Gene Expression of GABAA Receptor Subunits and Association with Patient Survival in Glioma.

Rapid neuronal inhibition in the brain is mediated by γ-aminobutyric acid (GABA) activation of GABAA receptors. The GABRA5 gene, which encodes the α5 subunit of the GABAA receptor, has been implicated in an aggressive subgroup of medulloblastoma (MB), a type of pediatric brain tumor. However, the possible role of GABAA receptor subunits in glioma remains poorly understood. Here, we examined the expression of genes encoding GABAA receptor subunits in different types of glioma, and its possible association with patient prognosis assessed by overall survival (OS). Data were obtained from the French and The Cancer Genome Atlas Brain Lower Grade Glioma (TCGA-LGG) datasets and analyzed for expression of GABAA receptor subunit genes. OS was calculated using the Kaplan-Meier estimate. We found that genes GABRA2, GABRA3, GABRB3, GABRG1, and GABRG2 showed a significant association with OS, with higher gene expression indicating better prognosis. In patients with GBM, high expression of GABRA2 was associated with shorter OS, whereas, in contrast, higher levels of GABRB3 were associated with better prognosis indicated by longer OS. In patients with lower grade gliomas, GABRA3, GABRB3, GABRG1, and GABRG2, were associated with longer OS. High GABRB3 expression was related to longer survival when low grade glioma types were analyzed separately. Our results suggest an overall association between higher expression of most genes encoding GABAA receptor subunits and better prognosis in different types of glioma. Our findings support the possibility that down-regulation of GABAA receptors in glioma contributes to promoting tumor progression by reducing negative inhibition. These findings might contribute to further evaluation of GABAA receptors as a therapeutic target in glioma.

Gene Expression of CSF3R/CD114 Is Associated with Poorer Patient Survival in Glioma.

Gliomas comprise most cases of central nervous system (CNS) tumors. Gliomas afflict both adults and children, and glioblastoma (GBM) in adults represents the clinically most important type of malignant brain cancer, with a very poor prognosis. The cell surface glycoprotein CD114, which is encoded by the CSF3R gene, acts as the receptor for the granulocyte colony stimulating factor (GCSF), and is thus also called GCSFR or CSFR. CD114 is a marker of cancer stem cells (CSCs), and its expression has been reported in several cancer types. In addition, CD114 may represent one among various cases where brain tumors hijack molecular mechanisms involved in neuronal survival and synaptic plasticity. Here, we describe CSF3R mRNA expression in human gliomas and their association with patient prognosis as assessed by overall survival (OS). We found that the levels of CSF3R/CD114 transcripts are higher in a few different types of gliomas, namely astrocytoma, pilocytic astrocytoma, and GBM, in comparison to non-tumoral neural tissue. We also observed that higher expression of CSF3R/CD114 in gliomas is associated with poorer outcome as measured by a shorter OS. Our findings provide early evidence suggesting that CSF3R/CD114 shows a potential role as a prognosis marker of OS in patients with GBM.

Comment on Boyde et al. (2023), 'Fleas and lesions in armadillo osteoderms'.

Boyde et al. (2023) stated that Moura et al. (2021) did not explain how fleas generated cavities in armadillo osteoderms, which is wrongly stated, also presenting a misrepresentation of what is written about this in Moura et al. (2021).

The origin of an invasive air sac system in sauropodomorph dinosaurs.

One of the most remarkable features in sauropod dinosaurs relates to their pneumatized skeletons permeated by a bird-like air sac system. Many studies described the late evolution and diversification of this trait in mid to late Mesozoic forms but few focused on the origin of the invasive respiratory diverticula in sauropodomorphs. Fortunately, it is possible to solve this thanks to the boom of new species described in the last decade as well as the broad accessibility of new technologies. Here we analyze the unaysaurid sauropodomorph Macrocollum itaquii from the Late Triassic (early Norian) of southern Brazil using micro-computed tomography. We describe the chronologically oldest and phylogenetically earliest unambiguous evidence of an invasive air sac system in a dinosaur. Surprisingly, this species presented a unique pattern of pneumatization in non-sauropod sauropodomorphs, with pneumatic foramina in posterior cervical and anterior dorsal vertebrae. This suggests that patterns of pneumatization were not cladistically consistent prior to the arrival of Jurassic eusauropods. Additionally, we describe the protocamerae tissue, a new type of pneumatic tissue with properties of both camellae and camerae. This reverts the previous hypothesis which stated that the skeletal pneumatization first evolved into camarae, and derived into delicate trabecular arrangements. This tissue is evidence of thin camellate-like tissue developing into larger chambers. Finally, Macrocollum is an example of the gradual evolution of skeletal tissues responding to the fastly specializing Respiratory System of saurischian dinosaurs.

