonstrating that this molecular mechanism can be targeted with small molecule therapeutics., (© 2023 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH.)

Background: Neuroendocrine carcinoma of the cervix (NECC) is more prone to lymphatic infiltration, lymph node involvement, local recurrence, and distant metastasis. Using concurrent chemoradiotherapy (CCRT) with or without adjuvant chemotherapy as the standard treatment for locally advanced NECCs and CCRT for patients with early lesions confined to the cervix. However, the prognosis of NECC patients treated with definitive radiotherapy (RT) is unknown. Immune checkpoint inhibitors are a promising therapeutic strategy for locally advanced cervical cancer. Some reports suggest that the expression of PD-L1 in solid tumors correlates with prognosis., Aim: This study investigates prognostic factors for survival in patients with neuroendocrine cervical carcinoma (NECC) treated with definitive RT and the relationship between PD-L1 expression and prognosis in these patients., Methods: This retrospective study included 66 patients with histologically confirmed NECC who received RT with or without chemotherapy. From January 2015 to December 2020, patients received routine extended-field irradiation (EFI), and PD-L1 expression was assessed by immunohistochemistry. The most commonly used chemotherapy agents were etoposide-platinum and paclitaxel-platinum., Results: PD-L1 expression was positive in 17 of 45 (37.8%) patients. There were 52 cases of pure NECC and 14 cases of mixed carcinoma. Sixty stage IB-III patients received definitive RT. The 3- and 5-year progression-free survival (PFS) was 39.8% and 34.1%, and 3- and 5-year overall survival (OS) was 48.0% and 40.2%, respectively. There was no significant difference in 3 and 5-year PFS and 3 and 5-year OS between patients with pure and mixed carcinoma. Positive PD-L1 expression was associated with higher 3-year PFS in patients with mixed histology. Univariate analysis showed that lymph node metastasis (LNM) and the International Federation of Gynecology and Obstetrics stages predicted 3- and 5-year PFS in patients who received definitive RT. The median OS in patients receiving less than four cycles and at least four cycles of chemotherapy (CT) was 26.0 and 44.0 months, respectively ( P = 0.038); moreover, 3- and 5-year PFS was 34.1% and 25.7% in the former and 46.4% and 40.4% in the latter. There were no significant differences in OS and PFS between pelvic irradiation and prophylactic EFI in patients treated with definitive RT. There were no significant differences in para-aortic failure rate after concurrent chemoradiotherapy between patients who underwent pelvic irradiation or prophylactic EFI ( P = 0.147)., Conclusion: In patients with mixed NECC, positive PD-L1 expression is correlated with higher 3-year PFS. Chemoradiotherapy was effective for NECCs. The LNM and stage predicted PFS. Four or more cycles of chemotherapy improve prognosis. Prophylactic EFI did not significantly improve PFS and OS., Relevance for Patients: This study is relevant to patients as it confirms that chemoradiotherapy is effective for both early and locally advanced NECC and that four or more cycles of chemotherapy improved prognosis. The regimen should be carefully evaluated to ensure that patients receive the most effective radiation therapy for the prophylactic of para-aortic LNM. Potential risk factors for the recurrence of radical radiotherapy should be fully understood to minimize these risks. This study observed that PD-L1 expression positive in patients with mixed NECC types is correlated with higher 3-year PFS., Competing Interests: The authors declare no conflict of interest regarding the publication of this paper., (Copyright: © 2023 Author(s).)

Remdesivir has been widely used for the treatment of Coronavirus (COVID) in hospitalized patients, but its nephrotoxicity is still under investigation 1 . Given the paucity of knowledge regarding the mechanism and optimal treatment of the development of acute kidney injury (AKI) in the setting of COVID, we analyzed the role of remdesivir and built multifactorial causal models of COVID-AKI by applying causal discovery machine learning techniques. Risk factors of COVID-AKI and renal function measures were represented in a temporal sequence using longitudinal data from EHR. Our models successfully recreated known causal pathways to changes in renal function and interactions with each other and examined the consistency of high-level causal relationships over a 4-day course of remdesivir. Results indicated a need for assessment of renal function on day 2 and 3 use of remdesivir, while uncovering that remdesivir may pose less risk to AKI than existing conditions of chronic kidney disease., (©2022 AMIA - All rights reserved.)

