Amazonian and indigenous Ecuadorian households have on average less access to basic public services, infrastructure and higher education. With the objective of evaluating intercultural access and practice at the Universidad Estatal Amazónica (UEA), through documentary analysis, the databases of the academic teaching system are studied to determine the evolution in ethnic self-identification in enrollment between the academic periods 2019-2019 to 2020-2021, two before the Covid-19 pandemic and two during it, and the specific guidelines proposed by Senescyt-Unesco for the mainstreaming of equality in Higher Education are compared with evidence of daily intercultural practices at UEA. The result indicates that enrollment by ethnic self-identification is strengthened in the first period of the pandemic and weakened in the last period elapsed, due to availabilities in virtual education. Also, UEA has met 27 of the 38 guidelines established by Senescyt & Unesco (2015), with greater approximation of compliance in the academic, research, linkage and management areas, while the training and administrative areas have lower performance. In the Amazonian territory, so diverse and rich in culture and nature, the Ecuadorian university has yet to find mechanisms to train and strengthen interculturality, despite the fact that public standards for the quality of higher education fail to incorporate mechanisms to recognize the wisdom of indigenous peoples.

Some Amazonian plants, beneficial to health, from Kichwa communities far from the colonized sector are described and laboratory results on antioxidants are presented. The methodology used was based on documentary searches and laboratory results as part of the functional food project, UEA. In the region, 107 species are reported in associated and rotating cultivation systems called chacras and up to 886 food, medicinal, ritual, flavoring, cosmetic and toxic uses of flora species known in the tropical rainforest, western Amazon, considered one of the most biodiverse areas of the planet; home of indigenous peoples with ancestral knowledge about medicinal and food plants, which deserve further laboratory verification. This work presents results of analysis of polyphenolic and antioxidant activity of Maytenus macrocarpa (chuchuhuaso), Theobroma cacao (cacao), Ilex guayusa (Guayusa), Musa acuminata AA ("orito" plantain), as well as the evaluation of phenolic compound contents in powders of Psidium guajava (guava), Tagetes filifolia Lag (wild anise), Allium ursinum (wild garlic), seeds of Plukenetia volubilis (sacha inchi) and tubers of Colocasia esculenta (Chinese potato) and their effect as phytobiotic additive, universities in the Amazonian territory should increase the exploration of sources of secondary metabolites for the pharmaceutical, cosmetic and functional food sectors, in order to promote strategies to improve social and economic conditions, such as ventures based on local resources and strengthening food security.

Software tests are empirical and technical investigations whose mission is to provide users with information about the quality of the software product or service under test.

Testing techniques include the process of running a program or application with the intention of finding bugs and verifying that the software product is suitable for use. Additionally, these tests involve running a software component or a system component to evaluate one or more properties of interest. In general, these properties report the extent to which the component or system under test:

• Meets the requirements that guided its design and development

• Responds correctly to all types of entries

• Performs their duties within an acceptable time frame

• Can be installed and run in intended environments

• Achieves results generally desired by its stakeholders

There are various classifications of software tests that vary depending on the execution mode with or without application, the content that is verified, test level etc.

Depending on the execution mode, there are static tests (to verify) and dynamic (to validate). The first is the type of tests that are performed without running the application code. For example, they are reviews, tours or inspections. While the second involves the execution of code programmed with a specific set of test cases. Static tests are often implicit, such as proofreading, in addition when programming tools / text editors check source code structure or compilers (precompilers) check syntax and data flow as static program analysis. Dynamic tests take place when the program runs. But such tests can begin before the program is 100% complete to test particular sections of code and apply to discrete modules or functions.

Depending on the test levels, generally speaking, there are at least three test levels: unit test, integration test, and system test. However, developers can include a fourth level, called acceptance testing, to ensure that the software meets expectations.

Unit tests

Unit tests are intended to ensure the proper functioning of a specific section, as well as to increase the quality and efficiency of the overall software development process.

