Miracle-dz/UniFAR-FTUs huggingface dataset:
Adversarial Multiscale Feature Learning Framework for Overlapping Chromosome Segmentation

Chromosome karyotype analysis is of great clinical importance in the diagnosis and treatment of diseases. Since manual analysis is highly time and effort consuming, computer-assisted automatic chromosome karyotype analysis based on images is routinely used to improve the efficiency and accuracy of the analysis. However, the strip-shaped chromosomes easily overlap each other when imaged, significantly affecting the accuracy of the subsequent analysis and hindering the development of chromosome analysis instruments. In this paper, we present an adversarial, multiscale feature learning framework to improve the accuracy and adaptability of overlapping chromosome segmentation. We first adopt the nested U-shaped network with dense skip connections as the generator to explore the optimal representation of the chromosome images by exploiting multiscale features. Then we use the conditional generative adversarial network (cGAN) to generate images similar to the original ones; the training stability of the network is enhanced by applying the least-square GAN objective. Finally, we replace the common cross-entropy loss with the advanced Lovász-Softmax loss to improve the model’s optimization and accelerate the model’s convergence. Comparing with the established algorithms, the performance of our framework is proven superior by using public datasets in eight evaluation criteria, showing its great potential in overlapping chromosome segmentation.
Fair Incentive Mechanism With Imperfect Quality in Privacy-Preserving Crowdsensing

Mobile crowdsensing (MCS) enables a platform to recruit users to collectively perform sensing tasks from requesters. In order to maximize the completion qualities of tasks, an incentive mechanism should be well designed for the platform to incentivize high-quality users’ participation. The existing works largely adopt the Stackelberg game to model the strategic interactions in the incentive mechanism. However, there are practical issues that are less investigated in the context of the Stackelberg-based incentive mechanism. First, the platform has no knowledge about users’ sensing qualities beforehand due to their private information. Second, the platform needs users’ continuous participation in the long run, which results in fairness requirements. Third, it is also crucial to protect users’ privacy due to the potential privacy leakage concerns (e.g., sensing qualities) after completing tasks. In this article, we jointly address these issues and propose the three-stage Stackelberg-based incentive mechanism for the platform to recruit participants. In detail, we leverage combinatorial volatile multiarmed bandits (CVMABs) to elicit unknown users’ sensing qualities. We use the drift-plus-penalty (DPP) technique in Lyapunov optimization to handle the fairness requirements. We blur the quality feedback with tunable Laplacian noise such that the incentive mechanism protects locally differential privacy (LDP). Finally, we carry out experiments to evaluate our incentive mechanism. The numerical results show that our incentive mechanism achieves sublinear regret performance to learn unknown quality with fairness and privacy guarantee.
COVID Feel Good: Evaluation of a Self-Help Protocol to Overcome the Psychological Burden of the COVID-19 Pandemic in a German Sample

The COVID-19 pandemic has severe consequences for physical as well as mental well-being. In times of restricted social contact, online self-help programs offer a low-threshold first aid to cope with the psychological burden. This current study evaluates the online self-help protocol “COVID Feel Good” in a German sample. The multicentric study was designed as a single cohort with a waiting list control condition. The convenience sample consisted of 38 German individuals who experienced at least two months of restrictions during the COVID-19 pandemic. The 7-day self-help protocol included the VR video “Secret Garden” as well as a social or cognitive exercise each day. General distress, depression, anxiety, stress, and hopelessness were assessed as primary outcomes. Social connectedness and fear of coronavirus were measured as secondary outcomes. Results showed a significant decrease in all primary outcomes except for hopelessness. Furthermore, the results indicated a significant improvement in social connectedness. Treatment effects on general distress, depression, stress, and anxiety persisted for two weeks after participation. The present study indicates that VR-based self-help protocols can mitigate the psychological burden associated with the pandemic, supporting recent findings.
Scanning Angle Extension of a Millimeter-Wave Antenna Array Using Electromagnetic Band Gap Ground

