Automated animal activity recognition has achieved great success due to the recent advances in deep learning, allowing staff to identify variations in the animal behavioural repertoire in real-time. The high performance of deep learning largely relies on the availability of big data, which inevitably brings data privacy issues when collecting a centralised dataset from different farms. Federated learning provides a promising solution to train a shared model by coordinating multiple farms (clients) without sharing their private data, in which a global server periodically aggregates local (client) gradients to update the global model. The study develops a novel federated learning framework called FedAAR to achieve automated animal activity recognition using decentralised sensor data and to address, in particular, two major challenges resulting from data heterogeneity when applying federated learning in this task. The experiments demonstrate the performance advantages of FedAAR compared to the state-of-the-art, proving the promising capability of our framework for enhancing animal activity recognition performance. This research opens new opportunities for developing animal monitoring systems using decentralised data from multiple farms without privacy leakage.Deep learning dominates automated animal activity recognition (AAR) tasks due to high performance on large-scale datasets. However, constructing centralised data across diverse farms raises data privacy issues. Federated learning (FL) provides a distributed learning solution to train a shared model by coordinating multiple farms (clients) without sharing their private data, whereas directly applying FL to AAR tasks often faces two challenges: client-drift during local training and local gradient conflicts during global aggregation. In this study, we develop a novel FL framework called FedAAR to achieve AAR with wearable sensors. Specifically, we devise a prototype-guided local update module to alleviate the client-drift issue, which introduces a global prototype as shared knowledge to force clients to learn consistent features. To reduce gradient conflicts between clients, we design a gradient-refinement-based aggregation module to eliminate conflicting components between local gradients during global aggregation, thereby improving agreement between clients. Experiments are conducted on a public dataset to verify FedAAR’s effectiveness, which consists of 87,621 two-second accelerometer and gyroscope data. The results demonstrate that FedAAR outperforms the state-of-the-art, on precision (75.23%), recall (75.17%), F1-score (74.70%), and accuracy (88.88%), respectively. The ablation experiments show FedAAR’s robustness against various factors . Monitoring and assessing animal activities provide rich insights into their physical status and circumstances, as activity is one of the most critical indicators of animal health and welfare . TraditiIn recent years, deep learning has dominated the tasks in AAR due to the high performance achievable with the help of large-scale training datasets ,4. For iDespite remarkable benefits provided by FL, directly applying FL to AAR tasks often faces two major challenges: client-drift during local training and local gradient conflicts during global aggregation, which easily increase the difficulty in model convergence and cause extreme degradation of performance . First, Second, gradients among clients often possess inconsistent directions and even have conflicting components due to the inconsistency between local optimisation objectives in the context of data heterogeneity. Directly aggregating all local gradients in standard FL methods easily leads to mutual interferences among clients’ knowledge, further hampering the process of model convergence and exacerbating the risk of model divergence. To alleviate this problem, most existing works ,19,20,21We propose a novel FL framework called FedAAR to automatically recognise animal activities based on distributed data. This gives it the considerable potential that multiple farms jointly train a shared model using their private dataset while protecting data privacy and ownership. To the best of our knowledge, this is the first time the FL method has been explored in automated AAR across multiple data sources.To alleviate client-drift issues, we devise a PLU module to replace the traditional local training process. The PLU module forces all clients to learn consistent feature knowledge by imposing a global prototype guidance constraint to local optimisation, further reducing the divergence between client updates.Different from existing aggregation mechanisms, we design a GRA module to reduce conflicts among local gradients. The GRA module eliminates conflicting components between local gradients during global aggregation, effectively guaranteeing that all refined local gradients point in a positive direction to improve the agreement among clients.Our experimental results demonstrate that our proposed FedAAR outperforms the state-of-the-art FL strategies and exhibits performance close to that of the centralised learning algorithm. This proves the promising capability of our method to enhance AAR performance without privacy leakage. We also validate the performance advantages of our approach compared to the baseline in different practical scenarios , providing rich insights into the appropriate future applications of our method.In this study, we propose a novel FL framework, namely FedAAR, to achieve automated AAR by uniting decentralised data while tackling the abovementioned issues. To alleviate the client-drift issue, we design a prototype-guided local update (PLU) module, in which we introduce a globally shared prototype (classwise feature representation) as shared knowledge to constrain local optimisation. To reduce conflicts between local gradients, we devise a new gradient-refinement-based aggregation (GRA) module to constantly recalibrate local gradients throughout the training process. The proposed FedAAR is trained based on a public dataset , and itsThe dataset used in this study is a public dataset created by . This daK clients to collectively train a global model while keeping their data stored locally, effectively reducing the potential for violating data privacy. In each client k, the local dataset kD, where C is the number of label categories, and kN is the data number of the k-th client. The training of the FL system mainly consists of T communication rounds between a global server and K clients, with the detailed procedure of each communication round divided into the following three steps:A federated learning (FL) system coordinates Step 1. globalw from a global server.All clients synchronously download a global model Step 2. k uses the global model globalw to initialise its local model kw . The above steps are performed repeatedly until the global model achieves convergence.The updated global model The implementation of standard FL  heavily relies on the assumption that no data heterogeneity issues occur across clients . However, this assumption does not hold in AAR tasks because the discrepancy of movement patterns among individual animals often results in data heterogeneity, thus giving rise to detrimental effects on the training of FL. Specifically, data heterogeneity across clients inevitably enlarges the inconsistency of learned features between clients, thereby inducing drift between client updates as each client model is optimised toward its local objective instead of global optima . In addiT communication rounds between a global server and K clients, where the detailed procedures of each communication round include three steps as follows. (1) Each client k first downloads the same global model globalw and global prototype k, the local model globalw and then trained in a prototype-guided local update (PLU) module . (3) All local gradients globalw (see Equation (3)). In addition, local prototypes globalw and global prototype In this study, we propose a novel FL framework called FedAAR to achieve automated AAR in the context of data heterogeneity between clients, as illustrated in ) module . After l) module  and thenTo alleviate client-drift issues in the local training, we devised a PLU module a, which k simultaneously downloads the same global model globalw and global prototype kD in each client k, the local model kw is initialised as the global model globalw and then trained in the proposed PLU module. Specifically, given batchwise samples C categories in client k, we first adopted the feature extractor to extract feature representation c, where B and cB represent the number of total samples and the c, i.e.,First, each client Herein, we empirically used the network that removed the last fully connected layer as the feature extractor .