Image Inpainting Algorithm for Region Filling Application

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Concomitant yield optimization of tannase and gallic acid by Bacillus licheniformis KBR6 through submerged fermentation : an industrial approach

The present study is concerned with the evaluation of tannase and gallic acid production effi cacy of Bacillus licheniformis KBR6 under diff erent environmental conditions through submerged fermentation. Results have shown that diff erent environmental conditions and mineral sources have diff erential infl uences on tannase and gallic acid production. Highest tannase and gallic acid yield was observed at incubation period of 18 h and 22 h, respectively. At tannic acid concentration of 15 g/l, maximum cell mass (0.75 g/l), cell yield coeffi cient (0.08 g/g), specifi c growth rate (37.5 mg/g/h), tannase yield (16.3 U/g) and specifi c tannase production rate (0.80 U/g/h) were observed, however, at higher tannic acid concentration a decrease in tannase yield and production rate were observed, but gallic acid production increased with increasing tannic acid concentration. Additional carbohydrate sources like glucose, fructose, and lactose showed positive infl uence on enzyme yield. Among the studied nitrogen sources urea and NH4Cl, and of the phosphate sources KH2PO4 showed favourable eff ects on cell growth and simultaneous enzyme and gallic acid production. Temperature of 35 °C was found to be optimum for tannase and gallic acid production. Of all the studied metal ions Ca2+, Mg2+ and Na+ showed positive eff ect whereas, Co2+, Ag2+, Pb2+, Hg2+ showed inhibitory eff ects

An Insight into the Gelatinization Properties Influencing the Modified Starches Used in Food Industry: A review

AbstractNative starch is subjected to various forms of modification to improve its structural, mechanical, and thermal properties for wider applications in the food industry. Physical, chemical, and dual modifications have a substantial effect on the gelatinization properties of starch. Consequently, this review explores and compares the different methods of starch modification applicable in the food industry and their effect on the gelatinization properties such as onset temperature (To), peak gelatinization temperature (Tp), end set temperature (Tc), and gelatinization enthalpy (ΔH), studied using differential scanning calorimetry (DSC). Chemical modifications including acetylation and acid hydrolysis decrease the gelatinization temperature of starch whereas cross-linking and oxidation result in increased gelatinization temperatures. Common physical modifications such as heat moisture treatment and annealing also increase the gelatinization temperature. The gelatinization properties of modified starch can be applied for the improvement of food products such as ready-to-eat, easily heated or frozen food, or food products with longer shelf life

Language models show human-like content effects on reasoning

Abstract reasoning is a key ability for an intelligent system. Large language models achieve above-chance performance on abstract reasoning tasks, but exhibit many imperfections. However, human abstract reasoning is also imperfect, and depends on our knowledge and beliefs about the content of the reasoning problem. For example, humans reason much more reliably about logical rules that are grounded in everyday situations than arbitrary rules about abstract attributes. The training experiences of language models similarly endow them with prior expectations that reflect human knowledge and beliefs. We therefore hypothesized that language models would show human-like content effects on abstract reasoning problems. We explored this hypothesis across three logical reasoning tasks: natural language inference, judging the logical validity of syllogisms, and the Wason selection task (Wason, 1968). We find that state of the art large language models (with 7 or 70 billion parameters; Hoffman et al., 2022) reflect many of the same patterns observed in humans across these tasks -- like humans, models reason more effectively about believable situations than unrealistic or abstract ones. Our findings have implications for understanding both these cognitive effects, and the factors that contribute to language model performance

Tell me why! Explanations support learning relational and causal structure

Inferring the abstract relational and causal structure of the world is a major challenge for reinforcement-learning (RL) agents. For humans, language--particularly in the form of explanations--plays a considerable role in overcoming this challenge. Here, we show that language can play a similar role for deep RL agents in complex environments. While agents typically struggle to acquire relational and causal knowledge, augmenting their experience by training them to predict language descriptions and explanations can overcome these limitations. We show that language can help agents learn challenging relational tasks, and examine which aspects of language contribute to its benefits. We then show that explanations can help agents to infer not only relational but also causal structure. Language can shape the way that agents to generalize out-of-distribution from ambiguous, causally-confounded training, and explanations even allow agents to learn to perform experimental interventions to identify causal relationships. Our results suggest that language description and explanation may be powerful tools for improving agent learning and generalization.Comment: ICML 2022; 23 page

Mortality in Norway and Sweden during the COVID-19 pandemic

Background: Norway and Sweden are similar countries in terms of socioeconomics and health care. Norway implemented extensive COVID-19 measures, such as school closures and lockdowns, whereas Sweden did not. Aims: To compare mortality in Norway and Sweden, two similar countries with very different mitigation measures against COVID-19. Methods: Using real-world data from national registries, we compared all-cause and COVID-19-related mortality rates with 95% confidence intervals (CI) per 100,000 person-weeks and mortality rate ratios (MRR) comparing the five preceding years (2015–2019) with the pandemic year (2020) in Norway and Sweden. Results: In Norway, all-cause mortality was stable from 2015 to 2019 (mortality rate 14.6–15.1 per 100,000 person-weeks; mean mortality rate 14.9) and was lower in 2020 than from 2015 to 2019 (mortality rate 14.4; MRR 0.97; 95% CI 0.96–0.98). In Sweden, all-cause mortality was stable from 2015 to 2018 (mortality rate 17.0–17.8; mean mortality rate 17.1) and similar to that in 2020 (mortality rate 17.6), but lower in 2019 (mortality rate 16.2). Compared with the years 2015–2019, all-cause mortality in the pandemic year was 3% higher due to the lower rate in 2019 (MRR 1.03; 95% CI 1.02–1.04). Excess mortality was confined to people aged ⩾70 years in Sweden compared with previous years. The COVID-19-associated mortality rates per 100,000 person-weeks during the first wave of the pandemic were 0.3 in Norway and 2.9 in Sweden. Conclusions: All-cause mortality in 2020 decreased in Norway and increased in Sweden compared with previous years. The observed excess deaths in Sweden during the pandemic may, in part, be explained by mortality displacement due to the low all-cause mortality in the previous year

