Search

Protecting freshwater biodiversity is considered an ultimate challenge but depends on reliable surveys of species distribution and abundance which eDNA metabarcoding (environmental DNA metabarcoding) may offer. To do so, a better understanding of the sources of temporal variation among species eDNA abundance and of data transformation in eDNA metabarcoding studies is needed. Here, we show that transformation based on relative abundance is critical to suitable analyses of eDNA metabarcoding data and that Hellinger transformation performed slightly better than other methods. Furthermore, we show that site localities significantly explain eDNA metabarcoding variation, while no variation is explained by time of sampling. This indicates that species communities vary more spatially than temporally within a dendritic system composed of small rivers. We then further documented the community structure in the St. Charles River (Québec City, Canada) and six of its tributaries. This revealed the existence of eight species communities explaining 82.1% of eDNA read variation within this river network. Moreover, variation in environmental variables among sites explained 53.0% of eDNA reads, while sampling events and temporal environmental variation explained no eDNA metabarcoding variation. Altogether, this supports the claim that eDNA metabarcoding is a powerful tool to document and monitor fish communities in watersheds composed of small river dendritic systems.

Large rivers and their estuaries are structurally complex and comprise a diversity of habitats supporting a rich biodiversity. As a result, identifying and monitoring fish communities using traditional methods in such systems may often be logistically challenging. Using the mitochondrial DNA 12S MiFish primers, we performed an eDNA metabarcoding analysis to assess the effect of spatial and environmental factors on the variation of the fish community structure along most of the St. Lawrence River/Estuary/Gulf (Québec Canada), a transect spanning 1300 km across a diversity of habitats from a fluviatile non‐tidal section to a marine environment. A total of 129 species were identified including freshwater and marine species. For the freshwater sectors, eDNA identified 80 species compared with the 85 species previously reported based on conventional sampling. eDNA also revealed similar species diversity and communities in the fluviatile section of the St. Lawrence River. Furthermore, our study improved current knowledge about the brackish and marine sections by describing community transition between freshwater and marine fish communities in association with a drastic shift in environmental conditions observed between the end of the fluvial estuary and the beginning of the middle (brackish) estuary. Altogether, this study exemplifies how eDNA metabarcoding is a powerful tool to document fish community shifts in large temperate lotic ecosystems.

Ascidians (Phylum Chordata, Class Ascidiacea) are a large group of invertebrates which occupy a central role in the ecology of marine benthic communities. Many ascidian species have become successfully introduced around the world via anthropogenic vectors. The botryllid ascidians (Order Stolidobranchia, Family Styelidae) are a group of 53 colonial species, several of which are widespread throughout temperate or tropical and subtropical waters. However, the systematics and biology of this group of ascidians is not well-understood. To provide a systematic framework for this group, we have constructed a well-resolved phylogenomic tree using 200 novel loci and 55 specimens. A Principal Components Analysis of all species described in the literature using 31 taxonomic characteristics revealed that some species occupy a unique morphological space and can be easily identified using characteristics of adult colonies. For other species, additional information such as larval or life history characteristics may be required for taxonomic discrimination. Molecular barcodes are critical for guiding the delineation of morphologically similar species in this group.

Induced systemic resistance (ISR) is a mechanism involved in the plant defense response against pathogens. Certain members of the Bacillus genus are able to promote the ISR by maintaining a healthy photosynthetic apparatus, which prepares the plant for future stress situations. The goal of the present study was to analyze the effect of the inoculation of Bacillus on the expression of genes involved in plant responses to pathogens, as a part of the ISR, during the interaction of Capsicum chinense infected with PepGMV. The effects of the inoculation of the Bacillus strains in pepper plants infected with PepGMV were evaluated by observing the accumulation of viral DNA and the visible symptoms of pepper plants during a time-course experiment in greenhouse and in in vitro experiments. The relative expression of the defense genes CcNPR1, CcPR10, and CcCOI1 were also evaluated. The results showed that the plants inoculated with Bacillus subtilis K47, Bacillus cereus K46, and Bacillus sp. M9 had a reduction in the PepGMV viral titer, and the symptoms in these plants were less severe compared to the plants infected with PepGMV and non-inoculated with Bacillus. Additionally, an increase in the transcript levels of CcNPR1, CcPR10, and CcCOI1 was observed in plants inoculated with Bacillus strains. Our results suggest that the inoculation of Bacillus strains interferes with the viral replication, through the increase in the transcription of pathogenesis-related genes, which is reflected in a lowered plant symptomatology and an improved yield in the greenhouse, regardless of PepGMV infection status.

The advantages of microbial induced carbonate mineralization for soil-stabilization and building-material industries are under extensive investigation. The pH is one of the influential parameters on the desired calcium carbonate mineralization due to the resulting textures of this mineral. Moreover, the decrease in microbial growth under the extreme alkaline environment compatible with the sustainability of concrete has been the bottleneck for an effective application of Microbial Induced Carbonate Precipitation (MICP) in the concrete industry. Microbial consortia have shown more robustness in their resistance to environmental fluctuations than pure cultures. In addition, microorganisms obtained from alkaline environments could facilitate their adaptation to extreme alkalinity. The aim of this study was to obtain urease producing bacteria (UPB) able to maintain a high MICP performance under extremely alkaline conditions compatible with concrete by adapting native microorganisms obtained from extreme environments. The growth performance, urease activity, strength of the generated biocement, and CaCO3 mineralogy were compared with the best-performer urease-producing bacteria (UPB), S. pasteurii DSMZ 33. The native bacteria presented a similar performance in growth and urease activity than S. pasteurii under extreme alkaline conditions (pH 12.5). However, the generated biocement of native Sporosarcina sp. achieved 461 % more unconfined compressive strength (UCS) and 120 % more CaCO3 content than the biocement generated by S. pasteurii DSMZ 33. The careful adaptation process performed in this study for native UPB and S. pasteurii DSMZ 33 is an interesting approach with promising and projectable results for future engineering and biotechnological applications. These results have important implications for the design of engineering solutions involving MICP. Statement of Significance A consolidated and strong biocement was generated by a native species obtained from extreme ecosystems in an effort of bioprospecting to enhance the performance of biotechnological solutions for geotechnical applications in the concrete and soil-improvement industries. Biocement generated by the native species was stronger than the generated by one of the best-described biocementation performers. This native species was able to actively growing and do perform microbial-induced-carbonate-mineralization under extreme alkalinity conditions after a careful laboratory adaptation process. The native species presented unique and differentiating traits that gave it a better adaptability and biocementation performance. The same occurs with a priceless microbial diversity inhabiting little explored and unprotected extreme ecosystems. Extreme environments house a fascinating biodiversity with potential value for ecosystem services.

