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Abstract Uncertainties in land surface processes notably limit subseasonal heat wave (HW) onset predictions. A better representation of the uncertainties in land surface processes using ensemble prediction methods may be an important way to improve HW onset predictions. However, generating ensemble members that adequately represent land surface process uncertainties, particularly those related to land surface parameters, remains challenging. In this study, a conditional nonlinear optimal perturbation related to parameters (CNOP-P) approach was employed to generate ensemble members for representing the uncertainties in land surface processes resulting from parameters. Via six strong and long-lasting HW events over the middle and lower reaches of the Yangtze River (MLYR), HW onset ensemble forecast experiments were conducted with the Weather Research and Forecasting (WRF) model. The performance of the CNOP-P approach and the traditional random parameter perturbation ensemble prediction method was evaluated. The results demonstrate that the deterministic and probabilistic skills of HW onset predictions show greater excellence using the CNOP-P approach, leading to much better predictions of extreme air temperatures than those using the traditional method. This occurred because the ensemble members generated by the CNOP-P method better represented the uncertainties in important land physical processes determining HW onsets over the MLYR, notably vegetation process uncertainties, whereas the ensemble members generated by the random parameter perturbation method could not. This finding suggests that the CNOP-P method is suitable for producing ensemble members that more appropriately represent model uncertainties through more reasonable parameter error characterization.

Seven new species of the primitive segmented spider genus Liphistius are described and assigned to species groups based on characters of the male palp and vulva plate. The bristowei group includes L. dawei Sivayyapram & Warrit, sp. nov. (♂♀) from southeastern Myanmar, L. choosaki Sivayyapram & Warrit, sp. nov. (♀) from northwestern Thailand, and L. lansak Sivayyapram & Warrit, sp. nov. (♀) from western Thailand; the trang group (Complex A) contains L. kaengkhoi Sivayyapram & Warrit, sp. nov. (♂♀), L. hintung Sivayyapram & Warrit, sp. nov. (♂♀), L. buyphradi Sivayyapram & Warrit, sp. nov. (♂♀), and L. champakpheaw Sivayyapram & Warrit, sp. nov. (♂♀) from central Thailand.

In the pursuit of better cancer classification, many studies have been conducted to identify the genes associated with cancer. However, the high dimensionality of gene expression data and the limited relevance of a few genes pose significant challenges to this endeavor. Existing gene selection methods yield divergent gene lists, further complicating the classification process. To overcome this issue, we developed a novel approach called Fuzzy Gene Selection (FGS), which combines the strengths of various gene selection methods in the field. FGS was developed using three feature selection techniques (Mutual Information, F-ClassIf, and Chi-squared) to rank genes based on their importance. These methods generated scores and rankings for each gene. Fuzzification and Defuzzification techniques were then applied to combine these scores into a single best score for each gene. This approach aids in identifying genes of significance in cancer classification, especially in multi-class scenarios. Classifiers often produce convergent decisions in such cases, where the predicted probabilities for different classes do not always correspond to the correct predicted class with the highest probability. To address this, we developed a novel Fuzzy classifier that leverages the contributions from each node’s traditional deep classifier. This novel approach combines the strengths of traditional deep classifiers at individual nodes, enabling the Fuzzy classifier to make more robust and informed predictions. The Fuzzy classifier (FC) has demonstrated improvements in accuracy and the generalization of the proposed algorithm to accurately classify different cancer types.

Quality of experience (QoE) metrics can be used to assess user perception and satisfaction in data services applications delivered over the Internet. End-to-end metrics are formed because QoE is dependent on both the users’ perception and the service used. Traditionally, network optimization has focused on improving network properties such as the quality of service (QoS). In this paper we examine adaptive streaming over a software-defined network environment. We aimed to evaluate and study the media streams, aspects affecting the stream, and the network. This was undertaken to eventually reach a stage of analysing the network’s features and their direct relationship with the perceived QoE. We then use machine learning to build a prediction model based on subjective user experiments. This will help to eliminate future physical experiments and automate the process of predicting QoE.

