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Backpropagation neural networks have been used to predict strain resulting from the maneuver in-flight loads in the empennage structure of a Cessna 172P. The purpose of this research was to develop a methodology for the prediction of strain in the tail section of a general aviation aircraft and to determine the minimum set of sensors necessary to adequately train the neural networks. Linear accelerometer, angular accelerometer, rate gyro, and strain gage signals were collected in flight using DAQBook portable data acquisition system for dutch-roll, roll, sideslip left, sideslip right, stabilized g turn left, stabilized g turn right, and push-pull maneuvers at airspeeds of 65 KIAS, 80 KIAS, and 95 KIAS. The sensor signals were filtered and used to train the neural networks. Modular Neural Networks were used to predict the strains. The horizontal tail neural network was trained with CGNz and x-, y-, and z-axis angular accelerometer signals and predicted 93% of all strains to within 50 :, of the measured value. The vertical tail neural network predicted 100% of all strains to within 50 :, of the measured va

Acoustic emission (AE) nondestructive testing can detect fatigue cracks as they occur in complex structures. One use for AE has been in-flight detection of fatigue cracks in aircraft. The KC-135 aircraft were successfully monitored as early as 1979. The main problem with this and subsequent applications was an unfavorable signal to noise ratio, the key being to separate the small amplitude crack signals from the large amplitude ambient noise. This was accomplished here through the use of a Kohonen self-organizing map (SOM) neural network. In order to simulate a fuselage undergoing fatigue, a pressure vessel was constructed from a 0.040 inch thick 2024-T3 aluminum cylinder. The vessel contained a rivet line, a round hole with a notch filed in it to provide a stress concentration, and a repair patch and was cyclically pressurized from 20 to 80 psi in order to fatigue the aluminum and generate typical in-flight signals. During the test, AE sensors, powered by a data acquisition system, collected the AE parameter data from metal rubbing at the patch, rivet fretting at the rivet line, and fatigue crack propagation at the stress concentration. The SOM successfully separated the crack signals from the rivet and rubbing signals. A prototype system is currently being built by Martingale Research Corporation to provide aircraft with a real time in-flight fatigue crack growth monitoring capability.

Acoustic emission nondestructive testing has been used for real-time monitoring of complex structures. All of the structures were made of materials at least 0.070 inch thick. The purpose of this research was to demonstrate the feasibility of using neural networks to classify acoustic emission signals gathered from a pressure vessel made of 2024-T3 aluminum 0.040 inches thick, i.e. thin aluminum sheet. AE waveforms were recorded during fatigue cycling of one pressure vessel using a wide band transducer and a digital oscilloscope connected to a computer. The source for each signal was determined using two narrow band transducers and a LOCAN-AT data acquisition system. The power spectrum was calculated for each waveform. A Kohonen self-organizing map (SOM) was used to cluster the spectra. The network clustered the data on a two-dimensional feature space according to the source of the signal. A total of 3,600 power spectra were used to train the neural network, and 1,800 were used to test the network. Initially there was overlap between the clusters on the two-dimensional feature space; however, this was found to be due to human error. The SOM itself correctly classified all of the signals.

This study used acoustic emission (AE) signal parameters to isolate the failure mechanisms in a 0° unidirectional, fiberglass/epoxy tensile test specimen. Since several failure mechanisms were known to be present, the lack of any distinctly identifiable bands in the original amplitude distribution indicated that there was considerable overlap between the AE signals of the various failure mechanisms. In order to separate the amplitude bands associated with each mechanism, it was necessary to sort on the duration of the AE signal. Two additional plots, counts versus amplitude and hits versus counts, were used to verify that the amplitude distributions were comprised of a single predominant mechanism. A total of seven failure mechanisms were isolated from the original data set, all but two having a shape similar to that of a normal distribution.