Can language models learn from explanations in context?

Large language models can perform new tasks by adapting to a few in-context examples. For humans, rapid learning from examples can benefit from explanations that connect examples to task principles. We therefore investigate whether explanations of few-shot examples can allow language models to adapt more effectively. We annotate a set of 40 challenging tasks from BIG-Bench with explanations of answers to a small subset of questions, as well as a variety of matched control explanations. We evaluate the effects of various zero-shot and few-shot prompts that include different types of explanations, instructions, and controls on the performance of a range of large language models. We analyze these results using statistical multilevel modeling techniques that account for the nested dependencies among conditions, tasks, prompts, and models. We find that explanations of examples can improve performance. Adding untuned explanations to a few-shot prompt offers a modest improvement in performance; about 1/3 the effect size of adding few-shot examples, but twice the effect size of task instructions. We then show that explanations tuned for performance on a small validation set offer substantially larger benefits; building a prompt by selecting examples and explanations together substantially improves performance over selecting examples alone. Hand-tuning explanations can substantially improve performance on challenging tasks. Furthermore, even untuned explanations outperform carefully matched controls, suggesting that the benefits are due to the link between an example and its explanation, rather than lower-level features of the language used. However, only large models can benefit from explanations. In summary, explanations can support the in-context learning abilities of large language models o

Sensitivity analysis and automation for intraoperative implementation of the atlas-based method for brain shift correction

ABSTRACT The use of biomechanical models to correct the misregistration due to deformation in image guided neurosurgical systems has been a growing area of investigation. In previous work, an atlas-based inverse model was developed to account for soft-tissue deformations during image-guided surgery. Central to that methodology is a considerable amount of pre-computation and planning. The goal of this work is to evaluate techniques that could potentially reduce that burden. Distinct from previous manual techniques, an automated segmentation technique is described for the cerebrum and dural septa. The shift correction results using this automated segmentation method were compared to those using the manual methods. In addition, the extent and distribution of the surgical parameters associated with the deformation atlas were investigated by a sensitivity analysis using simulation experiments and clinical data. The shift correction results did not change significantly using the automated method (correction of 73±13% ) as compared to the semi-automated method from previous work (correction of 76±13%). The results of the sensitivity analysis show that the atlas could be constructed by coarser sampling (six fold reduction) without substantial degradation in the shift reconstruction, a decrease in preoperative computational time from 13.1±3.5 hours to 2.2±0.6 hours. The automated segmentation technique and the findings of the sensitivity study have significant impact on the reduction of pre-operative computational time, improving the utility of the atlas-based method. The work in this paper suggests that the atlas-based technique can become a 'time of surgery' setup procedure rather than a pre-operative computing strategy

Childhood tonsillectomy alters the primary distribution of HPV‐related oropharyngeal squamous cell carcinoma

ObjectivesWe investigated how tonsillectomy during childhood may influence the distribution of human papillomavirus (HPV) positive cancer of the tonsils in adult life using p16 as a surrogate marker for HPV infection.Study DesignRetrospective observational study.MethodsA total of 280 patients diagnosed with oropharyngeal squamous cell carcinoma (OPSCC) and known p16 status were eligible for this study. Each participant was called to obtain the childhood tonsillectomy history. Respondents were subgrouped by p16 status and the primary tumor location. Patient demographic and clinical information was analyzed for association with Fisher’s exact and Wilcoxon rank sum tests. Location of tumor was modeled using univariate (UVA) and multivariate (MVA) logistic regression with associated odds ratios (OR) and 95% confidence intervals.ResultsOf the 280 patients, 115 (41%) were respondents: 104 (90.4%) were p16 positive and 11 (9.6%) were p16 negative. For p16 positive patients, we observed a majority (93%) of intact tonsils in those with tonsil cancer, compared to 45% of intact tonsils in patients with p16 positive cancer elsewhere in the oropharynx (P < .001). MVA logistic regression showed that female gender (OR = 4.16, P = .0675), prior smoking history (OR = 2.6, P = .0367), and intact tonsils (OR = 15.2, P < .0001) were associated with tonsillar OPSCC.ConclusionWe found that patients with p16 positive OPSCC at a non‐tonsil site were much more likely to have had prior tonsillectomy vs those with p16 positive OPSCC arising within the tonsil. Nevertheless, we do not advocate tonsillectomies as a public health policy to reduce HPV‐related OPSCC.Level of Evidence6Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154902/1/lio2342_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154902/2/lio2342.pd