Povezanost estrusa s tjelesnom aktivnošću i preživanjem mliječnih krava na farmama u Kini

This study aimed to describe the estrus-related changes in dairy cattle in parameters automatically recorded through an HR-Tag (SCR Engineers Ltd., Netanya, Israel) or a neck collar (Nedap Livestock Management, Groenlo, Netherlands). On two commercial dairy farms, the baseline period was defined as the mean of 3 d before and 3 d after estrus day. In the HR-Tag monitored herd, changes in physical activity and behavioral parameters (lying bouts, lying duration, total lying time, lying ratio) were studied in 78 estrous cycles. The cows were classified in groups according to parity (primiparous, n = 34; and multiparous, n = 44), milk production (MK1, n = 7, > 47 kg/d; MK2, n = 12, 42-47 kg/d; MK3, n = 43, 31-42 kg/d; and MK4, n = 16, 41 kg/d; MK2, n = 17, 32-41 kg/d; and MK3, n = 13, 47 kg/d; MK2, n = 12, 42 – 47 kg/d; MK3, n = 43, 31 – 42 kg/d; MK4, n = 16, 41 kg/d; MK2, n = 17, 32 – 41 kg/d; and MK3, n = 13, < 32 kg/d). Proveden je test ANOVA kako bi se ustanovile razlike u podacima i usporedile metode kojima su se procjenjivale razlike unutar skupine i među skupinama putem SPSS 23.0. Za prilagodbu podataka korištena je ordinalna logistička regresija kako bi se analizirali čimbenici koji utječu na tjelesnu aktivnost na dan estrusa. Rezultati u stadu praćenom HR-Tag-om pokazali su da se na dan estrusa tjelesna aktivnost povećala u odnosu na početno razdoblje, a prosječne su vrijednosti koje se odnose na ležanje na dan estrusa bile znatno niže nego u početnoj fazi, kao i ukupno vrijeme ležanja i omjer ležanja, s vrijednostima od -3,53 ± 0,55, -188,02 ± 21,46 i -14,05 ± 1,37. U stadu praćenom ovratnikom, kada se TFT u skupini MK2 povećao za jednu jedinicu, tjelesna aktivnost povećala se 3,19 puta (P = 0,03) u usporedbi sa skupinama u kojima se TFT nije povećao. Rezultati ovog istraživanja općenito upućuju na tjelesne i ponašajne promjene te promjene u preživanju koje su vezane za estrus, a otkrivene su automatiziranim sustavom praćenja. Pretpostavlja se stoga da sustav HR-Tag i ovratnici mogu biti prikladan način detekcije estrusa u komercijalnim stadima mliječnih krava

Co-immobilization multienzyme nanoreactor with co-factor regeneration for conversion of CO2

Multienzymatic conversion of carbon dioxide (CO2) into chemicals has been extensively studied. However, regeneration and reuse of co-factor are still the main problems for the efficient conversion of CO2. In this study, a nanoscale multienzyme reactor was constructed by encapsulating simultaneously carbonic anhydrase (CA), formate dehydrogenase (FateDH), co-factor (NADH), and glutamate dehydrogenases (GDH) into ZIF-8. In the multienzyme reactors, cationic polyelectrolyte (polyethyleneimine, PEI) was doped in the ZIF-8 by dissolving it in the precursors of ZIF-8. Co-factor (NADH) was anchored in ZIF-8 by ion exchange between PEI (positive charge) and co-factor (negative charge), and regenerated through GDH embedded in the ZIF-8, thus keeping high activity of FateDH. Activity recovery of FateDH in the multienzyme reactors reached 50%. Furthermore, the dissolution of CO2 in the reaction solution was increased significantly by the combination of CA and ZIF-8. As a result, the nanoscale multienzyme reactor exhibited superior capacity for conversion of CO2 to formate. Compared with free multienzyme system, formate yield was increased 4.6-fold by using the nanoscale multienzyme reactor. Furthermore, the nanoscale multienzyme reactor still retained 50% of its original productivity after 8 cycles, indicating excellent reusability

Bimetal based inorganic-carbonic anhydrase hybrid hydrogel membrane for CO2 capture

