We enhance this analysis by introducing marginal valuations. We use deep Q-learning to estimate the parameters of our model. We demonstrate our method by examining the positioning of knights and bishops, and also provide valuable insights into the valuation of pawns. Finally, we conclude by suggesting potential avenues for future research.We propose a neural network-based approach to calculate the value of a chess square–piece combination. Our model takes a triplet  as the input and calculates a value that measures the advantage/disadvantage of having this piece on this square. Our methods build on recent advances in chess AI, and can accurately assess the worth of positions in a game of chess. The conventional approach assigns fixed values to pieces ( Chess is not a game. Chess is a well-defined form of computation. You may not be able to work out the answers, but in theory, there must be a solution, a right procedure in any position. —John von NeumannThe development of chess AI can be traced back to pioneering work by ,2,3,4,5.All of these methods have now been replaced with either  search  or deep search ..α−β searQ-value of state While there has been much recent work conducted in chess AI ,12,13, t on For example, the notion that certain state combinations , our proposed model aims to predict the centipawn evaluation By using the augmented state representation and employing neural networks, our proposed model offers a more comprehensive and nuanced analysis of the chess game. This allows us to capture the intricate interplay between the color, piece type, square, and centipawn evaluation, providing a deeper understanding of the factors influencing the game’s outcome.This paper extends the work of  and propBy embracing a wider scope of analysis that covers all possible states, our research aims to provide a more comprehensive understanding of the game, surpassing the limitations imposed by narrow subsets. To achieve this, we employ advanced techniques, such as neural networks, to capture the intricate relationships between the components of a state and the corresponding probabilities of winning Through our research, we strive to advance the field by developing robust and effective models capable of accurately predicting the probabilities of winning and assessing the centipawn evaluations for any given state. By considering the full spectrum of states represented by Traditional chess literature often quotes the values of pieces as follows:ce, Ref.  adjusted lichess  refined V. The valuation of pieces plays a pivotal role in determining strategic decisions. The value attributed to a piece, such as a knight, is intrinsically tied to the probability of winning, as assessed by a chess engine. We can express this relationship through the identity: V = 3, is an aggregate of its utility across the entire game. As the game progresses and pieces are maneuvered, these values experience marginal shifts. Our primary objective is to accurately determine position-specific values, such as V.Our work will provide values for states consisting of a combination of pieces and squares. For example, we may wish to assess the value of a fianchettoed bishop on the queen’s side. The proposed method measures the advantage/disadvantage for states of the form (1)s. This dataset is derived from an extensive chess database, as described in A comprehensive dataset (2)s in Utilizing the state-of-the-art chess engine, Stockfish 16 [(3)s as determined by the neural network.A neural network architecture is designed and implemented. This network is specifically tailored to approximate the function (4)s, thereby assessing its results in relation to anecdotal chess maxims.In the applications section, the constructed neural network is rigorously tested by employing it to predict A brief overview of the methods in this paper is as follows:Q-learning. The value function, denoted as s. This state s belongs to the set In our approach, we begin by formalizing the theoretical functions used in To assess any legal chess position, we derive a centipawn evaluation denoted as For example, if White has a To address the sequential decision problem, we employ the dynamic programming technique known as Q-learning ,19,20). ,20. 19,2Bellman’s principle of optimality: An optimal policy has the property that whatever the initial state and initial decision are, the remaining decisions must constitute an optimal policy with regard to the state resulting from the first decision. To solve this sequential decision problem, we employ backward induction , which In recent years, the field of artificial intelligence has witnessed significant advancements, particularly in the realm of AI algorithms like deep learning ,23,24), ,24, 23,2One notable achievement stemming from these advancements is the ability to accurately assess chess positions. By leveraging AI algorithms, particularly deep learning techniques, we can now analyze and comprehend chess moves and strategies in a manner that was previously unimaginable. These algorithms have been specifically designed to process vast amounts of data, learn from patterns, and make informed decisions, ultimately resulting in highly accurate evaluations of chess positions.Moreover, the advent of advanced computer chess engines, exemplified by the likes of Stockfish 15 , has plaThe emergence of chess engines has effectively shifted the burden from human players and theorists to these intelligent systems. By leveraging their computational power and algorithmic prowess, chess engines have assumed the responsibility of assessing various lines of play, thus solving the Bellman equation.By adhering to Bellman’s optimality condition, computer chess engines fulfill the requirements of possessing complete knowledge about the chess environment and evaluating all possible actions and their consequences. Through this rigorous analysis, they provide insights into the optimal move in a given position.Q-value represents the probability of winning, given a move a in a given state s, by following the optimal Bellman path thereafter:For a given Q-learning, which calculates the Q-matrix , den, denQ-leQ-values, we obtain the value function for a given state.The policy function establishes the optimal mapping from states to actions, and by substituting the s. By harnessing the predictive capability of this neural network, we can subsequently determine the probability of a player winning, denoted as s.In s as input. Through a series of computations within the hidden layers, the model captures complex relationships and patterns inherent in the input data. Ultimately, the output layer produces the predicted value of The neural network model comprises interconnected layers, including an input layer that accepts the state s. These predicted values can then be utilized to compute the probability of a player winning, denoted as By employing this trained neural network model, we can make predictions of s. This information can be harnessed in various ways, including evaluating strategic moves, assessing the overall advantages or disadvantages of specific board configurations, and guiding decision-making during gameplay.With the ability to predict The neural network’s capacity to capture intricate patterns and relationships within the input data significantly contributes to more accurate predictions and a deeper understanding of the dynamics of the chess game. By incorporating the predicted values of Q-values, by predicting the value of a chess square and piece combination, denoted as We design a three-layer neural network aimed at determining the While the model can be made more sophisticated, the simplicity of this 3-layer architecture was chosen deliberately. The primary focus is on the novel method of determining values for chess squares and pieces, rather than leveraging computational prowess. By keeping the model straightforward, we ensure that results are more interpretable and that the emphasis remains on the innovation behind our method.s, and the corresponding centipawn loss (CPL) recorded for each state. Centipawn loss refers to the average difference in evaluation points between a chess engine’s top move and the move played by a player, expressed in hundredths of a pawn. Lower centipawn loss values indicate more accurate play, while higher values suggest greater inaccuracies or mistakes. The state information encompasses the variables discussed earlier that define the given chessboard position.To ensure effective training of the model, we curate a meticulously crafted dataset. This dataset consists of two essential elements: the state information, represented by Through supervised learning using this dataset, the model learns to associate the given state information with the corresponding CPL. Consequently, it acquires the ability to predict the CPL based on the provided state information as input. This training process involves iteratively adjusting the model’s parameters to minimize the disparity between its predictions and the actual CPL values present in the training dataset.x using a series of transformations:The neural network processes an input vector For the second layer, x.The third and final layer only has one neuron, and it directly outputs the weighted sum without any activation function. Therefore, Thus, the entire network can be represented by the composite function:We use an Adam optimizer with an MSE loss. The model processes the input through the layers, applying the weighted sums and hyperbolic tangent activations sequentially, to finally produce the desired output value.s, as well as the corresponding evaluation associated with that particular state.In order to train the neural network effectively, a training dataset is constructed, comprising two essential components. This dataset consists of elements that contain both the state information denoted by To gather the necessary chess game data for analysis, a vast mega database containing millions of previously played chess games is utilized. Within this database, each game is represented using the portable game notation (PGN) notation, which allows for standardized representation and compatibility with various chess software and applications.p within each game. The Forsyth–Edwards notation (FEN) is employed to determine the location of relevant chess pieces within each position p. As a result, all states The process of constructing the training dataset involves parsing and evaluating all positions p within the dataset, an evaluation is obtained. To accomplish this, the research incorporates the Stockfish engine, a widely recognized and powerful chess engine. Stockfish employs advanced algorithms and evaluation functions to assess the strength of positions. By leveraging the capabilities of Stockfish, the training dataset can determine the evaluation of each position p on the chessboard accurately.For every position s and the evaluation associated with the position p from which s was derived. This dataset serves as the foundation for training the neural network, enabling it to learn and make informed decisions based on the provided state information.Finally, this evaluation is associated with all states In this study, our proposed model is applied to a comprehensive dataset comprising over 2000 grandmaster games. The primary objective is to predict the probabilities of winning To provide a visual representation of the predicted values, heat maps are generated for both c is White and the piece p is the knight or the bishop. The resulting predictions are showcased in s.To illustrate the efficacy of our model, we first employ it to predict the centipawn evaluations By leveraging our proposed model, we gain a deeper understanding of the dynamics of the game, specifically in relation to the knight and bishop pieces within the context of the White color. This analysis not only facilitates strategic decision-making but also provides a basis for evaluating the effectiveness of various gameplay approaches. Moreover, the model’s expandability to encompass all pieces allows for a comprehensive examination of the game across different states, enabling us to uncover additional insights and enhance the overall understanding of chess strategies and pawn skeletons, as Nimzowitch surmised in his seminal work .The model is then used to determine Key squares for the bishops can be seen in The applications of the model on grandmaster games provide valuable insights into the dynamics and strategies employed by top-level chess players. By predicting the centipawn evaluations The predictions generated by the model offer a quantitative measure of the advantage/disadvantage provided by the knight and bishop pieces in specific states. Heat maps depicting the predicted centipawn evaluations By focusing on specific subsets of states, we can analyze the effectiveness of the Knight and bishop pieces individually, as well as their contributions to the overall gameplay strategies employed by grandmasters. This analysis aids in strategic decision-making, enabling players to assess the potential advantages or disadvantages associated with specific moves and piece configurations.Furthermore, the expandability