Anaerobic co-digestion of edible and inedible food waste with sewage sludge

Co-digestion processes of three specific mixing ratios of food waste (FW) with sewage sludge were tested in one-stage anaerobic digestion (AD)system induplicate to evaluate methane (CH4) and hydrogen sulfide(H2S) production. Two types of FW were considered in this study: a mixture of edible and inedible, termed "unsorted" and solely inedible FW. These two types of FW represent current observations of FW produced from the University of Missouri's Campus Dining Services (CDS) and an idealized scenario in which all edible FW is avoided. Over the whole period, all trials were under mesophilic condition (35.5+/-1 degrees C) with a stable hydraulic retention time (28 days). Under steady-state conditions, daily methane yield was 405L/kg VSadded in trial 2(25% unsorted FW). The increase of unsorted FW from 25 to 50% VS enhanced methane production in trial 1(50% unsorted FW), which was 460 L/kg VSadded. Trial 5 (50% unsorted FW) was also conducted to check the consistency of digestion process, showing that trial 1 and 5 produced similar methane yield in our study. Also, the daily methane yield in trial 4 (75% unsorted FW) was maintained at 438 L/kg VSadded, slightly lower than the one in trial 1. In addition, methane production was maintained at 349 and 386 L/kg VSadded in trials fed with 50% and 25% VS inedible FW, 31.81% and 4.92% lower than those in trial 1 and 2, respectively. Also, the production of H2S in trials with unsorted FW were approximately two times higher than those with inedible FW and further research is needed to explain this difference. In this study, energy potential output of unsorted and inedible FW from Columbia (MO) was calculated using an Anaerobic Digestion Development Iterative Tool (ADDIT model). The consideration of the cost of logistics, operations, maintenance and capital costs were also calculated in ADDIT model. It showed that this co-digestion of unsorted and inedible FW with sewage sludge could be able to produce 28,678,680 and 68,586 KWh/year total net energy. As a result, these co-digestion process will be able to take a desirable profit to Columbia city (MO) with an approximately $1,290,540 and$3086.4 annually income if all unsorted and inedible FW was sent to codigestion plant, assuming the average commercial electricity rate in MO is 4.50/kWh

RNAi-based Gene Therapy for Blood Genetic Diseases

Therapies for blood genetic diseases can be divided into different categories, including chemotherapy, radiotherapy, gene therapy, and hematopoietic stem cell transplantation. Among these treatments, gene targeting is progressively becoming a therapeutic alternative that offers the possibility of a permanent cure for certain blood genetic diseases. In recent years, gene therapy has played a more important role in curing genetic blood disorders. RNA interference (RNAi) is one of the directions for gene therapy, which was intensively studied in the past decades for its potentials in the treatment of diseases. In order to provide useful references and prospective directions for further studies concerning RNAi-based gene therapy for blood genetic diseases, current RNAi-based gene therapies for several typical blood genetic diseases have been summarized and discussed in this chapter

Multi-damage detection in composite structure

In this paper a pre-stack reverse-time migration concept of signal processing techniques is developed and adapted to guided-wave propagation in composite structure for multi-damage imaging by experimental studies. An anisotropic laminated composite plate with a surface-mounted linear piezoelectric ceramic (PZT) disk array is studied as an example. At first, Mindlin Plate Theory is used to model Lamb waves propagating in laminates. The group velocities of flexural waves are also derived from dispersion relations and validated by experiments. Then reconstruct the response wave fields with reflected data collected by the linear PZT array. Reverse-time migration technique is then performed to back-propagate the reflected energy to the damages using a two-dimensional explicit finite difference algorithm and damages are imaged. Stacking these images together gets the final image of multiple damages. The results show that the pre-stack migration method is hopeful for damage detection in composite structures

Physical-aware Cross-modal Adversarial Network for Wearable Sensor-based Human Action Recognition

Wearable sensor-based Human Action Recognition (HAR) has made significant strides in recent times. However, the accuracy performance of wearable sensor-based HAR is currently still lagging behind that of visual modalities-based systems, such as RGB video and depth data. Although diverse input modalities can provide complementary cues and improve the accuracy performance of HAR, wearable devices can only capture limited kinds of non-visual time series input, such as accelerometers and gyroscopes. This limitation hinders the deployment of multimodal simultaneously using visual and non-visual modality data in parallel on current wearable devices. To address this issue, we propose a novel Physical-aware Cross-modal Adversarial (PCA) framework that utilizes only time-series accelerometer data from four inertial sensors for the wearable sensor-based HAR problem. Specifically, we propose an effective IMU2SKELETON network to produce corresponding synthetic skeleton joints from accelerometer data. Subsequently, we imposed additional constraints on the synthetic skeleton data from a physical perspective, as accelerometer data can be regarded as the second derivative of the skeleton sequence coordinates. After that, the original accelerometer as well as the constrained skeleton sequence were fused together to make the final classification. In this way, when individuals wear wearable devices, the devices can not only capture accelerometer data, but can also generate synthetic skeleton sequences for real-time wearable sensor-based HAR applications that need to be conducted anytime and anywhere. To demonstrate the effectiveness of our proposed PCA framework, we conduct extensive experiments on Berkeley-MHAD, UTD-MHAD, and MMAct datasets. The results confirm that the proposed PCA approach has competitive performance compared to the previous methods on the mono sensor-based HAR classification problem.Comment: First IMU2SKELETON GANs approach for wearable HAR problem. arXiv admin note: text overlap with arXiv:2208.0809

