Evolution and loss of long-fringed petals

Background: The Cucurbitaceae genus Trichosanthes comprises 90–100 species that occur from India to Japan and southeast to Australia and Fiji. Most species have large white or pale yellow petals with conspicuously fringed margins, the fringes sometimes several cm long. Pollination is usually by hawkmoths. Previous molecular data for a small number of species suggested that a monophyletic Trichosanthes might include the Asian genera Gymnopetalum (four species, lacking long petal fringes) and Hodgsonia (two species with petals fringed). Here we test these groups’ relationships using a species sampling of c. 60% and 4759 nucleotides of nuclear and plastid DNA. To infer the time and direction of the geographic expansion of the Trichosanthes clade we employ molecular clock dating and statistical biogeographic reconstruction, and we also address the gain or loss of petal fringes. Results: Trichosanthes is monophyletic as long as it includes Gymnopetalum, which itself is polyphyletic. The closest relative of Trichosanthes appears to be the sponge gourds, Luffa, while Hodgsonia is more distantly related. Of six morphology-based sections in Trichosanthes with more than one species, three are supported by the molecular results; two new sections appear warranted. Molecular dating and biogeographic analyses suggest an Oligocene origin of Trichosanthes in Eurasia or East Asia, followed by diversification and spread throughout the Malesian biogeographic region and into the Australian continent. Conclusions: Long-fringed corollas evolved independently in Hodgsonia and Trichosanthes, followed by two losses in the latter coincident with shifts to other pollinators but not with long-distance ispersal events. Together with the Caribbean Linnaeosicyos, the Madagascan Ampelosicyos and the tropical African Telfairia, these cucurbit lineages represent an ideal system for more detailed studies of the evolution and function of petal fringes in plant-pollinator mutualisms

A data-driven linguistic characterization of hallucinated voices in clinical and non-clinical voice-hearers

Background: Auditory verbal hallucinations (AVHs) are heterogeneous regarding phenomenology and etiology. This has led to the proposal of AVHs subtypes. Distinguishing AVHs subtypes can inform AVHs neurocognitive models and also have implications for clinical practice. A scarcely studied source of heterogeneity relates to the AVHs linguistic characteristics. Therefore, in this study we investigate whether linguistic features distinguish AVHs subtypes, and whether linguistic AVH-subtypes are associated with phenomenology and voice-hearers' clinical status. Methods: Twenty-one clinical and nineteen non-clinical voice-hearers participated in this study. Participants were instructed to repeat verbatim their AVHs just after experiencing them. AVH-repetitions were audio-recorded and transcribed. AVHs phenomenology was assessed using the Auditory Hallucinations Rating Scale of the Psychotic Symptom Rating Scales. Hierarchical clustering analyses without a priori group dichotomization were performed using quantitative measures of sixteen linguistic features to distinguish sets of AVHs. Results: A two-AVHs-cluster solution best partitioned the data. AVHs-clusters significantly differed in linguistic features (p < .001); AVHs phenomenology (p < .001); and distribution of clinical voice-hearers (p < .001). The “expanded-AVHs” cluster was characterized by more determiners, more prepositions, longer utterances (all p < .01), and mainly contained non-clinical voice-hearers. The “compact-AVHs” cluster had fewer determiners and prepositions, shorter utterances (all p < .01), more negative content, higher degree of negativity (both p < .05), and predominantly came from clinical voice-hearers. Discussion: Two voice-speech clusters were recognized, differing in syntactic-grammatical complexity and negative phenomenology. Our results suggest clinical voice-hearers often hear negative, “compact-voices”, understandable under Broca's right hemisphere homologue and memory-based mechanisms. Conversely, non-clinical voice-hearers experience “expanded-voices”, better accounted by inner speech AVHs models

High-Throughput Assay for the Identification of Compounds Regulating Osteogenic Differentiation of Human Mesenchymal Stromal Cells

