Alex Batista Trentin

DNA extraction bias is usually treated as an intrinsic property of a cell's envelope, size, or physiology rather than a feature of its physical location. Most soil bacteria live attached to surfaces, and attached cells should lyse preferentially during bead-beating if a bead striking a particle crushes the cell against it, as on an anvil, whereas a free cell must be caught between colliding beads. We tested this 'anvil effect' in sterile synthetic soil microcosms using isogenic strain pairs that differ in adhesion in two bacteria with different modes of attachment. Agrobacterium tumefaciens anchors with a unipolar polysaccharide and aggregates in an exopolysaccharide matrix, whereas Caulobacter vibrioides attaches individually by a polar holdfast. Within each strain we calculated the ratio of DNA yield from mechanical to chemical lysis, then compared adherent with non-adherent strains. In support of the anvil effect, mechanical lysis favored adherent cells in A. tumefaciens (2.0-fold) and C. vibrioides (2.6-fold). The route by which soil was transferred to the lysis tube altered the extent of bias observed, further indicating the importance of considering physical context during DNA extraction. Our synthetic soil was standardized and aggregate-free, leaving cells exposed on particle surfaces, making these results a direct test of the anvil effect. In natural soils, the aggregation, texture, and moisture may offset or amplify this effect. Surface attachment therefore biases soil DNA extraction, complicating corrections that treat extraction efficiency as a fixed, strain-specific coefficient.

Biological treatment of dairy wastewater depends on complex microbial communities that vary widely across reactors, influent sources and operating conditions. Whether stable treatment performance arises from conserved taxa or from redundancy in functional potential remains unclear. In this study, we conducted a meta‐analysis and reprocessing of 239 samples from nine independent studies spanning six countries and multiple reactor configurations. Taxonomic profiles showed extreme heterogeneity, with no ASV detected in at least 50% of samples and most genera exhibiting strong study‐specific patterns. In contrast, the predicted functional repertoire was highly conserved. PICRUSt2 analysis identified 196 MetaCyc pathways present in at least 90% of samples, and these pathways accounted for approximately 90% of total predicted functional abundance. Generalised linear latent variable models showed that temperature and pH created consistent but modest ecological gradients, while study‐ and reactor‐level factors dominated community structure. Despite substantial taxonomic turnover, functional signatures associated with fermentation, biosynthesis, nitrogen cycling, and degradation of complex substrates remained stable across systems. Treatment‐stage transitions also followed predictable metabolic trajectories. These results show that dairy wastewater treatment systems are taxonomically flexible but functionally robust, and that extensive redundancy supports consistent metabolic capacity across diverse operational and environmental contexts.

Wastewater‐based epidemiology (WBE) has been widely applied for SARS‐CoV‐2 surveillance. Yet, operational parameters such as influent flow rate and sewer system characteristics may reflect underlying hydraulic variability relevant to detection performance, raising questions about the representativeness of results from small and medium‐sized plants. We conducted a systematic meta‐analysis of 10 published studies assessing SARS‐CoV‐2 RNA detection across wastewater treatment plants of different sizes and flow ranges. Data were extracted into 2 × 2 tables and analyzed in R using the meta and metafor packages. Odds ratios (ORs) were pooled with Mantel–Haenszel random‐effects models and DerSimonian–Laird τ ² estimation. Subgroup analyses compared studies from developed versus emerging economies, meta‐regression tested population size as a moderator, and publication bias was evaluated with funnel plots and Egger's test. The overall pooled OR was 2.08 (95% CI: 0.99–4.37, p = 0.053; I ² = 82.3%), indicating a non‐significant trend toward higher detection at higher flow rates. Subgroup analyses showed similar pooled effects across developed and emerging economies, though heterogeneity was lower in emerging settings. Meta‐regression revealed no significant association between population size and detection (slope = 0.29, p = 0.52). Funnel plots were relatively symmetric, and Egger's test did not indicate significant publication bias ( p = 0.169). Overall, these findings suggest that influent flow rate is not significantly associated with the probability of SARS‐CoV‐2 detection in WBE studies, supporting the inclusion of small and medium‐sized treatment plants in surveillance networks. At the same time, the concentration of studies in high‐income countries highlights inequities in the global WBE literature and in site deployment, with vulnerable populations often underrepresented. Expanding WBE infrastructure in underserved regions is necessary to ensure equitable and representative surveillance capacity.

Stable isotope probing (SIP) provides a powerful means to connect microbial sequence data with diverse metabolic activities, but the lack of a framework for SIP-derived data has limited its integration into broader strategies for ecological inference. Here, we introduce the SIPdb, an extensible SQLite database of curated nucleic acid SIP experiments (also in phyloseq format) paired with an interactive RShiny dashboard for analysis and visualization. The initial release compiles 22 studies covering 21 isotopolog substrates across diverse environments, with data standardized using the MISIP metadata standard. In creating the SIPdb, we have provided a standardized pipeline that accommodates the three most common SIP gradient fractionation strategies (binary, multi-fraction, and density-resolved), two isotope incorporator designation strategies (fixed- and sliding-window), and four complementary differential abundance methods (DESeq2, edgeR, limma-voom, and ALDEx2). Using our pipeline, we identified more than 42,000 unique amplicon sequence variants as isotope incorporators across 62 phyla. Benchmarking with synthetic datasets demonstrated consistent performance across incorporator designation strategies, with ALDEx2 providing the highest specificity. Validation against original publications showed that, on average, SIPdb recovered 70.1% of author-reported incorporator taxa, with discrepancies arising from differences in phylotyping or classification approaches. Finally, our reanalysis of a non-SIP study of 1,4-dioxane degradation showed how SIPdb can both validate known degraders and uncover additional candidate taxa involved in community metabolism. The SIPdb establishes a scalable platform for reverse ecology, enabling hypothesis generation, cross-study meta-analysis, and linking taxa to metabolic processes, while serving as an open, extensible resource to accelerate ecological interpretation in microbiome research.

