A Decade of Discovery-Eukaryotic Replisome Disassembly at Replication Termination.

The eukaryotic replicative helicase (CMG complex) is assembled during DNA replication initiation in a highly regulated manner, which is described in depth by other manuscripts in this Issue. During DNA replication, the replicative helicase moves through the chromatin, unwinding DNA and facilitating nascent DNA synthesis by polymerases. Once the duplication of a replicon is complete, the CMG helicase and the remaining components of the replisome need to be removed from the chromatin. Research carried out over the last ten years has produced a breakthrough in our understanding, revealing that replication termination, and more specifically replisome disassembly, is indeed a highly regulated process. This review brings together our current understanding of these processes and highlights elements of the mechanism that are conserved or have undergone divergence throughout evolution. Finally, we discuss events beyond the classic termination of DNA replication in S-phase and go over the known mechanisms of replicative helicase removal from chromatin in these particular situations.

Adaptive thresholding increases sensitivity to detect changes in the rate of skin conductance responses to psychologically arousing stimuli in both laboratory and ambulatory settings.

Psychophysiologists recording electrodermal activity (EDA) often derive measures of slow, tonic activity-skin conductance level (SCL)-and faster, more punctate changes-skin conductance responses (SCRs). A SCR is conventionally considered to have occurred when the local amplitude of the EDA signal exceeds a researcher-determined threshold (e.g., 0.05 µS), typically fixed across study participants and conditions. However, fixed SCR thresholds can preferentially exclude data from individuals with low SCL because their SCRs are smaller on average, thereby reducing statistical power for group-level analyses. Thus, we developed a fixed plus adaptive (FA) thresholding method that adjusts identification of SCRs based on an individual's SC at the onset of the SCR to increase statistical power and include data from more participants. We assess the utility of applying FA thresholding across two independent samples and explore age and race-related associations with EDA outcomes. Study 1 uses wired EDA measurements from 254 healthy adults responding to evocative images and sounds in a laboratory setting. Study 2 uses wireless EDA measurements from 20 children with autism in a clinical environment while they completed behavioral tasks. Compared to a 0.01, 0.03, and 0.05 µS fixed threshold, FA thresholding at 1.9% modestly increases statistical power to detect a difference in SCR rate between tasks with higher vs. lower subjective arousal and reduces exclusion of participants by up to 5% across both samples. This novel method expands the EDA analytical toolbox and may be useful in populations with highly variable basal SCL or when comparing groups with different basal SCL. Future research should test for reproducibility and generalizability in other tasks, samples, and contexts. IMPACT STATEMENTS: This article is important because it introduces a novel method to enhance sensitivity and statistical power in analyses of skin conductance responses from electrodermal data.

TRAIP resolves DNA replication-transcription conflicts during the S-phase of unperturbed cells.

Cell division is the basis for the propagation of life and requires accurate duplication of all genetic information. DNA damage created during replication (replication stress) is a major cause of cancer, premature aging and a spectrum of other human disorders. Over the years, TRAIP E3 ubiquitin ligase has been shown to play a role in various cellular processes that govern genome integrity and faultless segregation. TRAIP is essential for cell viability, and mutations in TRAIP ubiquitin ligase activity lead to primordial dwarfism in patients. Here, we have determined the mechanism of inhibition of cell proliferation in TRAIP-depleted cells. We have taken advantage of the auxin induced degron system to rapidly degrade TRAIP within cells and to dissect the importance of various functions of TRAIP in different stages of the cell cycle. We conclude that upon rapid TRAIP degradation, specifically in S-phase, cells cease to proliferate, arrest in G2 stage of the cell cycle and undergo senescence. Our findings reveal that TRAIP works in S-phase to prevent DNA damage at transcription start sites, caused by replication-transcription conflicts.

Deep Learning Assistance Closes the Accuracy Gap in Fracture Detection Across Clinician Types.