Network traffic monitoring uses empirical entropy to detect anomalous events such as various types of attacks. However, the exact computation of the entropy in high-speed networks is a difficult process due to the limited memory resources available in the data plane hardware. In this paper, we present a method and hardware accelerator to approximate the empirical entropy of a large data set with high throughput and sublinear memory requirements. Our method uses streaming algorithms that exploit the fine-grained parallelism of existing hardware platforms for data plane processing, such as field-programmable gate arrays (FPGAs). The method uses sketches to compute the cardinality of the stream and the frequencies of the top-K elements on line, and then it estimates the contribution to the entropy of the rest of the stream assuming a simple uniform distribution for these elements. Implemented on a Xilinx UltraScale+ ZCU102 FPGA, the accelerator implements the method using only on-chip memory, with less than 50% resource usage. Tested on real network traces of up to 120 million packets and more than 5 million flows, the accelerator estimates the empirical entropy with less than 1.5% mean relative error and $21~\mu \text{s}$ latency, and supports a minimum throughput of 204 gigabits per second.

The aphid Chaetosiphon fragaefolii Cockerell, 1901 is an agricultural pest and known vector of strawberry viruses. To better understand its biology and systematics, we performed a genomic analysis on C. fragaefolii collected from Quinalt strawberry plants from Pacific Grove, Monterey county, California, USA using Oxford Nanopore and Illumina sequencing. The resulting data were used to assemble the aphids complete mitogenome. The mitogenome of C. fragaefolii is 16,108 bp in length and contains 2 rRNA, 13 protein-coding, and 22 tRNA genes (GenBank accession number LC590896). The mitogenome is similar in content and organization to other Aphididae. Phylogenetic analysis of the C. fragaefolii mitogenome resolved it in a fully supported clade in the tribe Macrosiphini. Analysis of the cox1 barcode sequence of C. fragaefolii from California found exact and nearly identical sequences to C. fragaefolii and Chaetosiphon thomasi Hille Ris Lambers, 1953, suggesting the two species are conspecific.

Molecular techniques offer sensitive, specific, noninvasive monitoring of target species from a variety of environmental samples. We recently developed a CRISPR‐Cas‐based eDNA assay for rapid single‐species detection as a route to a simple, cost‐effective biosensor device. CRISPR‐Cas‐based diagnostic assays use isothermal conditions in combination with a highly specific sequence recognition system. This CRISPR‐Cas assay was designed to target Salmo salar, and we previously demonstrated its utility in eDNA samples from sites in Ireland. The aim of this study was to validate our assay in two larger sample sets from Canada (n = 16/n = 63) in comparison with an independent S. salar qPCR assay. We demonstrate that overall, the CRISPR‐Cas assay performs similarly to qPCR for assessing the presence or absence of S. salar from eDNA and provides a viable alternative approach where qPCR assay design and application have proven to be challenging.

The genus Rutilus is widespread in the western and central Palearctic region. In the Caspian Sea, the taxonomic status of different populations of Rutilus lacustris has been debated due to different sub-specific names attributed to each population. We genotyped 7,984 single nucleotide polymorphisms and sequenced the mitochondrial cytochrome C oxidase subunit I gene of 37 R. lacustris and Rutilus frisii from the southeast and southwest Caspian Sea and the Aras River in the Kura River drainage. We analysed data using clustering, Bayes factor delimitation, introgression, assessment of migration rate, and phylogenetic analyses. The results showed that the southeast and southwest Caspian Sea populations of R. lacustris were closely related, but highly differentiated from R. lacustris in the Aras River. The Bayes factor delimitation test supported the existence of three populations of R. lacustris in the studied area. Three hybrid individuals with mtDNA from Abramis brama or R. frisii and nuclear DNA from R. lacustris were detected. To protect R. lacustris in the southern Caspian Sea, we propose that the Aras River and sea-run R. lacustris be treated as two separate conservation units and the southern Caspian Sea R. lacustris populations should be viewed as two potentially separate management units.

Conservation of freshwater biodiversity requires being able to track the presence and abundance of entire fish communities. However, studying fish community composition within rivers remains a technical challenge because of high spatial and temporal physico‐chemical variability, anthropic activities and connections with other river catchments, which may all contribute to important variations in local ecology and communities. Here, we used environmental DNA metabarcoding to document spatial variation in fish communities at a small geographic scale in a large river system. The study was conducted in the Contrecoeur sector (5.5 km long and approximately 1–1.5 km wide) of the St. Lawrence River (Québec, Canada), where two water masses with different physico‐chemical properties, known as "brown waters" and "green waters," flow in parallel with limited admixing. Water samples were collected during two consecutive days at 53 stations located in both water masses. Using universal PCR MiFish 12S primers, Illumina MiSeq sequencing, and the Barque (www.github.com/enormandeau/barque) eDNA analysis software developed by our group, a total of 67 fish species were detected. PERMANOVA and redundancy analyses (RDA) performed on relative read abundance revealed that each water mass comprised distinct communities that depended on turbidity, depth, and to a lesser extent on the upstream versus downstream position along the study area. eDNA metabarcoding results were compared with those of traditional surveys conducted previously in the sector and up to 40 km upstream of it. As previously reported, higher species diversity was detected by eDNA and with substantially lower sampling effort. Our results represent one of the few studies documenting the potential of eDNA metabarcoding to investigate small‐scale variation in 2D spatial patterns of distribution of whole fish communities associated with habitat characteristics within a lotic system.

Investigating the relative importance of neutral versus selective processes governing the accumulation of genetic variants is a key goal in both evolutionary and conservation biology. This is particularly true in the context of small populations, where genetic drift can counteract the effect of selection. Using Brook Charr (Salvelinus fontinalis) from Québec, Canada, as a case study, we investigated the importance of demographic versus selective processes governing the accumulation of both adaptive and maladaptive mutations in closed versus open and connected populations to assess gene flow effect. This was achieved by using 14,779 high-quality filtered SNPs genotyped among 1,416 fish representing 50 populations from three life history types: lacustrine (closed populations), riverine and anadromous (connected populations). Using the PROVEAN algorithm, we observed a considerable accumulation of putative deleterious mutations across populations. The absence of correlation between the occurrence of putatively beneficial or deleterious mutations and local recombination rate supports the hypothesis that genetic drift might be the main driver of the accumulation of such variants. However, despite a lower genetic diversity observed in lacustrine than in riverine or anadromous populations, lacustrine populations do not exhibit more deleterious mutations than the two other history types, suggesting that the negative effect of genetic drift in lacustrine populations may be mitigated by that of relaxed purifying selection. Moreover, we also identified genomic regions associated with anadromy, as well as an overrepresentation of transposable elements associated with variation in environmental variables, thus supporting the importance of transposable elements in adaptation.

