Nonmechanical parfocal and autofocus features based on wave propagation distribution in lensfree holographic microscopy

Performing long-term cell observations is a non-trivial task for conventional optical microscopy, since it is usually not compatible with environments of an incubator and its temperature and humidity requirements. Lensless holographic microscopy, being entirely based on semiconductor chips without lenses and without any moving parts, has proven to be a very interesting alternative to conventional microscopy. Here, we report on the integration of a computational parfocal feature, which operates based on wave propagation distribution analysis, to perform a fast autofocusing process. This unique non-mechanical focusing approach was implemented to keep the imaged object staying in-focus during continuous long-term and real-time recordings. A light-emitting diode (LED) combined with pinhole setup was used to realize a point light source, leading to a resolution down to 2.76 μm. Our approach delivers not only in-focus sharp images of dynamic cells, but also three-dimensional (3D) information on their (x, y, z)-positions. System reliability tests were conducted inside a sealed incubator to monitor cultures of three different biological living cells (i.e., MIN6, neuroblastoma (SH-SY5Y), and Prorocentrum minimum). Altogether, this autofocusing framework enables new opportunities for highly integrated microscopic imaging and dynamic tracking of moving objects in harsh environments with large sample areas

Evaluasi Kinerja Rantai Pasok Gula Kelapa Kristal di Kecamatan Kutasari Kabupaten Purbalingga

Kecamatan Kutasari merupakan sentra industri gula kelapa kristal di Kabupaten Purbalingga. Perkembangan usaha gula kristal dipengaruhi pola distribusinya. Tujuan penelitian yaitu mengukur kinerja rantai pasok gula kristal dan menganalisis efisiensi kinerja rantai pasok gula kristal. Lokasi penelitian meliputi tiga desa yaitu Desa Candinata, Candiwulan, dan Karangcegak Kecamatan Kutasari. Metode penelitian yang digunakan adalah metode survei. Pengambilan sampel pengrajin menggunakan metode simple random sampling dan metode sensus untuk populasi pengepul kecil dan pengepul besar. Metode analisis data menggunakan analisis deskriptif, analisis SCOR dan Data Envelopment Analysis (DEA) dengan bantuan software LINDO 6.1. Hasil penelitian menunjukkan kinerja rantai pasok gula kristal berdasarkan analisis SCOR sudah memiliki kriteria yang baik. Analisis efisiensi kinerja pengepul kecil dan besar rantai pasok gula kristal dengan menggunakan DEA telah beroperasi secara efisien, sedangkan pengrajin belum efisien

Continuous Live-Cell Culture Imaging and Single-Cell Tracking by Computational Lensfree LED Microscopy

Continuous cell culture monitoring as a way of investigating growth, proliferation, and kinetics of biological experiments is in high demand. However, commercially available solutions are typically expensive and large in size. Digital inline-holographic microscopes (DIHM) can provide a cost-effective alternative to conventional microscopes, bridging the gap towards live-cell culture imaging. In this work, a DIHM is built from inexpensive components and applied to different cell cultures. The images are reconstructed by computational methods and the data are analyzed with particle detection and tracking methods. Counting of cells as well as movement tracking of living cells is demonstrated, showing the feasibility of using a field-portable DIHM for basic cell culture investigation and bringing about the potential to deeply understand cell motility

Penyiapan harga satuan pekerjaan untuk bill of quantity pekerjaan konstruksi

Estimasi biaya dalam suatu proyek konstruksi biasanya disajikan dalam bentuk Bill of Quantity. Bill of Quantity ini berisikan tiga hal pokok yaitu deskripsi pekerjaan, kuantitas (volume)+unit dan harga satuan pekerjaan. Penelitian ini merupakan kelanjutan dari penelitian yang telah dibuat sebelumnya tentang penyiapan data untuk estimasi biaya pekerjaan konstruksi, dengan penekanan pada tambahan untuk pekerjaan pondasi dan pekerjaan finishing. Data yang dikumpulkan dimaksudkan untuk memperoleh perhitungan harga satuan pekerjaan yang dibatasi berupa data berbagai jenis dan harga bahan serta data upah pekerjaan. Harga satuan pekerjaan itu sendiri dalam penelitian ini dibatasi hanya ditentukan dari harga bahan, upah pekerjaan dan faktor-faktor yang mempengaruhinya. Data-data tersebut dikumpulkan dan disimpan dalam suatu database menggunakan program Microsoft Access, selanjutnya dengan menggunakan program Microsoft Visual Basic dapat dibuat program Sistem Informasi Estimasi Biaya untuk mendapatkan harga satuan pekerjaan yang diinginkan. Untuk dapat mengaplikasikan harga satuan pekerjaan yang telah dibuat tersebut ke dalam Bill of Quantity dipergunakan program Microsoft Excel. Hasil akhir pemakaian program ini adalah sekumpulan harga satuan pekerjaan yang ragam atau jenisnya ditentukan dari ragam atau jenis pekerjaan sesuai kebutuhan proyek yang tertera dalam Bill of Quantity