λ is the weight coefficient of PGReg loss and its value equals 0 in the initial state. Then, we conducted the local training (see Equation (1)) to obtain the updated local model Inspired by prototype learning, in which gathering the prototypes across heterogeneous datasets enables the incorporation of feature representations over various data distributions ,25, we bcγ is an adaptively balanced coefficient controlling the updating degree of the global prototype and k. Considering that the updated global prototype may be transferred close to each other due to noise components, inducing similarity increases of inter-class feature vectors [cγ according to the intra-class and inter-class distance between local prototypes and the original global prototype:c’, that is the closest class to class c, i.e., c than the global prototype of its closest class  for equine activity recognition based on accelerometer and gyroscope data . The CMI−5 was used, and the learning rate decreased by 0.1 times every 20 epochs. The communication rounds T and batch size were set to 100 and 256, respectively. If not specified, the value of local training epochs E was set to 1, and the weighting coefficient λ in PLU was set to 0.05 by default. The global model was initialised randomly before being downloaded to clients in the first round. Over the communication rounds, the best global model with the highest testing accuracy was saved as the optimal model. To verify the model’s generalisation abilities, we performed the leave-one-out-based validation method. Specifically, we separately ran three times in each experiment. In each run (time), we randomly selected five horses from the original six horses and individually assigned these five horses to five farms (clients). Each of these five horses’ data serves as each client’s data, and all client data were used as training data to train a shared global model collectively. The data from the remaining horse (the sixth horse) were then used as the test data to verify the performance of the trained global model. The final test result of the model performance is presented in the format mean ± std from the three runs. This kind of data allocation can well simulate practical scenarios, i.e., data heterogeneity across farms, since the movement patterns of individual animals are often drawn from distinct distributions. All experiments were executed using the PyTorch framework on an NVIDIA Tesla V100 GPU. The source code is available at https://github.com/Max-1234-hub/FedAAR (accessed on 20 June 2022).During training, we used softmax cross-entropy loss with L2 regularisation (a weight decay of 0.15). An Adam optimiser with an initial learning rate of 5 × 10Overall, the experimental results demonstrate that our proposed FedAAR outperforms the state-of-the-art FL strategies from both quantitative and qualitative perspectives while exhibiting performance close to that of the centralised learning algorithm. Ablation studies were then carried out to evaluate the effectiveness of the PLU and GRA module on the classification capability. In addition, a comprehensive investigation of FedAAR’s performance was conducted at different levels of three practical conditions . The experimental results of FedAAR were compared with those of its corresponding baseline, further validating the performance advantages of our method. In the end, some possible future research directions are proposed. The details are described as follows.Quantitative comparison: We compared the performance of our proposed FedAAR with the state-of-the-art FL approaches  and with centralised learning. As illustrated in Qualitative comparison: To qualitatively verify our proposed approach, we visualise the feature vectors of the test set before the last fully connected layer within FedAAR and the state-of-the-art FL models, with the help of t-distributed stochastic neighbour embedding (t-SNE) [ (t-SNE) . As illuQuantitative analysis: To investigate the effectiveness of PLU and GRA on classification performance, we designed four different experimental settings as follows. (1) Baseline: we applied FedAvg as our baseline for the ablative comparison; (2) Baseline + PLU: we used the PLU module instead of the original optimisation process in the baseline during local training; (3) Baseline + GRA: we replaced the original weighted average mechanism in the baseline with our proposed GRA module during global aggregation; (4) FedAAR: we used our proposed framework involving both PLU and GRA modules simultaneously. The quantitative results are shown in Analysis of the hyper-parameterin PLU: The hyper-parameter Visualisation of refinements in GRA: To provide further insights into the enormous contributions of the GRA module, we visualise the counts of gradient refinement operations during the training process over three runs in To observe the behaviour of our proposed method over different data capacities, we present in E = 1, 2, 4, 8, 16). As expected [The communication frequency between local clients and the global server may influence learning behaviour. We decreased the communication frequency by increasing the local updating epochs and present in expected , both FeA larger number of clients may bring more conflicts among local gradients, which poses a great challenge to the practical application of FL. To further verify the performance advantages of FedAAR compared to the baseline under scenarios with more clients, we simulated the situations with varying client numbers by redistributing the original data based on the basic setting with five clients see . SpecifiDue to its privacy-preserving nature, FL has been increasingly adapted to various applications, including mobile edge devices, industrial engineering, and health care ,31,32. AOur method additionally requires the transmission of prototypes between clients and the server, increasing the dissemination of client information and communication overhead. However, this does not raise privacy concerns because prototypes only represent the average statistics across all already compressed local features . In addiOur method achieves automated AAR based on distributed data in the context of data heterogeneity. The major limitation is that our approach remains at the experimental level. To simulate practical scenarios, i.e., data heterogeneity across farms (clients), we assigned the horse dataset to multiple clients according to individuals since the movement patterns of individual animals are often drawn from distinct distributions. This kind of data allocation refers to some previous works ,15,34, wConsidering real-world applications, it is impossible for the client data to be fully labelled due to the time-consuming and costly process of data annotation. Thus, promising solutions are required for integrating FL with potential techniques  for exploiting the unlabelled data sufficiently. In addition, our method can be extended to AAR tasks with other forms of data  and different kinds of production management systems that need to use cross-silo data, further promoting the development of agriculture.In this study, we developed a novel FL framework called FedAAR involving a PLU module and a GRA module to achieve automated AAR by uniting decentralised sensor data while avoiding privacy leakage. The PLU module forces all clients to learn consistent classwise feature representations in local training, effectively reducing drift among client updates. The GRA module eliminates the conflicting components between local gradients during global aggregation, which ensures that all refined local gradients point in a positive direction to improve the agreement among clients. The experimental results reveal that our approach outperforms the state-of-the-art FL methods and achieves performance that is close to that of the centralised learning algorithm. Ablation studies further illuminated the effectiveness of the PLU and GRA modules. In addition, comparative analyses of the performance of FedAAR and the baseline at different levels of three practical conditions  confirm the performance advantages of our algorithm. These analyses also provide rich insights into the appropriate future applications of our method.


Point-of-care ultrasound (POCUS) training has been increasing among internal medicine (IM) residency programs, but few programs can provide longitudinal training due to barriers such as lack of trained faculty.Describe the development of a longitudinal POCUS track for IM residents using local and external resources, including a national POCUS certificate program.University-based IM residency program affiliated with a public and veterans affairs hospital.Twelve IM residents from 2018 to 2021.Residents complete a national POCUS certificate program by attending live courses and completing online modules, an image portfolio, and final knowledge/skills assessments. Locally, residents participate in 1-month procedure and diagnostic POCUS rotations and provide peer-to-peer POCUS teaching of residents and medical students.The POCUS track increased residents’ use and comfort with diagnostic and procedural applications. All residents rated being satisfied or very satisfied with the track and would recommend it to prospective applicants (100%). The most commonly reported barriers to utilizing POCUS per residents were time constraints (83%), lack of available ultrasound equipment (83%), and lack of trained faculty (58%).IM residency programs with limited faculty expertise in POCUS can leverage external resources to provide longitudinal POCUS training to its residents.The online version contains supplementary material available at 10.1007/s11606-022-07505-5. Here, we describe the development of our POCUS residency track including resources required, perspectives of residents and residency program leadership, and barriers to establishing a POCUS track.We developed a POCUS track for our IM residency, a university-based program with 95 categorical residents that is affiliated with a public and veterans affairs hospital. Candidates apply for the POCUS track through a separate NRMP number. Our program accepts 4 residents per year with a total of 12 residents in all three years on the POCUS track.POCUS track residents receive longitudinal ultrasound training during all three years .26 Interns are recommended to complete the modules during an elective rotation as the modules require 10–20 h of work.Interns complete self-directed online modules on the fundamentals of ultrasound and focused cardiac, pulmonary, and vascular ultrasound per the Society of Hospital Medicine – American College of Chest Physicians (SHM-ACCP) POCUS Certificate of Completion (COC) program.All IM interns participate in a 1-month procedure service rotation focusing on ultrasound-guided paracentesis, thoracentesis, and lumbar puncture. Large joint arthrocentesis and vascular access procedures are occasionally