BACKGROUND: Missed fractures are the most common diagnostic errors in musculoskeletal imaging and can result in treatment delays and preventable morbidity. Deep learning, a subfield of artificial intelligence, can be used to accurately detect fractures by training algorithms to emulate the judgments of expert clinicians. Deep learning systems that detect fractures are often limited to specific anatomic regions and require regulatory approval to be used in practice. Once these hurdles are overcome, deep learning systems have the potential to improve clinician diagnostic accuracy and patient care. QUESTIONS/PURPOSES: This study aimed to evaluate whether a Food and Drug Administration-cleared deep learning system that identifies fractures in adult musculoskeletal radiographs would improve diagnostic accuracy for fracture detection across different types of clinicians. Specifically, this study asked: (1) What are the trends in musculoskeletal radiograph interpretation by different clinician types in the publicly available Medicare claims data? (2) Does the deep learning system improve clinician accuracy in diagnosing fractures on radiographs and, if so, is there a greater benefit for clinicians with limited training in musculoskeletal imaging? METHODS: We used the publicly available Medicare Part B Physician/Supplier Procedure Summary data provided by the Centers for Medicare & Medicaid Services to determine the trends in musculoskeletal radiograph interpretation by clinician type. In addition, we conducted a multiple-reader, multiple-case study to assess whether clinician accuracy in diagnosing fractures on radiographs was superior when aided by the deep learning system compared with when unaided. Twenty-four clinicians (radiologists, orthopaedic surgeons, physician assistants, primary care physicians, and emergency medicine physicians) with a median (range) of 16 years (2 to 37) of experience postresidency each assessed 175 unique musculoskeletal radiographic cases under aided and unaided conditions (4200 total case-physician pairs per condition). These cases were comprised of radiographs from 12 different anatomic regions (ankle, clavicle, elbow, femur, forearm, hip, humerus, knee, pelvis, shoulder, tibia and fibula, and wrist) and were randomly selected from 12 hospitals and healthcare centers. The gold standard for fracture diagnosis was the majority opinion of three US board-certified orthopaedic surgeons or radiologists who independently interpreted the case. The clinicians' diagnostic accuracy was determined by the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, sensitivity, and specificity. Secondary analyses evaluated the fracture miss rate (1-sensitivity) by clinicians with and without extensive training in musculoskeletal imaging. RESULTS: Medicare claims data revealed that physician assistants showed the greatest increase in interpretation of musculoskeletal radiographs within the analyzed time period (2012 to 2018), although clinicians with extensive training in imaging (radiologists and orthopaedic surgeons) still interpreted the majority of the musculoskeletal radiographs. Clinicians aided by the deep learning system had higher accuracy diagnosing fractures in radiographs compared with when unaided (unaided AUC: 0.90 [95% CI 0.89 to 0.92]; aided AUC: 0.94 [95% CI 0.93 to 0.95]; difference in least square mean per the Dorfman, Berbaum, Metz model AUC: 0.04 [95% CI 0.01 to 0.07]; p < 0.01). Clinician sensitivity increased when aided compared with when unaided (aided: 90% [95% CI 88% to 92%]; unaided: 82% [95% CI 79% to 84%]), and specificity increased when aided compared with when unaided (aided: 92% [95% CI 91% to 93%]; unaided: 89% [95% CI 88% to 90%]). Clinicians with limited training in musculoskeletal imaging missed a higher percentage of fractures when unaided compared with radiologists (miss rate for clinicians with limited imaging training: 20% [95% CI 17% to 24%]; miss rate for radiologists: 14% [95% CI 9% to 19%]). However, when assisted by the deep learning system, clinicians with limited training in musculoskeletal imaging reduced their fracture miss rate, resulting in a similar miss rate to radiologists (miss rate for clinicians with limited imaging training: 9% [95% CI 7% to 12%]; miss rate for radiologists: 10% [95% CI 6% to 15%]). CONCLUSION: Clinicians were more accurate at diagnosing fractures when aided by the deep learning system, particularly those clinicians with limited training in musculoskeletal image interpretation. Reducing the number of missed fractures may allow for improved patient care and increased patient mobility. LEVEL OF EVIDENCE: Level III, diagnostic study.

Reevaluation of missed lung cancer with artificial intelligence.

Lung cancer is often missed on chest radiographs, despite chest radiography typically being the first imaging modality in the diagnosis pathway. We present a 46 year-old male with chest pain referred for chest X-ray, and initial interpretation reported no abnormality within the patient's lungs. The patient was discharged but returned 4 months later with persistent and worsening symptoms. At this time, chest X-ray was again performed and revealed an enlarging left perihilar mass with post-obstructive atelectasis in the left lower lobe. Follow-up chest computerized tomography scan confirmed lung cancer with post-obstructive atelectasis, and subsequent bronchoscopy-assisted biopsy confirmed squamous cell carcinoma. Retrospective analysis of the initial chest radiograph, which had reported normal findings, was performed with Chest-CAD, a Food and Drug Administration (FDA) cleared computer-assisted detection (CAD) software device that analyzes chest radiograph studies using artificial intelligence. The device highlighted the perihilar region of the left lung as suspicious. Additional information provided by artificial intelligence software holds promise to prevent missed detection of lung cancer on chest radiographs.