New proposal of viral genome representation applied in the classification of SARS-CoV-2 with deep learning.

BACKGROUND: In December 2019, the first case of COVID-19 was described in Wuhan, China, and by July 2022, there were already 540 million confirmed cases. Due to the rapid spread of the virus, the scientific community has made efforts to develop techniques for the viral classification of SARS-CoV-2. RESULTS: In this context, we developed a new proposal for gene sequence representation with Genomic Signal Processing techniques for the work presented in this paper. First, we applied the mapping approach to samples of six viral species of the Coronaviridae family, which belongs SARS-CoV-2 Virus. We then used the sequence downsized obtained by the method proposed in a deep learning architecture for viral classification, achieving an accuracy of 98.35%, 99.08%, and 99.69% for the 64, 128, and 256 sizes of the viral signatures, respectively, and obtaining 99.95% precision for the vectors with size 256. CONCLUSIONS: The classification results obtained, in comparison to the results produced using other state-of-the-art representation techniques, demonstrate that the proposed mapping can provide a satisfactory performance result with low computational memory and processing time costs.

SARS-CoV-2 virus classification based on stacked sparse autoencoder.

Since December 2019, the world has been intensely affected by the COVID-19 pandemic, caused by the SARS-CoV-2. In the case of a novel virus identification, the early elucidation of taxonomic classification and origin of the virus genomic sequence is essential for strategic planning, containment, and treatments. Deep learning techniques have been successfully used in many viral classification problems associated with viral infection diagnosis, metagenomics, phylogenetics, and analysis. Considering that motivation, the authors proposed an efficient viral genome classifier for the SARS-CoV-2 using the deep neural network based on the stacked sparse autoencoder (SSAE). For the best performance of the model, we explored the utilization of image representations of the complete genome sequences as the SSAE input to provide a classification of the SARS-CoV-2. For that, a dataset based on k-mers image representation was applied. We performed four experiments to provide different levels of taxonomic classification of the SARS-CoV-2. The SSAE technique provided great performance results in all experiments, achieving classification accuracy between 92% and 100% for the validation set and between 98.9% and 100% when the SARS-CoV-2 samples were applied for the test set. In this work, samples of the SARS-CoV-2 were not used during the training process, only during subsequent tests, in which the model was able to infer the correct classification of the samples in the vast majority of cases. This indicates that our model can be adapted to classify other emerging viruses. Finally, the results indicated the applicability of this deep learning technique in genome classification problems.

The absence of an invasive air sac system in the earliest dinosaurs suggests multiple origins of vertebral pneumaticity.

The origin of the air sac system present in birds has been an enigma for decades. Skeletal pneumaticity related to an air sac system is present in both derived non-avian dinosaurs and pterosaurs. But the question remained open whether this was a shared trait present in the common avemetatarsalian ancestor. We analyzed three taxa from the Late Triassic of South Brazil, which are some of the oldest representatives of this clade (233.23 ± 0.73 Ma), including two sauropodomorphs and one herrerasaurid. All three taxa present shallow lateral fossae in the centra of their presacral vertebrae. Foramina are present in many of the fossae but at diminutive sizes consistent with neurovascular rather than pneumatic origin. Micro-tomography reveals a chaotic architecture of dense apneumatic bone tissue in all three taxa. The early sauropodomorphs showed more complex vascularity, which possibly served as the framework for the future camerate and camellate pneumatic structures of more derived saurischians. Finally, the evidence of the absence of postcranial skeletal pneumaticity in the oldest dinosaurs contradicts the homology hypothesis for an invasive diverticula system and suggests that this trait evolved independently at least 3 times in pterosaurs, theropods, and sauropodomorphs.

Proposal of a Real-Time Test Platform for Tactile Internet Systems.

