Creating logistics, quality, and project plan for new spider bait product

This report contains the execution and resulting conclusions of project ‘Creating logistics, quality, and project plan for new spider bait product’ completed for Tumblar Products Ltd. This report describes a production plan for a 2L Mortein Spider Bait product contracted with Reckitt Benckiser. This included ensuring quality management and creating both a logistics and a project plan. Recommendations and solutions are suggested for implementation of the production plan. These include a value stream map, quality management plan covering the manufacturing process, and a design and implementation plan of the location, layout and equipment needed to meet Reckitt Benckiser’s requirements

MADGAN: unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction.

BACKGROUND: Unsupervised learning can discover various unseen abnormalities, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a 2D/3D single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer's disease (AD). Moreover, no study has shown how unsupervised anomaly detection is associated with either disease stages, various (i.e., more than two types of) diseases, or multi-sequence magnetic resonance imaging (MRI) scans. RESULTS: We propose unsupervised medical anomaly detection generative adversarial network (MADGAN), a novel two-step method using GAN-based multiple adjacent brain MRI slice reconstruction to detect brain anomalies at different stages on multi-sequence structural MRI: (Reconstruction) Wasserstein loss with Gradient Penalty + 100 [Formula: see text] loss-trained on 3 healthy brain axial MRI slices to reconstruct the next 3 ones-reconstructs unseen healthy/abnormal scans; (Diagnosis) Average [Formula: see text] loss per scan discriminates them, comparing the ground truth/reconstructed slices. For training, we use two different datasets composed of 1133 healthy T1-weighted (T1) and 135 healthy contrast-enhanced T1 (T1c) brain MRI scans for detecting AD and brain metastases/various diseases, respectively. Our self-attention MADGAN can detect AD on T1 scans at a very early stage, mild cognitive impairment (MCI), with area under the curve (AUC) 0.727, and AD at a late stage with AUC 0.894, while detecting brain metastases on T1c scans with AUC 0.921. CONCLUSIONS: Similar to physicians' way of performing a diagnosis, using massive healthy training data, our first multiple MRI slice reconstruction approach, MADGAN, can reliably predict the next 3 slices from the previous 3 ones only for unseen healthy images. As the first unsupervised various disease diagnosis, MADGAN can reliably detect the accumulation of subtle anatomical anomalies and hyper-intense enhancing lesions, such as (especially late-stage) AD and brain metastases on multi-sequence MRI scans

Interaksi Masyarakat Keturunan Arab dengan Masyarakat Setempat di Pekalongan

Dalam penelitian ini penulis mengeksplorasi interaksi antara masyarakat keturunan Arab dengan masyarakat setempat di Kelurahan Klego Kota Pekalongan serta mengetahui faktor pendorong dan penghambat terjadinya interaksi antara masyarakat keturunan Arab dengan masyarakat setempat. Penelitian ini menggunakan metode kualitatif. Teknik pengumpulan data melalui metode wawancara, observasi, dan dokumentasi. Hasil penelitian menunjukan bahwa terjadi interaksi antara masyarakat keturunan Arab dengan masyarakat setempat dengan intensitas dan kegiatan kebudayaan tertentu. Faktor pendukung terjadinya interaksi adalah adanya perkawinan campuran, terutama pada masyarakat keturunan Arab non-sayyid, dengan masyarakat setempat serta adanya kerjasama dalam bidang perdagangan. Sedangkan faktor penghambat terjadinya proses interaksi adalah adanya prasangka dan stereotip pada masyarakat keturunan Arab yang merasa masyarakat setempat kurang Islami, sebaliknya masyarakat setempat merasa masyarakat keturunan Arab itu sombong. Keturunan Arab yang tinggal di Kelurahan Klego terdiri dari golongan sayyid dan golongan non-sayyid. Keturunan Arab dari golongan non-sayyid sudah dapat berbaur dengan masyarakat setempat sedangkan keturunan Arab dari golongan sayyid belum berbaur dengan masyarakat non-Arab. Masyarakat keturunan Arab memiliki simbol-simbol seperti bahasa, pakaian, bangunan yang sangat mempengaruhi interaksi antara masyarakat keturunan Arab dengan masyarakat setempat. In this study, the author explores the interaction between people of Arab descent and the local people in the village of Klego Pekalongan city and also the factors that drive and inhibit the interaction between them. This study uses qualitative methods. The technique of collecting data are interviews, observation, and documentation. The results show that there is a pattern of interaction between people of Arab descent with the local people. Factors supporting the occurrence of interactions are the presence of mixed marriages, especially in the Arab non-sayyid descent, with the local community as well as the cooperation in the field of trade. While the factors inhibiting the interaction process is the existence of prejudice and stereotypes of people of Arab descent at a local community as less Islami. On the other hand, the local people feel that people of Arab descent are exclusive. The Arab descent living in the Village Klego consists of groups and classes of non-sayyid and sayyid. Arab descent from the class of non-sayyid are able to mingle with the local people, whereas Arab descent of sayyid cannot mingle with non-Arab communities. Society of Arab descent has symbols such as language, clothing, and building that strongly influence the interaction of people of Arab descent with the local community

