Archive for the ‘Machine Learning’ Category
Machine Learning Helps Scientists Locate the Neurological Origin of Psychosis – ExtremeTech
Researchers in the United States, Chile, and the United Kingdom have leveraged machine learning to hone in on the parts of the brain responsible for psychosis. Their findings help to illuminate a common yet elusive experience and could contribute to the development of novel treatments for psychosis and the conditions that cause it.
Around 3 in every 100 people will experience at least one psychotic episode in their lifetimes. Commonly misunderstood, these episodes are characterized by hallucinations (a false perception involving the senses) or delusions (false beliefs not rooted in reality). Many people who experience psychosis have a condition like schizophrenia or bipolar disorder; others have a history of substance abuse, and still others have no particular condition at all.
Regardless of its cause, psychosis can be debilitating for those who experience it, leading some people to seek out antipsychotic medication aimed at staving off future episodes. Though antipsychotic medications are often a godsend for the people who take them, they've historically disrupted neurological psychosis research. During brain scans, it's difficult to know whether specific brain activity can be attributed to the person's condition or to the drugs they're taking. This means medical professionals and pharmaceutical companies work with a fairly limited understanding of psychosis as they help patients manage their episodes.
Researchers at Stanford University, the University of California Los Angeles, Universidad del Desarrollo, and the University of Oxford relied on two strategies to circumvent this issue. To start, they gathered study participants from a wide range of ages and conditions in the hope of uncovering an overarching theme. The group of nearly 900 participants included people ages 6 to 39, some of whom had a history of psychosis or schizophrenia and some of whom had never experienced either. Just over 100 participants had 22q11.2 deletion syndrome, meaning they're missing part of one of their copies of chromosome 22a condition known to carry a 30% risk of experiencing psychosis, schizophrenia, or both. Another 120 participants experienced psychosis but had not been diagnosed with any particular hallucination- or delusion-causing condition.
Credit: Supekar et al, Molecular Psychiatry/DOI 10.1038/s41380-024-02495-8
The team also used machine learning to spot the minute distinctions between the brain activity of those who experience psychosis and the brain activity of those who don't. To map out the participants' neurological activity, the team used functional magnetic resonance imaging (fMRI). This technique allows medical professionals and researchers to track the tiny fluctuations in blood flow triggered by brain changes.
With a custom spatiotemporal deep neural network (stDNN), the researchers compared the functional brain signatures of all participants and found among those with 22q11.2 deletion syndrome. Regardless of demographic, these participants experienced what appeared to be "malfunctions" in the anterior insula and the ventral striatum. These two parts of the brain are involved in humans' cognitive filters and reward predictors, respectively. The stDNN continued to find clear discrepancies between the anterior insulae and ventral striata of those who experienced psychosis and those who did not, further indicating that these two regions of the brain played a vital role in hallucinations and delusions.
These findings, shared Friday in a paper for Molecular Psychiatry, support a standing theory regarding the reliance of psychosis on malfunctioning cognitive filters. Scientists have long wondered whether, during a psychotic episode, the brain struggles to distinguish what's true from what isn't. This is a key function of the brain's salience network, which detects and assigns importance to incoming stimuli. When the salience network cannot work correctly, the brain might incorrectly assign importance and attention to the wrong stimuli, resulting in a hallucination or delusion.
Our discoveries underscore the importance of approaching people with psychosis with compassion, said Stanford neuroscientist and senior study author Dr. Vinod Menon in a statement. Menon and his colleague, psychiatrist Kaustubh Supekar, hope their findings will assist in the development of antipsychotic treatments, especially for those with schizophrenia.
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Machine Learning Helps Scientists Locate the Neurological Origin of Psychosis - ExtremeTech
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Slack delivers native and secure generative AI powered by Amazon SageMaker JumpStart | Amazon Web Services – AWS Blog
This post is co-authored by Jackie Rocca, VP of Product, AI at Slack
Slack is where work happens. Its the AI-powered platform for work that connects people, conversations, apps, and systems together in one place. With the newly launched Slack AIa trusted, native, generative artificial intelligence (AI) experience available directly in Slackusers can surface and prioritize information so they can find their focus and do their most productive work.
