From a strategic and an economic point of view, patent prior art searches are very useful for several reasons such as avoiding the infringement of third parties’ rights, reinventing something already new in the state of the art, speeding up the prosecution of a patent application, and getting a patent granted with a lower associated cost. A prior art search is carried out in patent databases (free of charge or fee-based) through keywords or classification symbols, indexing codes (IPC – International Patent Classification or CPC – Cooperative Patent Classification), or a combination of these, using Boolean or proximity operators. The classification codes are independent of the language used and can increase the number of relevant documents retrieved. Patent databases are all incomplete and therefore a search in multiple sources is suggested.

Background: Gastric cancer (GC) is one of the leading malignancy diseases worldwide, especially in Asian. CAST is a potential oncogene in GC carcinogenesis process. The character of macrophage infiltration in GC microenvironment was also unaddressed. Methods: We first applied machine searching in gene candidate evaluation of GC. CAST expression was analyzed via the Human Protein Atlas (HPA) and Gene Expression Profiling Interactive Analysis 2 (GEPIA2) database. Protein-protein interaction (PPI) network was downloaded from STRING. We investigated the impact of CAST on clinical prognosis using Kaplan-Meier plotter. The correlations between CAST and Lgr5 and macrophage infiltration in GC was surveyed via TIMER 2.0. Finally, GeneMANIA was also used to evaluate the possible functional linkage between genes. Results: After machine-assisted searching, CAST expression was found signicant difference in the overall survival of GC patients. STRING revealed CAST related proteomics and transcriptomics associations, mainly about CAPN family. Moreover, CAST significantly impacts the prognosis of GC from other datasets validation. Notably, high CAST expression was correlated with worse overall survival in GC patients (hazard ratio = 1.59; logrank P = 9.4 x 10-8). CAST and Lgr5 expressions were both positively correlated with WNT 2 and WNT 2B. Among GC patients in several datasets, CAST and macrophage infiltration evaluated together showed no obvious trend toward poor clinical overall survival. Conclusion: CAST plays an important role in GC clinical prognosis and is associated with WNT 2/WNT 2B/Lgr5. Our study denmostrated that CAST in GC overall survival is regulated by macrophage infiltration.

To solve the problem of low efficiency of source seeking in implicit information field, this paper presents an autonomous souring method based on balanced searching strategy is enlighten by biological homing behaviors. At the beginning of the research, the source seeking task come down to a multi-objective convergence issue. By taking the feasibility search behaviors as the individual sample of the evolutionary population drawing on the thought of evolutionary algorithms, then combine motion searching with population evolution, so the carrier can be guide to complete source seeking task by solving the multi-objective problem. Furthermore, the distribution entropy is also considerated d to measure the searching bias in the process of source seeking. Combined with the demand of the source seeking process, a new source seeking method of balanced searching strategy is designed. Ultimately, through theoretical analysis and simulation verification confirm the effectiveness and rationality of the proposed method.

In this paper, a swarm trajectory planning method is proposed for multiple autonomous surface vehicles (ASVs) in an unknown and obstacle-rich environment. Specifically, based on the point cloud information of the surrounding environment obtained from local sensors, a kinodynamic path searching method is used to generate a series of waypoints in the discretized control space at first. Next, after fitting B-spline curves to the obtained waypoints, a nonlinear optimization problem is formulated to optimize the B-spline curves based on gradient-based local planning. Finally, a numerical optimization method is used to solve the optimization problems in real-time to obtain collision-free, smooth and dynamically feasible trajectories relying on a shared network. Simulation results demonstrate the effectiveness and efficiency of the proposed swarm trajectory planning method for a network of ASVs.

The COVID-19 pandemic has left no one indifferent: all around the globe, people have felt the massive impact of a virus that has stopped the world in its tracks. Many countries have been forced to impose strict lockdowns, causing devastating damage to the overall economy and welfare state, but also leaving an unprecedented emotional footprint on almost everyone. In this paper, we aim to quantify, by building our own method how, this pandemic has affected people emotionally by detecting the shifts in the emotional profile of representative users in selected countries. Our method processes the social media stream adapting the Pleasure-Arousal-Dominance (PAD) approach to measure emotional states on the Russell Circumplex plane, allowing for not only an overall emotional shift quantification but also a detailed analysis at mood level. By focusing on certain emotions and searching behavior, we establish a relationship between the pandemic and the emotional response. Although the method can be applied to any location with a significant and varied amount of geo-localized social media streams, the scope of this paper covers the most representative cities in Europe.

