AMR
Antimicrobial resistance (AMR) occurs when bacteria, viruses, fungi, and parasites evolve the ability to withstand antimicrobial drugs such as antibiotics, antivirals, antifungals, and antiparasitics, thereby evading treatments designed to eliminate them.[1][2] This resistance arises through genetic mutations and horizontal gene transfer, accelerated by selective pressures from antimicrobial exposure, leading to persistent infections, higher treatment costs, and prolonged illness.02724-0/fulltext)[3] In 2021, bacterial AMR was directly attributable to 1.14 million deaths worldwide and associated with 4.71 million deaths, reflecting a rising burden since 1990 driven by expanding pathogen-drug resistance combinations.01867-1/fulltext) Empirical analyses indicate disproportionate impacts in low- and middle-income countries, where limited diagnostics and regulatory oversight exacerbate misuse in human healthcare and livestock production.01867-1/fulltext)[4] Key drivers include overprescription of antibiotics for viral infections, prophylactic use in agriculture contributing to 70-80% of antimicrobial consumption in some regions, and inadequate infection control in hospitals.[3][5] Projections based on current trends forecast that AMR could cause over 10 million deaths annually by 2050 without scaled interventions, potentially surpassing other leading mortality causes through compounded effects on surgery, chemotherapy, and routine care.01867-1/fulltext)[6] Notable achievements in containment include national stewardship programs reducing unnecessary prescriptions by up to 30% in adherent settings, alongside genomic surveillance networks tracking resistance genes.[7] Controversies persist over economic incentives in pharmaceutical and agricultural sectors delaying reforms, and debates on whether alarmist projections sufficiently account for adaptive countermeasures like novel drug classes and phage therapies.[8][5] Global coordination, exemplified by the WHO's 2015 Global Action Plan, emphasizes surveillance, innovation, and cross-sectoral policies to preserve antimicrobial efficacy.[1]Health and Biology
Antimicrobial resistance
Antimicrobial resistance (AMR) refers to the ability of microorganisms, including bacteria, viruses, fungi, and parasites, to withstand the effects of antimicrobial drugs that were originally designed to inhibit or kill them, rendering standard treatments ineffective.[1][3] This phenomenon arises primarily through evolutionary processes where selective pressure from antimicrobial exposure favors the survival and proliferation of resistant variants.[9] In biological terms, AMR is not a novel pathology but an adaptive response rooted in microbial genetics, accelerated by human interventions such as widespread drug deployment.[10] At the molecular level, bacteria—the most studied microbes in AMR contexts—employ diverse mechanisms to evade antimicrobials, including enzymatic inactivation of drugs (e.g., beta-lactamases hydrolyzing penicillin-like antibiotics), efflux pumps expelling compounds from cells before they reach lethal concentrations, alteration of drug-binding targets (such as ribosomal modifications reducing aminoglycoside efficacy), and reduced cell wall permeability preventing entry.[10] These mechanisms often emerge via spontaneous mutations or horizontal gene transfer, such as conjugation plasmids disseminating resistance genes across species.[9] Fungi and parasites exhibit analogous strategies, like upregulated efflux in Candida species against azoles or mutated dihydrofolate reductase in malaria parasites evading antifolates, while viral resistance, as in HIV to antiretrovirals, typically involves rapid point mutations in polymerases.[1] Evolutionarily, these adaptations reflect trade-offs: resistance may impose fitness costs, such as slower growth rates in antibiotic-free environments, but compensatory mutations can mitigate them, enabling persistent lineages.[11] The global health burden of bacterial AMR underscores its causal impact on mortality and morbidity, with an estimated 1.27 million deaths directly attributable in 2019 and association with 4.95 million total deaths, disproportionately affecting low- and middle-income regions due to limited diagnostics and sanitation.[1][12] Surveillance data indicate escalating trends: between 2018 and 2023, resistance increased in over 40% of monitored pathogen-antibiotic combinations, with one in six laboratory-confirmed bacterial infections resistant to treatments in 2023.[13] Projections from systematic analyses forecast bacterial AMR causing nearly 2 million attributable deaths annually by 2050, alongside broader infectious mortality exceeding 90 million, driven by stagnant new drug development failing to match evolutionary rates.[14][15] Ecologically, AMR propagates through microbial communities, where interspecies interactions amplify gene dissemination, compounding selective pressures from agricultural and clinical antibiotic use.[16] Causal drivers of AMR emergence trace to overuse and misuse of antimicrobials, which impose Darwinian bottlenecks: sublethal exposures permit resistant subpopulations to dominate, while incomplete treatment courses sustain reservoirs of partially resistant strains.[3] In non-human sectors, veterinary applications—accounting for over 70% of antibiotics in some countries—facilitate zoonotic transfer to human pathogens.[4] Unlike media portrayals emphasizing systemic failures alone, first-principles analysis reveals that resistance is an inevitable outcome of deploying bactericidal agents in genetically diverse populations without concurrent stewardship, as microbes exploit every genetic avenue for survival.[9] Mitigation hinges on disrupting these evolutionary dynamics through targeted interventions, though institutional data from bodies like WHO, while empirically grounded, warrant scrutiny for potential overemphasis on regulatory solutions over biological realities.[1]Computing and Technology