We are excited to announce that Slack, a Salesforce company, has collaborated with Amazon SageMaker JumpStart to power Slack AIs initial search and summarization features and provide safeguards for Slack to use large language models (LLMs) more securely. Slack worked with SageMaker JumpStart to host industry-leading third-party LLMs so that data is not shared with the infrastructure owned by third party model providers.
This keeps customer data in Slack at all times and upholds the same security practices and compliance standards that customers expect from Slack itself. Slack is also using Amazon SageMaker inference capabilities for advanced routing strategies to scale the solution to customers with optimal performance, latency, and throughput.
With Amazon SageMaker JumpStart, Slack can access state-of-the-art foundation models to power Slack AI, while prioritizing security and privacy. Slack customers can now search smarter, summarize conversations instantly, and be at their most productive.
Jackie Rocca, VP Product, AI at Slack
SageMaker JumpStart is a machine learning (ML) hub that can help accelerate your ML journey. With SageMaker JumpStart, you can evaluate, compare, and select foundation models (FMs) quickly based on predefined quality and responsibility metrics to perform tasks like article summarization and image generation. Pretrained models are fully customizable for your use case with your data, and you can effortlessly deploy them into production with the user interface or SDK. In addition, you can access prebuilt solutions to solve common use cases and share ML artifacts, including ML models and notebooks, within your organization to accelerate ML model building and deployment. None of your data is used to train the underlying models. All the data is encrypted and is never shared with third-party vendors so you can trust that your data remains private and confidential.
Check out the SageMaker JumpStart model page for available models.
Slack launched Slack AI to provide native generative AI capabilities so that customers can easily find and consume large volumes of information quickly, enabling them to get even more value out of their shared knowledge in Slack. For example, users can ask a question in plain language and instantly get clear and concise answers with enhanced search. They can catch up on channels and threads in one click with conversation summaries. And they can access personalized, daily digests of whats happening in select channels with the newly launched recaps.
Because trust is Slacks most important value, Slack AI runs on an enterprise-grade infrastructure they built on AWS, upholding the same security practices and compliance standards that customers expect. Slack AI is built for security-conscious customers and is designed to be secure by designcustomer data remains in-house, data is not used for LLM training purposes, and data remains siloed.
SageMaker JumpStart provides access to many LLMs, and Slack selects the right FMs that fit their use cases. Because these models are hosted on Slacks owned AWS infrastructure, data sent to models during invocation doesnt leave Slacks AWS infrastructure. In addition, to provide a secure solution, data sent for invoking SageMaker models is encrypted in transit. The data sent to SageMaker JumpStart endpoints for invoking models is not used to train base models. SageMaker JumpStart allows Slack to support high standards for security and data privacy, while also using state-of-the-art models that help Slack AI perform optimally for Slack customers.
SageMaker JumpStart endpoints serving Slack business applications are powered by AWS instances. SageMaker supports a wide range of instance types for model deployment, which allows Slack to pick the instance that is best suited to support latency and scalability requirements of Slack AI use cases. Slack AI has access to multi-GPU based instances to host their SageMaker JumpStart models. Multiple GPU instances allow each instance backing Slack AIs endpoint to host multiple copies of a model. This helps improve resource utilization and reduce model deployment cost. For more information, refer to Amazon SageMaker adds new inference capabilities to help reduce foundation model deployment costs and latency.
The following diagram illustrates the solution architecture.
To use the instances most effectively and support the concurrency and latency requirements, Slack used SageMaker-offered routing strategies with their SageMaker endpoints. By default, a SageMaker endpoint uniformly distributes incoming requests to ML instances using a round-robin algorithm routing strategy called RANDOM. However, with generative AI workloads, requests and responses can be extremely variable, and its desirable to load balance by considering the capacity and utilization of the instance rather than random load balancing. To effectively distribute requests across instances backing the endpoints, Slack uses the LEAST_OUTSTANDING_REQUESTS (LAR) routing strategy. This strategy routes requests to the specific instances that have more capacity to process requests instead of randomly picking any available instance. The LAR strategy provides more uniform load balancing and resource utilization. As a result, Slack AI noticed over a 39% latency decrease in their p95 latency numbers when enabling LEAST_OUTSTANDING_REQUESTS compared to RANDOM.