Practical applications of environmental DNA (eDNA) are in exponential expansion, especially for the assessment and monitoring of freshwater metazoans. Because eDNA sampling and analysis is noninvasive, it improves the detection of threatened, invasive, and exploited species for which monitoring may be challenging. Species detection efforts using a combination of eDNA and qPCR have been highly successful and, as a result, their use in species monitoring is expanding rapidly. We developed qPCR primers and probes in order to monitor many invasive, threatened, or exploited aquatic species as part of various monitoring eDNA projects in the province of Québec, Canada. Here, we present a total of 60 species‐specific qPCR assays (including PCR protocols, primers, and TaqMan probes sequences) developed for the detection of 45 fishes, six amphibians, five reptiles, two mollusks, and two crustaceans. These comprised nine and 27 species, respectively, listed as invasive and threatened in Eastern Canada. These resources should be of broad usefulness not only for monitoring studies based in Québec but throughout the geographic range of the targeted species in North America.

Humans have the ability to permanently alter aquatic ecosystems and the introduction of species is often the most serious alteration. Non‐native Smallmouth Bass (Micropterus dolomieu) were identified in Miramichi Lake c. 2008, which is a headwater tributary to the Southwest Miramichi River, a renowned Atlantic Salmon (Salmo salar) river whose salmon population is dwindling. A containment programme managed by the Department of Fisheries and Oceans, Canada (DFO) was implemented in 2009 to confine Smallmouth Bass (SMB) to the lake. We utilized environmental DNA (eDNA) as a detection tool to establish the potential escape of SMB into the Southwest Miramichi River. We sampled at 26 unique sites within Miramichi Lake, the outlet of Miramichi Lake (Lake Brook), which flows into the main stem Southwest Miramichi River, and the main stem Southwest Miramichi River between August and October 2017. We observed n = 6 positive detections located in the lake, Lake Brook, and the main stem Southwest Miramichi downstream of the lake. No detections were observed upstream of the confluence of Lake Brook and the main stem Southwest Miramichi. The spatial pattern of positive eDNA detections downstream of the lake suggests the presence of individual fish versus lake‐sourced DNA in the outlet stream discharging to the main river. Smallmouth Bass were later confirmed by visual observation during a snorkeling campaign, and angling. Our results, both eDNA and visual confirmation, definitively show Smallmouth Bass now occupy the main stem of the Southwest Miramichi., Smallmouth Bass were believed to be contained within Miramichi Lake. Our eDNA results suggested a high probability of escapement from Miramichi Lake into the lotic Southwest Miramichi. A visual observation of a Smallmouth Bass validated our results.

Extracting discriminative k-mers is an important and challenging problem in DNA sequence analysis with applications in metagenomics and motif discovery. Despite the availability of multiple computational tools designed for this purpose, most discriminative k-mer discovery methods suffer from long execution times and high memory usage when processing large datasets. This paper presents a novel approach for discriminative k-mer discovery in DNA sequences, which leverages streaming and sketch algorithms to reduce space complexity and expose data parallelism, enabling the use of parallel platforms for accelerating the execution of computationally-intensive operations. To assess the performance of our method, we designed and implemented two versions of the algorithm that leverage parallelization at different levels: (i) a software version tailored for multithreading and vector instructions in commodity CPUs, and (ii) a custom architecture implemented on a Field-Programmable Gate Array (FPGA) accelerator that exploits fine-grain parallelism and deep pipelining on reconfigurable logic. Experimental results show that, when mining discriminative k-mers from a set of well-known ChIP-seq sequences, our parallel software implementation executes at least 15% faster than exact-counting tools, and requires at least five times less memory when processing large datasets. More importantly, we designed a custom FPGA-based accelerator for our algorithm on a Xilinx KCU1500 board, which achieves speedups above 78x with the largest datasets, compared to our parallel software implementation. The accelerator uses less than 3% of the logic resources available on the on-board XCKU115 Kintex-7 Ultrascale FPGA, and between 12% and 70% of the memory resources, depending on the size of the dataset.

We present an automatic algorithm for the group-wise parcellation of the cortical surface. The method is based on the structural connectivity obtained from representative brain fiber clusters, calculated via an inter-subject clustering scheme. Preliminary regions were defined from cluster-cortical mesh intersection points. The final parcellation was obtained using parcel probability maps to model and integrate the connectivity information of all subjects, and graphs to represent the overlap between parcels. Two inter-subject clustering schemes were tested, generating a total of 171 and 109 parcels, respectively. The resulting parcels were quantitatively compared with three state-of-the-art atlases. The best parcellation returned 69 parcels with a Dice similarity coefficient greater than 0.5. To the best of our knowledge, this is the first diffusion-based cortex parcellation method based on whole-brain inter-subject fiber clustering.

One of the most important steps in conservation of the subterranean life forms is to decipher their distribution and ecology, which is challenging using traditional approaches. Development of an environmental DNA (eDNA) assay provides an efficient means for discovering and monitoring subterranean life forms. In this study, the distribution of three Iranian blind cave fish species (blind Iran cave barb Garra typhlops, blind Lorestan cave barb Garra lorestanensis, and blind cave loach Eidinemacheilus smithi) was assessed using 12S rRNA eDNA metabarcoding performed using MiFish-U PCR primers and preliminary species distribution modeling (SDM) using bioclimatic data. The majority of sampling localities with positive detection of cave barb eDNA fall within suitable habitats in the Zagros Mountains of Iran. Our results revealed that Lorestan and Iran cave barbs have differential distribution patterns, with some extent of habitat overlap in the vicinity of the originally discovered cave barb locality. According to the observed distribution patterns, the blind Lorestan cave barb and cave loach are mostly distributed in habitats close to the Sezar River (Dez River drainage, Iran), and the blind Iran cave barb is distributed towards the west and probably in a few springs in the Karkheh River drainage. Our data support the previously proposed distribution pattern for the cave barbs, in which the species show partial niche separation and reproductive isolation, with the Lorestan cave barb being a water flow-dependent species and the Iran cave barb being a generalist species preferring variable flow rates. We showed eDNA metabarcoding to be a useful approach for ecological surveys of subterranean fish biodiversity with implications for conservation. Published version