of the model allows for a comprehensive examination of the game across different states. By extending the analysis to include all pieces, we can uncover additional insights into the dynamics of the game and evaluate the effectiveness of various gameplay approaches. This broader perspective enhances our overall understanding of chess strategies and gameplay dynamics.The predictions generated by the model can also be utilized for comparative analysis between different players or groups of players. By analyzing the centipawn evaluations and winning probabilities associated with specific states, we can identify patterns and trends in the strategies employed by grandmasters. This information can be leveraged to develop training materials and strategies for aspiring chess players, helping them improve their gameplay and decision-making abilities.For example, in The disadvantage of having a knight in the corner may stem from its limited mobility and restricted scope of influence. When placed in the corners, knights have fewer potential squares to reach and can easily become isolated from the central and more strategically significant areas of the board.On the other hand, as the knights move closer to the opponent’s side of the board, their positional value tends to increase. This is most likely due to the knights’ ability to infiltrate enemy territory, potentially attacking key squares, pieces, or pawns.The increasing value of knight-square states as the knights advance can be attributed to several factors. Firstly, the proximity to the opponent’s pieces and pawns provides more targets for the knight’s maneuvers and attacks. Secondly, knights positioned closer to the enemy’s side can exert greater control over central squares and influence the dynamics of the game. This control can restrict the opponent’s options and potentially create weaknesses in their position.Analyzing the values of knight-square states in different positions on the board, such as the corners and closer to the opponent’s side, supports the claim that the placement of knights significantly affects their effectiveness. Understanding the strengths and weaknesses associated with different knight positions helps players make informed decisions about piece placement, strategic plans, and tactical considerations. Key squares for the knight to occupy are marked in The applications of our model on grandmaster games provide valuable insights into the dynamics and strategies employed in high-level chess. The predictions of centipawn evaluations and winning probabilities offer a quantitative measure of the advantages and disadvantages associated with specific chess positions, aiding in strategic decision-making and gameplay evaluation. The expandability of the model allows for a comprehensive analysis of the game across different states, facilitating a deeper understanding of chess strategies and enhancing the overall gameplay experience.Our proposed model can be further applied to gain insights into the playing style and performance of specific players. In this section, we focus on the world-renowned chess player Magnus Carlsen . By applying our model to the games played by Carlsen, we aim to uncover unique patterns and characteristics that contribute to his success and distinguish his gameplay from other grandmasters.Our proposed model is applied to a dataset consisting of 2000+ Carlsen games played in the last 5 years. Similar to the previous section, we begin by predicting the centipawn evaluations s.Building upon this analysis, we further utilize the centipawn evaluations By focusing on Carlsen’s gameplay, we gain a deeper understanding of his preferred strategies and tendencies when employing the Knight piece as the “White” color. This analysis allows us to assess the effectiveness of Carlsen’s gameplay choices, providing insights into his decision-making process and potential areas of strength or improvement. Additionally, comparing Carlsen’s results to the general dataset of grandmaster games helps us evaluate his performance against the broader chess community.The model is then used to determine The applications of the model on Magnus Carlsen’s games provide valuable insights into the dynamics and strategies employed by one of the world’s top chess players. By predicting the centipawn evaluations The predictions generated by the model offer a quantitative measure of the advantage/disadvantage provided by the knight and bishop pieces in specific states encountered by Magnus Carlsen. Heat maps depicting the predicted centipawn evaluations By focusing on specific subsets of states in Carlsen’s games, we can analyze the effectiveness of the knight and bishop pieces individually, as well as their contributions to Carlsen’s overall gameplay strategies. This analysis aids in strategic decision-making, enabling players to assess the potential advantages or disadvantages associated with specific moves and piece configurations based on Carlsen’s approach.Furthermore, the expandability of the model allows for a comprehensive examination of the game across different states in Carlsen’s games. By extending the analysis to include all pieces, we can uncover additional insights into the dynamics of the game as played by Carlsen and evaluate the effectiveness of various gameplay approaches employed by him. This broader perspective enhances our overall understanding of Carlsen’s strategies and gameplay dynamics.The predictions generated by the model can also be utilized for comparative analysis between Magnus Carlsen and other players. By analyzing the centipawn evaluations and winning probabilities associated with specific states in Carlsen’s games, we can identify patterns and trends in his strategies. This information can be leveraged to develop training materials and strategies for aspiring chess players, helping them improve their gameplay and decision-making abilities while considering Carlsen’s approach.s is typically associated with favorable In By incorporating such insights into our analysis of Carlsen’s games, we gain a more comprehensive understanding of the strengths, weaknesses, and strategic implications of the knight and bishop pieces as employed by Magnus Carlsen.In sum, the applications of our model on Magnus Carlsen’s games provide valuable insights into the dynamics and strategies employed by this world-class chess player. The predictions of centipawn evaluations and winning probabilities offer a quantitative measure of the advantages and disadvantages associated with specific chess positions encountered by Carlsen, aiding in strategic decision-making and gameplay evaluation. The expandability of the model allows for a comprehensive analysis of Carlsen’s games, facilitating a deeper understanding of his strategies and enhancing the overall gameplay experience.No pawn exchanges, no file-opening, no attack—Aron NimzowitschOur study is not complete until we apply the model to the mighty pawn. Our proposed model is applied to a comprehensive dataset comprising over 2000 grandmaster games. The primary objective is to predict the probabilities of winning The results of the model when applied to the White pawn are shown in We note a few chess maxims that are reflected in the model predictions.Pawns gain value as they cross the fourth rank: This point highlights an important principle in chess, where advancing pawns beyond the fourth rank often leads to increased positional strength and potential threats. As pawns move forward, they gain control over more squares, restrict the opponent’s piece mobility, and open up lines for their own pieces. Crossing the fourth rank is a significant milestone that can significantly impact the dynamics of the game.h and a files are very good on the fifth rank: This point emphasizes the strategic importance of pawns positioned on the h and a files when they reach the fifth rank. Pawns on these files can have a powerful influence on the game, particularly in the endgame. Placing pawns on the fifth rank provides support for the central pawns, helps control key central squares, and may facilitate piece activity and potential attacks on the opponent’s position.Pawns on the Pawns on the sixth rank are deadly, especially when supported by a pawn on the fifth rank: This point highlights the strength of pawns on the sixth rank, which is just two steps away from promotion. Pawns advanced to this rank become highly dangerous as they pose a direct threat concerning promotion to a more powerful piece. When supported by a pawn on the fifth rank, these pawns can create a formidable pawn duo, exerting significant pressure on the opponent’s position and potentially leading to advantageous tactical opportunities.Edge pawns tend to be weaker than central pawns: This point draws attention to the relative weakness of pawns placed on the edges of the board (such as the a and h files) compared to pawns in central positions. Edge pawns have fewer potential squares to advance or support other pieces, limiting their mobility and influence. In contrast, central pawns control more critical squares, contribute to a stronger pawn structure, and have a greater impact on the overall game dynamics.g and h files for White) can have a more immediate and aggressive impact compared to advancing pawns on the queenside (a and b files for White). Advanced kingside pawns can create open lines, potentially exposing the opponent’s king to attacks or weakening their pawn structure. Understanding this distinction helps players assess the strategic implications of pawn advances on different sides of the board.We also found chess ideas that are not considered to be common knowledge. For example, kingside pawns are more dangerous when advanced than queenside pawns. We attempt to offer some anecdotal rationale for this: Advancing pawns on the kingside , the squares with the highest The square Also in the fourth row, we find the square In the fifth row, we encounter the square Finally, in the sixth row, the square By identifying these squares with high We next apply this model to the Black pawns. The results are shown in Similar conclusions can be drawn for Black pawns. By analyzing the highest centipawn evaluation Just like for the White pawns, the rows in the heatmap corresponding to the Black pawns reveal important squares. The squares with the highest The square The square In the fourth row, the square Furthermore, the square Lastly, the square Analyzing these key squares for Black pawns, namely By understanding the significance of these squares, players can make informed decisions regarding pawn placement, pawn breaks, and strategic plans to maximize their potential advantage and navigate the complexities of the game from the Black perspective.Q-learning, and neural networks to capture the complex dynamics of the game and facilitate informed decision-making.In this paper, we presented a comprehensive methodology for evaluating chess positions and predicting the probabilities of winning Q-learning, such as the value function s, while the centipawn evaluation measured the advantage in chess. We derived the win probability We began by formalizing the theoretical functions used in Q-value matrix represented the probability of winning given a policy/move in a specific state, and we determined the optimal policy and value function using the Q-values.To address the sequential decision problem, we employed the dynamic programming technique of Q-learning, which involved breaking down the problem into smaller sub-problems and solving the Bellman equation. The To predict centipawn evaluations Our methodology expanded upon previous work by considering a comprehensive state representation that encompassed color, piece type, and square information. This allowed for a more nuanced analysis of the game dynamics and a deeper understanding of the factors influencing the outcome. We also showcased the applications of our model, focusing on specific subsets of states, such as the knight and bishop pieces, and visualizing the predicted probabilities of winning and centipawn evaluations through heat maps.Further research in this area could explore the dynamic nature of square values, taking into account positional changes and the interaction between different pieces. By refining and expanding our methodology, we can continue to deepen our understanding of the intricate dynamics of chess positions and contribute to advancements in the field of chess AI.Q-learning, and neural networks, we achieved a comprehensive analysis of the game dynamics and enhanced our ability to assess strategic moves and guide decision-making. Our research contributes to the development of more sophisticated and intelligent chess AI systems, paving the way for deeper insights into the intricacies of the game.In conclusion, our methodology provides a robust framework for evaluating chess positions and making informed decisions during gameplay. By combining centipawn evaluation, With our methodology, we strive to unravel the logical relations of chess and provide a comprehensive understanding of the game, empowering players and researchers alike to unlock new levels of strategic thinking and mastery.


