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AbstractThe impacts of climate change on heritage collections in Mediterranean museums are serious and lead to accelerated material degradation, loss of value, increasing conservation costs and climatisation. Climate change scenarios and simulation models have been developed to predict the extreme and average future environmental conditions and to assess the future long-term risks caused by global warming for museum buildings and their collections in Mediterranean countries, with Egypt being particularly at risk. This paper presents the results of the risk analysis of the indoor and outdoor environments in Alexandria Museum of Fine Arts (AMFA) in Egypt to provide an overview of the current situation of conservation and museum management and to provide evidence-based data to support decision-making regarding preventive conservation given the museum's limited funding, capacity and resources. Unfortunately, the air quality in the museum cannot be considered satisfactory and specific measures need to be taken to improve the level of air quality and museum and building management. The results enabled an assessment of indoor air quality and provided information on potential risks to the museum building and collections, including variations in temperature (T) and relative humidity (RH), concentrations of NO2, SO2, O3, CO2, acetic and formic acid and lighting conditions, as well as the location and management of the museum. The results necessitate the development of a preventive conservation plan to address the challenges associated with high T/RH fluctuations and pollution pressure. This requires more regular use of the HVAC system within certain T/RH set points as well as minimising light exposure and the use of UV-filtering glazing. Care should be taken to ensure that housekeeping and emergency preparedness reduce the damping and salt florescence in the museum building. However, dealing with the impact of climate change on indoor and outdoor environments and museum collections in Mediterranean museums requires a holistic and adaptive approach that includes joint collaboration, research, training and strategic planning to ensure the long-term preservation of valuable cultural heritage collections in different climates with customised adaptations based on local environmental conditions, resources and needs. Resilience planning should be region-specific and take into account the potential impacts of extreme weather events, sea level rise and other climate-related challenges.

AbstractArchival boxes are used as a preventive measure to control the microenvironment in museums and archives storages. However, their efficiency in protecting valuable artefacts from fluctuations in relative humidity (RH) is not yet fully understood. In this study, the environmental performance of different archival boxes with different configurations (size, design, presence of holes/paper material, cardboard types, surface modification) was included in the evaluation of internal environment (RH). The effectiveness of archival boxes on reducing relative humidity fluctuations was investigated by testing various properties of boxes and boards, such as air exchange rates (AER), moisture sorption and water vapour transmission rates (WVTR). While most cardboard boxes showed only a limited buffering against the humidity ingress, strategies such as surface modification limited the interaction of a box with the external environment, resulting in a more stable internal environment. Material and box properties, such as moisture sorption, AER and WVTR proved to be useful quantitative tools for assessing the environmental performance of the selected archival boxes.

Background: Patients with cancer starting systemic treatment programs, such as chemotherapy, often develop depression. A prediction model may assist physicians and health care workers in the early identification of these vulnerable patients. Objective: This study aimed to develop a prediction model for depression risk within the first month of cancer treatment. Methods: We included 16,159 patients diagnosed with cancer starting chemo- or radiotherapy treatment between 2008 and 2021. Machine learning models (eg, least absolute shrinkage and selection operator [LASSO] logistic regression) and natural language processing models (Bidirectional Encoder Representations from Transformers [BERT]) were used to develop multimodal prediction models using both electronic health record data and unstructured text (patient emails and clinician notes). Model performance was assessed in an independent test set (n=5387, 33%) using area under the receiver operating characteristic curve (AUROC), calibration curves, and decision curve analysis to assess initial clinical impact use. Results: Among 16,159 patients, 437 (2.7%) received a depression diagnosis within the first month of treatment. The LASSO logistic regression models based on the structured data (AUROC 0.74, 95% CI 0.71-0.78) and structured data with email classification scores (AUROC 0.74, 95% CI 0.71-0.78) had the best discriminative performance. The BERT models based on clinician notes and structured data with email classification scores had AUROCs around 0.71. The logistic regression model based on email classification scores alone performed poorly (AUROC 0.54, 95% CI 0.52-0.56), and the model based solely on clinician notes had the worst performance (AUROC 0.50, 95% CI 0.49-0.52). Calibration was good for the logistic regression models, whereas the BERT models produced overly extreme risk estimates even after recalibration. There was a small range of decision thresholds for which the best-performing model showed promising clinical effectiveness use. The risks were underestimated for female and Black patients. Conclusions: The results demonstrated the potential and limitations of machine learning and multimodal models for predicting depression risk in patients with cancer. Future research is needed to further validate these models, refine the outcome label and predictors related to mental health, and address biases across subgroups. JMIR Medical Informatics, 12 (1) ISSN:2291-9694

