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This study aims to investigate the electric responses (complex modulus and complex impedance analysis) of hydroxyapatite/starch bone scaffold as a function of hydroxyapatite/starch proportion and the microstructural features. Hence, the non-porous and porous hydroxyapatite/starch composites were fabricated with various hydroxyapatite/starch proportions (70/30, 60/40, 50/50, 40/60, 30/70, 20/80, and 10/90 wt/wt%). Microstructural analysis of the porous hydroxyapatite/starch composites was carried out by using scanning electron microscopy. It shows that the formation of hierarchical porous microstructures with high porosity is more significant at a high starch proportion. The complex modulus and complex impedance analysis were conducted to investigate the electrical conduction mechanism of the hydroxyapatite/starch composites via dielectric spectroscopy within a frequency range from 5 MHz to 12 GHz. The electrical responses of the hydroxyapatite/starch composites are highly dependent on the frequency, material proportion, and microstructures. High starch proportion and highly porous hierarchical microstructures enhance the electrical responses of the hydroxyapatite/starch composite. The material proportion and microstructure features of the hydroxyapatite/starch composites can be indirectly reflected by the simulated electrical parameters of the equivalent electrical circuit models.

This paper presents an automated high-speed die edge detection framework, based on improved K-Mean and landscape analysis, that is highly suitable to be implemented in precision engineering for optical non-destructive testing of die-level defects inspection in the semiconductor industry. This paper specifically aims to achieve high accuracy (or yield) of greater than 99.95% with stable performance within a short computation time (i.e. less than 20 ms). To demonstrate the applicability of the proposed framework, it is validated using three production units (i.e. Production unit A: 6000 units; Production unit B: 3500 units; Production unit C: 4000 units) and is benchmarked to two baseline edge detection methods, namely cross-correlation and normalised cross-correlation methods, as well as state-of-the-art vision libraries, recent works, and several conventional edge detection methods. The results obtained show that the proposed framework is capable of performing die edge detection with promising accuracy and stable performance by achieving 100.0% yield in all three production units, having outperformed the benchmarking methods. Also, the overall computation time (considering die edge detection and die rotation) of the proposed framework is short, at approximately 15 ms.

Mechanistic studies of the interaction of electromagnetic (EM) fields with biomaterials has motivated a growing need for accurate models to describe the EM behavior of biomaterials exposed to these fields. In this paper, biodegradable bone scaffolds were fabricated using Wangi rice starch and nano-hydroxyapatite (nHA). The effects of porosity and composition on the fabricated scaffold were discussed via electrical impedance spectroscopy analysis. The fabricated scaffold was subjected to an electromagnetic field within the X-band and Ku-band (microwave spectrum) during impedance/dielectric measurement. The impedance spectra were analyzed with lumped-element models. The impedance spectra of the scaffold can be embodied in equivalent circuit models composed of passive components of the circuit, i.e., resistors, inductors and capacitors. It represents the morphological, structural and chemical characteristics of the bone scaffold. The developed models describe the impedance characteristics of plant tissue. In this study, it was found that the ε′ and ε″ of scaffold composites exhibited up and down trends over frequencies for both X-band and Ku-band. The circuit models presented the lowest mean percentage errors of Z′ and Z″, i.e., 3.60% and 13.80%, respectively.

Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a “one-stop center” synthesis and provide a holistic bird’s eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest.

This paper presents an automated high-speed die edge detection method that is meant to be implemented in non-destructive die-level defects inspection in the semiconductor industry. This paper specifically aims to achieve high accuracy (or yield) of greater than 99.95% with stable performance within a short computation time (i.e., less than 30 ms), provided the targeted unit-per-hour (UPH) is at 30,000. To demonstrate the applicability of the proposed method, the proposed method is validated using three production units (i.e., Production units A: 6000 units; Production unit B: 3500 units; Production unit C: 4000 units) and is benchmarked to two common edge detection methods that are widely implemented in the semiconductor industry nowadays (i.e., cross-correlation and normalized cross-correlation methods). The results obtained show that the proposed method is capable to perform die edge detection with promising accuracy and stable performance by achieving 100.0% yield in all three production units and outperformed the benchmarking methods. Also, the overall computation time (considering die edge detection and die rotation) of the proposed method is short, which is approximately 30 ms.