A novel method for the iodine-mediated reduction of phosphine oxides (sulfides) to phosphines using phosphonic acid under solvent-free conditions is described. By using a combination of H 3 PO 3 and I 2 , both tertiary monophosphine oxides and bis-phosphine oxides were reduced under this system, readily producing monodentate and bidentate phosphines, respectively, in good yields. Notably, chiral ( R )-(+)-2,2'-bis(diphenylphosphino)-1,1'-binaphthyl dioxide could be also tolerated without racemization. This new approach is inexpensive and features simple conditions and a wide substrate scope.

An accurate personality model is crucial to many research fields. Most personality models have been constructed using linear factor analysis (LFA). In this paper, we investigate if an effective deep learning tool for factor extraction, the Variational Autoencoder (VAE), can be applied to explore the factor structure of a set of personality variables. To compare VAE with LFA, we applied VAE to an International Personality Item Pool (IPIP) Big 5 dataset and an IPIP HEXACO (Humility-Honesty, Emotionality, Extroversion, Agreeableness, Conscientiousness, Openness) dataset. We found that LFA tends to break factors into ever smaller, yet still significant fractions, when the number of assumed latent factors increases, leading to the need to organize personality variables at the factor level and then the facet level. On the other hand, the factor structure returned by VAE is very stable and VAE only adds noise-like factors after significant factors are found as the number of assumed latent factors increases. VAE reported more stable factors by elevating some facets in the HEXACO scale to the factor level. Since this is a data-driven process that exhausts all stable and significant factors that can be found, it is not necessary to further conduct facet level analysis and it is anticipated that VAE will have broad applications in exploratory factor analysis in personality research., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Huang and Zhang.)

Acute kidney injury (AKI) is potentially catastrophic and commonly seen among inpatients. In the United States, the quality of administrative coding data for capturing AKI accurately is questionable and needs to be updated. This retrospective study validated the quality of administrative coding for hospital-acquired AKI and explored the opportunities to improve the phenotyping performance by utilizing additional data sources from the electronic health record (EHR). A total of34570 patients were included, and overall prevalence of AKI based on the KDIGO reference standard was 10.13%, We obtained significantly different quality measures (sensitivity.-0.486, specificity:0.947, PPV.0.509, NPV:0.942 in the full cohort) of administrative coding from the previously reported ones in the U.S. Additional use of clinical notes by incorporating automatic NLP data extraction has been found to increase the AUC in phenotyping AKI, and AKI was better recognized in patients with heart failure, indicating disparities in the coding and management of AKI., (©2021 AMIA - All rights reserved.)

Epitranscriptome is an exciting area that studies different types of modifications in transcripts and the prediction of such modification sites from the transcript sequence is of significant interest. However, the scarcity of positive sites for most modifications imposes critical challenges for training robust algorithms. To circumvent this problem, we propose MR-GAN, a generative adversarial network (GAN) based model, which is trained in an unsupervised fashion on the entire pre-mRNA sequences to learn a low dimensional embedding of transcriptomic sequences. MR-GAN was then applied to extract embeddings of the sequences in a training dataset we created for eight epitranscriptome modifications, including m 6 A, m 1 A, m 1 G, m 2 G, m 5 C, m 5 U, 2'- O -Me, Pseudouridine (Ψ) and Dihydrouridine (D), of which the positive samples are very limited. Prediction models were trained based on the embeddings extracted by MR-GAN. We compared the prediction performance with the one-hot encoding of the training sequences and SRAMP, a state-of-the-art m 6 A site prediction algorithm and demonstrated that the learned embeddings outperform one-hot encoding by a significant margin for up to 15% improvement. Using MR-GAN, we also investigated the sequence motifs for each modification type and uncovered known motifs as well as new motifs not possible with sequences directly. The results demonstrated that transcriptome features extracted using unsupervised learning could lead to high precision for predicting multiple types of epitranscriptome modifications, even when the data size is small and extremely imbalanced., Competing Interests: CONFLICT OF INTEREST There is no conflict of interest.

