Exploring undiscovered public knowledge in neuroscience.

In this essay, I argue that the combination of research synthesis and philosophical methods can fill an important methodological gap in neuroscience. While experimental research and formal modelling have seen their methods progressively increase in rigour and sophistication over the years, the task of analysing and synthesizing the vast literature reporting new results and models has lagged behind. The problem is aggravated because neuroscience has grown and expanded into a vast mosaic of related but partially independent subfields, each with their own literatures. This fragmentation not only makes it difficult to see the full picture emerging from neuroscience research but also limits progress in individual subfields. The current neuroscience literature has the perfect conditions to create what the information scientist Don Swanson called "undiscovered public knowledge"-knowledge that exists in the mutual implications of different published pieces of information but that is nonetheless undiscovered because those pieces have not been explicitly connected. Current methods for rigorous research synthesis, such as systematic reviews and meta-analyses, mostly focus on combining similar studies and are not suited for exploring undiscovered public knowledge. To that aim, they need to be adapted and supplemented. I argue that successful exploration of the hidden implications in the neuroscience literature will require the combination of these adapted research synthesis methods with philosophical methods for rigorous (and creative) analysis and synthesis.

Making wood inspection easier: FTIR spectroscopy and machine learning for Brazilian native commercial wood species identification.

The molecular structure of wood is mainly based on cellulose, lignin, and hemicellulose. However, low concentrations of lipids, phenolic compounds, terpenoids, fatty acids, resin acids, and waxes can also be found. In general, their color, smell, texture, quantity, and distribution of pores are used in human sensory analysis to identify native wood species, which may lead to erroneous classification, impairing quality control and inspection of commercialized wood. This study developed a fast and accurate method to discriminate Brazilian native commercial wood species using Fourier Transform Infrared Spectroscopy (FTIR) and machine learning algorithms. It not only solves the limitations of traditional methods but also goes beyond as it allows fast analyses to be obtained at low cost and high accuracy. In this work, we provide the identification of five Brazilian native wood species: Angelim-pedra (Hymenolobium petraeum Ducke), Cambara (Gochnatia polymorpha), Cedrinho (Erisma uncinatum), Champagne (Dipteryx odorata), and Peroba do Norte (Goupia glabra Aubl). The results showed the great potential of FTIR and multivariate analysis for wood sample classification; here, the Linear SVM differentiated the five wood species with an accuracy of 98%. The developed method allows industries, laboratories, companies, and control bodies to identify the nature of the wood product after being extracted and semi-manufactured.

Diagnosis of Cutaneous Leishmaniasis Using FTIR Spectroscopy and Machine Learning: An Animal Model Study.

Cutaneous leishmaniasis (CL) is a polymorphic and spectral skin disease caused by Leishmania spp. protozoan parasites. CL is difficult to diagnose because conventional methods are time-consuming, expensive, and low-sensitive. Fourier transform infrared spectroscopy (FTIR) with machine learning (ML) algorithms has been explored as an alternative to achieve fast and accurate results for many disease diagnoses. Besides the high accuracy demonstrated in numerous studies, the spectral variations between infected and noninfected groups are too subtle to be noticed. Since variability in sample set characteristics (such as sex, age, and diet) often leads to significant data variance and limits the comprehensive understanding of spectral characteristics and immune responses, we investigate a novel methodology for diagnosing CL in an animal model study. Blood serum, skin lesions, and draining popliteal lymph node samples were collected from Leishmania (Leishmania) amazonensis-infected BALB/C mice under experimental conditions. The FTIR method and ML algorithms accurately differentiated between infected (CL group) and noninfected (control group) samples. The best overall accuracy (∼72%) was obtained in an external validation test using principal component analysis and support vector machine algorithms in the 1800-700 cm-1 range for blood serum samples. The accuracy achieved in analyzing skin lesions and popliteal lymph node samples was satisfactory; however, notable disparities emerged in the validation tests compared to results obtained from blood samples. This discrepancy is likely attributed to the elevated sample variability resulting from molecular compositional differences. According to the findings, the successful functioning of prediction models is mainly related to data analysis rather than the differences in the molecular composition of the samples.

Turning chaotic sample group clusterization into organized ones by feature selection: Application on photodiagnosis of Brucella abortus serological test.