The server can assess the impact of mutations on the protein sequential and structural properties and apply a virtual screening to identify potential inhibitors that could be used as a highly individualized therapeutic approach, possibly based on the drug repurposing. PredictONCO integrates predictive algorithms and state-of-the-art computational tools combined with information from established databases. The user interface was carefully designed for the target specialists in precision oncology, molecular pathology, clinical genetics and clinical sciences. The tool summarizes the effect of the mutation on protein stability and function and currently covers 44 common oncological targets. The binding affinities of Food and Drug Administration/ European Medicines Agency -approved drugs with the wild-type and mutant proteins are calculated to facilitate treatment decisions. The reliability of predictions was confirmed against 108 clinically validated mutations. The server provides a fast and compact output, ideal for the often time-sensitive decision-making process in oncology. Three use cases of missense mutations, (i) K22A in cyclin-dependent kinase 4 identified in melanoma, (ii) E1197K mutation in anaplastic lymphoma kinase 4 identified in lung carcinoma and (iii) V765A mutation in epidermal growth factor receptor in a patient with congenital mismatch repair deficiency highlight how the tool can increase levels of confidence regarding the pathogenicity of the variants and identify the most effective inhibitors. The server is available at  In 2020 alone, more than 19 million cases of cancer were diagnosed, of which 10 million were mortal .Personalized medicine-based techniques have their limitations. Decisions are often based on next-generation sequencing technologies, which can generate a large amount of genomic or transcriptomic data. It can be difficult to analyze and interpret this amount of data in a clinically feasible manner. This leads to a divide between the generation of experimental data and their application during the decision-making process. For example, when targeted next-generation sequencing panels are used for analyzing multiple genes simultaneously, various coding sequence mutations are typically found. And when no previous data on the effect of such mutations on the expressed protein are available, the data cannot effectively support decisions since there is usually little space to study each mutation in sufficient detail .With the recent advances in protein modelling , 6, e.g.On the one hand, the traditional method of deliberation and discussion by multiple experts is naturally time-consuming. On the other hand, an alternative consisting of performing all the aforementioned analyses requires significant knowledge of computational biology and bioinformatics. It would also entail considerable time if performed manually. Here, we introduce PredictONCO, a web server for fully automated and fast analysis of the effect of mutations on protein structure, stability and function in 44 known protein targets relevant for oncology. PredictONCO applies computational methods and bioinformatic analyses and produces actionable reports within a few days. To ensure the high quality of the results, the entire pipeline was thoroughly validated using 108 clinically and experimentally characterized mutations covering all 44 proteins. The final report includes visualizations to help with data comprehension.The workflow of PredictONCO consists of two major steps: (i) selection of target protein and mutation and (ii) data analysis . In the The first step of the workflow is the specification of an input as one of the 44 pre-defined oncology-relevant proteins . CliniciAll of the 44 proteins available to the users were previously curated, following a number of steps. The most suitable calculation parameters were optimized for each target to increase the reliability of the results. First, the protein sequence and annotations were fetched from the UniProt database . The essOnce the protein is selected, users are requested to specify the mutation by typing in the substitution or selecting the position of the substituting amino acid using a visualized protein sequence. These calculations usually take up to 48 h, depending on the protein sequence length and other structural parameters (could be longer under heavy workload of the server). This time threshold was carefully selected as a balanced spot between time constraints of medical boards and the possibility to use the most reliable and accurate computational methods. However, all finished calculations are stored in a database and when a previously computed mutation is requested, the calculation output is immediately served from saved results. All 913 mutations used during the training and testing of the predictor see  are pre-Once the job is submitted, all calculations are executed automatically. The specific analyses depend on the region where the mutation is located and on the availability of a structure information for that region. If a high-quality 3D structure is available and the mutation is located in the cytoplasmic domain responsible for ligand binding, all analyses are performed, including the function-related and drug-related calculations to assess the potential inhibiting role of small molecules. The function-related and drug-related calculations are omitted if the mutation is in the extracellular unit. Only sequence-based analyses can be performed for the transmembrane and undefined regions, as these regions often lack the experimental structural information required for precise force-field calculations. Finally, the effect of the mutation is predicted using a newly developed machine learning-based predictor see .Ka change of catalytic residues using PROPKA3 [Initially, a mutant structure is constructed using the MODELLER  software PROPKA3  and iden PROPKA3 . In the  PROPKA3  is perfo PROPKA3 .The most important sequence analysis is a prediction of an effect of a mutation on a protein function performed by the meta-server PredictSNP , which cThe next step is the extraction of important and relevant information from publicly available database. We use the UniProt database  as the pIn the end, all obtained values from the structure and sequence analyses were used as features for our newly trained predictor: essentiality of the mutated residue (yes—1/no—0), the conservation of the position (the conservation grade and score), the domain where the mutation is located , the PredictSNP score, the number of essential residues, FoldX and Rosetta ddg_monomer scores, whether the residue is in the catalytic pocket (yes—1/no—0) and the pKa changes . The predictor is based on the XGBoost classification model and returns the probability of the oncogenic effect of a mutation. This model consists of a collection of small decision trees, trained sequentially by fitting each one of them to the gradients of the loss function from the previous iteration. On every iteration, the predictions of the current set of decision trees (the probabilities of a mutation being oncogenic) are weighted and evaluated against the ground truth to calculate the loss function. Therefore, even despite having weak predictive power separately , when combined they often show state-of-the-art performance, comparable with advanced methods from deep learning.https://zenodo.org/records/10013764), showing the areas under the receiver operating characteristic curve (ROC AUC) of 0.96 and 0.93 and the average precision from the precision–recall curve of 0.99 and 0.93 for the structure-based and sequence-based predictions, respectively  is a crucial molecule mediating the cell cycle . The CDKmelanoma . The mosmelanoma .Ka of catalytic residues are predicted to be negligible. Based on visualization, we can observe that the mutation is on the edge of the binding site and can have some effect on the interactions with critical molecules. Moreover, the protein loses an important interdomain salt bridge interaction. Therefore, an impact on the structure and/or substrate binding affinity can be expected based on these analyses.The tumour-derived DNA from a patient with melanoma was analyzed by whole-exome sequencing, and amongst other findings, CDK4 K22A alteration was identified. The mutation K22A previously showed to significantly decrease CDK4 binding to cyclin D1 and tumour suppressor p16 , 47 but Ebind = −9.5 kcal/mol), and, therefore, it is the top hit for potential treatment. Moreover, several other drugs were predicted as even better binders, with lumacaftor  being the top hit indicating a potential for the drug repurposing strategy  and palbociclib being the most commonly used. In the case of K22A, all four associated drugs show no significant difference between affinity in wild-type and mutant proteins. According to the docking results, none of the four drugs is directly influenced by the mutation, and their inhibitory effect should not be compromised. The highest affinity of the four associated drugs was observed for palbociclib  E1197K substitution. Since there is only limited evidence regarding the variant’s significance in the clinical databases of genetic variants, PredictONCO analysis was performed to evaluate the variant’s impact. The prediction results suggested that the variant is oncogenic with a 100% confidence score, and it affects the protein stability as predicted by both FoldX and Rosetta. Within the provided summary, substantial differences between the wild-type and mutant residue in their size and charge and a high level of evolutionary conservation of the wild-type residue are highlighted, underscoring the potential implications of this substitution. In terms of therapeutic options, the binding energies of second- or third-generation inhibitors such as ceritinib, alectinib and lorlatinib, which could be considered for subsequent treatment, do not appear to be significantly affected by the presence of the variant. This suggests that these inhibitors may remain viable options for following clinical management.The PDF report with all results for the E1197K mutation is available in the in silico evidence, PredictONCO analysis was conducted. The overall assessment returned a benign prediction with a 55% confidence score. In alignment with the overall prediction, PredictSNP and FoldX deemed this mutation to have a neutral impact on both sequential and structural levels, respectively. Stability prediction performed by Rosetta yielded discordant results, indicating a slightly deleterious effect on protein stability. In terms of evolutionary conservation, the residue displayed no significant conservation across species. When considering the entirety of the PredictONCO analysis, it did not corroborate the potential impact of the EGFR V765A variant. Consequently, this variant was not pursued further as a predictive biomarker.The DNA from a tumour of patient with congenital mismatch repair deficiency (CMMRD) was subjected to NGS analysis. CMMRD, characterized by a disruption in one of the fundamental DNA repair mechanisms, led to the presence of hundreds of variants in the tumour DNA. These variants often possess unknown functional significance, as they are randomly scattered throughout the DNA sequence . PatientThe PDF report with all results for the V765A mutation is