Transfer Learning on Small Datasets for Improved Fall Detection

Falls in the elderly are associated with significant morbidity and mortality. While numerous fall detection devices incorporating AI and machine learning algorithms have been developed, no known smartwatch-based system has been used successfully in real-time to detect falls for elderly persons. We have developed and deployed a SmartFall system on a commodity-based smartwatch which has been trialled by nine elderly participants. The system, while being usable and welcomed by the participants in our trials, has two serious limitations. The first limitation is the inability to collect a large amount of personalized data for training. When the fall detection model, which is trained with insufficient data, is used in the real world, it generates a large amount of false positives. The second limitation is the model drift problem. This means an accurate model trained using data collected with a specific device performs sub-par when used in another device. Therefore, building one model for each type of device/watch is not a scalable approach for developing smartwatch-based fall detection system. To tackle those issues, we first collected three datasets including accelerometer data for fall detection problem from different devices: the Microsoft watch (MSBAND), the Huawei watch, and the meta-sensor device. After that, a transfer learning strategy was applied to first explore the use of transfer learning to overcome the small dataset training problem for fall detection. We also demonstrated the use of transfer learning to generalize the model across the heterogeneous devices. Our preliminary experiments demonstrate the effectiveness of transfer learning for improving fall detection, achieving an F1 score higher by over 10% on average, an AUC higher by over 0.15 on average, and a smaller false positive prediction rate than the non-transfer learning approach across various datasets collected using different devices with different hardware specifications

Pharmacologic effects of cannabidiol on acute reperfused myocardial infarction in rabbits: evaluated with 3.0T cardiac magnetic resonance imaging and histopathology

Cannabidiol (CBD) has anti-inflammatory effects. We explored its therapeutic effects on cardiac ischemia-reperfusion injury with an experimental imaging platform. Reperfused acute myocardial infarction (AMI) was induced in rabbits with a 90-min coronary artery occlusion followed by 24-h reperfusion. Before reperfusion, rabbits received two intravenous doses of 100 μg/kg CBD (n=10) or vehicle (control, n=10). Evans blue was intravenously injected for later detection of the AMI-core. Cardiac magnetic resonance imaging (cMRI) was performed to evaluate cardiac morphology and function. After euthanasia, blood Troponin I (cTnI) was assessed, and the heart was excised and infused with multifunctional red-iodized-oil dye. The heart was sliced for digital radiography to quantify the perfusion density rate (PDR), area at risk (AAR), and myocardial salvage index (MSI), followed by histomorphologic staining. Compared to controls, CBD treatment improved systolic wall thickening (p<0.05), significantly increased blood flow in the AAR (p<0.05), significantly decreased microvascular obstruction (p<0.05), increased the PDR by 1.7-fold, lowered the AMI-core/AAR ratio (p<0.05), and increased the MSI (p<0.05). These improvements were associated with reductions in serum cTnI, cardiac leukocyte infiltration, and myocellular apoptosis (p<0.05). Thus, CBD therapy reduced AMI size and facilitated restoration of LV function. We demonstrated that this experimental platform has potential theragnostic utility.status: publishe

Effect of sperm DNA fragmentation on clinical outcome of frozen-thawed embryo transfer and on blastocyst formation.

During the last decades, many studies have shown the possible influence of sperm DNA fragmentation on assisted reproductive technique outcomes. However, little is known about the impact of sperm DNA fragmentation on the clinical outcome of frozen-thawed embryo transfer (FET) from cycles of conventional in vitro fertilization (IVF) and intra-cytoplasmic sperm injection (ICSI). In the present study, the relationship between sperm DNA fragmentation (SDF) and FET clinical outcomes in IVF and ICSI cycles was analyzed. A total of 1082 FET cycles with cleavage stage embryos (C-FET) (855 from IVF and 227 from ICSI) and 653 frozen-thawed blastocyst transfer cycles (B-FET) (525 from IVF and 128 from ICSI) were included. There was no significant change in clinical pregnancy, biochemical pregnancy and miscarriage rates in the group with a SDF >30% compared with the group with a SDF ≤30% in IVF and ICSI cycles with C-FET or B-FET. Also, there was no significant impact on the FET clinic outcome in IVF and ICSI when different values of SDF (such as 10%, 20%, 25%, 35%, and 40%) were taken as proposed threshold levels. However, the blastulation rates were significantly higher in the SDF ≤30% group in ICSI cycle. Taken together, our data show that sperm DNA fragmentation measured by Sperm Chromatin Dispersion (SCD) test is not associated with clinical outcome of FET in IVF and ICSI. Nonetheless, SDF is related to the blastocyst formation in ICSI cycles