The aphid Chaetosiphon fragaefolii Cockerell, 1901 is an agricultural pest and known vector of strawberry viruses. To better understand its biology and systematics, we performed a genomic analysis on C. fragaefolii collected from Quinalt strawberry plants from Pacific Grove, Monterey county, California, USA using Oxford Nanopore and Illumina sequencing. The resulting data were used to assemble the aphids complete mitogenome. The mitogenome of C. fragaefolii is 16,108 bp in length and contains 2 rRNA, 13 protein-coding, and 22 tRNA genes (GenBank accession number LC590896). The mitogenome is similar in content and organization to other Aphididae. Phylogenetic analysis of the C. fragaefolii mitogenome resolved it in a fully supported clade in the tribe Macrosiphini. Analysis of the cox1 barcode sequence of C. fragaefolii from California found exact and nearly identical sequences to C. fragaefolii and Chaetosiphon thomasi Hille Ris Lambers, 1953, suggesting the two species are conspecific.

Background The visualization and analysis of brain data such as white matter diffusion tractography and magnetic resonance imaging (MRI) volumes is commonly used by neuro-specialist and researchers to help the understanding of brain structure, functionality and connectivity. As mobile devices are widely used among users and their technology shows a continuous improvement in performance, different types of applications have been designed to help users in different work areas. Results We present, ABrainVis, an Android mobile tool that allows users to visualize different types of brain images, such as white matter diffusion tractographies, represented as fibers in 3D, segmented fiber bundles, MRI 3D images as rendered volumes and slices, and meshes. The tool enables users to choose and combine different types of brain imaging data to provide visual anatomical context for specific visualization needs. ABrainVis provides high performance over a wide range of Android devices, including tablets and cell phones using medium and large tractography datasets. Interesting visualizations including brain tumors and arteries, along with fiber, are given as examples of case studies using ABrainVis. Conclusions The functionality, flexibility and performance of ABrainVis tool introduce an improvement in user experience enabling neurophysicians and neuroscientists fast visualization of large tractography datasets, as well as the ability to incorporate other brain imaging data such as MRI volumes and meshes, adding anatomical contextual information.

The study of the superficial white matter and its description is essential for the understanding of human brain function and the study of pathogenesis. However, the study of these fibers is still an incomplete task due to the high inter-subject variability and the size of this kind of fibers. In this work, a superficial white matter bundle identification based on fiber clustering was performed using probabilistic tractography on 100 subjects from the The Human Connectome Project (HCP) data, aligned with a non-linear registration. The method starts with an intra-subject clustering, followed by a segmentation of fibers connecting the precentral (PrC) and postcentral (PoC) regions, based on a ROI atlas. Due to the high amount of fibers, they were randomly separated into groups. An inter-subject clustering was applied on the fibers of each group, and then two clustering levels were applied to select the most reproducible bundles. Seven bundles per hemisphere were obtained, connecting the PrC and PoC regions. These were compared with bundles from previous atlases, showing in general more coverage and some bundles not found in previous atlases.

Electronic Health (eHealth) is the use of information and communication technologies for health and plays a significant role in improving public health. The rapid expansion and development of eHealth initiatives allow researchers and healthcare providers to connect more effectively with patients. The aim of the CIHLMU Symposium 2020 was to discuss the current challenges facing the field, opportunities in eHealth implementation, to share the experiences from different healthcare systems, and to discuss future trends addressing the use of digital platforms in health. The symposium on eHealth explored how the health and technology sector must increase efforts to reduce the obstacles facing public and private investment, the efficacy in preventing diseases and improving patient quality of life, and the ethical and legal frameworks that influence the proper development of the different platforms and initiatives related to the field. This symposium furthered the sharing of knowledge, networking, and patient/user and practitioner experiences in low- and middle-income countries (LMIC) in both public and private sectors.

Estimating the empirical entropy of the elements in a dataset is an important task in data analysis. In particular, empirical entropy can be effectively used to detect anomalies in network traffic. However, computing the empirical entropy of a large dataset is computationally expensive and requires a large amount of memory. This is particularly important in high-speed network traffic analysis, where computing the entropy of a data flow in real time requires using hardware accelerators with restricted on-chip memory and arithmetic resources. In this work, we propose a method to estimate the entropy using a streaming algorithm with sublinear space requirements. Our approach uses a sketch to estimate the frequency of the elements in the stream, and a priority queue to store the top-k most frequent elements. We show that our method can provide a good approximation of the entropy of the dataset, and present the design of a hardware accelerator that can compute the entropy of the stream with a throughput of one packet per clock cycle. Implemented on a Xilinx Zynq UltraScale + MPSoC ZCU102 FPGA, our accelerator can operate at line rates above 181 Gbps, consuming 511 mW and using less than 24% of the resources available on the device.

This work presents an effective multiple subject clustering method using whole-brain tractography datasets. The method is able to obtain fiber clusters that are representative of the population. The proposed approach first applies a fast intra-subject clustering algorithm on each subject obtaining the cluster centroids for all subjects. Second, it compresses the collection of centroids to a latent space through the encoder of a trained autoencoder. Finally, it uses a modified HDBSCAN with adjusted parameters on the encoded centroids of all subjects to obtain the final inter-subject clusters. The results shows that the proposed method outperforms other clustering strategies, and it is able to retrieve known fascicles in a reasonable execution time, achieving a precision over 87% and F1 score above 86% on a collection of 20 simulated subjects.

