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G.B., D.B., K.W., A.P., R.F., T.R.H., S.K., and J.A.A. received support from Fundacja na rzecz Nauki Polskiej (FNP) (grant ID: MAB/3/2017). D.R.G. received support from Genome Canada & Genome BC (grant ID: 264PRO). D.J.H. received support from NuCana plc (grant ID: SMD0-ZIUN05). H.A. received support from Swedish Cancer Foundation (grant ID: 211709). H.G. received support from United Kingdom Research and Innovation (UKRI) (grant ID: EP/S02431X/1). C.P. received support from Fundação para a Ciência e a Tecnologia (FCT) through LASIGE Research Unit (grant ID: UIDB/00408/2020 and UIDP/00408/2020). A.L. F.M.Z., C.P., A.R., A.P., and J.A.A. received support from European Union’s Horizon 2020 research and innovation programme (grant ID: 101017453). C.B. received support from Agence Nationale de la Recherche (ANR) through GRAL LabEX (grant ID: ANR-10-LABX-49-01) and CBH-EUR-GS 32 (grant ID: ANR-17-EURE0003). S.N.S. received support from Cancer Research UK (CRUK) and the Chief Scientist's Office of Scotland (CSO): Experimental Cancer Medicine Centre (ECMC) (grant ID: ECMCQQR-2022/100017). A.L. received support from Chief Scientist's Office of Scotland (CSO) NRS Career Researcher Fellowship. R.O.N. received support from CRUK Cambridge Centre Thoracic Cancer Programme (grant ID: CTRQQR-2021\100012). Tumor antigens can emerge through multiple mechanisms, including translation of non-coding genomic regions. This non-canonical category of antigens has recently gained attention; however, our understanding of how they recur within and between cancer types is still in its infancy. Therefore, we developed a proteogenomic pipeline based on deep learning de novo mass spectrometry to enable the discovery of non-canonical MHC-associated peptides (ncMAPs) from non-coding regions. Considering that the emergence of tumor antigens can also involve post-translational modifications, we included an open search component in our pipeline. Leveraging the wealth of mass spectrometry-based immunopeptidomics, we analyzed 26 MHC class I immunopeptidomic studies of 9 different cancer types. We validated the de novo identified ncMAPs, along with the most abundant post-translational modifications, using spectral matching and controlled their false discovery rate (FDR) to 1%. Interestingly, the non-canonical presentation appeared to be 5 times enriched for the A03 HLA supertype, with a projected population coverage of 54.85%. Here, we reveal an atlas of 8,601 ncMAPs with varying levels of cancer selectivity and suggest 17 cancer-selective ncMAPs as attractive targets according to a stringent cutoff. In summary, the combination of the open-source pipeline and the atlas of ncMAPs reported herein could facilitate the identification and screening of ncMAPs as targeting agents for T-cell therapies or vaccine development. Publisher PDF

Legends for Supplementary tables, supplementary figures, and supplementary notes.

Annotation of the COD-dipp ncMAPs.

Open search peptide-spectrum matches (PSMs) information of serine acetylation, cysteinylation, carbamidomethylation, and cysteine trioxidation.

Datasets and samples considered in this study.

Mapping of ncMAPs to COSMIC frameshift mutations.

Tumor antigens can emerge through multiple mechanisms, including translation of noncoding genomic regions. This noncanonical category of tumor antigens has recently gained attention; however, our understanding of how they recur within and between cancer types is still in its infancy. Therefore, we developed a proteogenomic pipeline based on deep learning de novo mass spectrometry (MS) to enable the discovery of noncanonical MHC class I–associated peptides (ncMAP) from noncoding regions. Considering that the emergence of tumor antigens can also involve posttranslational modifications (PTM), we included an open search component in our pipeline. Leveraging the wealth of MS-based immunopeptidomics, we analyzed data from 26 MHC class I immunopeptidomic studies across 11 different cancer types. We validated the de novo identified ncMAPs, along with the most abundant PTMs, using spectral matching and controlled their FDR to 1%. The noncanonical presentation appeared to be 5 times enriched for the A03 HLA supertype, with a projected population coverage of 55%. The data reveal an atlas of 8,601 ncMAPs with varying levels of cancer selectivity and suggest 17 cancer-selective ncMAPs as attractive therapeutic targets according to a stringent cutoff. In summary, the combination of the open-source pipeline and the atlas of ncMAPs reported herein could facilitate the identification and screening of ncMAPs as targets for T-cell therapies or vaccine development.

