A Causal Model of Ion Interference Enables Assessment and Correction of Ratio Compression in Multiplex Proteomics.

Multiplex proteomics using isobaric labeling tags has emerged as a powerful tool for the simultaneous relative quantification of peptides and proteins across multiple experimental conditions. However, the quantitative accuracy of the approach is largely compromised by ion interference, a phenomenon that causes fold changes to appear compressed. The degree of compression is generally unknown, and the contributing factors are poorly understood. In this study, we thoroughly characterized ion interference at the MS2 level using a defined two-proteome experimental system with known ground-truth. We discovered remarkably poor agreement between the apparent precursor purity in the isolation window and the actual level of observed reporter ion interference in MS2 scans-a discrepancy that we found resolved by considering cofragmentation of peptide ions hidden within the spectral "noise" of the MS1 isolation window. To address this issue, we developed a regression modeling strategy to accurately predict reporter ion interference in any dataset. Finally, we demonstrate the utility of our procedure for improved fold change estimation and unbiased PTM site-to-protein normalization. All computational tools and code required to apply this method to any MS2 TMT dataset are documented and freely available.

SurgT challenge: Benchmark of soft-tissue trackers for robotic surgery.

This paper introduces the "SurgT: Surgical Tracking" challenge which was organized in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2022). There were two purposes for the creation of this challenge: (1) the establishment of the first standardized benchmark for the research community to assess soft-tissue trackers; and (2) to encourage the development of unsupervised deep learning methods, given the lack of annotated data in surgery. A dataset of 157 stereo endoscopic videos from 20 clinical cases, along with stereo camera calibration parameters, have been provided. Participants were assigned the task of developing algorithms to track the movement of soft tissues, represented by bounding boxes, in stereo endoscopic videos. At the end of the challenge, the developed methods were assessed on a previously hidden test subset. This assessment uses benchmarking metrics that were purposely developed for this challenge, to verify the efficacy of unsupervised deep learning algorithms in tracking soft-tissue. The metric used for ranking the methods was the Expected Average Overlap (EAO) score, which measures the average overlap between a tracker's and the ground truth bounding boxes. Coming first in the challenge was the deep learning submission by ICVS-2Ai with a superior EAO score of 0.617. This method employs ARFlow to estimate unsupervised dense optical flow from cropped images, using photometric and regularization losses. Second, Jmees with an EAO of 0.583, uses deep learning for surgical tool segmentation on top of a non-deep learning baseline method: CSRT. CSRT by itself scores a similar EAO of 0.563. The results from this challenge show that currently, non-deep learning methods are still competitive. The dataset and benchmarking tool created for this challenge have been made publicly available at https://surgt.grand-challenge.org/. This challenge is expected to contribute to the development of autonomous robotic surgery and other digital surgical technologies.

GSE1 links the HDAC1/CoREST co-repressor complex to DNA damage.

Post-translational modifications of histones are important regulators of the DNA damage response (DDR). By using affinity purification mass spectrometry (AP-MS) we discovered that genetic suppressor element 1 (GSE1) forms a complex with the HDAC1/CoREST deacetylase/demethylase co-repressor complex. In-depth phosphorylome analysis revealed that loss of GSE1 results in impaired DDR, ATR signalling and γH2AX formation upon DNA damage induction. Altered profiles of ATR target serine-glutamine motifs (SQ) on DDR-related hallmark proteins point to a defect in DNA damage sensing. In addition, GSE1 knock-out cells show hampered DNA damage-induced phosphorylation on SQ motifs of regulators of histone post-translational modifications, suggesting altered histone modification. While loss of GSE1 does not affect the histone deacetylation activity of CoREST, GSE1 appears to be essential for binding of the deubiquitinase USP22 to CoREST and for the deubiquitination of H2B K120 in response to DNA damage. The combination of deacetylase, demethylase, and deubiquitinase activity makes the USP22-GSE1-CoREST subcomplex a multi-enzymatic eraser that seems to play an important role during DDR. Since GSE1 has been previously associated with cancer progression and survival our findings are potentially of high medical relevance.

CholecTriplet2022: Show me a tool and tell me the triplet - An endoscopic vision challenge for surgical action triplet detection.