For more details on SageMaker routing strategies, see Minimize real-time inference latency by using Amazon SageMaker routing strategies.
Slack is delivering native generative AI capabilities that will help their customers be more productive and easily tap into the collective knowledge thats embedded in their Slack conversations. With fast access to a large selection of FMs and advanced load balancing capabilities that are hosted in dedicated instances through SageMaker JumpStart, Slack AI is able to provide rich generative AI features in a more robust and quicker manner, while upholding Slacks trust and security standards.
Learn more about SageMaker JumpStart, Slack AI and how the Slack team built Slack AI to be secure and private. Leave your thoughts and questions in the comments section.
Jackie Rocca is VP of Product at Slack, where she oversees the vision and execution of Slack AI, which brings generative AI natively and securely into Slacks user experience. Now shes on a mission to help customers accelerate their productivity and get even more value out of their conversations, data, and collective knowledge with generative AI. Prior to her time at Slack, Jackie was a Product Manager at Google for more than six years, where she helped launch and grow Youtube TV. Jackie is based in the San Francisco Bay Area.
Rachna Chadha is a Principal Solutions Architect AI/ML in Strategic Accounts at AWS. Rachna is an optimist who believes that the ethical and responsible use of AI can improve society in the future and bring economic and social prosperity. In her spare time, Rachna likes spending time with her family, hiking, and listening to music.
Marc Karp is an ML Architect with the Amazon SageMaker Service team. He focuses on helping customers design, deploy, and manage ML workloads at scale. In his spare time, he enjoys traveling and exploring new places.
Maninder (Mani) Kaur is the AI/ML Specialist lead for Strategic ISVs at AWS. With her customer-first approach, Mani helps strategic customers shape their AI/ML strategy, fuel innovation, and accelerate their AI/ML journey. Mani is a firm believer of ethical and responsible AI, and strives to ensure that her customers AI solutions align with these principles.
Gene Ting is a Principal Solutions Architect at AWS. He is focused on helping enterprise customers build and operate workloads securely on AWS. In his free time, Gene enjoys teaching kids technology and sports, as well as following the latest on cybersecurity.
Alan Tan is a Senior Product Manager with SageMaker, leading efforts on large model inference. Hes passionate about applying machine learning to the area of analytics. Outside of work, he enjoys the outdoors.
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Slack delivers native and secure generative AI powered by Amazon SageMaker JumpStart | Amazon Web Services - AWS Blog
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Accurate and rapid antibiotic susceptibility testing using a machine learning-assisted nanomotion technology platform – Nature.com
All experimental procedures and bioinformatic analyses in this work comply with ethical regulations and good scientific practices. An ethics approval for the pre-clinical experiments was not required as anonymized biological material, i.e., anonymized blood for the blood culture incubation, was provided by a blood donation center in Switzerland. The clinical study protocol for the PHENOTECH-1 study (NCT05613322) was approved by the Ethics Committee for Investigation with Medicinal Products (CEIm) in Madrid (ID 239/22), the Cantonal Commission for Ethics in Research on Human Beings (CER-VD) in Lausanne (ID 2022-02085), and the Ethics Committee of the Medical University of Innsbruck in Innsbruck (ID 1271/2022).
The strain collection used in this study consists of ATCC reference strains and clinical isolates either from patient blood samples at hospital sites or procured from strain collections (Supplementary Data1). In order to establish a methodology for nanomotion-based AST, we used the E. coli reference strain ATCC-25922, which is susceptible to ceftriaxone (CRO; ceftriaxone disodium salt hemi(heptahydrate) analytical standard, Merck & Cie, Schaffhausen, Switzerland), cefotaxime (CTX; cefotaxime sodium, Pharmaceutical Secondary Standard, Supelco, Merck & Cie, Schaffhausen, Switzerland), ciprofloxacin (CIP; ciprofloxacin, VETRANAL, analytical standard, Merck & Cie, Schaffhausen, Switzerland), and ceftazidime-avibactam (SigmaAldrich, Merck & Cie, Schaffhausen, Switzerland). Our reference strains for antibiotic resistance were BAA-2452 (resistant to CRO and CTX, blaNDM producer) and BAA-2469 (resistant to CIP). The K. pneumoniae reference isolates ATCC-27736 was susceptible to CRO.