Abstract Meaning Representation
Abstract Meaning Representation (AMR) is a semantic formalism that encodes the meaning of natural language sentences as rooted, labeled, directed acyclic graphs (DAGs), emphasizing predicate-argument structure while abstracting away from syntactic details such as word order and morphological inflections.[17] In AMR, nodes represent concepts—typically predicates from resources like PropBank or OntoNotes, or entities such as names and numbers—and edges denote semantic relations, including core roles (e.g., ARG0 for agents, ARG1 for patients) and non-core modifiers (e.g., location, time).[17] This graph-based approach facilitates tasks in natural language processing by providing a language-independent layer of meaning, though primarily developed and annotated for English.[18] AMR was introduced in 2013 by researchers at the University of Colorado Boulder and the University of Southern California as a framework for creating large-scale semantic annotations, or "sembanks," for English sentences drawn from corpora like broadcast news and web text.[17] The initial effort involved manually annotating over 10,000 sentences to capture who did what to whom, incorporating reification for complex phenomena like possession and negation, and using variables (e.g., "b" or "c") to link related concepts without relying on surface syntax.[17] Subsequent developments expanded AMR's expressivity, such as handling modality, polarity, and coreference through dedicated subgraphs, while efforts like AMR 2.0 (released around 2016) refined the inventory to reduce ambiguity and improve consistency in annotations.[18] The framework's utility stems from its focus on "who is doing what to what" semantics, enabling applications in semantic parsing—converting text to AMR graphs—and graph-to-text generation, where AMRs serve as intermediate representations for controlled language output.[19] Benchmarks for AMR parsing, such as those using the Little Prince corpus (initially 100 sentences, later expanded), evaluate Smatch scores measuring graph similarity via triple overlaps, with state-of-the-art models achieving around 80-85% by 2020 through neural architectures like graph neural networks and transition-based parsers.[19] Extensions to multilingual AMR have explored cross-lingual transfer, though challenges persist due to English-centric annotations, with adaptations for languages like Chinese, Spanish, and Turkish yielding lower inter-annotator agreement compared to English (e.g., 0.85-0.90 F-score for English vs. 0.70-0.80 for others).[20] AMR's graph structure also supports downstream tasks like question answering and summarization by aligning with event coreference and temporal ordering, as demonstrated in hybrid systems combining AMR with dependency parses.[18]Adaptive Multi-Rate
The Adaptive Multi-Rate (AMR) codec is a speech coding standard designed for digital cellular networks, encoding narrowband audio signals in the 200–3400 Hz frequency range at eight variable bit rates ranging from 4.75 to 12.2 kbit/s to balance speech quality and transmission efficiency under varying channel conditions.[21] It employs link adaptation mechanisms, such as those defined in 3GPP specifications, to dynamically select the optimal mode based on radio link quality, thereby enhancing network capacity and robustness against errors in GSM full-rate and enhanced full-rate channels, as well as UMTS.[22] The codec's core algorithm relies on algebraic code-excited linear prediction (ACELP) for efficient parametric representation of speech, incorporating voice activity detection (VAD) and comfort noise generation to reduce overhead during silence periods.[23] Standardized by the 3rd Generation Partnership Project (3GPP) as part of GSM Phase 2+ enhancements and UMTS Release 1999, AMR was frozen for implementation in October 1999, with detailed ANSI-C reference code specified in TS 26.073 to ensure interoperability across vendors.[22] Its adoption addressed limitations of prior fixed-rate codecs like GSM full-rate (13 kbit/s), offering up to 50% capacity gains in poor signal environments by dropping to lower rates during frame errors, while maintaining mean opinion score (MOS) quality comparable to or better than G.711 at higher modes.[24] The eight operational modes, each with distinct frame sizes and error concealment strategies, are:- Mode 0: 12.2 kbit/s (244 bits/frame, 20 ms)
- Mode 1: 10.2 kbit/s (204 bits/frame, 20 ms)
- Mode 2: 7.95 kbit/s (159 bits/frame, 20 ms)
- Mode 3: 7.40 kbit/s (148 bits/frame, 20 ms)
- Mode 4: 6.70 kbit/s (134 bits/frame, 20 ms)
- Mode 5: 5.90 kbit/s (118 bits/frame, 20 ms)
- Mode 6: 5.15 kbit/s (103 bits/frame, 20 ms)
- Mode 7: 4.75 kbit/s (95 bits/frame, 20 ms)