Bovine brucellosis diagnosis is a major problem to be solved; the disease has a tremendous economic impact with significant losses in meat and dairy products, besides the fact that it can be transmitted to humans. The sanitary measures instituted in Brazil are based on disease control through diagnosis, animal sacrifice, and vaccination. Although the currently available diagnostic tests show suitable quality parameters, they are time-consuming, and the incidence of false-positive and/or false-negative results is still observed, hindering effective disease control. The development of a low-cost, fast, and accurate brucellosis diagnosis test remains a need for proper sanitary measures at a large-scale analysis. In this context, spectroscopy techniques associated with machine learning tools have shown great potential for use in diagnostic tests. In this study, bovine blood serum was investigated by UV-vis spectroscopy and machine learning algorithms to build a prediction model for Brucella abortus diagnosis. Here we first pre-treated the UV raw data by using Standard Normal Deviate method to remove baseline deviation, then apply principal component analysis - a clustering method - to observe the group formation tendency; the first results showed no clustering tendency with a messy sample score distribution, then we properly select the main principal components to improve clusterization. Finally, by using machine learning algorithms (SVM and KNN), the predicting models achieved a 92.5% overall accuracy. The present methodology provides a test result in an average time of 5 min, while the standard diagnosis, with the screening and confirmatory tests, can take up to 48 h. The present result demonstrates the method's viability for diagnosing bovine brucellosis, which can significantly contribute to disease control programs in Brazil and other countries.

Multi-resistant diarrheagenic Escherichia coli identified by FTIR and machine learning: a feasible strategy to improve the group classification.

The identification of multidrug-resistant strains from E. coli species responsible for diarrhea in calves still faces many laboratory limitations and is necessary for adequately monitoring the microorganism spread and control. Then, there is a need to develop a screening tool for bacterial strain identification in microbiology laboratories, which must show easy implementation, fast response, and accurate results. The use of FTIR spectroscopy to identify microorganisms has been successfully demonstrated in the literature, including many bacterial strains; here, we explored the FTIR potential for multi-resistant E. coli identification. First, we applied principal component analysis to observe the group formation tendency; the first results showed no clustering tendency with a messy sample score distribution; then, we improved these results by adequately selecting the main principal components which most contribute to group separation. Finally, using machine learning algorithms, a predicting model showed 75% overall accuracy, demonstrating the method's viability as a screaming test for microorganism identification.

Canine visceral leishmaniasis diagnosis by UV spectroscopy of blood serum and machine learning algorithms.

Visceral leishmaniasis (VL) is a zoonotic disease caused by the protozoan Leishmania infantum, and dogs are considered the main urban hosts for future disease transmission. The first and most effective control against the spread of disease relies on identifying infected animals, followed by their treatment or sacrifice, to reduce the protozoan reservoirs. Despite the availability of various diagnostic tests for VL in dogs the development of a quick and accurate diagnosis is essential from a public health and ethical point of view. Here we analyze the use of UV-Vis spectroscopy as an alternative diagnostic method for VL diagnosis by using the antigen-antibody interaction in canine blood serum and machine learning algorithms. The main UV spectra in the 220 to 280 nm range exhibit nine electronic absorption bands, but no significative difference could be identified between the positive and negative group spectra. Finally, UV pre-proceed spectra by SNV (standard normal variate) were submitted to principal component analysis followed by Linear SVM algorithm, the prediction model was tested in a leave-one-out cross-validation and external validation test reaching an overall accuracy of 75%.

Reappraising the role of dopamine in adolescent risk-taking behavior.

Adolescence is characterized by increased risk-taking, which is often ascribed to developmental changes in dopaminergic signaling. Popular models propose that these behaviors are caused by dopamine-induced hypersensitivity to rewards, which overrides adolescents' immature self-control mechanisms. However, these models are often based on oversimplified notions about the workings and functions of dopamine. Here we discuss the relationship between changes in the dopaminergic system and adolescent behavior in light of current theories/models about the functions of dopamine. We show that dopamine is linked to learning, adaptive decision-making under uncertainty, and increased motivation to work for rewards. Thus, changes in the dopaminergic system contribute to the maturation of cognitive control through various mechanisms, contrary to the false dichotomy between reward processing and self-control. Finally, we note that dopamine interacts with a number of other neuromodulator systems, which also change during adolescence, but that have been largely ignored in the field of adolescent development. A full understanding of adolescent behavior will require these neurochemicals and their interactions with dopamine to be taken into account.

Sex-specific association between urinary kisspeptin and pubertal development.

Kisspeptins play a crucial role during pubertal development, but little is known about how their peripheral concentrations relate to sexual maturation. This is partly due to the lack of non-invasive, quick, and reliable peripheral kisspeptin measures, which limit widespread testing. Here, we investigated the relationship between kisspeptin concentrations measured from midstream urine samples with 2-h retention periods and developmental markers (age, self-reported pubertal status, and saliva concentrations of testosterone and DHEA sulphate ) in 209 typically developing 9- to 15-year-old males and females. As a result of the study, we found marked sex differences. Kisspeptin concentrations were similar between sexes until around 12 years of age, but, thereafter, kisspeptin concentrations in females did not change significantly, whereas, in males, there was a clear positive correlation with developmental measures. Our results replicate previous findings regarding kisspeptin concentration changes across the pubertal transition obtained from blood samples, suggesting that measuring these peptides in urine has the potential for exploring kisspeptins' peripheral effects and their associations with pubertal status.

