Although South Africa is well known for its incredible biodiversity, targeted efforts are still re-quired to accelerate the discovery and description of the still-unknown species in this floristically mega-diverse country. In addition, new species discovery is a new opportunity for the devel-opment of ethnobotanical knowledge. In the present study, we collected data on current knowledge of plant richness in the country and expert taxonomists’ opinions and used statistical modelling technique to predict what might still be missing in South Africa’s flora. According to experts’ opinions and our modelling, we might be missing 1400-1575 plant species in the country. We predicted that we might take 40-45 years to identify and describe these species and that be-tween 64 and 315 taxonomists will be required. The pool of taxonomists who took part in this study have spent a total of R 680 670 000 (US $40 039 411) to describe 419 species. This implies that, in theory, R 1 624 510.74 (US $95 559) was spent, on average, to describe 1 species. At this rate, R 2 558 604 415.00 (US $150 506 142) would be required to describe the 1575 (modelling) or R 2 274 315 036.00 (US $133 783 237) for the 1400 remaining species (expert opinion) in the current context where both full- and part-time taxonomists are committed to biodiversity assessment in the country. It is important to highlight that this estimate does not correspond to what is required specifically for only species description but does integrate all connected activities, e.g., running cost, bursary, salaries, grants, etc. Furthermore, we must bear in mind that these estimates do not account for the possibility of taxonomic revision which, on its own, needs to be funded, nor do they account for molecular laboratory requirement. However, if we consider that 15% of the predicted funds are spent solely on taxonomic activities, this means that we would need ~R243 673 (US$ 14 334) on 1 species. Overall, our study provides an important figure that can inform policy development including funding and recruitment strategies of taxonomists to fuel efforts towards a comprehensive assessment of the unique South Africa’s biodiversity. The implication of new species discovery is that it opens windows for traditional knowledge development.

BACKGROUND: Acute stroke is a time-critical emergency where diagnosis and acute management are highly dependent upon accuracy of patient’s history. We hypothesised that language barrier is associated with delayed onset time to thrombolysis and poor clinical outcomes. AIM: To evaluate the effect of language barriers on time to thrombolysis and clinical outcomes in acute ischemic stroke. METHODS: This is a retrospective study of all patients admitted to a metropolitan stroke unit (Melbourne, Victoria, Australia) with an acute ischemic stroke treated with tissue plasminogen activator between 1/2013 and 9/2017. Baseline characteristics, thrombolysis time intervals, length of stay, discharge destination, and in-hospital mortality were compared between patients with and without a language barrier using multivariate analysis after adjustment for age, sex, stroke severity, premorbid modified Rankin Scale (mRS) and Charlson Comorbidity Index (CCI). Language barriers were defined as a primary language other than English. RESULTS: A total of 374 patients were included. Of this, 76 patients (20.3%) had a language barrier. Mean age was 5 years older for patients with language barriers (76.7 vs 71.8 years, P=0.004). Less non-English speaking patients had pre-morbid mRS score of zero (P=0.002) and more had pre-morbid mRS score of one or two (P=0.04). There was no statistically significant difference between two groups in terms of stroke severity on presentation (P=0.06). The onset to needle time was significantly longer in patients with a language barrier (188 min vs 173 min, P=0.04). Onset to arrival and door to imaging times were surprisingly similar between the two groups. However, imaging to needle time was 9 minutes delayed in non-English speaking patients with a marginal P value (65 vs 56 min, P=0.06). patients with language barriers stayed longer in stroke unit stay (6 vs 4 days, P=0.02) and had higher rates of discharge to residential aged care facilities in those admitted from home (9.2% vs 2.3%, P=0.02). In-hospital mortality was not different between two groups (P=0.8) CONCLUSION: In this study language barriers were associated with almost 14 min delay in thrombolysis. The delay was mostly attributable to imaging to needle time. Language barriers were also associated with poorer clinical outcomes.

Telerehabilitation is an appealing service delivery option for optimising recovering. Internationally, the equity of telerehabilitation services for people from culturally and linguistically diverse (CALD) backgrounds has been questioned. Using a 31-item survey, our study explored the access, readiness, and willingness of 260 patients to use allied health telerehabilitation services for adults receiving interventions from a large tertiary health service located in Sydney, Australia. Overall, 72% patients reported having access to technology, 38% met our readiness criteria, and 53% reported willingness to engage in telerehabilitation. There were no difference in access, readiness and willingness to engage in telerehabilitation between patients from CALD and non-CALD backgrounds. Age was the only factor that influenced access, readiness and willingness to engage in telerehabilitation. Past experience of telerehabilitation was related to willingness, but not access or readiness. Findings highlight the importance of ensuring positive patient experiences to promote ongoing willingness to use telerehabilitation. Efforts are needed to improve patients’ digital health literacy, especially patients from older age groups, to ensure equitable access to telerehabilitation services.

In the evolving landscape of Portfolio Management (PM), the fusion of advanced machine 1 learning techniques with traditional financial methodologies has opened new avenues for innovation. 2 Our study introduces a cutting-edge model combining Deep Reinforcement Learning (DRL) with 3 a Non-stationary Transformer architecture. This model is specifically designed to decode complex 4 patterns in financial time series data, enhancing portfolio management strategies with deeper insights 5 and robustness. It effectively tackles the challenges of data heterogeneity and market uncertainty, 6 key obstacles in PM. Our approach integrates key macroeconomic indicators and targeted news 7 sentiment analysis into its framework, capturing a comprehensive picture of market dynamics. This 8 amalgamation of varied data types addresses the multifaceted nature of financial markets, enhancing 9 the model’s ability to navigate through the complexities of asset management. Rigorous testing 10 demonstrates the model’s efficacy, highlighting the benefits of blending diverse data sources and 11 sophisticated algorithmic approaches in mastering the nuances of PM.12