Precise segmentation of tumor regions plays prominent role in the grading of breast carcinoma using the Nottingham Histological Grading (NHG) system. A robust segmentation framework is expected to produce cost-effective, repeatable, and reproducible quantitative outputs. In this study, a spatial neighborhood intensity constraint (SNIC) clustering framework for tumor region in breast histopathology images is presented. The proposed framework consists of five main stages: (1) color normalization, (2) segmentation and removal of nucleus cells, (3) SNIC, (4) FCM with knowledge-based initial centroids selection, and (5) post-processing. The novelty of the proposed framework lies within its simple but powerful in clustering tumor regions precisely in a heterogenous environment. The SNIC is implemented to remove and replace the intensity of the nucleus cells based on the spatial constraints. Also, a knowledge-based initial centroids selection method is implemented to ease the FCM clustering algorithm. Both of these methods are posited to facilitate the clustering stage producing complementary results. To validate the hypothesis, careful justifications are performed to evaluate the role of SNIC and knowledge-based initial centroids selection. These methods are found plausible by achieving positive results in \(Acc\), \(F1\), \(AOM\), and \(CEI\) of 91.2%, 92.1%, 85.7%, and 90.1%, respectively. To further demonstrate the applicability of the proposed framework, four recent works are included for benchmarking purposes. The proposed framework found outperformed these methods with the lowest percentages in over-segmentation and under-segmentation: 8.7% and 6.6%, respectively.

Mitotic count assessment in breast carcinoma can be a considerable challenge especially when involve with algorithm development. The challenges lie within the hyperchromatic nucleus segmentation that served as a fundamental block in mitotic count assessment. In this study, we proposed an improved initialization based histogram of K-Mean clustering algorithm for hyperchromatic nucleus segmentation in breast carcinoma histopathological images. The focus is to segment the hyperchromatic nucleus from the background using K-Mean clustering algorithm. Conventional initialization method for K-Mean clustering was improved by establishing a relationship between the hyperchromatic nucleus and the intensity histogram. 75 images captured from 15 histopathological slides were used as dataset. The overall Sensitivity in ground truth segmentation of the proposed method was found to have a percentage of 100.0%. The values of precision (AreaPre) and sensitivity (AreaSen) in mitotic cells area segmentation were found to be promising with percentages of 95.2% and 89.2%, respectively. The promising results perhaps could be used to enhance performance of the true mitotic cell detection.

—Breast carcinoma represents a huge global health problem among women in both developed and developing countries. It is estimated that over 508,000 women worldwide died in 2011 due to breast carcinoma. Nottingham Histological Grading (NHG) system is recognized as the gold standard to provide overall grade for breast carcinoma. One of the breast carcinoma criteria considered in the grading system is tubule formation. The assessment of tubule formation starts with visual inspection on breast histopathological image using 10x magnification. However, not all regions in the image provide meaningful information. Histopathological image with score 3 in tubule formation usually has a small tubule size. Thus, a visual inspection at a higher magnification is required. A continuous inspection at a higher magnification is time consuming. By eliminating the irrelevant regions in the histopathological image, histopathologist can focus on the relevant region for further examination. This study proposed a simple method to detect relevant region on the breast histopathological images using landscape analysis. The proposed method was tested using three groups of histopathological images: Group 1: relevant and irrelevant regions, Group 2: relevant regions only and Group 3: irrelevant regions only. The proposed method is found to be effective in eliminating irrelevant regions as the overall accuracy for Groups 1, 2 and 3 are 86.6%, 100.0% and 100.0%, respectively.

Folylpolyglutamate synthetase (FPGS) catalyzes the sequential addition of several glutamates to folate, forming gamma-linked polyglutamate folates of varying lengths. To understand how this protein is capable of accommodating ligands of different length and net charge, we have performed docking studies for folate substrates and glutamate based on the ternary crystal structure of Lactobacillus casei FPGS. Our results suggest two locations for folate binding, the one seen in the crystal structure and another distinct cavity. According to our model and experimental data, it is highly probable that folate can bind in both sites, and we suggest that the new pocket is especially important for the initial addition of the first glutamate residue. Docking longer substrates, di- and triglutamylated folates, showed how these molecules bind in the same sites. The longer folates also adopted transition-state-like conformations that may help us to understand the ligation reaction in FPGS and influence the design of mechanism-based inhibitors for anticancer or antimicrobial therapy.