performed. Procedures are performed by interns under the supervision of a procedure chief resident or attending hospitalist.Interns attend an introductory 2- or 3-day POCUS course per the SHM-ACCP POCUS COC program that teaches goal-directed echocardiography, pulmonary, vascular, and abdominal ultrasound applications through a combination of lectures, image interpretation sessions, and hands-on scanning sessions with live models. Afterwards, interns are encouraged to start collecting images for their portfolios.Residents are registered for the SHM-ACCP POCUS COC program’s image portfolio in year 1 and focus on building their portfolios in year 2. The COC program requires 209 images of the heart, lungs, abdomen, and lower extremity vasculature. Expert SHM POCUS faculty use standardized image quality criteria to provide feedback on the images. Residents largely collect portfolio images during their inpatient rotations and POCUS elective.During this 4-week rotation, residents receive refresher training on focused cardiac, pulmonary, lower extremity vascular, abdominal, and other POCUS applications, including clinical integration of findings into the management of shock and cardiac arrest. Training is led by the POCUS track faculty director and includes a combination of didactics and hands-on scanning sessions in our Center for Clinical Ultrasound Education. During this rotation, residents attend four 2-h scanning sessions for image acquisition practice with live models, continue building their image portfolios, and meet weekly to bi-weekly with the POCUS track faculty director to review their collected images.SHM-ACCP POCUS COC participants must attend an approved regional POCUS course. Currently, approved courses are offered in San Francisco, Denver, New York, Minneapolis, Chicago, and San Antonio. In addition to reviewing core POCUS applications, these courses provide training in ultrasound-guided procedures; basic skin, soft tissue, and joint ultrasound; and discussion of ultrasound program development.Third-year POCUS track residents are assigned POCUS teaching activities to solidify their knowledge and skills and help meet the demand for POCUS training of residents and medical students. Our residency program utilizes a 4+1 block schedule, and one half-day is dedicated to POCUS teaching during each ambulatory week. Peer-to-peer instruction is provided during skills workshops and hands-on scanning sessions of the resident POCUS elective and during various medical student POCUS sessions. Additionally, the POCUS track residents themselves created and implemented new POCUS workshops for our IM residency program.rd year of IM residency during the SHM annual conference or an approved regional POCUS course. During the COVID-19 pandemic, both the written and skills exams were administered virtually in 2020–2021. The written exam was conducted online through the SHM learning portal and proctored by SHM staff virtually. The skills exam was administered virtually by an expert SHM POCUS faculty using REACTS tele-ultrasound software . After passing the final knowledge and skills exams, POCUS track residents were granted a certificate for completing the SHM-ACCP POCUS program.The final knowledge and skills exams are taken in the spring of the 3Resources for developing our POCUS track are summarized in Table Completing a training certificate through the SHM-ACCP POCUS COC program costs approximately $7000 per resident including travel. Our IM residency program had to be creative about securing institutional funding for this program. At our institution, university funds were reallocated to accept up to 4 residents/year on the POCUS track. We were fortunate that our Department of Medicine leadership supported the creation of the POCUS track.All POCUS track residents were surveyed in April 2021 with a response rate of 100% , lack of available ultrasound equipment (83%), and too few faculty trained in POCUS to supervise scanning (58%).st and 3rd years of residency.Frequency of use and comfort levels for different diagnostic and procedural POCUS applications are shown in Supplemental Tables 3 and 4. In general, the reported frequency of use and comfort levels increased between the 1We have described the development of an IM residency POCUS track combining local and external educational resources to provide longitudinal POCUS training. Direct benefits to POCUS track residents included the attainment of POCUS knowledge and skills and completion of a certificate program. Indirect benefits included increased institutional capacity for POCUS training by creating additional POCUS instructors for peer-to-peer teaching of residents and medical students. Our experience revealed important barriers and challenges that can help an IM residency program interested in developing a POCUS track.16 Longitudinal POCUS training has been shown to increase the frequency of ultrasound use and increase retention of knowledge and skills.24 Creation of a POCUS track allowed residents with a deep interest in POCUS to receive comprehensive, longitudinal training and complete a certification endorsed by two national specialty organizations. POCUS track residents’ comfort and frequency of POCUS use increased and overall satisfaction with the program was positive. However, we recognize that our total sample size is relatively small, and future surveys will give us a better understanding of residents’ comfort level with different POCUS applications.POCUS skills are highly desired but inconsistently taught in IM residency programs across the country.19 For programs with limited local expertise, our POCUS track can serve as a model for providing longitudinal training by leveraging available external educational resources. We utilized the SHM-ACCP POCUS COC program, a nationally recognized POCUS certificate program, to overcome a shortage of local POCUS-trained faculty to provide feedback and assess the knowledge and skills of our POCUS track residents. Second, peer-to-peer instruction has been shown to be effective for POCUS education.29 POCUS track residents increased our institutional capacity to provide POCUS training by serving as instructors to teach residents and medical students. Third, our residency program anticipated attracting competitive candidates due to the uniqueness of the POCUS track. The number of candidates matching the POCUS track coming from the first quartile of our residency program’s rank list has been increasing since 2019. Fourth, POCUS track residents have increased the residency program’s scholarly output, including peer-reviewed publications and national conference presentations. Additionally, a POCUS track can provide early faculty development during residency and may better prepare residents for careers in academic medicine.30 Thus far, one-third of our POCUS track residents have been recruited as academic hospitalist faculty or chief residents.Several institutional benefits were realized by the creation of a POCUS track for IM residents. First, the lack of POCUS-trained IM faculty is a major barrier to POCUS implementation nationwide.16 Specific challenges per POCUS track residents were completion of the image portfolio  and arranging schedules to attend required in-person courses. Lack of available ultrasound equipment presented challenges for both portfolio development and skills practice, and our IM residency program recently purchased two handheld ultrasound units specifically for the POCUS track residents to overcome this barrier.The most commonly reported barriers to POCUS use per POCUS track residents were time constraints, lack of available ultrasound equipment, and limited number of faculty trained in POCUS to supervise scanning. These barriers are consistent with past national surveys of POCUS training in IM residency programs.32 and collection of an image portfolio.33 However, the impact of a POCUS track on IM residents’ long-term knowledge and skills retention, and changes to clinical practice is unknown. Furthermore, the current training paradigm provides limited experience in clinical integration of POCUS findings into bedside decision-making, but as more faculty become trained, residents will have more frequent supervised clinical integration in the future. Second, peer-to-peer POCUS instruction has been shown to be effective for medical student POCUS training,29 but its effectiveness among IM residents has not been well studied which we plan to evaluate in the coming years. Third, POCUS use is beneficial in outpatient settings to monitor high-risk patients for decompensation, expedite workups, and improve the availability of diagnostic resources for underserved populations.34 Our POCUS track curriculum focuses primarily on inpatient applications, and additional training in outpatient applications, including skin, soft tissues, and joint ultrasound, shall be added to the curriculum in the future. Finally, the costs of the national certificate program and availability of a local POCUS faculty director may be limitations for residency programs desiring to start a POCUS track. Alternatively, institutions without local expertise could support interested faculty in completing the SHM-ACCP POCUS COC program. Investing in the development of institutional POCUS faculty champions could allow for the creation of a local certificate program for IM residents, similar to the SHM-ACCP POCUS COC program.We recognize our experience has limitations. First, core components of our POCUS track have been demonstrated to increase knowledge and skills of practicing clinicians, including participation in 2- or 3-day immersive POCUS CME coursesIn conclusion, we have described the development of a dedicated POCUS track in IM residency that can provide longitudinal POCUS training and certification for a select group of IM residents. Our POCUS track leverages external educational resources to help overcome local barriers to POCUS training for IM residents. Our POCUS track may serve as a model for IM residencies interested in providing longitudinal training to its residents but lack the required resources or local expertise to offer such training.ESM 1(DOCX 47 kb)