In this study, we synthesized for the first time a bimetal-based inorganic-carbonic anhydrase (CA) hybrid nanoflower to immobilize CA using Cu2+ and Zn2+ instead of single metal ion. Subsequently, the synthesized bimetallic hybrid nanoflowers (CANF) were embedded into the poly(vinyl alcohol) (PVA)-chitosan (CS) hydrogel networks to obtain PVA/CS@CANF hydrogel membrane. The CANF exhibited a significantly higher activity recovery of 70 % compared with 35 % with CA/Zn3(PO4)2 hybrid nanoflowers and 10 % with CA/Cu3(PO4)2 hybrid nanoflowers. The PVA/CS@CANF hydrogel membrane possessed excellent mechanical strength, high catalytic activity, and were easy to flow out without centrifugation or filtration. At the same time, the PVA/CS@CANF displayed higher thermostability, storage stability, and pH stability than free CA and CANF, and superior reusability and CO2 capture capacity. The hydrogel membrane maintained more than 75 % of its original activity after 8 cycles. However, CANF only maintained 12 % of its original activity. Furthermore, the amount of CaCO3 produced by PVA/CS@CANF membrane was 9.0-fold and 2.0-fold compared with free CA and CANF, respectively. Therefore, This approach to synthesizing bimetallic-based protein hybrid hydrogel membrane could have a bright future in CO2 capture

Self-assembly of activated lipase hybrid nanoflowers with superior activity and enhanced stability

Lipase-inorganic hybrid nanoflowers were prepared using Ca3(PO4)2 as the inorganic component and lipase from Aspergillus oryzae (A. oryzae) as the organic component. The influences of metal ions with different valence, various additives (surfactant), and synthesis conditions on the activity of the lipase hybrid nanoflowers were systematically investigated. Results revealed that the valence state of metal ions played an important role on the shape and activity of lipase hybrid nanoflowers. The synthesized lipase hybrid nanoflowers using bivalence metal ions (Ca2+, Mn2+, and Zn2+) as the inorganic components exhibited relative high activity. However, very low activities were observed in the lipase hybrid nanoflowers using univalent metal ions (Ag+) or trivalent metal ions (Al3+, Fe3+). More importantly, Ca2+ not only induced self-assemble of lipase hybrid nanoflowers, but also activated the enzyme activity by inducing conformational changes in lipase from A. oryzae. As a result, lipase/Ca3(PO4)2 hybrid nanoflowers (hNF-lipase) exhibited the high activity. The hNF-lipase displayed 9, 12, and 61 folds higher activity than lipase/Ag3PO4 hybrid nanoflowers, lipase/AlPO4 hybrid nanoflowers, and lipase/FePO4 nanoflowers, respectively. Compared with free lipase, the hNF-lipase displayed 172 % increase in activities by using 0.15 mM Tween-80 as an activity inducer (activated hNF-lipase). Furthermore, the hNF-lipase and activated hNF-lipase exhibited increased stability against high temperature and denaturant, and had good storage stability and reusability

Seismic Events Prediction Using Deep Temporal Convolution Networks

Seismic events prediction is a crucial task for preventing coal mine rock burst hazards. Currently, this task attracts increasing research enthusiasms from many mining experts. Considering the temporal characteristics of monitoring data, seismic events prediction can be abstracted as a time series prediction task. This paper contributes to address the problem of long-term historical dependence on seismic time series prediction with deep temporal convolution neural networks (CNN). We propose a dilated causal temporal convolution network (DCTCNN) and a CNN long short-term memory hybrid model (CNN-LSTM) to forecast seismic events. In particular, DCTCNN is designed with dilated CNN kernels, causal strategy, and residual connections; CNN-LSTM is established in a hybrid modeling way by utilizing advantage of CNN and LSTM. Based on these manners, both of DCTCNN and CNN-LSTM can extract long-term historical features from the monitoring seismic data. The proposed models are experimentally tested on two real-life coal mine seismic datasets. Furthermore, they are also compared with one traditional time series prediction method, two classic machine learning algorithms, and two standard deep learning networks. Results show that DCTCNN and CNN-LSTM are superior than the other five algorithms, and they successfully complete the seismic prediction task

A Coarse-to-Fine Approach to Robust 3D Facial Landmarking via Curvature Analysis and Active Normal Model

International audienceA Coarse-to-Fine Approach to Robust 3D Facial Landmarking via Curvature Analysis and Active Normal Mode

A Coarse-to-Fine Approach to Robust 3D Facial Landmarking via Curvature Analysis and Active Normal Model

International audienceA Coarse-to-Fine Approach to Robust 3D Facial Landmarking via Curvature Analysis and Active Normal Mode