A compact phased array with extended beam steering characteristics for the millimeter-waveband of the fifth-generation (5G) applications is presented in this communication. An E-shaped patch antenna placed on an electromagnetic bandgap ground (EBGG) with operating bandwidth of 24.2–27.5 GHz is used as the basic building block of the eight-element linear array. The array utilizes both the in-phase reflection characteristic for broad element radiation pattern and the bandgap characteristic for mutual coupling reduction for limited inter-element spacing of the proposed array. The existence of the EBGG enables a compact array with the inter-element spacing of around 0.4 wavelengths at the center frequency, yet the isolation between each element is still above 17 dB within the whole band of interest, which also helps to increase the array directivity at large scanning angles. The scanning angle of the array with the electromagnetic bandgap (EBG) structure ranges from −75° to 75° at 26 GHz within 3 dB scanning loss. The proposed EBGG extends the antenna array’s spatial coverage and enables a compact size of the array, which is very attractive for 5G millimeter-wave (mm-wave) application.
Process Optimization for Acid Hydrolysis and Characterization of Bioethanol from Leftover Injera Waste by Using Response Surface Methodology: Central Composite Design

In this study, leftover injera waste from the southwestern parts of Ethiopia was used as a raw material for bioethanol production. The conversion of this biomass into ethanol involved processing techniques, which include hydrolysis, fermentation, and distillation. This research focuses on determining optimal parameters that are temperature, acid concentration, and hydrolyzing time in a hydrolysis stage. Using response surface analysis, the suggested model is quadratic and has three independent factors, which had significant effects on the yield of ethanol. In this analysis, the temperature and hydrolyzing time had a positive relationship with the yield of ethanol whereas acid concentration had a negative relation. The optimum yield of ethanol obtained was 79.07%. The yield optimized in g/g was 29.99, which was obtained at a temperature of 109.99°C, at an acid concentration of 1.00%, and hydrolyzing time of 49.59 minutes. For this analysis, the mathematical model equation was developed and the R2 value was 99.9% and its desirability was 0.8867. The property of ethanol was characterized by the many parameters used in different standardization. The density, viscosity, flammability, boiling points, and pH were determined as 0.803 gcm−3, 1.1 cP, 14°C, 80°C, and 6.65, respectively.
The role of intermediaries in US workforce and education innovation

Abstract Intermediaries are specialised organisations that perform bridging and integrative functions to achieve improved results of social policies. Intermediaries have been most effective in the United States (US) when aligned with education and workforce system improvements and redesign, not as parallel and separate efforts. Intermediaries operate in many US social policy domains, including workforce and education, where they contribute to overcoming fragmented institutions and solving mismatch problems that interfere with hiring, career pathways and educational achievements. Intermediary solutions are especially important for creating better outcomes for low-income students and workers and improving overall racial and ethnic equity. Five mismatches especially prevalent in the US labour market require intermediary solutions: organising business to improve human resources, creating improved pathways from school to work, integrating and focussing multiple education investments, linking workforce and economic development, and improving access and use of data for design, performance management and advocacy. This paper discusses these mismatches and related intermediary solutions in more depth, offers examples of specific intermediaries in action and identifies intermediary challenges and opportunities faced by intermediaries to function better. The paper also points out that, more broadly, intermediaries are only a part of the solution to labour market and education inequalities and that broader changes in the political economy and policies are required to effect wage growth and labour market participation.
Viewpoint: plotting a way forward for service research in and out of Africa