Edge-welded metal bellows present an ongoing challenge: the prediction of an accurate cycle life. Current methods rely on physical leak detection to determine a bellow's cycle life to failure. It is known, however, that crack initiation begins many cycles before a leak path is present. Bellows manufacturers require a method for detection of fatigue cracks when they initiate but before they result in leak rates large enough to contaminate a process. Acoustic emission (AE) testing is one method which can meet this need and is a proven, reliable technique for detecting crack initiation and monitoring fatigue crack growth. Four sets of metal bellows samples were fatigue tested and AE parameter data recorded. The data sets were analyzed and the determination made that amplitude, duration, and time of occurrence were the AE data variables required for separation of the various failure mechanisms. For two of the four materials, an expanded set of tests were performed. Fourteen tests were used to train and test a back-propagation neural network for prediction of bellows cycle life. The input data consisted of a material identifier, AE parameter data consisting of the amplitude distribution (50-100 dB) of the first 250 hits, and the final cycle life. The network was structured with an input layer consisting of the identifier and amplitude data, two hidden layers for mapping failure mechanisms, and an output layer for predicting cycle life. The network required training on four samples for the Inconel 718 and five samples for the 350 stainless steel. Once trained the network was able to predict cycle life with a worst case error of-4.45 percent and 2.66 percent for the Inconel 718 and 350 stainless steel, respectively. Finally, through the use of multiple linear regression, a statistical analysis was made to develop a model capable of accurate prediction. Applying a natural log transformation to the independent variables of amplitude and energy resulted in a model capable of explaining 95 percent of the variability in cycle life prediction.

The purpose of this research was to demonstrate the capability of neural networks to discriminate between individual acoustic emission (AE) signals originating from crack growth and rivet rubbing (fretting) in aluminum lap joints. AE waveforms were recorded during tensile fatigue cycling of six notched and riveted 7075-T6 specimens using a broadband piezoelectric transducer and a computer interfaced oscilloscope. The source of 1,311 signals was identified based on triggering logic, amplitude relationships, and time of arrival data collected from the broad-band transducer and three additional 300 Hz resonant transducers bonded to the specimens. The power spectrum of each waveform was calculated and normalized to correct for variable specimen geometry and wave propagation effects. In order to determine the variation between individual signals of the same class, the normalized spectra were clustered onto a two-dimensional feature space using a Kohonen self organizing map (SOM). Then 132 crack growth and 137 rivet rubbing spectra were used to train a back-propagation neural network to provide automatic pattern classification. Although there was some overlap between the clusters mapped in the Kohonen feature space, the trained back-propagation neural network was able to classify the remaining 463 crack growth signals with a 94% accuracy and the 367 rivet rubbing signals with a 99% accuracy.

The acoustic emission (AE) given off by a structure as it is stressed provides a passive means to characterize flaw growth activity in complex structures. This thesis demonstrates how the qualitative analysis of AE data can be refined to provide a quantitative tool for predicting the ultimate strength of composite tensile test specimens. From an original sample set of only six specimens, a multivariate statistical analysis was used to generate an ultimate strength prediction equation. The variables of the multivariate statistical analysis were obtained through the mathematical modeling of the specimen's AE amplitude distributions produced during proof testing. Ultimate strengths were then accurately predicted at proof stresses less than 25% of the expected failure stress for five randomly drawn tensile coupons. The results of this and previously conducted composite pressure vessel research demonstrate the ability to accurately predict ultimate strengths in composite structures using AE amplitude distribution data.

"Embry-Riddle, with financial support from the FAA, has flight-tested a C-172P equipped with sensors to develop an ameliorated prediction of the empennage in-flight gust loads for general aviation aircraft using Neural Networks. Both the power spectral density and the FAA 'two-second' methods have been applied to separate maneuvers and gusts."--Leaf iv.

This work presents a statistical method for predicting the burst pressures in filament wound composite pressure vessels by analyzing acoustic emission data obtained during hydroproof testing. First the acoustic emission data is plotted in the form of amplitude distributions. Then, by using statistical models with characteristics similar to Rayleigh and Gaussian distributions, failure mechanism percentages are found, at which point multiple regression, analysis is used to predict burst pressures. Next, the predicted burst pressures are compared to the actual burst pressures and the significance of the data is analyzed using standard statistical theories. The research shows that the predicted burst pressures are within + 1% of the actual burst pressures. However, future experiments are required to clarify whether or not the Rayleigh and Gaussian models provide the best fit to the amplitude distributions. Finally, the problem of locating and isolating the acoustic emission sensors to remove extraneous noise must be studied.