This work aimed to develop a real-time test platform for systems associated with the tactile internet area. The proposal comprises a master device, a communication channel and a slave device. The master device is a tactile glove (wearable technology) that works as a tactile interface based on vibratory feedback. The master device can interact with virtual elements (local or remote). The Matlab/Simulink environment and a robotics toolbox form the communication channel and the slave device. The communication channel introduces a bidirectional connection of variable latency, and the slave device is defined as a robotic phantom omni manipulator emulated in Matlab/Simulink. The virtual robotic manipulator, the slave device, can generate different types of tactile sensations in the tactile glove, that is, in the master device. The platform can model tactile sensations such as coarse roughness, fine roughness, smoothness, dripping and softness. The proposed platform presented adequate results and can be used to test various algorithms and methods correlated to the tactile internet.

FPGA Applied to Latency Reduction for the Tactile Internet.

Tactile internet applications allow robotic devices to be remotely controlled over a communication medium with an unnoticeable time delay. In bilateral communication, the acceptable round trip latency is usually 1 ms up to 10 ms, depending on the application requirements. The communication network is estimated to generate 70% of the total latency, and master and slave devices produce the remaining 30%. Thus, this paper proposes a strategy to reduce 30% of the total latency produced by such devices. The strategy is to use FPGAs to minimize the execution time of device-associated algorithms. With this in mind, this work presents a new hardware reference model for modules that implement nonlinear positioning and force calculations and a tactile system formed by two robotic manipulators. In addition to presenting the implementation details, simulations and experimental tests are performed in order to validate the hardware proposed model. Results associated with the FPGA sampling rate, throughput, latency, and post-synthesis occupancy area are analyzed.

Convolutional Neural Network Applied to SARS-CoV-2 Sequence Classification.

COVID-19, the illness caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus belonging to the Coronaviridade family, a single-strand positive-sense RNA genome, has been spreading around the world and has been declared a pandemic by the World Health Organization. On 17 January 2022, there were more than 329 million cases, with more than 5.5 million deaths. Although COVID-19 has a low mortality rate, its high capacities for contamination, spread, and mutation worry the authorities, especially after the emergence of the Omicron variant, which has a high transmission capacity and can more easily contaminate even vaccinated people. Such outbreaks require elucidation of the taxonomic classification and origin of the virus (SARS-CoV-2) from the genomic sequence for strategic planning, containment, and treatment of the disease. Thus, this work proposes a high-accuracy technique to classify viruses and other organisms from a genome sequence using a deep learning convolutional neural network (CNN). Unlike the other literature, the proposed approach does not limit the length of the genome sequence. The results show that the novel proposal accurately distinguishes SARS-CoV-2 from the sequences of other viruses. The results were obtained from 1557 instances of SARS-CoV-2 from the National Center for Biotechnology Information (NCBI) and 14,684 different viruses from the Virus-Host DB. As a CNN has several changeable parameters, the tests were performed with forty-eight different architectures; the best of these had an accuracy of 91.94 ± 2.62% in classifying viruses into their realms correctly, in addition to 100% accuracy in classifying SARS-CoV-2 into its respective realm, Riboviria. For the subsequent classifications (family, genera, and subgenus), this accuracy increased, which shows that the proposed architecture may be viable in the classification of the virus that causes COVID-19.

Proposal of Smith-Waterman algorithm on FPGA to accelerate the forward and backtracking steps.

In bioinformatics, alignment is an essential technique for finding similarities between biological sequences. Usually, the alignment is performed with the Smith-Waterman (SW) algorithm, a well-known sequence alignment technique of high-level precision based on dynamic programming. However, given the massive data volume in biological databases and their continuous exponential increase, high-speed data processing is necessary. Therefore, this work proposes a parallel hardware design for the SW algorithm with a systolic array structure to accelerate the forward and backtracking steps. For this purpose, the architecture calculates and stores the paths in the forward stage for pre-organizing the alignment, which reduces the complexity of the backtracking stage. The backtracking starts from the maximum score position in the matrix and generates the optimal SW sequence alignment path. The architecture was validated on Field-Programmable Gate Array (FPGA), and synthesis analyses have shown that the proposed design reaches up to 79.5 Giga Cell Updates per Second (GCPUS).

Unsupervised Learning Applied to the Stratification of Preterm Birth Risk in Brazil with Socioeconomic Data.

