State of AI-based monitoring in smart manufacturing and introduction to focused section

Over the past few decades, intelligentization, supported by artificial intelligence (AI) technologies, has become an important trend for industrial manufacturing, accelerating the development of smart manufacturing. In modern industries, standard AI has been endowed with additional attributes, yielding the so-called industrial artificial intelligence (IAI) that has become the technical core of smart manufacturing. AI-powered manufacturing brings remarkable improvements in many aspects of closed-loop production chains from manufacturing processes to end product logistics. In particular, IAI incorporating domain knowledge has benefited the area of production monitoring considerably. Advanced AI methods such as deep neural networks, adversarial training, and transfer learning have been widely used to support both diagnostics and predictive maintenance of the entire production process. It is generally believed that IAI is the critical technologies needed to drive the future evolution of industrial manufacturing. This article offers a comprehensive overview of AI-powered manufacturing and its applications in monitoring. More specifically, it summarizes the key technologies of IAI and discusses their typical application scenarios with respect to three major aspects of production monitoring: fault diagnosis, remaining useful life prediction, and quality inspection. In addition, the existing problems and future research directions of IAI are also discussed. This article further introduces the papers in this focused section on AI-based monitoring in smart manufacturing by weaving them into the overview, highlighting how they contribute to and extend the body of literature in this area

Genetic analysis of amyotrophic lateral sclerosis identifies contributing pathways and cell types

Despite the considerable progress in unraveling the genetic causes of amyotrophic lateral sclerosis (ALS), we do not fully understand the molecular mechanisms underlying the disease. We analyzed genome-wide data involving 78,500 individuals using a polygenic risk score approach to identify the biological pathways and cell types involved in ALS. This data-driven approach identified multiple aspects of the biology underlying the disease that resolved into broader themes, namely, neuron projection morphogenesis, membrane trafficking, and signal transduction mediated by ribonucleotides. We also found that genomic risk in ALS maps consistently to GABAergic interneurons and oligodendrocytes, as confirmed in human single-nucleus RNA-seq data. Using two-sample Mendelian randomization, we nominated six differentially expressed genes (ATG16L2, ACSL5, MAP1LC3A, MAPKAPK3, PLXNB2, and SCFD1) within the significant pathways as relevant to ALS. We conclude that the disparate genetic etiologies of this fatal neurological disease converge on a smaller number of final common pathways and cell types

Characterization of Changes in Serum Anti-Glycan Antibodies in Crohn's Disease – a Longitudinal Analysis

INTRODUCTION: Anti-glycan antibodies are a promising tool for differential diagnosis and disease stratification of patients with Crohn's disease (CD). We longitudinally assessed level and status changes of anti-glycan antibodies over time in individual CD patients as well as determinants of this phenomenon. METHODS: 859 serum samples derived from a cohort of 253 inflammatory bowel disease (IBD) patients (207 CD, 46 ulcerative colitis (UC)) were tested for the presence of anti-laminarin (Anti-L), anti-chitin (Anti-C), anti-chitobioside (ACCA), anti-laminaribioside (ALCA), anti-mannobioside (AMCA) and anti-Saccharomyces cerevisiae (gASCA) antibodies by ELISA. All patients had at least two and up to eleven serum samples taken during the disease course. RESULTS: Median follow-up time for CD was 17.4 months (Interquartile range (IQR) 8.0, 31.6 months) and for UC 10.9 months (IQR 4.9, 21.0 months). In a subgroup of CD subjects marked changes in the overall immune response (quartile sum score) and levels of individual markers were observed over time. The marker status (positive versus negative) remained widely stable. Neither clinical phenotype nor NOD2 genotype was associated with the observed fluctuations. In a longitudinal analysis neither changes in disease activity nor CD behavior led to alterations in the levels of the glycan markers. The ability of the panel to discriminate CD from UC or its association with CD phenotypes remained stable during follow-up. In the serum of UC patients neither significant level nor status changes were observed. CONCLUSIONS: While the levels of anti-glycan antibodies fluctuate in a subgroup of CD patients the antibody status is widely stable over time

The Relationship between Anthropometry and Split Performance in Recreational Male Ironman Triathletes