available in the PredictONCO 1.0 is a novel web server for rapid analysis of the effects of missense mutations detected by exome sequencing in tumour tissue. It enables the analysis of important protein targets with a known relation to oncological diseases. The tool covers several important areas of analysis: (i) computational analysis of sequential and structural properties of the mutant enzyme, (ii) virtual screening of 4380 FDA/EMA-approved drugs and (iii) verified knowledge aggregated in the UniProt database. To make the tool accessible to researchers outside the bioinformatics community, the graphical user interface was designed to be highly interactive and visualize the critical information obtained from computations. PredictONCO provides the data in an easy-to-understand format, using various visual aids and comprehensible language. To meet the demands for a short execution time required by clinical practice, only the tools with high predictive accuracy and low computational time were selected, and they are executed in a highly parallel manner using our high-performance computing environment. Moreover, all previously calculated results are stored for their immediate re-use. To ensure the quality of provided data and prediction, the server was thoroughly validated using 108 experimentally characterized mutations with known clinical association with tumour.One of the limitations of the current version is its limited ability to target the mutations causing loss of function. Even though these mutations can be successfully identified during the calculations, the server itself cannot provide actionable insight into the treatment strategy. Repurposing the drugs, which are mostly inhibitors, on an already non-functional protein will not bring helpful effects. To overcome this limitation in the next version, we plan to incorporate knowledge from the biological pathways that will also allow inhibiting proteins downstream in the pathway. The second drawback of our method is its inability to predict effect of insertions and deletions even though they are also often related to oncological diseases. Unfortunately, this problem is still generally unresolved and there are no reliable tools available. However, we are actively monitoring newly published tools, and as soon as any promising options emerge, we will work to integrate them into PredictONCO. The third challenge concerns missing and low-quality regions of protein structures. We have already attempted to use models that AlphaFold 2  predictePredictONCO is a web server that analyzes the effects of mutations on proteins frequently altered in various cancer types.The server assess the impact of mutations on the protein sequential and structural properties and apply a virtual screening to identify potential inhibitors that could be used as a highly individualized therapeutic approach.PredictONCO integrates predictive algorithms and state-of-the-art computational tools combined with information from established databases.The server currently covers 44 common oncological targets and provides a fast and compact output ideal for the often time-sensitive decision-making process in oncology.Three use cases of missense mutations highlight how the tool can increase levels of confidence regarding the pathogenicity of the variants.report_ALK_E1197K_bbad441Click here for additional data file.list_of_protein_targets_bbad441Click here for additional data file.


PyPSA-Eur-Sec is a sector-coupled energy model of the European energy system and includes the electricity, heating, transportation, and industry sectors. The model and this extension are fully open-source, and all data sources and cost assumptions are openly available. The model allows for transparent, reliable, and computationally efficient analyses. These can form a solid basis for energy investments and policy advice. Additionally, for the first time, we present a diagram of the inner workings of the PyPSA-Eur-Sec model. We visualise exactly the possible energy flows, energy conversions, and couplings between the sectors in the model.  Specifications Table*Method detailsSectoral and national-sectoral emissions reduction caps enable the investigation of specific emission goals in the individual sectors. The purpose of the method is to allow the study and comparison of energy system configurations in which different sectoral emission reduction goals are fulfilled. The overall purpose is to enable the development of open, trusted, and nuanced policy advice.2 emission cap, thereby allowing the simulation and assessment of highly renewable future energy systems. Having a single emission cap, the decarbonisation pressure forces the cheapest-to-transit technologies and sectors to become renewable first. Even though this might at first seem economically desired, several potentially undesired effects can occur, and no control of where decarbonisation efforts are applied is given. In a pure cost-optimisation, the sectors are decarbonised one by one instead of simultaneously. This leads to rapid deployments of capacity that might lead to the rise and quick fall of investment opportunities leading to unstable business incentives. The proposed method allows for detailed studies of varying sectoral emissions and thereby provides a possibility to gain a deeper understanding of the issues as well as giving the modeller control over the individual sectoral emissions.The original method includes the possibility of setting a global COThe model is based on the open-source model PyPSA-Eur-Sec, first introduced in 2 constraint ensuring that the combined emissions remain below a given threshold. For the full implementation details see 2 emissions to an upper limit 2-tonne-per-MWhth emissions of the fuels 2 sequestration. The Lagrange multiplier 2. It is interpreted as the price per tonne of CO2 that emitting producers need to pay so that the price competition on the open market between emitting and non-emitting producers results in staying below the set emission cap. If the emission cap 2 price is zero.In the PyPSA model, the system configuration is optimised according to the following minimisation problem that is evaluated for every bus 2 emissions by emitters attributed to sectors and countries to individual upper limits We introduce additional constraints to limit the emissions from individual sectors and/or countries. They are similarly introduced directly into the minimisation problem as inequality constraints limiting the CO2 emissions are determined from the optimised dispatch. The national-sectoral CO2 prices are again determined as the Lagrange multipliers  from their respective constraints. Hence, the emission prices are an output from the model.The extended model allows to set a global emission constraint, national emission constraints, individual emission targets for the four sectors, or national-sectoral emission constraints that set upper emission caps on both national and sectoral levels. In the case of national-sectoral emission caps, one constraint per country and sector pair is implemented. Subsequently, the realised national-sectoral CO2 prices changes. A generator that is subject to multiple emission constraints will pay only the largest of the respective CO2 prices.The more emission constraints are implemented the larger the chance that they overlap and that some become non-binding. If multiple emission constraints are overlapping, like in the case of having both a constraint on the power sector and a global constraint on the whole energy system, the interpretation of the CO2 emission reductions are given in per cent relative to their corresponding 1990-levels.We apply the sectoral emissions constraints on the direct sectoral emissions instead of the indirect emissions. This allows us to detangle the interconnected sectors. Subsequently, the indirect sectoral emissions can be obtained from the optimised model. All COThe emissions in the transportation sector are exogenously defined by a fuel-switching parameter for road transport. This parameter sets the share of internal combustion engine (ICE) vehicles, battery electric vehicles (BEVs), and fuel cell electric vehicles (FCEVs). Half of the BEVs are assumed to do smart charging and allow feed-in back to the grid. Shipping demand is assumed to be supplied by hydrogen and aviation demand via traditional oil-based fuels. Thereby, the transport sector emissions are defined fully exogenously, and no additional sectorial constraint is necessary.The electricity sector and heating sector share the emissions from combined heat and power (CHP) units proportional to the respective electricity and heat output. For both sectors, direct emission constraints are implemented following 2 sequestration in the model to make sure that no carbon leakage between the sectors occurs.The remaining emissions are attributed to the industry sector and limited by the global emission constraint. Hence, no additional sectoral constraint is necessary. For steelmaking in the industry sector, we presume the use of Direct Reduced Iron (DRI) in the Electric Arc Furnace (EAF). In the model, we assume an exogenous fuel switching for the production of steel where we set the fraction of steel produced via the primary route (DRI + EAF) against the secondary route (EAF). Similarly, we assume for the production of aluminium a fraction via the primary route versus scrap reuse. We disable the possibility of COconfig.yaml), while the constraint definition is included in the script files scripts/prepare_sector_network.py and scripts/solve_network.py.The sectoral and national-sectoral emission caps are set directly in the configuration file  are assumed to be part of the industry sector. The generation of synthetic electrofuels is likewise included in the industry sector.The model performs a brownfield optimisation, i.e. taking today's capacities as a starting point for the further build out of the European energy system. It includes a detailed description of the European transmission network as shown in On 2 flows. Likewise, the PyPSA software architecture was designed around emission constraints and thereby integrates these constraints directly instead of having to build a custom module around it. The software that comes closes to PyPSA in terms of functionality is PLEXOS in which it is also possible to set emissions caps and allocate allowances. However, this framework is neither free nor open source and the inner workings can thereby not be compared. For a detailed comparison of PyPSA to other available software see Table 3 in PyPSA combines the strengths from traditional power system models with the universality from energy system models. Power system models offer their detailed electrical methodology that includes the physics of power flows while energy system models in turn include investment optimisation and energy system coupling. Hence, PyPSA can be applied for both purposes, but its real strength lies in combining the two approaches. PyPSA is often compared to the power system model PYPOWER Please note that the PyPSA-Eur-Sec model is still under active development. Extensive changes, as well as vital additions to the model, are still occurring frequently. Changes to the methodology are likewise possible to occur. Please see 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.