The research encompassed within this paper deals with the analysis and classification of fatigue cracks in aircraft structures. The particular structure that was examined was the vertical tail section of a Cessna T-303 Crusader aircraft. The analysis was performed using the nondestructive evaluation technique known as acoustic emission (AE), as well as the artificial intelligence of neural networks. Data were taken in a controlled laboratory environment as well as in a flying testbed aboard the aircraft. The first part of the research involved the analysis of a typical aircraft structure in a controlled laboratory environment. This support structure was fabricated from 7075-T6 aluminum, which is common in aircraft structures. Two different methods were used to fatigue the support, an MTS tensile test machine and a shaker table. Extensive AE data were taken throughout the laboratory tests in order to provide a known reference for the identification of fatigue cracks. The acoustic emission data derived from the laboratory tests were thoroughly examined and sorted into three distinct mechanisms: fatigue cracking, plastic deformation, and mechanical noise. The AE parameters associated with these mechanisms were in turn used to train a neural network. The neural network used was the Kohonen self-organizing map, as it is an excellent choice for the purpose of classification. Once the neural network was trained, it was possible to proceed to the second stage of the research. A support structure, identical to the one used in the laboratory tests, was installed in the vertical tail of the T-303 aircraft. Acoustic emission data were gathered during all aspects of aircraft maneuvers, from the initial taxiing and takeoff to the final approach and landing, including rolls and Dutch rolls. The AE parameters recorded from the in-flight tests were processed using the neural network trained in the first part of the research. Thus, the data were classified as being indicative of fatigue cracking, plastic deformation, or rubbing. These mechanisms were then analyzed with respect to the particular maneuver performed to further understand the stresses associated with different maneuvers. As a result of the ability to classify fatigue cracks, it is possible to develop a monitoring system for aircraft to determine the existence of fatigue cracks before they grow to the point where they become dangerous.

The purpose of analyzing annunciator panels, warning, and indicator lamps is to determine the state ("on" or "off") of each light bulb immediately before the impact. Determination of the state of lamps may help investigators determine the probable cause of the accident. The information gained from the lamp's filaments may be used to infer the aircraft's operational parameters prior to impact and to a qualitative appreciation of the severity of the accident. It is generally agreed that upon impact the filament of an unlit bulb will fracture without deformation and the filament of a lit bulb will display plastic deformation (Ellis, 1984). The main purpose of this study was to investigate the effects of current disruption as a result of initial impact forces upon light bulb filament behavior. One hundred and twenty commercially available T-1 five volt and T-1 twenty-eight volt aircraft light bulbs were subjected to inertial impact deceleration forces up to and including 90 times the force of gravity to investigate the effects of current disruption during the impact sequence. The age of the light bulbs ranged of from 50 to 1,600 hours. An air-cannon was used to accelerate the lamps along its horizontal barrel with impact occurring at a hydro-brake located 13 feet from the beginning of the barrel. The hydro-brake produced an inertial deceleration which neither broke the glass envelope nor destroyed the filament. Severance of power was accomplished through the use of a microswitch and a solid state relay. It was hypothesized that there would be a noticeable combination of ductile and brittle deformation characteristics in the filaments as the result of these deceleration forces. The filament analysis of the #6839 lamps displayed brittle fractures along with a combination of plastic deformations. The #6839 double helix filament displayed stretching, uncoiling, entanglement, and general deformation of its secondary coil with localized areas of stretching and general deformation of its primary coil. The #718 lamps exhibited plastic deformation characteristics typical of a filament at its brittle or "off" state. The #718 lamps that were aged 1,600 hours displayed plastic deformations typical of a lamp that was illuminated or "on" prior to impact. The filaments exhibited coil stretching and general deformation. The effects of aging had an important influence upon the behavior of the filaments in both lamp types. The on-set rate of notching depending upon the rated life of the lamp, the filament size, and rated current. Lamps with high rated life rates and low current requirements experienced a slower on-set of notching. Transient indications of the #6839 lamp included a combination of stretching, local, resonance, slight, uncoiling, and general deformations accompanied by brittle fractures. The transient indications of the #718 lamps, aged 200 hours and older, included slight, local, stretching, and general deformations. Aging effects have a major influence upon the deformation behavior of the filament. The lamp's rated life, operating voltage, and filament diameter control the degree and onset of the notching effect which affects the deformation behavior. Based on the results and conclusions obtained from this research, the following recommendations are suggested: (a) the development damage boundary curves for the T-1 series of lamps, (b) the investigation of resonance deformation in the T-1 lamp, and (c) additional testing of the T-1 lamp to better understand the relationship between the onset of notching and the lamp's rated life.