To differentiate between resistant and susceptible phenotypes, clinical isolates were selected based on their MIC in accordance with the European Committee on Antimicrobial Susceptibility Testing (EUCAST) interpretation guidelines59. MIC strips and disk diffusion tests were performed on MH Agar plates (Mueller-Hinton agar VWR International GmbH, Dietikon, Switzerland). During all nanomotion experiments, bacteria in the measurement chamber were incubated with filtered (0.02 m Polyethersulfone, PES, Corning, or Millipore) LB (Millers LB Broth, Corning) half-diluted in deionized water (Molecular Biology Grade Water, Cytiva), hereafter referred to as 50% LB.
All bacterial strains were stored at 80C in 20% glycerol. Bacterial samples for nanomotion experiments were prepared by first thawing new cell aliquots and growing them at 37C on Columbia agar medium solid plates (Columbia blood Agar, 5% sheep blood, VWR International GmbH, Dietikon, Switzerland). These cells were then used to inoculate blood culture medium and subsequently grown for nanomotion experimentation.
We performed MIC gradient tests (MIC strips) to determine the minimal inhibitory concentration (MIC) for each antibiotic used in this study. Cell suspensions were prepared by selecting three to five colonies grown overnight (ON) at 37C on a Columbia agar plate and resuspending them in 0.9% NaCl solution (Sodium Chloride, 0.9%, HuberLab, PanReac Applichem) at a density of 0.5 McFarland units (corresponding to OD600nm=0.07). This suspension was then spread on MH plates using a sterile cotton swab to create a confluent culture. MIC strips (ceftriaxone 0.016256g/mL, ciprofloxacin 0.00232g/mL, cefotaxime 0.016256g/mL, ceftazidime 0.016256g/mL, and ceftazidime-avibactam 0.016/4256/4g/mL MIC test strips, Liofilchem, Roseto degli Abruzzi, Teramo, Italy) were then placed onto inoculated plates using tweezers. The plates were subsequently incubated at 37C for 1620h, with the growth inhibition area surrounding the MIC strip present after this incubation period used to interpret MICs.
While MIC strips served as the primary AST reference method, some situations presented difficult interpretations or exceeded the scale of the CRO MIC strips. Here, broth microdilution assays were performed according to EUCAST recommendations59. Furthermore, a disk diffusion assay (DDA) was performed in parallel to each sample assessed using nanomotion technology for quality assurance purposes20,60.
To facilitate bacterial attachment and prevent cellular detachment during AST recording, we incubated the cantilever with 50l of 0.1mg/ml PDL (Poly-D-Lysine hydrobromide, MP Biomedicals, Santa Ana, California, USA) diluted in molecular biology grade water (HyClone, Logan, Utah, United States) for 20min at room temperature (RT). This treatment created a homogenous positive electric charge that enabled the attachment of negatively charged bacteria. Following incubation, thePDLdrop was removed and discarded, after which the cantilever tip was gently washed with 100l of molecular biology-grade water. The sensors on the cantilever were then allowed to dry for at least 15min before use.
Spiking refers to the process of inoculating blood culture samples with artificially infected blood. Here, we cultured strains of interest on Columbia Agar plates ON at 37C, isolated a single colony, and resuspended it in 0.9% NaCl with volumes adjusted to obtain a 0.5 McFarland density. We then performed two 1:10 serial dilutions, starting with that suspension, to generate a final dilution of 1:100. Finally, 10l of the final dilution were added to 9990l of EDTA blood from a donor provided by a blood donation center in Switzerland. Blood has been received fully anonymized.