They found that the spatial distribution of oxidizing agent  controls the advantage of remaining near the air-liquid interface, and hence the advantage that EPS-overproduction confers. All together this research paves the way for a deeper comprehension of the relationship between the environment's spatial structure and population dynamics.The formation of biofilm at the air-liquid interface of a still flask is related to the emergence of exopolysaccharides (EPS) overproducers. These mutants have the ability to remain near the surface, where oxygen is abundant. Yet, it is still unclear what role oxygen plays in cellular metabolism under this condition. A. Besse, M.-C. Groleau and E. Déziel (mSphere e00057-23, 2023;  A bacterial culture flask left on the bench for many days will result in the production of biofilm at the air-liquid interface (ALI). A study by Besse and colleagues reveals with clarity that the seemingly simple observation has profound implications .The formation of bacterial biofilm at the ALI is frequently triggered by the colonization of the surface by a mutant derived from the ancestral population. This mutant evolved to overproduce a polymer, which provides a competitive advantage at the ALI. Several mutants of exopolysaccharides (EPS) overproduction have been identified across species –4, as we–The abundance of oxygen at the ALI is generally accepted as the reason for colonization of this environment , 8. FastIn this context, the ability for bacterial cells to remain at the liquid surface is critical. The insightful research undertaken by Besse et al. provides a clear description of the cellular metabolic state in relation to the oxygen availability.anr that is incapable of metabolizing nitrate (due to the shutdown of the denitrification pathway by ANR) resulted in the selection of EPS-overproducing mutants even when nitrate was supplied. Moreover, they calculated the quantitative threshold of the NADH/NAD+ ratio below which the redox state of the population promotes the invasion of EPS-overproducing mutants.The authors went beyond the notion that dissolved oxygen is the driving force behind biofilm development and demonstrated that, instead, any oxidizing agent could play a role in biofilm formation. While oxygen is the most common oxidizing agent, an alternate agent, such as nitrate, can be used to maintain a healthy metabolism. To prove their point, they measured the cells' metabolic activity in a flask by measuring their redox state through the NADH/NAD+ ratio. When nitrate is supplied to the bacterial population in place of oxygen for oxidation, much fewer EPS-overproducing mutants were found to invade the ancestral population . On the Their research emphasizes that the spatial heterogeneity of an oxidizing agent is responsible for the selection of overproducer mutants at the ALI. Further evidence is shown when cultures are treated with pyocyanin, a diffuse metabolite that can transport electrons from the cell to a distant oxidant. Addition of pyocyanin to the flask makes the oxidizing agent's activity available across a greater distance. Therefore, the oxidizing environment becomes more homogeneous . The me


Self-administered paper or electronic surveys can create accessibility issues for people with language barriers and limited literacy, whereas face-to-face interviews can create privacy issues and give rise to reporting biases, particularly in the context of sensitive subject matters. An audio computer-assisted self-interview (ACASI) offers an alternative mode of survey administration, and its use has been tested against other survey modes to determine whether the presence of a background narration helps overcome literacy and privacy issues. There are still gaps with the ACASI survey administration because audio narration alone does not assist respondents with limited literacy in choosing response options. To overcome literacy issues, a few studies have used illustrated pictures for a limited number of response options.This study aimed to illustrate all the questions and response options in an ACASI application. This research is part of a larger study comparing different modes of survey administration  to collect data on hepatitis B knowledge, attitudes, and practices among the Myanmar-born community in Perth, Australia. This study describes the 2-phase process of developing a web-based ACASI application using illustrated pictures.The first phase was the preparation of the ACASI elements, such as questionnaire, pictures, brief descriptions of response options, and audio files. Each element was pretested on 20 participants from the target population. The second phase involved synchronizing all the elements into the web-based ACASI application and adapting the application features, in particular, autoplay audio and illustrated pictures. The preprototype survey application was tested for user acceptance on 5 participants from the target population, resulting in minor adjustments to the display and arrangement of response options.After a 12-month development process, the prototype ACASI application with illustrated pictures was fully functional for electronic survey administration and secure data storage and export.Pretesting each element separately was a useful approach because it saved time to reprogram the application at a later stage. Future studies should also consider the participatory development of pictures and visual design of user interfaces. This picture-assisted ACASI survey administration mode can be further developed and used to collect sensitive information from populations that are usually marginalized because of literacy and language barriers. In administering surveys of sensitive topics through face-to-face interview, self-administered paper, and computerized survey modes, research suggests that privacy, limited literacy, and language difficulties were common barriers ,2. AudioThe use of pictures in health communication has been shown to be helpful in overcoming literacy issues and language barriers among culturally and linguistically diverse (CaLD) populations ,11. A reAlthough a few studies have examined the use of pictures in web surveys and concluded that such practice could affect survey responses in various manners, for example, as accentuation, interference, and visual context effect -17. Few Despite the positive reports on the use of pictures in overcoming language barriers and literacy issues ,11, the This study describes the 2-phase process of developing a web-based ACASI application using illustrated pictures. Phase 1 involved the preparation of the ACASI elements, and phase 2 involved synchronizing all the elements into the web-based ACASI application and the adaptation of the application features. This study outlines the challenges, feasibility, and limitations of the development process and areas for future research.The development of a web-based ACASI application with illustrated pictures in 2020 involved following two phases: (1) the development and pretesting of the elements of the application and (2) synchronizing the pretested elements into an existing ACASI application and the adaptation of the application features. The steps involved in application development are summarized in Before the development of each element of ACASI application, potential application sources were explored. A range of ACASI application sources have been used in previous studies. Some researchers have purchased the subscription of ACASI platforms and built surveys on these platforms. Some studies contracted ACASI software developers who had adapted existing applications for each study. Other research teams have developed new applications that are specific to the corresponding studies. Examples of available ACASI platforms or software development services identified were Blaise, DatStat Illume Next, Questionnaire Development System, Research Triangle Institute, The Viewpoint Organization, Tufts University, and Westat ,27-32. ABecause internet access was readily available at the study location , a web-based ACASI application was selected for this study. The ACASI application was sourced from the Viewpoint Organization, which had developed web-based ACASI applications with different features customized to the aims of corresponding projects . It was The questionnaire topics included hepatitis B knowledge, attitudes, and practices. The questionnaire was adapted from similar previous questionnaires used among CaLD populations in Australia -41. The The questionnaire contained 40 questions. Only close-ended questions with multiple-choice response options were included. The forced answer option in the ACASI mode was used because respondents tend to skip questions unintentionally or intentionally, and the “forced answer” feature in web surveys has shown to overcome this issue ,47. To aFor the picture element of the ACASI application, the first author (NP) developed storyboards for each question and response item, because evidence suggests that specifications for the pictures should be determined by researchers, not by the artist . StoryboThe draft storyboards were reviewed, discussed, and revised by the research team. The questionnaire and revised storyboards were then presented to the artist who was based in Myanmar. The artist had 15 years of experience in creating comics, knowledge about hepatitis B, and an understanding of Myanmar’s culture. The artist used the Tayasui Sketch application  to develAt the same time as developing pictures, audio aids were developed. Text-to-speech (TTS) software was used to read the English questionnaire aloud in the ACASI application. For pretesting, the English questionnaire was read aloud using the Speak feature of Microsoft Word and was recorded using a voice recorder. The English voice file was approximately 12 minutes long. Owing to the high cost of the TTS software for the Myanmar language, a recorded speech was used for the Myanmar questionnaire. In reading aloud the questionnaire, to avoid measurement bias, the questionnaire was read aloud exactly in the way it was written . An ACASA pretest of audio-visual aids  was performed by the research team and the participants of 20 questionnaire face validity test. The draft pictures were reviewed for appropriateness and whether the pictures matched the corresponding text because viewers might interpret the same picture differently . The shoThe research team provided software programmers with the questionnaire, pictures, texts to display, wireframes (a sketch of each user interface), preferred color scheme, script for English narration, split speech files of Myanmar narration, and question skipping patterns. The research team designed only the user interface and not the administrator interface. In the user interface for survey respondents, ≥1 question items could be displayed on each page. In a study comparing scrolling (>1 question items per page) and paging (1 question item per page) designs of the same survey, a higher proportion of respondents left the survey on the first page with 8 question items than with 1 question item . TherefoEach page could be locked as a portrait or landscape display or the “autorotate” feature could be enabled. Studies that compared the users’ perspective of portrait or landscape view on different devices for different tasks concluded that the portrait view was more commonly used than the landscape view, especially when apps were used on small mobile devices -58. In tEach page contained display-only texts, images, and interactive buttons. The display-only images were the logo of software developers, flags of the major ethnic groups of the target population, and pictures illustrating the questions . The flaThe audio component of the current application could be played automatically and on demand. Once each page was launched, the audio played automatically, and pulsing radio waves highlighted the speaker button next to the picture related to the corresponding audio narration so that respondents would be able to match what they heard to what they saw . When a Regarding the position of pictures, a few studies examined the “banner blindness effect,” positioning the pictures or symbols before or after or left or right to the text, and the proximity of graphics to the text; however, there was no definitive evidence for best practice ,14,59,60Next to each response option picture were interactive radio buttons. The radio buttons were placed on the left-hand side of pictures and text because available literature suggests that this position could assist respondents in selecting the response options better than placing radio buttons on the right-hand side of the picture . An emptThe green check sign assured the users that the selection had been made. In the current application, the forced answer feature is deployed because 2 previous experimental studies concluded that there was an association between a forced answer feature and low item nonresponse rates ,47. WhenNavigation buttons were