Integration testing

It is a type of software test whose mission is to verify the interfaces between components with a software design. The software components can be integrated iteratively or all together. The former is typically considered a best practice as it allows for faster troubleshooting of the interface.

System tests

System tests are used to confirm that the system meets your requirements. For example, such a test might involve testing a login interface, then creating and editing an entry, as well as submitting or printing results, followed by a summary of processing or deleting the entries, and then logging out.

Acceptance Tests

Acceptance testing includes four levels of testing, which are user acceptance testing, operational acceptance testing, regulatory and contractual acceptance testing, and alpha and beta testing. Operational acceptance is used to carry out the operational preparation (pre-launch) of a product, service or as part of a quality management system. On the other hand, the contractual acceptance tests are carried out based on the acceptance criteria of the contract. While regulatory acceptance tests are proceeded based on the relevant regulations for the software product. On the other hand, alpha tests are simulated or real operational tests performed by potential users / customers or a test team independent of the developers. Finally, beta tests are tests that come after alpha tests. These tests are released to a limited audience outside of the programming team known as beta testers. Whereupon, the software is distributed to groups of people for further testing to ensure that the product has few bugs or errors.

Artificial Intelligence/Machine Learning (AI/ML) guided drug discovery is an interesting strategy to reduce costs in Drug discovery, Vaccine design, Nanoparticle-drug delivery systems assembly, Biomarkers validation, etc. These problems have multiple phases from chemical synthesis/isolation of molecular entities to preclinical studies (phase 0) to clinical studies (phase I, II, III) to pharmaco-epidemiology and post-marketing studies (phase IV) in real population. Consequently, integral Product Life Cycle (PLC) should incorporate analysis of all or at least various of these phases. However, relevant information for different phases of the PLC, most of the time, may be disperse on different databases. On this situation also emerge multiple cases of contradictory, incomplete, highly variable, sparse, over/under represented, large volume sub-sets of information. In addition, the information available has multiple labels or assay boundary conditions. Some of these conditions are continuous variables like dose, temperature, time of assay, multiple values pharmacological parameters (Ki, IC50, MIC, etc.). Nevertheless, many of these conditions are non-ordinated numeric labels. We can identify denote these conditions as cj. We are talking, for instance, of c0 = label of property measured (Ki, IC50, MIC, etc.), c1 = name of target protein, c2 = cell line, c3 = tissue, c4 = organism of assay, c5 = shape of nanoparticle, c6 = type of clinical assay, c7 = gender of patients, etc. In addition, many of these variables may be co-linear, co-dependent, or nested somehow among forming complex networks of interrelationships. For instance, we can measured the same set of parameters c0 to different drug for a subset of target proteins c1 expressed some of them in different tissues c3 of multiple organisms of assay c4, etc. This can be represented as a complex network of interconnections of these labels. Yet another point, usually these conditions can be managed as ontologies associated to an ontology dictionary cj = c0, c1, c2, c3, ... cn of deep n. Each one of these ontologies may have many levels or terms. For instance, organisms c4 may be multiple, eg.; human, mouse, rat, rabbit, etc. One last point, many of the instances of the dataset (not only the input variables) are complex systems (formed by sub-systems) with a network-like internal structure. We can see here structure as all the parts of the sub-system, the labels of these parts, the properties of weights of these parts, and the interconnection or links between these parts. This is for instance the case of drugs, proteins, metabolic networks, brain, etc. They all can be seen as sub-systems represented as molecular graphs of interconnected atoms, or protein structure network of interconnected aminoacids, metabolic network of interconnected reactions, etc. These graphs/networks may be constructed at different levels. For instance, the protein may be a network of atoms or a network of aminoacids, the brain may be seen as a network of neurons or a network of cortex regions. Also a population of patients in a sexual disease transmission network or flu epidemic break may be represented as a network of personal contacts or a network of towns. Due to the high amount and complexity of the information to be analyzed in a full/partial PLC analysis in this area this can be seen as a genuine Big Data problem. One approach to this problem may be the use of AI/ML as we mentioned at the beginning. However, the use of these methods from a PLC point of view implies the use of Information Fusion (IF) techniques to pre-process all the information from different sources and put all the pieces together in a single dataset susceptible of analysis by AI/ML method. In this context, we have proposed Information Fusion, Perturbation Theory, and Machine Learning (IFPTML) method for PLC analysis in Pharmaceutical industry. IFPTML (IF + PT + ML) have three phases. The first phase carry out the IF of all the previous information. The second phase calculate PT operators able to numerically codify and compact all information treated in IF phase related to labels, ontology, network-like structures, etc. Last the ML phase develops the ML model and implement it in a user-friendly software.