Preterm birth (PTB) is a phenomenon that brings risks and challenges for the survival of the newborn child. Despite many advances in research, not all the causes of PTB are already clear. It is understood that PTB risk is multi-factorial and can also be associated with socioeconomic factors. Thereby, this article seeks to use unsupervised learning techniques to stratify PTB risk in Brazil using only socioeconomic data. Through the use of datasets made publicly available by the Federal Government of Brazil, a new dataset was generated with municipality-level socioeconomic data and a PTB occurrence rate. This dataset was processed using various unsupervised learning techniques, such as k-means, principal component analysis (PCA), and density-based spatial clustering of applications with noise (DBSCAN). After validation, four clusters with high levels of PTB occurrence were discovered, as well as three with low levels. The clusters with high PTB were comprised mostly of municipalities with lower levels of education, worse quality of public services-such as basic sanitation and garbage collection-and a less white population. The regional distribution of the clusters was also observed, with clusters of high PTB located mostly in the North and Northeast regions of Brazil. The results indicate a positive influence of the quality of life and the offer of public services on the reduction in PTB risk.

Prediction Techniques on FPGA for Latency Reduction on Tactile Internet.

Tactile Internet (TI) is a new internet paradigm that enables sending touch interaction information and other stimuli, which will lead to new human-to-machine applications. However, TI applications require very low latency between devices, as the system's latency can result from the communication channel, processing power of local devices, and the complexity of the data processing techniques, among others. Therefore, this work proposes using dedicated hardware-based reconfigurable computing to reduce the latency of prediction techniques applied to TI. Finally, we demonstrate that prediction techniques developed on field-programmable gate array (FPGA) can minimize the impacts caused by delays and loss of information. To validate our proposal, we present a comparison between software and hardware implementations and analyze synthesis results regarding hardware area occupation, throughput, and power consumption. Furthermore, comparisons with state-of-the-art works are presented, showing a significant reduction in power consumption of ≈1300× and reaching speedup rates of up to ≈52×.

A Novel Deep Neural Network Technique for Drug-Target Interaction.

Drug discovery (DD) is a time-consuming and expensive process. Thus, the industry employs strategies such as drug repositioning and drug repurposing, which allows the application of already approved drugs to treat a different disease, as occurred in the first months of 2020, during the COVID-19 pandemic. The prediction of drug-target interactions is an essential part of the DD process because it can accelerate it and reduce the required costs. DTI prediction performed in silico have used approaches based on molecular docking simulations, including similarity-based and network- and graph-based ones. This paper presents MPS2IT-DTI, a DTI prediction model obtained from research conducted in the following steps: the definition of a new method for encoding molecule and protein sequences onto images; the definition of a deep-learning approach based on a convolutional neural network in order to create a new method for DTI prediction. Training results conducted with the Davis and KIBA datasets show that MPS2IT-DTI is viable compared to other state-of-the-art (SOTA) approaches in terms of performance and complexity of the neural network model. With the Davis dataset, we obtained 0.876 for the concordance index and 0.276 for the MSE; with the KIBA dataset, we obtained 0.836 and 0.226 for the concordance index and the MSE, respectively. Moreover, the MPS2IT-DTI model represents molecule and protein sequences as images, instead of treating them as an NLP task, and as such, does not employ an embedding layer, which is present in other models.

Exquisite air sac histological traces in a hyperpneumatized nanoid sauropod dinosaur from South America.

This study reports the occurrence of pneumosteum (osteohistological structure related to an avian-like air sac system) in a nanoid (5.7-m-long) saltasaurid titanosaur from Upper Cretaceous Brazil. We corroborate the hypothesis of the presence of an air sac system in titanosaurians based upon vertebral features identified through external observation and computed tomography. This is the fifth non-avian dinosaur taxon in which histological traces of air sacs have been found. We provided a detailed description of pneumatic structures from external osteology and CT scan data as a parameter for comparison with other taxa. The camellate pattern found in the vertebral centrum (ce) of this taxon and other titanosaurs shows distinct architectures. This might indicate whether cervical or lung diverticula pneumatized different elements. A cotylar internal plate of bone tissue sustains radial camellae (rad) in a condition similar to Alamosaurus and Saltasaurus. Moreover, circumferential chambers (cc) near the cotyle might be an example of convergence between diplodocoids and titanosaurs. Finally, we also register for the first time pneumatic foramina (fo) and fossae connecting camellate structures inside the neural canal in Titanosauria and the second published case in non-avian dinosaurs. The extreme pneumaticity observed in this nanoid titanosaur contrasts with previous assumptions that this feature correlates with the evolution of gigantic sizes in sauropodomorphs. This study reinforces that even small-bodied sauropod clades could present a hyperpneumatized postcranial skeleton, a character inherited from their large-bodied ancestors.