Bioinformatics is a rapidly developing field enabling scientific experiments through computer models and simulations. Considering the vast databases of biological data available, it is ex-tremely important to develop efficient methods and algorithms for their processing. Sequence comparison is the best method for studying the evolutionary interaction between genes. It is based on alignment – the process of arranging two or more sequences to achieve the maximum level of identity and degree of similarity. The paper presents a new version of the algorithm for pairwise DNA sequences alignment, based on a new method called CAT, where a dependency with a previous match and the closes neighbor are taken in consideration to increase uniqueness of the CAT profile and to reduce possible collisions, i.e. two or more sequence having same CAT profiles. This makes proposed algorithm suitable for finding exact match of a concrete DNA se-quence in a big set of DNA data faster. The generation of CAT profiles is made once before data has been uploaded to the database, allowing the profiles to be used as metadata for the sequenc-es. It consists of an algorithm to calculate a CAT profile against the selected reference sequences and an algorithm to compare two sequences based on the calculated CAT profiles. Improve-ments in generation of the CAT profiles, are detailed described in the paper. Block scheme, pseudo code tables and figures were updated according to the proposed new version and ex-perimental results. Experiments have been carried out with the new version of the method and different datasets to align DNA sequences based on the CAT method. New experimental results in terms of collisions, speed, and efficiency of the proposed solutions are presented. Experiments related to the performance comparison with Needleman-Wunsch were re-executed with the new version of the algorithm to confirm that we have the same performance. An analysis of the per-formance of the proposed CAT based algorithm against Knuth–Morris–Pratt algorithm, which has a complexity of O(n) and is one of the most widely used for searching biological data, was performed. The impact of prior matching dependencies on uniqueness for generated CAT pro-files is investigated. The analysis of the experimental results obtained by sequence alignment shows a small deviation of the proposed algorithm based on the CAT method, which can be ig-nored if this deviation is acceptable at the expense of performance. The time efficiency of the CAT algorithm remains constant, regardless of the length of the sequences. Therefore, the ad-vantage of the proposed method is its fast processing in the alignment of large sequences, for which the execution of the exact algorithms takes a long time. The example code realization of the CAT Method, under the protection of the GNU General Public License v3.0, can be accessed on GitHub at: https://github.com/HristoS/CATSequenceAnalysis.

Naturally occurring small molecules include a large variety of natural products from different sources that have confirmed activity against epigenetic targets. In this work we review chemoinformatic, molecular modeling and other computational approaches that have been used to uncover natural products as inhibitors of DNA metiltransferases, a major family of epigenetic targets with significant potential for the treatment of cancer and several other diseases. Examples of these computational approaches include docking, similarity-based virtual screening, and pharmacophore modeling. It is also commented the chemoinformatic-based exploration of the chemical space of naturally occurring compounds as epigenetic modulators which may have significant implications in epigenetic drug discovery and nutriepigenetics.

Peptide-based drugs are promising anticancer candidates due to their biocompatibility, and low toxicity. Particularly, tumor homing peptides (THPs) have the ability to bind specifically to can-cer cells receptors and tumor vasculature. Despite their potential to develop antitumor drugs, there are few available prediction tools to assist the discovery of new THPs. Two webservers based on machine learning models are currently active, the TumorHPD (https://webs.iiitd.edu.in/raghava/tumorhpd) and the THPep (http://codes.bio/thpep), and more recently the SCMTHP (SCMTHP (pmlabstack.pythonanywhere.com), based on scoring card method. Herein, a novel method based on network science and similarity searching implemented in the starPep toolbox (http://mobiosd-hub.com/starpep/) is presented for THPs discovery. The approach leverages from exploring the structural space of THPs with Chemical Space Networks (CSNs) and from applying centrality measures to identify the most relevant and non-redundant THPs sequences within the CSN. Such THPs were considered as queries (Qs) for multi-query similarity searches that applies a group fusion (MAX-SIM rule) model. The resulting multi-query similarity searching models (SSMs) were validated with three benchmarking datasets of THPs/non-THPs. Predictions achieved accuracies ranged from 92.64 to 99.18% and Matthews Correlation Coefficients between 0.894-0.98, outperforming state-of-the-art predictors. The best model was applied to repurpose AMPs from the starPep database as THPs, which were subse-quently optimized for the TH activity. Finally, 54 promising THP leads were discovered, and their sequences were analyzed to encounter novel motifs. These results demonstrate the potential of CSNs and multi-query similarity searching for a rapid and accurate identification of THPs.