18 research outputs found
PURIFICATION AND KINETIC STUDIES OF ORGANOPHOSPHORUS HYDROLASE FROM B. DIMINUTA
Objective: Extraction and purification of Organophosphorus hydrolase (OPH) enzyme from Brevundimonas diminuta and to study kinetic properties of the purified enzyme.
Methods: The enzyme was extracted from bacteria and purified by using a combination of gel filtration and ion-exchange chromatography and the purity of an enzyme was checked by sodium dodecyl sulfate-polyacrylamide gel electrophoresis. The activity of the purified enzyme was monitored by enzyme assay and total protein content was determined by using Lowry's method. The kinetic properties of the enzyme were also studied.
Results: A 72 kDa organophosphorus hydrolase (OPH) enzyme was extracted and purified. The purified enzyme was homodimer and showed a single band on SDS-PAGE. The Michaelis constant (Km) and maximal velocity (Vmax) values of free OPH enzyme for methyl parathion as substrate was 285.71 μM and 50 μM/min respectively. At optimum pH (7.5) and incubation temperature (35°C), free enzyme showed maximum activity with incubation time of 8 min.
Conclusion: The bacteria contain OPH enzyme with high potential to detoxify OP pesticides, attractive for bioremediation due to good pH & temperature conditions, were also useful in development of bio analytical techniques such as biosensors for OP pesticide detection
Do Large Code Models Understand Programming Concepts? A Black-box Approach
Large Language Models' success on text generation has also made them better
at code generation and coding tasks. While a lot of work has demonstrated their
remarkable performance on tasks such as code completion and editing, it is
still unclear as to why. We help bridge this gap by exploring to what degree
auto-regressive models understand the logical constructs of the underlying
programs. We propose Counterfactual Analysis for Programming Concept Predicates
(CACP) as a counterfactual testing framework to evaluate whether Large Code
Models understand programming concepts. With only black-box access to the
model, we use CACP to evaluate ten popular Large Code Models for four different
programming concepts. Our findings suggest that current models lack
understanding of concepts such as data flow and control flow
Stateful Defenses for Machine Learning Models Are Not Yet Secure Against Black-box Attacks
Recent work has proposed stateful defense models (SDMs) as a compelling
strategy to defend against a black-box attacker who only has query access to
the model, as is common for online machine learning platforms. Such stateful
defenses aim to defend against black-box attacks by tracking the query history
and detecting and rejecting queries that are "similar" and thus preventing
black-box attacks from finding useful gradients and making progress towards
finding adversarial attacks within a reasonable query budget. Recent SDMs
(e.g., Blacklight and PIHA) have shown remarkable success in defending against
state-of-the-art black-box attacks. In this paper, we show that SDMs are highly
vulnerable to a new class of adaptive black-box attacks. We propose a novel
adaptive black-box attack strategy called Oracle-guided Adaptive Rejection
Sampling (OARS) that involves two stages: (1) use initial query patterns to
infer key properties about an SDM's defense; and, (2) leverage those extracted
properties to design subsequent query patterns to evade the SDM's defense while
making progress towards finding adversarial inputs. OARS is broadly applicable
as an enhancement to existing black-box attacks - we show how to apply the
strategy to enhance six common black-box attacks to be more effective against
current class of SDMs. For example, OARS-enhanced versions of black-box attacks
improved attack success rate against recent stateful defenses from almost 0% to
to almost 100% for multiple datasets within reasonable query budgets.Comment: ACM CCS 202
Global diversity and antimicrobial resistance of typhoid fever pathogens: insights from a meta-analysis of 13,000 Salmonella Typhi genomes
Background:
The Global Typhoid Genomics Consortium was established to bring together the typhoid research community to aggregate and analyse Salmonella enterica serovar Typhi (Typhi) genomic data to inform public health action. This analysis, which marks 22 years since the publication of the first Typhi genome, represents the largest Typhi genome sequence collection to date (n=13,000).
Methods:
This is a meta-analysis of global genotype and antimicrobial resistance (AMR) determinants extracted from previously sequenced genome data and analysed using consistent methods implemented in open analysis platforms GenoTyphi and Pathogenwatch.