Background Increased coagulation biomarkers are associated with poor outcomes among people living with HIV (PLHIV). There are few data available from African cohorts demonstrating the effect of antiretroviral therapy (ART) on coagulation biomarkers. Methods From March 2014 to October 2014, ART-naïve PLHIV initiating non-nucleoside reverse transcriptase inhibitor-based ART were recruited from seven clinics in western Kenya and followed for up to 12 months. Demographics, clinical history and blood specimens were collected. Logistic regression models adjusted for intrasite clustering examined associations between HIV viral load and D-Dimer at baseline. Mixed linear effects models were used to estimate mean change from baseline to 6 months overall, and by baseline viral load, sex and TB status at enrollment. Mean change in D-dimer at 6 months is reported on the log10 scale and as percentage change from baseline. Results Among 611 PLHIV enrolled, 66% were female, median age was 34 years (interquartile range (IQR) 29–43 years), 31 (5%) participants had tuberculosis and median viral load was 113,500 copies/mL (IQR: 23,600-399,000). At baseline, 311 (50.9%) PLHIV had elevated D-dimer (> 500 ng/mL) and median D-dimer was 516.4 ng/mL (IQR: 302.7–926.6) (log baseline D-dimer: 2.7, IQR: 2.5–3.0). Higher baseline D-dimer was significantly associated with higher viral load (p p = 0.02) and tuberculosis (p = 0.02). After 6 months on ART, 518 (84.8%) PLHIV had achieved viral load p p 6.0 copies/mL (− 91.1, 95%CI −136.7, − 54.2; p Conclusions In this large Kenyan cohort of PLHIV, women, those with tuberculosis and higher viral load had elevated baseline D-dimer. ART initiation and viral load suppression among ART-naïve PLHIV in Kenya were associated with significant decrease in D-dimer at 6 months in this large African cohort.

Signaling pathways are responsible for the regulation of cell processes, such as monitoring the external environment, transmitting information across membranes, and making cell fate decisions. Given the increasing amount of biological data available and the recent discoveries showing that many diseases are related to the disruption of cellular signal transduction cascades, in silico discovery of signaling pathways in cell biology has become an active research topic in past years. However, reconstruction of signaling pathways remains a challenge mainly because of the need for systematic approaches for predicting causal relationships, like edge direction and activation/inhibition among interacting proteins in the signal flow. We propose an approach for predicting signaling pathways that integrates protein interactions, gene expression, phenotypes, and protein complex information. Our method first finds candidate pathways using a directed-edge-based algorithm and then defines a graph model to include causal activation relationships among proteins, in candidate pathways using cell cycle gene expression and phenotypes to infer consistent pathways in yeast. Then, we incorporate protein complex coverage information for deciding on the final predicted signaling pathways. We show that our approach improves the predictive results of the state of the art using different ranking metrics.

Electronic Health (eHealth) is the use of information and communication technologies for health and plays a significant role in improving public health. The rapid expansion and development of eHealth initiatives allow researchers and healthcare providers to connect more effectively with patients. The aim of the CIHLMU Symposium 2020 was to discuss the current challenges facing the field, opportunities in eHealth implementation, to share the experiences from different healthcare systems, and to discuss future trends addressing the use of digital platforms in health. The symposium on eHealth explored how the health and technology sector must increase efforts to reduce the obstacles facing public and private investment, the efficacy in preventing diseases and improving patient quality of life, and the ethical and legal frameworks that influence the proper development of the different platforms and initiatives related to the field. This symposium furthered the sharing of knowledge, networking, and patient/user and practitioner experiences in low- and middle-income countries (LMIC) in both public and private sectors.

Because significant global changes are currently underway in the Arctic, creating a large-scale standardized database for Arctic marine biodiversity is particularly pressing. This study evaluates the potential of aquatic environmental DNA (eDNA) metabarcoding to detect Arctic coastal biodiversity changes and characterizes the local spatio-temporal distribution of eDNA in two locations. We extracted and amplified eDNA using two COI primer pairs from ~80 water samples that were collected across two Canadian Arctic ports, Churchill and Iqaluit, based on optimized sampling and preservation methods for remote regions surveys. Results demonstrate that aquatic eDNA surveys have the potential to document large-scale Arctic biodiversity change by providing a rapid overview of coastal metazoan biodiversity, detecting nonindigenous species, and allowing sampling in both open water and under the ice cover by local northern-based communities. We show that DNA sequences of ~50% of known Canadian Arctic species and potential invaders are currently present in public databases. A similar proportion of operational taxonomic units was identified at the species level with eDNA metabarcoding, for a total of 181 species identified at both sites. Despite the cold and well-mixed coastal environment, species composition was vertically heterogeneous, in part due to river inflow in the estuarine ecosystem, and differed between the water column and tide pools. Thus, COI-based eDNA metabarcoding may quickly improve large-scale Arctic biomonitoring using eDNA, but we caution that aquatic eDNA sampling needs to be standardized over space and time to accurately evaluate community structure changes., Ecology and Evolution, 8 (16), ISSN:2045-7758

The International Chemistry Olympiad (IChO) is celebrating its 50th anniversary in 2018. Since 1968, 30 countries have hosted this prestigious event for preuniversity students, and 83 countries have participated in this annual tournament. The aim of the IChO includes promoting students’ interest in science, in particular, popularization of chemistry. For the jubilee celebration, IChO is returning to its birthplace, Czech Republic and Slovakia.

Culturally responsive teaching and design practices flip the online classroom by creating an environment that acknowledges, celebrates, and builds upon the cultural capital that learners and teachers bring to the online classroom. Challenges exist in all phases of online course design, including the ability to create online courses that reflect the instructor’s commitment to inclusive excellence, diversity, and social justice. Designing an online environment that supports all learners regardless of their backgrounds is important in their future success as professionals; thus, it is important for faculty to design courses with all students in mind. The purpose of this article is to share best practices in the design of culturally and linguistically responsive online courses that support the culturally and linguistically diverse (CLD) students we serve. Based on Gay’s (2010) culturally responsive teaching practices, this article provides examples of online activities that are validating; comprehensive; multi-dimensional; empowering; transformative, and emancipatory.

Age at maturity is a key life‐history trait of most organisms. In anadromous salmonid fishes such as Atlantic Salmon (Salmo salar), age at sexual maturity is associated with sea age, the number of years spent at sea before the spawning migration. For the first time, we investigated the presence of two nonsynonymous vgll3 polymorphisms in North American Atlantic Salmon populations that relate to sea age in European salmon and quantified the natural variation at these and two additional candidate SNPs from two other genes. A targeted resequencing assay was developed and 1,505 returning adult individuals of size‐inferred sea age and sex from four populations were genotyped. Across three of four populations sampled in Québec, Canada, the late‐maturing component (MSW) of the population of a given sex exhibited higher proportions of SNP genotypes 54Thrvgll3 and 323Lysvgll3 compared to early‐maturing fish (1SW), for example, 85% versus 53% of females from Trinité River carried 323Lysvgll3 (n MSW = 205 vs. n 1SW = 30; p 66%) to be female. In summary, two nonsynonymous vgll3 polymorphisms were confirmed in North American populations of Atlantic Salmon and our results suggest that variation at those loci correlates with sea age and sex. Our results also suggest that this correlation varies among populations. Future work would benefit from a more balanced sampling and from adding data on juvenile riverine life stages to contrast our data.

