Applying Machine Based Decomposition in 2-Machine Flow Shops

The Shifting Bottleneck (SB) heuristic is among the most successful approximation methods for solving the Job Shop problem. It is essentially a machine based decomposition procedure where a series of One Machine Sequencing Problems (OMSPs) are solved. However, such a procedure has been reported to be highly ineffective for the Flow Shop problems (Jain and Meeran 2002). In particular, we show that for the 2-machine Flow Shop problem, the SB heurisitc will deliver the optimal solution in only a small number of instances. We examine the reason behind the failure of the machine based decomposition method for the Flow Shop. An optimal machine based decomposition procedure is formulated for the 2-machine Flow Shop, the time complexity of which is worse than that of the celebrated Johnsons Rule. The contribution of the present study lies in showing that the same machine based decomposition procedures which are so successful in solving complex Job Shops can also be suitably modified to optimally solve the simpler Flow Shops.

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

The Pro-Coagulant Fibrinogenolytic Serine Protease Isoenzymes Purified from <i>Daboia russelii russelii</i> Venom Coagulate the Blood through Factor V Activation: Role of Glycosylation on Enzymatic Activity

<div><p>Proteases from Russell's viper venom (RVV) induce a variety of toxic effects in victim. Therefore, four new RVV protease isoenzymes of molecular mass 32901.044 Da, 333631.179 Da, 333571.472 Da, and 34594.776 Da, were characterized in this study. The first 10 N-terminal residues of these serine protease isoenzymes showed significant sequence homology with N-terminal sequences of snake venom thrombin-like and factor V-activating serine proteases, which was reconfirmed by peptide mass fingerprinting analysis. These proteases were found to be different from previously reported factor V activators isolated from snake venoms. These proteases showed significantly different fibrinogenolytic, BAEE-esterase and plasma clotting activities but no fibrinolytic, TAME-esterase or amidolytic activity against the chromogenic substrate for trypsin, thrombin, plasmin and factor Xa. Their <i>Km</i> and <i>Vmax</i> values towards fibrinogen were determined in the range of 6.6 to 10.5 µM and 111.0 to 125.5 units/mg protein, respectively. On the basis of fibrinogen degradation pattern, they may be classified as A/B serine proteases isolated from snake venom. These proteases contain ∼42% to 44% of N-linked carbohydrates by mass whereas partially deglycosylated enzymes showed significantly less catalytic activity as compared to native enzymes. <i>In vitro</i> these protease isoenzymes induce blood coagulation through factor V activation, whereas <i>in vivo</i> they provoke dose-dependent defibrinogenation and anticoagulant activity in the mouse model. At a dose of 5 mg/kg, none of these protease isoenzymes were found to be lethal in mice or house geckos, suggesting therapeutic application of these anticoagulant peptides for the prevention of thrombosis.</p></div

Activity of protease isoenzymes against various protein and chromogenic substrates.

†<p>Specific activity of partially deglycosylasted protease isoenzymes; ND: not determined.</p>#<p>Incubated for 180 min at 37°C.</p><p>Substrate(s) for ¹trypsin, ^2,3thrombin, ⁴plasmin, ⁵FXa.</p><p>Incubation was carried out with 1% (w/v) protein substrate at pH 8.0, 37°C for 90 min or ^#180 min. For amidolytic activity assay, protease was incubated with 0.2 mM chromogenic substrate for 10 min at 37°C. Values are mean±SD of triplicate determinations. Values in the same row with different subscripts (a–e) are significantly different (P<0.05).</p

Comparison of dose-dependent fibrinogenolytic and BAEE-esterase activities of protease isoenzymes purified from RVV.

<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086823#pone-0086823-g002" target="_blank">Fig.2</a> (A) shows fibrinogenolytic activity and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086823#pone-0086823-g002" target="_blank">Fig 2(B)</a> depcits BAEE-esterase activity of purified protease isoenzymes. The values are mean ± S.D of the three experiments.</p

Dose-dependent <i>in vivo</i> defibrinogenating activity and <i>in vitro</i> blood clotting activity of RVV protease isoenzymes in mouse model.

<p>The figure (A) shows <i>in vivo</i> defibrinogenating activity and figure (B) shows <i>in vitro</i> clotting blood of control and protease-treated (2 and 4 mg/kg dose) mice after 6 h i.p. injection. The values are mean ± S.D. of triplicate determinations. Significance of difference with respect to control, *p<0.01; ^†p<0.001.</p

Multiple sequence alignment of N-terminal sequence of RVV protease isoenzymes with other known serine proteases isolated from snake venom.

<p>NR: not reported.</p