This research investigates the feasibility of implementing an in-flight fatigue crack monitoring system in an airplane to identify fatigue crack growth. An acoustic emission data acquisition system coupled with a Kohonen self organizing map neural network were used to perform the analysis. Fatigue cracking was responsible for ripping the top of a fuselage off an Aloha Airline’s Boeing 737-200 as it carried passengers over the Pacific Ocean, killing some aboard. This tragedy is perhaps a precursor of problems to come, as our nation’s aircraft age. These planes experience fatigue as they perform their daily routine of ferrying passengers from location to location. Fatigue can initiate cracking within the aircraft’s structure and at least damage a small expendable part of the plane, or at most damage a vital part of the airplane leading to disaster as happened to the Aloha Airline’s flight. In an attempt to curb this sort of devastation, this research involves the development of an in-flight fatigue crack monitoring system. Such a system would have the ability to identify possible crack sources before the crack would have the chance to cause significant damage. Advantages of this type of system would be first, an obvious safety cushion, and second, lower maintenance costs because routine parts replacement and inspection could be minimized.

The purpose of this investigation was to determine the effects of biological fouling on hydrofoils. NACA 4412 and 4415 hydrofoil models with 6 inch chord and 8 inch span were used in this study. A water tunnel facility was designed and constructed to perform the tests, and the results were verified using computational fluid dynamic modeling. The CFD software package Fluent was used to create a two dimensional model of the water tunnel test section and hydrofoil. The model was meshed using a Laplacian grid smoothing technique that forced the structured grid lines to follow the same approximate contour as the stream lines, allowing for quicker convergence of the solution matrix and a lower degree of rounding error than normally associated with this type of modeling. The fouling was added to the model by introducing a porous medium on the surface of the hydrofoil to simulate the characteristics of biological fouling. The results showed a constant increase in zero-lift angle of attack by 10 degrees for as little as 10 percent fouling, and a reduction in maximum lift by as much as 80 percent.

To understand the experiences of emergency nurses who have returned to work after parental leave, specifically relating to the return to work transition, work-life balance, work engagement, and opportunities to continue human milk expression.Nurses (N = 19) were recruited from 5 emergency departments within 1 hospital system in the United States Midwest. Nurses (n = 11) were eligible to participate in a one-on-one interview if they had returned from parental leave within 6 months of the interview date. Nurses (n = 8) were eligible to participate in a focus group if they had returned from parental leave within 2 years of the interview date. Interviews were structured and data collection concluded when researchers believed data saturation was reached. Interviews were audio recorded and transcribed verbatim. Data were analyzed using Braun and Clarke's qualitative thematic analysis 6-phase framework.Three major themes from the data were identified: (1) work engagement, (2) lactation, and (3) childcare. Work engagement was broken down into the subthemes: lack of communication, perceived engagement expectations, and actual engagement. Lactation was broken down into the subthemes: the act of pumping, lactation breaks, and lactation rooms. The coronavirus disease 2019 pandemic impact on return-to-work is described under each major theme.Our findings provide insight into the unique challenges and experiences of nurses navigating parental leave and return-to-work in the emergency department. Strategies such as provision of managerial check-ins, return to work reorientation, lactation break coverage, enhanced supplementary lactation support, and leadership-provided accommodation may lighten the burden of these challenges and improve the returning nurse's job satisfaction.

Objectives Time-resolved, 2D-phase-contrast MRI (2D-CINE-PC-MRI) enables in vivo blood flow analysis. However, accurate vessel contour delineation (VCD) is required to achieve reliable results. We sought to evaluate manual analysis (MA) compared to the performance of a deep learning (DL) application for fully-automated VCD and flow quantification and corrected semi-automated analysis (corSAA). Methods We included 97 consecutive patients (age = 52.9 ± 16 years, 41 female) with 2D-CINE-PC-MRI imaging on 1.5T MRI systems at sinotubular junction (STJ), and 28/97 also received 2D-CINE-PC at main pulmonary artery (PA). A cardiovascular radiologist performed MA (reference) and corSAA (built-in tool) in commercial software for all cardiac time frames (median: 20, total contours per analysis: 2358 STJ, 680 PA). DL-analysis automatically performed VCD, followed by net flow (NF) and peak velocity (PV) quantification. Contours were compared using Dice similarity coefficients (DSC). Discrepant cases (> ± 10 mL or > ± 10 cm/s) were reviewed in detail. Results DL was successfully applied to 97% (121/125) of the 2D-CINE-PC-MRI series (STJ: 95/97, 98%, PA: 26/28, 93%). Compared to MA, mean DSC were 0.91 ± 0.02 (DL), 0.94 ± 0.02 (corSAA) at STJ, and 0.85 ± 0.08 (DL), 0.93 ± 0.02 (corSAA) at PA; this indicated good to excellent DL-performance. Flow quantification revealed similar NF at STJ (p = 0.48) and PA (p > 0.05) between methods while PV assessment was significantly different (STJ: p < 0.001, PA: p = 0.04). A detailed review showed noisy voxels in MA and corSAA impacted PV results. Overall, DL analysis compared to human assessments was accurate in 113/121 (93.4%) cases. Conclusions Fully-automated DL-analysis of 2D-CINE-PC-MRI provided flow quantification at STJ and PA at expert level in > 93% of cases with results being available instantaneously. Key Points • Deep learning performed flow quantification on clinical 2D-CINE-PC series at the sinotubular junction and pulmonary artery at the expert level in > 93% of cases. • Location detection and contouring of the vessel boundaries were performed fully-automatic with results being available instantaneously compared to human assessments which approximately takes three minutes per location. • The evaluated tool indicates usability in daily practice.