Is there a role for preoperative liver reducing diet in hepatectomy? A systematic review.

PURPOSE: Preoperative very low-calorie diets (VLCDs) have been shown to reduce liver volume, reduce the risk of liver injury and improve safety during bariatric surgery. Hepatic steatosis (HS) has been associated with poorer outcomes in liver resection. VLCD can be used to improve HS. We aim to explore if preoperative VLCD could improve outcomes for patients with HS undergoing liver resection. METHODS: We performed a systematic review of MEDLINE, EMBASE, PubMed and Cochrane databases. Studies were included if they were full-text articles investigating the effect of a preoperative dietary intervention in patients undergoing liver resection on intra-operative and post-operative outcomes. The last search was performed on 11 Jun 2020. Evidence quality was assessed by "GRADE". A narrative review was undertaken. RESULTS: Five studies were found: one RCT and four cohort studies including 133 patients in intervention groups and 181 controls. Three used diet-only strategies and two diet and exercise strategies with varying time courses and monitoring. The quality of evidence assessed by GRADE was "high" for the RCT and "low" for the four cohort studies. Steatosis objectively improved in three studies, with evidence of reduced liver volume and increased attenuation on imaging in one. All studies showed a reduction in body weight and body mass index (BMI). Intra-operative blood loss was decreased following a diet-only intervention in two studies, and liver mobility improved in one. No difference was found in morbidity, mortality or hospital length of stay between intervention and control groups. CONCLUSIONS: There is evidence of poorer outcomes in liver resection patients with existing HS. There is an expected role for a preoperative VLCD to optimise these patients for surgery. Existing publications support this, but diet interventions and outcome measures are inconsistent, and patient numbers are small. There is scope for a well-designed, multi-centre randomised trial to investigate this further.

Transcranial Neuromodulation Array With Imaging Aperture for Simultaneous Multifocus Stimulation in Nonhuman Primates.

Even simple behaviors arise from the simultaneous activation of multiple regions in the brain. Thus, the ability to simultaneously stimulate multiple regions within a brain circuit should allow for better modulation of function. However, performing simultaneous multifocus ultrasound neuromodulation introduces challenges to transducer design. Using 3-D Fullwave simulations, we have designed an ultrasound neuromodulation array for nonhuman primates that: 1) can simultaneously focus on multiple targets and 2) include an imaging aperture for additional functional imaging. This design is based on a spherical array, with 128 15-mm elements distributed in a spherical helix pattern. It is shown that clustering the elements tightly around the 65-mm imaging aperture located at the top of the array improves targeting at shallow depths, near the skull surface. Spherical arrays have good focusing capabilities through the skull at the center of the array, but focusing on off-center locations is more challenging due to the natural geometric configuration and the angle of incidence with the skull. In order to mitigate this, the 64 elements closest to the aperture were rotated toward and focusing on a shallow target, and the 64 elements farthest from the aperture were rotated toward and focusing on a deeper target. Data illustrated that this array produced focusing on the somatosensory cortex with a gain of 4.38 and to the thalamus with a gain of 3.82. To improve upon this, the array placement was optimized based on phase aberration simulations, allowing for the elements with the largest impact on the gain at each focal point to be found. This optimization resulted in an array design that can focus on the somatosensory cortex with a gain of 5.19 and the thalamus with a gain of 4.45. Simulations were also performed to evaluate the ability of the array to focus on 28 additional brain regions, showing that off-center target regions can be stimulated, but those closer to the skull will require corrective steps to deliver the same amount of energy to those locations. This simulation and design process can be adapted to an individual monkey or human skull morphologies and specific target locations within individuals by using orientable 3-D printing of the transducer case and by electronic phase aberration correction.

Does a Medical Home Buffer the Association between Child Poverty and Poor Health?

OBJECTIVE: To evaluate effects of the medical home on health status and health care use among children living in poverty compared with children not living in poverty. METHODS: Data from the 2016-2017 National Survey of Children's Health were used to examine the associations of having a medical home with health outcomes of interest, among children living in poverty and among children not living in poverty. RESULTS: Among 69,039 children ages 0-17 years, children living in poverty had lower medical home access (31%) compared with those not living in poverty (50%). Medical home access was favorably associated with health outcomes, but the strength of these association did not vary according to poverty status. CONCLUSIONS: Notwithstanding the efficacy of the medical home at promoting child health, children living in poverty did not have greater health benefits associated with medical home access compared with children not living in poverty.