Teacher preparation is a complex process; also complex are the standards that define what novice teachers need to know and be able to do to effectively meet the needs of all of their students. Add the demographic shift in the student population, and there are additional demands on teachers to provide students with the education they need and deserve. Given these changing needs, teacher educators must implement effective strategies to diversify the teaching force and to prepare all teachers to meet the needs of culturally and linguistically diverse students. This study sought to document the extent to which Latina/o elementary teacher candidates described and demonstrated culturally responsive teaching strategies using their students’ cultural and academic profiles to inform practice in classroom science and math instruction. The results and conclusions from this study indicate the Latina/o teacher candidates were learning to become culturally responsive classroom teachers. As student teachers they...

Clustering is an essential operation in many data analysis applications. In particular, bioinformatics and genome analysis use clustering to group similar components in sequence data, in order to find important patterns such as DNA motifs. In this paper, we present an algorithm that clusters DNA data using locality-sensitive hashing with MinHash to group similar subsequences in large Chip-seq datasets. Tested on a standard mESC dataset, the algorithm builds clusters that contain subsequences with high-score matches to known DNA motifs. We also describe the architecture and implementation of a hardware accelerator on a Xilinx Kintex-7 XC7K325T FPGA, that exploits the parallelism of the algorithm to cluster data with a throughput of one k-mer per clock cycle at 350MHz. The accelerator achieves a speedup of 91 compared to a parallel software implementation of the algorithm on a 24-core server.

We propose a new automatic algorithm for the tractography-based parcellation of the cortical surface. The method is based on segmented bundles of the superficial white matter (SWM), calculated for each subject, using a multi-subject SWM bundle atlas. The scheme uses a fast intersection algorithm to define cortical regions connected by each bundle and then it defines a graph representation to model the overlapping of the regions to derive the final parcellation. The algorithm was tested on one subject, and resulting parameters were applied to other 4 subjects. Results show a good correspondence between subjects.

We present an optimized algorithm that performs automatic classification of white matter fibers based on a multi-subject bundle atlas. We implemented a parallel algorithm that improves upon its previous version in both execution time and memory usage. Our new version uses the local memory of each processor, which leads to a reduction in execution time. Hence, it allows the analysis of bigger subject and/or atlas datasets. As a result, the segmentation of a subject of 4,145,000 fibers is reduced from about 14 minutes in the previous version to about 6 minutes, yielding an acceleration of 2.34. In addition, the new algorithm reduces the memory consumption of the previous version by a factor of 0.79., Comment: This research has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk{\l}odowska-Curie Actions H2020-MSCA-RISE-2015 BIRDS GA No. 690941, CONICYT PFCHA/ DOCTORADO NACIONAL/2016-21160342, CONICYT FONDECYT 1161427, CONICYT PIA/Anillo de Investigaci\'on en Ciencia y Tecnolog\'ia ACT172121, CONICYT BASAL FB0008 and from CONICYT Basal FB0001

International audience; Effective population size over a generation (Ne) or over a reproductive cycle (Nb) and the adult census size (Nc) are important parameters in both conservation and evolutionary biology. Ne provides information regarding the rate of loss of genetic diversity and can be tracked back in time to infer demographic history of populations, whereas Nb may often be more easily quantified than Nc for short-term abundance monitoring. In this study, we propose (1) an empirical context to Waples et al. (2014) who introduced a correction to bias due to overlapping generations, and (2) a mathematical relationship between Ne and Nb for direct application in Atlantic salmon populations in Quebec, Canada. To achieve this, we investigate the relationships between Ne, Nb and Nc in 10 Atlantic salmon populations, Canada, for which we genotyped 100 randomly sampled young-of-the year individuals for 5 consecutive years. The results show a positive correlation between Ne, Nb and Nc, suggesting that Nb is an indicative parameter for tracking effective population size and abundance of Atlantic salmon. However, our model allows predicting Nc from Nb values at 27% that can be partly explained by high variance in Nb/Nc both among populations (37%) and among years (19%). This result illustrates the need for thorough calibration of Nb/Nc before using Nb in monitoring programs, as well as a full understanding of the limits of such an approach. Finally, we discuss the importance of these results for the management of wild populations.

Automated methods that can identify white matter bundles from large tractography datasets have several applications in neuroscience research. In these applications, clustering algorithms have shown to play an important role in the analysis and visualization of white matter structure, generating useful data which can be the basis for further studies. This work proposes FFClust, an efficient fiber clustering method for large tractography datasets containing millions of fibers. Resulting clusters describe the whole set of main white matter fascicles present on an individual brain. The method aims to identify compact and homogeneous clusters, which enables several applications. In individuals, the clusters can be used to study the local connectivity in pathological brains, while at population level, the processing and analysis of reproducible bundles, and other post-processing algorithms can be carried out to study the brain connectivity and create new white matter bundle atlases. The proposed method was evaluated in terms of quality and execution time performance versus the state-of-the-art clustering techniques used in the area. Results show that FFClust is effective in the creation of compact clusters, with a low intra-cluster distance, while keeping a good quality Davies–Bouldin index, which is a metric that quantifies the quality of clustering approaches. Furthermore, it is about 8.6 times faster than the most efficient state-of-the-art method for one million fibers dataset. In addition, we show that FFClust is able to correctly identify atlas bundles connecting different brain regions, as an example of application and the utility of compact clusters.

Purpose A novel application of QuPath open-source digital analysis software is used to provide in-depth morphological analysis of progressive optic nerve (ON) degeneration in rats. Methods QuPath software was adapted to assess axon and gliotic morphology in toluidine blue-stained, Brown Norway rat ON light micrographs. QuPath axon numbers, density, size distributions, and gliotic areas were obtained from test images and ON cross-sections separated by damage grade. QuPath results were compared with manual counting, AxonJ, and electron microscopy axon estimates. Results QuPath-derived axon number, density, and diameter decreased with increasing ON damage. Axon density negatively correlated with gliotic areas in test images (R2 = 0.759; P < 0.0001; N = 40) and in ON cross-sections (R2 = 0.803; P < 0.0004; N = 10). Although axon losses occurred across most axon diameters, large axons were more susceptible to degeneration. The exception was swollen axons > 2 µm, which increased in moderately but not severely damaged images. QuPath axon counts correlated strongly with manual counts of test images (R2 = 0.956; P < 0.0001). QuPath outperformed AxonJ on test images and total ON axon counts. Compared to electron microscopy analysis, QuPath undercounted ON axons; however, correlation between the methods was robust (R2 = 0.797; P < 0.001; N = 10). Conclusions QuPath analysis reliably identified axon loss, axon morphology changes, and gliotic expansion that occurred in degenerating ONs. Translational Relevance QuPath is a valuable tool for rapid, automated, analysis of healthy and degenerating ONs. Reproducible preclinical studies for new glaucoma treatments depend on unbiased in-depth analysis of ON pathology. This was provided by the QuPath approach.