The objective of this study was to compare the rate of groin recurrence among women undergoing superficial or deep inguinal lymph node dissections in suspected early-stage vulvar carcinoma. Secondary objectives included comparison of overall survival and post-operative morbidity between the study groups.A retrospective cohort of 233 patients with squamous cell carcinoma (SCC) of the vulva who underwent an inguinal lymph node dissection at two major academic institutions from 1999 to 2017 were analyzed. Demographic, surgical, recurrence, survival, and post-operative morbidity data were collected for 233 patients, resulting in a total of 400 groin node dissections analyzed.Rates of overall primary recurrence of disease between superficial and deep inguinal LND (42.5 vs. 39.8%, p = 0.70) and rates of inguinal recurrence (3.4 vs. 8.3%, p = 0.16) were similar. Overall rates of postoperative morbidity were significantly higher in the cohort undergoing deep LND (70.3% vs 44.3%, p0.01). Rates of lymphedema (42.4 vs 15.9%, p0.01), readmission (26.3 vs 6.8%, p0.01), and infection (40.7 vs 14.8%, p0.01) were all significantly higher among patients undergoing deep LND. There was no significant difference noted in overall survival between the study groups when adjusting for stage and age (HR 1.08, p = 0.84).Superficial inguinal LND had no significant difference in rate of recurrence or overall survival when compared to deep inguinal LND in patients with vulvar SCC. Those who received a deep LND had a significant increase in overall morbidity, including lymphedema, readmission, and infection. For patients who cannot undergo or fail sentinel lymph node mapping, a superficial inguinal lymph node dissection may have similar outcomes in recurrence and overall survival with a reduction in overall morbidity as compared to a complete, or deep, lymph node dissection.

PURPOSE Metastatic breast cancer (mBrCa) is most often an incurable disease with only modest responses to available immunotherapies. This study investigates the immunogenicity of somatic mutations in breast cancer and explores the therapeutic efficacy in a pilot trial of mutation-reactive tumor-infiltrating lymphocytes (TILs) in patients with metastatic disease. PATIENTS AND METHODS Forty-two patients with mBrCa refractory to previous lines of treatment underwent surgical resection of a metastatic lesion(s), isolation of TIL cultures, identification of exomic nonsynonymous tumor mutations, and immunologic screening for neoantigen reactivity. Clinically eligible patients with appropriate reactivity were enrolled into one cohort of an ongoing phase II pilot trial of adoptive cell transfer of selected neoantigen-reactive TIL, with a short course of pembrolizumab (ClinicalTrials.gov identifier: NCT01174121 ). RESULTS TILs were isolated and grown in culture from the resected lesions of all 42 patients with mBrCa, and a median number of 112 (range: 6-563) nonsynonymous mutations per patient were identified. Twenty-eight of 42 (67%) patients contained TIL that recognized at least one immunogenic somatic mutation (median: 3 neoantigens per patient, range: 1-11), and 13 patients demonstrated robust reactivity appropriate for adoptive transfer. Eight patients remained clinically eligible for treatment, and six patients were enrolled on a protocol of adoptive cell transfer of enriched neoantigen-specific TIL, in combination with pembrolizumab (≤ 4 doses). Objective tumor regression was noted in three patients, including one complete response (now ongoing over 5.5 years) and two partial responses (6 and 10 months). CONCLUSION Most patients with breast cancer generated a natural immune response targeting the expressed products of their cancer mutations. Adoptive transfer of TIL is a highly personalized experimental option for patients with mBrCa shown to be capable of mediating objective responses in this pilot trial and deserves further study.