Characterizing dislocation loops in irradiated polycrystalline Zr alloys by X-ray line profile analysis of powder diffraction patterns with satellites.

This work extends the convolutional multiple whole profile (CMWP) line profile analysis (LPA) procedure to determine the total dislocation density and character of irradiation-induced dislocation loops in commercial polycrystalline Zr specimens. Zr alloys are widely used in the nuclear industry as fuel cladding materials in which irradiation-induced point defects evolve into dislocation loops. LPA has long been established as a powerful tool to determine the density and nature of lattice defects in plastically deformed materials. The CMWP LPA procedure is based on the Krivoglaz-Wilkens theory in which the dislocation structure is characterized by the total dislocation density ρ and the dislocation arrangement parameter M. In commercial Zr alloys irradiation-induced dislocation loops broaden the peak profiles, mainly in the tail regions, and occasionally generate small satellites next to the Bragg peaks. In this work, two challenges in powder diffraction patterns of irradiated Zr alloys are solved: (i) determination of the M values from the long tail regions of peaks has been made unequivocal and (ii) satellites have been fitted separately, using physically well established principles, in order to exclude them from the dislocation determination process. Referring to the theory of heterogeneous dislocation distributions, determination of the total dislocation density from the main peaks free of satellites has been justified. The dislocation loop structure has been characterized by the total dislocation density of loops and the M parameter correlated to the dipole character of dislocation loops. The extended CMWP procedure is applied to determine the total dislocation density, the dipole character of dislocation loops, and the fractions of 〈a〉- and 〈c〉-type loops in proton- or neutron-irradiated polycrystalline Zr alloys used in the nuclear energy industry.

Scn2a severe hypomorphic mutation decreases excitatory synaptic input and causes autism-associated behaviors.

SCN2A, encoding the neuronal voltage-gated Na+ channel NaV1.2, is one of the most commonly affected loci linked to autism spectrum disorders (ASDs). Most ASD-associated mutations in SCN2A are loss-of-function mutations, but studies examining how such mutations affect neuronal function and whether Scn2a mutant mice display ASD endophenotypes have been inconsistent. We generated a protein truncation variant Scn2a mouse model (Scn2aΔ1898/+) by CRISPR that eliminates the NaV1.2 channel's distal intracellular C-terminal domain, and we analyzed the molecular and cellular consequences of this variant in a heterologous expression system, in neuronal culture, in brain slices, and in vivo. We also analyzed multiple behaviors in WT and Scn2aΔ1898/+ mice and correlated behaviors with clinical data obtained in human subjects with SCN2A variants. Expression of the NaV1.2 mutant in a heterologous expression system revealed decreased NaV1.2 channel function, and cultured pyramidal neurons isolated from Scn2aΔ1898/+ forebrain showed correspondingly reduced voltage-gated Na+ channel currents without compensation from other CNS voltage-gated Na+ channels. Na+ currents in inhibitory neurons were unaffected. Consistent with loss of voltage-gated Na+ channel currents, Scn2aΔ1898/+ pyramidal neurons displayed reduced excitability in forebrain neuronal culture and reduced excitatory synaptic input onto the pyramidal neurons in brain slices. Scn2aΔ1898/+ mice displayed several behavioral abnormalities, including abnormal social interactions that reflect behavior observed in humans with ASD and with harboring loss-of-function SCN2A variants. This model and its cellular electrophysiological characterizations provide a framework for tracing how a SCN2A loss-of-function variant leads to cellular defects that result in ASD-associated behaviors.

An effort-based social feedback paradigm reveals aversion to popularity in socially anxious participants and increased motivation in adolescents.

We created a novel social feedback paradigm to study how motivation for potential social links is influenced in adolescents and adults. 88 participants (42F/46M) created online posts and then expended physical effort to show their posts to other users, who varied in number of followers and probability of positive feedback. We focused on two populations of particular interest from a social feedback perspective: adolescents relative to young adults (13-17 vs 18-24 years of age), and participants with social anxiety symptoms. Individuals with higher self-reported symptoms of social anxiety did not follow the typical pattern of increased effort to obtain social feedback from high status peers. Adolescents were more willing to exert physical effort on the task than young adults. Overall, participants were more likely to exert physical effort for high social status users and for users likely to yield positive feedback, and men were more likely to exert effort than women, findings that parallel prior results in effort-based tasks with financial rather than social rewards. Together the findings suggest social motivation is malleable, driven by factors of social status and the likelihood of a positive social outcome, and that age, sex, and social anxiety significantly impact patterns of socially motivated decision-making.