This cohort study quantifies changes in nursing home facility staffing during and after a severe COVID-19 outbreak. What is the association between severe COVID-19 outbreaks and US nursing home staffing patterns?In this cohort study of 2967 nursing homes in 2020, owing to increased absences and departures, there were statistically significant declines in staffing levels during a severe COVID-19 outbreak that remained statistically significantly reduced 16 weeks after the outbreak’s start. Facilities temporarily increased hiring, contract staff, and overtime to bolster staffing during outbreaks, but these measures did not fully replace lost staff, particularly certified nursing assistants.Considerable staffing challenges suggest a potential need for policy action to ensure adequate staffing levels during nursing home outbreaks to protect resident health. Staff absences and departures at nursing homes may put residents at risk and present operational challenges.To quantify changes in nursing home facility staffing during and after a severe COVID-19 outbreak.In this cohort study, daily staffing payroll data were used to construct weekly measures of facility staffing, absences, departures, and use of overtime and contract staff among US nursing homes experiencing a severe COVID-19 outbreak that started between June 14, 2020, and January 1, 2021. Facility outbreaks were identified using COVID-19 case data. An event-study design with facility and week fixed effects was used to investigate the association of severe outbreaks with staffing measures.Weeks since the beginning of a severe COVID-19 outbreak (4 weeks prior to 16 weeks after).Total weekly staffing hours, staff counts, staff absences, departures, new hires, overtime and contract staff hours measured for all nursing staff and separately by staff type , facility self-reported staff shortages, and resident deaths.Of the included 2967 nursing homes experiencing severe COVID-19 outbreaks, severe outbreaks were associated with a statistically significant drop in nursing staffing levels owing to elevated absences and departures. Four weeks after an outbreak’s start, around when average new cases peaked, staffing hours were 2.6%  of the mean below preoutbreak levels, despite facilities taking substantial measures to bolster staffing through increased hiring and the use of contract staff and overtime. Because these measures were mostly temporary, staffing declined further in later weeks; 16 weeks after an outbreak’s start, staffing hours were 5.5%  of the mean below preoutbreak levels. Staffing declines were greatest among certified nursing assistants, primarily owing to smaller increases in new hires of this staff type compared with licensed practical nurses and registered nurses.In this cohort study of nursing homes experiencing severe COVID-19 outbreaks, facilities experienced considerable staffing challenges during and after outbreaks. These results suggest the need for policy action to ensure facilities’ abilities to maintain adequate staffing levels during and after infectious disease outbreaks. Existing studies have established a large decline in industry-wide employment, which was 13.2% lower in June 2021 than at the pandemic’s start.This cohort study uses daily individual-level staffing data and an event-study design to quantify the association between severe COVID-19 outbreaks and changes in nursing home staffing. The main research questions were (1) whether severe outbreaks were associated with declines in staffing and by how much; (2) whether these declines were primarily driven by temporary absences, permanent departures, or a lack of new hiring; (3) how facilities may have used overtime or contract staffing to cope with these declines; and (4) how these effects may have varied across the 3 primary nursing staff types: registered nurses (RNs), licensed practical nurses (LPNs), and certified nursing assistants (CNAs). To the extent that CNAs are paid lower wages and may perform tasks with the highest exposure risk, outbreaks may particularly affect their likelihood of being absent or leaving their jobs, as well as facilities’ abilities to fill these positions. On the other hand, RN and LPN positions could be more affected because as more highly certified and higher-earning workers, they may be most willing or able to leave their jobs.16 While the data cover a broad range of staff types, we restricted this analysis to nursing staff  because these staff provide the majority of direct care for patients, and facilities may have deliberately reduced or deferred the use of other staff  during outbreaks. These staff care for both short-stay and long-stay patients at nursing homes. We used data from January 1, 2017, through June 31, 2021, which covered an average of 333 million daily shifts for 3.6 million staff members at 15 518 facilities each year.The primary data for this study are from the Centers for Medicare & Medicaid Services’ Payroll Based Journal (PBJ) system. Nursing homes are required to submit daily employee-level staffing information based on auditable payroll and contract data to the PBJ, including unique employee identifiers , staff type, employment arrangement , and hours worked.2 These data have been published weekly since May 24, 2020.We obtained data on weekly resident and staff COVID-19 cases, resident COVID-19–related deaths and total deaths, and self-reported staff shortages reported from the National Healthcare Safety Network (NHSN) COVID-19 Nursing Home Data set.STROBE) reporting guidelines for cross-sectional studies.Per Harvard institutional review board policy, institutional review board approval and written informed consent were not required because this study uses publicly available data. This study follows the Strengthening the Reporting of Observational Studies in Epidemiology , and January 1, 2021 (to focus on outbreaks prevaccination). We sorted these outbreaks by their severity, defined as total cases per bed, and defined the top decile of outbreaks across all nursing homes as severe outbreaks. Focusing on the top 10% of outbreaks ensured that we observed staffing patterns when facilities were most challenged and may have had the greatest difficulty maintaining adequate staffing levels.The final analysis sample consisted of all facilities experiencing severe outbreaks that were able to be matched to the PBJ data. We kept only the first severe outbreak for each facility because later outbreaks may have been less comparable owing to spillover effects  from the earlier severe outbreak , overtime hours (hours exceeding 40 hours each week worked by noncontract employees), and contract hours .We defined a facility as reporting a staffing shortage in any week they reported a shortage of nursing staff or aides in the NHSN.We used measures of resident deaths reported as due to COVID-19 and resident deaths not reported as due to COVID-19 from the NHSN data. Non-COVID-19–related deaths represent an extreme resident health outcome that could have been influenced by staff shortages.We used an event-study framework to study how staffing patterns changed during and after a severe outbreak. We ran multivariable linear regressions where the unit of analysis was all available facility-weeks for facilities that experienced a severe outbreak. The primary exposure variables were event-time indicators defined as weeks relative to the start of the outbreak. Models also included facility and calendar-time fixed effects. The facility fixed effects ensure that the event-time indicators are identified from within-facility variation in staffing over time. The time fixed effects control for secular time trends affecting all facilities . Thus, the specification is a 2-way fixed-effect event-study regression, where the identifying assumption is of parallel time trends . We graphed the coefficient estimates on the event-time indicators between −4 and 16 weeks, along with their 95% CIs. The effects prior to the start of the outbreak serve as a test of the plausibility of the parallel trends assumption. We clustered standard errors by facility.Using this method, we first explored how cases, total weekly nursing staff hours, resident census, and hours per resident evolved for the analysis sample during a severe outbreak. Next, we decomposed staffing changes into changes in absences, departures, new hires, overtime, and contract staffing, and studied the contribution of each of these components to the overall trend using the same event-study method. Next, we ran separate regressions for RNs, LPNs, and CNAs, and compared the average outbreak effect (the average coefficient across the first 16 weeks of an outbreak) across staff types. Finally, we plotted the event-study coefficients for self-reported staff shortages and resident deaths. Analyses were performed using Stata, version 16.1 (StataCorp).17In sensitivity analyses, we presented estimates for every outbreak decile to verify that effects were concentrated among larger outbreaks. We also redid the analysis defining severe outbreaks in terms of total cases, rather than total cases per bed. While this alternate definition is a more conventional way to define severe outbreaks, it naturally identifies larger facilities, which is why we used the per-bed definition in the main text. We also added controls for county-level case rates to provide reassurance that the observed associations are specific to a facility experiencing an outbreak, rather than a broader community-level outbreak. Finally, because