tRNA guanine transglycosylase (TGT) is a tRNA-modifying enzyme which catalyzes the posttranscriptional exchange of guanine in position 34 of tRNAY,H,N,D with the modified base queuine in eukaryotes or its precursor, preQ1 base, in eubacteria. Thus, TGT must recognize the guanine in tRNA and the free base queuine or preQ1 to catalyze this exchange. The crystal structure of Zymomonas mobilis TGT with preQ1 bound suggests that a key aspartate is critically involved in substrate recognition. To explore this, a series of site-directed mutants of D143 in Escherichia coli TGT were made and characterized to investigate heterocyclic substrate recognition. Our data confirm that D143 has significant impact on KM of guanine; however, the trend in the KM data (D143A < D143N < D143S < D143T) is unexpected. Computational studies were used to further elucidate the interactions between guanine and the D143 mutants. A homology model of E. coli TGT was created, and the role of D143 was investigated by molecular dynamic simulations of guanine bound to the wild-type and D143-mutant TGTs. To validate the model systems against our kinetic data, free energies of binding were fit using the linear interaction energy (LIE) method. This is a unique application of the LIE method because the same ligand is bound to several mutant proteins rather than one protein binding several ligands. The atomic detail gained from the simulations provided a better understanding of the binding affinities of guanine with the mutant TGTs, revealing that water molecules enter the active site and hydrogen bond to the ligand and compensate for lost protein-ligand interactions. The trend of binding affinity for wild-type > D143A > D143N > D143S > D143T appears to be directly related to the degree of hydrogen bonding available to guanine in the binding site.

Quantum chemical DFT-B3LYP/6-31G* method and IR spectrometry have been used to investigate the natural and binding structures of Huperzine B (HupB) in order to better understand the interaction nature between acetylcholinesterase (AChE) and its inhibitor, with the view of designing new AChE inhibitors. The predicted and experimental results reveal that both the natural state and binding form of HupB adopt the chair conformation. Furthermore, the B3LYP/6-31G* results suggest that structure S1 should be the dominant form of the two possible chair structures (S1 and S2, Fig. 2). The calculated results also show that the condensed ring structure composing of rings A, B and C is very rigid. Therefore, its flexibility does not need to be considered when we try to dock this structure to its target. Indeed, this supposition is confirmed by the excellent alignment of the binding structure produced from our recent/break X-ray crystallographic structure of the HupB-AChE complex with the B3LYP/6-31G* predicted geometry. Among all the 111 predicted vibrational bands, the mode 110, which is resulted from the stretching of the bond N2–H and having the second highest frequency, is essential for the geometrical identification. The difference between our predicted strongest absorption band and experimental IR spectrum suggests that a strong intermolecular interaction, which could be a hydrogen bond, exists in HupB crystal. The electrostatic potential surface of HupB derived from our B3LYP/6-31G* CHelpG atomic charge suggests a mechanism of how HupB would interact with its target. In addition, the good agreement between predicted vibrational bands (scaled by a factor of 0.96) and experimental result shows that B3LYP/6-31G* is a good tool for studying such kind of natural compound.

The epoxidation of three stereolabeled methyl-substituted chiral allylic alcohols with (1,2)A and/or (1,3)A allylic strain, namely 3-methylbut-3-en-2-ol (1a), pent-3-en-2-ol (1b), and 3-methylpent-3-en-2-ol (1c), have been studied by the density-functional theory method, B3LYP/6-31+G(d,p). For each substrate we calculated the two prereaction complexes with Ti(OH)(4)/MeOOH (the oxidant model for Ti(O-i-Pr)(4)/t-BuOOH), their threo and erythro transition states for oxygen transfer, and the corresponding product complexes. For substrate 1a, the erythro transition state is 0.91 kcal/mol of lower energy than the threo one; for substrates 1b and 1c, the threo compared to the erythro transition states are by 1.05 and 0.21 kcal/mol more favorable, respectively. The threo/erythro product ratios have been estimated from the computed free energies for the competing threo and erythro transition states 3a-c in CH(2)Cl(2) solution to be 12:88 (1a), 92:8 (1b), and 77:23 (1c), which are in good accordance with the experimental values 22:78 (1a), 91:9 (1b), and 83:17 (1c). The diastereoselectivity of this diastereoselective oxyfunctionalization is rationalized in terms of the competition between (1,3)A and (1,2)A strain and the electronic advantage for the spiro transition state. In addition, solvent effects are also play a role for the diastereoselectivity at the same time.