Human mesenchymal stromal cells are regarded as the golden standard for cell-based therapies. They present multilineage differentiation potential and trophic and immunosuppressive abilities, making them the best candidate for clinical applications. Several molecules have been described to increase bone formation and were mainly discovered by candidate approaches towards known signaling pathways controlling osteogenesis. However, their bone forming potential is still limited, making the search for novel molecules a necessity. High-throughput screening (HTS) not only allows the screening of a large number of diverse chemical compounds, but also allows the discovery of unexpected signaling pathways and molecular mechanisms for a certain application, even without the prior knowledge of the full molecular pathway. Typically HTS is performed in cell lines, however, in this manuscript we have performed a phenotypical screen on more clinically relevant human mesenchymal stromal cells, as a proof of principle that HTS can be performed in those cells and can be used to find small molecules that impact stem cell fate. From a library of pharmacologically active small molecules, we were able to identify novel compounds with increased osteogenic activity. These compounds allowed achieving levels of bone-specific alkaline phosphatase higher than any other combination previously known. By combining biochemical techniques, we were able to demonstrate that a medium to high-throughput phenotypic assay can be performed in academic research laboratories allowing the discovery of novel molecules able to enhance stem cell differentiation

Traps as treats: a traditional sticky rice snack persisting in rapidly changing Asian kitchens

Background: An accessory to modern developing economies includes a shift from traditional, laborious lifestyles and cuisine to more sedentary careers, recreation and convenience-based foodstuffs. Similar changes in the developed western world have led to harmful health consequences. Minimization of this effect in current transitional cultures could be met by placing value on the maintenance of heritage-rich food. Vitally important to this is the preservation and dissemination of knowledge of these traditional foods. Here, we investigate the history and functionality of a traditional rice snack cooked in Nepenthes pitchers, one of the most iconic and recognizable plants in the rapidly growing economic environment of Southeast Asia. Methods: Social media was combined with traditional ethnobotanical surveys to conduct investigations throughout Malaysian Borneo. Interviews were conducted with 25 market customers, vendors and participants from various ethnical groups with an in-depth knowledge of glutinous rice cooked in pitcher plants. The acidity of pitcher fluid was measured during experimental cooking to analyze possible chemical avenues that might contribute to rice stickiness. Results: Participants identifying the snack were almost all (96%) from indigenous Bidayuh or Kadazandusun tribal decent. They prepare glutinous rice inside pitcher traps for tradition, vessel functionality and because they thought it added fragrance and taste to the rice. The pH and chemical activity of traps analyzed suggest there is no corresponding effect on rice consistency. Harvest of pitchers does not appear to decrease the number of plants in local populations. Conclusions: The tradition of cooking glutinous rice snacks in pitcher plants, or peruik kera in Malay, likely carries from a time when cooking vessels were more limited, and persists only faintly in tribal culture today because of value placed on maintaining cultural heritage. Social media proved a valuable tool in our research for locating research areas and in interviewing respondents, and we endorse its further use in ethnobotanical investigations. Our gathered data urges for the preservation of sustainable, tribal plant use for the prosperity of both health and cultur

Assessing coherence through linguistic connectives:Analysis of speech in patients with schizophrenia-spectrum disorders

BackgroundIncoherent speech is a core diagnostic symptom of schizophrenia-spectrum disorders (SSD) that can be studied using semantic space models. Since linguistic connectives signal relations between words, they and their surrounding words might represent linguistic loci to detect unusual coherence in speech. Therefore, we investigated whether connectives' measures are useful to assess incoherent speech in SSD.MethodsConnectives and their surrounding words were extracted from transcripts of spontaneous speech of 50 SSD-patients and 50 control participants. Using word2vec, two different cosine similarities were calculated: those of connectives and their surrounding words (connectives-related similarity), and those of free-of-connectives words-chunks (non-connectives similarity). Differences between groups in proportion of five types of connectives were assessed using generalized logistic models, and connectives-related similarity was analyzed through non-parametric multivariate analysis of variance. These features were evaluated in classification tasks to differentiate between groups.ResultsSSD-patients used less contingency (e.g., because) (p = .008) and multiclass connectives (e.g., as) (p &lt; .001) than control participants. SSD-patients had higher minimum similarity of multiclass (adj-p = .04) and temporality connectives (e.g., after) (adj-p &lt; .001), narrower similarity-range of expansion (e.g., and) (adj-p = .002) and multiclass connectives (adj-p = .04), and lower maximum similarity of expansion connectives (adj-p = .005). Using connectives' features alone, SSD-patients and controls could be distinguished with 85 % accuracy.DiscussionOur results show that SSD-speech can be distinguished from speech of control participants with high accuracy, based solely on connectives' features. We conclude that including connectives could strengthen computational models to categorize SSD

Assessing coherence through linguistic connectives:Analysis of speech in patients with schizophrenia-spectrum disorders

