Systems Biology-Based Analysis Indicates Global Transcriptional Impairment in Lead-Treated Human Neural Progenitor Cells

Lead poisoning effects are wide and include nervous system impairment, peculiarly during development, leading to neural damage. Lead interaction with calcium and zinc-containing metalloproteins broadly affects cellular metabolism since these proteins are related to intracellular ion balance, activation of signaling transduction cascades, and gene expression regulation. In spite of lead being recognized as a neurotoxin, there are gaps in knowledge about the global effect of lead in modulating the transcription of entire cellular systems in neural cells. In order to investigate the effects of lead poisoning in a systemic perspective, we applied the transcriptogram methodology in an RNA-seq dataset of human embryonic-derived neural progenitor cells (ES-NP cells) treated with 30 µM lead acetate for 26 days. We observed early downregulation of several cellular systems involved with cell differentiation, such as cytoskeleton organization, RNA, and protein biosynthesis. The downregulated cellular systems presented big and tightly connected networks. For long treatment times (12 to 26 days), it was possible to observe a massive impairment in cell transcription profile. Taking the enriched terms together, we observed interference in all layers of gene expression regulation, from chromatin remodeling to vesicle transport. Considering that ES-NP cells are progenitor cells that can originate other neural cell types, our results suggest that lead-induced gene expression disturbance might impair cells’ ability to differentiate, therefore influencing ES-NP cells’ fate

Sistema Modular para Detecção e Reconhecimento de Disparos de Armas de Fogo

The urban violence has been increasing in almost Brazilian state and in order to face this threat, new technological tools are required by the police authorities in order to support their decisions on how and when the few available resources should be employed to combat criminality. In this context, this work presents an embedded computational tool that is suitable for detecting gun-shots automatically. To provide the necessary knowledge to understand the work, a brief description about impulsive sounds, re guns and the gun-shot characteristics are initially presented. Latter, a system based on modules is proposed to detect and recognize impulsive sound, which are characteristics of gun-shots. However, since the system contain several modules in this work we have focus only on two of them: the module for detecting impulsive sounds and the module for distinguish a gun-shot from any other impulsive sound. For the impulsive detection module, three well-known algorithms were analyzed on the same condition: the fourth derivative of the Root Median Square (RMS), the Conditional Median Filter (CMF) and the Variance Method (VM). The algorithms were tested based on four measured performance parameters: accuracy, precision, sensibility and speci city. And in order to determine the most e cient algorithm for detecting impulsive sounds, a cadence test with impulsive sounds, without or with additional noise (constant or increasing) was performed. After this analysis, the parameters employed on the CMF and VM method were tested in a wide range of con gurations to verify any possibility of optimization. Once this optimal method was determined, the classi cation module to recognize gun-shots started to be implemented. For this, two distinguish methods were compared, one based on the signal wrapped over the time and the other based on most relevant frequencies obtained from the Fourier transform. From the comparison between the two methods it was observed that the wrapped method provided 54% of accuracy in the classi cation of impulsive sounds, while with the frequency analysis this value was 72%.A violência urbana vem crescendo anualmente em praticamente todos os estados brasileiros e para fazer face a essa amea ca, as autoridades policiais necessitam cada vez mais de ferramentas tecnológicas que os auxiliem na tomada de decisões sobre quando e como empregar os parcos recursos disponíveis a repressão do crime. Neste contexto, e apresentado nesse trabalho uma ferramenta computacional, passível de ser embarcada em dispositivos m oveis, que possibilita realizar a detecção e reconhecimento automático de disparos de armas de fogo. Para tanto, são descritos inicialmente os fundamentos básicos sobre sons impulsivos, armas de fogo e caracter sticas de disparos. Posteriormente, descreve-se uma proposta de um sistema modular de detecção e reconhecimento de disparos. No entanto, devido ao sistema conter diversos m odulos complexos, este trabalho teve foco em dois deles: o modulo de detecção de sons impulsivos e o modulo de classificação, que permite distinguir disparos de armas de fogo de outros sons impulsivos. Para o módulo de detecção de sons impulsivos foram analisados três algoritmos amplamente descritos na literatura: o algoritmo da quarta derivada da RMS, o da Conditional Median Filter (CMF) e o Método da Variância (VM). Os algoritmos foram testados com base nas medidas de desempenho da acurácia, precisão, sensibilidade e especificidade. E a para determinar o método mais e ciente, foram realizados testes de cadências, com sons impulsivos sem adição de ru do sonoro, com adição de ruído constante e com ruído variável. Ao final dessa anáise, os par^ametros preconizados na literatura para os m etodos CMF e VM foram alterados para uma verificação de possibilidade de otimização. De nido o algoritmo de detecção de impulso mais e ciente, iniciou-se o desenvolvimento do módulo de classificação. Para isso, foram propostas duas t ecnicas para o reconhecimento de disparos de armas de fogo, uma utilizando uma compara c~ao da envolt oria do som no dom nio do tempo e outra baseada na comparação de frequências dominantes obtidas por meio da transformada de Fourier. Numa comparação entre as duas técnicas observou-se que com a técnica da envoltória e poss vel identi car 54% dos sons impulsivos, enquanto que com a t ecnica baseada no dom nio da frequ^encia, este percentual foi de 72%