Purpose This study aims to critically evaluate and reflect on the current state of service research in Africa. The purpose is to develop a roadmap to guide future service research in and out of Africa. Design/methodology/approach This study is a conceptual reflection and in situ observation of service research and practice in Africa. Findings This study delineates scholarly, policy and managerial pathways for further service research in the African context. Service research in Africa is often experience-based rather than concept-based, and such research is often conducted in close collaboration with the local business community. Theoretical development and empirical exploration through collaboration initiatives among institutions with mutual research interests are encouraged. Research limitations/implications This study is a theoretical analysis of service research in Africa. Further empirical exploration is needed to delineate service research priorities and methodological directions so as to balance local needs and global relevance. Practical implications Africa represents fertile terrain for experience-based insights regarding financial behavior, ecosystem services and nation branding. Grassroots-level involvement in research represents a key component of managerial relevance. Social implications This study highlights the role of service research in and about Africa. The discussion demonstrates that the African Ubuntu values of community involvement and a collectivist orientation can expand the relevance of research from the academic business field to broader society. Originality/value This study represents a unique perspective on service research in the African context. In doing so, this paper lays the groundwork for more meaningful participation on the part of African-based researchers in the global service research domain. Experience-based research projects focusing on the empirical starting point complement theory development and serve as useful anchors for theory implementation in practice.
Effect of prepartum dietary-cation anion difference on performance, blood mineral and metabolite concentrations in Holstein dairy cows

Dietary cation-anion difference (DCAD) initiates a compensatory metabolic acidosis, which improves calcium (Ca) absorption and mobilization before calving and minimizes clinical and subclinical hypocalcaemia after calving. The goal of this study was to evaluate how mineral concentrations, blood metabolites, parathyroid hormone (PTH), and productive performance were affected by prepartum dietary cation-anion difference (DCAD mEq [(Na + K – Cl + S)]/kg of dry matter (DM)) in postpartum dairy cows. Forty-eight multiparous Holstein cows ranging from 2 to 5 lactations with an average body weight of 685 ± 10 kg (mean ± SD) were allocated in a randomized block design with three prepartum diets differing in DCAD (0, –100, and –180 mEq/ kg DM). All cows were fed the same postpartum diet and kept on trial for 66 days. Cows fed –180 and –100 mEq DCAD had greater prepartum non-esterified fatty acid (NEFA) concentrations than cows fed 0 mEq DCAD. Cows fed –180 mEq DCAD had greater serum Ca concentrations than cows fed –100 and 0 mEq DCAD. PTH concentrations were greater in cows fed 0 mEq DCAD than in cows fed –100 or –180 mEq DCAD. Cows fed 0 mEq DCAD had higher milk protein and solid-not-fat (SNF) levels than cows fed –100 or –180 mEq DCAD. Treatment and interaction had no effect on milk yield, 3.5% fat-corrected milk (FCM), energy-corrected milk (ECM), milk fat, total solids (TS), lactose, milk urea nitrogen (MUN), magnesium (Mg), sodium (Na), chloride (Cl), potassium (K), beta hydroxybutyric acid (BHBA), and liver or kidney functions. We concluded that adding anionic salts to dairy cows’ diets (–100 and –180 mEq DCAD) improved postpartum Ca availability and reduced clinical and subclinical hypocalcaemia while having no effect on milk yield or fat-corrected milk.
Comparative Study to Evaluate Ethanol and Ethyl Acetate Extracts of Different 'Vidanga' Species for Antioxidant Efficacy and Phyto-Constituents Screening

In Ayurveda, ‘Vidanga’ is one such species high in demand for its various uses. All the species of genus Embelia and Maesa belonging to the family Myrsinaceae are reported as ‘Vidanga’. Considering the availability of plant material in the market there is an ambiguity in supplying the authentic species as ‘Vidanga’. In the present study, a comparative analysis was carried out to determine the efficacy of different 'Vidanga’ spp. in terms of their phyto-constituents, antioxidant potential, and free radical scavenging activity. The highest total phenolic contents (TPCs) and total flavonoid contents (TFCs) were found to be in ethanolic and ethyl acetate extract. Quantitative measurements also showed that abundance of phenolic and flavonoid phytoconstituents was significantly (P<0.001) greater in ethanolic extract of all the ‘Vidanga’ fractions (1.773±0.01 to 137.17±0.19 mg/g GAE and 4.84±0.001 to 302.29±0.07 mg/g of quercetin respectively) than in ethyl acetate extract (1.15±0.003 to 15.12±0.01 mg/g GAE and 7.94±0.05 to 25.20±0.001 mg/g of quercetin respectively). Ethanolic extract of Embelia ribes had significant activity in terms of IC50 than ethyl acetate extracts in the case of 2,2-diphenyl,1- picryl hydrazyl (DPPH), ferric reducing antioxidant power (FRAP) and anti-lipid peroxidation (ALP) (9.53, 9.73 and 6.21 µg/mL respectively) indicates Embelia ribes found to be most effective species as ‘Vidanga’. Pearson’s correlation (r2) analysis also suggests a significant correlation between different antioxidant parameters and bioactive constituents. This study may helpful to draw attention of researchers to characterize the various bioactive compounds from the Embelia ribes in terms of their antioxidant prospective.
Foundations of Popfly: Rapid Mashup Development (Foundations)