Purpose: The aim of this study was to investigate the relation between anthropometric variables and total race time including split times in 184 recreational male Ironman triathletes. Methods: Body mass, body height, body mass index, lengths and circumferences of limbs, thicknesses of skin-folds, sum of skin-fold thicknesses, and percent body fat were related to total race time including split times using correlation analysis and effect size. Results: A large effect size (r>0.37) was found for the association between body mass index and time in the run split and between both the sum of skin-folds and percent body fat with total race time. A medium effect size (r=0.24-0.36) was observed in the association between body mass and both the split time in running and total race time, between body mass index and total race time, between both the circumferences of upper arm and thigh with split time in the run and between both the sum of skin-folds and percent body fat with split times in swimming, cycling and running. Conclusions: The results of this study showed that lower body mass, lower body mass index and lower body fat were associated with both a faster Ironman race and a faster run split; lower circumferences of upper arm and thigh were also related with a faster run split

Pairing of Homologous Regions in the Mouse Genome Is Associated with Transcription but Not Imprinting Status

This work was funded by the BBSRC, grant BB/H088071/1 (www.bbsrc.ac.uk), MRC, grant G0700760 (www.mrc.ac.uk), Wellcome Trust, grant 095645/Z/11/Z (www.wellcome.ac.uk) and the EU through EpiGeneSys (www.epigenesys.eu) and Blueprint (www.blueprint-epigenome.eu). C.K. was funded by the DFG, personal fellowship KR 3317/2-1 (www.dfg.de) and CTR, personal short term fellowship (www.trophoblast.cam.ac.uk). M.J.H. received funding through grant NCI/NIH 2RO1 CA089426 (www.nih.gov)

In quest of a systematic framework for unifying and defining nanoscience

This article proposes a systematic framework for unifying and defining nanoscience based on historic first principles and step logic that led to a “central paradigm” (i.e., unifying framework) for traditional elemental/small-molecule chemistry. As such, a Nanomaterials classification roadmap is proposed, which divides all nanomatter into Category I: discrete, well-defined and Category II: statistical, undefined nanoparticles. We consider only Category I, well-defined nanoparticles which are >90% monodisperse as a function of Critical Nanoscale Design Parameters (CNDPs) defined according to: (a) size, (b) shape, (c) surface chemistry, (d) flexibility, and (e) elemental composition. Classified as either hard (H) (i.e., inorganic-based) or soft (S) (i.e., organic-based) categories, these nanoparticles were found to manifest pervasive atom mimicry features that included: (1) a dominance of zero-dimensional (0D) core–shell nanoarchitectures, (2) the ability to self-assemble or chemically bond as discrete, quantized nanounits, and (3) exhibited well-defined nanoscale valencies and stoichiometries reminiscent of atom-based elements. These discrete nanoparticle categories are referred to as hard or soft particle nanoelements. Many examples describing chemical bonding/assembly of these nanoelements have been reported in the literature. We refer to these hard:hard (H-n:H-n), soft:soft (S-n:S-n), or hard:soft (H-n:S-n) nanoelement combinations as nanocompounds. Due to their quantized features, many nanoelement and nanocompound categories are reported to exhibit well-defined nanoperiodic property patterns. These periodic property patterns are dependent on their quantized nanofeatures (CNDPs) and dramatically influence intrinsic physicochemical properties (i.e., melting points, reactivity/self-assembly, sterics, and nanoencapsulation), as well as important functional/performance properties (i.e., magnetic, photonic, electronic, and toxicologic properties). We propose this perspective as a modest first step toward more clearly defining synthetic nanochemistry as well as providing a systematic framework for unifying nanoscience. With further progress, one should anticipate the evolution of future nanoperiodic table(s) suitable for predicting important risk/benefit boundaries in the field of nanoscience