BackgroundIncoherent speech is a core diagnostic symptom of schizophrenia-spectrum disorders (SSD) that can be studied using semantic space models. Since linguistic connectives signal relations between words, they and their surrounding words might represent linguistic loci to detect unusual coherence in speech. Therefore, we investigated whether connectives' measures are useful to assess incoherent speech in SSD.MethodsConnectives and their surrounding words were extracted from transcripts of spontaneous speech of 50 SSD-patients and 50 control participants. Using word2vec, two different cosine similarities were calculated: those of connectives and their surrounding words (connectives-related similarity), and those of free-of-connectives words-chunks (non-connectives similarity). Differences between groups in proportion of five types of connectives were assessed using generalized logistic models, and connectives-related similarity was analyzed through non-parametric multivariate analysis of variance. These features were evaluated in classification tasks to differentiate between groups.ResultsSSD-patients used less contingency (e.g., because) (p = .008) and multiclass connectives (e.g., as) (p &lt; .001) than control participants. SSD-patients had higher minimum similarity of multiclass (adj-p = .04) and temporality connectives (e.g., after) (adj-p &lt; .001), narrower similarity-range of expansion (e.g., and) (adj-p = .002) and multiclass connectives (adj-p = .04), and lower maximum similarity of expansion connectives (adj-p = .005). Using connectives' features alone, SSD-patients and controls could be distinguished with 85 % accuracy.DiscussionOur results show that SSD-speech can be distinguished from speech of control participants with high accuracy, based solely on connectives' features. We conclude that including connectives could strengthen computational models to categorize SSD

Plant biodiversity assessment through soil eDNA reflects temporal and local diversity

1. Several studies have shown the potential of eDNA-based proxies for plant identification, but little is known about their spatial and temporal resolution. This limits its use for plant biodiversity assessments and monitoring of vegetation responses to environmental changes. Here we calibrate the temporal and spatial plant signals detected with soil eDNA surveys by comparing with a standard visual above-ground vegetation survey. 2. Our approach compares vegetation in an old-growth boreal forest in southern Norway, surveyed in 100 permanent 1-m2 plots seven times over a 30-year period, with a single soil eDNA metabarcoding-based survey from soil samples collected at the same 100 plots in the year of the last vegetation survey. 3. On average, 60% and 10% of the vascular plants and bryophytes recorded across all vegetation surveys were detected by soil eDNA. Taxa detected by soil eDNA were more representative for the local taxa pool than for the specific plot, and corresponded to those surveyed over the 30-year period although most closely matched the current taxa composition. Soil eDNA detected abundant taxa better than rare ones although both rare taxa and taxa unrecorded by the visual survey were detected. 4. Our study highlights the potential of soil eDNA assessments for monitoring of vegetation responses over broad spatial and temporal scales. The method's ability to detect abundant taxa makes it suitable for assessment of vegetation composition in a specific area and for broad-scale plant diversity assessments

Semantic and Acoustic Markers in Schizophrenia-Spectrum Disorders:A Combinatory Machine Learning Approach

BACKGROUND AND HYPOTHESIS: Speech is a promising marker to aid diagnosis of schizophrenia-spectrum disorders, as it reflects symptoms like thought disorder and negative symptoms. Previous approaches made use of different domains of speech for diagnostic classification, including features like coherence (semantic) and form (acoustic). However, an examination of the added value of each domain when combined is lacking as of yet. Here, we investigate the acoustic and semantic domains separately and combined. STUDY DESIGN: Using semi-structured interviews, speech of 94 subjects with schizophrenia-spectrum disorders (SSD) and 73 healthy controls (HC) was recorded. Acoustic features were extracted using a standardized feature-set, and transcribed interviews were used to calculate semantic word similarity using word2vec. Random forest classifiers were trained for each domain. A third classifier was used to combine features from both domains; 10-fold cross-validation was used for each model. RESULTS: The acoustic random forest classifier achieved 81% accuracy classifying SSD and HC, while the semantic domain classifier reached an accuracy of 80%. Joining features from the two domains, the combined classifier reached 85% accuracy, significantly improving on separate domain classifiers. For the combined classifier, top features were fragmented speech from the acoustic domain and variance of similarity from the semantic domain. CONCLUSIONS: Both semantic and acoustic analyses of speech achieved ~80% accuracy in classifying SSD from HC. We replicate earlier findings per domain, additionally showing that combining these features significantly improves classification performance. Feature importance and accuracy in combined classification indicate that the domains measure different, complementing aspects of speech.</p