Detection of eye contact with deep neural networks is as accurate as human experts.

Eye contact is among the most primary means of social communication used by humans. Quantification of eye contact is valuable as a part of the analysis of social roles and communication skills, and for clinical screening. Estimating a subject's looking direction is a challenging task, but eye contact can be effectively captured by a wearable point-of-view camera which provides a unique viewpoint. While moments of eye contact from this viewpoint can be hand-coded, such a process tends to be laborious and subjective. In this work, we develop a deep neural network model to automatically detect eye contact in egocentric video. It is the first to achieve accuracy equivalent to that of human experts. We train a deep convolutional network using a dataset of 4,339,879 annotated images, consisting of 103 subjects with diverse demographic backgrounds. 57 subjects have a diagnosis of Autism Spectrum Disorder. The network achieves overall precision of 0.936 and recall of 0.943 on 18 validation subjects, and its performance is on par with 10 trained human coders with a mean precision 0.918 and recall 0.946. Our method will be instrumental in gaze behavior analysis by serving as a scalable, objective, and accessible tool for clinicians and researchers.

Visual Search for Circumscribed Interests in Autism Is Similar to That of Neurotypical Individuals.

Intense interests are a core symptom of autism spectrum disorders (ASD) and can be all-encompassing for affected individuals. This observation raises the hypothesis that intense interests in ASD are related to pervasive changes in visual processing for objects within that category, including visual search. We assayed visual processing with two novel tasks, targeting category search and exemplar search. For each task, three kinds of stimuli were used: faces, houses, and images personalized to each participant's interest. 25 children and adults with ASD were compared to 25 neurotypical (NT) children and adults. We found no differences in either visual search task between ASD and NT controls for interests. Thus, pervasive alterations in perception are not likely to account for ASD behavioral symptoms.

Assessment of a deep-learning system for fracture detection in musculoskeletal radiographs.

Missed fractures are the most common diagnostic error in emergency departments and can lead to treatment delays and long-term disability. Here we show through a multi-site study that a deep-learning system can accurately identify fractures throughout the adult musculoskeletal system. This approach may have the potential to reduce future diagnostic errors in radiograph interpretation.

Prevalent and sex-biased breathing patterns modify functional connectivity MRI in young adults.

Resting state functional connectivity magnetic resonance imaging (fMRI) is a tool for investigating human brain organization. Here we identify, visually and algorithmically, two prevalent influences on fMRI signals during 440 h of resting state scans in 440 healthy young adults, both caused by deviations from normal breathing which we term deep breaths and bursts. The two respiratory patterns have distinct influences on fMRI signals and signal covariance, distinct timescales, distinct cardiovascular correlates, and distinct tendencies to manifest by sex. Deep breaths are not sex-biased. Bursts, which are serial taperings of respiratory depth typically spanning minutes at a time, are more common in males. Bursts share features of chemoreflex-driven clinical breathing patterns that also occur primarily in males, with notable neurological, psychiatric, medical, and lifespan associations. These results identify common breathing patterns in healthy young adults with distinct influences on functional connectivity and an ability to differentially influence resting state fMRI studies.

Autism spectrum disorder.

Autism spectrum disorder is a construct used to describe individuals with a specific combination of impairments in social communication and repetitive behaviours, highly restricted interests and/or sensory behaviours beginning early in life. The worldwide prevalence of autism is just under 1%, but estimates are higher in high-income countries. Although gross brain pathology is not characteristic of autism, subtle anatomical and functional differences have been observed in post-mortem, neuroimaging and electrophysiological studies. Initially, it was hoped that accurate measurement of behavioural phenotypes would lead to specific genetic subtypes, but genetic findings have mainly applied to heterogeneous groups that are not specific to autism. Psychosocial interventions in children can improve specific behaviours, such as joint attention, language and social engagement, that may affect further development and could reduce symptom severity. However, further research is necessary to identify the long-term needs of people with autism, and treatments and the mechanisms behind them that could result in improved independence and quality of life over time. Families are often the major source of support for people with autism throughout much of life and need to be considered, along with the perspectives of autistic individuals, in both research and practice.