Dietary supplement adverse events are potentially severe, yet knowledge regarding the safety of dietary supplements is limited. The CFSAN Adverse Event Reporting System (CAERS) contains records of adverse events attributed to supplements and is potentially useful for dietary supplement pharmacovigilance. This study investigates the feasibility of mining CAERS for dietary supplement adverse events as well as for monitoring the safety of dietary supplement products. Using three online resources, we mapped products in CAERS to their listed ingredients. We then ran four standard signal detection algorithms over the ingredient-adverse event and product-adverse event pairs extracted from CAERS and ranked the detected associations. Comparing 130 signals detected by all four algorithms with a dietary supplement resource, we found evidence for 73 (56%) associations. In addition, some detected product-adverse event signals were consistent with product safety information. We have made a database of the detected adverse events publicly available at https://github.com/zhang-informatics/DDSAE.

Motivation: Among many large-scale proteomic quantification methods, 18 O/ 16 O labeling requires neither specific amino acid in peptides nor label incorporation through several cell cycles, as in metabolic labeling; it does not cause significant elution time shifts between heavy- and light-labeled peptides, and its dynamic range of quantification is larger than that of tandem mass spectrometry-based quantification methods. These properties offer 18 O/ 16 O labeling the maximum flexibility in application. However, 18 O/ 16 O labeling introduces large quantification variations due to varying labeling efficiency. There lacks a processing pipeline that warrants the reliable identification of differentially expressed proteins (DEPs). This motivates us to develop a quantitative proteomic approach based on 18 O/ 16 O labeling and apply it on Kaposi sarcoma-associated herpesvirus (KSHV) microRNA (miR) target prediction. KSHV is a human pathogenic γ-herpesvirus strongly associated with the development of B-cell proliferative disorders, including primary effusion lymphoma. Recent studies suggest that miRs have evolved a highly complex network of interactions with the cellular and viral transcriptomes, and relatively few KSHV miR targets have been characterized at the functional level. While the new miR target prediction method, photoactivatable ribonucleoside-enhanced cross-linking and immunoprecipitation (PAR-CLIP), allows the identification of thousands of miR targets, the link between miRs and their targets still cannot be determined. We propose to apply the developed proteomic approach to establish such links., Method: We integrate several 18 O/ 16 O data processing algorithms that we published recently and identify the messenger RNAs of downregulated proteins as potential targets in KSHV miR-transfected human embryonic kidney 293T cells. Various statistical tests are employed for picking DEPs, and we select the best test by examining the enrichment of PAR-CLIP-reported targets with seed match to the miRs of interest among top ranked DEPs returned by statistical tests. Subsequently, the list of DEPs picked by the selected statistical test is filtered with the criteria that they must have downregulated gene expressions, must have reported as targets by an miR target prediction algorithm SVMcrio, and must have reported as targets by PAR-CLIP., Result: We test the developed approach in the problem of finding targets of KSHV miR-K1. The RNAs of three DEPs are identified as miR-K1 targets, among which RAB23 and HNRNPU are novel. Results from both Western blotting and Luciferase reporter assays confirm the novel targets. These results show that the developed quantitative approach based on 18 O/ 16 O labeling can be combined with genomic, PAR-CLIP, and target prediction algorithms for the confident identification of KSHV miR targets. The developed approach could also be applied in other applications., Competing Interests: Authors disclose no potential conflicts of interest.

In liquid chromatography-mass spectrometry (LC-MS), parts of LC peaks are often corrupted by their co-eluting peptides, which results in increased quantification variance. In this paper, we propose to apply accurate LC peak boundary detection to remove the corrupted part of LC peaks. Accurate LC peak boundary detection is achieved by checking the consistency of intensity patterns within peptide elution time ranges. In addition, we remove peptides with erroneous mass assignment through model fitness check, which compares observed intensity patterns to theoretically constructed ones. The proposed algorithm can significantly improve the accuracy and precision of peptide ratio measurements.

Motivation: Peptide detection is a crucial step in mass spectrometry (MS) based proteomics. Most existing algorithms are based upon greedy isotope template matching and thus may be prone to error propagation and ineffective to detect overlapping peptides. In addition, existing algorithms usually work at different charge states separately, isolating useful information that can be drawn from other charge states, which may lead to poor detection of low abundance peptides., Results: BPDA2d models spectra as a mixture of candidate peptide signals and systematically evaluates all possible combinations of possible peptide candidates to interpret the given spectra. For each candidate, BPDA2d takes into account its elution profile, charge state distribution and isotope pattern, and it combines all evidence to infer the candidate's signal and existence probability. By piecing all evidence together--especially by deriving information across charge states--low abundance peptides can be better identified and peptide detection rates can be improved. Instead of local template matching, BPDA2d performs global optimization for all candidates and systematically optimizes their signals. Since BPDA2d looks for the optimal among all possible interpretations of the given spectra, it has the capability in handling complex spectra where features overlap. BPDA2d estimates the posterior existence probability of detected peptides, which can be directly used for probability-based evaluation in subsequent processing steps. Our experiments indicate that BPDA2d outperforms state-of-the-art detection methods on both simulated data and real liquid chromatography-mass spectrometry data, according to sensitivity and detection accuracy., Availability: The BPDA2d software package is available at http://gsp.tamu.edu/Publications/supplementary/sun11a/.