included immediately below response options. In the current application, only the “next” navigation button was included, not the “back” button, and manual forwarding was applied. Automatic forwarding saves time because users are not required to select “next” for each question; however, previous studies have indicated that this could be associated with a high item nonresponse rate . AutomatAnother study compared the positions of navigation buttons . The stuThe current application avoided the color yellow. A few qualitative studies have reported that jaundice or yellowish discoloration of the skin and sclera is a well-known symptom related to hepatitis B among migrants in Australia, and some misunderstood that all people living with hepatitis B had jaundice or those without jaundice did not have hepatitis B -69. A whProgrammers synchronized the provided elements into the existing web-based ACASI application and adjusted the application features. The two new features added were as follows: (1) automatically played audio narration and (2) pictures hardwired to the response option buttons. In previous ACASI applications, speech played when the speaker button was pressed. In the adapted version, speech played automatically. In the current version, the pictures were included for both questions and response options, and pictures illustrating response options were hardwired to radio buttons. Among the existing features, the question skip feature was used in this study. The logic check feature was not included in this study because previous reports have suggested that it might take longer for the participants to go back to previous questions, introduce confusion, and interfere with the sense of privacy . If the The web-based ACASI application was written in HTML revision 5. The application was written to be used with any browser and to work on any computerized device and most mobile devices that had built-in sound and speakers. The base questionnaire was created in English from which a translation template was created. In the translation template, the questions and response options were translated into the Myanmar language, and then the Myanmar template was imported into the database.The questions, responses, and pictures were built into a questionnaire database that allowed the analysis tool to monitor the progress of data collection, analysis, and export. The data were stored in the Structured Query Language database. The 2 servers used by ACASI application were a live production server used for production and a live spare server that mirrored the production server, which is available as an immediate backup. The ACASI application stored the data immediately after the response was entered. A respondent could not move on to the next page before the response was securely stored in the server. No data were stored in the device used; therefore, no storage space was used in the corresponding devices, and there was no risk of losing data if the device was damaged or stolen.The application stored data in the English language, regardless of the language used by the respondents when completing the survey. The data were exported using the data extraction feature in Excel format, which could be configured for statistical software. Reports could be generated to show the languages used by the respondents. In addition, individual records could be displayed in the input language, although the data were stored in English language. The data analysis feature was for exporting individual records, frequency reports, cross-tabulation reports, and any open text.The research team and 5 participants from the target community conducted user acceptance testing of the preprototype ACASI application. The research team tested compatibility, functionality, and usability -72. For The test users performed general user interface testing. They provided feedback on whether it was easy to use the application, hear and follow the speech, select and input their answers, and navigate to the next pages. To avoid the fatigue of pretest participants, it was decided not to seek detailed feedback on subjective user satisfaction regarding the general appearance of application interfaces, such as colors, font size and picture size, the functionality of speaker buttons and push buttons, and the clarity of instructions. After the review by the test users, suggestions for minimal changes were made. The test users commented that it was not user-friendly when they had to drag the page left and right to see all the contents on the touchscreen device. For pages with 4 or 5 response options in a row, the interface was larger than the frame of the device used during the user acceptance test; hence, not all the contents were visible without scrolling or dragging the page. Another comment was that when there were 4 or 5 response options in the same row and they chose the last or second last option, because those 2 options were not visible without dragging the page, they were confused whether their answer of choice had correctly been chosen. As a result, minimal adjustments were made to the preprototype application. The application interface was resized to autofit with the frames of the devices such that no scrolling or dragging would be required. The layout of the response options was limited to 3 in a row.This study was approved by the Curtin University Human Research Ethics Committee (HRE2020-034). The draft questionnaire and audio-visual aids were pretested by 20 adults recruited from the Myanmar-born community in Perth. In total, 5 Myanmar-born adults participated in a user acceptance test of the preprototype application. Informed consent was obtained from all the participants before data collection. No identifiable information was collected. For each pretest, each participant received Aus $20 (US $15.2) as an acknowledgment for their time. The pretest audio recordings and databases were stored in a secure storage facility at Curtin University.After 1 year of preparation and development of the application, the prototype application was functional with ACASI survey features, such as audio narration, computerized survey administration, and data storage and export. Users were required to log in to the ACASI application with user-specific log-in credentials. The language choice option was displayed on the log-in and welcome page. The welcome, consent, title of each session, each question item, and closing page were shown on separate screens. For the 40 question items in the questionnaire of this study, the survey was shown on 48 separate pages in the application. It took 8-15 minutes to complete the survey, depending on the language used and whether the users selected responses before or after listening to the complete speech file of each page. The user had to press the “next” button to progress to the next page. There was an “exit” button on every page, which allowed users to quit the survey at any time. The narration played automatically once the user accessed each page. Touchscreen devices were used by respondents to enter the survey responses. To enter a response, the users had to tap on the corresponding picture. Because it did not require the users to press exactly on the circular radio button for each response option, it was easy for the users to enter their responses. When the user pressed “next” without choosing any response option, there was a prompt  saying, This paper describes the process of developing a picture-assisted ACASI application, including its challenges and good practices. In the first phase of the development process, the development and pretesting of questionnaires, pictures, audio recordings, and a brief description of the response options were presented. The second phase discussed the synchronization of pretested questionnaires and audio-visual aids into the existing web-based ACASI application; the technical information of the application; and the adaptation of the application features, such as the visual design of the user interface, the functions of radio buttons, navigation buttons, push buttons, and audio narration with pulsing radio waves, highlighting the corresponding response options.Major challenges in the development of this web-based ACASI application with illustrated pictures were the time-consuming and resource-intensive nature of developing, pretesting, and revising pictures; splitting the audio files into separate tracks; setting up the questionnaire in the ACASI application; and adjusting the features of the existing web-based application. This finding is consistent with the time-consuming nature of designing, setting up, and implementing ACASI software reported by a study that piloted a station-based ACASI application in 3 languages in South Africa in 2008 .The development process for pictures and audio files in this study was found to be useful because no major revisions were required. In this study, the artist had a cultural background similar to that of the target population. An ACASI study in India in 2010 required making pictures developed in the United States more discreet and culturally appropriate for study participants in India, and this warranted the reproduction of pictures by a local artist ,75. For A good practice learned in this study is that the elements of ACASI were pretested and revised before being synchronized into the application. In doing so, time and other resources were saved to avoid making multiple changes during development and in a later version of the application. If changes were required, a new questionnaire database would be required rather than amending the former database, which would require additional time investment and associated costs. Similar recommendations have been made by researchers who formerly developed ACASI applications that translated questionnaires, pictures, and audio files should be pretested ,37,74. PThe limitations of this study include the small sample size for the user acceptance test. However, the feedback collected from the user acceptance test participants was constructive in refining the preprototype version of the application. To improve the application further, an alert prompt can be accompanied by an audio track. The response options for radio buttons can be made larger. Each response option (radio button and picture) can be enclosed in a boundary that functions as a wide button. The speaker button can be enlarged and positioned on the left side of the corresponding wide button.This study shares the experience of developing a web-based ACASI application with illustrated pictures. The newly developed application was found to be fully functional with essential ACASI web survey features, such as background audio narration, computer-assisted self-administration of surveys, question skip commands, secure data storage, and versatile data export. The new features added to the current application were illustrated pictures and autoplay audio features with pulsing radio waves. Including several elements in the web-based ACASI application requires careful consideration of multiple factors in its development and use. Pretesting each element was found to be a useful approach. On the basis of our experiences, we offer suggestions to assist other researchers during phase one of the development of a web-based ACASI application with illustrated pictures.Agree on the specifications of each element of ACASI at the beginning of the process: The type of ACASI application to be used must be determined at the beginning of the process such that application-specific picture file formats can be produced. For example, 4 classes of bitmap file formats were required for each picture button in Questionnaire Development System ACASI software .Shorten the preparatory phase: It would save time to use a pretested questionnaire in the development of a similar ACASI application in the future. A shared repository of pictures can be developed from which pictures can be reused whenever relevant.Itemize audio tracks: When the background narration has to be recorded in multiple languages and if the person responsible for splitting the audio files into separate tracks does not understand the languages, each separate track can be numbered, and the narrator can read the numbers in English during recording.Consult consumers on pictures and application interface: If time and resources allow, representatives of the target population should be consulted for picture development and visual design of the application.Pretest each element before developing an ACASI application or setting up the elements on an ACASI platform: This would save time and resources to reprogram the application at a late stage.With the possibility of using pictures in ACASI applications, ACASI with pictures could be further tested as an innovative mode of administering surveys about sensitive topics among people who use different languages and dialects, who have different competencies of the languages used in the survey, and who have different literacy levels.