Supplementary Table 1. Treatment discontinuation due to AE. Supplementary Table 2. Worst grade toxicity related to pembrolizumab in >=5% of patients. Supplementary Table 3. PDL1 staining by objective responder status. Supplementary Figure S1. CONSORT diagram illustrates distribution of patients after enrollment. Supplementary Figure S2. Gating strategies for the main immune populations in PBMC. Supplementary Figure S3. The FlowSOM tree for the PBMC collected at C1D1 showing unsupervised metaclustering.

Purpose:The objective of this study was to assess the efficacy and safety of pembrolizumab in combination with standard carboplatin/paclitaxel in patients with advanced endometrial cancer.Patients and Methods:This single-arm, open-label, multicenter phase II study enrolled patients with RECIST measurable advanced endometrial cancer. Patients could have received ≤ 1 prior platinum-based regimen and ≤ one non-platinum chemotherapy. The primary endpoint was objective response rate (ORR). Planned sample size of 46 subjects provided 80% power to detect 15% ORR improvement compared with historical control rate of 50%.Results:A total of 46 patients were enrolled and 43 were evaluable for ORR. Median age was 66 (range: 43–86). Thirty-four (73.9%) patients had recurrent and 12 (26.1%) primary metastatic endometrial cancer. Patients received carboplatin AUC6, paclitaxel 175 mg/m2, and pembrolizumab 200 mg i.v. every 3 weeks for up to six cycles. ORR was 74.4% (32/43), higher than historic controls (P = 0.001). Median progression-free survival (PFS) was 10.6 months (95% confidence interval, 8.3–13.9 months). The most common grade 1–2 treatment-related adverse event (TRAE) included anemia (56.5%), alopecia (47.8%), fatigue (47.8%), and neuropathy (13%), while the most common grade 3–4 TRAEs were lymphopenia, leukopenia, and anemia (19.6% each). High-dimensional spectral flow cytometry (CyTEK) identified enrichment in peripheral CD8+ and CD4+ T-cell populations at baseline in responders. The CD8+ T-cell compartment in responders exhibited greater expression levels of PD-1 and PD-L1 and higher abundance of effector memory CD8+ cells compared with nonresponders.Conclusions:Addition of pembrolizumab to carboplatin and paclitaxel for advanced endometrial cancer was tolerated and improved ORR compared with historical outcomes.Significance:The results of the study support that the combination of pembrolizumab with carboplatin and paclitaxel is well tolerated and active in patients with advanced endometrial cancer. The duration of response and the PFS were significantly longer in patients with mismatch repair deficient/microsatellite instability-high compared with mismatch repair proficient/microsatellite stable tumors. Responders to treatment tend to have enriched CD8+ T-cell and CD4+ T-cell populations among peripheral blood mononuclear cells at baseline.

Supplementary Figure S1. Dose-response curves of combinations of orlistat or NanoOrl with taxanes; Supplementary Figure S2. Synergy analysis with Combenefit (Loewe model);Supplementary Figure S3. Synergy analysis with Combenefit [Highest Single Agent (HSA) model];Supplementary Figure. S4. Morphological changes associated with combination treatment;Supplementary Figure S5. Combination of NanoOrl plus docetaxel stabilizes microtubules in taxane-resistant cells;Supplementary Fig. S6. Example of overestimation of synergy; Supplementary Fig. S7. Example of underestimation of synergy.

The casual justification of the influence of a technology, particularly artificial intelligence, by appeal to the existence of the technology constitutes an artificialistic fallacy, analogous to the naturalistic fallacy that is well-known in philosophy. Similar to an invocation of nature to provide moral warrant (the naturalistic fallacy), modern tech evangelists invoke the burgeoning of hardware and software products in order to promote that burgeoning (the artificialistic fallacy). This fallacy is often tacit or committed by omission. Emerging ethical initiatives emphasize the refinement, explanation, and oversight of AI products rather than their fundamental ethical effect, making the fallacy recursive.

