Citrix (Switzerland)

Transient pain and ocular inflammation may occur with ophthalmic surgery. Pain is an important determinant of patient satisfaction, and inflammation can increase the risk for suboptimal visual outcomes. Topical anti-inflammatory therapies can reduce the incidence of postoperative pain and inflammation but have many limitations, including nonadherence and significant potential side effects such as cataract formation and intraocular pressure elevation. There is unmet need for a therapy that provides anti-inflammatory and analgesic activity while overcoming the limitations of topical therapy. The intracanalicular dexamethasone insert (DEX) overcomes many of the limitations of topical therapy. DEX is a fluorescent yellow cylindrical-shaped polyethylene glycol-based hydrogel insert incorporating 0.4mg preservative-free dexamethasone. The insert was designed based on human anatomy and measurements of the canalicular system and continuously releases a tapered dose of dexamethasone over approximately 30days onto the surface of the eye. The insert is completely resorbable and does not require removal. DEX was approved by the US Food and Drug Administration for the treatment of pain (in 2018) and inflammation (in 2019) following ocular surgery; in 2021, the treatment of ocular itching associated with allergic conjunctivitis was added as an indication. We present a comprehensive review of the current therapeutic landscape for management of postoperative pain and inflammation following cataract surgery, including the limitations of topical eye drop medications. We review data from the key phase2 and 3 clinical trials that supported the approval and label expansion for DEX. We also summarize the broader literature of smaller studies evaluating the role of DEX in controlling postoperative pain and inflammation for a variety of ophthalmic surgery beyond cataract surgery as well as in management of ocular itching associated with allergic conjunctivitis.

As the climate warms, the interaction between surface water and groundwater provides a critical buffer for salmonids facing thermal stress. In the Coldwater River, summer water temperatures often exceed 25 ºC, well above the survival threshold for the system’s vulnerable Chinook, Coho, and Steelhead populations. Using a thermal infrared drone, we conducted video overflights to map potential thermal refuges across the lower Coldwater River, followed by snorkel and habitat surveys to verify fish abundance and habitat quality. We identified 38 cold-water patches, primarily composed of side channels and alcoves. We also identified three notable 3-4 km gaps between cold-water patches which may act as thermal barriers to both upstream or downstream migration. While cold-water patches provided water temperatures up to 10 ºC cooler than the mainstem, snorkel surveys revealed these sites were predominantly used by juvenile salmonids, as the shallow depths and low velocities were unsuitable for adults. Furthermore, high solar exposure at these sites resulted in warming of up to 5 ºC from upwelling source to downstream end, likely as a function of low riparian vegetation. In conclusion, drone-based thermal video overflights were effective at mapping cold-water patches across a large section of river. Site visits revealed many of these habitats are degraded and offer poor habitat, limited to juvenile fish. More research is necessary to identify the natural river processes that maintain and create thermal refuges and determine practical methods of improving the ecological function of cold-water patches.

Accurate classification of forest types and tree species is an important aspect of forest monitoring but it requires vast amounts of spatio-temporal data. Remotely sensed data provides a viable solution for acquiring the necessary global-scale information. Historically multispectral data has been used for forest monitoring, but the limited number of spectrally-broad bands often do not provide sufficient differentiation between similar tree species, lowering classification accuracy. Hyperspectral data offers improved spectral resolution which enables to differentiate similar tree species. Nevertheless, the quality of ground truth data used for classification remains a challenge, as it is often limited and noisy.This study presents a hierarchical, three-stage classification approach utilizing hyperspectral data, cascaded machine learning models and spectral unmixing algorithms to classify forest types and individual tree species. The approach integrates coarse level dataset for broad level classification and finer resolution hyperspectral imagery for fine-scale spectral and structural variability. Furthermore, to address the possibility of low quality ground truth labels we propose a semi-supervised training framework leveraging pseudo-labeling.The cascaded three-stage architecture sequentially processes the data, with each stage consisting of an XGBoost model trained to address specific challenges. The first stage is a coarse classifier, classifying forest into three broad categories: Evergreen, Deciduous, and Mixed. This model is trained on coarse resolution 60m GSD EMIT data and supervised labels generated using the National Land Cover Database. The second stage further refines the three classes into 28 different forest group types labels as defined by the USDA Forest Service's Forest Inventory and Analysis (FIA). The third and the final stage classifies each of the forest pixels by its dominant tree species, leveraging the outputs from the previous stage and AVIRIS-NG high resolution 4m GSD hyperspectral data as additional input features. Non-dominant tree species are identified using Vertex Component Analysis based spectral unmixing and classified into pure tree species spectras using spectral similarity metrics. The abundances of dominant and non-dominant spectras are then mapped using the Fully Constrained Least Squares approach.This method was tested over two regions: Shasta-Trinity National Forest, California, USA and Grand Mesa National Forest, Colorado, USA. The resulting tree distribution mapped 10 different individual tree species and were validated against USDA’s Treemap product . For these test regions the resulting overall accuracy from the entire 3-stage model is 80%. The individual stage accuracies for stage 1, stage 2, and stage 3 classification, were 94%, 92%,  and 92% respectively.Despite these promising results, the approach is constrained by the availability of high-quality ground-truth data for supervised training. To address this, a pseudo-labeling technique that generates additional training data by iteratively assigning labels to unlabeled samples with high model confidence was explored. The preliminary results indicate that the inclusion of pseudo-labeled data training can enhance the classification accuracy of the proposed hierarchical cascaded approach for forest applications.