BackgroundPreterm birth is a major public health concern with the largest burden of morbidity and mortality falling within low- and middle-income countries (LMIC).Materials and methodsThis sequential explanatory mixed methods study was conducted in special care baby units (SCBUs) serving migrants and refugees along the Myanmar-Thailand border. It included a retrospective medical records review, qualitative interviews with mothers receiving care within SCBUs, and focus group discussions with health workers. Changes in neonatal mortality and four clinical outcomes were described. A mix of ethnographic phenomenology and implementation frameworks focused on cultural aspects, the lived experience of participants, and implementation outcomes related to SCBU care.ResultsFrom 2008–2017, mortality was reduced by 68% and 53% in very (EGA 28–32 weeks) and moderate (EGA 33–36 weeks) preterm neonates, respectively. Median SCBU stay was longer in very compared to moderate preterm neonates: 35 (IQR 22, 48 days) vs. 10 days (IQR 5, 16). Duration of treatments was also longer in very preterm neonates: nasogastric feeding lasted 82% (IQR 74, 89) vs. 61% (IQR 40, 76) of the stay, and oxygen therapy was used a median of 14 (IQR 7, 27) vs. 2 (IQR 1, 6) days respectively. Nine interviews were conducted with mothers currently receiving care in the SCBU and four focus group discussions with a total of 27 local SCBU staff. Analysis corroborated quantitative analysis of newborn care services in this setting and incorporated pertinent implementation constructs including coverage, acceptability, appropriateness, feasibility, and fidelity. Coverage, acceptability, and appropriateness were often overlapping outcomes of interest highlighting financial issues prior to or while admitted to the SCBU and social issues and support systems adversely impacting SCBU stays. Interview and FGD findings highlight the barriers in this resource-limited setting as they impact the feasibility and fidelity of providing evidence-based SCBU care that often required adaptation to fit the financial and environmental constraints imposed by this setting.DiscussionThis study provides an in-depth look at the nature of providing preterm neonatal interventions in a SCBU for a vulnerable population in a resource-limited setting. These findings support implementation of basic evidence-based interventions for preterm and newborn care globally, particularly in LMICs.

Machine learning approaches are powerful techniques commonly employed for developing cancer prediction models using associated gene expression and mutation data. This manuscript provides a comprehensive review of recent cancer studies that have employed gene expression data from several cancer types (breast, lung, kidney, ovarian, liver, central nervous system and gallbladder) for survival prediction, tumor identification and stratification. We also provide an overview of biomarker studies that are associated with these cancer types. The survey captures multiple aspects of machine learning associated cancer studies, including cancer classification, cancer prediction, identification of biomarker genes, microarray, and RNA-Seq data. We discuss the technical issues with current cancer prediction models and the corresponding measurement tools for determining the activity levels of gene expression between cancerous tissues and noncancerous tissues. Additionally, we investigate how identifying putative biomarker gene expression patterns can aid in predicting future risk of cancer and inform the provision of personalized treatment.

We determined the complete mitochondrial genome sequence of a holothurian Euapta godeffroyi belonging to the order Apodida. The complete mitogenome of E. godeffroyi was 16,410 bp in length and consisted of 13 protein-coding genes (PCGs), two ribosomal RNA genes, and 22 transfer RNA genes. The orders of PCGs and rRNAs did not match those of any recorded holothurian mitogenomes. The maximum likelihood (ML) phylogenetic tree placed E. godeffroyi as the sister group to chiridotid species and supported the monophyly of the order Apodida.

Objectives Traumatic brain injury (TBI) is a global health problem, whose management in low-resource settings is hampered by fragile health systems and lack of access to specialist services. Improvement is complex, given the interaction of multiple people, processes and institutions. We aimed to develop a mixed-method approach to understand the TBI pathway based on the lived experience of local people, supported by quantitative methodologies and to determine potential improvement targets.Design We describe a systems approach based on narrative exploration, participatory diagramming, data collection and discrete event simulation (DES), conducted by an international research collaborative.Setting The study is set in the tertiary neurotrauma centre in Yangon General Hospital, Myanmar, in 2019–2020 (prior to the SARS-CoV2 pandemic).Participants The qualitative work involved 40 workshop participants and 64 interviewees to explore the views of a wide range of stakeholders including staff, patients and relatives. The 1-month retrospective admission snapshot covered 85 surgical neurotrauma admissions.Results The TBI pathway was outlined, with system boundaries defined around the management of TBI once admitted to the neurosurgical unit. Retrospective data showed 18% mortality, 71% discharge to home and an 11% referral rate. DES was used to investigate the system, showing its vulnerability to small surges in patient numbers, with critical points being CT scanning and observation ward beds. This explorative model indicated that a modest expansion of observation ward beds to 30 would remove the flow-limitations and indicated possible consequences of changes.Conclusions A systems approach to improving TBI care in resource-poor settings may be supported by simulation and informed by qualitative work to ground it in the direct experience of those involved. Narrative interviews, participatory diagramming and DES represent one possible suite of methods deliverable within an international partnership. Findings can support targeted improvement investments despite coexisting resource limitations while indicating concomitant risks.

