Research on prediction of China’s financial systematic risk based on the hybrid model

An important feature of the outbreak of systemic financial risk is that the linkage and contagion of risk amongst the various sub-markets of the financial system have increased significantly. In addition, research on the prediction of systemic financial risk plays a significant role in the sustainable development of the financial market. Therefore, this paper takes China’s financial market as its research object, considers the risks co-activity among major financial sub-markets, and constructs a financial composite indicator of systemic stress (CISS) for China, describing its financial systemic stress based on 12 basic indicators selected from the money market, bond market, stock market, and foreign exchange market. Furthermore, drawing on the decomposition and integration technology in the TEI@I complex system research methodology, this paper introduces advanced variational mode decomposition (VMD) technology and extreme learning machine (ELM) algorithms, constructing the VMD-DE-ELM hybrid model to predict the systemic risk of China’s financial market. According to e RMSE , e MAE , and e MAPE , the prediction model’s multistep-ahead forecasting effect is evaluated. The empirical results show that the China’s financial CISS constructed in this paper can effectively identify all kinds of risk events in the sample range. The results of a robustness test show that the overall trend of China’s financial CISS and its ability to identify risk events are not affected by parameter selection and have good robustness. In addition, compared with the benchmark model, the VMD-DE-ELM hybrid model constructed in this paper shows superior predictive ability for systemic financial risk.

On the Subrange and Its Application to the R-Chart

The conventional sample range is widely used for the construction of an R-chart. In an R-chart, the sample range estimates the standard deviation, especially in the case of a small sample size. It is well known that the performance of the sample range degrades in the case of a large sample size. In this paper, we investigate the sample subrange as an alternative to the range. This subrange includes the range as a special case. We recognize that we can improve the performance of estimating the standard deviation by using the subrange, especially in the case of a large sample size. Note that the original sample range is biased. Thus, the correction factor is used to make it unbiased. Likewise, the original subrange is also biased. In this paper, we provide the correction factor for the subrange. To compare the sample subranges with different trims to the conventional sample range or the sample standard deviation, we provide the theoretical relative efficiency and its values, which can be used to select the best trim of the subrange with the sense of maximizing the relative efficiency. For a practical guideline, we also provide a simple formula for the best trim amount, which is obtained by the least-squares method. It is worth noting that the breakdown point of the conventional sample range is always zero, while that of the sample subrange increases proportionally to a trim amount. As an application of the proposed method, we illustrate how to incorporate it into the construction of the R-chart.

Automated Peripheral Blood Cell Differentiation Using Artificial Intelligence - a Study with More Than 10,000 Routine Samples in a Specialized Leukemia Laboratory