To generate spiked blood cultures, we added 10ml of artificially infected blood to either anaerobic (ANH) or aerobic (AEH) blood culture bottles (BD BACTECTM Lytic Anaerobic medium and BD BACTECTM Standard Aerobic medium Culture Vials; Becton Dickinson, Eysins, Switzerland) using a syringe. These culture bottles were then incubated until positivity, as determined by the BACTECTM 9240 automated blood culture system (Becton Dickinson), was reached. In most cases, this process took 12h or an overnight incubation.
To generate and purify bacterial pellets for nanomotion recordings, we used either the MBT Sepsityper IVD Kit (Bruker) or the direct attachment method (DA). When using the MBT Sepsityper IVD Kit, we followed the manufacturers instructions. Briefly, 1ml of blood culture was combined with 200l Lysis Buffer, mixed by vortexing, and then centrifuged for 2min at 12,000g to obtain a bacterial pellet. The supernatant was discarded, while the bacterial pellet was resuspended in 1ml of Washing Buffer. The resuspension was then centrifuged again for 1min at 12,000g to remove debris. For DA, 1ml of positive blood culture (PBC) was syringe filtered (5m pore size, Acrodisc Syringe Filters with Supor Membrane, Pall, Fribourg, Switzerland). The pellet was then used for attachment to the cantilever.
Bacterial cells from prepared pellets needed to be immobilized onto the surface of the functionalized cantilever for nanomotion recording. First, pellets were resuspended in a PBS (Phosphate Buffer Saline, Corning) solution containing 0.04% agarose. Next, the sensor was placed on a clean layer of Parafilm M (Amcor, Victoria, Australia). The tip of the sensor, containing the chip with the cantilever, was placed into contact with a single drop of bacterial cell suspension for 1min. After this, the sensor was removed, gently washed with PBS, and assessed using phase microscopy for attachment quality. In the event of unsatisfactory attachment, the sensor was re-incubated in the cell suspension for an additional 3060s, or until satisfactory attachment was achieved. We aimed for an even bacterial distribution across the sensor (Fig.1b, c, and Supplementary Fig.2). The attachment of bacteria is part of a filed patent (PCT/EP2020/087821).
Our nanomotion measurement platform, the Resistell Phenotech device (Resistell AG, Muttenz, Switzerland), comprises a stainless-steel head with a measurement fluid chamber, an active vibration damping system, acquisition and control electronics, and a computer terminal.
Nanomotion-based AST strategies utilize technologies that are well-established in atomic-force microscopy (AFM). Specifically, our nanomotion detection system is based on an AFM setup for cantilever-based optical deflection detection. However, in contrast to standard AFM devices, in the Phenotech device the light source and the photodetector are placed below the cantilever to facilitate the experimental workflow. A light beam, focused at the cantilever end, originates in a superluminescent diode (SLED) module (wavelength: 650mm, optical power: 2mW), is reflected, and reaches a four-sectional position-sensitive photodetector that is a part of a custom-made precision preamplifier (Resistell AG). The flexural deflection of the cantilever is transformed into an electrical signal, which is further processed by a custom-made dedicated electronic module (Resistell AG) and recorded using a data acquisition card (USB-6212; National Instruments, Austin, TX, USA). The device is controlled using a dedicated AST software (custom-made, Resistell AG).
The custom-made sensors used for the described experiments (Resistell AG) contain quartz-like tipless cantilevers with a gold coating acting as a mirror for the light beam (SD-qp-CONT-TL, spring constant: 0.1N/m, length width thickness: 130400.75m, resonant frequency in air: 32kHz; NanoWorld AG, Neuchtel, Switzerland). During an AST experiment, bacterial nanoscale movements actuate the cantilever to deflect in specific frequencies and amplitudes.
For the development of temperature-controlled experiments with CZA at 37C, we used modular NanoMotion Device (NMD) prototypes. It allowed the reconfiguration of the hardware setup to work with either a standard incubator or a modified measurement head to warm up only the measurement chamber. For the merge of an NMD with a BINDER BD 56 incubator, the size of the incubator fits the entire NMD head with the active vibration damping module, also permitting the user a comfortable manual operation. The incubator shelf was rigid and able to hold the vibration isolator and NMD head (ca. 10kg), and the incubator was modified with an access port to pass through control cables operating the light source, photodetector, and vibration damping module from the outside. Another NMD prototype was equipped with a locally-heated measurement chamber, thermally insulated from the measurement head set-up. A Peltier module as a heating element was installed under the measurement chamber, adapted to temperature control by adding a Pt100 temperature sensor. Temperature was kept at 37C by a Eurotherm EPC3016 PID controller (Eurotherm Ltd, Worthing, United Kingdom) and a custom-made Peltier module driver. Both setups had a temperature stability <0.2C, which is a matching requirement for stable culture conditions.
