Role of Zinc (Zn) in Human Reproduction: A Journey from Initial Spermatogenesis to Childbirth

Zinc (Zn), the second-most necessary trace element, is abundant in the human body. The human body lacks the capacity to store Zn; hence, the dietary intake of Zn is essential for various functions and metabolism. The uptake of Zn during its transport through the body is important for proper development of the three major accessory sex glands: the testis, epididymis, and prostate. It plays key roles in the initial stages of germ cell development and spermatogenesis, sperm cell development and maturation, ejaculation, liquefaction, the binding of spermatozoa and prostasomes, capacitation, and fertilization. The prostate releases more Zn into the seminal plasma during ejaculation, and it plays a significant role in sperm release and motility. During the maternal, labor, perinatal, and neonatal periods, the part of Zn is vital. The average dietary intake of Zn is in the range of 8–12 mg/day in developing countries during the maternal period. Globally, the dietary intake of Zn varies for pregnant and lactating mothers, but the average Zn intake is in the range of 9.6–11.2 mg/day. The absence of Zn and the consequences of this have been discussed using critical evidence. The events and functions of Zn related to successful fertilization have been summarized in detail. Briefly, our current review emphasizes the role of Zn at each stage of human reproduction, from the spermatogenesis process to childbirth. The role of Zn and its supplementation in in vitro fertilization (IVF) opens opportunities for future studies on reproductive biology.     

Optical scattering in beef steak to predict tenderness using hyperspectral imaging in the VIS-NIR region

The objective of this research is to develop a non-destructive method for predicting cooked beef tenderness using optical scattering of light on fresh beef muscle tissue. A hyperspectral imaging system (λ = 496–1,036 nm) that consists of a CCD camera and an imaging spectrograph, was used to acquire beef steak images. The hyperspectral image consisted of 120 bands with spectral intervals of 4.54 nm. Sixty-one fresh beef steaks, including 44 strip loin and 17 tenderloin cuts, were collected. After imaging, the steaks were cooked and Warner-Bratzler shear (WBS) force values were collected as tenderness references. The optical scattering profiles were derived from the hyperspectral images and fitted to the modified Lorentzian function. Parameters, such as the peak height, full scattering width at half maximum (FWHM), and the slope around the FWHM were determined at each wavelength. Stepwise regression was used to identify 7 key wavelengths and parameters. The parameters were then used to predict the WBS scores. The model was able to predict WBS scores with an R = 0.67. Optical scattering implemented with hyperspectral imaging shows limited success for predicting current status of tenderness in beef steak.     

TopoAct: Visually Exploring the Shape of Activations in Deep Learning

Deep neural networks such as GoogLeNet, ResNet, and BERT have achieved impressive performance in tasks such as image and text classification. To understand how such performance is achieved, we probe a trained deep neural network by studying neuron activations, i.e.combinations of neuron firings, at various layers of the network in response to a particular input. With a large number of inputs, we aim to obtain a global view of what neurons detect by studying their activations. In particular, we develop visualizations that show the shape of the activation space, the organizational principle behind neuron activations, and the relationships of these activations within a layer. Applying tools from topological data analysis, we present TopoAct , a visual exploration system to study topological summaries of activation vectors. We present exploration scenarios using TopoAct that provide valuable insights into learned representations of neural networks. We expect TopoAct to give a topological perspective that enriches the current toolbox of neural network analysis, and to provide a basis for network architecture diagnosis and data anomaly detection.     

Theorem Proving Guided Development of Formal Assertions in a Resource-Constrained Scheduler for High-Level Synthesis

This paper presents a formal specification and a proof of correctness for the widely-used Force-Directed List Scheduling (FDLS) algorithm for resource-constrained scheduling of data flow graphs in high-level synthesis systems. The proof effort is conducted using a higher-order logic theorem prover. During the proof effort many interesting properties of the FDLS algorithm are discovered. These properties are formally stated and proved in a higher-order logic theorem proving environment. These properties constitute a detailed set of formal assertions and invariants that should hold at various steps in the FDLS algorithm. They are then inserted as programming assertions in the implementation of the FDLS algorithm in a production-strength high-level synthesis system. When turned on, the programming assertions (1) certify whether a specific run of the FDLS algorithm produced correct schedules and, (2) in the event of failure, help discover and isolate programming errors in the FDLS implementation.We present a detailed example and several experiments to demonstrate the effectiveness of these assertions in discovering and isolating errors. Based on this experience, we discuss the role of the formal theorem proving exercise in developing a useful set of assertions for embedding in the scheduler code and argue that in the absence of such a formal proof checking effort, discovering such a useful set of assertions would have been an arduous if not impossible task.     