Mashups are big; everybody and their dog are creating new and interesting ways of combining information on the Web. But if all the talk of APIs and complex code leaves you cold, youll be pleased to meet Microsoft Popfly. Popfly was created as a tool for designers to build mashups in a visual manner, rapidly, without needing to know any code. By simply dragging out blocks that represent services such as Flickr onto your workspace and then drawing connection lines between them, you can create a mashup. Its as easy as that! Get Popfly and Silverlight up and running on your computer, and then learn how to use the Popfly Mashup Creator and Popfly Web Page Creator to share your mashups with the world. Author Eric Griffin will introduce you to Popfly and show you everything you need to start creating your own hybrid web applications. Build mashups visually without the need to know any code. Leverage technologies such as Flickr and Google Maps. Save your creation as a Vista Sidebar Gadget. What youll learn How to get Popfly and Silverlight up and running on your computer How to use the Popfly Mashup Creator How to combine blocks to create mashups How to create web pages in the Popfly Web Page Creator How to share your mashups with the world How to save your mashups on your Vista desktop Who is this book for? Designers (and developers) who want to quickly and easily create mashups in a visual environment Related Titles Pro Web 2.0 Mashups: Remixing Data and Web Services Creating Mashups with Adobe Flex and AIR
Silibinin Improves the Clinical Scores of Memory Function in Patients with Mild Cognitive Impairment: A Double-Blind, Placebo-Controlled Pilot Study

The aim of pharmacotherapy for moderate cognitive impairment (MCI) is to reduce the present clinical signs and delay the progression of Alzheimer's disease (AD). There is currently no viable pharmacological therapy for the treatment of AD. Early intake of nutraceuticals, on the other hand, may help to alleviate and delay MCI. The goal of this study was to see how silibinin (SIL) supplementation affected cognitive function in older people with memory problems. A total of 85 subjects with memory impairment were randomly assigned to one of two groups: SIL (n = 42) supplied with 250 mg twice daily, or placebo (n = 43) for 16 weeks. A computerized neurocognitive function test was used to assess cognitive function (CNT). When comparing the SIL group to the placebo group, the "verbal learning test index" items of the CNT were significantly improved in the SIL arm (P < 0.05). Supplementing older people with memory impairment with SIL for 16 weeks appears to have a positive impact on their state of "verbal memory. " Further studies are highly recommended.
Ligands for oral delivery of peptides across the blood-brain-barrier

Peptides are short chains of amino acids linked by peptide bonds. Many peptides and proteins are limited by their poor enzymatic stability and permeability across the intestinal epithelial membranes and/or blood-brain barrier (BBB). Parenteral administration of these peptides is unfavorable because of procedural complications and low patient compliance with treatments. Instead, oral delivery is the preferred route of administration because it allows for self-administration and has a high degree of patient acceptability and compliance. Oral delivery of these peptides poses a major challenge, because the peptide drug must overcome both the physical and biochemical barriers of the gastrointestinal tract and BBB. An oral drug delivery system is beneficial because it can protect peptide drugs against degradation and deliver them to the brain, where they exert their pharmacological actions. The use of active-targeting ligands and/or cell-penetrating peptides increases penetration and uptake across the BBB. This review focuses on the diverse combinations of drug delivery systems, active-targeting ligands, and cell-penetrating peptides used to deliver peptides to the brain.
Unsupervised clustering of materials properties using hierarchical techniques