Each sampled nanomotion signal was split into 10s timeframes. For each timeframe, the linear trend was removed and the variance of the residue frame was estimated. For some experiments, the variance signal was too noisy for classification, necessitating the application of an additional smoothing procedure. A running median with a 1min time window was applied to smooth the variance signal and allow plot interpretation. For the calculation of the SP slope of the variance in the drug phase used for determining the nanomotion dose response in Fig.2b and Supplementary Fig.4, we used the formula log(x)=log(C) + at, where t is time (in min), a is the slope of the common logarithm of the variance trend, and log(C) is the intercept. Variance plots were used here for the visual inspection of results, and are currently the primary tool accessible for investigators. However, more sophisticated SPs are necessary for reliably classifying phenotypes in ASTs.
Nanomotion-based AST was performed using Resistell Phenotech devices (Resistell AG, Muttenz, Switzerland) on a standard laboratory benchtop. Each recording comprises two phases: a 2-h medium phase and a 2-h drug phase. In addition, a short blank phase is conducted to measure the baseline deflections of a new, bare, functionalized cantilever in 50% LB medium for 510min. Raw nanomotion recordings were used to develop classification models using machine learning.
The signal during the blank phase is expected to be constant and primarily flat (variance around 2.6 E-6 or lower). Higher median values or the clear presence of peaks are indicators of potential contamination of the culture medium inside the measurement fluid chamber, sensor manufacturing errors, or an unusual external environmental noise source that should be identified and rectified. In particular, contamination (OD600<0.01) can cause deflection signals that are several orders of magnitude higher than expected for sterile media due to interactions between floating particles in the fluid chamber and the laser beam. The blank phase serves as a quality control but is not used for classification models and, therefore, can be performed several hours prior to recording medium and drug phases.
The medium phase records cantilever deflections after bacterial attachment, showing the oscillations caused by natural bacterial nanomotions stemming from metabolic and cellular activity. Here, variance is expected to be greater (105 to 103) than during the blank phase. The 2-h medium phase duration allows cells to adapt to their new environment within the fluid chamber and generates a baseline that can be compared to bacterial nanomotions during the drug phase. The drug phase measures cellular vibrations after an antibiotic has been introduced to the fluid chamber. The antibiotic is directly pipetted into the medium already present within the measurement chamber.
The Phenotech device detects nanomotion signals resulting from the activity of living cells. However, other sources can create detectable noise during cantilever-based sensing61. Thermal drift occurring on the cantilever62, as well as external sources such as acoustic noise and mechanical vibrations, can all impact measurements. Distinguishing cell-generated vibrations from background noise can be challenging. As such, we employed a supervised machine learning-based approach to extract signal parameters (SPs) containing diagnostic information while minimizing overall background noise. The entire procedure of analyzing motional activity of particles is part of a filed patent (PCT/EP 2023/055596).
First, a batch of initial SPs related to frequency and time domains were extracted, with time and frequency resolution being high to allow for further statistical analysis at this level. Next, different statistical parameters were created with a much coarser time and frequency scale. Finally, various combinations (differences, ratios, etc.) were calculated, forming a final batch of SPs that are more related to antibiotic susceptibility. SPs were estimated for experiments with cells and conditions with well-defined and known outputs (e.g., susceptibility to a given antibiotic could be known through reference AST methods). Here, extracted SPs and outputs formed labeled datasets that could be used for supervised machine learning.