Background: About 6 million Americans suffer from heart failure and 70% of heart failure cases are caused by myocardial infarction (MI). Following myocardial infarction, increased cytokines induce two major types of macrophages: classically activated macrophages which contribute to extracellular matrix destruction and alternatively activated macrophages which contribute to extracellular matrix construction. Though experimental results have shown the transitions between these two types of macrophages, little is known about the dynamic progression of macrophages activation. Therefore, the objective of this study is to analyze macrophage activation patterns post-MI., Results: We have collected experimental data from adult C57 mice and built a framework to represent the regulatory relationships among cytokines and macrophages. A set of differential equations were established to characterize the regulatory relationships for macrophage activation in the left ventricle post-MI based on the physical chemistry laws. We further validated the mathematical model by comparing our computational results with experimental results reported in the literature. By applying Lyaponuv stability analysis, the established mathematical model demonstrated global stability in homeostasis situation and bounded response to myocardial infarction., Conclusions: We have established and validated a mathematical model for macrophage activation post-MI. The stability analysis provided a possible strategy to intervene the balance of classically and alternatively activated macrophages in this study. The results will lay a strong foundation to understand the mechanisms of left ventricular remodelling post-MI.

The ultrasound echo attenuation depends on frequency, propagating depth and tissue characteristics. Thus, the attenuation dependent on frequency results in a larger attenuation of high frequencies than lower when the wave propagates through the tissue. As a result, the central frequency of the echo generates the increasing downshift with the increasing of depth. In the traditional I/Q demodulation method, it is assumed that the central frequency of the echo is the same as the transmitting frequency and unchanged all time. The assumption directly causes that the acquired I/Q signals are not perfect baseband ones but biased due to the echo attenuation. In addition, the unreasonable assumption will keep the echo from getting better signal-to-noise ratio. A quadrature demodulation method based on tracking the ultrasound echo frequency is proposed in this paper. The method consists of the traditional I/Q demodulator, the frequency tracking module, the phase compensation module and the dynamic filtering module. The outputs of I/Q demodulator are biased. Autocorrelation technique is utilized in the frequency tracking unit to estimate the frequency bias according to the outputs of I/Q demodulator. The estimated bias feeds to the phase compensation unit which can eliminate the frequency bias by simple trigonometric function transform. The compensated signals feed to the dynamic filter and are further processed. The bandwidth of the dynamic filter decreases with the increasing of the depth, which makes the echo acquire better SNR in different depth. The efficiency of the proposed method is testified by both simulations and experiments.

In color flow imaging systems, the ultrasound Doppler signal scattered from blood is heavily corrupted by the strong clutter signal reflected from the slow-moving muscular tissue. Moving target indicator (MTI) is a key technique of rejecting the clutter signal and detecting blood flow. To improve the performance of detecting blood flow, a new MTI scheme is proposed in this paper. It consists of a pre-filter, a clutter weak-rejector and a 2-order AR estimator. After the processing of pre-filtering and clutter weak-rejection, the strong clutter signal is effectively eliminated and its strength is comparable with blood flow. Under such condition, the optimal performance can be attained by the 2-order AR estimator. It is illustrated in the simulation that, compared with the data from traditional methods, the blood flow parameters are estimated with smaller deviation and the lower speed flow is detected.

In order to test the performance of wall filters, we propose a method for simulation of Hybrid Doppler testing signal, which adopts a rectangular power spectrum. In the method, the parameters of clutter, blood flow and system noise can be adjusted with flexibility. The problem about choosing model parameters is discussed as an emphasis. Several reasonable and typical parameter combination patterns are presented. Furthermore, an example is given to explain the process of choosing the parameters and testing the wall filter performance.