微流道交流电水力泵的数值模拟及优化

描述了微通道内交流电水力泵(EHD)的工作原理,并用IntelliSuite(R)的Microfluidic模块对电水力泵进行数值模拟.并分析了影响电水力泵速度场分布的因素,包括流体电导率、驱动交流电频率和相位、电极维度以及电极间隙和数量等.通过数值模拟不同参数下的电水力泵,比较驱动压力大小,最后给出优化的设计结果,为给电水力泵设计参数的选择提供依据.本文对流道长度为5mm,宽0.4mm,厚度0.4mm的电水力泵做了优化设计的数值模拟,最后得到驱动电极宽度为0.01mm,电极间距为0.02mm.     

Effect of Humidity on Wear of TiN Coatings: Role of Capillary Condensation

Metallurgical and Materials Transactions A - Coated ball-on-disk wear configuration was used to study the effect of relative humidity, water vapor pressure, and water on wear of TiN coatings in the...     

Surface modification technique to enhance metallurgical and mechanical properties of alloy C-276 weldment by laser shock peening without coating

Microsegregation of alloying elements is prone to hot cracking in the weldment of alloy C-276. The formation of topologically close packed phases P and µ is largely responsible for the hot cracking. The present study articulates the effect of laser shock peening (LSP) to improve the metallurgical and mechanical properties of the weld joint. The weld joint was fabricated by pulsed current gas tungsten arc welding (PCGTAW) using an ERNiCrMo-3 filler wire. LSP without coating was carried out on the cap surface of the weldment. Microstructural studies were carried out to compare the as-welded and laser-peened microstructure on the fusion zone. The results show that a fine equiaxed dendritic structure was observed in both conditions. EDS analysis was carried out to evaluate the microsegregation of alloying elements. EDS analysis indicates that there are no secondary intermetallic phases. X-ray diffraction analysis was carried out to evaluate the phase change and crystallite size in the as-welded and laser shock peened fusion zone. The result shows 48.99% reduction in crystallite size after LSP. Hardness and tensile strength results indicate there is a consequential increase in laser shock peened specimen compared with as-welded specimen.     

Hot Corrosion Behaviour of Refractory and Rare Earth Oxide Reinforced CoCrAlY APS Coatings at 700 °C

This paper investigates cyclic hot corrosion of plasma sprayed CoCrAlY?+?Al₂O₃?+?YSZ (C1) and CoCrAlY?+?CeO₂ (C2) composite coatings on MDN 321 and Superni 76 substrates in molten salt (Na₂SO₄-60%V₂O₅) environment exposed to 700 °C. Weight change technique is used to evaluate the corrosion performance. Both C1 and C2 coatings showed better corrosion resistance than uncoated alloy. Both the coatings showed linear weight gain during the initial cycles and parabolic weight gain nature with subsequent hot corrosion cycles. The parabolic rate constant (K_p) of C1 and C2 coating was observed to be in the range 0.29–0.32?×?10^?10 g² cm^?4 s^?1 and 1.0–1.13?×?10^?10 g² cm^?4 s^?1 respectively. In C1 coating, the globular and continuously packed structure on the corroded surface having CoO, Cr₂O₃, CoCr₂O₄ and CoAl₂O₄ spinel oxides provided superior hot corrosion resistance. While in case of C2 coating, the outward growth of CeVO₄ irregular crystals as a corrosion product of CeO₂ and V₂O₅ salt deteriorated the oxide scales resulting in higher corrosion rate.     

Partial least squares analysis of near-infrared hyperspectral images for beef tenderness prediction

Tenderness is a primary determinant of consumer satisfaction of beef steaks. The objective of this study was to implement and test near-infrared (NIR) hyperspectral imaging to forecast 14-day aged, cooked beef tenderness from the hyperspectral images of fresh ribeye steaks (n = 319) acquired at 3–5 day post-mortem. A pushbroom hyperspectral imaging system (wavelength range: 900–1700 nm) with a diffuse-flood lighting system was developed. After imaging, steaks were vacuum-packaged and aged until 14 days postmortem. After aging, the samples were cooked and slice shear force (SSF) values were collected as a tenderness reference. After reflectance calibration, a Region-of-Interest (ROI) of 150 × 300 pixels at the center of longissimus muscle was selected. Partial least squares regression (PLSR) was carried out on each ROI image to reduce the dimension along the spectral axis. Gray-level textural co-occurrence matrix analysis with two quantization levels (64 and 256) was conducted on the PLSR bands to extract second-order statistical textural features. These features were then used in a canonical discriminant model to predict three beef tenderness categories, namely tender (SSF ≤ 205.80 N), intermediate (205.80 N < SSF < 254.80 N), and tough (SSF ≥ 254.80 N). The model with a quantization level of 256 performed better than the one with a quantization level of 64. This model correctly classified 242 out of 314 samples with an overall accuracy of 77.0%. Fat, protein, and water absorption bands were identified between 900 and 1700 nm. Our results show that NIR hyperspectral imaging holds promise as an instrument for forecasting beef tenderness.