MGnify: the microbiome sequence data analysis resource in 2023.

The MGnify platform (https://www.ebi.ac.uk/metagenomics) facilitates the assembly, analysis and archiving of microbiome-derived nucleic acid sequences. The platform provides access to taxonomic assignments and functional annotations for nearly half a million analyses covering metabarcoding, metatranscriptomic, and metagenomic datasets, which are derived from a wide range of different environments. Over the past 3 years, MGnify has not only grown in terms of the number of datasets contained but also increased the breadth of analyses provided, such as the analysis of long-read sequences. The MGnify protein database now exceeds 2.4 billion non-redundant sequences predicted from metagenomic assemblies. This collection is now organised into a relational database making it possible to understand the genomic context of the protein through navigation back to the source assembly and sample metadata, marking a major improvement. To extend beyond the functional annotations already provided in MGnify, we have applied deep learning-based annotation methods. The technology underlying MGnify's Application Programming Interface (API) and website has been upgraded, and we have enabled the ability to perform downstream analysis of the MGnify data through the introduction of a coupled Jupyter Lab environment.

Creating safer cancer care with ethnic minority patients: A qualitative analysis of the experiences of cancer service staff.

INTRODUCTION: Effective consumer engagement practices can enhance patient safety. This is important for consumers from ethnic minority backgrounds who are exposed to increased risk of patient safety events. Using the Systems Engineering Initiative for Patient Safety model, this study explored staff experiences of creating opportunities for engagement with consumers from ethnic minority backgrounds to contribute to their cancer care safety. METHOD: A qualitative study was conducted using semistructured interviews with cancer service staff from four cancer services across two states in Australia. Purposive sampling was used to recruit healthcare staff from a diverse range of professions. Data were analysed using the Framework Analysis method. RESULTS: Fifty-four interviews were conducted with healthcare staff. Analysis of the qualitative interview data identified enablers and associated challenges that contributed to creating a shared understanding between consumers and staff of the information, processes, expectations and problems arising in care. Enablers and challenges are reported in relation to four themes: (1) co-creating safety through shared understanding of care processes; (2) tools and technologies support planned communication; (3) organisational policy levers exist but lack implementation in direct care and (4) formal tasks incorporate consumer engagement more readily than informal interactions. CONCLUSION: The availability of infrastructure and resources to support communication with consumers from ethnic minority backgrounds was limited to specific tasks across the cancer care continuum. Strategies implemented by health services to foster effective communication during formal interactions now require expansion to support and create conditions for effective consumer engagement during informal and everyday care tasks. The use of innovative language support tools and cultural considerations are required at the service and system level to support consumer engagement in all types of care interactions. PUBLIC AND PATIENT INVOLVEMENT: The study was embedded within a larger project that included a consumer investigator and was guided by a consumer advisory group (CAG). These consumer team members have lived experience of cancer and are from diverse ethnic backgrounds. CAG members provided feedback on the draft interview guide and participant information for this study.

Conceptualising factors impacting nutrition services coverage of treatment for acute malnutrition in children: an application of the Three Delays Model in Niger.

OBJECTIVE: The Three Delays Model is a conceptual model traditionally used to understand contributing factors of maternal mortality. It posits that most barriers to health services utilisation occur in relation to one of three delays: (1) Delay 1: delayed decision to seek care; (2) Delay 2: delayed arrival at health facility and (3) Delay 3: delayed provision of adequate care. We applied this model to understand why a community-based management of acute malnutrition (CMAM) services may have low coverage. DESIGN: We conducted a Semi-Quantitative Evaluation of Access and Coverage (SQUEAC) over three phases using mixed methods to estimate programme coverage and barriers to care. In this manuscript, we present findings from fifty-one semi-structured interviews with caregivers and programme staff, as well as seventy-two structured interviews among caregivers only. Recurring themes were organised and interpreted using the Three Delays Model. SETTING: Madaoua, Niger. PARTICIPANTS: Totally, 123 caregivers and CMAM program staff. RESULTS: Overall, eleven barriers to CMAM services were identified in this setting. Five barriers contribute to Delay 1, including lack of knowledge around malnutrition and CMAM services, as well as limited family support, variable screening services and alternative treatment options. High travel costs, far distances, poor roads and competing demands were challenges associated with accessing care (Delay 2). Finally, upon arrival to health facilities, differential caregiver experiences around quality of care contributed to Delay 3. CONCLUSIONS: The Three Delays Model was a useful model to conceptualise the factors associated with CMAM uptake in this context, enabling implementing agencies to address specific barriers through targeted activities.