In this paper, recent research on terrestrial ecosystem predictability using the conditional nonlinear optimal parameter perturbation (CNOP-P) method is summarized. The main findings include the impacts of uncertainties in climate change on uncertainties in simulated terrestrial ecosystems, the identification of key physical parameters that lead to large uncertainties in terrestrial ecosystem modeling and prediction, and the evaluation of the simulation ability and prediction skill of terrestrial ecosystems by reducing key physical parameter errors. The study areas included the Inner Mongolia region, north–south transect of eastern China, and Qinghai–Tibet Plateau region. The periods of the studies were from 1961 to 1970 for the impacts of uncertainties in climate change on uncertainties in simulated terrestrial ecosystems, and from 1951 to 2000 for the identification of the most sensitive combinations of physical parameters. Climatic Research Unit (CRU) data were employed. The numerical results indicate the important role of nonlinear changes in climate variability due to the occurrences of extreme events characterized by CNOP-P in the abrupt grassland ecosystem equilibrium state and formation of carbon sinks in China. Second, the most sensitive combinations of physical parameters to the uncertainties in simulations and predictions of terrestrial ecosystems identified by the CNOP-P method were more sensitive than those obtained by traditional methods (e.g., one-at-a-time (OAT) and stochastic methods). Furthermore, the improvement extent of the simulation ability and prediction skill of terrestrial ecosystems by reducing the errors of the sensitive physical parameter combinations identified by the CNOP-P method was higher than that by the traditional methods.

To provide scientific support for improvements in land surface modeling on the Tibetan Plateau (TP) by reducing uncertainties in the physical parameters of models, comprehensive uncertainty and sensitivity evaluations were performed for the simulation of surface soil moisture (SSM). Five observational stations were selected for the study. The conditional nonlinear optimal perturbation related to parameters (CNOP-P) approach and the Common Land Model (CoLM) with 28 uncertain parameters were employed to evaluate the maximal uncertainty of the simulated SSM. The uncertainty analysis indicated that the parameter errors could induce large uncertainties. These uncertainties in the SSM generally fluctuated over the range from 0.33 to 0.64 m3 m−3 in terms of absolute changes and from 235% to 510% in terms of percentage changes. The uncertainty analysis addressed the necessity of decreasing the uncertainties in the parameters. When resources are limited, the most important and sensitive parameter set should first be identified in order to reduce uncertainties. To find this parameter set, a sensitivity analysis framework based on the CNOP-P approach was applied. The results showed that the most sensitive and important combinations of 4 parameters changed slightly among the study sites and consisted of soil texture-related parameters. Although the most sensitive and important parameter combinations only had 4 elements, the uncertainties that they could induce accounted for a large proportion of the uncertainties caused by all 28 uncertain parameters. Furthermore, the decreases in parameter errors, which were derived from the CNOP-Ps of the most sensitive and important parameter combinations, led to the maximal reductions in the uncertainties of the simulated SSM. The above results imply that we should prioritize reducing the uncertainty of sensitive parameters or parameter combinations in order to improve prediction and simulation abilities.

Abstract Soil carbon is becoming increasingly difficult to predict due to uncertainties in climate warming. The main objective of this research is to estimate the scope of uncertainties in soil carbon, which is the difference between maximal and minimal estimations of soil carbon, caused by uncertainties in climate change in China, by applying the Lund–Potsdam–Jena (LPJ) model and an improved optimization method. Based on an original optimization method (conditional nonlinear optimal parameter perturbation, CNOP‐P), a cost function is revised to evaluate the scope of uncertainties in predicting soil carbon sequestration, avoiding the overestimation of uncertainties. Two types of climate change projections are obtained from 10 global circulation models (GCMs) under the Representative Concentration Pathway (RCP) 4.5 scenario using two methods. For the first type of climate change projection, the upper and lower limits of estimated future soil carbon sequestration are 102.7 and 65.5 Gt, respectively, whereas the reference soil carbon sequestration driven by an ensemble of 10 GCMs is 79.6 Gt. The estimated future soil carbon sequestration ranges from 79.4 to 91.6 Gt for the second type of climate change. The numerical results imply that these two types of climate change, constrained by the projection of 10 GCMs, are the main factors affecting the uncertainty range of estimated soil carbon.