Supplementary Figures S1 to S7.

Supplementary notes 1 and 2.

A longstanding disconnect between the growing number of MHC Class I immunopeptidomic studies and genomic medicine hinders cancer vaccine design. We develop COD-dipp to genomically map the full spectrum of detected canonical and non-canonical (non-exonic) MHC Class I antigens from 26 cancer studies. We demonstrate that patient mutations in regions overlapping physically identified antigens better predict immunotherapy response when compared to neoantigen predictions. We suggest a vaccine design approach using 140,966 highly immune-visible regions of the genome annotated by their expression and haplotype frequency in the human population. These regions tend to be highly conserved, mutated in cancer and harbor 7.8 times more immunogenicity. Intersecting pan-cancer mutations with these immune surveilled regions revealed a potential to create off-the-shelf multi-epitope vaccines against public neoantigens. Here we release COD-dipp, a cancer vaccine toolkit as a web-application (https://www.proteogenomics.ca/COD-dipp) and open-source high-throughput resource.

The diagnostics of the condition of athletes has become a field of special scientific interest and activity. The aim of this study was to verify the effect of a long (100 km) run on a group of runners, as well as to assess the recovery time that is required for them to return to the pre-run state. The heart rate (HR) data presented were collected the day before the extreme physical effort, on the same day as, but after, the physical effort, as well as 24 and 48 h after. The Wavelet Transform (WT) and the Wavelet-based Fractal Analysis (WBFA) were implemented in the analysis. A tool was constructed that, based on quantitative data, enables one to confirm the completion of the recovery process that is related to the extreme physical effort. Indirectly, a tool was constructed that enables one to confirm the completion of the recovery process. The obtained information proves that the return to the resting state of the body after a significant physical effort can be observed after two days entirely through the analysis of the HR. Certain practical measures were used to differentiate between two substantially different states of the human body, i.e., pre- and post-effort states were constructed. The obtained results allow for us to state that WBFA appears to be a useful and robust tool in the determination of hidden features of stochastic signals, such as HR time signals. The proposed method allows one to differentiate between particular days of measurements with a mean probability of 92.2%.

Bioethanol is a liquid fuel for which a significant increase in the share of energy sources has been observed in the economies of many countries. The most significant factor in popularizing bioethanol is the profitability of investments in construction of facilities producing this energy source, as well as the profitability of its supply chain. With the market filled with a large amount of equipment used in the bioethanol production process, it is often difficult to make an optimal decision regarding the investment. Another issue is the location of the plant itself. Economic benefits are strongly associated with costs of equipment and materials, the amount of revenue from sales, and transportation costs. This article presents an attempt to solve this problem by using several swarm algorithms – new and fast-growing optimisation techniques. By employing ant colony optimization, river formation dynamics, particle swarm optimization, and cuckoo search algorithms in the task of bioethanol plant investment planning, the overall suitability of this type of technique has been tested. Moreover, the results allow us to determine which of the preceding algorithms is the most efficient in the given task.

Body surface area (BSA) may be computed using a variety of formulas, but the computed BSA differs from real BSA values for particular subjects. This is presented in the paper by computing BSA values for selected subject and comparing them to the real BSA value obtained with the use of a 3D body scanner. The results show inequalities in the relevant BSA computing formulas. Hence, there is a need to determine a method that will allow to select the best formula for calculating BSA in a particular case. For this purpose, the pairwise comparisons (PC) method is suggested. This article presents a proposition of using consistency-driven PC, as well as the basic and most important aspects of using PC to determine the appropriate BSA calculation formula.

Musculoskeletal disorders can completely take away the possibility of one’s locomotion, and in most cases require intensive rehabilitation. Medical services are still one of the least automated, while in the era of increasing emphasis on personalized medicine, the only effective way to overcome most problems can be to automate the rehabilitation process. This paper presents parts of a methodological basis for an automatic expert platform assisting in the process of rehabilitation. We test four machine learning models in tasks that involve assessment of limb exercises and joint rotation estimation, solely based on electromyography signals. In the best case, the models achieved 72% of accuracy in the former, and 0.08 of mean absolute error in the later. The level of errors qualifies these models as acceptable for further development for rehabilitation systems.