A feature selection algorithm extracted SPs related to the phenomenon of interest. These SPs were selected from the overall batch of SPs to optimize the performance of this so-called machine learning model. In this case, the model was a classifier validated by analysis of metrics measuring the degree of distinguishing antibiotic susceptibility. Therefore, a forward selection method was applied. All SPs were subsequently evaluated in the classifier with repeated stratified cross-validation. The SPs that enabled the classifier to reach maximal accuracy were added to the stack of selected SPs and deleted from the remaining SPs. In the next iteration, all remaining SPs were again tested with the already-selected SPs. The best-performing SP was again added to the selected SP stack. This process was repeated several times until the overall performance reached a plateau or a predefined number of SPs were selected. In the final model (iii), these newly found SPs were then used as machine learning model features. Classifier models were trained using the complete available dataset and could now be used to classify previously unseen data. The Supplementary information elaborates in more detail on that process and lists all SPs used in the different classification models.
After achieving Pareto optimality, the models were tested on independent test datasets consisting exclusively of strains of K. pneumoniae or E. coli that were not used in the training of the corresponding model. We used either spiked blood cultures or directly anonymized remnant PBC from the Lausanne University Hospital (CHUV) in Lausanne. Spiking was predominantly utilized to increase the fraction of resistant strains to obtain more representative specificity (classification performance of resistant strains), as resistance rates at that hospital are around 10 % for CRO and CIP and close to non-existent for CZA. Each nanomotion recording was classified separately and combined using the median to a sample reporting accuracy, sensitivity and specificity exactly as described for reporting the training performance.
In addition to this, we performed an interim analysis of the multicentric clinical performance study PHENOTECH-1 (NCT05613322), conducted in Switzerland (Lausanne University Hospital, Lausanne), Spain (University Hospital Ramn y Cajal, Madrid) and Austria (Medical University of Innsbruck, Innsbruck). The study evaluates the performance of the nanomotion AST with the Phenotech device using the CRO model on E. coli and K. pneumoniae from fresh residual PBC. Ethical review and approval were obtained by the hospital ethics committee at each participating site. In Lausanne and Innsbruck, only samples from patients who had previously agreed to the use of their residual biological material were utilized. In Madrid, consent for participation was not required for this study in accordance with institutional requirements. No compensation was paid to participants. The interim results reported here comprise the first included 85 samples with complete data entry. The eventual sample size of 250 was estimated based on the expected rate of E. coli and K. pneumoniae samples susceptible to the antibiotic in the three countries (i.e., 80%). Allowing for up to 10% samples with missing data or technical errors, an overall sample size of 250 would include 180 truly susceptible samples with 98% power to demonstrate that sensitivity is at least 90%. The PHENOTECH-1 study is expected to conclude in 2024. The endpoints of this study include the accuracy, sensitivity, and specificity of the device according to ISO-20776-2 (2021), as well as the time to result from the start of the AST to the generation of the result in form of a time stamped report. Regarding inclusion criteria, patients aged 18 years or older, with positive blood cultures for either E. coli or K. pneumoniae, are eligible for participation in the study. Additionally, Phenotech AST needs to be performed within 24h of the blood culture turning positive. Patients with polymicrobial samples are excluded from the study.
Qualitative results of the Kirby Bauer disk diffusion assay, i.e., either R or S, were used for benchmarking. Clinical breakpoints for the class definition were according to EUCAST in 2022. The samples coming from one PBC were measured in technical triplicates for 4h. The results from each recording were automatically combined to a sample. Instead of the median score, a majority voting system was in place that is, RRR, RRS and RR- return predicted resistance, SSS, SSR, SS- return susceptibility. In this way even if one recording needed to be excluded because of technical errors, or detection of substantial elongation of the specimen, the sample could be interpretated. Only if two or more recordings were excluded, or the exclusion of one recording resulted in the disagreement between the two remaining recordings, the sample would be classified as non-conclusive. The experiments were not randomized and the investigators were unblinded during experiments and outcome assessment. Information on sex, gender, and age of participants was not collected in this study as having no impact on the generalizability and translation of the findings. At the time of analysis, the data set included 119 samples, of which 12 screening failures, 5 with technical errors or elongation, and 12 incomplete/unverified. Samples with complete, verified and cleaned data accounted to 90. Of these, the first 85 samples were selected of which 20 samples derived from CHUV, 48 samples from Ramon y Cajal Hospital and 17 samples from Medical University of Innsbruck.