Formalizing surgical activities as triplets of the used instruments, actions performed, and target anatomies is becoming a gold standard approach for surgical activity modeling. The benefit is that this formalization helps to obtain a more detailed understanding of tool-tissue interaction which can be used to develop better Artificial Intelligence assistance for image-guided surgery. Earlier efforts and the CholecTriplet challenge introduced in 2021 have put together techniques aimed at recognizing these triplets from surgical footage. Estimating also the spatial locations of the triplets would offer a more precise intraoperative context-aware decision support for computer-assisted intervention. This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection. It includes weakly-supervised bounding box localization of every visible surgical instrument (or tool), as the key actors, and the modeling of each tool-activity in the form of triplet. The paper describes a baseline method and 10 new deep learning algorithms presented at the challenge to solve the task. It also provides thorough methodological comparisons of the methods, an in-depth analysis of the obtained results across multiple metrics, visual and procedural challenges; their significance, and useful insights for future research directions and applications in surgery.

Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the HeiChole benchmark.

PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robotic assistance or improve training of surgeons via data-driven feedback. In surgical workflow analysis up to 91% average precision has been reported for phase recognition on an open data single-center video dataset. In this work we investigated the generalizability of phase recognition algorithms in a multicenter setting including more difficult recognition tasks such as surgical action and surgical skill. METHODS: To achieve this goal, a dataset with 33 laparoscopic cholecystectomy videos from three surgical centers with a total operation time of 22 h was created. Labels included framewise annotation of seven surgical phases with 250 phase transitions, 5514 occurences of four surgical actions, 6980 occurences of 21 surgical instruments from seven instrument categories and 495 skill classifications in five skill dimensions. The dataset was used in the 2019 international Endoscopic Vision challenge, sub-challenge for surgical workflow and skill analysis. Here, 12 research teams trained and submitted their machine learning algorithms for recognition of phase, action, instrument and/or skill assessment. RESULTS: F1-scores were achieved for phase recognition between 23.9% and 67.7% (n = 9 teams), for instrument presence detection between 38.5% and 63.8% (n = 8 teams), but for action recognition only between 21.8% and 23.3% (n = 5 teams). The average absolute error for skill assessment was 0.78 (n = 1 team). CONCLUSION: Surgical workflow and skill analysis are promising technologies to support the surgical team, but there is still room for improvement, as shown by our comparison of machine learning algorithms. This novel HeiChole benchmark can be used for comparable evaluation and validation of future work. In future studies, it is of utmost importance to create more open, high-quality datasets in order to allow the development of artificial intelligence and cognitive robotics in surgery.

Domain generalization improves end-to-end object detection for real-time surgical tool detection.

PURPOSE: Computer assistance for endoscopic surgery depends on knowledge about the contents in an endoscopic scene. An important step of analysing the video contents is real-time surgical tool detection. Most methods for tool detection nevertheless depend on multi-step algorithms building upon prior knowledge like anchor boxes or non-maximum suppression which ultimately decrease performance. A real-world difficulty encountered by learning-based methods are limited datasets. Training a neural network on data matching a specific distribution (e.g. from a single hospital or showing a specific type of surgery) can result in a lack of generalization. METHODS: In this paper, we propose the application of a transformer based architecture for end-to-end tool detection. This architecture promises state-of-the-art accuracy while decreasing the complexity resulting in improved run-time performance. To improve the lack of cross-domain generalization due to limited datasets, we enhance the architecture with a latent feature space via variational encoding to capture common intra-domain information. This feature space models the linear dependencies between domains by constraining their rank. RESULTS: The trained neural networks show a distinct improvement on out-of-domain data indicating better generalization to unseen domains. Inference with the end-to-end architecture can be performed at up to 138 frames per second (FPS) achieving a speedup in comparison to older approaches. CONCLUSIONS: Experimental results on three representative datasets demonstrate the performance of the method. We also show that our approach leads to better domain generalization.

A toolbox for class I HDACs reveals isoform specific roles in gene regulation and protein acetylation.