We present a custom hardware architecture for fast heavy hitter detection in large data streams. The architecture probabilistically estimates the frequency of each element in the data stream using the Countmin-CU sketch with the H3 family of hash functions. The sketch is stored in on-chip memory, and the architecture exploits the parallelism available in the data by simultaneously processing each row of the sketch. The hash functions map each element to a set of counters on the sketch, and the sketch increments the counters that hold the minimum value, which corresponds to the estimated frequency of the element. The hash functions and sorting network are implemented in hardware as fully pipelined circuits, in order to maximize their operating clock frequency. We show a prototype of the architecture running on a Xilinx Kintex-7 XC7K325T FPGA operating with a 300MHz clock, which can process a stream of 3,982,496 32-bit elements and detect the heavy hitters with an 4x16,384-element sketch in 13.27ms, achieving a speedup of 768 compared to a modern desktop computer.

This work characterizes raw and decopperized slimes, with the objective of identifying the phases in these two sub-products. The main phases in copper anodes are metallic copper, including CuO, which are present in free form or associated with the presence of copper selenide or tellurides (Cu2(Se,Te)) and several Cu-Pb-Sb-As-Bi oxides. During electrorefining, the impurities in the anode release and are not deposited in the cathode, part of them dissolving and concentrated in the electrolyte, and others form a raw anode slime that contains Au, Ag, Cu, As, Se, Te and PGM, depending on the composition of the anode. There are several recovery processes, most of which involve acid leaching in the first step to dissolve copper, whose product is decopperized anode slime. SEM analysis revealed that the mineralogical species present in the raw anode slime under study were mainly eucarite (CuAgSe), naumannite (Ag2Se), antimony arsenate (SbAsO4), and lead sulfate (PbSO4). In the case of decopperized slime, the particles were mainly composed of SbAsO4 (crystalline appearance), non-stoichiometric silver selenide (Ag(2−x)Se), and chlorargyrite (AgCl).

Many proteins work together with others in groups called complexes in order to achieve a specific function. Discovering protein complexes is important for understanding biological processes and predict protein functions in living organisms. Large-scale and throughput techniques have made possible to compile protein-protein interaction networks (PPI networks), which have been used in several computational approaches for detecting protein complexes. Those predictions might guide future biologic experimental research. Some approaches are topology-based, where highly connected proteins are predicted to be complexes; some propose different clustering algorithms using partitioning, overlaps among clusters for networks modeled with unweighted or weighted graphs; and others use density of clusters and information based on protein functionality. However, some schemes still require much processing time or the quality of their results can be improved. Furthermore, most of the results obtained with computational tools are not accompanied by an analysis of false positives. We propose an effective and efficient mining algorithm for discovering highly connected subgraphs, which is our base for defining protein complexes. Our representation is based on transforming the PPI network into a directed acyclic graph that reduces the number of represented edges and the search space for discovering subgraphs. Our approach considers weighted and unweighted PPI networks. We compare our best alternative using PPI networks from Saccharomyces cerevisiae (yeast) and Homo sapiens (human) with state-of-the-art approaches in terms of clustering, biological metrics and execution times, as well as three gold standards for yeast and two for human. Furthermore, we analyze false positive predicted complexes searching the PDBe (Protein Data Bank in Europe) database in order to identify matching protein complexes that have been purified and structurally characterized. Our analysis shows that more than 50 yeast protein complexes and more than 300 human protein complexes found to be false positives according to our prediction method, i.e., not described in the gold standard complex databases, in fact contain protein complexes that have been characterized structurally and documented in PDBe. We also found that some of these protein complexes have recently been classified as part of a Periodic Table of Protein Complexes. The latest version of our software is publicly available at http://doi.org/10.6084/m9.figshare.5297314.v1.

Home computer ownership and Internet access have become essential to education, job security and economic opportunity. The digital divide remains greatest for ethnic/racial groups placing them at a disadvantage. The purpose of the study was to gain understanding and insight into the sociocultural factors influencing the digital divide as related to Latinos as a group, specifically a group of non-traditional Latina pre-service teachers from the midwest in a required online instructional media and technology course. The following sociocultural factors (1) generational status, (2) English language ability, (3) educational attainment, (4) socioeconomic status and (5) gender identity are defined and identified in relationship to the success of these participants. Of the five sociocultural factors explored in this study, only English language ability did not appear to have any direct influence on the success of these particular students. These sociocultural factors also influenced one another, particularly as generational status influences English language ability, educational attainment, gender roles, and ultimately socioeconomic status.

This qualitative theoretical study was conducted in response to the current need for an inclusive and comprehensive model to guide the preparation and assessment of teacher candidates for culturally responsive teaching. The process of developing a model of culturally responsive teaching involved three steps: a comprehensive review of the literature; a synthesis of the literature into thematic categories to capture the dispositions and behaviors of culturally responsive teaching; and the piloting of these thematic categories with teacher candidates to validate the usefulness of the categories and to generate specific exemplars of behavior to represent each category. The model of culturally responsive teaching contains five thematic categories: (1) content integration, (2) facilitating knowledge construction, (3) prejudice reduction, (4) social justice, and (5) academic development. The current model is a promising tool for comprehensively defining culturally responsive teaching in the context of teacher education as well as to guide curriculum and assessment changes aimed to increase candidates’ culturally responsive knowledge and skills in science and mathematics teaching.