the estimates exploit staggered event timing, we followed the literature and redid the analyses using alternative estimators that are robust to negative weighting.There were 39 390 COVID-19 outbreaks identified in the data with a mean of 29.6 resident and staff cases (0.30 cases per bed). The top decile of these outbreaks was labeled as severe outbreaks, which averaged 135.1 cases, corresponding to 1.5 cases per bed (eFigure 1 in the Finally, Event study coefficients for the weeks prior to an outbreak are near zero for most dependent variables. This finding supports the assumption of parallel trends that forms the basis of this article’s identification. In addition, the findings of clear shocks starting in the exact week of treatment and peaks roughly coinciding with the peak in cases offer additional validation of the identification strategy.eFigure 3 in the This study found that severe COVID-19 outbreaks were associated with statistically significantly reduced nursing staffing levels at nursing homes. Staffing reductions were driven by both temporary absences and permanent departures. Facilities were only able to partially offset these losses during outbreaks through increased new hires and the potentially costly use of contract staff and overtime. Because the use of contract hours and overtime declined after the peak outbreak weeks, staffing levels fell further after these weeks and did not recover even 16 weeks after the start of an outbreak, suggesting a lasting effect of severe outbreaks on facility staffing.16 We confirmed this to be true even during severe outbreaks. However, we also found that facility managers are much more likely to report staffing shortages during severe outbreaks, suggesting that per-resident staffing may not be a suitable benchmark for understanding staffing capacity during these times. The shifting composition of nursing home residents caused by declines in admissions of short-stay rehabilitative residents,18 and adoption of intensive infection-control practices, likely drastically altered the nature of resident care in nursing homes, potentially necessitating greater staffing per resident.Previous work found that per-resident staffing levels were stable or higher than prepandemic levels owing to a declining resident census that outpaced reductions in staffing.6 Similar factors may impede the filling of available CNA positions during outbreaks and raise concerns about the long-term outlook of this occupation.19 In contrast, RN staffing appeared to be the most stable during outbreaks, largely owing to substantial increases in new RN hires. This study is unable to distinguish whether this pattern reflects the available supply of RNs or a focused effort to bring in additional RN hours owing to their additional training and ability to implement and oversee infection control practices.We found staffing losses were greatest for CNAs, primarily owing to a disproportionate lack of hiring to fill losses created by absences and departures. Previous work has demonstrated that CNA positions have higher rates of turnover, likely owing to their low wages and limited benefits.Future work is needed to determine whether these staffing reductions had adverse effects on resident quality of life, morbidity, and mortality, including by putting facilities at risk for additional outbreaks. Although descriptive, the contemporaneous increase in staffing shortages and resident non-COVID-19–related deaths is consistent with worsened resident health owing to inadequate staffing.1This study is limited by the fact that we are unable to observe the reasons for changes in absences, departures, and new hires. For example, we do not know whether staff absences were because of sickness and quarantine, fear of exposure, or other factors. In addition, we do not know if facilities intentionally chose to lower staffing levels owing to the occupancy declines or if facilities were unable to achieve their desired staffing because of turnover and hiring constraints. We also miss early outbreaks that were not captured by the NHSN data.21 These teams can be organized at the state or federal level and could provide supplemental staffing to facilities experiencing severe outbreaks.22 The American Rescue Plan Act of 2021 provided temporary funding for such teams, and anecdotal reports indicate that they were helpful in addressing emergency staff shortages.24 Policy makers might also consider broad investment in nursing home workers through better pay and benefits, such as increasing Medicaid reimbursements alongside wage pass-through requirements.25 Finally, policy makers should question whether traditional staffing measures accurately capture the adequacy of staffing levels during a pandemic or if new measures are needed.In light of the considerable challenges documented in this cohort study, preparations for future infectious disease outbreaks should include emergency staffing plans for nursing homes to ensure resident safety, such as centralized “strike teams” that can be temporarily deployed.


Though rare, acute macular neuroretinopathy is a well-described clinical entity. We report for the first time a detailed analysis of macular hyalocyte count and morphology during the acute phase of acute macular neuroretinopathy. We present a case of a 19-year-old man with bilateral acute onset paracentral scotomas in the setting of an antecedent viral infection. Multimodal imaging demonstrated classic features of acute macular neuroretinopathy. Further analysis revealed increased macular hyalocyte count and an activated hyalocyte morphology during the acute phase of the disease course. Multimodal imaging not only allows for greater confidence in a diagnosis and prognosis but also helps to shed light on underlying pathophysiology. The new clinical capability of monitoring cellular behavior, such as hyalocyte count and morphology, in physiological and pathological conditions may prove useful as biomarkers of early disease activity or response to therapy. Acute macular neuroretinopathy (AMN) is a rare condition characterized by acute onset paracentral scotomas with corresponding intraretinal reddish-brown, wedge-shaped lesions that tend to point toward the fovea , 2. AlthA 19-year-old man presented to the emergency department with two days of progressive bilateral blurry vision. Two days prior to his symptoms, he had fever and cough and tested positive for influenza, negative for COVID-19, and had recently taken his second dose of oseltamivir (Tamiflu). On initial examination, he had count finger vision at one foot in both eyes, but with eccentric gaze, he was able to see 20/60 in each eye. Presence of improved visual acuity in eccentric gaze prompted Amsler grid testing which revealed bilateral inferior paracentral scotomas with no metamorphopsia. The patient was unable to read Ishihara color plates. Intraocular pressure, extraocular motility, and confrontation visual field testing were full. Pupils were equal, round, and reactive, with no relative afferent pupil defect. Anterior and posterior segment exams were remarkable only for asymmetric cup to disc ratio (0.6 OD and 0.3 OS). No macular or optic nerve head edema was seen. MRI brain and orbits with and without contrast were unremarkable.On repeat examination two days later, best corrected visual acuity with noneccentric gaze had improved to 20/25 OD and 20/70 OS. The patient reported improvement in the paracentral scotomas. Dilated fundus exam was significant for mild macular edema and subtle red-brown foveal pigmentation notably not present at the time of initial. Optical coherence tomography showed outer retinal layer hyperreflectivity with distortion of the ellipsoid layer but intact retinal pigment epithelium (RPE) Figures . Hyporefμm OCT-Reflectance (OCT-R) image slab located above the inner limiting membrane (ILM) surface using MATLAB . Relative to a 35-year-old healthy control, there was a notable increased macular hyalocyte count in our patient with AMN. The morphology of the surface hyalocytes was distinctively round with few processes, consistent with an “activated” phenotype  imaging revealed choriocapillaris flow voids that roughly correspond to the lesions seen on infrared reflectance photographs . To assehenotype .μm region above the inner limiting membrane (ILM), entangled with the collagen fibrils that compose the formed component of the vitreous cortex [Hyalocytes are the primary resident macrophages within the vitreous. They act as the first responders to neuronal injury, playing an integral role in maintaining homeostasis in the retinal microenvironment, modulating the immune response, and synthesizing extracellular matrix –9. They s cortex , 11.Advances in retinal imaging have recently allowed visualization of vitreous cortex hyalocytes (VCH) in humans in vivo , 13. CliDuring the acute inflammatory phase, our patient demonstrated classic features of AMN consistent with descriptions in the literature: outer retinal hyperreflectivity on OCT cross-sections, parafoveal petalloid hyporeflective lesions on infrared reflectance photography, corresponding areas of hypofluorescence on ICG, and the appearance of localized choriocapillaris flow voids in OCT-A scans. In this case, we were able to image the density, distribution, and morphology of VCH during the acute phase of AMN. Compared to the healthy control presented in this paper, as well as 18 other healthy controls previously published by Castanos et al. , this paRefinement of imaging strategies makes it likely that similar events in the future will be able to be studied more extensively, enabling a more complete in vivo description of the cellular transformations as they develop. Such investigations will provide greater understanding of the cellular-level events occurring and possibly suggest therapeutic approaches.


