*2.1. The Corpora*

Parallel corpora are a valuable resource for many NLP tasks and for linguistics studies. Translation documents preserve the same meaning and functions, to a certain extent, across languages. This allows analysis/comparison of the morphological and typological features of languages.

We used two different parallel corpora that are available for a wide set of languages. On one hand, we used a portion of the Parallel Bible Corpus [15]; in particular, we used a subset of 1150 parallel verses that overlapped across 47 languages (the selection of languages and pre-processing of this dataset was part of the Interactive Workshop on Measuring Language Complexity (IWMLC 2019) http://www.christianbentz.de/MLC2019\_index.html). These languages are part of the WALS 100-language sample, a selection of languages that are typologically diverse [16] (https://wals.info/ languoid/samples/100).

On the other hand, we used the JW300 parallel corpus that compiles magazine articles for many languages [17] (these articles were originally obtained from the Jehovah's Witnesses website https: //www.jw.org). In this case, we extracted a subset of 68 parallel magazine articles that overlapped across 133 languages. Table 1 summarizes information about the corpora.

**Table 1.** General information about the parallel corpora.

We ran the experiments in both corpora independently. The intersection of languages covered by the two parallel corpora is 25. This shared set of languages was useful to compare the complexity rankings obtained with our measures, i.e., test if our complexity measures are consistent across different corpora.

It is important to mention that no sentence alignment was applied to the corpora. The Bibles corpus was already aligned at the verse level while the JW300 corpus was only aligned at the document level. However, for the aim of our experiments, alignment annotation (at the sentence or verse level) was not required.

#### *2.2. Type-Token Relationship (TTR)*

The type-token relationship (TTR) has proven to be a simple, yet effective, way to quantify the morphological complexity of a language using relatively small corpora [14]. It has also shown a high correlation with other types of complexity measures such as paradigm-based approaches that are based on typological information databases [11].

Morphologically rich languages will produce many different word forms (types) in a text, this is captured by measures such as TTR. From a linguistic perspective, Joan Bybee [18] affirms that "the token frequency of certain items in constructions [i.e., words] as well as the range of types [. . . ] determines representation of the construction as well as its productivity".

TTR can be influenced by the size of a text (Heaps' law) or even by the domain of a corpus [19,20]. Some alternatives to make TTR more comparable include normalizing the text size or using logarithm, however, Covington and McFall [19] argue that these strategies are not fully successful, and they propose the moving-Average Type-Token Ratio. On the other hand, using parallel corpora has shown to be a simple way to make TTR more comparable across languages [21,22]. In principle, translations preserve the same meaning in two languages, therefore, there is no need for the texts to have the exact same length in tokens.

We calculated the TTR for a corpus by simply using Equation (1). Where #*types* are the different word types in the corpus (vocabulary size), and #*tokens* is the total number of word tokens in the

corpus. Values closer to 1 would represent greater complexity. This simple way of measuring TTR, without any normalization, has been used in similar works [11,22,23].

$$\text{TTR} = \frac{\text{\#types}}{\text{\#tokens}} \tag{1}$$

We use this measure as an easy way to approach the e-complexity dimension; i.e., different morphosyntactic distinctions, and their productivity, could be reflected in the type and token distribution over a corpus.

#### *2.3. Entropy Rate of a Sub-Word Language Model*

Entropy as a measure of unpredictability represents a useful tool to quantify different linguistic phenomena, in particular, the complexity of morphological systems [9,12,24].

Our method aims to reflect the predictability of the internal structure of words in a language. We conjecture that morphological processes that are irregular/suppletive, unproductive, etc., will increase the entropy of a model that predicts the probability of sequences of morphs/sub-word units within a word.

To do this, we estimate a stochastic matrix *P*, where each cell contains the transition probability between two sub-word units in that language (see example Table 2). These probabilities are estimated using the corpus and a neural language model that we will describe below.

**Table 2.** Toy example of a stochastic matrix using the trigrams contained in the word 'cats'. The symbols #, \$ indicate beginning/end of a word.