The class I histone deacetylases are essential regulators of cell fate decisions in health and disease. While pan- and class-specific HDAC inhibitors are available, these drugs do not allow a comprehensive understanding of individual HDAC function, or the therapeutic potential of isoform-specific targeting. To systematically compare the impact of individual catalytic functions of HDAC1, HDAC2 and HDAC3, we generated human HAP1 cell lines expressing catalytically inactive HDAC enzymes. Using this genetic toolbox we compare the effect of individual HDAC inhibition with the effects of class I specific inhibitors on cell viability, protein acetylation and gene expression. Individual inactivation of HDAC1 or HDAC2 has only mild effects on cell viability, while HDAC3 inactivation or loss results in DNA damage and apoptosis. Inactivation of HDAC1/HDAC2 led to increased acetylation of components of the COREST co-repressor complex, reduced deacetylase activity associated with this complex and derepression of neuronal genes. HDAC3 controls the acetylation of nuclear hormone receptor associated proteins and the expression of nuclear hormone receptor regulated genes. Acetylation of specific histone acetyltransferases and HDACs is sensitive to inactivation of HDAC1/HDAC2. Over a wide range of assays, we determined that in particular HDAC1 or HDAC2 catalytic inactivation mimics class I specific HDAC inhibitors. Importantly, we further demonstrate that catalytic inactivation of HDAC1 or HDAC2 sensitizes cells to specific cancer drugs. In summary, our systematic study revealed isoform-specific roles of HDAC1/2/3 catalytic functions. We suggest that targeted genetic inactivation of particular isoforms effectively mimics pharmacological HDAC inhibition allowing the identification of relevant HDACs as targets for therapeutic intervention.

MIcro-surgical anastomose workflow recognition challenge report.

BACKGROUND AND OBJECTIVE: Automatic surgical workflow recognition is an essential step in developing context-aware computer-assisted surgical systems. Video recordings of surgeries are becoming widely accessible, as the operational field view is captured during laparoscopic surgeries. Head and ceiling mounted cameras are also increasingly being used to record videos in open surgeries. This makes videos a common choice in surgical workflow recognition. Additional modalities, such as kinematic data captured during robot-assisted surgeries, could also improve workflow recognition. This paper presents the design and results of the MIcro-Surgical Anastomose Workflow recognition on training sessions (MISAW) challenge whose objective was to develop workflow recognition models based on kinematic data and/or videos. METHODS: The MISAW challenge provided a data set of 27 sequences of micro-surgical anastomosis on artificial blood vessels. This data set was composed of videos, kinematics, and workflow annotations. The latter described the sequences at three different granularity levels: phase, step, and activity. Four tasks were proposed to the participants: three of them were related to the recognition of surgical workflow at three different granularity levels, while the last one addressed the recognition of all granularity levels in the same model. We used the average application-dependent balanced accuracy (AD-Accuracy) as the evaluation metric. This takes unbalanced classes into account and it is more clinically relevant than a frame-by-frame score. RESULTS: Six teams participated in at least one task. All models employed deep learning models, such as convolutional neural networks (CNN), recurrent neural networks (RNN), or a combination of both. The best models achieved accuracy above 95%, 80%, 60%, and 75% respectively for recognition of phases, steps, activities, and multi-granularity. The RNN-based models outperformed the CNN-based ones as well as the dedicated modality models compared to the multi-granularity except for activity recognition. CONCLUSION: For high levels of granularity, the best models had a recognition rate that may be sufficient for applications such as prediction of remaining surgical time. However, for activities, the recognition rate was still low for applications that can be employed clinically. The MISAW data set is publicly available at http://www.synapse.org/MISAW to encourage further research in surgical workflow recognition.

Co-occurrence balanced time series classification for the semi-supervised recognition of surgical smoke.

PURPOSE: Automatic recognition and removal of smoke in surgical procedures can reduce risks to the patient by supporting the surgeon. Surgical smoke changes its visibility over time, impacting the vision depending on its amount and the volume of the body cavity. While modern deep learning algorithms for computer vision require large amounts of data, annotations for training are scarce. This paper investigates the use of unlabeled training data with a modern time-based deep learning algorithm. METHODS: We propose to improve the state of the art in smoke recognition by enhancing a image classifier based on convolutional neural networks with a recurrent architecture thereby providing temporal context to the algorithm. We enrich the training with unlabeled recordings from similar procedures. The influence of surgical tools on the smoke recognition task is studied to reduce a possible bias. RESULTS: The evaluations show that smoke recognition benefits from the additional temporal information during training. The use of unlabeled data from the same domain in a semi-supervised training procedure shows additional improvements reaching an accuracy of 86.8%. The proposed balancing policy is shown to have a positive impact on learning the discrimination of co-occurring surgical tools. CONCLUSIONS: This study presents, to the best of our knowledge, the first use of a time series algorithm for the recognition of surgical smoke and the first use of this algorithm in the described semi-supervised setting. We show that the performance improvements with unlabeled data can be enhanced by integrating temporal context. We also show that adaption of the data distribution is beneficial to avoid learning biases.