Results:
Compared with previous global snapshots, the data highlight that genotype 4.3.1 (H58) has not spread beyond Asia and Eastern/Southern Africa; in other regions, distinct genotypes dominate and have independently evolved AMR. Data gaps remain in many parts of the world, and we show the potential of travel-associated sequences to provide informal ‘sentinel’ surveillance for such locations. The data indicate that ciprofloxacin non-susceptibility (>1 resistance determinant) is widespread across geographies and genotypes, with high-level ciprofloxacin resistance (≥3 determinants) reaching 20% prevalence in South Asia. Extensively drug-resistant (XDR) typhoid has become dominant in Pakistan (70% in 2020) but has not yet become established elsewhere. Ceftriaxone resistance has emerged in eight non-XDR genotypes, including a ciprofloxacin-resistant lineage (4.3.1.2.1) in India. Azithromycin resistance mutations were detected at low prevalence in South Asia, including in two common ciprofloxacin-resistant genotypes.
Conclusions:
The consortium’s aim is to encourage continued data sharing and collaboration to monitor the emergence and global spread of AMR Typhi, and to inform decision-making around the introduction of typhoid conjugate vaccines (TCVs) and other prevention and control strategies
Global diversity and antimicrobial resistance of typhoid fever pathogens: Insights from a meta-analysis of 13,000 Salmonella Typhi genomes
Background: The Global Typhoid Genomics Consortium was established to bring together the typhoid research community to aggregate and analyse Salmonella enterica serovar Typhi (Typhi) genomic data to inform public health action. This analysis, which marks 22 years since the publication of the first Typhi genome, represents the largest Typhi genome sequence collection to date (n=13,000). Methods: This is a meta-analysis of global genotype and antimicrobial resistance (AMR) determinants extracted from previously sequenced genome data and analysed using consistent methods implemented in open analysis platforms GenoTyphi and Pathogenwatch. Results: Compared with previous global snapshots, the data highlight that genotype 4.3.1 (H58) has not spread beyond Asia and Eastern/Southern Africa; in other regions, distinct genotypes dominate and have independently evolved AMR. Data gaps remain in many parts of the world, and we show the potential of travel-associated sequences to provide informal ‘sentinel’ surveillance for such locations. The data indicate that ciprofloxacin non-susceptibility (>1 resistance determinant) is widespread across geographies and genotypes, with high-level ciprofloxacin resistance (=3 determinants) reaching 20% prevalence in South Asia. Extensively drug-resistant (XDR) typhoid has becomedominant in Pakistan (70% in 2020) but has not yet become established elsewhere. Ceftriaxone resistance has emerged in eight non-XDR genotypes, including a ciprofloxacin-resistant lineage (4.3.1.2.1) in India. Azithromycin resistance mutations were detected at low prevalence in South Asia, including in two common ciprofloxacin-resistant genotypes. Conclusions: The consortium’s aim is to encourage continued data sharing and collaboration to monitor the emergence and global spread of AMR Typhi, and to inform decision-making around the introduction of typhoid conjugate vaccines (TCVs) and other prevention and control strategies
Global diversity and antimicrobial resistance of typhoid fever pathogens : insights from a meta-analysis of 13,000 Salmonella Typhi genomes
DATA AVAILABILITY : All data analysed during this study are publicly accessible. Raw Illumina sequence reads have been submitted to the European Nucleotide Archive (ENA), and individual sequence accession numbers are listed in Supplementary file 2. The full set of n=13,000 genome assemblies generated for this study are available for download from FigShare: https://doi.org/10.26180/21431883. All assemblies of suitable quality (n=12,849) are included as public data in the online platform Pathogenwatch (https://pathogen.watch). The data are organised into collections, which each comprise a neighbour-joining phylogeny annotated with metadata, genotype, AMR determinants, and a linked map. Each contributing study has its own collection, browsable at https://pathogen.watch/collections/all?organismId= 90370. In addition, we have provided three large collections, each representing roughly a third of the total dataset presented in this study: Typhi 4.3.1.1 (https://pathogen.watch/collection/ 2b7mp173dd57-clade-4311), Typhi lineage 4 (excluding 4.3.1.1) (https://pathogen.watch/collection/ wgn6bp1c8bh6-clade-4-excluding-4311), and Typhi lineages 0-3 (https://pathogen.watch/collection/ 9o4bpn0418n3-clades-0-1-2-and-3). In addition, users can browse the full set of Typhi genomes in Pathogenwatch and select subsets of interest (e.g. by country, genotype, and/or resistance) to generate a collection including neighbour-joining tree