Biosurfactant as the next antimicrobial agents in pharmaceutical applications

The number of patients with antimicrobial resistance is growing as a result of new emerging microbes or overuse of antibiotics. A new substitute to the existing antimicrobial agents is important in time to come to control the mortality rate in the global population. Natural substances, like biosurfactants or commonly known as microbial surfactants could be a potential antimicrobial agent to medical personnel’s consideration as some biosurfactants exhibits antimicrobial activity. Hence, this paper will briefly highlight some of the findings from contemporary researchers who have tested different biosurfactants for potential antimicrobial activity

Linear-model-based estimation in wall turbulence: Improved stochastic forcing and eddy viscosity terms

We use Navier–Stokes-based linear models for wall-bounded turbulent flows to estimate large-scale fluctuations at different wall-normal locations from their measurements at a single wall-normal location. In these models, we replace the nonlinear term by a combination of a stochastic forcing term and an eddy dissipation term. The stochastic forcing term plays a role in energy production by the large scales, and the eddy dissipation term plays a role in energy dissipation by the small scales. Based on the results in channel flow, we find that the models can estimate large-scale fluctuations with reasonable accuracy only when the stochastic forcing and eddy dissipation terms vary with wall distance and with the length scale of the fluctuations to be estimated. The dependence on the wall distance ensures that energy production and energy dissipation are not concentrated close to the wall but are evenly distributed across the near-wall and logarithmic regions. The dependence on the length scale of the fluctuations ensures that lower wavelength fluctuations are not excessively damped by the eddy dissipation term and hence that the dominant scales shift towards lower wavelengths towards the wall. This highlights that, on the one hand, energy extraction in wall turbulence is predominantly linear and thus physics-based linear models give reasonably accurate results. On the other hand, the absence of linearly unstable modes in wall turbulence means that the nonlinear term still plays an essential role in energy extraction and thus the modelled terms should include the observed wall distance and length scale dependencies of the nonlinear term.This work was supported by the National Natural Science Foundation of China (grant nos. 91752201, 12002147 and 12050410247), the Shenzhen Science and Technology Innovation Committee (KQTD20180411143441009), the Department of Science and Technology of Guangdong Province (grant nos. 2019B21203001 and 2020B1212030001) and the Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory, Guangzhou (GML2019ZD0103). We acknowledge support from the Centers for Mechanical Engineering Research and Education at MIT and SUSTech, as well as from the Center for Computational Science and Engineering at SUSTech. AM and SJI are grateful for the financial support of the Australian Research Council

Is It Time to Call Time on Bone Marrow Biopsy for Staging Ewing Sarcoma (ES)?

Primary malignant bone sarcomas are rare and Ewing sarcoma (ES), along with osteosarcoma, predominates in teenagers and young adults. The well-established multimodality treatment incorporates systemic chemotherapy with local control in the form of surgery, with or without radiation. The presence and extent of metastases at diagnosis remains the most important prognostic factor in determining patient outcome; patients with skeletal metastases or bone marrow infiltration having a significantly worse outcome than those with lung metastases alone. There is, however, no accepted staging algorithm for ES. Large cooperative groups and national guidelines continue to advocate bone marrow biopsy (BMB) for staging but functional imaging techniques, such as 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) with computerised tomography (CT) have been increasingly used for staging cancers and whole-body magnetic resonance imaging (WB-MRI) for staging skeletal metastases. This review outlines the current literature, from which we conclude that BMB is no longer required for the staging of ES as it does not influence the standard of care management. BMB may, however, provide prognostic information and insights into the biology of ES in selected patients on prospective clinical trials

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in standard RNN models may be an over-simplification of what real neuronal networks compute. Here, we suggest that the RNN approach may be made both neurobiologically more plausible and computationally more powerful by its fusion with Bayesian inference techniques for nonlinear dynamical systems. In this scheme, we use an RNN as a generative model of dynamic input caused by the environment, e.g. of speech or kinematics. Given this generative RNN model, we derive Bayesian update equations that can decode its output. Critically, these updates define a 'recognizing RNN' (rRNN), in which neurons compute and exchange prediction and prediction error messages. The rRNN has several desirable features that a conventional RNN does not have, for example, fast decoding of dynamic stimuli and robustness to initial conditions and noise. Furthermore, it implements a predictive coding scheme for dynamic inputs. We suggest that the Bayesian inversion of recurrent neural networks may be useful both as a model of brain function and as a machine learning tool. We illustrate the use of the rRNN by an application to the online decoding (i.e. recognition) of human kinematics

The Paleoproterozoic Chibaisong Mafic-Ultramafic Intrusion and Cu-Ni Deposit, North China Craton: SHRIMP Zircon U-Pband Re-Os Geochronology and Geodynamic Implications

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Health poverty among people with type 2 diabetes mellitus (T2DM) in Malaysia