Intelligent Mobile Electronic Nose System Comprising a Hybrid Polymer-Functionalized Quartz Crystal Microbalance Sensor Array

We devised a low-cost mobile electronic nose (e-nose) system using a quartz crystal microbalance (QCM) sensor array functionalized with various polymer-based thin active films (i.e., polyacrylonitrile, poly(vinylidene fluoride), poly(vinyl pyrrolidone), and poly(vinyl acetate)). It works based on the gravimetric detection principle, where the additional mass of the adsorbed molecules on the polymer surface can induce QCM resonance frequency shifts. To collect and process the obtained sensing data sets, a multichannel data acquisition (DAQ) circuitry was developed and calibrated using a function generator resulting in a device frequency resolution of 0.5 Hz. Four prepared QCM sensors demonstrated various sensitivity levels with high reproducibility and consistency under exposure to seven different volatile organic compounds (VOCs). Moreover, two types of machine learning algorithms (i.e., linear discriminant analysis and support vector machine models) were employed to differentiate and classify those tested analytes, in which classification accuracies of up to 98 and 99% could be obtained, respectively. This high-performance e-nose system is expected to be used as a versatile sensing platform for performing reliable qualitative and quantitative analyses in complex gaseous mixtures containing numerous VOCs for early disease diagnosis and environmental quality monitoring

Artificial Neural Networks for Automated Cell Quantification in Lensless LED Imaging Systems

Cell registration by artificial neural networks (ANNs) in combination with principal component analysis (PCA) has been demonstrated for cell images acquired by light emitting diode (LED)-based compact holographic microscopy. In this approach, principal component analysis was used to find the feature values from cells and background, which would be subsequently employed as neural inputs into the artificial neural networks. Image datasets were acquired from multiple cell cultures using a lensless microscope, where the reference data was generated by a manually analyzed recording. To evaluate the developed automatic cell counter, the trained system was assessed on different data sets to detect immortalized mouse astrocytes, exhibiting a detection accuracy of ~81% compared with manual analysis. The results show that the feature values from principal component analysis and feature learning by artificial neural networks are able to provide an automatic approach on the cell detection and registration in lensless holographic imaging

Hybrid learning method based on feature clustering and scoring for enhanced COVID-19 breath analysis by an electronic nose

Breath pattern analysis based on an electronic nose (e-nose), which is a noninvasive, fast, and low-cost method, has been continuously used for detecting human diseases, including the coronavirus disease 2019 (COVID-19). Nevertheless, having big data with several available features is not always beneficial because only a few of them will be relevant and useful to distinguish different breath samples (i.e., positive and negative COVID-19 samples). In this study, we develop a hybrid machine learning-based algorithm combining hierarchical agglomerative clustering analysis and permutation feature importance method to improve the data analysis of a portable e-nose for COVID-19 detection (GeNose C19). Utilizing this learning approach, we can obtain an effective and optimum feature combination, enabling the reduction by half of the number of employed sensors without downgrading the classification model performance. Based on the cross-validation test results on the training data, the hybrid algorithm can result in accuracy, sensitivity, and specificity values of (86 ± 3)%, (88 ± 6)%, and (84 ± 6)%, respectively. Meanwhile, for the testing data, a value of 87% is obtained for all the three metrics. These results exhibit the feasibility of using this hybrid filter-wrapper feature-selection method to pave the way for optimizing the GeNose C19 performance

Continuous Live-Cell Culture Imaging and Single-Cell Tracking by Computational Lensfree LED Microscopy

Continuous cell culture monitoring as a way of investigating growth, proliferation, and kinetics of biological experiments is in high demand. However, commercially available solutions are typically expensive and large in size. Digital inline-holographic microscopes (DIHM) can provide a cost-effective alternative to conventional microscopes, bridging the gap towards live-cell culture imaging. In this work, a DIHM is built from inexpensive components and applied to different cell cultures. The images are reconstructed by computational methods and the data are analyzed with particle detection and tracking methods. Counting of cells as well as movement tracking of living cells is demonstrated, showing the feasibility of using a field-portable DIHM for basic cell culture investigation and bringing about the potential to deeply understand cell motility

Continuous Live-Cell Culture Monitoring by Compact Lensless LED Microscopes

A compact lensless microscope comprising a custom-made LED engine and a CMOS imaging sensor has been developed for live-cell culture imaging inside a cell incubator environment. The imaging technique is based on digital inline-holographic microscopy, while the image reconstruction is carried out by angular spectrum approach with a custom written software. The system was tested with various biological samples including immortalized mouse astrocyte cells inside a petri dish. Besides the imaging possibility, the capability of automated cell counting and tracking could be demonstrated. By using image sensors capable of video frame rate, time series of cell movement can be captured