Compressed representations have become effective to store and access large Web and social graphs, in order to support various graph querying and mining tasks. The existing representations exploit various typical patterns in those networks and provide basic navigation support. In this paper, we obtain unprecedented results by finding "dense subgraph" patterns and combining them with techniques such as node orderings and compact data structures. On those representations, we support out-neighbor and out/in-neighbor queries, as well as mining queries based on the dense subgraphs. First, we propose a compression scheme for Web graphs that reduces edges by representing dense subgraphs with "virtual nodes"; over this scheme, we apply node orderings and other compression techniques. With this approach, we match the best current compression ratios that support out-neighbor queries (i.e., nodes pointed from a given node), using 1.0---1.8 bits per edge (bpe) on large Web graphs, and retrieving each neighbor of a node in 0.6---1.0 microseconds ( $$\upmu $$ s). When supporting both out- and in-neighbor queries, instead, our technique generally offers the best time when using little space. If the reduced graph, instead, is represented with a compact data structure that supports bidirectional navigation, we obtain the most compact Web graph representations (0.9---1.5 bpe) that support out/in-neighbor navigation; yet, the time per neighbor extracted raises to around 5---20 $$\upmu $$ s. We also propose a compact data structure that represents dense subgraphs without using virtual nodes. It allows us to recover out/in-neighbors and answer other more complex queries on the dense subgraphs identified. This structure is not competitive on Web graphs, but on social networks, it achieves 4---13 bpe and 8---12 $$\upmu $$ s per out/in-neighbor retrieved, which improves upon all existing representations.

Global monthly rainfall estimates and other hydrological products have been produced from 1987 to the present using measurements from the Defense Meteorological Satellite Program (DMSP) series of the Special Sensor Microwave Imager (SSM/I). The aim of this paper is twofold: to present the recent efforts to improve the quality control (QC) of historical antenna temperature of the SSM/I sensor (1987–2008) and how this improvement impacts the different hydrological products that are generated at NOAA/National Environmental Satellite, Data, and Information Service (NESDIS). Beginning in 2005, the DMSP Special Sensor Microwave Imager/Sounder (SSMI/S) has been successfully operating on the F-16, F-17, and F-18 satellites. The second objective of this paper is focused on the application of SSMI/S channels to evaluate the performance of several hydrological products using the heritage of existing SSM/I algorithms and to develop an improved strategy to extend the SSM/I time series into the SSMI/S era, starting with data in 2009 for F-17. The continuity of hydrological products from SSM/I to SSMI/S has shown to be a valuable contribution for the precipitation and climate monitoring community but several sensor issues must be accounted for to meet this objective.

This chapter seeks to describe an alternative licensure program, Aggie Prep, developed to address the needs of underrepresented students in the Southwest. The aim of the program was to prepare culturally and linguistically responsive first-year teachers seeking secondary alternative certification in science or mathematics. We discuss how this innovative alternative licensure program for secondary STEM teachers utilizes a competency-based curriculum with a practice-based approach to strategically identify opportunities to integrate science, mathematics, literacy, and language instruction for culturally and linguistically diverse youth. We specifically outline the use of ongoing professional development in the form of coursework, field coaching, mentoring and targeted professional development. The program assists beginning teachers to (1) make science, mathematics and language learning accessible; (2) elevate content rigor; (3) plan with print and non-print text in mind; and (4) build collaborative learning environments with attention to integrated content, language and literacy through a contextualized science, mathematics, and engineering approach. An integral part of this approach was the emphasis on the use of engaging inquiry-based activities. We share how we support our students’ ability to reflect on the nature of content, how adolescents learn, and implement instructional best practices of science and mathematics that accommodate the language and learning needs of students in their classrooms.

BACKGROUND/SUMMARY OF THE PROBLEM The long examination times in MRI create the need to reduce access time and implement an efficientmodel to reduce patient turnaround time to a minimum [1]. A commonly encountered cause of delay is the screening process, an essential and indispensable step to ensure patient safety and delivery of high-quality care [2,3]. Traditionally, the MRI screening process is completed with a paper questionnaire, which is then scanned into the electronic medical record (EMR) or uploaded to the PACS for documentation purposes. The MRI screening process is commonly completed after patient arrival for an MRI appointment. If a patient reports a positive pertinent history during MRI screening (eg, placement of a medical device or presence of foreign bodies), further investigation is required, for example, obtaining specific information about the make and model of an implanted device before “clearing” the patient [2-6]. This process has several challenges. First,many patients are unaware of the specifics of their implanted device or may not be aware of the need or may simply forget to bring device-specific documentation to their MRI

La inyección intracitoplasmática del espermatozoide (ICSI) ha permitido resolver problemas masculinos severos con excelentes resultados. Sin embargo, la difusión de su técnica llevó a la aplicación en casos donde no existe una indicación clara, desplazando muchas veces a la fertilización in vitro (FIV) sin suficientes argumentos. Esta revisión pretende, partiendo de conceptos fisiológicos de fecundación, llamar la atención sobre los eventos involucrados en el ICSI que transgreden dicha fisiología y constituyen grandes enigmas en cuanto a sus implicaciones en el desarrollo futuro. Las diversas observaciones descritas por los grupos de investigación básica dedicados a estos análisis, si bien no establecen un efecto indeseable inherente a la técnica, dejan abierta la investigación y permiten concluir que el ICSI es una técnica con claras indicaciones que no debe utilizarse indiscriminadamente.The intracytoplasmic sperm injection (ICSI) from its introduction in humans has let to treat severe male infertility with great results. However, the diffusion of this technology has led to its application in several cases without a clear indication, displacing in many cases the in vitro fertilization (FIV), without enough reasons. This review looks for calling the attention in the physiology of fertilization, recalls the events involved in ICSI which transgress the principles of this physiology and are great enigmas about future development implications. The different findings described by the basic research groups in this area, if they do not give a relation between the ICSI technology and any undesirable effect, they leave the investigation open and let us conclude that ICSI is an assisted reproductive technology with clear indications and should not be used so widely.

La inyección intracitoplasmática del espermatozoide (ICSI) ha permitido resolver problemas masculinos severos con excelentes resultados. Sin embargo, la difusión de su técnica llevó a la aplicación en casos donde no existe una indicación clara, desplazando muchas veces a la fertilización in vitro (FIV) sin suficientes argumentos. Esta revisión pretende, partiendo de conceptos fisiológicos de fecundación, llamar la atención sobre los eventos involucrados en el ICSI que transgreden dicha fisiología y constituyen grandes enigmas en cuanto a sus implicaciones en el desarrollo futuro. Las diversas observaciones descritas por los grupos de investigación básica dedicados a estos análisis, si bien no establecen un efecto indeseable inherente a la técnica, dejan abierta la investigación y permiten concluir que el ICSI es una técnica con claras indicaciones que no debe utilizarse indiscriminadamente.