In the context of the escalating burden of diabetes in low and middle-income countries (LMICs), there is a pressing concern about the widening disparities in care and outcomes across socioeconomic groups. This paper estimates health poverty measures among individuals with type 2 diabetes mellitus (T2DM) in Malaysia. Using data from the National Diabetes Registry between 2009 and 2018, the study linked 932,855 people with T2DM aged 40–75 to death records. Cox proportional hazards models were used to estimate the 5-year survival probabilities for each patient, stratified by age and sex, while controlling for comorbidities and area-based indicators of socio-economic status (SES), such as district-level asset-based indices and night-time luminosity. Measures of health poverty, based on the Foster-Greer-Thorbecke (FGT) measures, were employed to capture excessive risk of premature mortality. Two poverty line thresholds were used, namely a 5% and 10% reduction in survival probability compared to age and sex-adjusted survival probability of the general population. Counterfactual simulations estimated the extent to which comorbidities contribute to health poverty. 43.5% of the sample experienced health poverty using the 5% threshold, and 8.9% were health poor using the 10% threshold. Comorbidities contribute 2.9% for males and 5.4% for females, at the 5% threshold. At the 10% threshold, they contribute 7.4% for males and 3.4% for females. If all patients lived in areas of highest night-light intensity, poverty would fall by 5.8% for males and 4.6% for females at the 5% threshold, and 4.1% for males and 0.8% for females at the 10% threshold. In Malaysia, there is a high incidence of health poverty among people with diabetes, and it is strongly associated with comorbidities and area-based measures of SES. Expanding the application of health poverty measurement, through a combination of clinical registries and open spatial data, can facilitate simulations for health poverty alleviation.</p

Sequence and Phylogenetic Analysis of SSU rRNA Gene of Five Microsporidia

The complete small subunit rRNA (SSU rRNA) gene sequences of five microsporidia including Nosemaheliothidis, and four novel microsporidia isolated from Pieris rapae, Phyllobrotica armta, Hemerophila atrilineata, and Bombyx mori, respectively, were obtained by PCR amplification, cloning, and sequencing. Two phylogenetic trees based on SSU rRNA sequences had been constructed by using Neighbor-Joining of Phylip software and UPGMA of MEGA4.0 software. The taxonomic status of four novel microsporidia was determined by analysis of phylogenetic relationship, length, G+C content, identity, and divergence of the SSU rRNA sequences. The results showed that the microsporidia isolated from Pieris rapae, Phyllobrotica armta, and Hemerophila atrilineata have close phylogenetic relationship with the Nosema, while another microsporidium isolated from Bombyx mori is closely related to the Endoreticulatus. So, we temporarily classify three novel species of microsporidia to genus Nosema, as Nosema sp. PR, Nosema sp. PA, Nosema sp. HA. Another is temporarily classified into genus Endoreticulatus, as Endoreticulatus sp. Zhenjiang. The result indicated as well that it is feasible and valuable to elucidate phylogenetic relationships and taxonomic status of microsporidian species by analyzing information from SSU rRNA sequences of microsporidia

Retrospective model-based inference guides model-free credit assignment

An extensive reinforcement learning literature shows that organisms assign credit efficiently, even under conditions of state uncertainty. However, little is known about credit-assignment when state uncertainty is subsequently resolved. Here, we address this problem within the framework of an interaction between model-free (MF) and model-based (MB) control systems. We present and support experimentally a theory of MB retrospective-inference. Within this framework, a MB system resolves uncertainty that prevailed when actions were taken thus guiding an MF credit-assignment. Using a task in which there was initial uncertainty about the lotteries that were chosen, we found that when participants’ momentary uncertainty about which lottery had generated an outcome was resolved by provision of subsequent information, participants preferentially assigned credit within a MF system to the lottery they retrospectively inferred was responsible for this outcome. These findings extend our knowledge about the range of MB functions and the scope of system interactions

Trends in coagulase-negative staphylococci (CoNS), England, 2010-2021.

OBJECTIVE: To review the epidemiology of coagulase-negative staphylococci (CoNS) in England over the recent 12 year period. METHODS: Laboratory-confirmed CoNS reported from sterile sites in patients in England to the UK Health Security Agency (UKHSA) between 2010 and 2021 were extracted from the national laboratory database and analysed. RESULTS: Overall, 668 857 episodes of CoNS were reported. Unspeciated CoNS accounted for 56 % (374 228) of episodes, followed by Staphylococcus epidermidis (26 %; 174 050), S. hominis (6.5 %; 43 501) and S. capitis (3.9 %; 25 773). Unspeciated CoNS increased by 8.2 % (95 % CI, 7.1-9.3) annually between 2010 and 2016, then decreased annually by 6.4 % (95 % CI: -4.8 to -7.9) until 2021. Speciated CoNS increased by 47.6 % (95 % CI, 44.5-50.9) annually between 2010 and 2016 and increased annually by 8.9 % (95 % CI: 5.1 to 12.8) until 2021. Antimicrobial susceptibility profiles differed by species. CONCLUSIONS: Reports of CoNS from normally sterile body sites in patients in England increased between 2010 and 2016 and remained stable between 2017 and 2021. There has been a striking improvement in species-level identification of CoNS in recent years. Monitoring trends in CoNS epidemiology is crucial for development of observational and clinical intervention studies on individual species