Dual-axis cubic cavity for drift-compensated multi-wavelength laser stabilisation.

We describe a 'clock control unit' based on a dual-axis cubic cavity (DACC) for the frequency stabilisation of lasers involved in a strontium optical lattice clock. The DACC, which ultimately targets deployment in space applications, provides a short-term stable reference for all auxiliary lasers-i.e. cooling, clear-out, and optical lattice-in a single multi-band cavity. Long-term cavity drift is compensated by a feed-forward scheme exploiting a fixed physical relation to an orthogonal second cavity axis; either by reference to an ultrastable 698 nm clock laser, or by exploiting the differential drift between orthogonal axes extracted by a single laser in common view. Via a change of mirror set in the cavity axis accessed by the clock laser, the system could also provide stabilisation for sub-Hz linewidths at the 698 nm clock wavelength, fulfilling all stabilisation requirements of the clock.

Frontostriatal Brain Activation Is Associated With the Longitudinal Progression of Cardiometabolic Risk.

OBJECTIVE: Cardiometabolic risk refers to a set of interconnected factors of vascular and metabolic origin associated with both cardiovascular disease and various brain disorders. Although midlife cardiometabolic risk is associated with future brain dysfunction, emerging evidence suggests that alterations in autonomic and central nervous system function may precede increases in cardiometabolic risk. METHODS: The present study tested whether patterns of cerebral blood flow in brain areas associated with autonomic regulation were associated with increases in overall cardiometabolic risk. A community sample of 109 adults with resting systolic blood pressure between 120 and 139 mm Hg, diastolic blood pressure between 80 and 89 mm Hg, or both underwent pseudocontinuous arterial spin labeling to quantify cerebral blood flow responses to cognitively challenging tasks. Cardiometabolic risk and cerebral blood flow measurements were collected at baseline and at a 2-year follow-up. RESULTS: Regression analyses showed that greater frontostriatal cerebral blood flow responses to cognitive challenge were associated with higher cardiometabolic risk at follow-up (ß = 0.26 [95% confidence interval = 0.07 to 0.44], t = 2.81, p = .006, ΔR = 0.04). These findings were specific to frontostriatal brain regions, as frontoparietal, insular-subcortical, and total cerebral blood flow were not associated with progression of cardiometabolic risk. Moreover, cardiometabolic risk was not associated with frontostriatal cerebral blood flow responses 2 years later. CONCLUSIONS: Frontostriatal brain function may precede and possibly forecast the progression of cardiometabolic risk.

Enrichment of Nitrogen-Fixing Bacteria in a Nitrogen-Deficient Wastewater Treatment System.

Anthropogenic nitrogen fixation is essential to sustain a global population of 7.7 billion. However, there has been a long-standing desire to find cheaper and more environmentally friendly alternatives to the Haber-Bosch process. In this study, we developed a new strategy of nitrogen fixation by enriching free-living N2-fixing bacteria (NFB) in reactors fed with low nitrogen wastewater, analogous to those usually found in certain industrial effluents such as paper mills. Our reactors fixed appreciable quantities of nitrogen with a rate of 11.8 mg N L-1 day-1. This rate is comparable to recent "breakthrough" nitrogen-fixing technologies and far higher than observed in low C/N reactors (fed with organic matter and nitrogen). NFB were quantified using quantitative polymerase chain reaction (qPCR) of the nifH (marker gene used to identify biological nitrogen fixation) and 16S rRNA genes. The nifH gene was enriched by a factor of 10 in the nitrogen-fixing reactors (compared to controls) attaining 13% of the bacterial population (1:4.2 copies of nifH to 16S rRNA). The Illumina MiSeq 16S rRNA gene amplicon sequencing of reactors showed that the microbial community was dominated (19%) by Clostridium pasteurianum. We envisage that nitrogen-enriched biomass could potentially be used as a biofertilizer and that the treated wastewater could be released to the environment with very little post-treatment.

Active and adaptive case finding to estimate therapeutic program coverage for severe acute malnutrition: a capture-recapture study.