Background: Cytomorphology is the gold standard for quick assessment of peripheral blood (PB) and bone marrow samples in hematological neoplasms and is used to orchestrate specific diagnostics. Artificial Intelligence (AI) promises to provide an unbiased way of interrogating blood smear data as reproducibility varies across labs. This is a prospective clinical study (ClinicalTrials.gov Identifier: NCT04466059) conducted on our approach outlined at ASH 2020. Aim: Use an AI model to classify cell images to produce differential counts of PB smears side-by-side to routine diagnostics. Methods: We enrolled 10,082 patient samples which were sent to our lab between 01/2021 and 07/2021 for cytomorphology with a suspected hematologic neoplasm. Blood smears were differentiated by highly skilled technicians (median 5y in lab) and all were reviewed by hematologists. In parallel, all samples were scanned on a MetaSystems (Altlussheim, Germany) Metafer Scanning System (Zeiss (Oberkochen, Germany) Axio Imager.Z2 microscope, automatic slide feeder). Areas of interest were defined and leukocyte positions were flagged by pre-scan in 10x magnification followed by high resolution scan in 40x to generate cell images for analysis. We set up a supervised Machine Learning model based on ImageNet-pretrained Xception using Amazon Sagemaker (AS) and trained it on 8,425 carefully annotated color images to identify 21 predefined classes (including 1 garbage class). Overall accuracy of this model against hold-out-set (10%) was 96%. The algorithm consumes 144x144pixel cell images and produces probability scores (PS) for each class in every image. Results: For routine diagnostics in median 100 cells/sample (range 82 - 103) were differentiated manually, overall 988,130. The automated process gathered 500 cell images/sample (range 101 - 500), overall 4,937,389. Average capture times for 500 cells: 4:37 min. Cropped images were uploaded to a cloud storage and exposed to an AS endpoint to initiate classification and the computation of a PS for each of the predefined 21 classes in the model. For the study we only considered images with a probability of at least 90% (n=3,781,670/4,937,389) and excluded normoblasts, smudge cells and images identified as garbage (together n=2,120,258). Final diagnosis included: no lymphoma detectable (2,186), MDS (1,152), AML (369), in these 11 APL, MPN (658), CLL (558), other mature B-cell neoplasms (377), CML (326), multiple myeloma (155), but also rare entities such as hairy cell leukemia variant (2) or PPBL (3). Comparing the benign normal cells in peripheral blood we identified (all values normalized) segmented neutrophils (manual (M): 516,648=52% vs AI: 882,538=53%), eosinophils (M: 24,860=2.52% vs. AI: 55,699=3.36%), basophils (M: 7159=0,72% vs. AI: 11,957=0,72%), monocytes (M: 74,113=7.5% vs. AI: 110,126=6.64%), lymphocytes (M: 313,518=31.7% vs. AI: 399,249=24%). Pathogenic blasts were detected in 16,048 (0.97%) images by AI (M: 16,290=1.65%). In routine diagnostics 536 cases with blast cells, including "questionable blasts" were identified. The AI identified 493 (91%) of these cases. At least one atypical/malignant lymphocyte was found in 2,323 samples manually, out of which the AI identified 2,279 (98%). In few cases manual differentiation relies on the number of pathogenic cells from an immunophenotyping analysis, which the AI does not had. During the course of the study by chance we identified at least 3 instances, were the AI detected pathogenic cells (blasts, atypical promyelocytes (APL) or bilobulated promyelocytes (APL-v)) which were initially missed manually (in some case WBC below .5 G/l) or flagged during subsequent immunophenotyping/molecular genetic analysis. Upon manually revisiting the smear, we could verify the presence of the AI-anticipated cells, revealing the higher sensitivity of the 5 time increase in cells/sample investigated by AI and power of algorithms. Conclusion: We present data of a prospective, blinded clinical study comparing blood smear analysis between humans and AI head-to-head. The concordance is extremely high with 95% for pathogenic cases. Misclassified cells are used for retraining to continuously improve the model and benefit from large datasets even for rare cell types. The model's cloud based implementation makes it easy to connect scanning devices for automated, unbiased classification. Disclosures Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Kern: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership.

Proximate Composition of Three Head Organs (Brain, Eyes, Tongue), Three Visceral Organs (Liver, Heart, Gizzard), Skin and Muscle of Muscovy Duck- hen

The proximate compositions of brain (A), eyes (B), tongue (E), liver (D), heart (F), gizzard (C), skin (H) and muscle (G) of Muscovy duck-hen were determined. The proximate composition values ranged as follows (values in g/100g on dry weight basis) ash (0.18 – 3.77 ± 1.40), moisture (0.50 – 4.78± 1.40), protein (3.24 – 79.9 ± 29.7), fat (0.23 – 5.60 ± 2.04), carbohydrate (6.19 – 95.8 ± 33.6), dry matter (95.22 – 99.5± 1.40) and organic matter (91.45 – 99.27± 2.63) with all the parameters being significantly different among the samples. Metabolizable energy contribution from protein, fat and carbohydrate in the samples ranged from (kJ/100g/kcal/100g): 740(180) – 7924(1864). Percentage energy contribution range was 5.53/5.70 – 59.2/59.1. Whereas the crude fat ranged from 0.23 – 5.60 g/100g, the total fatty acid (TFA) ranged from 0.217 – 5.08 g/100g or EPg/100g with corresponding energy of (kJ/100g versus kcal/100g): 8.51/2.07 – 207/50.4 and 8.03/1.95 – 188/45.7 respectively. UEDP% (assuming 60% energy utilization) range was 1.95/1.96 – 49.0/48.9. Approximate sample weight equivalents to the energy requirements of adults and infants had ranges of : for 2500kcal per day, sample range was 617 – 644g (adults) and at 3000kcal per day, requirement was 741 – 773g (adults); infant at 740kcal would require 183 – 191g. Water balance for protein metabolism had value range of 6.48 – 160ml. Correlational analyses of samples at r=0.01 gave these results: A/B (0.3024), B/E (0.1794), A/E (0.9916), C/D (0.9994), D/F (0.9892), C/F (0.9923) and G/H (-0.2014). Hence, Muscovy duck-hens are good sources of protein, metabolizable energy and low fat.