In Myanmar, yellow mosaic and leaf curl diseases caused by whitefly-transmitted begomoviruses are serious problems for vegetables such as tomatoes and peppers. To investigate the incidence of begomoviruses in Myanmar between 2017 and 2019, a field survey of tomato and pepper plants with virus-like symptoms was conducted in the Naypyitaw, Tatkon, and Mohnyin areas of Myanmar. Among the 59 samples subjected to begomovirus detection using polymerase chain reaction, 59.3% were infected with begomoviruses. Complete genome sequences using rolling circle amplification identified five begomovirus species: tomato yellow leaf curl Thailand virus (TYLCTHV), tomato yellow leaf curl Kanchanaburi virus (TYLCKaV), tobacco leaf curl Yunnan virus (TbLCYnV), chili leaf curl Pakistan virus (ChiLCV/PK), and tobacco curly shoot Myanmar virus (TbCSV-[Myanmar]). Excluding the previously reported TYLCTHV, three begomoviruses (ChiLCV/PK, TYLCKaV, and TbLCYnV) were identified in Myanmar for the first time. Based on the 91% demarcation threshold of begomovirus species, TbCSV-[Myanmar] was identified as a new species in this study. Among these, ChiLCV/PK and TbCSV-[Myanmar] were the most predominant in tomato and pepper fields in Myanmar. Identification of begomovirus species may be helpful for predicting the origin of viruses and preventing their spread.

Objectives: Myanmar is a World Health Organization high tuberculosis (TB) burden country with a high multidrug-resistant (MDR)-TB burden. Of significance, a high prevalence of the Beijing genotype of Mycobacterium tuberculosis (MTB) among MDR-MTB has been reported previously. A detailed genetic characterization of TB clinical isolates was performed in order to explore whether there is an association between the prevalence of the Beijing MTB genotype and MDR-TB in Myanmar. Methods: A total of 265 MDR-MTB clinical isolates collected in 2010 and 2012 were subjected to spoligotyping, mycobacterial interspersed repetitive unit–variable number tandem repeat (MIRU-VNTR) analysis, single nucleotide polymorphism (SNP) typing, and drug resistance-associated gene sequencing, including rpoC to detect potential compensatory evolution. Results: Of the total MDR-MTB isolates, 79.2% (210/265) were of the Beijing genotype, the majority of which were the ‘modern’ subtype. Beijing genotype isolates were differentiated by 15-locus MIRU-VNTR and a high clustering rate (53.0%) was observed in the modern subtype. These MIRU-VNTR patterns were similar to Beijing genotype clones spreading across Russia and Central Asia. A high prevalence of katG Ser315Thr, and genetic evidence of extensive drug resistance (XDR) and pre-XDR and compensatory mutations in rpoC were observed among clustered isolates. Conclusions: MDR-MTB strains of the Beijing genotype might be spreading in Myanmar and present a major challenge to TB control in this country. Keywords: Mycobacterium tuberculosis, Multidrug-resistant TB, Beijing genotype, MIRU-VNTR, Myanmar