BACKGROUND: Coverage is an important indicator to assess both the performance and effectiveness of public health programs. Recommended methods for coverage estimation for the treatment of severe acute malnutrition (SAM) can involve active and adaptive case finding (AACF), an informant-driven sampling procedure, for the identification of cases. However, as this procedure can yield a non-representative sample, exhaustive or near exhaustive case identification is needed for valid coverage estimation with AACF. Important uncertainty remains as to whether an adequate level of exhaustivity for valid coverage estimation can be ensured by AACF. METHODS: We assessed the sensitivity of AACF and a census method using a capture-recapture design in northwestern Nigeria. Program coverage was estimated for each case finding procedure. RESULTS: The sensitivity of AACF was 69.5% (95% CI: 59.8, 79.2) and 91.9% (95% CI: 85.1, 98.8) with census case finding. Program coverage was estimated to be 40.3% (95% CI 28.6, 52.0) using AACF, compared to 34.9% (95% CI 24.7, 45.2) using the census. Depending on the distribution of coverage among missed cases, AACF sensitivity of at least ≥70% was generally required for coverage estimation to remain within ±10% of the census estimate. CONCLUSION: Given the impact incomplete case finding and low sensitivity can have on coverage estimation in potentially non-representative samples, adequate attention and resources should be committed to ensure exhaustive or near exhaustive case finding. TRIAL REGISTRATION: ClinicalTrials.gov ID NCT03140904. Registered on May 3, 2017.

Estimating program coverage in the treatment of severe acute malnutrition: a comparative analysis of the validity and operational feasibility of two methods.

BACKGROUND: Many health programs can assess coverage using standardized cluster survey methods, but estimating the coverage of nutrition programs presents a special challenge due to low disease prevalence. Used since 2012, the Semi-Quantitative Evaluation of Access and Coverage (SQUEAC) employs both qualitative and quantitative methods to identify key barriers to access and estimate coverage of therapeutic feeding programs. While the tool has been increasingly used in programs, the validity of certain methodological elements has been the subject of debate. METHODS: We conducted a study comparing a SQUEAC conjugate Bayesian analysis to a two-stage cluster survey estimating the coverage of a therapeutic feeding program in Niger in 2016. RESULTS: We found that the coverage estimate from the conjugate Bayesian analysis was sensitive to the prior estimation. With the exception of prior estimates produced by an external support team, all prior estimates resulted in a conflict with the likelihood result, excluding interpretation of the final coverage estimate. Allowing for increased uncertainty around the prior estimate did not materially affect conclusions. CONCLUSION: SQUEAC is a demanding analytical method requiring both qualitative and quantitative data collection and synthesis to identify program barriers and estimate coverage. If the necessary technical capacity is not available to objectively specify an accurate prior for a conjugate Bayesian analysis, alternatives, such as a two-stage cluster survey or a larger likelihood survey, may be considered to ensure valid coverage estimation. TRIAL REGISTRATION: NCT03280082 . Retrospectively registered on September 12, 2017.

Prospective population-based cohort of inflammatory bowel disease in the biologics era: Disease course and predictors of severity.

BACKGROUND AND AIM: We have previously found high incidence of inflammatory bowel disease (IBD) in Australia. A population-based registry was established to assess disease severity, frequency of complications, and prognostic factors. METHODS: Incident cases were prospectively identified over 4 years. Early disease severity was assessed according to need for hospitalization and resective surgery and medication use. RESULTS: We report on the early outcomes (median 18 months, range 12-60 months) for 252 patients comprising 146 with Crohn's disease (CD), 96 with ulcerative colitis (UC), and 10 IBD undifferentiated. Eighty-seven percent of CD patients had inflammatory disease at diagnosis, and this reduced to 73% at 5 years (n = 38). Immunomodulators were prescribed in 57% of CD patients and 19% with UC. A third of all CD patients were hospitalized, the majority (77%) in the first 12 months. Risk factors for hospitalization included penetrating, perianal, and ileocolonic disease (P < 0.05). Twenty-four percent of UC patients were hospitalized, most within the first 12 months. Intestinal resection rates were 13% at 1 year in CD and 26% at 5 years. Risk factors include penetrating and stricturing disease (P < 0.001) and ileal involvement (P < 0.05). Colectomy rates in UC were 2% and 13% at 1 and 5 years. High C-reactive protein (CRP) at diagnosis was associated with colectomy. CONCLUSIONS: A high rate of inflammatory disease, frequent immunomodulator use in CD, and a low rate of surgery in both CD and UC were identified. In CD, ileal involvement and complex disease behavior are associated with a more severe disease course, while in UC a high CRP predicted this outcome.