LeMMINGs – II. The e-MERLIN legacy survey of nearby galaxies. The deepest radio view of the Palomar sample on parsec scale

ABSTRACT We present the second data release of high-resolution (≤0.2 arcsec) 1.5-GHz radio images of 177 nearby galaxies from the Palomar sample, observed with the e-MERLIN array, as part of the Legacy e-MERLIN Multi-band Imaging of Nearby Galaxies Sample (LeMMINGs) survey. Together with the 103 targets of the first LeMMINGs data release, this represents a complete sample of 280 local active (LINER and Seyfert) and inactive galaxies (H ii galaxies and absorption line galaxies, ALG). This large program is the deepest radio survey of the local Universe, ≳1017.6 W Hz−1, regardless of the host and nuclear type: we detect radio emission ≳0.25 mJy beam−1 for 125/280 galaxies (44.6 per cent) with sizes of typically ≲100 pc. Of those 125, 106 targets show a core which coincides within 1.2 arcsec with the optical nucleus. Although we observed mostly cores, around one third of the detected galaxies features jetted morphologies. The detected radio core luminosities of the sample range between ∼1034 and 1040 erg s−1. LINERs and Seyferts are the most luminous sources, whereas H ii galaxies are the least. LINERs show FR I-like core-brightened radio structures while Seyferts reveal the highest fraction of symmetric morphologies. The majority of H ii galaxies have single radio core or complex extended structures, which probably conceal a nuclear starburst and/or a weak active nucleus (seven of them show clear jets). ALGs, which are typically found in evolved ellipticals, although the least numerous, exhibit on average the most luminous radio structures, similar to LINERs.

Alcohol Withdrawal Cases As a Consequence of COVID-19: A Clinical Analysis

Background: The COVID-19 pandemic has been followed by the shutting down of bars and liquor shops. This condition has led to the acute unavailability of alcohol, and subsequently increasing the number of cases of alcohol withdrawal. Objectives: This paper reports the clinical profile of cases of alcohol withdrawal presented to the psychiatry department following the non-availability of alcohol due to the COVID-19 lockdown. Methods: The patients were referred to the psychiatry department from the emergency medicine department and some of them directly to the psychiatry department. Thirty-two patients with alcohol use disorder and alcohol withdrawal were included in the study. The study data were collected using a semi-structured proforma and then were tabulated. The obtained data were assessed by the Chi-square test and unpaired t-test where appropriate. Results: The Mean±SD age of the study patients were 38.84±11.64 years. The Mean±SD years of consumption of alcohol was 13.50±7.8 years in the sample (range 1-30 years) and Mean±SD days of last consumption of alcohol was 3.88±1.8 days (range 2-10 days). There were no significant differences between stockers and non-stockers in various parameters. Conclusion: Alcohol and substance withdrawal have increased in the wake of the lockdown and COVID-19 pandemic and there is a need for non-COVID-19 setups to be created to cater to the needs of these patients.