We calculate the stochastic matrix *P* as follows (2):

$$P = p\_{i\bar{j}} = p(w\_{\bar{j}}|w\_{\bar{i}}) \tag{2}$$

where *wi* and *wj* are sub-word units. We used a neural probabilistic language model to estimate a probability function.


**1. Introduction**

*Salmonella* is a species in the genus with worldwide public health implications and is the major cause of foodborne disease, accounting for deaths of thousands of people worldwide [1–9]. *Salmonella* is anaerobic in nature and is a Gram-negative, rod-shaped bacterium belonging to the Enterobacteriaceae family. *Salmonella* is divided into two species: *Salmonella enterica* and *Salmonella bongori*. More than 2600 *S. enterica* serovars have been defined so far, with most of these serotypes likely to cause diseases in both humans and animals [10], whereas a few *S. enterica* variants, such as *Salmonella* Gallinarum (SG) and *Salmonella* Pullorum (SP), are non-flagellated and non-motile, the large percentage of *Salmonella* members are motile by peritrichous flagella. The SG and SP are linked to clinical disease in poultry

**Figure 1.** *Sources of Salmonella enterica.*

and cause significant economic losses to poultry farming, particularly in developing countries [11–13]. According to recent data from the United States, Europe, and Low and Middle Income Countries (LMICs), *Salmonella* is frequently occurring international cause of foodborne disease. *Salmonella* also enhances food contamination in many natural environments [14]. *Salmonella enteric* found in the gut of food animals more persistently, is characterized by chronic transmitters which remove the bacterium with their own fecal matter. As a result, these carriers act as a reservoir for future bacterial contamination, allowing *Salmonella* to spread through infected milk, meat, eggs, and other agricultural products fertilized and developed in *Salmonella*-infested manure [14]. *Salmonella* have been isolated from variety of animals and their food products. These include poultry, ovine, porcine, bovine, lizards and snakes (**Figure 1**). This book chapter attempts to discuss different aspects of *Salmonella* serovars and *Salmonella* infection in different animals, with special emphasis to understand the mechanism of its pathogenesis.


**1. Introduction**

250 Biogas

Zhu, H.;. Fang, HP.; Zhang, T.;. Beaudette, LA. (2007). Effect of ferrous ion on photo

*Energy*, vol.32, pp.4112-4118.

heterotrophic hydrogen production by *Rhodobacter sphaeroides*, *Int J Hydrogen*

Constantly increasing demand for energy has created extensive consumption of fossil fuels and the thread of their exhaustion has became a serious concern. At the same time it has been an inspiration for search for new, environmental friendly energy sources, out of which hydrogen seems to be one of the most promising. It is easily accessible, harmless, renewable and effective (high heat of combustion) energy carrier (Ball, 2009). Within the numerous methods of hydrogen production, biological methods (so called "green technology") have gained substantial importance. These methods consist of fermentative decomposition of organic substances, biophotolysis of water by algae and cyanobacteria, decomposition of organic compounds by photosynthetic bacteria and two-stage hybrid systems with fermentative and photosynthetic bacteria (Waligórska, 2006, Koku, 2002, Su, 2009).

Photofermentation represents the process where heterotrophic bacteria in the presence of light decompose organic substances and produce hydrogen and CO2. It has been already shown that purple non-sulphur bacteria *Rhodobacter sphaeroides* act as efficient biocatalyst in the process of hydrogen production from the wastes coming from breweries and dairy industry. Brewery wastes carry high concentration of organic compounds (COD 0.8- 2.5kg/hl of beer) and represent high volumes (1.3-1.8 hl/hl of beer). The amount of waste during beer production is enormous and equals the amount of water applied for production diminished with water present in beer (usually 3-4 hl of waste per 1 hl of beer). A chemical composition of waste strongly depends on the kind of beer produced and fermentation degree. Such waste can contain aminoacids, proteins, organic acids, sugers, alcohols, as well as vitamins of the B group. (Wojnowska-Baryła, 2002, Srikanth, 2009, Cui, 2009) As far as dairy wastes are concerned, they contain an average of 5-50 g O2 /l. These wastes are mainly composed of remaining of milk, fats and whey. Typical Polish dairy produces 450-600 m3 of wastes per day, half of which goes directly to rivers, lakes and to the ground. These wastes easily undergo fermentation, which causes acidification, intense oxygen consumption, bottom sedimentation and growth of fungi. The organics in both dairy and brewery wastes represent the efficient substrate for *Rhodobacter sphaeroides* and seem to be a promising source for energy production. The efficient use of food wastes in hydrogen generation with