Seasonal pigment fluctuation in diploid and polyploid Arabidopsis revealed by machine learning-based phenotyping method PlantServation

Long-term field monitoring of leaf pigment content is informative for understanding plant responses to environments distinct from regulated chambers but is impractical by conventional destructive measurements. We developed PlantServation, a method incorporating robust image-acquisition hardware and deep learning-based software that extracts leaf color by detecting plant individuals automatically. As a case study, we applied PlantServation to examine environmental and genotypic effects on the pigment anthocyanin content estimated from leaf color. We processed >4 million images of small individuals of four Arabidopsis species in the field, where the plant shape, color, and background vary over months. Past radiation, coldness, and precipitation significantly affected the anthocyanin content. The synthetic allopolyploid A. kamchatica recapitulated the fluctuations of natural polyploids by integrating diploid responses. The data support a long-standing hypothesis stating that allopolyploids can inherit and combine the traits of progenitors. PlantServation facilitates the study of plant responses to complex environments termed "in natura"

Orangutans (Pongo pygmaeus) remember old acquaintances.

Many social animals can discriminate between familiar and unfamiliar faces. Orangutans, however, lead a semi-solitary life and spend much of the day alone. As such, they may be less adept at recognizing conspecifics and are a good model for determining how social structure influences the evolution of social cognition such as facial recognition. The present study is the first report of whether orangutans can distinguish among individual faces. We adopted a preferential looking method and found that orangutans used facial discrimination to identify known conspecifics. This suggests that frequent and intense social interaction is not necessary for facial discrimination, although our findings were limited by the small number of stimuli and the unequal numbers of male and female orangutans depicted in the stimuli

Mean (± SEM) preference scores for familiar faces.

<p>The preference scores were calculated as percentages. Blue bars indicate the percentage of time spent gazing at currently familiar faces, and red bars indicate the percentage of time spent gazing at historically familiar faces. Gypsy, Julie, and Borneo are the orangutans used in this study. </p

Examples of familiar (left) and unfamiliar (right) faces used in this experiment.

<p>Examples of familiar (left) and unfamiliar (right) faces used in this experiment.</p

IMACEL: A cloud-based bioimage analysis platform for morphological analysis and image classification.

Automated quantitative image analysis is essential for all fields of life science research. Although several software programs and algorithms have been developed for bioimage processing, an advanced knowledge of image processing techniques and high-performance computing resources are required to use them. Hence, we developed a cloud-based image analysis platform called IMACEL, which comprises morphological analysis and machine learning-based image classification. The unique click-based user interface of IMACEL's morphological analysis platform enables researchers with limited resources to evaluate particles rapidly and quantitatively without prior knowledge of image processing. Because all the image processing and machine learning algorithms are performed on high-performance virtual machines, users can access the same analytical environment from anywhere. A validation study of the morphological analysis and image classification of IMACEL was performed. The results indicate that this platform is an accessible and potentially powerful tool for the quantitative evaluation of bioimages that will lower the barriers to life science research

MADGAN: unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction

Abstract: Background: Unsupervised learning can discover various unseen abnormalities, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a 2D/3D single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer’s disease (AD). Moreover, no study has shown how unsupervised anomaly detection is associated with either disease stages, various (i.e., more than two types of) diseases, or multi-sequence magnetic resonance imaging (MRI) scans. Results: We propose unsupervised medical anomaly detection generative adversarial network (MADGAN), a novel two-step method using GAN-based multiple adjacent brain MRI slice reconstruction to detect brain anomalies at different stages on multi-sequence structural MRI: (Reconstruction) Wasserstein loss with Gradient Penalty + 100 ℓ1 loss—trained on 3 healthy brain axial MRI slices to reconstruct the next 3 ones—reconstructs unseen healthy/abnormal scans; (Diagnosis) Average ℓ2 loss per scan discriminates them, comparing the ground truth/reconstructed slices. For training, we use two different datasets composed of 1133 healthy T1-weighted (T1) and 135 healthy contrast-enhanced T1 (T1c) brain MRI scans for detecting AD and brain metastases/various diseases, respectively. Our self-attention MADGAN can detect AD on T1 scans at a very early stage, mild cognitive impairment (MCI), with area under the curve (AUC) 0.727, and AD at a late stage with AUC 0.894, while detecting brain metastases on T1c scans with AUC 0.921. Conclusions: Similar to physicians’ way of performing a diagnosis, using massive healthy training data, our first multiple MRI slice reconstruction approach, MADGAN, can reliably predict the next 3 slices from the previous 3 ones only for unseen healthy images. As the first unsupervised various disease diagnosis, MADGAN can reliably detect the accumulation of subtle anatomical anomalies and hyper-intense enhancing lesions, such as (especially late-stage) AD and brain metastases on multi-sequence MRI scans