for interactive exploration.SUPPLEMENTARY FILES : Available at https://elifesciences.org/articles/85867/figures#content. SUPPLEMENTARY FILE 1. Details of local ethical approvals provided for studies that were unpublished at the time of contributing data to this consortium project. Most data are now published, and the citations for the original studies are provided here. National surveillance programs in Chile (Maes et al., 2022), Colombia (Guevara et al., 2021), France, New Zealand, and Nigeria (Ikhimiukor et al., 2022b) were exempt from local ethical approvals as these countries allow sharing of non-identifiable pathogen sequence data for surveillance purposes. The US CDC Internal Review Board confirmed their approval was not required for use in this project (#NCEZID-ARLT- 10/ 20/21-fa687). SUPPLEMENTARY FILE 2. Line list of 13,000 genomes included in the study. SUPPLEMENTARY FILE 3. Source information recorded for genomes included in the study. ^Indicates cases included in the definition of ‘assumed acute illness’. SUPPLEMENTARY FILE 4. Summary of genomes by country. SUPPLEMENTARY FILE 5. Genotype frequencies per region (N, %, 95% confidence interval; annual and aggregated, 2010–2020). SUPPLEMENTARY FILE 6. Genotype frequencies per country (N, %, 95% confidence interval; annual and aggregated, 2010–2020). SUPPLEMENTARY FILE 7. Antimicrobial resistance (AMR) frequencies per region (N, %, 95% confidence interval; aggregated 2010–2020). SUPPLEMENTARY FILE 8. Antimicrobial resistance (AMR) frequencies per country (N, %, 95% confidence interval; annual and aggregated, 2010–2020). SUPPLEMENTARY FILE 9. Laboratory code master list. Three letter laboratory codes assigned by the consortium.BACKGROUND : The Global Typhoid Genomics Consortium was established to bring together the
typhoid research community to aggregate and analyse Salmonella enterica serovar Typhi (Typhi)
genomic data to inform public health action. This analysis, which marks 22 years since the publication
of the first Typhi genome, represents the largest Typhi genome sequence collection to date
(n=13,000).
METHODS : This is a meta-analysis
of global genotype and antimicrobial resistance (AMR) determinants
extracted from previously sequenced genome data and analysed using consistent methods
implemented in open analysis platforms GenoTyphi and Pathogenwatch.
RESULTS : Compared with previous global snapshots, the data highlight that genotype 4.3.1 (H58)
has not spread beyond Asia and Eastern/Southern Africa; in other regions, distinct genotypes dominate
and have independently evolved AMR. Data gaps remain in many parts of the world, and we
show the potential of travel-associated
sequences to provide informal ‘sentinel’ surveillance for
such locations. The data indicate that ciprofloxacin non-susceptibility
(>1 resistance determinant) is
widespread across geographies and genotypes, with high-level
ciprofloxacin resistance (≥3 determinants)
reaching 20% prevalence in South Asia. Extensively drug-resistant
(XDR) typhoid has become dominant in Pakistan (70% in 2020) but has not yet become established elsewhere. Ceftriaxone
resistance has emerged in eight non-XDR
genotypes, including a ciprofloxacin-resistant
lineage
(4.3.1.2.1) in India. Azithromycin resistance mutations were detected at low prevalence in South
Asia, including in two common ciprofloxacin-resistant
genotypes.
CONCLUSIONS : The consortium’s aim is to encourage continued data sharing and collaboration to
monitor the emergence and global spread of AMR Typhi, and to inform decision-making
around the
introduction of typhoid conjugate vaccines (TCVs) and other prevention and control strategies.Fellowships from the European Union (funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 845681), the Wellcome Trust (SB, Wellcome Trust Senior Fellowship), and the National Health and Medical Research Council.https://elifesciences.org/am2024Medical MicrobiologySDG-03:Good heatlh and well-bein
MOLECULAR TECHNIQUES FOR MEDICAL MICROBIOLOGY LABORATORIES: FUTURISTIC APPROACH IN DIAGNOSTICS OF INFECTIOUS DISEASES
Diagnostic methods for infectious diseases have stagnated in the last 20–30 years. Conventional diagnostic approaches are not able to fulfill all the desires needed for the effective diagnosis of microbial diseases. Few major advances in clinical diagnostic tools have been achieved after the introduction of PCR, although new techniques are under investigation. Many tools that are used in the “modern ” microbiology laboratory are based on very old and labor-intensive technologies. The need to develop new diagnostic tools include more rapid tests without sacrificing sensitivity, reliable, accurate, value-added tests, and point-of-care test. Research has been focused toward development of new alternative methods to improve the diagnosis of microbial diseases. These include molecular-based approaches. This review summarizes some of the new molecular approaches in microbial disease diagnosis