Background: Physicians are hesitant to forgo the opportunity of entering unstructured clinical notes for structured data entry in electronic health records. Does free text increase informational value in comparison with structured data? Objective: This study aims to compare information from unstructured text-based chief complaints harvested and processed by a natural language processing (NLP) algorithm with clinician-entered structured diagnoses in terms of their potential utility for automated improvement of patient workflows. Methods: Electronic health records of 293,298 patient visits at the emergency department of a Swiss university hospital from January 2014 to October 2021 were analyzed. Using emergency department overcrowding as a case in point, we compared supervised NLP-based keyword dictionaries of symptom clusters from unstructured clinical notes and clinician-entered chief complaints from a structured drop-down menu with the following 2 outcomes: hospitalization and high Emergency Severity Index (ESI) score. Results: Of 12 symptom clusters, the NLP cluster was substantial in predicting hospitalization in 11 (92%) clusters; 8 (67%) clusters remained significant even after controlling for the cluster of clinician-determined chief complaints in the model. All 12 NLP symptom clusters were significant in predicting a low ESI score, of which 9 (75%) remained significant when controlling for clinician-determined chief complaints. The correlation between NLP clusters and chief complaints was low (r=−0.04 to 0.6), indicating complementarity of information. Conclusions: The NLP-derived features and clinicians’ knowledge were complementary in explaining patient outcome heterogeneity. They can provide an efficient approach to patient flow management, for example, in an emergency medicine setting. We further demonstrated the feasibility of creating extensive and precise keyword dictionaries with NLP by medical experts without requiring programming knowledge. Using the dictionary, we could classify short and unstructured clinical texts into diagnostic categories defined by the clinician. JMIR Medical Informatics, 12 ISSN:2291-9694

Landscapes are socially produced and reproduced spaces. This is easily recognizable for large-scale urban groups with built environments that dominate living places. But it also pertains to all types of societies and cultures, even small-scale hunter–gatherers, once the ontological beliefs structuring landscape perception and use are acknowledged. The foragers of south–central and southern California and the Great Basin illustrate this fact. They maintained a widely shared ontological perspective supported by a fundamental cognitive postulate. This is that supernatural power, the principle causative agent in the universe, was differentially distributed among individuals and places. The distribution of power, revealed by certain geomorphological features and natural events, structured their perceptions of landscape. These perceptions were expressed in ritual and symbolism, including petroglyphs and pictographs as durable manifestations of ceremonies on the landscape. The ontological relationship between power and landscape explains a longstanding question in hunter–gatherer archaeology: Why were rock writing sites created at specific locations? It also explains another equally significant but rarely considered and related problem: Why do some localities have massive quantities of rock writings that dwarf most other sites? The landscape symbolism of and the placement of sites by Native Californian and Great Basin tribes is explained by reference to their shared ontological beliefs, illustrating how they structured their ritual practices and archaeological record.

Abstract The digitization of the US Patent and Trademark Office’s (USPTO) backfile of six million patents undertaken between 1951 and 2001 was a five-decade struggle, featuring several media transitions from print and microfilm to CD-ROMs and, finally, the Web. This mass digitization project is on a similar scale to Google Books and the Internet Archive, but it is rarely discussed within critical digitization scholarship or for its significance as a tool for knowledge production. In this article, I focus on the USPTO’s patent document’s digital and physical material form and how the current paradigm of access and storage of the digital backfile emerged. Through this case study, I build upon Ian Milligan’s distinction between the ‘text’ and ‘platform’ layers of a digitization project to demonstrate how historical decisions regarding format and metadata continue to influence how users retrieve and interpret documents, such as patents, online.

So-called ‘forgotten’ or ‘orphan’ crops are an important component of strategies aimed at preserving and promoting biodiversity. Knowledge of historical cultivation, usage, and geographic and evolutionary trajectories of plants, that is, crop history research, is important for the long-term success of such efforts. However, research biases in the crops chosen for study may present hurdles. This review attempts to systematically identify patterns in crop species representativeness within archaeology-based crop history research. A meta-analysis and synthesis of archaeo- botanical evidence (and lack thereof) is presented for 268 species known to have been cultivated for food prior to 1492 CE from the Mediterranean region to South Asia. We identified 39 genera with known crop plants in this geographical and histor- ical context that are currently absent from its archaeobotanical record, constituting ‘orphan’ crops of archaeobotany. In addition, a worldwide synthesis of crop species studied using geometric morphometric, archaeogenetic and stable isotope analyses of archaeological plant remains is presented, and biases in the species represented in these disciplines are discussed. Both disciplinary methodological biases and economic agenda-based biases affecting species representativeness in crop history research are apparent. This study also highlights the limited geographic diffusion of most crops and the potential for deeper historical perspectives on how crops become marginal- ized and ‘forgotten’. L'agrobiodiversité est un puissant levier d'action de l'agriculture durable à l'échelle mondiale. La culture, la conservation et la réintroduction de diverses espèces végétales, y compris les cultures ‘oubliées’ et ‘sous-utilisées’, contribuent à l'agrobiodiversité mondiale, aux écosystèmes vivants et à la production alimentaire durable. Ces actions bénéficient des connaissances traditionnelles et historiques des trajectoires évolutives et culturelles des plantes cultivées. Cette étude représente une première tentative de mesure systématique de la représentativité des espèces dans les études des restes archéologiques de plantes. Les résultats indiquent que, en plus des sources de biais méthodologiques spécifiques à la discipline, les biais agricoles modernes peuvent se répliquer dans la recherche sur l'histoire des cultures et influencer la compréhension des ‘cultures oubliées’. Reconnaître ces biais est un premier pas vers leur rectification et la promotion de l'agrobiodiversité dans la recherche et les applications pratiques. International audience