Layer 5 (L5) serves as the main output layer of cortical structures, where long-range projecting pyramidal neurons broadcast the columnar output to other cortical and extracortical regions of the brain. L5 pyramidal neurons are grouped into two subclasses based on their projection targets; while intratelencephalic (IT) neurons project to cortical areas and the striatum, extratelencephalic (ET) neurons project to subcortical areas such as the thalamus, midbrain, and brainstem. Each L5 subclass possesses distinct morphological and electrophysiological properties and is incorporated into a unique synaptic network. Thanks to recent advances in genetic tools and methodologies, it has now become possible to distinguish between the two subclasses in the living brain. There is increasing evidence indicating that each subclass plays a unique role in sensory processing, decision-making, and learning. This review first summarizes the anatomical and physiological properties as well as the neuromodulation of IT and ET neurons in the rodent neocortex, and then reviews recent literature on their roles in sensory processing and rodent behavior. Our ultimate goal is to provide a comprehensive understanding of the role of each subclass in cortical function by examining their operational regimes based on their cellular properties. The mammalian neocortex is a collection of functionally distinct circuits that are heavily interconnected and operate in parallel. Although each region of the neocortex serves different tasks, they share basic organizational rules, including a six-layer columnar structure and a diversity of cell types that comprise each layer. The main output of cortical columns is localized in layer 5 (L5), which consists of intermingled, non-overlapping populations of pyramidal cells, namely intratelencephalic (IT) and extratelencephalic (ET) neurons. L5 IT neurons project to other cortical areas and the striatum bilaterally. In contrast, ET neurons project to subcortical areas, including the ipsilateral striatum, higher-order thalamic nuclei, superior colliculus (SC), and pons. These projection patterns of IT and ET neurons can be further segregated into additional subcategories . Here, wIT neurons have relatively small cell bodies in the upper half of L5 (L5a), whereas ET neurons with large cell bodies are found primarily in lower L5 (L5b) . One of As the neocortex consists of six layers, each of which receives different inputs from local circuits or distant brain areas, the anatomical and morphological difference between IT and ET neurons already implies differences in inputs that they primarily process. For example, L4 and L5b in the primary somatosensory cortex (S1) receive feed-forward sensory signals from the ventral posteromedial (VPM) thalamus—the primary thalamic nucleus that relays somatosensory information from the peripheries to the cortex. VPM thalamic axons terminate on the basal dendrites of ET neurons, feeding ET neurons with early somatosensory information . In contElectrophysiological properties of IT and ET neurons have been extensively characterized by brain slice experiments . As discRegarding somatic excitability and action potentials (APs), IT neurons have higher input resistance, higher firing thresholds, and higher rheobase than ET neurons . IT neurIn contrast, ET neurons are distinguished by the tendency for their APs to appear in high-frequency bursts—intrinsically bursting . In respIh), measured as a “sag” potential in response to hyperpolarization. ET neurons show a pronounced sag response to hyperpolarizing currents, whereas IT neurons show little sag response (Ih currents are mediated by hyperpolarization-activated cyclic nucleotide-gated non-selective cation (HCN) channels and prevent the membrane potential from being too hyperpolarized. HCN channels are tonically active near the resting membrane potential, and thus act as a leak conductance that impedes the accumulation of synaptic potentials  . bAPs prThe differences between IT and ET neurons include their response to neuromodulatory inputs. Neuromodulatory projections typically extend throughout the neocortex, but most work on the influence of neuromodulators has concentrated on the mPFC circuit due to its involvement in higher-order cognitive functions. We therefore present work mainly from mPFC, if not stated otherwise, but it is important to keep in mind that neuromodulatory effects may differ between brain regions. Here, we focus on dopamine (DA), acetylcholine (ACh), norepinephrine (NE), and serotonin (5-HT) as these neuromodulators have shown to exert distinct effects on IT and ET neurons due to differential expression of each respective receptor subunit and subtypes.Ih current, putative IT neurons, but activation of these receptors only slightly enhanced their excitability , which projects to many areas, including the neocortex , and has1A and 5-HT2A receptors  in activity space (repertoire of possible activity patterns), known as attractor dynamics. Attractor networks have been proposed as a model of cortical learning and memory . The synET neurons receive broadly-tuned sensory information mainly at the basal dendrites, while non-sensory (feedback) information, mostly from IT neurons in distant cortical areas, targets the distal apical dendrites. Due to the electrical distance between the distal dendrites and the soma, feedback inputs alone have little influence on the somatic AP output. Feedback inputs act as a filter that gates coincidental sensory inputs arriving at the soma through activating calcium spikes in the apical dendrites. The spatial and temporal characteristics of the filter depend on the target specificity of the feedback inputs and their duration, respectively. The spatiotemporal characteristics are further shaped by the intrinsic properties of the dendritic membrane. Finally, gated sensory information is boosted into bursts of APs and transmitted to the subcortical regions. It is intriguing that neuromodulators such as ACh and NE, which are elevated in arousal and alert states, promote calcium spikes in ET neurons, thereby enhancing the interaction between feedback and sensory information. As a result, the internal model could edit or highlight specific cortical representations of the sensory environment and output the results to subcortical structures to exert control over subcortical sensory-motor processing.via subcortical-to-cortical ascending pathways. One such pathway is thalamocortical projections via the higher-order thalamus  of the thalamus. A recent study demonstrated that PoM-targeting ET neurons regulate the mouse’s perceptual threshold for detecting tactile stimuli . ImportaThe motor cortical areas also form a strong recurrent loop with higher-order motor thalamic nuclei. In a delayed motor task, the loop between the anterior lateral motor cortex (ALM) and the motor thalamus builds persistent preparatory activity reflecting upcoming behavioral choices . ThalamuThe striatum is a unique structure in the sense that it is innervated by both IT and ET neurons, albeit bilaterally versus unilaterally, respectively. Interestingly, in the striatum, axon terminals of IT neurons are characteristically small (0.4–0.5 μm) compared with those of ET neurons (0.8–0.9 μm) . This main vivo operation of each subtype during behavior (This review has highlighted differences in morphological, anatomical, and physiological properties between IT and ET neurons, as well as in neuromodulatory effects on cellular excitability. Based on these differences, we hypothesized a distinct role for IT and ET neurons in cortical sensory processing: IT neurons generate an internal model, which acts as a spatiotemporal filter that gates the output of a selected population of ET neurons to the subcortical structures. With recent methodological advances, such as genetic targeting, population recording and optogenetics, it has now become possible to investigate their modes of operation in the living brain and even during behavior. As a result, distinct modes of operation between subtypes in sensory and motor processing are becoming evident, each impacting behavior in different ways. However, it remains to be determined how the morphological and physiological characteristics contribute to the behavior . The higIn this study, we reviewed IT and ET neurons in neocortical L5, focusing on rodent studies. Recently, attempts have begun to characterize human L5 neurons physiologically, morphologically and genetically using surgically resected human cortical tissue. These studies have revealed many conserved properties of rodent and human L5 neurons, supporting the translational relevance of the findings in rodents, while some divergent properties have also been identified . For exaBoth authors wrote the manuscript and approved the submitted version.


Clammy are my hands squirming inside latex gloves,Picking at zigzag wrinklesCascading down ill-fitting scrub pants.Under trimmed bangs, my wide eyes peer outThrough safety goggles one exhalation awayFrom a blanket of condensation.My mask—it obscures both my frozen smileAnd the heart in my throat.Every third of an hour in the geriatric clinic,A cleaned room seats a new, frail,Patient in the flesh.Their hands, gripping sheets of written griefs,Refrain from shaking mineAs they ready to recount the reasonsThey dared break strict isolationFor these precious minutesWith not their loved ones,But a tremulous trainee.I should have shriveled from pure unworthinessWhen the dainty lady recessed in the corner chair,With her reading glasses and notebook ready,Student DoctorIgnored the first half of my introduction as And waved off my stammered correction with,“You’re a full doctor to me.”Yet in an instant, my back stood straighterThe light at the end of the tunnel shone brighterThe gut-wrenching, heart-rending,Learning moments of school nights pastSlackened their fierce grip around my lungs,And my throat buried deep the primal screamThat so often punctuates twilight-hour studying.doctorAs the two-syllable melody of Swelled my chestAnd ricocheted in my eardrums,I carefully harbored them within every muscle of my beingFor the longest of nights and days to come.