Abstract The targeted observation is that the additional observation is implemented in important regions to improve a numerical forecast of a weather or climate event. These additional observations are aimed to reduce the uncertainties of the initial state with the data‐assimilation system. In the study, the targeted observation strategy is attempted in the uncertainties of the model's physical parameters to improve the numerical simulation and forecast skills of surface turbulent fluxes (sensible heat flux—SH and latent heat flux—LH). First, an approach for determining important parameter combinations based on conditional nonlinear optimal perturbation related to parameters (CNOP‐P) is employed to choose sensitive parameter combinations for the SH and LH. The characteristic of the new approach is to judge the sensitivity of the parameter combination and consider the nonlinear effect of the combination. The sensitivity of the parameter combination using the new approach is compared with that using the traditional one‐at‐a‐time (OAT) approach. The latter method ranks parameters one by one according to the sensitivity of each parameter and it fails to judge the sensitivity of the parameter combination. Within the Common Land Surface Model (CoLM), numerical results show differences between the sensitivity of the parameter combinations using the new approach and the top‐ranked sensitive parameter using the OAT approach in dry and wet regions of China for the SH and LH. Second, the benefits of forecast skills of the SH and LH are evaluated by reducing the parameter error within the sensitive parameter combination. It is found that the prediction skills of the SH and LH are improved in China. For example, elevated extents of the SH (84.3%) and LH (78.3%) calculated from the sensitive parameter combination using the new approach are greater than those of the SH (57.2%) and LH (48.5%) using the OAT approach in semi‐arid regions. This result suggests that it is practicable to apply the targeted observation to reduce the model physical parameters to improve the simulation skill. The new approach for determining parameter combinations based on the CNOP‐P is a promising method with which to explore the problems of the targeted observation to the model physical parameters.

Apicomplexan parasites of the genus Babesia cause babesiosis in humans and animals worldwide. Human babesiosis is a predominantly zoonotic disease transmitted by hard ticks that is of increasing health concern in the USA and many other countries. Microscopic examination of stained blood smears, detection of serum antibodies by immunoassays and identification of parasite nucleic acid in blood by qPCR and fluorescence in situ hybridization (FISH) are some methods available for diagnosing babesiosis. This study investigated the use of a Babesia genus-specific FISH test for detecting Babesia parasites in blood smears and immunofluorescence assay (IFA) for detecting serum antibodies to Babesia duncani and Babesia microti, two common species that cause human babesiosis in the USA. The findings with clinical samples originating from USA, Australia, Europe and elsewhere demonstrate that the parallel use of Babesia genus-specific FISH and IFA tests for B. duncani and B. microti provides more useful diagnostic information in babesiosis and that B. duncani infections are more widespread globally than presently recognized.

Investigations of the intrinsic low-frequency variability and predictability of the Kuroshio Current and of its extension jet (the Kuroshio Extension, KE) are reviewed. The Kuroshio and KE in the North Pacific constitute a western boundary current system of great relevance from climatological and ecological viewpoints. Both the Kuroshio south of Japan and the KE display remarkable changes of bimodal character on interannual time scales that are believed to be intrinsic, i.e., basically generated by nonlinear oceanic mechanisms rather than by direct atmospheric forcing. Model studies of the Kuroshio and KE with climatological forcing are thus reviewed. Moreover, as these changes are chaotic, their predictability requires peculiar mathematical approaches: theoretical results concerning this important issue are therefore reviewed as well. Model studies aimed at determining the optimal precursors and optimally growing initial errors for the Kuroshio are described. Techniques based on Lyapunov exponents (including their Lagrangian extension) and on data assimilation techniques (namely, sequential importance sampling using a particle-filtering approach) are reviewed for the KE. The key problem of how to identify the areas where targeted observations can improve the forecast is also addressed. The role of wind forcing in triggering the KE oscillations is finally considered.

To reveal key parameter-related physical mechanisms in simulating Madden-Julian Oscillation (MJO), seven physical parameters in the convection and cloud parameterization schemes of Beijing Climate Center Climate System Model (BCC_CSM1.2) are perturbed with Latin hypercube sampling method. A new strategy is proposed to select runs with good and poor MJO simulations among 85 generated ones. Outputs and parameter values from good and poor simulations are composited separately for comparison. Among the seven chosen parameters, a decreased value of precipitation efficiency for shallow convection, higher values of relative humidity threshold for low stable clouds and evaporation efficiency for deep convective precipitation are crucial to simulate a better MJO. Changes of the three parameters act together to suppress heavy precipitation and increase the frequency of light rainfall over the Indo-Pacific region, supplying more moisture in low and middle troposphere. As a result of a wetter lower troposphere ahead of the MJO main convection, the low-level moisture preconditioning along with the leading shallow convection tends to be enhanced, favorable for MJO’s further development and eastward propagation. The MJO’s further propagation across the Maritime Continent (MC) in good simulations is accompanied with more land precipitation dominated by shallow convection. Therefore, the above-mentioned three parameters are found to be crucial parameters out of the seven ones for MJO simulation, providing an inspiration for better MJO simulation and prediction with this model. This work is valuable as it highlights the key role of moisture-shallow convection feedback in the MJO dynamics.