Who takes risks in high-risk sport?: the role of alexithymia.

People who have difficulty identifying and describing their emotions are more likely to seek out the experience of emotions in the high-risk domain. This is because the high-risk domain provides the experience of more easily identifiable emotions (e.g., fear). However, the continued search for intense emotion may lead such individuals to take further risks within this domain, which, in turn, would lead to a greater likelihood of experiencing accidents. Across three studies, we provide the first evidence in support of this view. In Study 1 (n = 762), alexithymia was associated with greater risk taking and a greater propensity to experience accidents and close calls. In Study 2 (n = 332) and Study 3 (n = 356), additional bootstrapped mediation models confirmed these relationships. The predictive role of alexithymia remained significant when controlling for sensation seeking (Study 1) and anhedonia (Study 2 and Study 3). We discuss the practical implications of the present model as they pertain to minimizing accidents and close calls in the high-risk domain.

The Promise of Explainable AI in Digital Health for Precision Medicine: A Systematic Review.

This review synthesizes the literature on explaining machine-learning models for digital health data in precision medicine. As healthcare increasingly tailors treatments to individual characteristics, the integration of artificial intelligence with digital health data becomes crucial. Leveraging a topic-modeling approach, this paper distills the key themes of 27 journal articles. We included peer-reviewed journal articles written in English, with no time constraints on the search. A Google Scholar search, conducted up to 19 September 2023, yielded 27 journal articles. Through a topic-modeling approach, the identified topics encompassed optimizing patient healthcare through data-driven medicine, predictive modeling with data and algorithms, predicting diseases with deep learning of biomedical data, and machine learning in medicine. This review delves into specific applications of explainable artificial intelligence, emphasizing its role in fostering transparency, accountability, and trust within the healthcare domain. Our review highlights the necessity for further development and validation of explanation methods to advance precision healthcare delivery.

An interpretable machine learning model of cross-sectional U.S. county-level obesity prevalence using explainable artificial intelligence.

BACKGROUND: There is considerable geographic heterogeneity in obesity prevalence across counties in the United States. Machine learning algorithms accurately predict geographic variation in obesity prevalence, but the models are often uninterpretable and viewed as a black-box. OBJECTIVE: The goal of this study is to extract knowledge from machine learning models for county-level variation in obesity prevalence. METHODS: This study shows the application of explainable artificial intelligence methods to machine learning models of cross-sectional obesity prevalence data collected from 3,142 counties in the United States. County-level features from 7 broad categories: health outcomes, health behaviors, clinical care, social and economic factors, physical environment, demographics, and severe housing conditions. Explainable methods applied to random forest prediction models include feature importance, accumulated local effects, global surrogate decision tree, and local interpretable model-agnostic explanations. RESULTS: The results show that machine learning models explained 79% of the variance in obesity prevalence, with physical inactivity, diabetes, and smoking prevalence being the most important factors in predicting obesity prevalence. CONCLUSIONS: Interpretable machine learning models of health behaviors and outcomes provide substantial insight into obesity prevalence variation across counties in the United States.

Discovering Themes in Deep Brain Stimulation Research Using Explainable Artificial Intelligence.

Deep brain stimulation is a treatment that controls symptoms by changing brain activity. The complexity of how to best treat brain dysfunction with deep brain stimulation has spawned research into artificial intelligence approaches. Machine learning is a subset of artificial intelligence that uses computers to learn patterns in data and has many healthcare applications, such as an aid in diagnosis, personalized medicine, and clinical decision support. Yet, how machine learning models make decisions is often opaque. The spirit of explainable artificial intelligence is to use machine learning models that produce interpretable solutions. Here, we use topic modeling to synthesize recent literature on explainable artificial intelligence approaches to extracting domain knowledge from machine learning models relevant to deep brain stimulation. The results show that patient classification (i.e., diagnostic models, precision medicine) is the most common problem in deep brain stimulation studies that employ explainable artificial intelligence. Other topics concern attempts to optimize stimulation strategies and the importance of explainable methods. Overall, this review supports the potential for artificial intelligence to revolutionize deep brain stimulation by personalizing stimulation protocols and adapting stimulation in real time.

Secure key distribution exploiting error rate criticality for radio frequency links.