In this study, three Perturbation Theory Machine Learning (PTML) models were created to optimize preclinical assays on antimalarial compounds of the parasitic species of the genus Plasmodium falciparum. Between General Discriminant Analysis (GDA), Classification Tree with Univariate Splits (CTUS) and Classification Tree with Linear Combinations (CTLC). The PTML-CTLC presented the best performance with a Sensitivity percentage equal to 83.6 for the training data set and 85.1 for validation; for specificity with a percentage of 89.8 for training and 89.7 for validation. The PTML-CTLC model has significant variables that could be a good option for pharmaceutical companies to optimize preclinical testing processes.

PTML is a combination of Machine Learning (ML) and Perturbation Theory (PT) that allows to create prediction models in many areas of knowledge mainly in Medicinal Chemistry to handle large amounts of data representing physical and chemical properties of different organisms and biological systems under different input conditions. PTML allows to establish dispersion measurements on descriptors of physicochemical properties of different organisms with high values of sensitivity, specificity and accuracy higher than 70%

The primary metabolites, very abundant in nature, are essential for the physiological development of the plant; they are present in large quantities, they are easy to extract, and their exploitation is relatively cheap. In order to establish mathematical expressions that relate the regrowth age with the nitrogen and sugar content (glucose, fructose and sucrose) of the whole plant and its fractions. The experiment was developed, following a randomized block design, with 5 treatments (60, 90, 120, 150 and 180 d). In the first part of this communication the content of Nitrogen (N) in the integral plant, leaves and stems were evaluated. In the second part we studied sugars like Glu, Frut and Suc. Reporting that N, Glu, Frut and Suc decreased with the highest results at 60 days and quadratic and cubic equations with R2 higher than 0.90 were adjusted. The established regression equations explain the close relationship between regrowth age and the contents of precursor metabolites (N, Glu, Frut and Suc), which explains the fluctuations found in sugars influenced by the phenological state of the plants and photosynthetic activity of this. Insert abstract text here.

Haematological cancers are a heterogeneous family of tumours that manifests as clonal expansions of a single cell at different phases of haemopoietic development. They are divided into leukaemias, lymphomas and myelomas, each comprising a wide range of subtypes. They are often called "liquid tumours" since they do not produce nodules or masses, as other tumour types called "solid tumours" do. Due to this peculiarity, they cannot be surgically removed, hence chemotherapy is the mainstay of their treatment. Over the last 15 years, biological and chemical research has produced an enormous volume of data that has been digitalized, the majority of them are freely available in open access databases (Pubchem, ChEMBL, etc.). Through these, the modern drug discovery process has entered the Big Data era. Use of Big Data has transformed the way chemical molecules data are derived and used in research. Pivotal in this change has been the incorporation of artificial intelligence approaches, such as machine learning and deep learning algorithms, which have been successfully employed in Computer Aided Drug Design (CADD). By combining Big Data, CADD and artificial intelligence, it is possible to create computational models capable of predicting specific biological activity. The submentioned models can be employed for virtual screening, which allows to identify new molecules with the desired activity and exclude those lacking that activity and/or with adverse side effects, thereby creating a bottleneck process leading to the experimentation of the most promising molecules only. This in silico strategy could be effectively adopted in the research of new anticancer drugs rendering the drug discovery process more rapid, affordable and sustainable.