This article presents a comprehensive analysis of automated security configuration management for enterprise networking products, addressing the growing challenges in maintaining secure network configurations across complex modern infrastructures. It examines the evolution of network configuration management from traditional manual approaches to automated solutions, highlighting the critical role of automation in maintaining security posture and compliance. Through analysis of recent research and industry reports, the article explores the challenges posed by increasing network complexity, particularly in hybrid environments combining on-premises and cloud-based services. It investigates the components of automated configuration analysis systems, including scanning engines, security rule engines, and remediation recommendation systems. The article demonstrates the benefits of automated configuration management, including improved security consistency, reduced human error, enhanced proactive risk management, and streamlined compliance processes. It also provides detailed implementation considerations for organizations adopting automated configuration management solutions, emphasizing the importance of establishing clear security baselines, robust change management procedures, and regular updates to security rules and best practices.

In the realm of cloud infrastructure management, Infrastructure as Code (IaC) has become a pivotal framework, catalyzing advancements in modern digital marketplaces. The study at hand delves into the nuanced application of IaC within the Distributed Open Marketplace for Europe (DOME) project. Utilizing the GitOps methodology, complemented by tools such as ArgoCD, Helm, Kustomize, and Bitnami Sealed Secrets, this project achieves heightened levels of operational efficacy, scalability, and security. The empirical analysis underscores that IaC not only simplifies the provisioning and administration of infrastructure assets but also fosters operational consistency and agility. This establishes a robust blueprint for deploying dependable cloud and edge services throughout Europe.

Bugungi kunda barchamiz doimiy o‘zgaruvchan ijtimoiy-iqtisodiy va ijtimoiy-madaniy sharoitda yashamoqdamiz. Globallashuv jarayonlari shiddat bilan kechayotgan, axborot oqimi ortib borayotgan bir davrda har bir shaxsdan uzluksiz rivojlanish hamda moslashuv talab etilib, u yangi sharoitlarga moslashishga yordam beradigan shaxsiy kompetentsiyaga ega bo‘lishi kerak. Hozirgi kunda shunday muhim kompetentsiyalardan biri - kreativ tafakkur, yuqori ijodiy faollik va kreativ xulq-atvordir. Mustaqil ijodiy fikrlash qobiliyatini shakllantirishda tanqidiy fikrlashi va o‘z faoliyatiga munosabatlarini o‘zgartirish masalasi o‘ta muhimdir. Chunki bu masala bevosita inson ruhiyatiga, ongiga, tafakkuriga ta’sir etish yo‘li bilan amalga oshiriladi.

Crude oil spilled at sea is chemically altered through environmental processes such as dissolution, biodegradation, and photodegradation. Transformation of hydrocarbons to oxygenated species increases water-solubility. Metabolites and oxidation products largely remain uncharacterized by common analytical methods but may be more bioavailable to aquatic organisms. Studies have shown that unresolved (i.e. unidentified) polar compounds (‘UPCs’) may constitute > 90% of the water-accommodated fraction (WAF) of heavily weathered crude oils, but still there is a paucity of information characterizing their toxicological significance in relation to other oil-derived toxicants. In this study, low-energy WAFs (no droplets) were generated from two field-weathered oils (collected during the 2010 Deepwater Horizon incident) and their polar fractions were isolated through fractionation. To allow establishment of thresholds for acute toxicity (LC50) of the dissolved and polar fraction of field collected oils, we concentrated both WAFs and polar fractions to beyond field-documented concentrations, and the acute toxicity of both to the marine copepod Acartia tonsa was measured and compared to the toxicity of the native WAF (non-concentrated). The difference in toxic units (TUs) between the total of the mixture and of identified compounds of known toxicity (polycyclic aromatic hydrocarbons [PAHs] and alkyl phenols) in both WAF and polar fractions was used to estimate the contribution of the UPC to overall toxicity. This approach identified that UPC had a similar contribution to toxicity as identified compounds within the WAFs of the field-weathered oils. This signifies the relative importance of polar compounds when assessing environmental impacts of spilled and weathered oil.

Abstract Aiming to verify the predictive value of oral narrative structure on reading acquisition, we followed 253 children (first and second graders) during an entire school year, assessing oral narratives and reading performances in five sessions. Transcriptions of oral narratives were represented as word-recurrence graphs to measure connectedness attributes. Connectedness predicted performance in phonological awareness, reading comprehension, and word reading accuracy 3–4 months in advance.

Este estudio ofrece un análisis detallado del lenguaje empleado en la cobertura de la pandemia por parte de The Guardian y El País, examinando la representación de las posturas gubernamentales y su influencia en la respuesta de los ciudadanos. Se investiga el papel de estos medios como canales de comunicación gubernamental y su efecto en la percepción pública durante la crisis sanitaria. A través de un análisis riguroso, confirmamos nuestra hipótesis de que los gobiernos de Reino Unido y España ejercieron una influencia ambivalente sobre la población mediante el uso estratégico de un discurso manipulador. Este estudio se apoyó en la creación de un corpus bilingüe y en el uso de análisis textual avanzado para desentrañar las tácticas de control y persuasión aplicadas. Los hallazgos revelan una tendencia al uso de un léxico cargado negativamente y de estrategias emocionales, así como el empleo de metáforas y jerga técnica para establecer una asimetría informativa entre las autoridades y la ciudadanía.