Peroxiredoxin promotes longevity and H2O2-resistance in yeast through redox-modulation of protein kinase A.

Peroxiredoxins are H2O2 scavenging enzymes that also carry out H2O2 signaling and chaperone functions. In yeast, the major cytosolic peroxiredoxin, Tsa1 is required for both promoting resistance to H2O2 and extending lifespan upon caloric restriction. We show here that Tsa1 effects both these functions not by scavenging H2O2, but by repressing the nutrient signaling Ras-cAMP-PKA pathway at the level of the protein kinase A (PKA) enzyme. Tsa1 stimulates sulfenylation of cysteines in the PKA catalytic subunit by H2O2 and a significant proportion of the catalytic subunits are glutathionylated on two cysteine residues. Redox modification of the conserved Cys243 inhibits the phosphorylation of a conserved Thr241 in the kinase activation loop and enzyme activity, and preventing Thr241 phosphorylation can overcome the H2O2 sensitivity of Tsa1-deficient cells. Results support a model of aging where nutrient signaling pathways constitute hubs integrating information from multiple aging-related conduits, including a peroxiredoxin-dependent response to H2O2.

A constitutive active allele of the transcription factor Msn2 mimicking low PKA activity dictates metabolic remodeling in yeast.

In yeast, protein kinase A (PKA) adjusts transcriptional profiles, metabolic rates, and cell growth in accord with carbon source availability. PKA affects gene expression mostly via the transcription factors Msn2 and Msn4, two key regulators of the environmental stress response. Here we analyze the role of the PKA-Msn2 signaling module using an Msn2 allele that harbors serine-to-alanine substitutions at six functionally important PKA motifs (Msn2A6) . Expression of Msn2A6 mimics low PKA activity, entails a transcription profile similar to that of respiring cells, and prevents formation of colonies on glucose-containing medium. Furthermore, Msn2A6 leads to high oxygen consumption and hence high respiratory activity. Substantially increased intracellular concentrations of several carbon metabolites, such as trehalose, point to a metabolic adjustment similar to diauxic shift. This partial metabolic switch is the likely cause for the slow-growth phenotype in the presence of glucose. Consistently, Msn2A6 expression does not interfere with growth on ethanol and tolerated is to a limited degree in deletion mutant strains with a gene expression signature corresponding to nonfermentative growth. We propose that the lethality observed in mutants with hampered PKA activity resides in metabolic reprogramming that is initiated by Msn2 hyperactivity.

Light-sensing via hydrogen peroxide and a peroxiredoxin.

Yeast lacks dedicated photoreceptors; however, blue light still causes pronounced oscillations of the transcription factor Msn2 into and out of the nucleus. Here we show that this poorly understood phenomenon is initiated by a peroxisomal oxidase, which converts light into a hydrogen peroxide (H2O2) signal that is sensed by the peroxiredoxin Tsa1 and transduced to thioredoxin, to counteract PKA-dependent Msn2 phosphorylation. Upon H2O2, the nuclear retention of PKA catalytic subunits, which contributes to delayed Msn2 nuclear concentration, is antagonized in a Tsa1-dependent manner. Conversely, peroxiredoxin hyperoxidation interrupts the H2O2 signal and drives Msn2 oscillations by superimposing on PKA feedback regulation. Our data identify a mechanism by which light could be sensed in all cells lacking dedicated photoreceptors. In particular, the use of H2O2 as a second messenger in signalling is common to Msn2 oscillations and to light-induced entrainment of circadian rhythms and suggests conserved roles for peroxiredoxins in endogenous rhythms.

Implementation of an intensified infection control program to reduce MRSA transmissions in a German tertiary care hospital.

