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.     

Molecular cloning of a novel melanocortin receptor

Using the technique of the polymerase chain reaction primed with oligonucleotides based on the homologous transmembrane regions of seven transmembrane G protein-linked receptors, we isolated three full-length human genes that encode a novel subgroup of this receptor family. Recently, two of these receptors were identified as specific for alpha-melanocyte-stimulating hormone (alpha-MSH) and adrenocorticotropic hormone. We report the molecular cloning and pharmacologic characterization of a third member of this subgroup. The gene for this receptor encodes a protein of 361 amino acids in length. Its pharmacology characterizes it as an MSH receptor specific to the heptapeptide core common to adrenocorticotropic hormone and alpha-, beta-, and gamma-MSH. By Northern blot hybridization and polymerase chain reaction, it is expressed in brain, placental, and gut tissues but not in melanoma cells or in the adrenal gland. These findings may yield insight into the physiology of peptides derived from pro-opiomelanocortin post-translational processing.     

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.     

A facile antistatic modification of polyester fiber based on ion-exchange reaction of sulfonate-modified polyester and various cationic surfactants

A new technique for imparting antistatic properties to poly(ethylene terephthalate) (PET) fiber has been developed. In this technique, blend polyester fibers containing poly(ethylene terephthalate/5-sulfoisophthalate) (SIP-PET) were prepared by blend spinning and then treated with various cationic surfactants in the process of dyeing. The surfactants could effectively be immobilized on the fiber as the counter cations of the sulfonate groups of the 5-sulfoisophthalate (SIP) units and aid the release of static electrons formed in the fiber. Thus, the half-life time (t_1/2) of leakage of static charge and the surface resistivity (Rs) of the blend PET fibers became much lower after treating. The best result was obtained with a methylated quaternary ammonium salt of a stearylamine-ethylene oxide (EO) adduct or hydrochloride of a laurylamine-EO adduct as the surfactant of which the number of EO units was around ten. Even after five washing cycles the t_1/2 value of the fibers treated with these surfactants was kept lower than 30 s with the Rs value maintained in the order of 10¹³ Ω cm^-2. Therefore, the present technique could be useful for practical production of polyester fibers with “semi-permanent” antistatic properties which can be recovered by re-treatment even if they were lost.     

Uncertainty propagation in puff-based dispersion models using polynomial chaos

Atmospheric dispersion is a complex nonlinear physical process with numerous uncertainties in model parameters, inputs, source parameters, initial and boundary conditions. Accurate propagation of these uncertainties through the dispersion models is crucial for a reliable prediction of the probability distribution of the states and assessment of risk. A simple three-dimensional Gaussian puff-based dispersion model is used as a test case to study the effect of uncertainties in the model parameters and initial conditions on the output concentration. A polynomial chaos based approach is used to numerically investigate the evolution of the model output uncertainties due to initial condition and parametric uncertainties. The polynomial chaos solution is found to be an accurate approximation to ground truth, established by Monte Carlo simulation, while offering an efficient computational approach for large nonlinear systems with a relatively small number of uncertainties.     

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.     

Linear stochastic approximation driven by slowly varying Markov chains

We study a linear stochastic approximation algorithm that arises in the context of reinforcement learning. The algorithm employs a decreasing step-size, and is driven by Markov noise with time-varying statistics. We show that under suitable conditions, the algorithm can track the changes in the statistics of the Markov noise, as long as these changes are slower than the rate at which the step-size of the algorithm goes to zero.     

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...