Paracoccidioidomycosis screening diagnosis by FTIR spectroscopy and multivariate analysis.

Paracoccidioidomycosis (PCM) is a systemic mycosis with high incidence in Latin America, caused by species of the genus Paracoccidioides spp. Brazil is considered to be the endemic center of this disease, which is identified as the eighth cause of mortality from chronic infectious disease in the country. There are several specific diagnostic methods in PCM, such as microbiological, immunological, histopathological, and molecular. However, the standard laboratory diagnosis depends mostly on fungus direct observation - the gold standard of PCM diagnosis. The implementation of new technologies, such as Fourier Transform Infrared (FTIR), can contribute to the clinical diagnosis trial of this disease. Here, we evaluated a new strategy for the diagnosis of PCM by using blood serum FTIR spectra from 20 patients with PCM and 20 healthy individuals. Machine learning algorithms were able to provide an overall accuracy of 91.67% by using Cubic SVM in the PCA data from FTIR results.

Censo dos cursos de graduação em enfermagem brasileiros presenciais e à distância / Census of undergraduate and distance learning brazilian nursing courses / Censo de cursos de enfermería brasileños de pregrado y educación a distancia

Objetivo: Realizar um censo dos cursos de graduação em Enfermagem em instituições de Ensino Superior brasileiras, segundo credenciamento presencial e à distância. Método: Estudo de abordagem quantitativa, de base documental, do tipo analítico-descritivo, que utilizou dados sobre os cursos de Enfermagem brasileiros do portal e-MEC e das páginas destes, nos sites das IES. Resultados: Dos 1.668 cursos de Enfermagem brasileiros, 105 (6,29%) eram EAD e 1.563 (93,70%) presenciais. Todos os cursos EAD eram privados. Vale ressaltar que, dos 105 cursos EAD localizados, 50 se autodenominavam semipresenciais. Discussões: Na Enfermagem, profissão que essencialmente envolve o cuidado humano, questiona-se a qualidade de educação, que o ensino à distância oferece. Conclusões: Existem questionamentos com relação à qualidade da educação empregada pelo EAD que, por ser aplicada fora do ambiente palpável e observável de prática, coloca em xeque a confiabilidade da metodologia de formação, bem como a competência dos profissionais formados. (AU)

Objective: To carry out a census of undergraduate nursing courses in Brazilian Higher Education institutions, according to face-to-face and distance accreditation. Methods: Study of quantitative approach, based on documents, analytical-descriptive type, which used data on Brazilian Nursing courses from the e-MEC portal and their pages, on the websites of the HEIs. Results: Of the 1,668 Brazilian nursing courses, 105 (6.29%) were distance learning and 1,563 (93.70%) in person. All distance learning courses were private. It is worth mentioning that, of the 105 distance learning courses located, 50 called themselves semi-presential. In Nursing, a profession that essentially involves human care, the quality of education that distance learning offers is questioned. Conclusion: There are questions regarding the quality of education employed by EAD, which, being applied outside the palpable and observable environment of practice, puts in check the reliability of the training methodology, as well as the competence of the trained professionals. (AU)

Objetivo: Realizar un censo de los cursos de licenciatura en enfermería en las instituciones de educación superior brasileñas, según acreditación presencial y a distancia. Métodos: Estudio de abordaje cuantitativo, basado en documentos, tipo analítico-descriptivo, que utilizó datos de los cursos de Enfermería brasileños del portal e-MEC y sus páginas, en los sitios web de las IES. Resultados: De los 1.668 cursos de enfermería brasileños, 105 (6,29%) fueron a distancia y 1.563 (93,70%) presenciales. Todos los cursos de educación a distancia fueron privados. Cabe mencionar que, de los 105 cursos a distancia localizados, 50 se autodenominaron semipresenciales. En Enfermería, profesión que involucra esencialmente el cuidado humano, se cuestiona la calidad de la educación que ofrece la educación a distancia. Conclusion: Existen interrogantes sobre la calidad de la educación empleada por EAD, que al ser aplicada fuera del ámbito de práctica palpable y observable, pone en jaque la confiabilidad de la metodología de formación, así como la competencia de los profesionales capacitados. (AU)

Predictive processing: Is the future just a memory?

Predictive processing seems like a radical departure from traditional theories of information processing in the brain, but a broader view of predictions highlights many similarities with standard frameworks. Predictive processing is memory and competitive bias in a new outlook-and we should use this correspondence to advance research on both fronts.

Hippocampal place cells are topographically organized, but physical space has nothing to do with it.