Fecal microbiota transplantation (FMT) is a promising therapeutic approach for microbiota-associated pathologies, but our understanding of the post-FMT microbiome assembly process and its ecological and clinical determinants is incomplete. Here we perform a comprehensive fecal metagenome analysis of 14 FMT trials, involving five pathologies and >250 individuals, and determine the origins of strains in patients after FMT. Independently of the underlying clinical condition, conspecific coexistence of donor and recipient strains after FMT is uncommon and donor strain engraftment is strongly positively correlated with pre-FMT recipient microbiota dysbiosis. Donor strain engraftment was enhanced through antibiotic pretreatment and bowel lavage and dependent on donor and recipient ɑ-diversity; strains from relatively abundant species were more likely and from predicted oral, oxygen-tolerant, and gram-positive species less likely to engraft. We introduce a general mechanistic framework for post-FMT microbiome assembly in alignment with ecological theory, which can guide development of optimized, more targeted, and personalized FMT therapies.  •Pre-FMT patient preparation has larger impact on strain engraftment than disease•Patient antibiotic pretreatment and lavage increase post-FMT donor strain engraftment•Models indicate leverage for personalized FMT optimization with patient-donor pairing•Engraftment rates not different in responders/non-responders with IBD or melanoma Using strain-level microbiota profiling in patients treated with fecal microbiota transplantation (FMT), Podlesny et al. identify a mechanistic framework for post-FMT microbiome assembly that aligns with ecological theory. Donor strain engraftment is dependent on microbiome and clinical parameters, including antibiotic treatment and bowel lavage, independently of the treated disease. Clostridioides difficile infection (rCDI), with a success rate of ∼90% that surpasses those of conventional antibiotic treatments.,,,Fecal microbiota transplantation (FMT) infuses the microbiota of healthy donor feces into the intestinal tract of a patient in the clinical attempt to treat a microbiota-associated disease.The dynamics of post-FMT microbiota organization and the clinical, host, and ecological factors that govern microbiome assembly in different FMT-treated populations and at the level of individual patients and microbes have not been extensively studied. Previous studies mostly relied on 16S rRNA analysis, which lacks the taxonomic resolution to track specific recipient and donor-derived microbiota members after FMT. This limits our ability to elucidate the ecological principles that govern post-FMT microbiota assembly, as interactions between microbiome members are hypothesized to be most competitive among closely related taxa, including conspecific strains belonging to the same species.,C. difficile infection,Although in many aspects FMT represents an ecosystem restoration approach, ecological framework theories have only recently been applied to understand and predict FMT outcomes.In this study, we characterize post-FMT microbiome assemblies at the strain level in patients who present with a range of disease and treatment backgrounds. We develop generalizable models to predict and control FMT outcomes, determine the relationship between post-FMT microbiota assembly and clinical response, thereby defining the opportunities and limitations of FMT-based precision microbiota therapies and outlining clinical strategies to optimize them.C. difficile infection , the IBDs ulcerative colitis (2 studies) and Crohn disease (one study); metabolic syndrome, type 2 diabetes mellitus, or obesity ; drug-resistant Enterobacteriaceae carriage ; or immune checkpoint inhibitor-refractory melanoma . Post-FMT samples were relatively evenly distributed between patient groups over the first 4 months after FMT (+/−), the FMT application type , as well as the number of FMTs received (which varied between a single and up to 41 applications). All rCDI patients were treated with antibiotics as part of their therapeutic regimens, while each of the ICI, IBD, and MDR groups included at least one study in which antibiotics were used as a bowel cleansing strategy to deplete the resident microbiota and prepare patients for donor microbiota engraftment  B. This aMost strains detected in patients after FMT could be assigned to either the donor or the recipient, or they represented new, i.e., previously undetected, strains or species from environmental or other sources A. The coABx−, IBDABx−, and ICIABx− patients was dominated by recipient strains , followed by recipient-derived strains (12.2% ± 20.1%) A. Howeve strains C. In con for FMT C. The an for FMT D, where ications .Donor strains from the majority of species (62.1% ± 15.4%) did not engraft in patients after FMT . Only 10In summary, donor strain engraftment after FMT appears clinically expandable, including for different medical conditions, using antibiotic microbiota disruption to induce dysbiosis in preparation for FMT, as well as other FMT protocol parameters.In order to gain a conceptual understanding of the factors that determine donor microbiota engraftment after FMT in individual patients, we wanted to determine the effects of both ecological microbiota and clinical FMT parameters on post-FMT patient microbiota assembly. As ecological parameters, recipient and donor microbiota (ɑ/β) diversity, and as clinical parameters, patient pretreatment  and FMT modalities (no. of FMTs) were used as input variables for a generalized linear mixed model (GLMM) to estimate donor-derived strain fractions after FMT per patient  and S5. Of the microbiota variables, donor ɑ-diversity (Shannon index) had the strongest estimated positive effect  on overall donor strain engraftment, whereas recipient ɑ-diversity had an estimated negative effect . Simulations with the GLMM to estimate the specific marginal effects of recipient and donor ɑ-diversity, using alternative, artificial values for the Shannon index, i.e., the highest and lowest value that was detected in any donor from the cohort, indicated that a disproportionately large fraction of strains from high-diversity donors would engraft in low-diversity patients after FMT B. ComposOf the clinical variables, antibiotic pretreatment , bowel lavage , and multiple FMT applications  were all predicted to increase donor strain engraftment based on our GLMM A. Time sIn summary, post-FMT microbiota assembly appears to be driven both by the recipient (ɑ/β-diversity) and donor (ɑ-diversity) microbiota, as well as the FMT procedure, i.e., resident microbiota depletion before FMT (by ABx treatment and lavage) and repeated exposure of the patient to the donor microbiota (multiple FMTs).As the previous model estimated the community-wide (ɑ/β-diversity) determinants of donor strain engraftment for individual patients, we next sought to compare the engraftment probabilities for individual strains from different bacterial taxa in a separate GLMM. This second model outlines the taxonomic and functional boundaries for donor strain engraftment, i.e., whether strains from specific taxa or with taxon-specific properties are more or less likely to engraft after FMT. For this GLMM, we used the same set of variables as described above, but in combination with relative abundance and other species features, such as Gram stain, oral habitat, spore formation, or oxygen tolerance, as estimated using functional databases see . SpeciesBacteroidetes compared with Firmicutes, Proteobacteria, and Actinobacteria (Megamonas (21.7%), Desulfovibrio (16.8%), and Paraprevotella (13.7%) representing the most and Klebsiella, Veillonella, and Haemophilus (<2e-07%) the least likely engrafted genera , spore formation , and oxygen tolerance  were all negatively correlated with engraftment probability  were generally higher for members of the phylum bacteria A. On thed genera B. In gend genera B. Donor-bability . Across In conclusion, the engraftment of specific donor strains appears to be less dependent on microbiome (ɑ/β-diversity) and clinical  parameters, than on associated species properties, such as  lifestyle and (recipient and donor) relative abundance.Firmicutes . Across all simulated recipient/donor pairs, the predicted total number of engrafted donor strains per patient varied in a range of log2(±5)-fold between the worst and best-engrafting donors relative to the actual donor D. DonorsCure rates of ∼90% after FMT have been reported for rCDI in patients after repeated antibiotic treatment failure,The FMT meta-cohort further included a subset of two ICI  and two IBD  studies for which clinical response data were available A. BetweeBetween responders and non-responders from the two analyzed IBD studies, consistent differences regarding donor microbiota engraftment could neither be detected across nor within any of the studies or their subgroups . FurtherFMT shows potential as a therapeutic tool for microbiome-associated pathologies and can be used as an experimental approach to establish causality for alterations of the human gut microbiota that have been associated with disease. However, the relationship between donor microbiota engraftment and FMT outcomes in treated patients has not yet been comprehensively characterized, compared, and correlated with patient parameters, FMT modalities, and clinical outcomes. Moreover, the ecological principles that govern the post-FMT microbiota assembly process are poorly understood. Our analyses identify clinically modifiable factors as targets for FMT outcome optimization and differentiate between two distinct therapeutic goals: (1) to maximize broad, untargeted engraftment of the donor microbiota after FMT with antibiotic patient pretreatment, with likely clinical benefits for the treatment of rCDI; and (2) to increase the engraftment probability of specific donor strains by increasing the relative abundance of the corresponding species in the transplant sample or matching a recipient to an optimal donor. The second goal offers the prospect of developing personalized FMT applications for the treatment of different microbiota-associated pathologies in a framework of precision medicine.,A key finding of our study is that donor strain engraftment after FMT is strongly dependent on recipient microbiota composition and dysbiosis. In rCDI, where dysbiosis is rampant  and linkEcosystems assemble through a combination of deterministic, neutral, and historical processes.in situ evolution or genetic adaptation to the host environment) mechanisms,,Findings from our strain-level analysis provide insights into the ecological principles that shape the post-FMT patient microbiome, as well as the influence of clinical factors on these principles, specifically as they relate to (1) the conspecific coexistence of strains, and (2) the ratio by which donor strains engraft at the expense of recipient strains. In a community that is shaped by deterministic, niche-specific processes, such as the competition between members based on fitness differences, coexistence theory would postulate that closely related taxa with resource niche overlaps, such as conspecific strains, are more likely to compete than to coexist.Although we cannot rule out the possibility that a reduced sensitivity of our bioinformatic analysis toward low-abundant strains is responsible for the decreased detection of strain coexistence, our findings are in agreement with strong niche-related competition and priority effects favoring recipient strains in undisturbed communities. Depletion of the resident microbiota is necessary to remove recipient strains, free ecological niches, and effectively overcome priority effects, resulting in increased donor strain engraftment in rCDI patients or recipients pretreated with antibiotics and bowel cleansing. The ability to overcome priority effects through antibiotic treatment has recently been demonstrated for consecutive strain colonization experiments in gnotobiotic mice.Previous microbiome studies mostly lacked the taxonomic resolution to assess the role of donor strain engraftment for the clinical response to FMT in patients with microbiota-associated immunological or metabolic diseases, such as IBD and ICI. We compared donor strain engraftment in responders and non-responders from four IBD and ICI studies and did not observe differences in donor strain engraftment in ICI and IBD responders and non-responders. Moreover, although donor engraftment rates substantially varied between studies , clinicaBlautia producta strains that inhibit vancomycin-resistant enterococci.Studies are beginning to outline personalized FMT strategies by drawing attention to both recipient and donor microbiome features to predict and optimize clinical FMT outcomes.In summary, our findings characterize the contribution of donor-derived strains to the post-FMT patient microbiota across a diverse set of patient, microbiome, and clinical conditions. They suggest a major impact of adjustable patient pretreatment modalities on donor strain engraftment and present generalizable predictive models for patient and strain-specific donor microbiota engraftment that are in agreement with ecological theory. They further illustrate the theoretical potential for personalized FMT applications through fecal supplementation with select strains and recipient/donor matching to increase the engraftment probability of specific strains. With this work, we lay the groundwork for future developments of precision microbiota modulation therapies.,Most clinical trials that were included in the analysis involved small patient numbers, which were sampled at different and inconsistent time points. Similarly, the control dataset of fecal metagenomes from healthy individuals was significantly larger than the patient or donor datasets from the FMT meta-cohort. Although our models identified strong and distinctive associations, these were partially based on study and patient subsets from the meta-cohort, and the temporal trajectories of the post-FMT assembly process were not characterized in detail. However, the use of a heterogeneous, but extensive, meta-cohort was also instrumental in the development of robust predictive generalized linear mixed models and the identification of what appear to be the universal drivers of post-FMT microbiota assembly. Our predictions for donor strain engraftment in other recipient/donor combinations or with fecal samples supplemented with bacterial cultures should be experimentally tested in follow-up clinical trials, as our retrospective study design did not allow for independent validation of our simulated FMT outcomes. Contributions of non-bacterial microbes, such as fungi and viruses/phages, to the post-FMT microbiome assembly, which may be clinically relevant,w.florian.fricke@uni-hohenheim.de).Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, W. Florian Fricke  and their donors and to apply strain-level microbiota profiling in order to determine donor-derived strain contributions to post-FMT microbiota assembly. This information was used to inform predictive models to describe the post-FMT microbiota assembly process and to determine the role of recipient and donor microbiota features, taxonomic and ecological microbiota parameters, and clinical modalities of patient preparation and FMT for donor strain engraftment.Clostridioides difficile infection (rCDI),,,,,,,Enterobacteriaceae carriage (MDR),,,We assembled a comprehensive meta-cohort of available metagenomics data from clinical FMT trials, including 254 distinct clinical cases in which FMT was used to modulate the microbiota of patients with different medical conditions A. PublisClinical response information was obtained from the original publications and defined as follows: For ICI patients . To remove host contamination, trimmed reads were mapped against the human genome (GRCh37/hg19) with Bowtie2 v2.2.3.,Taxonomic analyses were carried out to provide an overview of sample microbiota compositions and to generate the marker gene alignments that served as input for the strain-level analysis with SameStr. Preprocessed sequence reads from each sample were mapped against the MetaPhlAn clade-specific marker gene database  was incorporated as a fixed effect, using samples from the control cohort as a reference. Only post-FMT patient sample data were included that were collected at least five days after FMT. We controlled for study effects in the control cohort and repeated post-FMT patient sampling by including study and case as random effects. Multicollinearity was assessed by calculating the variance inflation factor with the performance package v0.7.2 , individual donor samples that the authors had combined in batches for FMT were combined. For Li et al. (BOR), which included data from a single donor sampled at three distinct time points without providing information about their specific use for distinct patients, donor samples were combined, as described in the original publication. For Kump et al. (GOR), which included repeated FMT, all available sequence data for each donor were combined across treatment rounds. Ng et al. (CHA) did not disclose sample metadata in the publication, including information about patient/donor pairing and patient assignments to different treatment groups , and this information was not made available upon requests to the authors. For this study, concatenated sequence files of all five donor samples were used and sham cases identified and excluded based on the lack of shared strains with any post-FMT sample see . In caseGeneralized linear mixed-effects models (GLMMs) were used to estimate the effects of recipient and donor microbiota parameters and clinical modalities on the post-FMT microbiota assembly process. Separate GLMMs were used to determine the role of these parameters for donor-derived strain fractions in individual patients after FMT  and of tOverall donor microbiota engraftment  was calc,We expanded our model to estimate the relevance of relative abundances and functional features  on the engraftment probabilities of individual strains in the meta-cohort . Using all species that were detected in a donor and provided only with donor and pre-FMT patient microbiota information, the model predicted whether the corresponding donor-derived strain would be detected in the post-FMT patient or not. Functional features were coded on the species level as +1  and −1 , replacing missing values with each feature’s mean frequency across all species (in order to avoid an influence on the model). Since some donors were used to treat multiple patients, in addition to repeated sampling and study effects, we also controlled for donor-specific influences with random effects in our model. The adjusted engraftment probability of each genus  is shownThe GLMM for the engraftment of individual strains  closely fit the underlying data, both for predicted donor strain engraftment events of individual genera and aggregating total numbers of engrafted strains . Therefore, we used the model to simulate FMT outcomes for other recipient/donor pairs from the meta-cohort. For these simulations, species-level taxonomic compositional profiles of recipient and donor samples, as determined with MetaPhlAn3, were used as input for the GLMM and the predicted total number of engrafted donor strains  were aggregated for each patient and time point and compared to actually detected numbers from the real recipient/donor pairs by calculating (log2) fold-changes D.https://www.github.com/danielpodlesny/SameStr).The code for SameStr, including documentation, examples and notebooks, is available at Github (