Stable isotope probing (SIP) connects microbial sequence data to diverse metabolic activities, but the lack of a unifying framework for SIP-derived data has limited its integration into broader strategies for ecological inference. Here, we introduce the SIPdb, an extensible SQLite database of curated nucleic acid SIP experiments (also in phyloseq format) paired with an interactive RShiny dashboard for analysis and visualization. The initial release compiles 22 studies covering 21 isotopolog substrates across diverse environments, standardized using the MISIP metadata standard. SIPdb provides a standardized pipeline accommodating the three most common SIP gradient fractionation strategies (binary, multi-fraction, and density-resolved), two incorporator designation strategies (fixed- and sliding-window), and four complementary differential abundance methods (DESeq2, edgeR, limma-voom, and ALDEx2). Using this pipeline, we identified over 42 000 unique amplicon sequence variants as isotope incorporators across 62 phyla. Benchmarking with SIPSim-generated synthetic datasets showed that position-resolved designs performed best and that differential abundance method contributed comparably to variation in incorporator designation, with DESeq2 suggested as a balanced default. Validation against original publications showed that, on average, SIPdb recovered 70.1% of author-reported incorporators, with discrepancies arising from differences in phylotyping or classification approaches. Finally, reanalysis of a non-SIP study of 1,4-dioxane degradation showed how SIPdb can both validate known degraders and uncover additional candidate taxa involved in community metabolism. SIPdb establishes a scalable platform for reverse ecology, enabling hypothesis generation, cross-study meta-analysis, and linking taxa to metabolic processes, while serving as an open, extensible resource to accelerate ecological interpretation in microbiome research.

The teaching of viral diseases is of utmost importance for student development in basic education. However, it is necessary to find ways to capture students' attention and make them more central to the teaching-learning process. This study aimed to conduct a systematic literature review using a scientometric analysis methodology, with the aim of understanding the state of the art regarding the use of active methodologies in the teaching of viral diseases in basic education. Google Scholar was used as the search tool, and the terms: "active methodology" AND "virus" were employed to obtain a set of published studies. The selected publications were from 2013 to 2023, covering a decade and totaling 120 documents, with more than 50% being academic works. Four main clusters of viral diseases were identified: STIs (39 publications), general viral diseases (43), arboviruses (27), and coronaviruses (11). The years with the highest number of publications were 2019 and 2022, and the Southeast region stood out among Brazilian states publishing on the topic. Well-established methodologies, such as inquiry-based learning, circle dynamics, and workshops, appeared throughout the dataset. However, gamification was the most employed methodology, especially in recent years and during the period of remote learning. Thus, this study provided an overall overview of publications on active methodologies in the teaching of viral diseases and highlights that, although these methodologies are being encouraged, scientific dissemination about them is still highly necessary.

The dairy industry plays a crucial economic role worldwide but also contributes significantly to environmental pollution through the discharge of effluents. Effective management and treatment of these effluents are essential to reduce their environmental impact, prompting ongoing research into improved treatment methods. In this review, we analyze and summarize current trends in dairy wastewatertreatment technologies using a scientometric approach, with a focus on identifying prevalent treatment methods and emerging research directions. A comprehensive literature search was performed in the Web of Science (WoS) database using the keywords “dairy effluent” and “dairy wastewater” for publications spanning from 1945 to 2021. This initial search yielded 1,258 documents, which were manually refined to 1,191 relevant papers. The scientometric analysis, conducted using CiteSpace software and Microsoft Excel, categorized the publications by language, publication year, citation frequency, country, knowledge areas, keywords, and treatment methods (physical, chemical, and biological). The results indicated a significant growth in research on dairy effluent treatment, peaking in 2021 and projected to continue expanding through 2030. English emerged as the dominant publication language, and India was identified as the leading contributor. The focus of the research was predominantly on effluent treatment methods, with biological treatments accounting for 70% of the studies. Notable biological methods included bioreactor-based treatments, microalgae or aquatic plant cultivation, anaerobic digestion, microbial fuel cells, enzymatic processes, and techniques involving activated sludge and biofilters. These findings highlight critical trends in dairy effluent treatment and underscore the dominance and efficacy of biological methods. The insights provided can guide future research directions and support technological advancements aimed at sustainable and environmentally friendly effluent management practices.

Functional foods enriched with probiotics can help maintain health and enhance mental and cognitive function. This research focused on developing and characterizing a high-protein dietary supplement containing a blend of plant proteins and probiotic cells (Lactobacillus reuteri LRE 02 ID 1774). An additional systematic review aimed to showcase the current state of the art of the research topic and complement the study’s findings. The supplement demonstrated high nutritional quality, particularly in protein content and amino acid profile, providing all essential amino acids and branched-chain amino acids (BCAAs). It also contains omega-3 fatty acids (150 mg 100 g⁻¹), omega-6 (1420 mg 100 g⁻¹), and omega-9 (1180 mg 100 g⁻¹, including 20 mg 100 g⁻¹ of cis-11-eicosenoic acid). Additionally, the supplement serves as a source of dietary fiber and probiotics (10⁹ CFUg⁻¹) and exhibits moderate antioxidant activity. It is considered innovative due to its hypoallergenic properties, low concentration of anti-nutritional factors, good digestibility, and the fact that it is gluten- and lactose-free. Furthermore, it contains Lactobacillus reuteri, a probiotic with potential immunomodulatory and neurocognitive benefits.