Adapting Regulations Post-Brexit

The UK government is taking advantage of the new regulatory flexibility, afforded by Brexit, to boost the country's competitiveness in pharma On 1 Jan. 2021, the United Kingdom formally left the European Union to become a third country and no longer a member of the Union's single market and customs union. The UK has, for example, decided to draw up its own version of the EU chemicals legislation-called REACH (Registration, Evaluation, Authorization, and Restriction of Chemicals)-which sets technical standards for chemical ingredients for medicines (1). Because the UK is a separate legal entity-a third country-the UK's excipient producers and their raw material suppliers have started to be concerned about procedures like customs declarations and rules of origin. [...]by 18 Feb. 2021 the UK had vaccinated 26% of its population versus 8% in Denmark-the leading EU country for vaccines availability-6% in Germany, and 5% in France (6). NICE needs to change Industry believes that the National Institute for Health and Care Excellence (NICE), the government's health technology assessment (HTA) body, is being too restrictive with its evaluation of digitalization products, which ultimately sets the price paid by the government for them (9).

Automating Biopharma Manufacturing

"With the advance of data science enabling factors such as easy access to scalable memory and computing resources;our growing competence in collecting, storing, and contextualizing data;advances in robotics;[and] the quickly evolving method landscape driven by the open-source community, the benefits of automation and simulation are becoming accessible in the notoriously complicated realm of biopharma manufacturing," says Marcel von der Haar, head of product strategy data analytics at Sartorius. "Plug-and-play" capabilities of automation systems, which enable flexible manufacturing and faster technology transfer, are more important than ever, he says. Walvax Biotech's new COVID-19 mRNA vaccine plant in China is another example of an intelligent and digital plant;it uses Honeywell's batch process control, building and energy management solution systems, and digital twins to monitor assets (5). "Automation brings in the data for machine learning to model the dynamic processes of cell growth and map it against the multiple dimensions provided by advanced sensors," explains Brandl.

Diversity Monitoring of Coexisting Birds in Urban Forests by Integrating Spectrograms and Object-Based Image Analysis

In the context of rapid urbanization, urban foresters are actively seeking management monitoring programs that address the challenges of urban biodiversity loss. Passive acoustic monitoring (PAM) has attracted attention because it allows for the collection of data passively, objectively, and continuously across large areas and for extended periods. However, it continues to be a difficult subject due to the massive amount of information that audio recordings contain. Most existing automated analysis methods have limitations in their application in urban areas, with unclear ecological relevance and efficacy. To better support urban forest biodiversity monitoring, we present a novel methodology for automatically extracting bird vocalizations from spectrograms of field audio recordings, integrating object-based classification. We applied this approach to acoustic data from an urban forest in Beijing and achieved an accuracy of 93.55% (±4.78%) in vocalization recognition while requiring less than ⅛ of the time needed for traditional inspection. The difference in efficiency would become more significant as the data size increases because object-based classification allows for batch processing of spectrograms. Using the extracted vocalizations, a series of acoustic and morphological features of bird-vocalization syllables (syllable feature metrics, SFMs) could be calculated to better quantify acoustic events and describe the soundscape. A significant correlation between the SFMs and biodiversity indices was found, with 57% of the variance in species richness, 41% in Shannon’s diversity index and 38% in Simpson’s diversity index being explained by SFMs. Therefore, our proposed method provides an effective complementary tool to existing automated methods for long-term urban forest biodiversity monitoring and conservation.