Food Use and Health Effects of Soybean and Sunflower Oils

This review provides a scientific assessment of current knowledge of health effects of soybean oil (SBO) and sunflower oil (SFO). SBO and SFO both contain high levels of polyunsaturated fatty acids (PUFA) (60.8 and 69%, respectively), with a PUFA:saturated fat ratio of 4.0 for SBO and 6.4 for SFO. SFO contains 69% C18:2n-6 and less than 0.1% C18:3n-3, while SBO contains 54% C18:2n-6 and 7.2% C18:3n-3. Thus, SFO and SBO each provide adequate amounts of C18:2n-6, but of the two, SBO provides C18:3n-3 with a C18:2n-6:C18:3n-3 ratio of 7.1. Epidemiological evidence has suggested an inverse relationship between the consumption of diets high in vegetable fat and blood pressure, although clinical findings have been inconclusive. Recent dietary guidelines suggest the desirability of decreasing consumption of total and saturated fat and cholesterol, an objective that can be achieved by substituting such oils as SFO and SBO for animal fats. Such changes have consistently resulted in decreased total and low-density-lipoprotein cholesterol, which is thought to be favorable with respect to decreasing risk of cardiovascular disease. Also, decreases in high-density-lipoprotein cholesterol have raised some concern. Use of vegetable oils such as SFO and SBO increases C18:2n-6, decreases C20:4n-6, and slightly elevated C20:5n-3 and C22:6n-3 in platelets, changes that slightly inhibit platelet generation of thromboxane and ex vivo aggregation. Whether chronic use of these oils will effectively block thrombosis at sites of vascular injury, inhibit pathologic platelet vascular interactions associated with atherosclerosis, or reduce the incidence of acute vascular occlusion in the coronary or cerebral circulation is uncertain. Linoleic acid is needed for normal immune response, and essential fatty acid (EFA) deficiency impairs B and T cell-mediated responses. SBO and SFO can provide adequate linoleic acid for maintenance of the immune response. Excess linoleic acid has supported tumor growth in animals, an effect not verified by data from diverse human studies of risk, incidence, or progression of cancers of the breast and colon. Areas yet to be investigated include the differential effects of n-6- and n-3-containing oil on tumor development in humans and whether shorter-chain n-3 PUFA of plant origin such as found in SBO will modulate these actions of linoleic acid, as has been shown for the longer-chain n-3 PUFA of marine oil

Antiinflammatory Therapy with Canakinumab for Atherosclerotic Disease

Background: Experimental and clinical data suggest that reducing inflammation without affecting lipid levels may reduce the risk of cardiovascular disease. Yet, the inflammatory hypothesis of atherothrombosis has remained unproved. Methods: We conducted a randomized, double-blind trial of canakinumab, a therapeutic monoclonal antibody targeting interleukin-1β, involving 10,061 patients with previous myocardial infarction and a high-sensitivity C-reactive protein level of 2 mg or more per liter. The trial compared three doses of canakinumab (50 mg, 150 mg, and 300 mg, administered subcutaneously every 3 months) with placebo. The primary efficacy end point was nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death. RESULTS: At 48 months, the median reduction from baseline in the high-sensitivity C-reactive protein level was 26 percentage points greater in the group that received the 50-mg dose of canakinumab, 37 percentage points greater in the 150-mg group, and 41 percentage points greater in the 300-mg group than in the placebo group. Canakinumab did not reduce lipid levels from baseline. At a median follow-up of 3.7 years, the incidence rate for the primary end point was 4.50 events per 100 person-years in the placebo group, 4.11 events per 100 person-years in the 50-mg group, 3.86 events per 100 person-years in the 150-mg group, and 3.90 events per 100 person-years in the 300-mg group. The hazard ratios as compared with placebo were as follows: in the 50-mg group, 0.93 (95% confidence interval [CI], 0.80 to 1.07; P = 0.30); in the 150-mg group, 0.85 (95% CI, 0.74 to 0.98; P = 0.021); and in the 300-mg group, 0.86 (95% CI, 0.75 to 0.99; P = 0.031). The 150-mg dose, but not the other doses, met the prespecified multiplicity-adjusted threshold for statistical significance for the primary end point and the secondary end point that additionally included hospitalization for unstable angina that led to urgent revascularization (hazard ratio vs. placebo, 0.83; 95% CI, 0.73 to 0.95; P = 0.005). Canakinumab was associated with a higher incidence of fatal infection than was placebo. There was no significant difference in all-cause mortality (hazard ratio for all canakinumab doses vs. placebo, 0.94; 95% CI, 0.83 to 1.06; P = 0.31). Conclusions: Antiinflammatory therapy targeting the interleukin-1β innate immunity pathway with canakinumab at a dose of 150 mg every 3 months led to a significantly lower rate of recurrent cardiovascular events than placebo, independent of lipid-level lowering. (Funded by Novartis; CANTOS ClinicalTrials.gov number, NCT01327846.