Currently used body surface area (BSA) formulas give satisfactory results only for individuals with typical physique, while for elderly, obese or anorectic people accurate results cannot be expected. Particularly noteworthy are the results for individuals with severe obesity (body-mass index greater than 35 kg/m2), for which BSA estimation errors reached 80%. The main goal of our study is the development of precise BSA models for specific body parts. We have achieved satisfactory results for a wide range of patients. Using regression models, such as: support vector regression, multilayer perceptron regressor, stochastic gradient descent, or ridge regression, a fourfold decrease in errors proportion is achieved. Machine learning algorithms led to reduction from 1.2 to 8 times for mean estimation error.

Symptoms caused by cerebral palsy or stroke deprive a person partially or even completely of his ability to move. Nowadays we can observe more technologically advanced rehabilitation devices which incorporate biofeedback into the process of rehabilitation of such people. However, there is still a lack of devices that would analyse, assess, and control (independently or with limited support) specialised movement exercises. Here we propose an idea of an automated exercise evaluation mechanism based on machine learning techniques, such as: support vector machines, decision trees, random forest, and k-nearest neighbours. While being only a preliminary case study, our research showed that with appropriate processing even a 100% accuracy score can be achieved in classifying whether an exercise is executed well or not.

Human body surface area (BSA) is one of the major parameters used in several medical fields. Its heterogeneity caused by individual human characteristics sustains a many-decades-long research on the matter. Today’s technology allows to create exact body models in mere seconds. However, an extensive research that includes scanning people with major disfigurements or people suffering from diseases, which often prevent their mobility, requires a specific approach. Here, we present the entire scanning procedure and graphical processing methods used in investigation of changes in BSA in function of different anthropometric parameters. The Artec 3D Eva hand-held scanner has been used as the measurement device. We performed a total of five scans for every subject—four for each limb and one for the main body part. After a series of processing methods, the resulting body model can be further used as an accurate basis for BSA formulae investigation.

Human body surface area (BSA) is an established parameter for the calculation of chemotherapy drugs dosage, treatment of chronic hepatitis B, treatment of burns or for establishing a dosing regimen for antimicrobials. Although being a critical parameter, usage of modern 3D scanners, which would measure the exact BSA value, is often impossible in time-sensitive operations or for patients unable to withstand the scanning process. Therefore, over the last decades considerable research efforts have been devoted to development of simple formulae for BSA approximation. The formulae use a small number of state variables (weight, height, age, sex), which are intended to be easily obtained for every patient. The formulae parameters were estimated independently for isolated groups of subjects, which should rise suspicion whether any of the formulae used is indeed effective and safe for medical treatment. Here, we provide an extended analysis of 43 BSA formulae based on 152 patients scanned with a hand-held 3D scanner. Upon comparison of the real BSA values with estimations made by the formulae, we can conclude that most of the formulae exhibit a high relative BSA error, ranging from 9.83% to 43.27%.

The paper presents a comparison of four optimisation algorithms implemented for the purpose of finding the shortest path in static and dynamic environments with obstacles. Two classical graph algorithms --- the Dijkstra complete algorithm and A* heuristic algorithm --- were compared with metaheuristic River Formation Dynamics swarm algorithm and its newly introduced modified version. Moreover, another swarm algorithm has been compared --- the Ant Colony Optimization and its modification. Terms and conditions of the simulation are thoroughly explained, paying special attention to the new, modified River Formation Dynamics algorithm. The algorithms were used for the purpose of generating the shortest path in three different types of environments, each served as a static environment and as a dynamic environment with changing goal or changing obstacles. The results show that the proposed modified River Formation Dynamics algorithm is efficient in finding the shortest path, especially when compared to its original version. In cases where the path should be adjusted to changes in the environment, calculations carried out by the proposed algorithm are faster than the A*, Dijkstra, and Ant Colony Optimization algorithms. This advantage is even more evident the more complex and extensive the environment is.

Body surface area (BSA) plays a key role in several medical fields, including cancer chemotherapy, transplantology, burn treatment and toxicology. BSA is often a major factor in the determination of the course of treatment and drug dosage. A series of formulae to simplify the process have been developed. Because easy-to-identify, yet general, body coefficient results of those formulae vary considerably, the question arises as to whether the choice of a particular formula is valid and safe for patients. Here we show that discrepancies between most of the known BSA formulae can reach 0.5 m2 for the standard adult physique. Although many previous studies have demonstrated that certain BSA formulae provide an almost exact fit with the patients examined, all of these studies have been performed on a limited and isolated group of people. Our analysis presents a broader perspective, considering 25 BSA formulae. The analysis revealed that the choice of a particular formula is a difficult task. Differences among calculations made by the formulae are so great that, in certain cases, they may considerably affect patients’ mortality, especially for people with an abnormal physique or for children.