We propose a method by which two radio frequency (RF) communication terminals exchange encryption keys or other data securely. This method draws on the approach developed for quantum key distribution (QKD) for detecting eavesdroppers but our method does not use any quantum properties at all. Instead, by exploiting the effects an eavesdropper has on channel stability, we explore a line-of-sight link radio in which data transfer rates are so high as to approach the Shannon limit. With very steep rises in bit error rate accompanying a small degradation of signal-to-noise limits for certain forward error correction codes, it becomes possible to infer the existence of an eavesdropper before they are able to obtain a complete key. We describe our method and analyse one possible implementation using low density parity check codes with quadrature phase shift keying modulation. The proposed technique is in principle far easier to implement than quantum-based approaches for RF and optical wireless links since the required hardware is readily available and the basic principles are well known and well understood. Finally, we show our method to have a higher key rate and spectral efficiency than those of QKD.

Diversity and metabolic energy in bacteria.

Why are some groups of bacteria more diverse than others? We hypothesize that the metabolic energy available to a bacterial functional group (a biogeochemical group or 'guild') has a role in such a group's taxonomic diversity. We tested this hypothesis by looking at the metacommunity diversity of functional groups in multiple biomes. We observed a positive correlation between estimates of a functional group's diversity and their metabolic energy yield. Moreover, the slope of that relationship was similar in all biomes. These findings could imply the existence of a universal mechanism controlling the diversity of all functional groups in all biomes in the same way. We consider a variety of possible explanations from the classical (environmental variation) to the 'non-Darwinian' (a drift barrier effect). Unfortunately, these explanations are not mutually exclusive, and a deeper understanding of the ultimate cause(s) of bacterial diversity will require us to determine if and how the key parameters in population genetics (effective population size, mutation rate, and selective gradients) vary between functional groups and with environmental conditions: this is a difficult task.

Digitally deployed, GP remote consultation video intervention that aims to reduce opioid prescribing in primary care: protocol for a mixed-methods evaluation.

INTRODUCTION: Opioid prescribing rates are disproportionately high in the North of England. In addition to patients' complex health needs, clinician prescribing behaviour is also a key driver. Although strategies have been initiated to reduce opioid prescribing nationally, the COVID-19 pandemic has interrupted service provision and created challenges for the system and health professionals to tackle this complex issue. A pilot intervention using smartphone video messaging has been developed to remotely explain the rationale for opioid reduction and facilitate self-initiation of support. The aim of this study is to evaluate the potential benefits, risks and economic consequences of 'at scale' implementation. METHODS AND ANALYSIS: This will be a mixed-methods study comprising a quasi-experimental non-randomised before-and-after study and qualitative interviews. The intervention arm will comprise 50 General Practitioner (GP) Practices using System 1 (a clinical computer system hosting the intervention) who will deliver the video to their patients via text message. The control arm will comprise 50 practices using EMIS (a different computer system) who will continue usual care. Monthly practice level prescribing and consultation data will be observed for 6 months postintervention. A general linear model will be used to estimate the association between the exposure and the main outcome (opioid prescribing; average daily quantity (ADQ)/1000 specific therapeutic group age-sex related prescribing unit). Semi-structured interviews will be undertaken remotely with purposively selected participants including patients who received the video, and health professionals involved in sending out the videos and providing additional support. Interviews will be audio recorded, transcribed and analysed thematically. ETHICS AND DISSEMINATION: Ethics approval has been granted by the NHS Health Research Authority Research Ethics Committee (22/PR/0296). Findings will be disseminated to the participating sites, participants, and commissioners, and in peer-reviewed journals and academic conferences. TRIAL REGISTRATION NUMBER: NCT05276089.

Using Explainable Artificial Intelligence to Discover Interactions in an Ecological Model for Obesity.

Ecological theories suggest that environmental, social, and individual factors interact to cause obesity. Yet, many analytic techniques, such as multilevel modeling, require manual specification of interacting factors, making them inept in their ability to search for interactions. This paper shows evidence that an explainable artificial intelligence approach, commonly employed in genomics research, can address this problem. The method entails using random intersection trees to decode interactions learned by random forest models. Here, this approach is used to extract interactions between features of a multi-level environment from random forest models of waist-to-height ratios using 11,112 participants from the Adolescent Brain Cognitive Development study. This study shows that methods used to discover interactions between genes can also discover interacting features of the environment that impact obesity. This new approach to modeling ecosystems may help shine a spotlight on combinations of environmental features that are important to obesity, as well as other health outcomes.