The theoretical investigation of tetramethylammonium (TMA)−imidazole and TMA−furan complexes has been performed to justify the preferred structure of the cation−aromatic complexes predicated by the...

A detailed theoretical investigation of the tetramethylammonium(TMA)-benzene and TMA-pyrrole complexes has been performed to obtain the interaction properties of TMA with aromatics. Diffuse functions have been found to be important in the computational studies of these noncovalent complexes. Adding diffuse functions to the basis set decreases the binding energy by about 10% for the TMA-aromatic systems. Dispersion interactions in the TMA-aromatic systems are very important. They enhance the binding interactions between the TMA and the aromatic ring systems by about 0.5 kcal·mol-1 per interacting atomic pair, which is in agreement with the estimates of Rappe and Bernstein.1 Also, for the TMA-pyrrole complex, the presence of the dispersion interaction leads to a dramatic change in the optimized structure. Because B3LYP cannot handle properly the dispersion in the calculation, use of the Moller−Plesset second-order perturbation or other sophisticated methods should be considered in computational studies of c...

Quantum chemical ab initio (U)HF/4-31G investigation on buckminsterfullerene and some proposed intermediates in its formation is carried out in this study with a view to better understanding how small carbon species carry out self-assembly to form fullerenes. The calculations on 19 carefully designed fullerene intermediates reveal that the core of an intermediate, rather than the number of its dangling bonds or abutting pentagon rings, has an intrinsic effect on its energy. The computational results show that hexagonal-core structures have lower energies than pentagonal-core structures. In addition, the pentagonal core enclosed completely by hexagonal rings has the highest energy. The UHF/4-31G results also suggest that some intermediates such as C18, C21 and C30 with hexagonal cores have unusually low energies in comparison with their isomers or neighbours. Based on these calculated results, we outline the possible pathways from precursor to intermediates to fullerenes, subject to synthesis conditions and raw materials. These pathways support some existing proposals, such as medium monocyclic ring stacking and small ring polymerization mechanisms. However, our results do not suggest that the numbers of dangling bonds or abutting pentagonal rings have the highest impact on fullerene formation. The calculated thermodynamic parameters of the dimerization and addition reactions between two bowl-shaped intermediates suggest that these reactions are favorable to fullerene formation, and that the concentration of bowl-shaped fullerene intermediates should be very low in all detectable carbon species.

DFT/B3LYP calculations were carried out on complexes formed by NH4+ with aromatics, viz. benzene, phenol, pyrrole, imidazole, pyridine, indole, furane, and thiophene, to characterize the forces involved in such interactions and to gain further insight into the nature and diversity of cation−aromatic interactions. Such calculations may provide valuable information for understanding molecular recognition in biological systems and for force-field development. B3LYP/6-31G** optimization on 35 initial structures resulted in 11 different finally optimized geometries, which could be divided into three types: NH4+−π complexes, protonated heterocyclic−NH3 hydrogen bond complexes, and heterocyclic−NH4+ hydrogen bond complexes. For NH4+−π complexes, NH4+ always tilts toward the carbon−carbon bond rather than toward the heteroatom or the carbon−heteroatom bond. The calculated CHelpG charges suggest that the charge distribution of a free heterocyclic may be used to predict the geometry of its complex. Charge populati...

Tetraethylammonium (TEA) and other quaternary ammonium (QA) ions are potent blockers of potassium channels. In order to shed light on the blockade mechanism of QA ions, we have carried out a series of computations on the phenol–ammonium model with density functional theory (DFT) and Moller–Plesset second order perturbation (MP2) methods at levels of 6-31G* and 6-31G** basis sets. NH–aromatic π interaction and NH–OH hydrogen bond interaction, which are important in biological systems, are responsible for the binding of NH4+ to phenol. From analysis of structures, energies, charge populations and transition state features, both the cation–π interaction and hydrogen bond or electrostatic interaction between QAs and the key amino acid residues at the entryways of K+ channels are seen to be significant in the blockade mechanism of QA ions.