Unique Genomic Landscape of High-Grade Neuroendocrine Cervical Carcinoma: Implications for Rethinking Current Treatment Paradigms

PURPOSE High-grade neuroendocrine cervical cancer (HGNECC) is an uncommon malignancy with limited therapeutic options; treatment is patterned after the histologically similar small-cell lung cancer (SCLC). To better understand HGNECC biology, we report its genomic landscape. PATIENTS AND METHODS Ninety-seven patients with HGNECC underwent comprehensive genomic profiling (182-315 genes). These results were subsequently compared with a cohort of 1,800 SCLCs. RESULTS The median age of patients with HGNECC was 40.5 years; 83 patients (85.6%) harbored high-risk human papillomavirus (HPV). Overall, 294 genomic alterations (GAs) were identified (median, 2 GAs/sample; average, 3.0 GAs/sample, range, 0-25 GAs/sample) in 109 distinct genes. The most frequently altered genes were PIK3CA (19.6% of cohort), MYC (15.5%), TP53 (15.5%), and PTEN (14.4%). RB1 GAs occurred in 4% versus 32% of HPV-positive versus HPV-negative tumors ( P < .0001). GAs in HGNECC involved the following pathways: PI3K/AKT/mTOR (41.2%); RAS/MEK (11.3%); homologous recombination (9.3%); and ERBB (7.2%). Two tumors (2.1%) had high tumor mutational burden (TMB; both with MSH2 alterations); 16 (16.5%) had intermediate TMB. Seventy-one patients (73%) had ≥ 1 alteration that was theoretically druggable. Comparing HGNECC with SCLC, significant differences in TMB, microsatellite instability, HPV-positive status, and in PIK3CA, MYC, PTEN, TP53, ARID1A, and RB1 alteration rates were found. CONCLUSION This large cohort of patients with HGNECC demonstrated a genomic landscape distinct from SCLC, calling into question the biologic and therapeutic relevance of the histologic similarities between the entities. Furthermore, 73% of HGNECC tumors had potentially actionable alterations, suggesting novel treatment strategies for this aggressive malignancy.

Combined Effect of Sonication and Acid Whey on Antioxidant and Angiotensin-Converting Enzyme Inhibitory Activities of Peptides Obtained from Dry-Cured Pork Loin

This study aimed to determine the effects of sonication and acid whey maceration on the oxidative stability, antioxidant activity and angiotensin-converting enzyme (ACE) inhibitory activity of peptides obtained from dry-cured pork loins. The changes in the selected parameters were documented over 7, 21 and 42 days of storage. The lowest antioxidant and angiotensin-converting enzyme inhibitory activities of peptides were noted in batches with curing salt (C) and acid whey (SW) compared to batches with sea salt (S). In this sample range, the lowest oxidation–reduction power values were associated with the use of ultrasound. In addition, higher antiradical activity (against ABTS•+) and reducing power values were observed for the sea salt ultrasound (SU) batches (after 21 and 42 days) and for the acid whey ultrasound (SWU) batches (after 7 and 21 days). Contrasting results were obtained for samples with sea salt (S and SU), which were characterized by a higher content of peptides, better antiradical properties and the highest potential to inhibit ACE (after seven days).

A Critical Exploration of Some Fundamental Terms and Terminologies in Statistics

This article focuses on the understanding of definitions of several widely used statistical terms such as degrees of freedom, locations, range, dispersion, grouped and ungrouped data. The terms have been redefined along with examples so that they stand alone to express their meaning. In this article, a new term ‘the smallest unit’ in a statistical sense has been defined and illustrated in some instances. It is also indicated how statisticians or practitioners of statistics are using it knowingly or unknowingly. We have mentioned the application of the smallest unit in the classification of data. Moreover, the concept of the smallest unit has been synced with the definition of the sample range so that the range can cover the entire space of values. Therefore, the proposed sample range can now better approximate the population range. We have shown that researchers can end up with misleading result if they treat a dataset as ungrouped data when it is truly a grouped data. This has been discussed in the computation of different percentiles. Moreover, the crux of the definition of degrees of freedom and dispersion has been pointed out which has helped repelled the confusion behind these terms. We have shown how the concept of linearly independent pieces of information is related to the definition of degrees of freedom. We have also emphasized not to mix the definition of standard deviation and/or variance with the whole concept of dispersion because the former is merely a single measure among many measures of the latter.