Statistical details can be found in the figure legends. Data are presented as mean or medianSD or representative single experiments and provided in the Source data file. In Figs.3, 5, and 6, the performance calculation is based on single recordings for which a score was calculated. Each recording is depicted as a datapoint representing a biological replicate originating from a different PBC. Performance calculation in Fig.4 is based on the median of the scores calculated for each technical replicate originating from the same PBC. Thus, each datapoint represents the median score as it is currently implemented in the PHENOTECH-1 clinical performance study. In each case, scores are logits predicted by the corresponding logistic regression model. In Fig.5e the two-tailed MannWhitney U test was performed for calculating a p-value. Statistical analysis and graphs were generated with GraphPad Prism 10.
Further information on research design is available in theNature Portfolio Reporting Summary linked to this article.
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Accurate and rapid antibiotic susceptibility testing using a machine learning-assisted nanomotion technology platform - Nature.com
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AI reveals the complexity of a simple birdsong – The Washington Post
To a human ear the songs of all male zebra finches sound more or less the same. But faced with a chorus of this simple song, female finches can pick the performer who sings most beautifully.
Zebra finches are found in Australia, and they usually mate monogamously for life making this a high-stakes decision for the female finches. The zebra finch is among about a third of songbirds who learn a single song from their fathers early in life, and sing it over and over, raising the question of how female songbirds distinguish between them to choose a mate.
Listen to the song of a male zebra finch:
Scientists believe that most male songbirds evolved to sing a variety of songs to demonstrate their fitness. Under that theory, the fittest songbirds will have more time and energy to work on their vocal stylings and attract females with their varied vocal repertoire.
New research using machine learning shows finches may be sticking to one tune, but how they sing it makes a big difference. Published Wednesday in the journal Nature, the study reveals the complexity of a single zebra finch song and what female songbirds might be hearing in their prospective mates seemingly simple songs.
When researchers analyze birdsongs, theyre often not listening to them but rather looking at spectrograms, which are visualizations of audio files.
So I put together that, Hey, what humans are doing is looking at images of these audio files. Can we use machine learning and deep learning to do this? said Danyal Alam, the lead author on the new study and a postdoctoral researcher at the University of California at San Francisco.
Alam, along with Todd Roberts, an associate professor at UT Southwestern Medical Center and another colleague, used machine learning to analyze hundreds of thousands of zebra finch songs to figure out how they were different from each other and which variations were more attractive to female songbirds.
The researchers found one metric that seemed to get females attention: the spread of syllables in the song. The females seemed to prefer longer paths between syllables. This isnt something humans can easily pick up by listening to the songs or looking at the spectrograms but based on how these algorithms mapped the syllables, the researchers were able to see them in a new way.
To check their hypothesis, the researchers brought the findings back to the birds.
They generated synthetic bird songs to see if females preferred those with a longer path and they did, suggesting the birds intended audience picked up on the same pattern as the researchers computers.
Listen to see if you can tell the difference between a synthetic finch song that doesnt spread out its syllables:
Alam and his colleagues also found that baby birds had a harder time learning the long-distance song patterns than the shorter ones which suggests fitter birds would be more able to learn them, the researchers said.
The studys finding is consistent with whats been shown in other species: The more complexity or difficulty in a song, the more appealing it is to female birds.
A lot of signals in animal communication are meant to be an honest signal of some underlying quality, said Kate Snyder, a researcher at Vanderbilt who wasnt involved in the new paper.
For example, she said, if you look at a peacock, you see the male birds with the longer and more beautiful tails are better at attracting mates. Maintaining a tail like that is expensive for the bird so it must be good at finding food and surviving in its environment to have the time to devote to keeping its tail nice.
Learning takes a lot of time, energy, brain space, Snyder said. Only the fittest male birds will have the time and energy to devote to learning to sing.
Among finches, that work has just been harder to spot until now.
We used to think of this single song repertoire as perhaps a simple behavior, said Roberts. But what we see is that its perhaps much more complicated than we previously appreciated.
See more here:
AI reveals the complexity of a simple birdsong - The Washington Post
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