Diagnostic performance of serum calprotectin in discriminating active from inactive ulcerative colitis in an outpatient setting.

There are limited and conflicting data on the value of serum calprotectin (sCp) in discriminating active from inactive disease activity in ulcerative colitis (UC). Faecal calprotectin (fCp), sCp, serum C-reactive protein (sCRP) and platelets were compared in patients with UC who had clinically active (n = 29) and clinically inactive (n = 42) disease. Serum calprotectin was measured with Bühlmann® (BMN sCp) and ImmunodiagnostikTM (IDK sCp) assays. Median (interquartile range) fCp was higher in active than inactive disease [1004 (466-1922) versus 151 (55-280) µg/g; p < 0.0001). BMN sCp [4534 (3387-6416) versus 4031 (2401-5414) ng/mL; p = 0.1825], IDK sCp [4531 (2920-6433) versus 3307 (2104-4789) ng/mL; p = 0.1065], sCRP [ 4 (2-8) versus 2 (1-4) mg/L; p = 0.0638) and platelets [269 (233-331) versus 280 (227-325) ×10-9/L; p = 0.8055] were similar in active and inactive disease respectively. The area under the receiver operator characteristics curves with 95% confidence limits were 0.85 (0.76-0.94) for fCp, 0.61 (0.47-0.74) for BMN sCp, 0.61 (0.48-0.75) for IDK sCp, 0.69 (0.56-0.81) for sCRP and 0.52 (0.38-0.66) for blood platelets. Faecal calprotectin is the optimum biomarker for discriminating between active and inactive UC. The diagnostic performance of sCp, irrespective of assay, and systemic biomarkers was poor; of these sCRP performed best.

Looking for lipases and lipolytic organisms in low-temperature anaerobic reactors treating domestic wastewater.

Poor lipid degradation limits low-temperature anaerobic treatment of domestic wastewater even when psychrophiles are used. We combined metagenomics and metaproteomics to find lipolytic bacteria and their potential, and actual, cold-adapted extracellular lipases in anaerobic membrane bioreactors treating domestic wastewater at 4 and 15 °C. Of the 40 recovered putative lipolytic metagenome-assembled genomes (MAGs), only three (Chlorobium, Desulfobacter, and Mycolicibacterium) were common and abundant (relative abundance ≥ 1%) in all reactors. Notably, some MAGs that represented aerobic autotrophs contained lipases. Therefore, we hypothesised that the lipases we found are not always associated with exogenous lipid degradation and can have other roles such as polyhydroxyalkanoates (PHA) accumulation/degradation and interference with the outer membranes of other bacteria. Metaproteomics did not provide sufficient proteome coverage for relatively lower abundant proteins such as lipases though the expression of fadL genes, long-chain fatty acid transporters, was confirmed for four genera (Dechloromonas, Azoarcus, Aeromonas and Sulfurimonas), none of which were recovered as putative lipolytic MAGs. Metaproteomics also confirmed the presence of 15 relatively abundant (≥ 1%) genera in all reactors, of which at least 6 can potentially accumulate lipid/polyhydroxyalkanoates. For most putative lipolytic MAGs, there was no statistically significant correlation between the read abundance and reactor conditions such as temperature, phase (biofilm and bulk liquid), and feed type (treated by ultraviolet light or not). Results obtained by metagenomics and metaproteomics did not confirm each other and extracellular lipases and lipolytic bacteria were not easily identifiable in the anaerobic membrane reactors used in this study. Further work is required to identify the true lipid degraders in these systems.

Experimental evaluation of a linear angular momentum multiplexed radio link for moving platforms.

Linear angular momentum multiplexing (LAMM) has recently been proposed for high spectral-efficiency communications between moving platforms, such as between trains and ground infrastructure. We present performance results obtained from a scale experimental system comprising a 2 × 2 antenna system operating at 2.35 GHz. The link transmitted two independent video streams, using RF pre-coding and software-defined radios to modulate and up/down-convert the signals. Linear motion is introduced to demonstrate the translation-invariance of the technique. We interpret the measured data with the aid of an analytical model to show that crosstalk between the two channels is at levels low enough to consistently support the video streams without interruption. Specifically, our results show spectral efficiency is consistently higher when LAMM coding is enabled compared with an uncoded channel.