BACKGROUND: Germany has witnessed increasing national methicillin-resistant Staphylococcus aureus (MRSA) rates during the past 2 decades. In our 900-bed tertiary care community hospital, a similar increase was noted during the period from 1994 to 2002, although single-room isolation and decolonization therapy were the standard of care. METHODS: An intensified infection control program aimed at the reduction of nosocomial MRSA transmissions was developed in 2002 and translated into clinical practice in 2003. Essential components of the program were a detailed written MRSA standard, acquisition of signal-colored isolation gowns and storage carts facilitating the use of separate supplies for MRSA patients, intensified surveillance and feedback of MRSA data, "flagging" of formerly positive MRSA patients, and a general MRSA screening policy for all newly admitted patients on the surgical intensive care unit (ICU). The effect of the program was monitored by continuous surveillance of MRSA cases on all wards. The transmission index was defined as the ratio between secondary and "imported" MRSA cases. RESULTS: Comparing the preintervention (2002) and postintervention (2005-2006) periods, the total number of MRSA patients, MRSA rates on the ICUs, and invasive MRSA infections on the ICUs were reduced. The MRSA transmission index fell from 2.1 (2002) to 0.8 (2006). The rate of deep incisional and organ/space infections due to MRSA occurring after orthopedic surgery was lowered from 0.74 to 0.15%. CONCLUSIONS: Our data indicate that the efficacy of single-room isolation and decolonization therapy can be strongly enhanced by means of a multicomponent, comprehensive MRSA control program. The program was effective despite an increasing "import" of new MRSA cases. Programs of this type may be suited to achieve a downward turn of MRSA figures in Germany.

Nuclear localization destabilizes the stress-regulated transcription factor Msn2.

The transcriptional program of yeast cells undergoes dramatic changes during the shift from fermentative growth to respiratory growth. A large part of this response is mediated by the stress responsive transcription factor Msn2. During glucose exhaustion, Msn2 is activated and concentrated in the nucleus. Simultaneously, Msn2 protein levels also drop significantly under this condition. Here we show that the decrease in Msn2 concentration is due to its increased degradation. Moreover, Msn2 levels are also reduced under chronic stress or low protein kinase A (PKA) activity, both conditions that cause a predominant nuclear localization of Msn2. Similar effects were found in msn5 mutant cells that block Msn2 nuclear export. To approximate the effect of low PKA activity on Msn2, we generated a mutant form with alanine substitutions in PKA phosphorylation sites. High expression of this Msn2 mutant is detrimental for growth, suggesting that the increased degradation of nuclear Msn2 might be necessary to adapt cells to low PKA conditions after the diauxic shift or to allow growth under chronic stress conditions.

Measurements of complement factor H-related protein (BTA-TRAK assay) and nuclear matrix protein (NMP22 assay)--useful diagnostic tools in the diagnosis of urinary bladder cancer?

Between 1997 and 2000 we investigated in a prospective study the voided urine samples of all consecutive patients undergoing cystoscopy independent from their clinical background (n = 705) with the BTA-TRAK assay (Bard Diagnostics, Redmont, USA) detecting a complement factor H-related protein (CFHrP) and the NMP22 assay (Matritech, Newton, USA) measuring a nuclear matrix protein, which is supposed to be specific for bladder cancer. The individuals were divided into three groups concerning the clinical background: 233 patients had urological diseases, 268 patients had urinary bladder cancer and 150 patients had other urological malignancies. Based on the clinical findings we compared our results with well established diagnostic methods for urinary bladder cancer such as cytology and the detection of hematuria. In addition, we investigated urine samples from 30 healthy individuals and 24 patients with urinary tract infection without performing cystoscopy. Following the recommendations of the European Group on Tumor Markers we used 95% specificity for benign urological diseases and urinary tract infections, which resulted in a sensitivity of 17% for active bladder cancer for the BTA-TRAK assay and 31% for NMP22. We compared these results with the detection of hematuria (specificity: 72%) and cytology, which had a sensitivity of 64% and 89%, respectively. Subsequently, we calculated sensitivity and specificity for the detection of relapse of the disease. Again using 95% specificity, in this case for patients with no evidence of disease (NED), in patients with recurrent disease the BTA-TRAK assay showed 8% sensitivity as compared to 12% for the NMP22 assay. Due to an insufficient specificity and sensitivity, both tests can neither be clinically useful in screening of high risk patients, nor in primary diagnosis of bladder cancer. They cannot replace neither cystoscopy nor cytology. In the follow-up care more investigations may be necessary to prove the benefit of existing diagnostic strategies for the discrimination between active and inactive bladder cancer.