Introduction: Oxford Nanopore Technologies (ONT) is a third generation sequencing approach that allows the analysis of individual, full-length nucleic acids. ONT records the alterations of an ionic current flowing across a nano-scaled pore while a DNA or RNA strand is threading through the pore. Basecalling methods are then leveraged to translate the recorded signal back to the nucleic acid sequence. However, basecall generally introduces errors that hinder the process of barcode demultiplexing, a pivotal task in single-cell RNA sequencing that allows for separating the sequenced transcripts on the basis of their cell of origin.Methods: To solve this issue, we present a novel framework, called UNPLEX, designed to tackle the barcode demultiplexing problem by operating directly on the recorded signals. UNPLEX combines two unsupervised machine learning methods: autoencoders and self-organizing maps (SOM). The autoencoders extract compact, latent representations of the recorded signals that are then clustered by the SOM.Results and Discussion: Our results, obtained on two datasets composed of in silico generated ONT-like signals, show that UNPLEX represents a promising starting point for the development of effective tools to cluster the signals corresponding to the same cell. Single-cell RNA sequencing (scRNA-seq) is a widely adopted approach to carry out high-precision analysis of complex biological systems, with particular impact on stem cell biology and cancer research . In factbasecalling using dedicated algorithms. Both ONT and the scientific community have developed several alternative basecallers in an attempt to maximize the accuracy of the generated sequences.In this context, nanopore sequencing is a recent nucleic acid sequencing approach developed by Oxford Nanopore Technologies (ONT) that allows for sequencing individual, full-length DNA or RNA molecules. The principle of this technology is based on ratcheting a nucleic acid strand through a proteic nanopore in the presence of an ionic current across the pore itself. As the molecule is threaded through the pore, the chemical composition of the nucleotides residing within the pore at any given moment alters the ionic flow. Thanks to a current sensor coupled to each pore, these current alterations are continuously recorded, producing a trace of current (picoAmperes) over time. This signal can then be translated into a nucleic acid sequence in a process called Short-read scRNA-seq methods  allow for effectively reconstructing the biological complexity in terms of gene expression; though, the fact they rely on sequencing just the 3′ or 5’ end of transcripts prevents an accurate exploration of other levels of heterogeneity, such as expressed mutations and alternative splicing events. On the contrary, long-read sequencing methods  enable to cover the entire length of the transcripts and to perform the aforementioned analyses, but they can only exploit bulk RNA that is not multiplexed at the cellular level  to prodar level . Subsequar level . FinallyOne critical issue of this process is BCs demultiplexing, which consists of re-assigning every read to its origin sample after all reads have been sequenced together. This task is generally performed by the software of the sequencing platform, exploiting as information the sequence of the BC associated with each sample. BCs demultiplexing on Illumina data relies on the low error rate (around 0.1%) of the sequencing platform ; insteadi.e., dilation or compression of the signal along the time axis. To the best of our knowledge, DeepBinner . Specifically, we use autoencoders to build a compact, latent representation of the BC regions of the nanopore electrical signals; then, we cluster such representations using a SOM. To test and evaluate the performance of UNPLEX we used in silico, and explain the unsupervised machine learning approaches—autoencoders and self-organizing maps—used in this work. Then, we introduce the UNPLEX framework and describe the hyper-parameter optimization process employed to enhance UNPLEX performance. In Section 3 we show the results obtained by running UNPLEX on two in silico datasets. Finally, in Section 4 we discuss the results obtained and draw future research directions for this work.The paper is structured as follows. In Section 2 we describe how raw signals were generated The generation of raw signals consists of two consecutive phases, starting with the creation of random nucleic acids sequences, followed by the generation of the corresponding current signals .Dseq in the following) consists of three steps: (i) a set of A different BCs are created by sampling each nucleotide from a uniform distribution so that, for each position of every BC sequence, all nucleotides have the same probability to be sampled; (ii) a set of B different UMI sequences are created by sampling each nucleotide from a uniform distribution so that, for each position of every UMI sequence, all nucleotides have the same probability to be sampled; (iii) each UMI is concatenated to each BC.Phase 1. The creation of the dataset containing the nucleic acids sequences  of the longest signal in the dataset Dsig. To this aim, we first isolate the first half of each signal: this is a heuristic used to remove a portion of the UMI signal, since the BC and UMI sequences have similar lengths. Then, each half of the signal is concatenated to itself until the signal length is equal to l. In case the concatenation step produces signals with a length higher than l, the signal is truncated at length l. Finally, the intensity values of the signals are normalized in the unit interval by using as the minimum (maximum) value the lowest (highest) intensity value among all signals used to train the autoencoder. It is worth noting that the isolation of the first half is performed on the signals and not on the original sequences because, in real-world applications, the information regarding the original sequences is not available.Being based on convolutional layers, the input data of the autoencoder must be of a fixed length. Since the ONT-like signals have different lengths, as described in Demb by extracting the latent representations, or embeddings, of all signals belonging to Dtsig. The dataset Demb will thus contain vectors of real numbers used to train the Self-Organizing Map, as described in Dtsig is partitioned into a training set and validation set according to a 80 − 20 split. We exploit the Adam optimizer, and an early stopping criterion monitoring the Mean Squared Error (MSE) computed on the validation set.The autoencoder is used to generate a new dataset Self-Organizing Maps (SOMs) are a class of unsupervised neural networks that exploit a competitive learning approach to cluster the input data in a regular grid of elements called neurons . NeuronsDemb, and the weights are then sampled from the hyper-plane defined by the two eigen-vectors. The learning rate is a real-valued number that determines the magnitude of each neuron’s update and prevents the neurons from “forgetting too quickly” input vectors previously received. The topology determines how the neurons are organized in a regular grid; in this work, we consider squared tiles and hexagon-shaped tiles. The topology directly impacts on the choice of the most suitable neighborhood function, which determines how the neurons in the BMU’s neighborhood interact, compete and learn to better match the samples. The neighborhood functions are time-dependent, meaning that the size of the neighborhood they identify decreases as the training of the SOM proceeds. The neighborhood function also influences the magnitude of the update of the weights: the further from the BMU the neuron is, the weaker the update. In this work, we test four neighborhood functions: Gaussian, Bubble, Mexican hat, and Triangle  , which wngle see . The Gaungle see .i) a couple of samples is supposed to belong to the same cluster and it is correctly grouped into the same cluster; (ii) a couple of samples is not supposed to belong to the same cluster and it does not belong to the same cluster. Similarly, FMS considers all the possible couples of samples and rewards the clustering when the couples are correctly clustered together. Conversely, a penalization is used in FMS when two samples that should belong to different clusters are grouped together and when the samples that should belong to different clusters are grouped together.At the end of the training process, the SOM is employed to determine the BMU of each element of the input dataset, thus grouping the elements according to their representative neuron. In particular, it is possible to apply a clustering algorithm to the SOM’s neurons to obtain a mapping from the input vector to the BMU. To evaluate the quality of a clustering, extrinsic metrics can be leveraged when the ground truth for each sample is available. In this work, we use two extrinsic metrics: Adjusted Random Score (ARS)  and FowlThe performance of any machine learning algorithm is influenced by the values of its hyper-parameters, whose tuning is typically based on a trial-and-error approach that is time-consuming, error-prone, and hampers the repeatability of the experiments . The trii) build a surrogate model on the basis of the results of randomly sampled parameterizations; (ii) find the optimum of the surrogate model; (iii) evaluate the corresponding parameterization; (iv) update the surrogate model. Steps (ii)-(iv) are repeated until a termination criterion is met  have been performed). The main advantage of this sequential approach is that the first surrogate model is built by leveraging a fraction of the MEs available. So doing, the remaining MEs are used to simultaneously update the surrogate model and effectively search for an optimal parameterization. The sequential approach also allows for performing a reduced number of MEs, which is ideal when the training and evaluation of a model are expensive in terms of computational resources. In fact, the execution of a sequential search can be interrupted at any iteration and the method provides a sub-optimal solution.In this work, we use the Sequential Model Based