Fully Parallel Implementation of Otsu Automatic Image Thresholding Algorithm on FPGA.

This work proposes a high-throughput implementation of the Otsu automatic image thresholding algorithm on Field Programmable Gate Array (FPGA), aiming to process high-resolution images in real-time. The Otsu method is a widely used global thresholding algorithm to define an optimal threshold between two classes. However, this technique has a high computational cost, making it difficult to use in real-time applications. Thus, this paper proposes a hardware design exploiting parallelization to optimize the system's processing time. The implementation details and an analysis of the synthesis results concerning the hardware area occupation, throughput, and dynamic power consumption, are presented. Results have shown that the proposed hardware achieved a high speedup compared to similar works in the literature.

A full-parallel implementation of Self-Organizing Maps on hardware.

Self-Organizing Maps (SOMs) are extensively used for data clustering and dimensionality reduction. However, if applications are to fully benefit from SOM based techniques, high-speed processing is demanding, given that data tends to be both highly dimensional and yet "big". Hence, a fully parallel architecture for the SOM is introduced to optimize the system's data processing time. Unlike most literature approaches, the architecture proposed here does not contain sequential steps - a common limiting factor for processing speed. The architecture was validated on FPGA and evaluated concerning hardware throughput and the use of resources. Comparisons to the state of the art show a speedup of 8.91× over a partially serial implementation, using less than 15% of hardware resources available. Thus, the method proposed here points to a hardware architecture that will not be obsolete quickly.

Data stream dataset of SARS-CoV-2 genome.

As of May 25, 2020, the novel coronavirus disease (called COVID-19) spread to more than 185 countries/regions with more than 348,000 deaths and more than 5,550,000 confirmed cases. In the bioinformatics area, one of the crucial points is the analysis of the virus nucleotide sequences using approaches such as data stream techniques and algorithms. However, to make feasible this approach, it is necessary to transform the nucleotide sequences string to numerical stream representation. Thus, the dataset provides four kinds of data stream representation (DSR) of SARS-CoV-2 virus nucleotide sequences. The dataset provides the DSR of 1557 instances of SARS-CoV-2 virus, 11540 other instances of other viruses from the Virus-Host DB dataset, and three instances of Riboviria viruses from NCBI (Betacoronavirus RaTG13, bat-SL-CoVZC45, and bat-SL-CoVZXC21).

Proposal of the CAD System for Melanoma Detection Using Reconfigurable Computing.

This work proposes dedicated hardware to real-time cancer detection using Field-Programmable Gate Arrays (FPGA). The presented hardware combines a Multilayer Perceptron (MLP) Artificial Neural Networks (ANN) with Digital Image Processing (DIP) techniques. The DIP techniques are used to extract the features from the analyzed skin, and the MLP classifies the lesion into melanoma or non-melanoma. The classification results are validated with an open-access database. Finally, analysis regarding execution time, hardware resources usage, and power consumption are performed. The results obtained through this analysis are then compared to an equivalent software implementation embedded in an ARM A9 microprocessor.

Chaos game representation dataset of SARS-CoV-2 genome.

As of April 16, 2020, the novel coronavirus disease (called COVID-19) spread to more than 185 countries/regions with more than 142,000 deaths and more than 2,000,000 confirmed cases. In the bioinformatics area, one of the crucial points is the analysis of the virus nucleotide sequences using approaches such as data stream, digital signal processing, and machine learning techniques and algorithms. However, to make feasible this approach, it is necessary to transform the nucleotide sequences string to numerical values representation. Thus, the dataset provides a chaos game representation (CGR) of SARS-CoV-2 virus nucleotide sequences. The dataset provides the CGR of 100 instances of SARS-CoV-2 virus, 11540 instances of other viruses from the Virus-Host DB dataset, and three instances of Riboviria viruses from NCBI (Betacoronavirus RaTG13, bat-SL-CoVZC45, and bat-SL-CoVZXC21).