Photofermentative Hydrogen Generation in Presence of Waste Water from Food Industry 253

deionized water and dried at 80oC for 4 h. Elemental analysis of the foot wastes (C,H,N,O) was performed in triplicate using an elemental analyser (Vario EL III Elementary). Concentration of Fe, Ca, Mg in purified wastes was measured by ICP OES spectroscopy. The value of pH was measured with glass electrode ERH-11. The intensity of luminance was measured at the external wall of the bottle with a luxometer Lx204 made by Slandi, Poland and a pyranometer CMP3 by Kipp & Zonen (Waligórska, 2006). The light conversion

( ) *tAI*

where "V" is the volume of produced H2 in liters, "ρ" is the density of the produced hydrogen gas in g/l, "I" is the light intensity in W/m2, "A" is the irradiated area in m2 and

Substrate efficiency Ysub (l /l waste) was calculated as final hydrogen concentration per l of

where Hmax is a final hydrogen concentration in l, Vwaste is waste concentration in l. Specific efficiency Ysp (l H2 /g COD) was calculated based on following equation:

*Y*

hydrogen and carbon dioxide (Mu, 2007, Nath, 2008, Chen, 2006):

max

(h), λ – lag time (h), e – exp = 2.718.

**3. Results and discussion 3.1 Pretreatment of wastes**

61.33 %

⋅⋅ ⋅⋅ = ρ

max *H Ysub Vwaste*

> max *H*

> > ( ) max, 2

λ

max exp exp 1

*H*

= − − +

where: H - cumulative hydrogen (l/lmedium), Hmax – maximum cumulative hydrogen (l/lmedium), Rmax, H2 –maximum rate of hydrogen production (l/l/h), t – fermentation time

The wastes applied in this series of experiments required high temperature pretreatment (120oC for 20 min), which had significantly increased the efficiency of hydrogen production by removing from the crude waste microorganisms realizing competitive fermentation. The crude wastes were acidic (dairy waste pH 4.2, brewery waste pH 4.7) and contained high concentration of NH4+ (40 mg/l dairy waste and 96 mg/l brewery waste), which can significantly reduce hydrogen production (Waligórska, 2009). High concentration of

*sp CODloss*

The modified Gompertz (Eq. 4) was applied for calculations of cumulative amounts of

*R e H H H t*

*V*

(1)

= (2)

= (3)

(4)

efficiency (η) was calculated based on the following formula (Koku, 2002):

η

"t" is the duration of hydrogen production in hours.

waste:

simultaneous degradation of these laborious wastes seems to be a very environmentally friendly solution. The US Department of Energy Hydrogen Program in United States estimates that contribution of hydrogen to total energy market will be 8-10% by 2025 (National Hydrogen Energy Roapmap, 2002). It is predicted that hydrogen will become the main carrier of energy in the near future due to environmental and universal applications reasons. It is clean, highly energetic energy carrier (142.35 kJ/g), with almost tripled gravimetric energy density compared to ordinary hydrocarbons. Although the described method is relatively simple and cheap it still requires optimization due to the obtained unsatisfied yields.


**5. Conclusion**

Our study revealed that associations between monocyte cytokine parameters and specific co-morbid medical conditions exist in the ASD subjects studied, independent of other clinical variables. This indicates that there is a possibility that monocyte cytokine profiles may be used for assessing treatment options in ASD subjects with specific co-morbid medical conditions.