Optimization (SMBO), a general-purpose method specifically defined to optimize the hyper-parameters of an algorithm by means of a Bayesian optimization process . The SMBThe UNPLEX framework leverages a pipeline composed of three unsupervised machine learning techniques—autoencoder, SOM, clustering—to cluster ONT-like raw signals according to their BCs . We opteDsig are pre-processed to generate the dataset Dtsig; then, the convolutional autoencoder is trained by exploiting all available signals and produce their latent representations, which are collected in dataset Demb. In the second step, the SOM is trained using all embeddings in Demb, and a BMU is assigned to each embedding. In the third step, the BMUs are clustered using an agglomerative approach to identify the neurons of the SOM that represent the embeddings of all raw signals sharing the same BC  and B = 1 000 (UMIs) for D1, and A = 100 and B = 1 000 for D2. The chosen value of A represents the number of cells in each dataset, and B roughly represents the number of transcripts derived from each cell. Given the different size of the two initial datasets, the hyper-parameters of UNPLEX are optimized by leveraging a budget of 60 MEs for D1, and 30 MEs for D2 due to the higher computational costs. The score function used to guide the optimization process of SMBO is defined as the average between the FMS and ARS score values. For both datasets, the number of clusters to be identified by UNPLEX is set equal to A, i.e., the number of BCs present in the datasets.Two in silico generated signal (left panel), the corresponding pre-processed signal in Dtsig (middle panel), and the signal reconstructed by the autoencoder (right panel). Noteworthy, the autoencoder output signal is a de-noised version of the input signal: the embedding does not contain any information about the noise introduced by the recording process of the nanopore. At the end of the training, the reconstructed signals are thus similar but not identical to the input signals, suggesting that the autoencoder successfully learn an embedding that can be used to reconstruct the input.In D1 with the best hyper-parameterization found by SMBO. Each SOM’s neuron is highlighted with a color representing the cluster it is assigned to. D1 are perfectly grouped according to their BC, producing a sequence of uniformly colored vertical bars. In the right plot, the signals are displayed with the colors corresponding to the clusters they are assigned to by UNPLEX, and are arranged in vertical bars that can follow a different order with respect to those of the gold standard. If the right panel shows only uniformly colored columns, this means that UNPLEX perfectly clusters all the signals, i.e., both FMS and ARS are maximized. In the case of D1 (ARS = 0.90 and FMS = 0.92), which provide a numerical indication of the clustering results, confirm the good outcome achieved by UNPLEX.In D2 with the best parameterization found by SMBO. D2 (right). In this case, although the column structure is maintained in most cases, the percentage of misclustered signals is increased with respect to the results obtained on dataset D1. Indeed, the performance metrics indicate a slight decrement of the quality of the clustering (ARS = 0.82 and FMS = 0.81) but yet confirm the effectiveness of UNPLEX.D1 and D2 is not caused by the inherent stochasticity of the employed methods, but by the higher number of BCs considered in D2. Indeed, since the number of BCs in D2 is twice the amount of D1, the dissimilarity of the signals produced by different BCs decreases, causing an increase in the error rate of UNPLEX. In fact, in the tests performed on both datasets, we observe that the misclustered signals that should have been assigned to a specific cluster, are instead all inserted into a unique other cluster, i.e., UNPLEX assigns these signals to only one other BC. We hypothesize that this behavior is caused by the fact that such signals are assigned to BMUs of the SOM that lie on the border between two clusters. Stated in other words, considering the graphical representations of the SOM in We argue that the difference in performance between Long-read sequencing methods such as ONT, coupled with high-throughput single-cell sequencing platforms, hold the promise to greatly expand the scope of scRNA-seq analyses towards multi-omics approaches, enabling the exploration of additional layers of biological complexity . In thisIn our tests, UNPLEX reaches high values of ARS and FMS on two datasets containing 50 000 and 100 000 signals; this result highlights that the raw signals can be properly clustered according to their BC. Contrary to other published methods , UNPLEX in silico. So doing, when a new, real-world dataset has to be clustered, the optimal hyper-parameters for the respective dataset size can be identified by looking at the previous analysis and at the signals properly clustered without any optimization step.The main drawback of the current implementation of UNPLEX is related to the running time, caused by the quadratic scale with respect to the SOM dimension, and the necessity to tune the SOM hyper-parameters to obtain a better clustering of the embeddings. A possible strategy to limit the impact of the computational costs would be analyzing the relationship between the hyper-parameters and the clustering quality of datasets composed of different numbers of signals. In fact, if the optimal hyper-parameterization depends only on the number of signals in the dataset, and not on the signals themselves, it will be possible to perform preemptive hyper-parameter optimizations for different sizes of datasets generated k-mer models used in this work  to assess the clustering quality instead of leveraging a ground truth. In this way, it would be possible to perform the optimization of hyper-parameters also for datasets whose ground truth is unknown. Finally, we will also consider the development of a novel signal generator that leverages the latest signals reported in the literature, since the his work  are no lin silico generated signals due to the great challenge of measuring classification accuracy on a real Nanopore sequencing dataset, where the ground truth is unknown. Indeed, the performance of UNPLEX cannot be compared to existing methods because of two reasons. First and foremost, all existing general-purpose methods rely on basecallers, which cannot (by definition) be considered ground truth. As a matter of fact, UNPLEX could (in principle) be more accurate in clustering similar BCs than a basecaller-based approach, but that would be paradoxically counted as an error, affecting the overall accuracy. Second, all existing methods require a high coverage to yield high quality results, which is not the case for UNPLEX that is designed to be effective using a single ONT experiment . We are currently working on the generation of a reference dataset to further assess the performance of UNPLEX. Generating ground truth data that include known barcodes typically requires implementing a separate protocol for creating synthetic datasets. A recommended approach would use deep learning techniques to simulate and verify the dataset, taking into account the 5%–15% error rate typically observed in nanopore data due to substitution, deletion, and insertion events. In any case, the obtained signals should be clean enough to avoid misinterpretation by the Dynamic Time Warping (DTW) method, which requires an additional step to pre-process the signals. A validation step of the generated signals is also necessary to make sure they are very close to reality. DTW is a technique used to compare time series data that may have variations in the timing and alignment of their features. Applying DTW to barcode identification in Nanopore sequencing data can be challenging due to the computational complexity of the algorithm, the need for appropriately selecting parameter values, and the requirement for sufficient training data. However, with careful optimization and pre-processing, DTW can be a powerful tool for identifying barcodes in Nanopore sequencing data with high accuracy and enhancing UNPLEX performance.At present, UNPLEX was only tested on Although our data generation procedure aimed to faithfully represent a Nanopore signal generated by scRNA-seq libraries, the analysis of real sequencing datasets will likely pose additional challenges in terms of signal complexity and noise profile. Future experimental and analytical work will allow us to extend the applicability of UNPLEX to real Nanopore sequencing datasets. In any case, novel nanopore models and strategies to control the speed of the DNA/RNA strand in the pore are nowadays under investigation by many research groups; therefore, UNPLEX need to be equipped with additional features to keep up with the ONT that is currently evolving .Finally, we plan to further improve our methods by studying the neurons of the SOM which fire in response to signals derived from different BCs. In fact, if such neurons are located on the borders between two groups of neurons representing two different BCs, a fine-grained analysis can be performed to mark these signals as “unsure” to the user. Another possible strategy to mitigate this issue is to perform an additional clustering on signals whose BMUs are located on the borders. To this aim, it could be possible to develop a “two-step” framework whereby, after the execution of UNPLEX, only the signals located at the borders are considered and further clustered. During this second step, larger embeddings could be computed to introduce in the compact representations the features that might help to properly cluster such signals; then, such embeddings can be clustered by leveraging a SOM or another clustering approach. Larger embeddings would be computed only for a small subset of signals, since computing larger embeddings for the whole dataset would significantly increase the computational burden, thus making UNPLEX unfeasible due to computational or time limitations. We envision that UNPLEX could be easily adapted to cluster different types of signals, for example, to discriminate UMIs or methylated nucleotide sequences.