Distinct and similar patterns of emotional development in adolescents and young adults.

Adolescence is a developmental period of increased sensitivity to social emotional cues, but it is less known whether young adults demonstrate similar social emotional sensitivity. The current study tested variation in reaction times to emotional face cues during different phases of emotional development. Ex-Gaussian parameters mu, sigma, and tau were computed, in addition to mean, median and standard deviation (SD) in reaction times (RT) during an emotional go/nogo-paradigm with fearful, happy, and calm facial expressions in 377 participants, 6-30 years of age. Across development, mean RT showed slowing to fearful facial expressions relative to both calm and happy facial cues, but mu revealed that this pattern was specific to adolescence. In young adulthood, increased variability to fearful expressions relative to both happy and calm ones was captured by SD and tau. The findings that adolescents had longer response latencies to fearful faces, whereas young adults demonstrated greater response variability to fearful faces, together reflect how social emotional processing continues to evolve from adolescence into early adulthood. The findings suggest that young adulthood is also a vulnerable period for processing social emotional cues that ultimately may be important to better understand why different psychopathologies emerge in early adulthood.

Adolescent-Specific Motivation Deficits in Autism Versus Typical Development.

Differences in motivation during adolescence relative to childhood and adulthood in autism was tested in a cross-sectional study. 156 Typically developing individuals and 79 individuals with autism ages 10-30 years of age completed a go/nogo task with social and non-social cues. To assess age effects, linear and quadratic models were used. Consistent with prior studies, typically developing adolescents and young adults demonstrated more false alarms for positive relative to neutral social cues. In autism, there were no changes in attention across age for social or non-social cues. Findings suggest reduced orienting to motivating cues during late adolescence and early adulthood in autism. The findings provide a unique perspective to explain the challenges for adolescents with autism transitioning to adulthood.

Characteristics of respiratory measures in young adults scanned at rest, including systematic changes and "missed" deep breaths.

Breathing rate and depth influence the concentration of carbon dioxide in the blood, altering cerebral blood flow and thus functional magnetic resonance imaging (fMRI) signals. Such respiratory fluctuations can have substantial influence in studies of fMRI signal covariance in subjects at rest, the so-called "resting state functional connectivity" technique. If respiration is monitored during fMRI scanning, it is typically done using a belt about the subject's abdomen to record abdominal circumference. Several measures have been derived from these belt records, including the windowed envelope of the waveform (ENV), the windowed variance in the waveform (respiration variation, RV), and a measure of the amplitude of each breath divided by the cycle time of the breath (respiration volume per time, RVT). Any attempt to gauge respiratory contributions to fMRI signals requires a respiratory measure, but little is known about how these measures compare to each other, or how they perform beyond the small studies in which they were initially proposed. Here, we examine the properties of these measures in hundreds of healthy young adults scanned for an hour each at rest, a subset of the Human Connectome Project chosen for having high-quality physiological records. We find: 1) ENV, RV, and RVT are all correlated, and ENV and RV are more highly correlated to each other than to RVT; 2) respiratory events like deep breaths exhibit characteristic heart rate elevations, fMRI signal changes, head motions, and image quality abnormalities time-locked to large deflections in the belt traces; 3) all measures can "miss" deep breaths; 4) RVT "misses" deep breaths more than ENV or RV; 5) all respiratory measures change systematically over the course of a 14.4-min scan. We discuss the implications of these findings for the literature and ways to move forward in modeling respiratory influences on fMRI scans.

The Chemistry of Mordançage, a Historic Photographic Process.

Based on a 19th century method, the process known as mordançage has been used to dramatically alter black and white photographs since the 1960s. Mordançage subjects a fully developed photograph to a series of wet chemical reactions, beginning with an acidic copper(II) chloride and hydrogen peroxide bleaching solution. Different types of photographic paper, time in mordançage solution, and the concentration of hydrogen peroxide, copper(II) chloride, and acetic acid are all variables that influence the degree of emulsion lift and corresponding veils observed. We conducted experiments to explore these variables and analyzed processed prints using ImageJ to measure changes in pixel intensity and SEM-EDS for compositional analysis. Fiber-based and cotton rag papers resulted in more emulsion lift than resin-coated paper, and increased time in the mordançage solution had similar results. Quantitative analysis of the bleaching solution reveals how the process variables affect the formation of veils, and a process scheme is presented. This work provides insight into the chemical transformations involved in this artistic process.