The distribution of monoaminergic cell bodies in the brainstem of the cat has been examined with Falck-Hillarp fluorescence histochemical technique. Quantitative determinations indicate that the cat brainstem contains about 60,300 indolaminergic (IA) cells. The majority of these (about 46,700, or 77.5%) are located within raphe nuclei. The largest number is contained within nucleus raphe dorsalis (RD), accounting for around 24,300 IA cells, while raphe pallidus (RP) holds about 8,000 raphe centralis superior (RCS) 7,400, raphe magnus (RM) 2,400, raphe obscurus (RO) 2,300, linearis intermedius (LI) 2,100, and the raphe pontis (RPo) only some 280 IA cells. The IA cells represent, however, only part of the neuronal population of raphe nuclei, which, in addition, hold varying numbers of other medium-sized and small-sized neurons. Thus, quantification in Nissl-stained material indicate that the IA cells make up about 70% of the medium-sized cells in RD, 50% in RP, 35% in RCS and RO, 25% in LI, 15% in RM, and only 10% in RPo. The substantial numbers of small-sized perikarya observed in all raphe nuclei may represent interneurons. Significant numbers of IA cells were consistently located outside the raphe nuclei at all brainstem levels. In all, these amounted to approximately 13,600, or 22.5% of the total number of IA cells. Thus, IA cells occurred in the myelinated bundles, and sometimes in reticular formation, bordering the raphe nuclei; in the ventral brainstem forming a lateral extension from the ventral raphe (RP, RM, RPo, RCS, and LI) to the position of the rubrospinal bundle; in the periventricular gray and subjacent tegmentum of dorsal pons and caudal mesencephalon; in the locus coeruleus (LC) complex; around the motor trigeminal nucleus; caudal to the red nucleus; and in the interpeduncular and interfascicular nuclei. The wide distribution of IA cells leads to a considerable mixing with catecholaminergic (CA) cell groups. Our observations on CA cell distribution are essentially in accordance with previous reports. Quantifications indicate that the LC complex contains about 9,150 CA cells, unilaterally. A previously unnoticed group of scattered CA cells was found in relation to the vestibular nuclei and extending dorsally toward the deep cerebellar nuclei.

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In this study, galactose dehydrogenase (EC 1.1.1.48) was chosen as a prototype target protein to investigate the capability of metal affinity precipitation to facilitate the purification of genetically engineered proteins. A DNA fragment encoding five histidine residues was fused to the 3′‐terminal end of the galactose dehydrogenase gene from Pseudomonas fluorescens and thereafter expressed in Escherichia coli. The additional five histidines functioned as an affinity tail and the modified enzyme could be purified using metal affinity precipitation when the metal‐chelate complex with ethylene glycol‐bis‐(β‐aminoethyl ether) N,N,N′,N′‐tetra‐acetic acid, EGTA(Zn)2, was added to the protein solution. The affinity tail could also be applied for the purification of the fusion protein utilising immobilised metal affinity chromatography. After purification, the pentahistidine affinity tail could be removed enzymatically by carboxypeptidase A. Furthermore, growth rate experiments demonstrated that the expression of the metal‐binding affinity tail in E. coli cells enhanced the tolerance to zinc ions when added to the growth medium.

Purpose: Evaluate if automated vulnerability scanning accurately identifies vulnerabilities in computer networks and if this accuracy is contingent on the platforms used.Design/methodology/approach: Both qualitative comparisons of functionality and quantitative comparisons of false positives and false negatives are made for seven different scanners. The quantitative assessment includes data from both authenticated and unauthenticated scans. Experiments were conducted on a computer network of 28 hosts with various operating systems, services and vulnerabilities. This network was set up by a team of security researchers and professionals. Findings:The data collected in this study show that authenticated vulnerability scanning is usable. However, automated scanning is not able to accurately identify all vulnerabilities present in computer networks. Also, scans of hosts running Windows are more accurate than scans of hosts running Linux. Research limitations/implications:This paper focuses on the direct output of automated scans with respect to the vulnerabilities they identify. Areas such as how to interpret the results assessed by each scanner (e.g. regarding remediation guidelines) or aggregating information about individual vulnerabilities into risk measures are out of scope. Practical implications:This paper describes how well automated vulnerability scanners perform when it comes to identifying security issues in a network. The findings suggest that a vulnerability scanner is a useable tool to have in your security toolbox given that user credentials are available for the hosts in your network. Manual effort is however needed to complement automated scanning in order to get satisfactory accuracy regarding network security problems. Originality/value: Previous studies have focused on the qualitative aspects on vulnerability assessment. This study presents a quantitative evaluation of seven of the most popular vulnerability scanners available on the market.

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