Topographical organization can be found in many areas of the cerebral cortex, although its presence in higher order cortices is debated. Some studies evaluated whether this pattern of organization is present in the hippocampus, trying to determine whether hippocampal place cells are organized around a topographical map of space. Those studies indicated that the topographical organization of hippocampal place cells is either very limited or simply nonexistent. In this paper, we argue for a different interpretation of available evidence and suggest that there is a topographical organization in hippocampal place cells, but the topographical map formed is not a map of the physical space. Although place cell firing is correlated with the animal's position and is important to spatial navigation, place cells encode much more information than just location. Thus, we should not expect the topographical map to be organized around physical space, but around an abstract, multidimensional space containing the receptive fields of place cells. We show that this conclusion is supported by two of the main theories of hippocampal function-cognitive map theory and index theory-which, when carefully analyzed, make exactly the same predictions about hippocampal topography. Such abstract topographical map would be extremely hard to find using the methods commonly employed in the literature, but there are some approaches that may, in the future, make possible to characterize the topographical organization in the hippocampus and other high-order brain regions.

Isolating the key factors defining the magnitude of hippocampal neurogenesis' effects on anxiety, memory and pattern separation.

In this paper, I analyze the hypothesis that hippocampal neurogenesis (HN) exerts its effects on behavior via activation of inhibitory circuits in the hippocampus. Using a very simple mathematical model (half-borrowed from biochemistry) to aid the reasoning, I show that the key factors determining the magnitude of HN's effects on behavior are: the baseline levels of HN in the animal, the efficiency of the animal's inhibitory circuits, the strength/intensity of the stimulus presented to the animal and how much accuracy the behavioral task requires from the information contained in the hippocampal representations. Taken together, those factors can help explain patterns observed in the behavioral results for memory, pattern separation and anxiety. The conclusions of the analysis suggest that HN's effects on inhibitory circuits can explain the impact of neurogenesis on both emotion and cognition and provide a framework to interpret future studies about the effects of HN on different behaviors, with animals of different ages and of different species.

Writing Papers to Be Memorable, Even When They Are Not Really Read.

This paper discusses how our bad reading habits are starting to influence how we write. This short abstract and the picture next to it summarize the arguments in this paper. Just kidding, they do not. One really needs to read the paper for that.

To Read More Papers, or to Read Papers Better? A Crucial Point for the Reproducibility Crisis.

The overflow of scientific literature stimulates poor reading habits which can aggravate science's reproducibility crisis. Thus, solving the reproducibility crisis demands not only methodological changes, but also changes in our relationship with the scientific literature, especially our reading habits. Importantly, this does not mean reading more, it means reading better.

The evolutionary significance of hippocampal neurogenesis.

Hippocampal neurogenesis (HN) has been implicated in a variety of hippocampus-dependent behaviors in the laboratory but its evolutionary significance is still debated. Some authors have argued that HN is an adaptation while others argued it is an atavism. However, recent analyses lead to the conclusion that HN likely evolved concurrently with the dentate gyrus itself, both being dependent on a migration of neural stem and progenitor cells out of the periventricular zone that occurs during development. This may render the previous debates obsolete, as selective pressure was likely acting upon the mammalian dentate gyrus itself, with neurogenesis being a mere spandrel in dentate gyrus' evolution.

How the Hippocampus Represents Memories: Making Sense of Memory Allocation Studies.

In recent years there has been a wealth of studies investigating how memories are allocated in the hippocampus. Some of those studies showed that it is possible to manipulate the identity of neurons recruited to represent a given memory without affecting the memory's behavioral expression. Those findings raised questions about how the hippocampus represents memories, with some researchers arguing that hippocampal neurons do not represent fixed stimuli. Herein, an alternative hypothesis is argued. Neurons in high-order brain regions can be tuned to multiple dimensions, forming complex, abstract representations. It is argued that such complex receptive fields allow those neurons to show some flexibility in their responses while still representing relatively fixed sets of stimuli. Moreover, it is pointed out that changes induced by artificial manipulation of cell assemblies are not completely redundant-the observed behavioral redundancy does not imply cognitive redundancy, as different, but similar, memories may induce the same behavior.

Plasticity and redundancy in the integration of adult born neurons in the hippocampus.

Hippocampal neurogenesis (HN) is an extreme form of plasticity that inevitably rewires the hippocampal circuit and this rewiring was put forward as a possible mechanism for neurogenesis' behavioral effects. Here, I critically evaluate multiple lines of evidence to argue that structural plasticity induced by HN is, to a large extent, functionally redundant and thus has limited impact on behavior. The associative plasticity rules along with properties of immature neurons should only allow the survival of new neurons whose pre and postsynaptic partners have correlated activity, leading to functional redundancy. Moreover, non-redundant rewiring, even with its computational benefits, would impair meaningful communication between the hippocampus and other brain regions. This implies that associative plasticity rules constrain structural plasticity induced by neurogenesis, allowing the brain to balance plasticity and stability to maintain proper functioning. It also implies that behavioral effects of HN are mediated by other mechanisms apart from circuit rewiring.