Data mining (DM) algorithms arose as a promising and flourishing discipline at manufacturing and industrial engineering. This paper proposes an efficient decision support approach for manufacturing engineering. The proposed approach tackles clustering challenges for engineering materials properties. It adopts the hierarchal clustering for mining engineering materials properties. Extensive experiments and comparisons are conducted on three different real-world datasets for engineering materials properties. In addition, a study of different similarity measures is carried out to choose the best fit similarity measure to engineering material datasets. A comparison of the results with other competitors clearly shows the robustness of the proposed approach. Therefore, it is highly recommended to use the proposed approach as a scalable engineering material properties tool.
Comparative proteomics identify HSP90A, STIP1 and TAGLN-2 in serum extracellular vesicles as potential circulating biomarkers for human adenomyosis

Extracellular vesicles (EVs) carry specific proteins involved in intercellular communication. EVs with different protein contents are released into circulation in different diseases. Recent studies have identified proteins in adenomyosis (AM)-derived EVs (AMEVs) from blood as biomarkers for this disease. AM is an extension of endometrial tissue into the uterine myometrium. Magnetic resonance imaging (MRI) is the most accurate imaging tool for identifying adenomyosis. Therefore, the present study aimed to investigate the role of EVs in diagnosing AM. In the present study, tissue AMEVs (T-AMEVs) were isolated from lesion homogenates of patients with adenomyosis, and blood AMEVs (B-AMEVs) were isolated from peripheral blood of patients with AM via differential centrifugation and density gradient centrifugation. T-AMEVs and B-AMEVs were characterized by electron microscopy, western blotting and mass spectrometry and analysed using FunRich3.1. 3 software. T-AMEVs (average diameter, 150.9±102.2 nm) and B-AMEVs (194.1±66.81 nm) expressed the CD9, CD63 and flotillin-2 EV markers. A total of 211 proteins expressed in T-AMEVs and B-AMEVs overlapped with Vesiclepedia database entries, including 2 epithelial-to-mesenchymal transition (EMT)-associated proteins and 6 invasion-associated proteins. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that these 211 proteins were associated with the ‘regulation of cell morphogenesis’ and ‘cytoskeletal organization’ terms, as well as the PPAR and HIF-1 signalling pathways, which are related to the proliferation and metastasis of endometrial cells that cannot invade the myometrium under normal circumstances. Among the 211 proteins, HSP90A, STIP1 and TAGLN-2 were expressed in T-AMEVs and B-AMEVs, but not in serum EVs of women without adenomyosis/endometriosis, and these proteins might be the potential biomarkers for adenomyosis. These findings provide insights into the molecular features of adenomyosis and the new candidate biomarkers for diagnosis.
Identification of construction and demolition waste based on change detection and deep learning

ABSTRACT With the gradual adjustment of urban expansion and discontinuation of non-essential functions in Beijing, many decades-old buildings have been demolished. Thus, construction and demolition waste (CDW) has become the focus of urban and dust pollution management. However, CDW piles are volatile and present irregular boundaries. Therefore, it is essential to map CDW regions in a timely and accurate manner to achieve urban development while protecting the environment. To address this issue, we proposed a method of CDW identification based on change detection and deep learning. First, ZY-3 multispectral images from 2016 and 2019 and their difference images were used for initial sample preparation. We expanded the samples using the post-classification comparison method of change detection, resulting in a 25.4% increase in the valid sample set. The expanded samples were then used as an input to the DeepLabV3+ for training. Thereafter, combined with the change information from the digital elevation model, specific forms, such as demolition remains, landfill CDW, and large-scale dump, were extracted using spatial analysis methods. The overall accuracy of CDW recognition was 91.67%, with a Kappa coefficient of 0.8642. In addition, we calculated the accuracy indices using only the initial samples, obtaining a mean Intersection-over-Union value that was 0.086 lower than that obtained using the expanded sample set. Similar results were obtained in PSPNet and UNet. This suggests that change detection is useful in improving the accuracy of the deep learning models. This study is the first to identify three existing forms of CDW and can effectively address the misclassification between CDW and bare land to identify CDW efficiently.