A DFT calculation was performed at the B3LYP/6-31G* level on the complexes formed by NH4+ and aromatic nitrogen heterocyclics, viz. pyrrole, imidazole, pyridine and indole, in order to investigate the mechanism and complexity of the interaction between the ammonium group and the aromatic heterocyclic in biomacromolecules. The optimized geometries suggested that there are two different types of complexes: one is a cation–π complex and the other is a hydrogen bond complex. A cation–π complex will be formed if the heteroatom has no localized lone-pair electrons. A hydrogen bond complex will be formed by proton transfer from NH4+ to the heteroatom if the heteroatom has localized lone-pair electrons. In the case of the cation–π complex, the predicted geometries, atomic charges and thermodynamic parameters revealed that ammonium binds more strongly to heterocyclics than it binds to benzene. The calculated orbital coefficient and the optimized structures implied that NH4+ interacts with the π electrons of the CC bond of heterocyclics to form a cation–π complex mainly through one hydrogen atom. Regarding the hydrogen bond complex, although the calculated binding strength is similar to that for the cation–π complex, the ΔH of the whole reaction process suggested that the formation of the hydrogen bond complex is favorable to the stability of the whole system. Calculated IR spectra showed that three groups of new bands appear when NH4+ binds to heterocyclics. Normal mode analysis showed that these new bands are all related to the relative motion of the two parts in the formed complexes. All these results suggest that the NH4+–heterocyclic system is a better model for studying the nature and complexity of the interaction between the ammonium group and the aromatic ring structure in biomolecules.

Quantum chemical ab initio (U)HF/4-31G investigation on buckminsterfullerene and some proposed intermediates in its formation is carried out in this study with a view to better understanding how small carbon species carry out self-assembly to form fullerenes. The calculations on 19 carefully designed fullerene intermediates reveal that the core of an intermediate, rather than the number of its dangling bonds or abutting pentagon rings, has an intrinsic effect on its energy. The computational results show that hexagonal-core structures have lower energies than pentagonal-core structures. In addition, the pentagonal core enclosed completely by hexagonal rings has the highest energy. The UHF/4-31G results also suggest that some intermediates such as C18, C21 and C30 with hexagonal cores have unusually low energies in comparison with their isomers or neighbours. Based on these calculated results, we outline the possible pathways from precursor to intermediates to fullerenes, subject to synthesis conditions and raw materials. These pathways support some existing proposals, such as medium monocyclic ring stacking and small ring polymerization mechanisms. However, our results do not suggest that the numbers of dangling bonds or abutting pentagonal rings have the highest impact on fullerene formation. The calculated thermodynamic parameters of the dimerization and addition reactions between two bowl-shaped intermediates suggest that these reactions are favorable to fullerene formation, and that the concentration of bowl-shaped fullerene intermediates should be very low in all detectable carbon species.

We report a nonepisodic autosomal dominant (AD) spinocerebellar ataxia (SCA) not caused by a nucleotide repeat expansion that is, to our knowledge, the first such SCA. The AD SCAs currently comprise a group ofor =16 genetically distinct neurodegenerative conditions, all characterized by progressive incoordination of gait and limbs and by speech and eye-movement disturbances. Six of the nine SCAs for which the genes are known result from CAG expansions that encode polyglutamine tracts. Noncoding CAG, CTG, and ATTCT expansions are responsible for three other SCAs. Approximately 30% of families with SCA do not have linkage to the known loci. We recently mapped the locus for an AD SCA in a family (AT08) to chromosome 19q13.4-qter. A particularly compelling candidate gene, PRKCG, encodes protein kinase C gamma (PKC gamma), a member of a family of serine/threonine kinases. The entire coding region of PRKCG was sequenced in an affected member of family AT08 and in a group of 39 unrelated patients with ataxia not attributable to trinucleotide expansions. Three different nonconservative missense mutations in highly conserved residues in C1, the cysteine-rich region of the protein, were found in family AT08, another familial case, and a sporadic case. The mutations cosegregated with disease in both families. Structural modeling predicts that two of these amino acid substitutions would severely abrogate the zinc-binding or phorbol ester-binding capabilities of the protein. Immunohistochemical studies on cerebellar tissue from an affected member of family AT08 demonstrated reduced staining for both PKC gamma and ataxin 1 in Purkinje cells, whereas staining for calbindin was preserved. These results strongly support a new mechanism for neuronal cell dysfunction and death in hereditary ataxias and suggest that there may be a common pathway for PKC gamma-related and polyglutamine-related neurodegeneration.