In this paper, we analyze observational and reanalysis data to demonstrate that the Atlantic Multidecadal Oscillation (AMO) significantly modulates winter Eurasian surface air temperature through its impact on the shape, frequency and persistence of Ural blocking (UB) events that last for 10–20 d. This impact results from changes in mid-high latitude westerly winds over Eurasia associated with the warming in the Barents–Kara Seas (BKS) through the AMO-driven high sea surface temperature and sea-ice decline and resultant weakening in meridional temperature gradients. The BKS warming has a strongest positive correlation with the AMO at a time lag of about 14 years. During the recent positive AMO phase, more persistent northwest–southeast (NW–SE) oriented UB events are favored by weakened westerly winds in Eurasian mid-high latitudes. Through cold atmospheric advection and radiative cooling, such UB events produce a strong, persistent and widespread cooling over Eurasia and enhance BKS warming during 1999–2015. However, the positive AMO phase cannot directly produce the Eurasian cooling if the UB is absent. Thus, we conclude that the recent AMO phase change is a major cause of the recent winter cooling over Eurasia through its impact on BKS temperature and sea ice, which in turn affect the meridional temperature gradient, the westerly winds and the UB events.

A 1.5-layer reduced-gravity shallow-water ocean model in spherical coordinates is described and discretized in a staggered grid (standard Arakawa C-grid) with the forward-time central-space (FTCS) method and the Leap-frog finite difference scheme. The discrete Fourier analysis method combined with the Gershgorin circle theorem is used to study the stability of these two finite difference numerical models. A series of necessary conditions of selection criteria for the time-space step sizes and model parameters are obtained. It is showed that these stability conditions are more accurate than the Courant-Friedrichs-Lewy (CFL) condition and other two criterions (Blumberg and Mellor, 1987; Casulli, 1990, 1992). Numerical experiments are proposed to test our stability results, and numerical model that is designed is also used to simulate the ocean current.

We report two new localities of Hemiphyllodactylus montawaensis and one new locality of H. ngwelwini, located to the east of Taunggyi in Shan State, Myanmar. We collected five specimens from Site 1 and four specimens from Site 2. All specimens from Site 1 were identified as H. montawaensis, while Site 2 included three specimens of H. montawaensis and one specimen of H. ngwelwini. These represent the first record of the syntopic occurrence of these two species. These species were similar to each other in scalation but distinguishable by color pattern. Specimens of H. montawaensis from Site 1 did not show significant genetic differentiation from previously known localities, whereas those from Site 2 exhibited notable genetic differentiation. The new specimen of H. ngwelwini did not show significant genetic differentiation from previously known populations. [ABSTRACT FROM AUTHOR]

Uncertainties in land surface processes notably limit subseasonal heat wave (HW) onset predictions. A better representation of the uncertainties in land surface processes using ensemble prediction methods may be an important way to improve HW onset predictions. However, generating ensemble members that adequately represent land surface process uncertainties, particularly those related to land surface parameters, remains challenging. In this study, a conditional nonlinear optimal perturbation related to parameters (CNOP-P) approach was employed to generate ensemble members for representing the uncertainties in land surface processes resulting from parameters. Via six strong and long-lasting HW events over the middle and lower reaches of the Yangtze River (MLYR), HW onset ensemble forecast experiments were conducted with the Weather Research and Forecasting (WRF) model. The performance of the CNOP-P approach and the traditional random parameter perturbation ensemble prediction method was evaluated. The results demonstrate that the deterministic and probabilistic skills of HW onset predictions show greater excellence using the CNOP-P approach, leading to much better predictions of extreme air temperatures than those using the traditional method. This occurred because the ensemble members generated by the CNOP-P method better represented the uncertainties in important land physical processes determining HW onsets over the MLYR, notably vegetation process uncertainties, whereas the ensemble members generated by the random parameter perturbation method could not. This finding suggests that the CNOP-P method is suitable for producing ensemble members that more appropriately represent model uncertainties through more reasonable parameter error characterization. [ABSTRACT FROM AUTHOR]