Urban litterfall that is deposited on impervious surface leaches nutrients into stormwater, contributing to downstream eutrophication. Previous studies have focused on the leaching potential of deciduous leaf litter, while other smaller-volume litterfall types-such as blossoms and fruit-may leach significant amounts of nitrogen, phosphorus, and carbon. These additional litterfall types represent an unaccounted-for source of nutrients to urban stormwater. We explored variation in leaching potential of dissolved nutrients and organic carbon across litter types and species by collecting litterfall (blossoms, fruit, leaves) from ten common urban tree species. After 24 h of leaching, we measured total phosphorus (TP), total dissolved nitrogen (TDN), and dissolved organic carbon (DOC) contributions and compared differences across litter types and species. Litter basket estimates then allowed us to quantify annual litterfall inputs. We found that blossoms leached 3-20 times more TDN and 1.5-7 times more TP than leaves of the same species. Furthermore, considering litterfall mass, several species had greater springtime nutrient-leaching potential compared to fall due to high leaching potential in blossoms and lower potential in leaves. We found mixed effects of leaf crushing and leachate solution (stormwater, salinity) on leaching rates. This study highlights the need to consider all litterfall types as well as variation in urban forest communities and conditions when seeking to budget, control, and maintain for potential nutrient sources from the urban forest.The online version contains supplementary material available at 10.1007/s11252-022-01217-8.

Conventional ecological risk assessment (ERA) predominately evaluates the impact of individual chemical stressors on a limited range of taxa, which are assumed to act as proxies to predict impacts on freshwater ecosystem function. However, it is recognized that this approach has limited ecological relevance. We reviewed the published literature to identify measures that are potential functional indicators of down-the-drain chemical stress, as an approach to building more ecological relevance into ERA. We found wide variation in the use of the term "ecosystem function," and concluded it is important to distinguish between measures of processes and measures of the capacity for processes (i.e., species' functional traits). Here, we present a classification of potential functional indicators and suggest that including indicators more directly connected with processes will improve the detection of impacts on ecosystem functioning. The rate of leaf litter breakdown, oxygen production, carbon dioxide consumption, and biomass production have great potential to be used as functional indicators. However, the limited supporting evidence means that further study is needed before these measures can be fully implemented and interpreted within an ERA and regulatory context. Sensitivity to chemical stress is likely to vary among functional indicators depending on the stressor and ecosystem context. Therefore, we recommend that ERA incorporates a variety of indicators relevant to each aspect of the function of interest, such as a direct measure of a process (e.g., rate of leaf litter breakdown) and a capacity for a process (e.g., functional composition of macroinvertebrates), alongside structural indicators (e.g., taxonomic diversity of macroinvertebrates). Overall, we believe that the consideration of functional indicators can add value to ERA by providing greater ecological relevance, particularly in relation to indirect effects, functional compensation (Box 1), interactions of multiple stressors, and the importance of ecosystem context. Environ Assess Manag 2022;18:1135-1147. © 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental ToxicologyChemistry (SETAC).

The molecular events and transcriptional plasticity driving brain metastasis in clinically relevant breast tumor subtypes has not been determined. Here we comprehensively dissect genomic, transcriptomic and clinical data in patient-matched longitudinal tumor samples, and unravel distinct transcriptional programs enriched in brain metastasis. We report on subtype specific hub genes and functional processes, central to disease-affected networks in brain metastasis. Importantly, in luminal brain metastases we identify homologous recombination deficiency operative in transcriptomic and genomic data with recurrent breast mutational signatures A, F and K, associated with mismatch repair defects, TP53 mutations and homologous recombination deficiency (HRD) respectively. Utilizing PARP inhibition in patient-derived brain metastatic tumor explants we functionally validate HRD as a key vulnerability. Here, we demonstrate a functionally relevant HRD evident at genomic and transcriptomic levels pointing to genomic instability in breast cancer brain metastasis which is of potential translational significance., The molecular landscape of breast cancer brain metastases (BCBM) is still understudied, especially for different breast cancer subtypes. Here, the authors characterise subtype-specific BCBMs using genomics and transcriptomics and identify homologous recombination deficiency as a key therapeutic vulnerability.

This protein–iron oxide hybrid biomaterial aims to integrate the drug encapsulating potential of a coiled-coil protein with peptide-driven iron oxide biomineralization to serve as drug-carrying, MRI-detectable mesofiber.

Supplementary Tables 1-6 from Genome-Wide DNA Methylation Profiling of CpG Islands in Breast Cancer Identifies Novel Genes Associated with Tumorigenicity