Systems Biology-Based Analysis Indicates Global Transcriptional Impairment in Lead-Treated Human Neural Progenitor Cells

Lead poisoning effects are wide and include nervous system impairment, peculiarly during development, leading to neural damage. Lead interaction with calcium and zinc-containing metalloproteins broadly affects cellular metabolism since these proteins are related to intracellular ion balance, activation of signaling transduction cascades, and gene expression regulation. In spite of lead being recognized as a neurotoxin, there are gaps in knowledge about the global effect of lead in modulating the transcription of entire cellular systems in neural cells. In order to investigate the effects of lead poisoning in a systemic perspective, we applied the transcriptogram methodology in an RNA-seq dataset of human embryonic-derived neural progenitor cells (ES-NP cells) treated with 30 µM lead acetate for 26 days. We observed early downregulation of several cellular systems involved with cell differentiation, such as cytoskeleton organization, RNA, and protein biosynthesis. The downregulated cellular systems presented big and tightly connected networks. For long treatment times (12 to 26 days), it was possible to observe a massive impairment in cell transcription profile. Taking the enriched terms together, we observed interference in all layers of gene expression regulation, from chromatin remodeling to vesicle transport. Considering that ES-NP cells are progenitor cells that can originate other neural cell types, our results suggest that lead-induced gene expression disturbance might impair cells’ ability to differentiate, therefore influencing ES-NP cells’ fate

Household-based biodigesters promote reduction of enteric virus and bacteria in vulnerable and poverty rural area.

The present study evaluated the river water quality improvement by implementation of household-based biodigesters in vulnerability and poverty rural area, in Minas Gerais State-Brazil. For that, 78 household-based biodigesters were installed for domestic wastewater treatment. Wastewater was collected before and after treatment and the physicochemical parameters and pathogens removal (human adenovirus (HAdV), hepatitis A (HAV) virus, Salmonella sp. and Escherichia coli) were evaluated; Additionally, river water was sampled before and after the household-based biodigesters implementation, to verify the contamination reduction and the positive impact of domestic wastewater treatment on waterborne pathogen reduction, considering HAdV, HAV, Salmonella sp. and E. coli quantification. The applicability in real-scale of decentralized treatment systems using household-based biodigesters promoted reduction of 90, 99, 99.99 and 99.999% from HAV, Salmonella sp., E. coli and HAdV from domestic wastewater, respectively; The river water quality improvement before the wastewater treatment application was highlight in the present study, considering that the reduction of waterborne pathogens in this water in 90, 99.99 and 99.999% of E. coli, HAV and HAdV, respectively (Salmonella sp. was not detected in river water). In general, this is an important study for encouraging the decentralized sanitation in vulnerable and poverty area, as well in rural sites, considering the positive impact of this implementation on public health