The purpose of this study was to create a multi-target Quantitative Structure-Activity Relationship (mt-QSAR) classification model, based on machine learning techniques, for the prediction of cytotoxic drugs simultaneously active against leukaemia, lymphoma and myeloma cell lines. More precisely, a dataset of about 11,000 molecules tested against 39 cell lines was extrapolated from the ChEMBL database. Although the data were extracted from a single database the bioactivity assays came from different experiments obtained by various research groups, thus they were sufficient to ensure a wide experimental diversity. The anti-tumour activity was reported as IC50 (concentration capable of inhibiting 50% of cell viability), and a cutoff value of 1 µM was chosen to discriminate active from inactive molecules. For each molecule, a set of 2D descriptors were calculated using AlvaDesc software.

The resulting dataset was submitted to the QSAR-Co-X software, which implements the Box-Jenkins moving average approach, allowing several experimental assay conditions to be incorporated into a single model for the prediction of simultaneous activity. This approach made it possible to discriminate the behaviour of molecules depending on the cell line, the type of assay and the time point used to investigate cytotoxic activity. In addition, the QSAR-Co-X software was employed to identify the best machine learning technique that yielded the best mt-QSAR model. This approach was proven to be Random Forest.

The result is a model with good predictive capabilities, as demonstrated by the accuracy metric (Acc), greater than 88%, and the Matthews correlation coefficient (MCC), greater than 0.83 in both the test set and the validation set.

Objective To investigate the effects of serum 25-hydroxyvitamin D on insulin resistance and IVF pregnancy outcomes in the patients with PCOS. Methods The patients with PCOS were divided into vitamin D deficiency group and sufficient group according to the concentration of serum vitamin D. To compare and analyze the relationship between 25(OH) D and indicators of insulin resistance and secretion function in patients, and effects on pregnancy outcomes. Results VDD group with significantly higher proportion of high risk of 1h postprandial blood sugar, hyperinsulinemia and insulin resistance, but with significantly lower ISIcomp (P<0.05). G60、 time-glycemic AUC has predictive value on VDD in PCOS. Vitamin D sufficient is in favor of improving the quality of embryos (r=0.3, P=0.014), but does not affect IVF pregnancy outcomes. Conclusion VDD of the patients with PCOS incline to be combined with insulin regulatory dysfunction, but it has no harm to IVF pregnancy outcomes.

Objective To investigate the relationship between the outcome of in vitro fertilization and embryo transfer (IVF-ET) and the visceral fat area (VFA). Methods From January 2019 to December 2019, the Reproductive Center of the First Hospital of Lanzhou University was selected from the IVF patients who were under 38 years old, the number of follicles was 7-15, and the AMH was 1.1-9, and polycystic follicles were excluded. Patients with endocrine disorders such as PCOS and hyperthyroidism and hypothyroidism were divided into two groups according to VFA values. The prospective cohort was used to analyze the relationship between visceral fat content (VFA) and IVF assisted pregnancy outcomes. Patients were divided into three groups according to VFA: the first group (VFA≤50cm2), the second group (50 cm2 <VFA≤70 cm2), and the third group (VFA> 70 cm2). Result There were statistically significant differences in the number of infertility years (P = 0.033), total propulsion (P = 0.019), and clinical pregnancy rate (X2 = 396.0, P <0.001) in different VFA groups: patients with VFA> 70 cm2 The number of years of infertility increased significantly, and the total amount of propulsion increased significantly; the clinical pregnancy rate of VFA ≤50cm2 decreased significantly; the time of down-regulation (P = 0.762), number of down-regulations (P = 0.535), Total amount of adjustment (P = 0.378), time to promote ovulation (P = 0.285), E2 of trigger day (P = 0.130), number of eggs obtained (P = 0.953), number of cleavages (P = 0.415), high quality embryo rate ( P = 0.149). Conclusion Increasing or decreasing visceral fat area (VFA) affects IVF-ET clinical medication and fertility outcomes.